From 93da5cd74e8f51f25d774be190158fd1553b66cd Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 12 Jul 2023 17:16:40 +0200 Subject: [PATCH 001/250] Additional tests --- .coveragerc | 2 +- docs/res/code/utils.rst | 4 - .../_tests/test_model_framework.py | 48 ++++ napari_cellseg3d/_tests/test_models.py | 26 +++ napari_cellseg3d/_tests/test_utils.py | 76 ++++++- .../code_models/model_framework.py | 23 +- .../code_models/models/model_TRAILMAP.py | 212 +++++++++--------- .../code_models/models/wnet/soft_Ncuts.py | 176 +++++++-------- napari_cellseg3d/dev_scripts/thread_test.py | 5 +- napari_cellseg3d/utils.py | 212 +----------------- 10 files changed, 372 insertions(+), 412 deletions(-) diff --git a/.coveragerc b/.coveragerc index 69f74700..a2bc84e8 100644 --- a/.coveragerc +++ b/.coveragerc @@ -4,4 +4,4 @@ exclude_lines = [run] omit = - napari_cellseg3d/setup.py, napari_cellseg3d/code_models/models/wnet/train_wnet.py + napari_cellseg3d/setup.py, napari_cellseg3d/code_models/models/wnet/train_wnet.py, napari_cellseg3d/code_models/models/wnet/model.py,napari_cellseg3d/code_models/models/TEMPLATE_model.py, napari_cellseg3d/code_models/models/unet/*, napari_cellseg3d/dev_scripts/thread_test.py diff --git a/docs/res/code/utils.rst b/docs/res/code/utils.rst index d9fdcfa2..88046b6f 100644 --- a/docs/res/code/utils.rst +++ b/docs/res/code/utils.rst @@ -27,10 +27,6 @@ get_time_filepath ************************************** .. autofunction:: napari_cellseg3d.utils::get_time_filepath -save_stack -************************************** -.. autofunction:: napari_cellseg3d.utils::save_stack - get_padding_dim ************************************** .. autofunction:: napari_cellseg3d.utils::get_padding_dim diff --git a/napari_cellseg3d/_tests/test_model_framework.py b/napari_cellseg3d/_tests/test_model_framework.py index 5734b329..48f320bc 100644 --- a/napari_cellseg3d/_tests/test_model_framework.py +++ b/napari_cellseg3d/_tests/test_model_framework.py @@ -1,5 +1,6 @@ from pathlib import Path +from napari_cellseg3d.config import MODEL_LIST from napari_cellseg3d.code_models import model_framework @@ -52,3 +53,50 @@ def test_create_train_dataset_dict(make_napari_viewer): ] assert widget.create_train_dataset_dict() == expect + +def test_log(make_napari_viewer): + mock_test = "test" + framework = model_framework.ModelFramework(viewer=make_napari_viewer()) + framework.log.print_and_log(mock_test) + assert len(framework.log.toPlainText()) != 0 + assert framework.log.toPlainText() == "\n" + mock_test + + framework.results_path = str(Path(__file__).resolve().parent / "res") + framework.save_log(do_timestamp=False) + log_path = Path(__file__).resolve().parent / "res/Log_report.txt" + assert log_path.is_file() + with Path.open(log_path.resolve(), "r") as f: + assert f.read() == "\n" + mock_test + + # remove log file + log_path.unlink(missing_ok=False) + log_path = Path(__file__).resolve().parent / "res/Log_report.txt" + framework.save_log_to_path(str(log_path.parent), do_timestamp=False) + assert log_path.is_file() + with Path.open(log_path.resolve(), "r") as f: + assert f.read() == "\n" + mock_test + log_path.unlink(missing_ok=False) + +def test_display_elements(make_napari_viewer): + framework = model_framework.ModelFramework(viewer=make_napari_viewer()) + + framework.display_status_report() + framework.display_status_report() + + framework.custom_weights_choice.setChecked(False) + framework._toggle_weights_path() + assert not framework.weights_filewidget.isVisible() + + +def test_available_models_retrieval(make_napari_viewer): + framework = model_framework.ModelFramework(viewer=make_napari_viewer()) + assert framework.get_available_models() == MODEL_LIST + +def test_update_weights_path(make_napari_viewer): + framework = model_framework.ModelFramework(viewer=make_napari_viewer()) + assert framework._update_weights_path(framework._default_weights_folder) is None + name = str(Path.home() / "test/weight.pth") + framework._update_weights_path([name]) + assert framework.weights_config.path == name + assert framework.weights_filewidget.text_field.text() == name + assert framework._default_weights_folder == str(Path.home() / "test") \ No newline at end of file diff --git a/napari_cellseg3d/_tests/test_models.py b/napari_cellseg3d/_tests/test_models.py index ebb3a614..994ca3bc 100644 --- a/napari_cellseg3d/_tests/test_models.py +++ b/napari_cellseg3d/_tests/test_models.py @@ -1,4 +1,7 @@ import numpy as np +from pathlib import Path + +import pytest import torch from numpy.random import PCG64, Generator @@ -51,6 +54,14 @@ def test_soft_ncuts_loss(): assert isinstance(res, torch.Tensor) assert 0 <= res <= 1 # ASSUMES NUMBER OF CLASS IS 2, NOT CORRECT IF K>2 + loss = SoftNCutsLoss( + data_shape=[dims, dims, dims], + device="cpu", + intensity_sigma=4, + spatial_sigma=4, + radius=None, + ) + assert loss.radius == 5 def test_crf_batch(): dims = 8 @@ -95,3 +106,18 @@ def on_yield(result): result = next(crf._run_crf_job()) on_yield(result) + + +def test_pretrained_weights_compatibility(): + from napari_cellseg3d.code_models.workers import WeightsDownloader + from napari_cellseg3d.config import MODEL_LIST + from napari_cellseg3d.config import PRETRAINED_WEIGHTS_DIR + + for model_name in MODEL_LIST.keys(): + file_name = MODEL_LIST[model_name].weights_file + WeightsDownloader().download_weights(model_name, file_name) + model = MODEL_LIST[model_name](input_img_size=[128, 128, 128]) + try: + model.load_state_dict(torch.load(str(Path(PRETRAINED_WEIGHTS_DIR) / file_name))) + except RuntimeError: + pytest.fail(f"Failed to load weights for {model_name}") \ No newline at end of file diff --git a/napari_cellseg3d/_tests/test_utils.py b/napari_cellseg3d/_tests/test_utils.py index dc680b35..3f89a0a6 100644 --- a/napari_cellseg3d/_tests/test_utils.py +++ b/napari_cellseg3d/_tests/test_utils.py @@ -1,12 +1,67 @@ +import random from functools import partial from pathlib import Path - import numpy as np +import pytest import torch from napari_cellseg3d import utils from napari_cellseg3d.dev_scripts import thread_test +def test_singleton_class(): + class TestSingleton(metaclass=utils.Singleton): + def __init__(self, value): + self.value = value + + a = TestSingleton(1) + b = TestSingleton(2) + + assert a.value == b.value +def test_save_folder(): + test_path = Path(__file__).resolve().parent / "res" + folder_name = "test_folder" + images = [np.random.rand(5, 5, 5) for _ in range(10)] + images_paths = [f"{i}.tif" for i in range(10)] + + utils.save_folder(test_path, folder_name, images, images_paths, exist_ok=True) + assert (test_path / folder_name).is_dir() + for i in range(10): + assert (test_path / folder_name / images_paths[i]).is_file() + +def test_normalize_y(): + test_array = np.array([0, 255, 127.5]) + results = utils.normalize_y(test_array) + expected = test_array / 255 + assert np.all(results == expected) + assert np.all(test_array == utils.denormalize_y(results)) + +def test_sphericities(): + for i in range(100): + mock_volume = random.randint(1, 10) + mock_surface = random.randint(100, 1000) # assuming surface is always larger than volume + sphericity_vol = utils.sphericity_volume_area(mock_volume, mock_surface) + assert 0 <= sphericity_vol <= 1 + + semi_major = random.randint(10, 100) + semi_minor = random.randint(10, 100) + try: + sphericity_axes = utils.sphericity_axis(semi_major, semi_minor) + except ZeroDivisionError: + sphericity_axes = 0 + except ValueError: + sphericity_axes = 0 + assert 0 <= sphericity_axes <= 1 + +def test_normalize_max(): + test_array = np.array([0, 255, 127.5]) + expected = np.array([0, 1, 0.5]) + assert np.all(utils.normalize_max(test_array) == expected) + +def test_dice_coeff(): + test_array = np.random.randint(0,2,(64, 64, 64)) + test_array_2 = np.random.randint(0,2,(64, 64, 64)) + assert utils.dice_coeff(test_array, test_array) == 1 + assert utils.dice_coeff(test_array, test_array_2) <= 1 def test_fill_list_in_between(): test_list = [1, 2, 3, 4, 5, 6] @@ -102,12 +157,29 @@ def test_get_padding_dim(): assert pad == [128, 128, 128] + tensor_wrong = torch.randn(65, 70, 80, 90) + with pytest.raises(ValueError): + utils.get_padding_dim(tensor_wrong.size()) + def test_normalize_x(): test_array = utils.normalize_x(np.array([0, 255, 127.5])) expected = np.array([-1, 1, 0]) assert np.all(test_array == expected) +def test_load_images(): + + path = Path(__file__).resolve().parent / "res" + with pytest.raises(ValueError): + images = utils.load_images(str(path), as_folder=True) + with pytest.raises(NotImplementedError): + images = utils.load_images(str(path), as_folder=True, filetype=".tif") + # assert len(images) == 1 + + path = path / "test.tif" + images = utils.load_images(str(path)) + assert images.shape == (6,6,6) + def test_parse_default_path(): user_path = Path().home() @@ -117,7 +189,7 @@ def test_parse_default_path(): path = [test_path, None, None] assert utils.parse_default_path(path) == test_path - long_path = "D:/very/long/path/what/a/bore/ifonlytherewassomethingtohelpmenottypeitiallthetime" + long_path = "D:/very/long/path/what/a/bore/ifonlytherewas/something/tohelpmenotsearchit/allthetime" path = [test_path, None, None, long_path, ""] assert utils.parse_default_path(path) == long_path diff --git a/napari_cellseg3d/code_models/model_framework.py b/napari_cellseg3d/code_models/model_framework.py index ddd9cd28..4860d59e 100644 --- a/napari_cellseg3d/code_models/model_framework.py +++ b/napari_cellseg3d/code_models/model_framework.py @@ -121,16 +121,21 @@ def send_log(self, text): if self.log is not None: self.log.print_and_log(text) - def save_log(self): + def save_log(self, do_timestamp=True): """Saves the worker's log to disk at self.results_path when called""" if self.log is not None: log = self.log.toPlainText() path = self.results_path + if do_timestamp: + log_name = f"Log_report_{utils.get_date_time()}.txt" + else: + log_name = "Log_report.txt" + if len(log) != 0: with Path.open( - path + f"/Log_report_{utils.get_date_time()}.txt", + Path(path) / log_name, "x", ) as f: f.write(log) @@ -142,7 +147,7 @@ def save_log(self): else: logger.warning(f"No logger defined : Log is {self.log}") - def save_log_to_path(self, path): + def save_log_to_path(self, path, do_timestamp=True): """Saves the worker log to a specific path. Cannot be used with connect. Args: @@ -150,8 +155,14 @@ def save_log_to_path(self, path): """ log = self.log.toPlainText() + + if do_timestamp: + log_name = f"Log_report_{utils.get_date_time()}.txt" + else: + log_name = "Log_report.txt" + path = str( - Path(path) / Path(f"Log_report_{utils.get_date_time()}.txt") + Path(path) / log_name ) if len(log) != 0: @@ -256,10 +267,6 @@ def create_train_dataset_dict(self): return data_dicts - def get_model(self, key): # TODO remove - """Getter for module (class and functions) associated to currently selected model""" - return self.models_dict[key] - @staticmethod def get_available_models(): """Getter for module (class and functions) associated to currently selected model""" diff --git a/napari_cellseg3d/code_models/models/model_TRAILMAP.py b/napari_cellseg3d/code_models/models/model_TRAILMAP.py index e6bbad55..118e1e85 100644 --- a/napari_cellseg3d/code_models/models/model_TRAILMAP.py +++ b/napari_cellseg3d/code_models/models/model_TRAILMAP.py @@ -1,106 +1,106 @@ -import torch -from torch import nn - - -class TRAILMAP(nn.Module): - def __init__(self, in_ch, out_ch, *args, **kwargs): - super().__init__() - self.conv0 = self.encoderBlock(in_ch, 32, 3) # input - self.conv1 = self.encoderBlock(32, 64, 3) # l1 - self.conv2 = self.encoderBlock(64, 128, 3) # l2 - self.conv3 = self.encoderBlock(128, 256, 3) # l3 - - self.bridge = self.bridgeBlock(256, 512, 3) - - self.up5 = self.decoderBlock(256 + 512, 256, 2) - - self.up6 = self.decoderBlock(128 + 256, 128, 2) - self.up7 = self.decoderBlock(128 + 64, 64, 2) # l2 - self.up8 = self.decoderBlock(64 + 32, 32, 2) # l1 - self.out = self.outBlock(32, out_ch, 1) - - def forward(self, x): - conv0 = self.conv0(x) # l0 - conv1 = self.conv1(conv0) # l1 - conv2 = self.conv2(conv1) # l2 - conv3 = self.conv3(conv2) # l3 - - bridge = self.bridge(conv3) # bridge - # print("bridge :") - # print(bridge.shape) - - up5 = self.up5(torch.cat([conv3, bridge], 1)) # l3 - # print("up") - # print(up5.shape) - up6 = self.up6(torch.cat([up5, conv2], 1)) # l2 - # print(up6.shape) - up7 = self.up7(torch.cat([up6, conv1], 1)) # l1 - # print(up7.shape) - - up8 = self.up8(torch.cat([up7, conv0], 1)) # l1 - # print(up8.shape) - return self.out(up8) - # print("out:") - # print(out.shape) - - def encoderBlock(self, in_ch, out_ch, kernel_size, padding="same"): - return nn.Sequential( - nn.Conv3d(in_ch, out_ch, kernel_size=kernel_size, padding=padding), - nn.BatchNorm3d(out_ch), - nn.ReLU(), - nn.Conv3d( - out_ch, out_ch, kernel_size=kernel_size, padding=padding - ), - nn.BatchNorm3d(out_ch), - nn.ReLU(), - nn.MaxPool3d(2), - ) - - def bridgeBlock(self, in_ch, out_ch, kernel_size, padding="same"): - return nn.Sequential( - nn.Conv3d(in_ch, out_ch, kernel_size=kernel_size, padding=padding), - nn.BatchNorm3d(out_ch), - nn.ReLU(), - nn.Conv3d( - out_ch, out_ch, kernel_size=kernel_size, padding=padding - ), - nn.BatchNorm3d(out_ch), - nn.ReLU(), - ) - - def decoderBlock(self, in_ch, out_ch, kernel_size, padding="same"): - return nn.Sequential( - nn.Conv3d(in_ch, out_ch, kernel_size=kernel_size, padding=padding), - nn.BatchNorm3d(out_ch), - nn.ReLU(), - nn.Conv3d( - out_ch, out_ch, kernel_size=kernel_size, padding=padding - ), - nn.BatchNorm3d(out_ch), - nn.ReLU(), - nn.ConvTranspose3d( - out_ch, out_ch, kernel_size=kernel_size, stride=(2, 2, 2) - ), - ) - - def outBlock(self, in_ch, out_ch, kernel_size, padding="same"): - return nn.Sequential( - nn.Conv3d(in_ch, out_ch, kernel_size=kernel_size, padding=padding), - ) - - -class TRAILMAP_(TRAILMAP): - use_default_training = True - weights_file = "TRAILMAP_PyTorch.pth" # model additionally trained on Mathis/Wyss mesoSPIM data - # FIXME currently incorrect, find good weights from TRAILMAP_test and upload them - - def __init__(self, in_channels=1, out_channels=1, **kwargs): - super().__init__(in_channels, out_channels, **kwargs) - - # def get_output(model, input): - # out = model(input) - # - # return out - - # def get_validation(model, val_inputs): - # return model(val_inputs) +# import torch +# from torch import nn +# +# +# class TRAILMAP(nn.Module): +# def __init__(self, in_ch, out_ch, *args, **kwargs): +# super().__init__() +# self.conv0 = self.encoderBlock(in_ch, 32, 3) # input +# self.conv1 = self.encoderBlock(32, 64, 3) # l1 +# self.conv2 = self.encoderBlock(64, 128, 3) # l2 +# self.conv3 = self.encoderBlock(128, 256, 3) # l3 +# +# self.bridge = self.bridgeBlock(256, 512, 3) +# +# self.up5 = self.decoderBlock(256 + 512, 256, 2) +# +# self.up6 = self.decoderBlock(128 + 256, 128, 2) +# self.up7 = self.decoderBlock(128 + 64, 64, 2) # l2 +# self.up8 = self.decoderBlock(64 + 32, 32, 2) # l1 +# self.out = self.outBlock(32, out_ch, 1) +# +# def forward(self, x): +# conv0 = self.conv0(x) # l0 +# conv1 = self.conv1(conv0) # l1 +# conv2 = self.conv2(conv1) # l2 +# conv3 = self.conv3(conv2) # l3 +# +# bridge = self.bridge(conv3) # bridge +# # print("bridge :") +# # print(bridge.shape) +# +# up5 = self.up5(torch.cat([conv3, bridge], 1)) # l3 +# # print("up") +# # print(up5.shape) +# up6 = self.up6(torch.cat([up5, conv2], 1)) # l2 +# # print(up6.shape) +# up7 = self.up7(torch.cat([up6, conv1], 1)) # l1 +# # print(up7.shape) +# +# up8 = self.up8(torch.cat([up7, conv0], 1)) # l1 +# # print(up8.shape) +# return self.out(up8) +# # print("out:") +# # print(out.shape) +# +# def encoderBlock(self, in_ch, out_ch, kernel_size, padding="same"): +# return nn.Sequential( +# nn.Conv3d(in_ch, out_ch, kernel_size=kernel_size, padding=padding), +# nn.BatchNorm3d(out_ch), +# nn.ReLU(), +# nn.Conv3d( +# out_ch, out_ch, kernel_size=kernel_size, padding=padding +# ), +# nn.BatchNorm3d(out_ch), +# nn.ReLU(), +# nn.MaxPool3d(2), +# ) +# +# def bridgeBlock(self, in_ch, out_ch, kernel_size, padding="same"): +# return nn.Sequential( +# nn.Conv3d(in_ch, out_ch, kernel_size=kernel_size, padding=padding), +# nn.BatchNorm3d(out_ch), +# nn.ReLU(), +# nn.Conv3d( +# out_ch, out_ch, kernel_size=kernel_size, padding=padding +# ), +# nn.BatchNorm3d(out_ch), +# nn.ReLU(), +# ) +# +# def decoderBlock(self, in_ch, out_ch, kernel_size, padding="same"): +# return nn.Sequential( +# nn.Conv3d(in_ch, out_ch, kernel_size=kernel_size, padding=padding), +# nn.BatchNorm3d(out_ch), +# nn.ReLU(), +# nn.Conv3d( +# out_ch, out_ch, kernel_size=kernel_size, padding=padding +# ), +# nn.BatchNorm3d(out_ch), +# nn.ReLU(), +# nn.ConvTranspose3d( +# out_ch, out_ch, kernel_size=kernel_size, stride=(2, 2, 2) +# ), +# ) +# +# def outBlock(self, in_ch, out_ch, kernel_size, padding="same"): +# return nn.Sequential( +# nn.Conv3d(in_ch, out_ch, kernel_size=kernel_size, padding=padding), +# ) +# +# +# class TRAILMAP_(TRAILMAP): +# use_default_training = True +# weights_file = "TRAILMAP_PyTorch.pth" # model additionally trained on Mathis/Wyss mesoSPIM data +# # FIXME currently incorrect, find good weights from TRAILMAP_test and upload them +# +# def __init__(self, in_channels=1, out_channels=1, **kwargs): +# super().__init__(in_channels, out_channels, **kwargs) +# +# # def get_output(model, input): +# # out = model(input) +# # +# # return out +# +# # def get_validation(model, val_inputs): +# # return model(val_inputs) diff --git a/napari_cellseg3d/code_models/models/wnet/soft_Ncuts.py b/napari_cellseg3d/code_models/models/wnet/soft_Ncuts.py index e0f92ff7..db049526 100644 --- a/napari_cellseg3d/code_models/models/wnet/soft_Ncuts.py +++ b/napari_cellseg3d/code_models/models/wnet/soft_Ncuts.py @@ -135,91 +135,91 @@ def gaussian_kernel(self, radius, sigma): (1, 1, kernel.shape[0], kernel.shape[1], kernel.shape[2]) ) - def get_distances(self): - """Precompute the spatial distance of the pixels for the weights calculation, to avoid recomputing it at each iteration. - - Returns: - distances (dict): for each pixel index, we get the distances to the pixels in a radius around it. - """ - - distances = dict() - indexes = np.array( - [ - (i, j, k) - for i in range(self.H) - for j in range(self.W) - for k in range(self.D) - ] - ) - - for i in indexes: - iTuple = (i[0], i[1], i[2]) - distances[iTuple] = dict() - - sliceD = indexes[ - i[0] * self.H - + i[1] * self.W - + max(0, i[2] - self.radius) : i[0] * self.H - + i[1] * self.W - + min(self.D, i[2] + self.radius) - ] - sliceW = indexes[ - i[0] * self.H - + max(0, i[1] - self.radius) * self.W - + i[2] : i[0] * self.H - + min(self.W, i[1] + self.radius) * self.W - + i[2] : self.D - ] - sliceH = indexes[ - max(0, i[0] - self.radius) * self.H - + i[1] * self.W - + i[2] : min(self.H, i[0] + self.radius) * self.H - + i[1] * self.W - + i[2] : self.D * self.W - ] - - for j in np.concatenate((sliceD, sliceW, sliceH)): - jTuple = (j[0], j[1], j[2]) - distance = np.linalg.norm(i - j) - if distance > self.radius: - continue - distance = math.exp( - -(distance**2) / (self.spatial_sigma**2) - ) - - if jTuple not in distances: - distances[iTuple][jTuple] = distance - - return distances, indexes - - def get_weights(self, inputs): - """Computes the weights matrix for the Soft N-Cuts loss. - - Args: - inputs (torch.Tensor): Tensor of shape (N, C, H, W, D) containing the input images. - - Returns: - list: List of the weights dict for each image in the batch. - """ - - # Compute the brightness distance of the pixels - flatted_inputs = inputs.view( - inputs.shape[0], inputs.shape[1], -1 - ) # (N, C, H*W*D) - I_diff = torch.subtract( - flatted_inputs.unsqueeze(3), flatted_inputs.unsqueeze(2) - ) # (N, C, H*W*D, H*W*D) - masked_I_diff = torch.mul(I_diff, self.mask) # (N, C, H*W*D, H*W*D) - squared_I_diff = torch.pow(masked_I_diff, 2) # (N, C, H*W*D, H*W*D) - - W_I = torch.exp( - torch.neg(torch.div(squared_I_diff, self.intensity_sigma)) - ) # (N, C, H*W*D, H*W*D) - W_I = torch.mul(W_I, self.mask) # (N, C, H*W*D, H*W*D) - - # Get the spatial distance of the pixels - unsqueezed_W_X = self.W_X.view( - 1, 1, self.W_X.shape[0], self.W_X.shape[1] - ) # (1, 1, H*W*D, H*W*D) - - return torch.mul(W_I, unsqueezed_W_X) # (N, C, H*W*D, H*W*D) + # def get_distances(self): + # """Precompute the spatial distance of the pixels for the weights calculation, to avoid recomputing it at each iteration. + # + # Returns: + # distances (dict): for each pixel index, we get the distances to the pixels in a radius around it. + # """ + # + # distances = dict() + # indexes = np.array( + # [ + # (i, j, k) + # for i in range(self.H) + # for j in range(self.W) + # for k in range(self.D) + # ] + # ) + # + # for i in indexes: + # iTuple = (i[0], i[1], i[2]) + # distances[iTuple] = dict() + # + # sliceD = indexes[ + # i[0] * self.H + # + i[1] * self.W + # + max(0, i[2] - self.radius) : i[0] * self.H + # + i[1] * self.W + # + min(self.D, i[2] + self.radius) + # ] + # sliceW = indexes[ + # i[0] * self.H + # + max(0, i[1] - self.radius) * self.W + # + i[2] : i[0] * self.H + # + min(self.W, i[1] + self.radius) * self.W + # + i[2] : self.D + # ] + # sliceH = indexes[ + # max(0, i[0] - self.radius) * self.H + # + i[1] * self.W + # + i[2] : min(self.H, i[0] + self.radius) * self.H + # + i[1] * self.W + # + i[2] : self.D * self.W + # ] + # + # for j in np.concatenate((sliceD, sliceW, sliceH)): + # jTuple = (j[0], j[1], j[2]) + # distance = np.linalg.norm(i - j) + # if distance > self.radius: + # continue + # distance = math.exp( + # -(distance**2) / (self.spatial_sigma**2) + # ) + # + # if jTuple not in distances: + # distances[iTuple][jTuple] = distance + # + # return distances, indexes + + # def get_weights(self, inputs): + # """Computes the weights matrix for the Soft N-Cuts loss. + # + # Args: + # inputs (torch.Tensor): Tensor of shape (N, C, H, W, D) containing the input images. + # + # Returns: + # list: List of the weights dict for each image in the batch. + # """ + # + # # Compute the brightness distance of the pixels + # flatted_inputs = inputs.view( + # inputs.shape[0], inputs.shape[1], -1 + # ) # (N, C, H*W*D) + # I_diff = torch.subtract( + # flatted_inputs.unsqueeze(3), flatted_inputs.unsqueeze(2) + # ) # (N, C, H*W*D, H*W*D) + # masked_I_diff = torch.mul(I_diff, self.mask) # (N, C, H*W*D, H*W*D) + # squared_I_diff = torch.pow(masked_I_diff, 2) # (N, C, H*W*D, H*W*D) + # + # W_I = torch.exp( + # torch.neg(torch.div(squared_I_diff, self.intensity_sigma)) + # ) # (N, C, H*W*D, H*W*D) + # W_I = torch.mul(W_I, self.mask) # (N, C, H*W*D, H*W*D) + # + # # Get the spatial distance of the pixels + # unsqueezed_W_X = self.W_X.view( + # 1, 1, self.W_X.shape[0], self.W_X.shape[1] + # ) # (1, 1, H*W*D, H*W*D) + # + # return torch.mul(W_I, unsqueezed_W_X) # (N, C, H*W*D, H*W*D) diff --git a/napari_cellseg3d/dev_scripts/thread_test.py b/napari_cellseg3d/dev_scripts/thread_test.py index a48f6db0..c511b71c 100644 --- a/napari_cellseg3d/dev_scripts/thread_test.py +++ b/napari_cellseg3d/dev_scripts/thread_test.py @@ -1,5 +1,4 @@ import time - import napari from napari.qt.threading import thread_worker from numpy.random import PCG64, Generator @@ -15,6 +14,10 @@ rand_gen = Generator(PCG64(12345)) +#################################### +# Tutorial code from napari forums # +#################################### +# not covered by tests @thread_worker def two_way_communication_with_args(start, end): diff --git a/napari_cellseg3d/utils.py b/napari_cellseg3d/utils.py index 663872c4..84ccc380 100644 --- a/napari_cellseg3d/utils.py +++ b/napari_cellseg3d/utils.py @@ -1,4 +1,5 @@ import logging +import math from datetime import datetime from pathlib import Path from typing import TYPE_CHECKING, Union @@ -28,7 +29,7 @@ #################### # viewer utils -def save_folder(results_path, folder_name, images, image_paths): +def save_folder(results_path, folder_name, images, image_paths, exist_ok=False): """ Saves a list of images in a folder @@ -39,7 +40,7 @@ def save_folder(results_path, folder_name, images, image_paths): image_paths: list of filenames of images """ results_folder = results_path / Path(folder_name) - results_folder.mkdir(exist_ok=False, parents=True) + results_folder.mkdir(exist_ok=exist_ok, parents=True) for file, image in zip(image_paths, images): path = results_folder / Path(file).name @@ -92,10 +93,6 @@ def show_result(viewer, layer, image, name): f"Results not shown, unsupported layer type {type(layer)}" ) - -#################### - - class Singleton(type): """ Singleton class that can only be instantiated once at a time, @@ -111,18 +108,6 @@ def __call__(cls, *args, **kwargs): return cls._instances[cls] -# class TiffFileReader(ImageReader): -# def __init__(self): -# super().__init__() -# -# def verify_suffix(self, filename): -# if filename == "tif": -# return True -# def read(self, data, **kwargs): -# return imread(data) -# -# def get_data(self, data): -# return data, {} def normalize_x(image): """Normalizes the values of an image array to be between [-1;1] rather than [0;255] @@ -158,7 +143,7 @@ def sphericity_volume_area(volume, surface_area): sphericity =\\frac {\\pi^\\frac{1}{3} (6 V_{p})^\\frac{2}{3}} {A_p} """ - return np.pi ** (1 / 3) * (6 * volume) ** (2 / 3) / surface_area + return (np.pi ** (1 / 3)) * ((6 * volume) ** (2 / 3)) / surface_area def sphericity_axis(semi_major, semi_minor): @@ -185,10 +170,14 @@ def sphericity_axis(semi_major, semi_minor): print(f"Error encountered in calculation : {e}") result = "Error in calculation" + if math.isnan(result): + print("NaN in sphericity calculation was replaced by 0") + result = 0 + return result -def dice_coeff(y_true, y_pred): +def dice_coeff(y_true, y_pred, smooth=1.0): """Compute Dice-Sorensen coefficient between two numpy arrays Args: @@ -198,7 +187,6 @@ def dice_coeff(y_true, y_pred): Returns: dice coefficient """ - smooth = 1.0 y_true_f = y_true.flatten() y_pred_f = y_pred.flatten() intersection = np.sum(y_true_f * y_pred_f) @@ -375,82 +363,6 @@ def denormalize_y(image): return image * 255 -def annotation_to_input(label_ermito): - mito = (label_ermito == 1) * 255 - er = (label_ermito == 2) * 255 - mito = normalize_y(mito) - er = normalize_y(er) - mito_anno = np.zeros_like(mito) - er_anno = np.zeros_like(er) - mito = gaussian(mito, sigma=2) * 255 - er = gaussian(er, sigma=2) * 255 - mito_anno[:, :] = mito - er_anno[:, :] = er - anno = np.concatenate( - [mito_anno[:, :, np.newaxis], er_anno[:, :, np.newaxis]], 2 - ) - anno = normalize_x(anno[np.newaxis, :, :, :]) - return anno - - -# def check_csv(project_path, ext): -# if not Path(Path(project_path) / Path(project_path).name).is_file(): -# cols = [ -# "project", -# "type", -# "ext", -# "z", -# "y", -# "x", -# "z_size", -# "y_size", -# "x_size", -# "created_date", -# "update_date", -# "path", -# "notes", -# ] -# df = DataFrame(index=[], columns=cols) -# filename_pattern_original = Path(project_path) / Path( -# f"dataset/Original_size/Original/*{ext}" -# ) -# images_original = dask_imread(filename_pattern_original) -# z, y, x = images_original.shape -# record = Series( -# [ -# Path(project_path).name, -# "dataset", -# ".tif", -# 0, -# 0, -# 0, -# z, -# y, -# x, -# datetime.datetime.now(), -# "", -# Path(project_path) / Path("dataset/Original_size/Original"), -# "", -# ], -# index=df.columns, -# ) -# df = df.append(record, ignore_index=True) -# df.to_csv(Path(project_path) / Path(project_path).name) -# else: -# pass - - -# def check_annotations_dir(project_path): -# if not Path( -# Path(project_path) / Path("annotations/Original_size/master") -# ).is_dir(): -# os.makedirs( -# os.path.join(project_path, "annotations/Original_size/master") -# ) -# else: -# pass - - def fill_list_in_between(lst, n, fill_value): """Fills a list with n * elem between each member of list. Example with list = [1,2,3], n=2, elem='&' : returns [1, &, &,2,&,&,3,&,&] @@ -475,30 +387,6 @@ def fill_list_in_between(lst, n, fill_value): for _j in range(n): new_list.append(fill_value) return new_list - return None - - -# def check_zarr(project_path, ext): -# if not len( -# list( -# (Path(project_path) / "dataset" / "Original_size").glob("./*.zarr") -# ) -# ): -# filename_pattern_original = os.path.join( -# project_path, f"dataset/Original_size/Original/*{ext}" -# ) -# images_original = dask_imread(filename_pattern_original) -# images_original.to_zarr( -# os.path.join(project_path, f"dataset/Original_size/Original.zarr") -# ) -# else: -# pass - - -# def check(project_path, ext): -# check_csv(project_path, ext) -# check_zarr(project_path, ext) -# check_annotations_dir(project_path) def parse_default_path(possible_paths): @@ -580,86 +468,6 @@ def load_images( ) # images_original = dask_imread(filename_pattern_original) - return imread(filename_pattern_original) # tifffile imread - - -# def load_predicted_masks(mito_mask_dir, er_mask_dir, filetype): -# -# images_mito_label = load_images(mito_mask_dir, filetype) -# # TODO : check that there is no problem with compute when loading as single file -# images_mito_label = images_mito_label.compute() -# images_er_label = load_images(er_mask_dir, filetype) -# # TODO : check that there is no problem with compute when loading as single file -# images_er_label = images_er_label.compute() -# base_label = (images_mito_label > 127) * 1 + (images_er_label > 127) * 2 -# return base_label - - -# def load_saved_masks(mod_mask_dir, filetype, as_folder: bool): -# images_label = load_images(mod_mask_dir, filetype, as_folder) -# if as_folder: -# images_label = images_label.compute() -# base_label = images_label -# return base_label - - -def save_stack(images, out_path, filetype=".png", check_warnings=False): - """Saves the files in labels at location out_path as a stack of len(labels) .png files - - Args: - images: array of label images - out_path: path to the directory for saving - """ - num = images.shape[0] - p = Path(out_path) - p.mkdir(exist_ok=True) - for i in range(num): - label = images[i] - io.imsave( - Path(out_path) / Path(str(i).zfill(4) + filetype), - label, - check_contrast=check_warnings, - ) + return imread(str(filename_pattern_original)) # tifffile imread -def select_train_data(dataframe, ori_imgs, label_imgs, ori_filenames): - train_img_names = list() - for node in dataframe.itertuples(): - if node.train == "Checked": - train_img_names.append(node.filename) - - train_ori_imgs = list() - train_label_imgs = list() - for ori_img, label_img, train_filename in zip( - ori_imgs, label_imgs, ori_filenames - ): - if train_filename in train_img_names: - train_ori_imgs.append(ori_img) - train_label_imgs.append(label_img) - - return np.array(train_ori_imgs), np.array(train_label_imgs) - - -# def format_Warning(message, category, filename, lineno, line=""): -# """Formats a warning message, use in code with ``warnings.formatwarning = utils.format_Warning`` -# -# Args: -# message: warning message -# category: which type of warning has been raised -# filename: file -# lineno: line number -# line: unused -# -# Returns: format -# -# """ -# return ( -# str(filename) -# + ":" -# + str(lineno) -# + ": " -# + category.__name__ -# + ": " -# + str(message) -# + "\n" -# ) From 452e12d59ddfe8bf52f509f5ccd25b61599e6905 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 12 Jul 2023 17:31:16 +0200 Subject: [PATCH 002/250] Fixed weight compatibility test --- napari_cellseg3d/_tests/test_models.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/_tests/test_models.py b/napari_cellseg3d/_tests/test_models.py index 994ca3bc..68e790c8 100644 --- a/napari_cellseg3d/_tests/test_models.py +++ b/napari_cellseg3d/_tests/test_models.py @@ -116,8 +116,12 @@ def test_pretrained_weights_compatibility(): for model_name in MODEL_LIST.keys(): file_name = MODEL_LIST[model_name].weights_file WeightsDownloader().download_weights(model_name, file_name) - model = MODEL_LIST[model_name](input_img_size=[128, 128, 128]) + model = MODEL_LIST[model_name](input_img_size=[128,128,128]) try: - model.load_state_dict(torch.load(str(Path(PRETRAINED_WEIGHTS_DIR) / file_name))) + if model_name == "WNet": + strict = False + else: + strict = True + model.load_state_dict(torch.load(str(Path(PRETRAINED_WEIGHTS_DIR) / file_name), map_location="cpu"), strict=strict) except RuntimeError: pytest.fail(f"Failed to load weights for {model_name}") \ No newline at end of file From 3fafbb4c275df1cac5a89ca892e6c50364a8a548 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 12 Jul 2023 17:57:55 +0200 Subject: [PATCH 003/250] Added newest weight to HF for new WNet --- .gitignore | 1 + napari_cellseg3d/_tests/test_models.py | 6 +----- 2 files changed, 2 insertions(+), 5 deletions(-) diff --git a/.gitignore b/.gitignore index 929bcfa0..102de4e1 100644 --- a/.gitignore +++ b/.gitignore @@ -11,6 +11,7 @@ __pycache__/ *.tiff napari_cellseg3d/_tests/res/*.csv *.pth +*.tar.gz *.db # Distribution / packaging diff --git a/napari_cellseg3d/_tests/test_models.py b/napari_cellseg3d/_tests/test_models.py index 68e790c8..a47ef5ea 100644 --- a/napari_cellseg3d/_tests/test_models.py +++ b/napari_cellseg3d/_tests/test_models.py @@ -118,10 +118,6 @@ def test_pretrained_weights_compatibility(): WeightsDownloader().download_weights(model_name, file_name) model = MODEL_LIST[model_name](input_img_size=[128,128,128]) try: - if model_name == "WNet": - strict = False - else: - strict = True - model.load_state_dict(torch.load(str(Path(PRETRAINED_WEIGHTS_DIR) / file_name), map_location="cpu"), strict=strict) + model.load_state_dict(torch.load(str(Path(PRETRAINED_WEIGHTS_DIR) / file_name), map_location="cpu"), strict=True) except RuntimeError: pytest.fail(f"Failed to load weights for {model_name}") \ No newline at end of file From 9dd8172da8e7f3aed163eb6fc43758cca0e62dce Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 13 Jul 2023 11:21:42 +0200 Subject: [PATCH 004/250] Improving worker tests --- .gitignore | 2 + napari_cellseg3d/_tests/test_dock_widget.py | 4 +- napari_cellseg3d/_tests/test_helper.py | 4 +- .../_tests/test_model_framework.py | 24 +- napari_cellseg3d/_tests/test_models.py | 3 +- .../_tests/test_plugin_inference.py | 72 +++-- napari_cellseg3d/_tests/test_plugin_utils.py | 4 +- napari_cellseg3d/_tests/test_plugins.py | 6 +- napari_cellseg3d/_tests/test_review.py | 4 +- napari_cellseg3d/_tests/test_training.py | 8 +- napari_cellseg3d/_tests/test_utils.py | 4 +- napari_cellseg3d/_tests/test_workers.py | 24 ++ .../code_models/models/model_WNet.py | 2 +- .../pretrained/pretrained_model_urls.json | 1 + napari_cellseg3d/code_models/workers.py | 29 +- .../code_plugins/plugin_model_inference.py | 282 +++++++++--------- 16 files changed, 255 insertions(+), 218 deletions(-) create mode 100644 napari_cellseg3d/_tests/test_workers.py diff --git a/.gitignore b/.gitignore index 102de4e1..7dbc9185 100644 --- a/.gitignore +++ b/.gitignore @@ -11,6 +11,8 @@ __pycache__/ *.tiff napari_cellseg3d/_tests/res/*.csv *.pth +*.pt +*.onnx *.tar.gz *.db diff --git a/napari_cellseg3d/_tests/test_dock_widget.py b/napari_cellseg3d/_tests/test_dock_widget.py index 8063c92b..4474729e 100644 --- a/napari_cellseg3d/_tests/test_dock_widget.py +++ b/napari_cellseg3d/_tests/test_dock_widget.py @@ -5,10 +5,10 @@ from napari_cellseg3d.code_plugins.plugin_review_dock import Datamanager -def test_prepare(make_napari_viewer): +def test_prepare(make_napari_viewer_proxy): path_image = str(Path(__file__).resolve().parent / "res/test.tif") image = imread(str(path_image)) - viewer = make_napari_viewer() + viewer = make_napari_viewer_proxy() viewer.add_image(image) widget = Datamanager(viewer) viewer.window.add_dock_widget(widget) diff --git a/napari_cellseg3d/_tests/test_helper.py b/napari_cellseg3d/_tests/test_helper.py index 7c93b7d5..53183edf 100644 --- a/napari_cellseg3d/_tests/test_helper.py +++ b/napari_cellseg3d/_tests/test_helper.py @@ -1,8 +1,8 @@ from napari_cellseg3d.code_plugins.plugin_helper import Helper -def test_helper(make_napari_viewer): - viewer = make_napari_viewer() +def test_helper(make_napari_viewer_proxy): + viewer = make_napari_viewer_proxy() widget = Helper(viewer) dock = viewer.window.add_dock_widget(widget) diff --git a/napari_cellseg3d/_tests/test_model_framework.py b/napari_cellseg3d/_tests/test_model_framework.py index 48f320bc..ff486e4c 100644 --- a/napari_cellseg3d/_tests/test_model_framework.py +++ b/napari_cellseg3d/_tests/test_model_framework.py @@ -8,8 +8,8 @@ def pth(path): return str(Path(path)) -def test_update_default(make_napari_viewer): - view = make_napari_viewer() +def test_update_default(make_napari_viewer_proxy): + view = make_napari_viewer_proxy() widget = model_framework.ModelFramework(view) widget.images_filepaths = [] @@ -39,8 +39,8 @@ def test_update_default(make_napari_viewer): ] -def test_create_train_dataset_dict(make_napari_viewer): - view = make_napari_viewer() +def test_create_train_dataset_dict(make_napari_viewer_proxy): + view = make_napari_viewer_proxy() widget = model_framework.ModelFramework(view) widget.images_filepaths = [str(f"{i}.tif") for i in range(3)] @@ -54,9 +54,9 @@ def test_create_train_dataset_dict(make_napari_viewer): assert widget.create_train_dataset_dict() == expect -def test_log(make_napari_viewer): +def test_log(make_napari_viewer_proxy): mock_test = "test" - framework = model_framework.ModelFramework(viewer=make_napari_viewer()) + framework = model_framework.ModelFramework(viewer=make_napari_viewer_proxy()) framework.log.print_and_log(mock_test) assert len(framework.log.toPlainText()) != 0 assert framework.log.toPlainText() == "\n" + mock_test @@ -77,8 +77,8 @@ def test_log(make_napari_viewer): assert f.read() == "\n" + mock_test log_path.unlink(missing_ok=False) -def test_display_elements(make_napari_viewer): - framework = model_framework.ModelFramework(viewer=make_napari_viewer()) +def test_display_elements(make_napari_viewer_proxy): + framework = model_framework.ModelFramework(viewer=make_napari_viewer_proxy()) framework.display_status_report() framework.display_status_report() @@ -88,12 +88,12 @@ def test_display_elements(make_napari_viewer): assert not framework.weights_filewidget.isVisible() -def test_available_models_retrieval(make_napari_viewer): - framework = model_framework.ModelFramework(viewer=make_napari_viewer()) +def test_available_models_retrieval(make_napari_viewer_proxy): + framework = model_framework.ModelFramework(viewer=make_napari_viewer_proxy()) assert framework.get_available_models() == MODEL_LIST -def test_update_weights_path(make_napari_viewer): - framework = model_framework.ModelFramework(viewer=make_napari_viewer()) +def test_update_weights_path(make_napari_viewer_proxy): + framework = model_framework.ModelFramework(viewer=make_napari_viewer_proxy()) assert framework._update_weights_path(framework._default_weights_folder) is None name = str(Path.home() / "test/weight.pth") framework._update_weights_path([name]) diff --git a/napari_cellseg3d/_tests/test_models.py b/napari_cellseg3d/_tests/test_models.py index a47ef5ea..d1db76c9 100644 --- a/napari_cellseg3d/_tests/test_models.py +++ b/napari_cellseg3d/_tests/test_models.py @@ -59,10 +59,11 @@ def test_soft_ncuts_loss(): device="cpu", intensity_sigma=4, spatial_sigma=4, - radius=None, + radius=None, # test radius=None init ) assert loss.radius == 5 + def test_crf_batch(): dims = 8 mock_image = rand_gen.random(size=(1, dims, dims, dims)) diff --git a/napari_cellseg3d/_tests/test_plugin_inference.py b/napari_cellseg3d/_tests/test_plugin_inference.py index 0258f243..2ce72f2e 100644 --- a/napari_cellseg3d/_tests/test_plugin_inference.py +++ b/napari_cellseg3d/_tests/test_plugin_inference.py @@ -1,6 +1,7 @@ from pathlib import Path - from tifffile import imread +import numpy as np +from napari.qt.threading import GeneratorWorker from napari_cellseg3d._tests.fixtures import LogFixture from napari_cellseg3d.code_models.instance_segmentation import ( @@ -11,16 +12,25 @@ InferenceResult, Inferer, ) -from napari_cellseg3d.config import MODEL_LIST +from napari_cellseg3d.config import MODEL_LIST, InferenceWorkerConfig + + +class WorkerFixture(GeneratorWorker): + def __init__(self): + super().__init__(self.mock_inference) + def mock_inference(self): + while True: + yield InferenceResult(result=None, instance_labels=None) + break -def test_inference(make_napari_viewer, qtbot): +def test_inference(make_napari_viewer_proxy, qtbot): im_path = str(Path(__file__).resolve().parent / "res/test.tif") image = imread(im_path) assert image.shape == (6, 6, 6) - viewer = make_napari_viewer() + viewer = make_napari_viewer_proxy() widget = Inferer(viewer) widget.log = LogFixture() viewer.window.add_dock_widget(widget) @@ -48,22 +58,44 @@ def test_inference(make_napari_viewer, qtbot): assert widget.worker_config is not None assert widget.model_info is not None widget.window_infer_box.setChecked(False) - worker = widget._create_worker_from_config(widget.worker_config) - - assert worker.config is not None - assert worker.config.model_info is not None - worker.config.layer = viewer.layers[0].data - worker.config.post_process_config.instance.enabled = True - worker.config.post_process_config.instance.method = ( - INSTANCE_SEGMENTATION_METHOD_LIST["Watershed"]() - ) - assert worker.config.layer is not None - worker.log_parameters() + # worker = widget._create_worker_from_config(widget.worker_config) + # assert worker.config is not None + # assert worker.config.model_info is not None + # worker.config.layer = viewer.layers[0].data + # worker.config.post_process_config.instance.enabled = True + # worker.config.post_process_config.instance.method = ( + # INSTANCE_SEGMENTATION_METHOD_LIST["Watershed"]() + # ) + + # assert worker.config.layer is not None + # worker.log_parameters() + + # res = next(worker.inference()) + # assert isinstance(res, InferenceResult) + # assert res.result.shape == (8, 8, 8) + # assert res.instance_labels.shape == (8, 8, 8) + # widget.on_yield(res) + + mock_image = np.random.rand(10, 10, 10) + mock_results = InferenceResult( + image_id=0, + original=mock_image, + instance_labels=np.random.randint(0, 10, (10, 10, 10)), + crf_results=mock_image, + stats=None, + result=mock_image, + model_name="test", + ) + num_layers = len(viewer.layers) + widget._display_results(mock_results) + assert len(viewer.layers) == num_layers + 3 - res = next(worker.inference()) - assert isinstance(res, InferenceResult) - assert res.result.shape == (8, 8, 8) - assert res.instance_labels.shape == (8, 8, 8) + assert widget.check_ready() + widget._setup_worker() + # widget.config.show_results = True + with qtbot.waitSignal(widget.worker.yielded, timeout=10000) as blocker: + blocker.connect(widget.worker.errored) # Can add other signals to blocker + widget.worker.start() - widget.on_yield(res) + assert widget.on_finish() diff --git a/napari_cellseg3d/_tests/test_plugin_utils.py b/napari_cellseg3d/_tests/test_plugin_utils.py index 60c25ccc..98f60108 100644 --- a/napari_cellseg3d/_tests/test_plugin_utils.py +++ b/napari_cellseg3d/_tests/test_plugin_utils.py @@ -9,8 +9,8 @@ rand_gen = Generator(PCG64(12345)) -def test_utils_plugin(make_napari_viewer): - view = make_napari_viewer() +def test_utils_plugin(make_napari_viewer_proxy): + view = make_napari_viewer_proxy() widget = Utilities(view) image = rand_gen.random((10, 10, 10)).astype(np.uint8) diff --git a/napari_cellseg3d/_tests/test_plugins.py b/napari_cellseg3d/_tests/test_plugins.py index c58d26af..a91e67ea 100644 --- a/napari_cellseg3d/_tests/test_plugins.py +++ b/napari_cellseg3d/_tests/test_plugins.py @@ -4,12 +4,12 @@ from napari_cellseg3d.code_plugins import plugin_metrics as m -def test_all_plugins_import(make_napari_viewer): +def test_all_plugins_import(make_napari_viewer_proxy): plugins.napari_experimental_provide_dock_widget() -def test_plugin_metrics(make_napari_viewer): - viewer = make_napari_viewer() +def test_plugin_metrics(make_napari_viewer_proxy): + viewer = make_napari_viewer_proxy() w = m.MetricsUtils(viewer=viewer, parent=None) viewer.window.add_dock_widget(w) diff --git a/napari_cellseg3d/_tests/test_review.py b/napari_cellseg3d/_tests/test_review.py index fb61fb29..356e0140 100644 --- a/napari_cellseg3d/_tests/test_review.py +++ b/napari_cellseg3d/_tests/test_review.py @@ -3,8 +3,8 @@ from napari_cellseg3d.code_plugins import plugin_review as rev -def test_launch_review(make_napari_viewer): - view = make_napari_viewer() +def test_launch_review(make_napari_viewer_proxy): + view = make_napari_viewer_proxy() widget = rev.Reviewer(view) # widget.filetype_choice.setCurrentIndex(0) diff --git a/napari_cellseg3d/_tests/test_training.py b/napari_cellseg3d/_tests/test_training.py index e7f1e07b..f7f23aa1 100644 --- a/napari_cellseg3d/_tests/test_training.py +++ b/napari_cellseg3d/_tests/test_training.py @@ -10,10 +10,10 @@ from napari_cellseg3d.config import MODEL_LIST -def test_training(make_napari_viewer, qtbot): +def test_training(make_napari_viewer_proxy, qtbot): im_path = str(Path(__file__).resolve().parent / "res/test.tif") - viewer = make_napari_viewer() + viewer = make_napari_viewer_proxy() widget = Trainer(viewer) widget.log = LogFixture() viewer.window.add_dock_widget(widget) @@ -62,8 +62,8 @@ def test_training(make_napari_viewer, qtbot): # assert widget.worker is not None -def test_update_loss_plot(make_napari_viewer): - view = make_napari_viewer() +def test_update_loss_plot(make_napari_viewer_proxy): + view = make_napari_viewer_proxy() widget = Trainer(view) widget.worker_config = config.TrainingWorkerConfig() diff --git a/napari_cellseg3d/_tests/test_utils.py b/napari_cellseg3d/_tests/test_utils.py index 3f89a0a6..89e5de39 100644 --- a/napari_cellseg3d/_tests/test_utils.py +++ b/napari_cellseg3d/_tests/test_utils.py @@ -194,7 +194,7 @@ def test_parse_default_path(): assert utils.parse_default_path(path) == long_path -def test_thread_test(make_napari_viewer): - viewer = make_napari_viewer() +def test_thread_test(make_napari_viewer_proxy): + viewer = make_napari_viewer_proxy() w = thread_test.create_connected_widget(viewer) viewer.window.add_dock_widget(w) diff --git a/napari_cellseg3d/_tests/test_workers.py b/napari_cellseg3d/_tests/test_workers.py new file mode 100644 index 00000000..0193c7dd --- /dev/null +++ b/napari_cellseg3d/_tests/test_workers.py @@ -0,0 +1,24 @@ +import pytest +import numpy as np +from pathlib import Path + +from napari_cellseg3d.code_models.workers import WeightsDownloader, InferenceWorker +from napari_cellseg3d.config import MODEL_LIST, PRETRAINED_WEIGHTS_DIR, InferenceWorkerConfig + +def test_onnx_inference(make_napari_viewer_proxy): + downloader = WeightsDownloader() + downloader.download_weights("WNet_ONNX", "wnet.onnx") + + config = InferenceWorkerConfig() + config.weights_config.path = str(Path(PRETRAINED_WEIGHTS_DIR).resolve() / "wnet.onnx") + assert Path(config.weights_config.path).is_file() + + viewer = make_napari_viewer_proxy() + viewer.add_image(np.random.rand(10, 10, 10)) + assert viewer.layers[0].data.shape == (10, 10, 10) + config.layer = viewer.layers[0].data + + worker = InferenceWorker(worker_config=config) + + res = next(worker.inference()) + diff --git a/napari_cellseg3d/code_models/models/model_WNet.py b/napari_cellseg3d/code_models/models/model_WNet.py index a2fce724..4058b603 100644 --- a/napari_cellseg3d/code_models/models/model_WNet.py +++ b/napari_cellseg3d/code_models/models/model_WNet.py @@ -32,7 +32,7 @@ def __init__( # # enc = self.forward_encoder(x) # # return self.forward_decoder(enc) - def load_state_dict(self, state_dict, strict=False): + def load_state_dict(self, state_dict, strict=True): """Load the model state dict for inference, without the decoder weights.""" encoder_checkpoint = state_dict.copy() for k in state_dict: diff --git a/napari_cellseg3d/code_models/models/pretrained/pretrained_model_urls.json b/napari_cellseg3d/code_models/models/pretrained/pretrained_model_urls.json index cde5e332..b235a550 100644 --- a/napari_cellseg3d/code_models/models/pretrained/pretrained_model_urls.json +++ b/napari_cellseg3d/code_models/models/pretrained/pretrained_model_urls.json @@ -4,5 +4,6 @@ "VNet": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/VNet.tar.gz", "SwinUNetR": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/Swin64.tar.gz", "WNet": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/wnet.tar.gz", + "WNet_ONNX": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/wnet_onnx.tar.gz", "test": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/test.tar.gz" } diff --git a/napari_cellseg3d/code_models/workers.py b/napari_cellseg3d/code_models/workers.py index 50f85395..827e9fb8 100644 --- a/napari_cellseg3d/code_models/workers.py +++ b/napari_cellseg3d/code_models/workers.py @@ -379,36 +379,9 @@ def load_folder(self): # TODO : better solution than loading first image always ? data_check = LoadImaged(keys=["image"])(images_dict[0]) - check = data_check["image"].shape - - # self.log("\nChecking dimensions...") pad = utils.get_padding_dim(check) - # dims = self.model_dict["model_input_size"] - # - # if self.model_dict["name"] == "SegResNet": - # model = self.model_dict["class"].get_net( - # input_image_size=[ - # dims, - # dims, - # dims, - # ] - # ) - # elif self.model_dict["name"] == "SwinUNetR": - # model = self.model_dict["class"].get_net( - # img_size=[dims, dims, dims], - # use_checkpoint=False, - # ) - # else: - # model = self.model_dict["class"].get_net() - # - # self.log_parameters() - # - # model.to(self.config.device) - - # logger.debug("FILEPATHS PRINT") - # logger.debug(self.images_filepaths) if self.config.sliding_window_config.is_enabled(): load_transforms = Compose( [ @@ -443,7 +416,7 @@ def load_folder(self): def load_layer(self): self.log("\nLoading layer\n") - data = np.squeeze(self.config.layer) + data = np.squeeze(self.config.layer.data) volume = np.array(data, dtype=np.int16) diff --git a/napari_cellseg3d/code_plugins/plugin_model_inference.py b/napari_cellseg3d/code_plugins/plugin_model_inference.py index 256cffa4..e8a4ac2e 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_inference.py +++ b/napari_cellseg3d/code_plugins/plugin_model_inference.py @@ -543,6 +543,124 @@ def _build(self): # self.setMinimumSize(180, 100) # self.setBaseSize(210, 400) + def _update_progress_bar(self, image_id: int, total: int): + pbar_value = image_id // total + if pbar_value == 0: + pbar_value = 1 + + self.progress.setValue(100 * pbar_value) + + def _display_results(self, result: InferenceResult): + viewer = self._viewer + zoom = self.worker_config.post_process_config.zoom.zoom_values + image_id = result.image_id + model_name = self.model_choice.currentText() + + viewer.dims.ndisplay = 3 + viewer.scale_bar.visible = True + + if self.config.show_original and result.original is not None: + viewer.add_image( + result.original, + colormap="inferno", + name=f"original_{image_id}", + scale=zoom, + opacity=0.7, + ) + + out_colormap = "twilight" + if self.worker_config.post_process_config.thresholding.enabled: + out_colormap = "turbo" + + viewer.add_image( + result.result, + colormap=out_colormap, + name=f"pred_{image_id}_{model_name}", + opacity=0.8, + ) + if result.crf_results is not None: + logger.debug( + f"CRF results shape : {result.crf_results.shape}" + ) + viewer.add_image( + result.crf_results, + name=f"CRF_results_image_{image_id}", + colormap="viridis", + ) + if ( + result.instance_labels is not None + and self.worker_config.post_process_config.instance.enabled + ): + method_name = ( + self.worker_config.post_process_config.instance.method.name + ) + + number_cells = ( + np.unique(result.instance_labels.flatten()).size - 1 + ) # remove background + + name = f"({number_cells} objects)_{method_name}_instance_labels_{image_id}" + + viewer.add_labels(result.instance_labels, name=name) + + from napari_cellseg3d.utils import LOGGER as log + + if result.stats is not None and isinstance( + result.stats, list + ): + log.debug(f"len stats : {len(result.stats)}") + + for i, stats in enumerate(result.stats): + # stats = result.stats + + if ( + self.worker_config.compute_stats + and stats is not None + ): + stats_dict = stats.get_dict() + stats_df = pd.DataFrame(stats_dict) + + self.log.print_and_log( + f"Number of instances in channel {i} : {stats.number_objects[0]}" + ) + + csv_name = f"/{method_name}_seg_results_{image_id}_channel_{i}_{utils.get_date_time()}.csv" + stats_df.to_csv( + self.worker_config.results_path + csv_name, + index=False, + ) + + # self.log.print_and_log( + # f"OBJECTS DETECTED : {number_cells}\n" + # ) + def _setup_worker(self): + if self.folder_choice.isChecked(): + self.worker_config.images_filepaths = self.images_filepaths + self.worker = InferenceWorker(worker_config=self.worker_config) + + elif self.layer_choice.isChecked(): + self.worker_config.layer = self.image_layer_loader.layer() + self.worker = InferenceWorker(worker_config=self.worker_config) + + else: + raise ValueError("Please select to load a layer or folder") + + self.worker.set_download_log(self.log) + + self.worker.started.connect(self.on_start) + + self.worker.log_signal.connect(self.log.print_and_log) + self.worker.warn_signal.connect(self.log.warn) + self.worker.error_signal.connect(self.log.error) + + self.worker.yielded.connect(partial(self.on_yield)) # + self.worker.errored.connect(partial(self.on_error)) + self.worker.finished.connect(self.on_finish) + + if self.get_device(show=False) == "cuda": + self.worker.finished.connect(self.empty_cuda_cache) + return self.worker + def start(self): """Start the inference process, enables :py:attr:`~self.worker` and does the following: @@ -580,39 +698,13 @@ def start(self): else: self.log.print_and_log("Starting...") self.log.print_and_log("*" * 20) - self._set_worker_config() - ##################### - ##################### - ##################### - - if self.folder_choice.isChecked(): - self.worker_config.images_filepaths = self.images_filepaths - self.worker = InferenceWorker(worker_config=self.worker_config) - - elif self.layer_choice.isChecked(): - self.worker_config.layer = self.image_layer_loader.layer_data() - self.worker = InferenceWorker(worker_config=self.worker_config) - - else: - raise ValueError("Please select to load a layer or folder") - - self.worker.set_download_log(self.log) - - self.worker.started.connect(self.on_start) - - self.worker.log_signal.connect(self.log.print_and_log) - self.worker.warn_signal.connect(self.log.warn) - self.worker.error_signal.connect(self.log.error) - - self.worker.yielded.connect(partial(self.on_yield)) # - self.worker.errored.connect(partial(self.on_error)) - self.worker.finished.connect(self.on_finish) - - if self.get_device(show=False) == "cuda": - self.worker.finished.connect(self.empty_cuda_cache) + if self.worker_config is None: + raise RuntimeError("Worker config was not set correctly") + self._setup_worker() self.btn_close.setVisible(False) + if self.worker.is_running: # if worker is running, tries to stop self.log.print_and_log( "Stop request, waiting for next inference & saving to occur..." @@ -630,6 +722,19 @@ def _create_worker_from_config( raise TypeError("Please provide a valid worker config object") return InferenceWorker(worker_config=worker_config) + def _set_self_config(self): + self.config = config.InfererConfig( + model_info=self.model_info, + show_results=self.view_checkbox.isChecked(), + show_results_count=self.display_number_choice_slider.slider_value, + show_original=self.show_original_checkbox.isChecked(), + anisotropy_resolution=self.anisotropy_wdgt.resolution_xyz, + ) + if self.layer_choice.isChecked(): + self.config.show_results = True + self.config.show_results_count = 5 + self.config.show_original = False + def _set_worker_config(self) -> config.InferenceWorkerConfig: self.model_info = config.ModelInfo( name=self.model_choice.currentText(), @@ -696,19 +801,7 @@ def _set_worker_config(self) -> config.InferenceWorkerConfig: def on_start(self): """Catches start signal from worker to call :py:func:`~display_status_report`""" self.display_status_report() - - self.config = config.InfererConfig( - model_info=self.model_info, - show_results=self.view_checkbox.isChecked(), - show_results_count=self.display_number_choice_slider.slider_value, - show_original=self.show_original_checkbox.isChecked(), - anisotropy_resolution=self.anisotropy_wdgt.resolution_xyz, - ) - if self.layer_choice.isChecked(): - self.config.show_results = True - self.config.show_results_count = 5 - self.config.show_original = False - + self._set_self_config() self.log.print_and_log(f"Worker started at {utils.get_time()}") self.log.print_and_log(f"Saving results to : {self.results_path}") self.log.print_and_log("Worker is running...") @@ -718,14 +811,9 @@ def on_error(self, error): self.log.print_and_log("!" * 20) self.log.print_and_log("Worker errored...") self.log.error(error) - # self.log.print_and_log("Trying to clean up...") self.worker.quit() - self.btn_start.setText("Start") - self.btn_close.setVisible(True) + self.on_finish() - self.worker_config = None - self.worker = None - self.empty_cuda_cache() def on_finish(self): """Catches finished signal from worker, resets workspace for next run.""" @@ -737,6 +825,7 @@ def on_finish(self): self.worker = None self.worker_config = None self.empty_cuda_cache() + return True # signal clean exit def on_yield(self, result: InferenceResult): """ @@ -756,103 +845,18 @@ def on_yield(self, result: InferenceResult): # check that viewer checkbox is on and that max number of displays has not been reached. # widget.log.print_and_log(result) try: + image_id = result.image_id - model_name = result.model_name if self.worker_config.images_filepaths is not None: total = len(self.worker_config.images_filepaths) else: total = 1 - - viewer = self._viewer - - pbar_value = image_id // total - if pbar_value == 0: - pbar_value = 1 - - self.progress.setValue(100 * pbar_value) + self._update_progress_bar(image_id, total) if ( - self.config.show_results - and image_id <= self.config.show_results_count + self.config.show_results + and image_id <= self.config.show_results_count ): - zoom = self.worker_config.post_process_config.zoom.zoom_values - - viewer.dims.ndisplay = 3 - viewer.scale_bar.visible = True - - if self.config.show_original and result.original is not None: - viewer.add_image( - result.original, - colormap="inferno", - name=f"original_{image_id}", - scale=zoom, - opacity=0.7, - ) - - out_colormap = "twilight" - if self.worker_config.post_process_config.thresholding.enabled: - out_colormap = "turbo" - - viewer.add_image( - result.result, - colormap=out_colormap, - name=f"pred_{image_id}_{model_name}", - opacity=0.8, - ) - if result.crf_results is not None: - logger.debug( - f"CRF results shape : {result.crf_results.shape}" - ) - viewer.add_image( - result.crf_results, - name=f"CRF_results_image_{image_id}", - colormap="viridis", - ) - if ( - result.instance_labels is not None - and self.worker_config.post_process_config.instance.enabled - ): - method_name = ( - self.worker_config.post_process_config.instance.method.name - ) - - number_cells = ( - np.unique(result.instance_labels.flatten()).size - 1 - ) # remove background - - name = f"({number_cells} objects)_{method_name}_instance_labels_{image_id}" - - viewer.add_labels(result.instance_labels, name=name) - - from napari_cellseg3d.utils import LOGGER as log - - if result.stats is not None and isinstance( - result.stats, list - ): - log.debug(f"len stats : {len(result.stats)}") - - for i, stats in enumerate(result.stats): - # stats = result.stats - - if ( - self.worker_config.compute_stats - and stats is not None - ): - stats_dict = stats.get_dict() - stats_df = pd.DataFrame(stats_dict) - - self.log.print_and_log( - f"Number of instances in channel {i} : {stats.number_objects[0]}" - ) - - csv_name = f"/{method_name}_seg_results_{image_id}_channel_{i}_{utils.get_date_time()}.csv" - stats_df.to_csv( - self.worker_config.results_path + csv_name, - index=False, - ) - - # self.log.print_and_log( - # f"OBJECTS DETECTED : {number_cells}\n" - # ) + self._display_results(result) except Exception as e: self.on_error(e) From 0a31889976848f7dfd0d9b2cc7d95bc7bb1845fa Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 13 Jul 2023 11:34:07 +0200 Subject: [PATCH 005/250] Fix style --- .../_tests/test_model_framework.py | 28 ++++++++--- napari_cellseg3d/_tests/test_models.py | 19 ++++--- .../_tests/test_plugin_inference.py | 21 ++++---- napari_cellseg3d/_tests/test_utils.py | 50 ++++++++++++++----- napari_cellseg3d/_tests/test_workers.py | 26 +++++++--- .../code_models/model_framework.py | 4 +- .../code_plugins/plugin_model_inference.py | 29 ++++------- napari_cellseg3d/dev_scripts/thread_test.py | 4 +- napari_cellseg3d/utils.py | 10 ++-- 9 files changed, 120 insertions(+), 71 deletions(-) diff --git a/napari_cellseg3d/_tests/test_model_framework.py b/napari_cellseg3d/_tests/test_model_framework.py index ff486e4c..8ed3d33a 100644 --- a/napari_cellseg3d/_tests/test_model_framework.py +++ b/napari_cellseg3d/_tests/test_model_framework.py @@ -1,7 +1,7 @@ from pathlib import Path -from napari_cellseg3d.config import MODEL_LIST from napari_cellseg3d.code_models import model_framework +from napari_cellseg3d.config import MODEL_LIST def pth(path): @@ -54,9 +54,12 @@ def test_create_train_dataset_dict(make_napari_viewer_proxy): assert widget.create_train_dataset_dict() == expect + def test_log(make_napari_viewer_proxy): mock_test = "test" - framework = model_framework.ModelFramework(viewer=make_napari_viewer_proxy()) + framework = model_framework.ModelFramework( + viewer=make_napari_viewer_proxy() + ) framework.log.print_and_log(mock_test) assert len(framework.log.toPlainText()) != 0 assert framework.log.toPlainText() == "\n" + mock_test @@ -77,8 +80,11 @@ def test_log(make_napari_viewer_proxy): assert f.read() == "\n" + mock_test log_path.unlink(missing_ok=False) + def test_display_elements(make_napari_viewer_proxy): - framework = model_framework.ModelFramework(viewer=make_napari_viewer_proxy()) + framework = model_framework.ModelFramework( + viewer=make_napari_viewer_proxy() + ) framework.display_status_report() framework.display_status_report() @@ -89,14 +95,22 @@ def test_display_elements(make_napari_viewer_proxy): def test_available_models_retrieval(make_napari_viewer_proxy): - framework = model_framework.ModelFramework(viewer=make_napari_viewer_proxy()) + framework = model_framework.ModelFramework( + viewer=make_napari_viewer_proxy() + ) assert framework.get_available_models() == MODEL_LIST + def test_update_weights_path(make_napari_viewer_proxy): - framework = model_framework.ModelFramework(viewer=make_napari_viewer_proxy()) - assert framework._update_weights_path(framework._default_weights_folder) is None + framework = model_framework.ModelFramework( + viewer=make_napari_viewer_proxy() + ) + assert ( + framework._update_weights_path(framework._default_weights_folder) + is None + ) name = str(Path.home() / "test/weight.pth") framework._update_weights_path([name]) assert framework.weights_config.path == name assert framework.weights_filewidget.text_field.text() == name - assert framework._default_weights_folder == str(Path.home() / "test") \ No newline at end of file + assert framework._default_weights_folder == str(Path.home() / "test") diff --git a/napari_cellseg3d/_tests/test_models.py b/napari_cellseg3d/_tests/test_models.py index d1db76c9..ec8f6edc 100644 --- a/napari_cellseg3d/_tests/test_models.py +++ b/napari_cellseg3d/_tests/test_models.py @@ -1,6 +1,6 @@ -import numpy as np from pathlib import Path +import numpy as np import pytest import torch from numpy.random import PCG64, Generator @@ -111,14 +111,19 @@ def on_yield(result): def test_pretrained_weights_compatibility(): from napari_cellseg3d.code_models.workers import WeightsDownloader - from napari_cellseg3d.config import MODEL_LIST - from napari_cellseg3d.config import PRETRAINED_WEIGHTS_DIR + from napari_cellseg3d.config import MODEL_LIST, PRETRAINED_WEIGHTS_DIR - for model_name in MODEL_LIST.keys(): + for model_name in MODEL_LIST: file_name = MODEL_LIST[model_name].weights_file WeightsDownloader().download_weights(model_name, file_name) - model = MODEL_LIST[model_name](input_img_size=[128,128,128]) + model = MODEL_LIST[model_name](input_img_size=[128, 128, 128]) try: - model.load_state_dict(torch.load(str(Path(PRETRAINED_WEIGHTS_DIR) / file_name), map_location="cpu"), strict=True) + model.load_state_dict( + torch.load( + str(Path(PRETRAINED_WEIGHTS_DIR) / file_name), + map_location="cpu", + ), + strict=True, + ) except RuntimeError: - pytest.fail(f"Failed to load weights for {model_name}") \ No newline at end of file + pytest.fail(f"Failed to load weights for {model_name}") diff --git a/napari_cellseg3d/_tests/test_plugin_inference.py b/napari_cellseg3d/_tests/test_plugin_inference.py index 2ce72f2e..7bd8a001 100644 --- a/napari_cellseg3d/_tests/test_plugin_inference.py +++ b/napari_cellseg3d/_tests/test_plugin_inference.py @@ -1,18 +1,18 @@ from pathlib import Path -from tifffile import imread -import numpy as np + from napari.qt.threading import GeneratorWorker +from numpy.random import PCG64, Generator +from tifffile import imread from napari_cellseg3d._tests.fixtures import LogFixture -from napari_cellseg3d.code_models.instance_segmentation import ( - INSTANCE_SEGMENTATION_METHOD_LIST, -) from napari_cellseg3d.code_models.models.model_test import TestModel from napari_cellseg3d.code_plugins.plugin_model_inference import ( InferenceResult, Inferer, ) -from napari_cellseg3d.config import MODEL_LIST, InferenceWorkerConfig +from napari_cellseg3d.config import MODEL_LIST + +rand_gen = Generator(PCG64(12345)) class WorkerFixture(GeneratorWorker): @@ -24,6 +24,7 @@ def mock_inference(self): yield InferenceResult(result=None, instance_labels=None) break + def test_inference(make_napari_viewer_proxy, qtbot): im_path = str(Path(__file__).resolve().parent / "res/test.tif") image = imread(im_path) @@ -77,11 +78,11 @@ def test_inference(make_napari_viewer_proxy, qtbot): # assert res.instance_labels.shape == (8, 8, 8) # widget.on_yield(res) - mock_image = np.random.rand(10, 10, 10) + mock_image = rand_gen.random(size=(10, 10, 10)) mock_results = InferenceResult( image_id=0, original=mock_image, - instance_labels=np.random.randint(0, 10, (10, 10, 10)), + instance_labels=rand_gen.integers(0, 10, (10, 10, 10)), crf_results=mock_image, stats=None, result=mock_image, @@ -95,7 +96,9 @@ def test_inference(make_napari_viewer_proxy, qtbot): widget._setup_worker() # widget.config.show_results = True with qtbot.waitSignal(widget.worker.yielded, timeout=10000) as blocker: - blocker.connect(widget.worker.errored) # Can add other signals to blocker + blocker.connect( + widget.worker.errored + ) # Can add other signals to blocker widget.worker.start() assert widget.on_finish() diff --git a/napari_cellseg3d/_tests/test_utils.py b/napari_cellseg3d/_tests/test_utils.py index 89e5de39..448548c6 100644 --- a/napari_cellseg3d/_tests/test_utils.py +++ b/napari_cellseg3d/_tests/test_utils.py @@ -1,13 +1,18 @@ import random from functools import partial from pathlib import Path + import numpy as np import pytest import torch +from numpy.random import PCG64, Generator from napari_cellseg3d import utils from napari_cellseg3d.dev_scripts import thread_test +rand_gen = Generator(PCG64(12345)) + + def test_singleton_class(): class TestSingleton(metaclass=utils.Singleton): def __init__(self, value): @@ -17,17 +22,22 @@ def __init__(self, value): b = TestSingleton(2) assert a.value == b.value + + def test_save_folder(): test_path = Path(__file__).resolve().parent / "res" folder_name = "test_folder" - images = [np.random.rand(5, 5, 5) for _ in range(10)] + images = [rand_gen.random((5, 5, 5)) for _ in range(10)] images_paths = [f"{i}.tif" for i in range(10)] - utils.save_folder(test_path, folder_name, images, images_paths, exist_ok=True) + utils.save_folder( + test_path, folder_name, images, images_paths, exist_ok=True + ) assert (test_path / folder_name).is_dir() for i in range(10): assert (test_path / folder_name / images_paths[i]).is_file() + def test_normalize_y(): test_array = np.array([0, 255, 127.5]) results = utils.normalize_y(test_array) @@ -35,11 +45,16 @@ def test_normalize_y(): assert np.all(results == expected) assert np.all(test_array == utils.denormalize_y(results)) + def test_sphericities(): - for i in range(100): + for _i in range(100): mock_volume = random.randint(1, 10) - mock_surface = random.randint(100, 1000) # assuming surface is always larger than volume - sphericity_vol = utils.sphericity_volume_area(mock_volume, mock_surface) + mock_surface = random.randint( + 100, 1000 + ) # assuming surface is always larger than volume + sphericity_vol = utils.sphericity_volume_area( + mock_volume, mock_surface + ) assert 0 <= sphericity_vol <= 1 semi_major = random.randint(10, 100) @@ -52,17 +67,20 @@ def test_sphericities(): sphericity_axes = 0 assert 0 <= sphericity_axes <= 1 + def test_normalize_max(): test_array = np.array([0, 255, 127.5]) expected = np.array([0, 1, 0.5]) assert np.all(utils.normalize_max(test_array) == expected) + def test_dice_coeff(): - test_array = np.random.randint(0,2,(64, 64, 64)) - test_array_2 = np.random.randint(0,2,(64, 64, 64)) + test_array = rand_gen.integers(0, 2, (64, 64, 64)) + test_array_2 = rand_gen.integers(0, 2, (64, 64, 64)) assert utils.dice_coeff(test_array, test_array) == 1 assert utils.dice_coeff(test_array, test_array_2) <= 1 + def test_fill_list_in_between(): test_list = [1, 2, 3, 4, 5, 6] res = [ @@ -158,7 +176,10 @@ def test_get_padding_dim(): assert pad == [128, 128, 128] tensor_wrong = torch.randn(65, 70, 80, 90) - with pytest.raises(ValueError): + with pytest.raises( + ValueError, + match="Please check the dimensions of the input, only 2 or 3-dimensional data is supported currently", + ): utils.get_padding_dim(tensor_wrong.size()) @@ -167,18 +188,23 @@ def test_normalize_x(): expected = np.array([-1, 1, 0]) assert np.all(test_array == expected) -def test_load_images(): +def test_load_images(): path = Path(__file__).resolve().parent / "res" - with pytest.raises(ValueError): + with pytest.raises( + ValueError, match="If loading as a folder, filetype must be specified" + ): images = utils.load_images(str(path), as_folder=True) - with pytest.raises(NotImplementedError): + with pytest.raises( + NotImplementedError, + match="Loading as folder not implemented yet. Use napari to load as folder", + ): images = utils.load_images(str(path), as_folder=True, filetype=".tif") # assert len(images) == 1 path = path / "test.tif" images = utils.load_images(str(path)) - assert images.shape == (6,6,6) + assert images.shape == (6, 6, 6) def test_parse_default_path(): diff --git a/napari_cellseg3d/_tests/test_workers.py b/napari_cellseg3d/_tests/test_workers.py index 0193c7dd..1e65f527 100644 --- a/napari_cellseg3d/_tests/test_workers.py +++ b/napari_cellseg3d/_tests/test_workers.py @@ -1,24 +1,34 @@ -import pytest -import numpy as np from pathlib import Path -from napari_cellseg3d.code_models.workers import WeightsDownloader, InferenceWorker -from napari_cellseg3d.config import MODEL_LIST, PRETRAINED_WEIGHTS_DIR, InferenceWorkerConfig +from numpy.random import PCG64, Generator + +from napari_cellseg3d.code_models.workers import ( + InferenceWorker, + WeightsDownloader, +) +from napari_cellseg3d.config import ( + PRETRAINED_WEIGHTS_DIR, + InferenceWorkerConfig, +) + +rand_gen = Generator(PCG64(12345)) + def test_onnx_inference(make_napari_viewer_proxy): downloader = WeightsDownloader() downloader.download_weights("WNet_ONNX", "wnet.onnx") config = InferenceWorkerConfig() - config.weights_config.path = str(Path(PRETRAINED_WEIGHTS_DIR).resolve() / "wnet.onnx") + config.weights_config.path = str( + Path(PRETRAINED_WEIGHTS_DIR).resolve() / "wnet.onnx" + ) assert Path(config.weights_config.path).is_file() viewer = make_napari_viewer_proxy() - viewer.add_image(np.random.rand(10, 10, 10)) + viewer.add_image(rand_gen.random((10, 10, 10))) assert viewer.layers[0].data.shape == (10, 10, 10) config.layer = viewer.layers[0].data worker = InferenceWorker(worker_config=config) - res = next(worker.inference()) - + next(worker.inference()) diff --git a/napari_cellseg3d/code_models/model_framework.py b/napari_cellseg3d/code_models/model_framework.py index 4860d59e..ee9834c2 100644 --- a/napari_cellseg3d/code_models/model_framework.py +++ b/napari_cellseg3d/code_models/model_framework.py @@ -161,9 +161,7 @@ def save_log_to_path(self, path, do_timestamp=True): else: log_name = "Log_report.txt" - path = str( - Path(path) / log_name - ) + path = str(Path(path) / log_name) if len(log) != 0: with Path.open( diff --git a/napari_cellseg3d/code_plugins/plugin_model_inference.py b/napari_cellseg3d/code_plugins/plugin_model_inference.py index e8a4ac2e..27093754 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_inference.py +++ b/napari_cellseg3d/code_plugins/plugin_model_inference.py @@ -579,24 +579,22 @@ def _display_results(self, result: InferenceResult): opacity=0.8, ) if result.crf_results is not None: - logger.debug( - f"CRF results shape : {result.crf_results.shape}" - ) + logger.debug(f"CRF results shape : {result.crf_results.shape}") viewer.add_image( result.crf_results, name=f"CRF_results_image_{image_id}", colormap="viridis", ) if ( - result.instance_labels is not None - and self.worker_config.post_process_config.instance.enabled + result.instance_labels is not None + and self.worker_config.post_process_config.instance.enabled ): method_name = ( self.worker_config.post_process_config.instance.method.name ) number_cells = ( - np.unique(result.instance_labels.flatten()).size - 1 + np.unique(result.instance_labels.flatten()).size - 1 ) # remove background name = f"({number_cells} objects)_{method_name}_instance_labels_{image_id}" @@ -605,18 +603,13 @@ def _display_results(self, result: InferenceResult): from napari_cellseg3d.utils import LOGGER as log - if result.stats is not None and isinstance( - result.stats, list - ): + if result.stats is not None and isinstance(result.stats, list): log.debug(f"len stats : {len(result.stats)}") for i, stats in enumerate(result.stats): # stats = result.stats - if ( - self.worker_config.compute_stats - and stats is not None - ): + if self.worker_config.compute_stats and stats is not None: stats_dict = stats.get_dict() stats_df = pd.DataFrame(stats_dict) @@ -633,6 +626,7 @@ def _display_results(self, result: InferenceResult): # self.log.print_and_log( # f"OBJECTS DETECTED : {number_cells}\n" # ) + def _setup_worker(self): if self.folder_choice.isChecked(): self.worker_config.images_filepaths = self.images_filepaths @@ -704,7 +698,6 @@ def start(self): self._setup_worker() self.btn_close.setVisible(False) - if self.worker.is_running: # if worker is running, tries to stop self.log.print_and_log( "Stop request, waiting for next inference & saving to occur..." @@ -814,7 +807,6 @@ def on_error(self, error): self.worker.quit() self.on_finish() - def on_finish(self): """Catches finished signal from worker, resets workspace for next run.""" self.log.print_and_log(f"\nWorker finished at {utils.get_time()}") @@ -845,7 +837,6 @@ def on_yield(self, result: InferenceResult): # check that viewer checkbox is on and that max number of displays has not been reached. # widget.log.print_and_log(result) try: - image_id = result.image_id if self.worker_config.images_filepaths is not None: total = len(self.worker_config.images_filepaths) @@ -854,9 +845,9 @@ def on_yield(self, result: InferenceResult): self._update_progress_bar(image_id, total) if ( - self.config.show_results - and image_id <= self.config.show_results_count + self.config.show_results + and image_id <= self.config.show_results_count ): - self._display_results(result) + self._display_results(result) except Exception as e: self.on_error(e) diff --git a/napari_cellseg3d/dev_scripts/thread_test.py b/napari_cellseg3d/dev_scripts/thread_test.py index c511b71c..112ec0ce 100644 --- a/napari_cellseg3d/dev_scripts/thread_test.py +++ b/napari_cellseg3d/dev_scripts/thread_test.py @@ -1,4 +1,5 @@ import time + import napari from napari.qt.threading import thread_worker from numpy.random import PCG64, Generator @@ -19,6 +20,7 @@ #################################### # not covered by tests + @thread_worker def two_way_communication_with_args(start, end): """Both sends and receives values to & from the main thread. @@ -134,7 +136,7 @@ def on_finish(): if __name__ == "__main__": - viewer = napari.view_image(rand_gen.random(512, 512)) + viewer = napari.view_image(rand_gen.random((512, 512))) w = create_connected_widget(viewer) viewer.window.add_dock_widget(w) diff --git a/napari_cellseg3d/utils.py b/napari_cellseg3d/utils.py index 84ccc380..3dff19c0 100644 --- a/napari_cellseg3d/utils.py +++ b/napari_cellseg3d/utils.py @@ -7,8 +7,6 @@ import napari import numpy as np from monai.transforms import Zoom -from skimage import io -from skimage.filters import gaussian from tifffile import imread, imwrite if TYPE_CHECKING: @@ -29,7 +27,9 @@ #################### # viewer utils -def save_folder(results_path, folder_name, images, image_paths, exist_ok=False): +def save_folder( + results_path, folder_name, images, image_paths, exist_ok=False +): """ Saves a list of images in a folder @@ -93,6 +93,7 @@ def show_result(viewer, layer, image, name): f"Results not shown, unsupported layer type {type(layer)}" ) + class Singleton(type): """ Singleton class that can only be instantiated once at a time, @@ -387,6 +388,7 @@ def fill_list_in_between(lst, n, fill_value): for _j in range(n): new_list.append(fill_value) return new_list + return None def parse_default_path(possible_paths): @@ -469,5 +471,3 @@ def load_images( # images_original = dask_imread(filename_pattern_original) return imread(str(filename_pattern_original)) # tifffile imread - - From 6a5fa93490037faca776f81742a92f40e3c77b77 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 13 Jul 2023 11:48:50 +0200 Subject: [PATCH 006/250] Improve stats display test coverage --- napari_cellseg3d/_tests/test_plugin_inference.py | 9 ++++++--- napari_cellseg3d/code_plugins/plugin_model_inference.py | 8 ++++---- 2 files changed, 10 insertions(+), 7 deletions(-) diff --git a/napari_cellseg3d/_tests/test_plugin_inference.py b/napari_cellseg3d/_tests/test_plugin_inference.py index 7bd8a001..2e3dc1b9 100644 --- a/napari_cellseg3d/_tests/test_plugin_inference.py +++ b/napari_cellseg3d/_tests/test_plugin_inference.py @@ -5,6 +5,7 @@ from tifffile import imread from napari_cellseg3d._tests.fixtures import LogFixture +from napari_cellseg3d.code_models.instance_segmentation import volume_stats from napari_cellseg3d.code_models.models.model_test import TestModel from napari_cellseg3d.code_plugins.plugin_model_inference import ( InferenceResult, @@ -79,18 +80,20 @@ def test_inference(make_napari_viewer_proxy, qtbot): # widget.on_yield(res) mock_image = rand_gen.random(size=(10, 10, 10)) + mock_labels = rand_gen.integers(0, 10, (10, 10, 10)) mock_results = InferenceResult( image_id=0, original=mock_image, - instance_labels=rand_gen.integers(0, 10, (10, 10, 10)), + instance_labels=mock_labels, crf_results=mock_image, - stats=None, + stats=[volume_stats(mock_labels)], result=mock_image, model_name="test", ) num_layers = len(viewer.layers) + widget.worker_config.post_process_config.instance.enabled = True widget._display_results(mock_results) - assert len(viewer.layers) == num_layers + 3 + assert len(viewer.layers) == num_layers + 4 assert widget.check_ready() widget._setup_worker() diff --git a/napari_cellseg3d/code_plugins/plugin_model_inference.py b/napari_cellseg3d/code_plugins/plugin_model_inference.py index 27093754..f9d6cb21 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_inference.py +++ b/napari_cellseg3d/code_plugins/plugin_model_inference.py @@ -601,10 +601,10 @@ def _display_results(self, result: InferenceResult): viewer.add_labels(result.instance_labels, name=name) - from napari_cellseg3d.utils import LOGGER as log - - if result.stats is not None and isinstance(result.stats, list): - log.debug(f"len stats : {len(result.stats)}") + if result.stats is not None and isinstance( + result.stats, list + ): # list for several channels + # logger.debug(f"len stats : {len(result.stats)}") for i, stats in enumerate(result.stats): # stats = result.stats From b4b6280030e7f771483354a17edf573b82ab303f Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 13 Jul 2023 11:52:56 +0200 Subject: [PATCH 007/250] Disable direct thread worker test for GH actions --- .../_tests/test_plugin_inference.py | 51 ++++++++++--------- 1 file changed, 27 insertions(+), 24 deletions(-) diff --git a/napari_cellseg3d/_tests/test_plugin_inference.py b/napari_cellseg3d/_tests/test_plugin_inference.py index 2e3dc1b9..475d8e2c 100644 --- a/napari_cellseg3d/_tests/test_plugin_inference.py +++ b/napari_cellseg3d/_tests/test_plugin_inference.py @@ -5,7 +5,10 @@ from tifffile import imread from napari_cellseg3d._tests.fixtures import LogFixture -from napari_cellseg3d.code_models.instance_segmentation import volume_stats +from napari_cellseg3d.code_models.instance_segmentation import ( + INSTANCE_SEGMENTATION_METHOD_LIST, + volume_stats, +) from napari_cellseg3d.code_models.models.model_test import TestModel from napari_cellseg3d.code_plugins.plugin_model_inference import ( InferenceResult, @@ -61,23 +64,23 @@ def test_inference(make_napari_viewer_proxy, qtbot): assert widget.model_info is not None widget.window_infer_box.setChecked(False) - # worker = widget._create_worker_from_config(widget.worker_config) - # assert worker.config is not None - # assert worker.config.model_info is not None - # worker.config.layer = viewer.layers[0].data - # worker.config.post_process_config.instance.enabled = True - # worker.config.post_process_config.instance.method = ( - # INSTANCE_SEGMENTATION_METHOD_LIST["Watershed"]() - # ) + worker = widget._create_worker_from_config(widget.worker_config) + assert worker.config is not None + assert worker.config.model_info is not None + worker.config.layer = viewer.layers[0].data + worker.config.post_process_config.instance.enabled = True + worker.config.post_process_config.instance.method = ( + INSTANCE_SEGMENTATION_METHOD_LIST["Watershed"]() + ) - # assert worker.config.layer is not None - # worker.log_parameters() + assert worker.config.layer is not None + worker.log_parameters() - # res = next(worker.inference()) - # assert isinstance(res, InferenceResult) - # assert res.result.shape == (8, 8, 8) - # assert res.instance_labels.shape == (8, 8, 8) - # widget.on_yield(res) + res = next(worker.inference()) + assert isinstance(res, InferenceResult) + assert res.result.shape == (8, 8, 8) + assert res.instance_labels.shape == (8, 8, 8) + widget.on_yield(res) mock_image = rand_gen.random(size=(10, 10, 10)) mock_labels = rand_gen.integers(0, 10, (10, 10, 10)) @@ -95,13 +98,13 @@ def test_inference(make_napari_viewer_proxy, qtbot): widget._display_results(mock_results) assert len(viewer.layers) == num_layers + 4 - assert widget.check_ready() - widget._setup_worker() - # widget.config.show_results = True - with qtbot.waitSignal(widget.worker.yielded, timeout=10000) as blocker: - blocker.connect( - widget.worker.errored - ) # Can add other signals to blocker - widget.worker.start() + # assert widget.check_ready() + # widget._setup_worker() + # # widget.config.show_results = True + # with qtbot.waitSignal(widget.worker.yielded, timeout=10000) as blocker: + # blocker.connect( + # widget.worker.errored + # ) # Can add other signals to blocker + # widget.worker.start() assert widget.on_finish() From 509d63c6541b50c7f86628efd045011b7c08777c Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 13 Jul 2023 13:10:21 +0200 Subject: [PATCH 008/250] Update tox.ini Fix ONNX reqs --- tox.ini | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tox.ini b/tox.ini index 1b9b5e22..b8c76091 100644 --- a/tox.ini +++ b/tox.ini @@ -35,6 +35,8 @@ deps = pytest-qt qtpy git+https://github.com/lucasb-eyer/pydensecrf.git@master#egg=pydensecrf + onnx + onnxruntime ; pyopencl[pocl] ; opencv-python extras = crf From ccf0f7747a9c63961d54fc344fe5c8d96cde39a9 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 13 Jul 2023 13:11:00 +0200 Subject: [PATCH 009/250] Update .coveragerc Disable dev scripts coverage --- .coveragerc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.coveragerc b/.coveragerc index a2bc84e8..4f06b4f7 100644 --- a/.coveragerc +++ b/.coveragerc @@ -4,4 +4,4 @@ exclude_lines = [run] omit = - napari_cellseg3d/setup.py, napari_cellseg3d/code_models/models/wnet/train_wnet.py, napari_cellseg3d/code_models/models/wnet/model.py,napari_cellseg3d/code_models/models/TEMPLATE_model.py, napari_cellseg3d/code_models/models/unet/*, napari_cellseg3d/dev_scripts/thread_test.py + napari_cellseg3d/setup.py, napari_cellseg3d/code_models/models/wnet/train_wnet.py, napari_cellseg3d/code_models/models/wnet/model.py,napari_cellseg3d/code_models/models/TEMPLATE_model.py, napari_cellseg3d/code_models/models/unet/*, napari_cellseg3d/dev_scripts/* From 20939277e917e9bcc4294c31a9f0fe12f0d46726 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 13 Jul 2023 15:02:56 +0200 Subject: [PATCH 010/250] More tests & fixes Tests : - Base plugin - ONNX inference - Cropping Fixes : - Fixed review image loading not working - Fixed scaling when running inference on a folder and showing originals --- napari_cellseg3d/_tests/test_base_plugin.py | 19 +++++ napari_cellseg3d/_tests/test_plugin_utils.py | 27 +++++++ napari_cellseg3d/_tests/test_training.py | 2 - napari_cellseg3d/_tests/test_utils.py | 21 +++--- napari_cellseg3d/_tests/test_workers.py | 28 +++---- napari_cellseg3d/code_models/workers.py | 2 +- napari_cellseg3d/code_plugins/plugin_base.py | 75 +++++++++++-------- napari_cellseg3d/code_plugins/plugin_crop.py | 20 ++--- .../code_plugins/plugin_metrics.py | 2 +- .../code_plugins/plugin_model_inference.py | 7 +- .../code_plugins/plugin_model_training.py | 6 +- .../code_plugins/plugin_review.py | 9 +-- napari_cellseg3d/utils.py | 33 ++++---- 13 files changed, 149 insertions(+), 102 deletions(-) create mode 100644 napari_cellseg3d/_tests/test_base_plugin.py diff --git a/napari_cellseg3d/_tests/test_base_plugin.py b/napari_cellseg3d/_tests/test_base_plugin.py new file mode 100644 index 00000000..98c35ce6 --- /dev/null +++ b/napari_cellseg3d/_tests/test_base_plugin.py @@ -0,0 +1,19 @@ +from pathlib import Path + +from napari_cellseg3d.code_plugins.plugin_base import ( + BasePluginSingleImage, +) + + +def test_base_single_image(make_napari_viewer_proxy): + viewer = make_napari_viewer_proxy() + plugin = BasePluginSingleImage(viewer) + + test_folder = Path(__file__).parent.resolve() + test_image = str(test_folder / "res/test.tif") + + assert plugin._check_results_path(str(test_folder)) + plugin.image_path = test_image + assert plugin._default_path[0] != test_image + plugin._update_default() + assert plugin._default_path[0] == test_image diff --git a/napari_cellseg3d/_tests/test_plugin_utils.py b/napari_cellseg3d/_tests/test_plugin_utils.py index 98f60108..1427021a 100644 --- a/napari_cellseg3d/_tests/test_plugin_utils.py +++ b/napari_cellseg3d/_tests/test_plugin_utils.py @@ -1,6 +1,7 @@ import numpy as np from numpy.random import PCG64, Generator +from napari_cellseg3d.code_plugins.plugin_crop import Cropping from napari_cellseg3d.code_plugins.plugin_utilities import ( UTILITIES_WIDGETS, Utilities, @@ -32,3 +33,29 @@ def test_utils_plugin(make_napari_viewer_proxy): assert len(image_layer.data.shape) == 3 assert len(label_layer.data.shape) == 3 widget.utils_widgets[i]._start() + + +def test_crop_widget(make_napari_viewer_proxy): + view = make_napari_viewer_proxy() + widget = Cropping(view) + + image = rand_gen.random((10, 10, 10)).astype(np.uint8) + image_layer_1 = view.add_image(image, name="image") + image_layer_2 = view.add_labels(image, name="image2") + + view.window.add_dock_widget(widget) + view.dims.ndisplay = 3 + assert len(image_layer_1.data.shape) == 3 + assert len(image_layer_2.data.shape) == 3 + widget.crop_second_image_choice.setChecked(True) + widget.aniso_widgets.checkbox.setChecked(True) + + widget._start() + widget.create_new_layer.setChecked(True) + widget.quicksave() + + widget.sliders[0].setValue(2) + widget.sliders[1].setValue(2) + widget.sliders[2].setValue(2) + + widget._start() diff --git a/napari_cellseg3d/_tests/test_training.py b/napari_cellseg3d/_tests/test_training.py index f7f23aa1..728eb3c2 100644 --- a/napari_cellseg3d/_tests/test_training.py +++ b/napari_cellseg3d/_tests/test_training.py @@ -23,8 +23,6 @@ def test_training(make_napari_viewer_proxy, qtbot): assert not widget.check_ready() - assert widget.filetype_choice.currentText() == ".tif" - widget.images_filepaths = [im_path] widget.labels_filepaths = [im_path] widget.epoch_choice.setValue(1) diff --git a/napari_cellseg3d/_tests/test_utils.py b/napari_cellseg3d/_tests/test_utils.py index 448548c6..b1342afe 100644 --- a/napari_cellseg3d/_tests/test_utils.py +++ b/napari_cellseg3d/_tests/test_utils.py @@ -191,17 +191,16 @@ def test_normalize_x(): def test_load_images(): path = Path(__file__).resolve().parent / "res" - with pytest.raises( - ValueError, match="If loading as a folder, filetype must be specified" - ): - images = utils.load_images(str(path), as_folder=True) - with pytest.raises( - NotImplementedError, - match="Loading as folder not implemented yet. Use napari to load as folder", - ): - images = utils.load_images(str(path), as_folder=True, filetype=".tif") - # assert len(images) == 1 - + # with pytest.raises( + # ValueError, match="If loading as a folder, filetype must be specified" + # ): + # images = utils.load_images(str(path), as_folder=True) + # with pytest.raises( + # NotImplementedError, + # match="Loading as folder not implemented yet. Use napari to load as folder", + # ): + # images = utils.load_images(str(path), as_folder=True, filetype=".tif") + # # assert len(images) == 1 path = path / "test.tif" images = utils.load_images(str(path)) assert images.shape == (6, 6, 6) diff --git a/napari_cellseg3d/_tests/test_workers.py b/napari_cellseg3d/_tests/test_workers.py index 1e65f527..235763a5 100644 --- a/napari_cellseg3d/_tests/test_workers.py +++ b/napari_cellseg3d/_tests/test_workers.py @@ -1,14 +1,14 @@ from pathlib import Path +import torch from numpy.random import PCG64, Generator from napari_cellseg3d.code_models.workers import ( - InferenceWorker, + ONNXModelWrapper, WeightsDownloader, ) from napari_cellseg3d.config import ( PRETRAINED_WEIGHTS_DIR, - InferenceWorkerConfig, ) rand_gen = Generator(PCG64(12345)) @@ -17,18 +17,12 @@ def test_onnx_inference(make_napari_viewer_proxy): downloader = WeightsDownloader() downloader.download_weights("WNet_ONNX", "wnet.onnx") - - config = InferenceWorkerConfig() - config.weights_config.path = str( - Path(PRETRAINED_WEIGHTS_DIR).resolve() / "wnet.onnx" - ) - assert Path(config.weights_config.path).is_file() - - viewer = make_napari_viewer_proxy() - viewer.add_image(rand_gen.random((10, 10, 10))) - assert viewer.layers[0].data.shape == (10, 10, 10) - config.layer = viewer.layers[0].data - - worker = InferenceWorker(worker_config=config) - - next(worker.inference()) + path = str(Path(PRETRAINED_WEIGHTS_DIR).resolve() / "wnet.onnx") + assert Path(path).is_file() + dims = 64 + batch = 2 + x = torch.randn(size=(batch, 1, dims, dims, dims)) + worker = ONNXModelWrapper(file_location=path) + assert worker.eval() is None + assert worker.to(device="cpu") is None + assert worker.forward(x).shape == (batch, 2, dims, dims, dims) diff --git a/napari_cellseg3d/code_models/workers.py b/napari_cellseg3d/code_models/workers.py index 827e9fb8..2ba5e7db 100644 --- a/napari_cellseg3d/code_models/workers.py +++ b/napari_cellseg3d/code_models/workers.py @@ -360,7 +360,7 @@ def log_parameters(self): if config.keep_on_cpu: self.log("Dataset loaded to CPU") else: - self.log(f"Dataset loaded on {config.device}") + self.log(f"Dataset loaded on {config.device} device") if config.post_process_config.zoom.enabled: self.log( diff --git a/napari_cellseg3d/code_plugins/plugin_base.py b/napari_cellseg3d/code_plugins/plugin_base.py index cfa3f0d7..b406ee20 100644 --- a/napari_cellseg3d/code_plugins/plugin_base.py +++ b/napari_cellseg3d/code_plugins/plugin_base.py @@ -61,6 +61,7 @@ def __init__( ################ self.layer_choice = ui.RadioButton("Layer", parent=self) self.folder_choice = ui.RadioButton("Folder", parent=self) + self.filetype = None self.radio_buttons = ui.combine_blocks( self.folder_choice, self.layer_choice ) @@ -68,7 +69,10 @@ def __init__( ################ # Image widgets self.image_filewidget = ui.FilePathWidget( - "Image path", self._show_dialog_images, self + "Image path", + self._show_dialog_images, + self, + required=True, ) self.image_layer_loader: ui.LayerSelecter = ui.LayerSelecter( @@ -81,7 +85,10 @@ def __init__( ################ # Label widgets self.labels_filewidget = ui.FilePathWidget( - "Label path", self._show_dialog_labels, parent=self + "Label path", + self._show_dialog_labels, + parent=self, + required=True, ) self.label_layer_loader: ui.LayerSelecter = ui.LayerSelecter( @@ -96,10 +103,6 @@ def __init__( self.results_filewidget = ui.FilePathWidget( "Saving path", self._load_results_path, parent=self ) - - self.filetype_choice = ui.DropdownMenu( - [".tif", ".tiff"], text_label="File format" - ) ######## qInstallMessageHandler(ui.handle_adjust_errors_wrapper(self)) @@ -125,7 +128,7 @@ def _build_io_panel(self): self.radio_buttons, self.image_layer_loader, self.label_layer_loader, - self.filetype_choice, + # self.filetype_choice, self.image_filewidget, self.labels_filewidget, self.save_label, @@ -165,7 +168,7 @@ def _set_io_visibility(self): # Show when folder is selected f = self.folder_choice - self._show_io_element(self.filetype_choice, f) + # self._show_io_element(self.filetype_choice, f) if self._show_image_io: self._show_io_element(self.image_filewidget, f) else: @@ -217,44 +220,49 @@ def _build(self): """Method to be defined by children classes""" raise NotImplementedError("To be defined in child classes") - def _show_filetype_choice(self): - """Method to show/hide the filetype choice when "loading as folder" is (de)selected""" - show = self.load_as_stack_choice.isChecked() - if show is not None: - self.filetype_choice.setVisible(show) - # self.lbl_ft.setVisible(show) + # def _show_filetype_choice(self): # TODO(cyril): remove? + # """Method to show/hide the filetype choice when "loading as folder" is (de)selected""" + # show = self.load_as_stack_choice.isChecked() + # if show is not None: + # self.filetype_choice.setVisible(show) + # # self.lbl_ft.setVisible(show) def _show_file_dialog(self): - """Open file dialog and process path depending on single file/folder loading behaviour""" - if self.load_as_stack_choice.isChecked(): - choice = ui.open_folder_dialog( - self, - self._default_path, - filetype=f"Image file (*{self.filetype_choice.currentText()})", - ) - else: - f_name = ui.open_file_dialog(self, self._default_path) - choice = str(f_name[0]) - self.filetype = str(Path(choice).suffix) + """Open file dialog and process path for a single file""" + # if self.load_as_stack_choice.isChecked(): + # return ui.open_folder_dialog( + # self, + # self._default_path, + # filetype=f"Image file (*{self.filetype_choice.currentText()})", + # ) + # else: + logger.debug("Opening file dialog") + f_name = ui.open_file_dialog(self, self._default_path) + logger.debug(f"File dialog returned {f_name}") + choice = str(f_name[0]) + self.filetype = str(Path(choice).suffix) + logger.debug(f"Filetype set to {self.filetype}") return choice def _show_dialog_images(self): """Show file dialog and set image path""" f_name = self._show_file_dialog() - if type(f_name) is str and f_name != "": + if type(f_name) is str and Path(f_name).is_file(): self.image_path = f_name + logger.debug(f"Image path set to {self.image_path}") self.image_filewidget.text_field.setText(self.image_path) self._update_default() def _show_dialog_labels(self): """Show file dialog and set label path""" f_name = self._show_file_dialog() - if isinstance(f_name, str) and f_name != "": + if isinstance(f_name, str) and Path(f_name).is_file(): self.label_path = f_name + logger.debug(f"Label path set to {self.label_path}") self.labels_filewidget.text_field.setText(self.label_path) self._update_default() - def _check_results_path(self, folder): + def _check_results_path(self, folder: str): if folder != "" and isinstance(folder, str): if not Path(folder).is_dir(): Path(folder).mkdir(parents=True, exist_ok=True) @@ -262,6 +270,8 @@ def _check_results_path(self, folder): return False logger.info(f"Created missing results folder : {folder}") return True + if not isinstance(folder, str): + raise TypeError(f"Expected string, got {type(folder)}") return False def _load_results_path(self): @@ -302,7 +312,6 @@ def _make_next_button(self): def remove_from_viewer(self): """Removes the widget from the napari window. Can be re-implemented in children classes if needed""" - self.remove_docked_widgets() self._viewer.window.remove_dock_widget(self) @@ -394,14 +403,14 @@ def load_dataset_paths(self): Returns: array(str): all loaded file paths """ - filetype = self.filetype_choice.currentText() + # filetype = self.filetype_choice.currentText() directory = ui.open_folder_dialog(self, self._default_folders) - file_paths = sorted(Path(directory).glob("*" + filetype)) + file_paths = sorted(Path(directory).glob("*" + ".tif")) if len(file_paths) == 0: logger.warning( - f"The folder does not contain any compatible {filetype} files.\n" - f"Please check the validity of the folder and images." + "The folder does not contain any compatible .tif files.\n" + "Please check the validity of the folder and images." ) return file_paths diff --git a/napari_cellseg3d/code_plugins/plugin_crop.py b/napari_cellseg3d/code_plugins/plugin_crop.py index 74691e1f..df65ac5e 100644 --- a/napari_cellseg3d/code_plugins/plugin_crop.py +++ b/napari_cellseg3d/code_plugins/plugin_crop.py @@ -99,6 +99,7 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): self._x = 0 self._y = 0 self._z = 0 + self.sliders = [] self._crop_size_x = DEFAULT_CROP_SIZE self._crop_size_y = DEFAULT_CROP_SIZE @@ -385,15 +386,15 @@ def add_isotropic_layer( ) return layer - def _check_for_empty_layer(self, layer, volume_data): - if layer.data.all() == np.zeros_like(layer.data).all(): - layer.colormap = "red" - layer.data = np.random.random(layer.data.shape) - layer.refresh() - else: - layer.colormap = "twilight_shifted" - layer.data = volume_data - layer.refresh() + # def _check_for_empty_layer(self, layer, volume_data): # tries to recolor empty layers so that cropping is visible + # if layer.data.all() == np.zeros_like(layer.data).all(): + # layer.colormap = "red" + # layer.data = np.random.random(layer.data.shape) + # layer.refresh() + # else: + # layer.colormap = "twilight_shifted" + # layer.data = volume_data + # layer.refresh() def _add_crop_layer(self, layer, cropx, cropy, cropz): crop_data = layer.data[:cropx, :cropy, :cropz] @@ -557,6 +558,7 @@ def set_slice( ui.Slider(text_label=axis, lower=0, upper=end, step=step) for axis, end, step in zip("zyx", ends, stepsizes) ] + self.sliders = sliders for axis, slider in enumerate(sliders): slider.valueChanged.connect( lambda event, axis=axis: set_slice( diff --git a/napari_cellseg3d/code_plugins/plugin_metrics.py b/napari_cellseg3d/code_plugins/plugin_metrics.py index 1dc5e7de..b0aa3df8 100644 --- a/napari_cellseg3d/code_plugins/plugin_metrics.py +++ b/napari_cellseg3d/code_plugins/plugin_metrics.py @@ -77,7 +77,7 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): def _build(self): """Builds the layout of the widget.""" - self.filetype_choice.label.setVisible(False) + # self.filetype_choice.label.setVisible(False) w = ui.ContainerWidget() self.layout = w.layout diff --git a/napari_cellseg3d/code_plugins/plugin_model_inference.py b/napari_cellseg3d/code_plugins/plugin_model_inference.py index f9d6cb21..560cef3d 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_inference.py +++ b/napari_cellseg3d/code_plugins/plugin_model_inference.py @@ -552,7 +552,10 @@ def _update_progress_bar(self, image_id: int, total: int): def _display_results(self, result: InferenceResult): viewer = self._viewer - zoom = self.worker_config.post_process_config.zoom.zoom_values + if self.worker_config.post_process_config.zoom.enabled: + zoom = self.worker_config.post_process_config.zoom.zoom_values + else: + zoom = [1, 1, 1] image_id = result.image_id model_name = self.model_choice.currentText() @@ -781,7 +784,7 @@ def _set_worker_config(self) -> config.InferenceWorkerConfig: model_info=self.model_info, weights_config=self.weights_config, results_path=self.results_path, - filetype=self.filetype_choice.currentText(), + filetype=".tif", keep_on_cpu=self.keep_data_on_cpu_box.isChecked(), compute_stats=self.save_stats_to_csv_box.isChecked(), post_process_config=self.post_process_config, diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 3e666dcc..3e245281 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -475,9 +475,9 @@ def _build(self): ui.add_widgets( data_layout, [ - ui.combine_blocks( - self.filetype_choice, self.filetype_choice.label - ), # file extension + # ui.combine_blocks( + # self.filetype_choice, self.filetype_choice.label + # ), # file extension self.image_filewidget, self.labels_filewidget, self.results_filewidget, diff --git a/napari_cellseg3d/code_plugins/plugin_review.py b/napari_cellseg3d/code_plugins/plugin_review.py index dd98bcd7..4e1a7e66 100644 --- a/napari_cellseg3d/code_plugins/plugin_review.py +++ b/napari_cellseg3d/code_plugins/plugin_review.py @@ -96,7 +96,7 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): def _update_results_path(self): p = self.image_filewidget.text_field.text() - if p is not None and p != "" and Path(p).is_file(): + if p is not None and Path(p).is_file(): self.results_filewidget.text_field.setText(str(Path(p).parent)) def _build(self): @@ -109,7 +109,7 @@ def _build(self): # ui.add_blank(self, layout) ########################### - self.filetype_choice.setVisible(False) + # self.filetype_choice.setVisible(False) layout.addWidget(self.io_panel) self._set_io_visibility() self.layer_choice.toggle() @@ -137,7 +137,6 @@ def _build(self): b=5, ), self.new_csv_choice, - self.results_filewidget, ], ) @@ -202,7 +201,7 @@ def _prepare_data(self): self.config.model_name = self.csv_textbox.text() self.config.new_csv = self.new_csv_choice.isChecked() - self.config.filetype = self.filetype_choice.currentText() + self.config.filetype = self.filetype if self.anisotropy_widgets.enabled: zoom = self.anisotropy_widgets.scaling_zyx() @@ -359,7 +358,7 @@ def quicksave(): canvas_dock._close_btn = False @viewer.mouse_drag_callbacks.append - def update_canvas_canvas(viewer, event): + def update_canvas(viewer, event): if "shift" in event.modifiers: try: cursor_position = np.round(viewer.cursor.position).astype( diff --git a/napari_cellseg3d/utils.py b/napari_cellseg3d/utils.py index 3dff19c0..0f9eee69 100644 --- a/napari_cellseg3d/utils.py +++ b/napari_cellseg3d/utils.py @@ -430,9 +430,7 @@ def get_time_filepath(): return f"{datetime.now():%H_%M_%S}" -def load_images( - dir_or_path, filetype="", as_folder: bool = False -): # TODO(cyril):refactor w/o as_folder +def load_images(dir_or_path, filetype="", as_folder: bool = False): """Loads the images in ``directory``, with different behaviour depending on ``filetype`` and ``as_folder`` * If ``as_folder`` is **False**, will load the path as a single 3D **.tif** image. @@ -455,19 +453,18 @@ def load_images( np.array: array with loaded images """ - if not as_folder: - filename_pattern_original = Path(dir_or_path) - # print(filename_pattern_original) - elif as_folder and filetype != "": - filename_pattern_original = Path(dir_or_path + "/*" + filetype) - # print(filename_pattern_original) - else: - raise ValueError("If loading as a folder, filetype must be specified") - - if as_folder: - raise NotImplementedError( - "Loading as folder not implemented yet. Use napari to load as folder" - ) - # images_original = dask_imread(filename_pattern_original) - + # if not as_folder: + filename_pattern_original = Path(dir_or_path) return imread(str(filename_pattern_original)) # tifffile imread + # print(filename_pattern_original) + # elif as_folder and filetype != "": + # filename_pattern_original = Path(dir_or_path + "/*" + filetype) + # print(filename_pattern_original) + # else: + # raise ValueError("If loading as a folder, filetype must be specified") + + # if as_folder: + # raise NotImplementedError( + # "Loading as folder not implemented yet. Use napari to load as folder" + # images_original = dask_imread(filename_pattern_original) + # ) From 972a7cbd426fe31ba807b2b0bfc4f97d25e9c913 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 13 Jul 2023 15:55:16 +0200 Subject: [PATCH 011/250] More worker tests --- napari_cellseg3d/_tests/test_models.py | 11 +++ napari_cellseg3d/_tests/test_workers.py | 69 +++++++++++++++++++ .../code_models/models/model_SegResNet.py | 8 +-- napari_cellseg3d/code_models/workers.py | 5 +- 4 files changed, 87 insertions(+), 6 deletions(-) diff --git a/napari_cellseg3d/_tests/test_models.py b/napari_cellseg3d/_tests/test_models.py index ec8f6edc..898b4e4c 100644 --- a/napari_cellseg3d/_tests/test_models.py +++ b/napari_cellseg3d/_tests/test_models.py @@ -11,6 +11,7 @@ crf_batch, crf_with_config, ) +from napari_cellseg3d.code_models.models.model_TRAILMAP_MS import TRAILMAP_MS_ from napari_cellseg3d.code_models.models.wnet.soft_Ncuts import SoftNCutsLoss from napari_cellseg3d.config import MODEL_LIST, CRFConfig @@ -127,3 +128,13 @@ def test_pretrained_weights_compatibility(): ) except RuntimeError: pytest.fail(f"Failed to load weights for {model_name}") + + +def test_trailmap_init(): + test = TRAILMAP_MS_( + input_img_size=[128, 128, 128], + in_channels=1, + out_channels=1, + dropout_prob=0.3, + ) + assert isinstance(test, TRAILMAP_MS_) diff --git a/napari_cellseg3d/_tests/test_workers.py b/napari_cellseg3d/_tests/test_workers.py index 235763a5..30ebae08 100644 --- a/napari_cellseg3d/_tests/test_workers.py +++ b/napari_cellseg3d/_tests/test_workers.py @@ -1,14 +1,21 @@ from pathlib import Path +import napari +import numpy as np +import pytest import torch +from monai.data import DataLoader from numpy.random import PCG64, Generator from napari_cellseg3d.code_models.workers import ( + InferenceResult, + InferenceWorker, ONNXModelWrapper, WeightsDownloader, ) from napari_cellseg3d.config import ( PRETRAINED_WEIGHTS_DIR, + InferenceWorkerConfig, ) rand_gen = Generator(PCG64(12345)) @@ -26,3 +33,65 @@ def test_onnx_inference(make_napari_viewer_proxy): assert worker.eval() is None assert worker.to(device="cpu") is None assert worker.forward(x).shape == (batch, 2, dims, dims, dims) + + +def test_load_folder(): + config = InferenceWorkerConfig() + worker = InferenceWorker(worker_config=config) + + images_path = Path(__file__).resolve().parent / "res/test.tif" + worker.config.images_filepaths = [str(images_path)] + dataloader = worker.load_folder() + assert isinstance(dataloader, DataLoader) + assert len(dataloader) == 1 + worker.config.sliding_window_config.window_size = [64, 64, 64] + dataloader = worker.load_folder() + assert isinstance(dataloader, DataLoader) + assert len(dataloader) == 1 + + mock_layer = napari.layers.Image(data=rand_gen.random((64, 64, 64))) + worker.config.layer = mock_layer + input_image = worker.load_layer() + assert input_image.shape == (1, 1, 64, 64, 64) + + mock_layer = napari.layers.Image(data=rand_gen.random((5, 2, 64, 64, 64))) + worker.config.layer = mock_layer + assert len(mock_layer.data.shape) == 5 + with pytest.raises( + ValueError, + match="Data array is not 3-dimensional but 5-dimensional, please check for extra channel/batch dimensions", + ): + worker.load_layer() + + +def test_inference_on_folder(): + config = InferenceWorkerConfig() + config.filetype = ".tif" + config.images_filepaths = [ + str(Path(__file__).resolve().parent / "res/test.tif") + ] + + def mock_work(x): + return x + + worker = InferenceWorker(worker_config=config) + worker.aniso_transform = mock_work + + image = torch.Tensor(rand_gen.random((1, 1, 64, 64, 64))) + res = worker.inference_on_folder( + {"image": image}, 0, model=mock_work, post_process_transforms=mock_work + ) + assert isinstance(res, InferenceResult) + + +def test_post_processing(): + config = InferenceWorkerConfig() + worker = InferenceWorker(worker_config=config) + + image = rand_gen.random((1, 1, 64, 64, 64)) + labels = rand_gen.random((1, 2, 64, 64, 64)) + + results = worker.run_crf(image, labels, lambda x: x) + assert results.shape == (2, 64, 64, 64) + + worker.stats_csv(np.squeeze(labels)) diff --git a/napari_cellseg3d/code_models/models/model_SegResNet.py b/napari_cellseg3d/code_models/models/model_SegResNet.py index 8b6e6e65..60b74d64 100644 --- a/napari_cellseg3d/code_models/models/model_SegResNet.py +++ b/napari_cellseg3d/code_models/models/model_SegResNet.py @@ -19,10 +19,10 @@ def forward(self, x): # logger.debug(f"SegResNetVAE.forward: {res[0].shape}") return res[0] - def get_model_test(self, size): - return SegResNetVAE( - size, in_channels=1, out_channels=1, dropout_prob=0.3 - ) + # def get_model_test(self, size): + # return SegResNetVAE( + # size, in_channels=1, out_channels=1, dropout_prob=0.3 + # ) # def get_output(model, input): # out = model(input)[0] diff --git a/napari_cellseg3d/code_models/workers.py b/napari_cellseg3d/code_models/workers.py index 2ba5e7db..1b1210d7 100644 --- a/napari_cellseg3d/code_models/workers.py +++ b/napari_cellseg3d/code_models/workers.py @@ -547,7 +547,7 @@ def create_inference_result( original=None, stats=None, i=0, - ): + ) -> InferenceResult: if not from_layer and original is None: raise ValueError( "If the image is not from a layer, an original should always be available" @@ -696,9 +696,10 @@ def inference_on_folder(self, inf_data, i, model, post_process_transforms): self.save_image(out, i=i) instance_labels, stats = self.get_instance_result(out, i=i) if self.config.use_crf: + crf_in = inputs.detach().cpu().numpy() try: crf_results = self.run_crf( - inputs, + crf_in, out, aniso_transform=self.aniso_transform, image_id=i, From d2fa556f8c77b6b311978e01bda1d7a2e81e3190 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 13 Jul 2023 19:04:44 +0200 Subject: [PATCH 012/250] More extensive training tests --- .../_tests/test_plugin_inference.py | 11 -- napari_cellseg3d/_tests/test_training.py | 100 ++++++++++-------- .../code_plugins/plugin_model_training.py | 18 +++- 3 files changed, 68 insertions(+), 61 deletions(-) diff --git a/napari_cellseg3d/_tests/test_plugin_inference.py b/napari_cellseg3d/_tests/test_plugin_inference.py index 475d8e2c..309c5ab7 100644 --- a/napari_cellseg3d/_tests/test_plugin_inference.py +++ b/napari_cellseg3d/_tests/test_plugin_inference.py @@ -1,6 +1,5 @@ from pathlib import Path -from napari.qt.threading import GeneratorWorker from numpy.random import PCG64, Generator from tifffile import imread @@ -19,16 +18,6 @@ rand_gen = Generator(PCG64(12345)) -class WorkerFixture(GeneratorWorker): - def __init__(self): - super().__init__(self.mock_inference) - - def mock_inference(self): - while True: - yield InferenceResult(result=None, instance_labels=None) - break - - def test_inference(make_napari_viewer_proxy, qtbot): im_path = str(Path(__file__).resolve().parent / "res/test.tif") image = imread(im_path) diff --git a/napari_cellseg3d/_tests/test_training.py b/napari_cellseg3d/_tests/test_training.py index 728eb3c2..cf327ef9 100644 --- a/napari_cellseg3d/_tests/test_training.py +++ b/napari_cellseg3d/_tests/test_training.py @@ -10,6 +10,49 @@ from napari_cellseg3d.config import MODEL_LIST +def test_update_loss_plot(make_napari_viewer_proxy): + view = make_napari_viewer_proxy() + widget = Trainer(view) + + widget.worker_config = config.TrainingWorkerConfig() + widget.worker_config.validation_interval = 1 + widget.worker_config.results_path_folder = "." + + epoch_loss_values = [1] + metric_values = [] + + widget.update_loss_plot(epoch_loss_values, metric_values) + + assert widget.dice_metric_plot is None + assert widget.train_loss_plot is None + + widget.worker_config.validation_interval = 2 + + epoch_loss_values = [0, 1] + metric_values = [0.2] + + widget.update_loss_plot(epoch_loss_values, metric_values) + + assert widget.dice_metric_plot is None + assert widget.train_loss_plot is None + + epoch_loss_values = [0, 1, 0.5, 0.7] + metric_values = [0.2, 0.3] + + widget.update_loss_plot(epoch_loss_values, metric_values) + + assert widget.dice_metric_plot is not None + assert widget.train_loss_plot is not None + + epoch_loss_values = [0, 1, 0.5, 0.7, 0.5, 0.7] + metric_values = [0.2, 0.3, 0.5, 0.7] + + widget.update_loss_plot(epoch_loss_values, metric_values) + + assert widget.dice_metric_plot is not None + assert widget.train_loss_plot is not None + + def test_training(make_napari_viewer_proxy, qtbot): im_path = str(Path(__file__).resolve().parent / "res/test.tif") @@ -35,17 +78,27 @@ def test_training(make_napari_viewer_proxy, qtbot): ################# MODEL_LIST["test"] = TestModel widget.model_choice.addItem("test") - widget.model_choice.setCurrentIndex(len(MODEL_LIST.keys()) - 1) - + widget.model_choice.setCurrentText("test") worker_config = widget._set_worker_config() + assert worker_config.model_info.name == "test" worker = widget._create_worker_from_config(worker_config) worker.config.train_data_dict = [{"image": im_path, "label": im_path}] worker.config.val_data_dict = [{"image": im_path, "label": im_path}] worker.config.max_epochs = 1 + worker.config.validation_interval = 2 worker.log_parameters() res = next(worker.train()) assert isinstance(res, TrainingReport) + assert res.epoch == 0 + + widget.worker = worker + res.show_plot = True + res.loss_values = [1, 1, 1, 1] + res.validation_metric = [1, 1, 1, 1] + widget.on_yield(res) + assert widget.loss_values == [1, 1, 1, 1] + assert widget.validation_values == [1, 1, 1, 1] # def on_error(e): # print(e) @@ -58,46 +111,3 @@ def test_training(make_napari_viewer_proxy, qtbot): # widget.worker.error_signal.connect(on_error) # widget.worker.train() # assert widget.worker is not None - - -def test_update_loss_plot(make_napari_viewer_proxy): - view = make_napari_viewer_proxy() - widget = Trainer(view) - - widget.worker_config = config.TrainingWorkerConfig() - widget.worker_config.validation_interval = 1 - widget.worker_config.results_path_folder = "." - - epoch_loss_values = [1] - metric_values = [] - - widget.update_loss_plot(epoch_loss_values, metric_values) - - assert widget.dice_metric_plot is None - assert widget.train_loss_plot is None - - widget.worker_config.validation_interval = 2 - - epoch_loss_values = [0, 1] - metric_values = [0.2] - - widget.update_loss_plot(epoch_loss_values, metric_values) - - assert widget.dice_metric_plot is None - assert widget.train_loss_plot is None - - epoch_loss_values = [0, 1, 0.5, 0.7] - metric_values = [0.2, 0.3] - - widget.update_loss_plot(epoch_loss_values, metric_values) - - assert widget.dice_metric_plot is not None - assert widget.train_loss_plot is not None - - epoch_loss_values = [0, 1, 0.5, 0.7, 0.5, 0.7] - metric_values = [0.2, 0.3, 0.5, 0.7] - - widget.update_loss_plot(epoch_loss_values, metric_values) - - assert widget.dice_metric_plot is not None - assert widget.train_loss_plot is not None diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 3e245281..676ab78c 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -1052,15 +1052,23 @@ def _make_csv(self): logger.warning("No loss values to add to csv !") return + val = utils.fill_list_in_between( + self.validation_values, + self.worker_config.validation_interval - 1, + "", + )[: len(size_column)] + + if len(val) != len(self.loss_values): + err = f"Validation and loss values don't have the same length ! Got {len(val)} and {len(self.loss_values)}" + logger.error(err) + # return None + raise ValueError(err) + self.df = pd.DataFrame( { "epoch": size_column, "loss": self.loss_values, - "validation": utils.fill_list_in_between( - self.validation_values, - self.worker_config.validation_interval - 1, - "", - )[: len(size_column)], + "validation": val, } ) path = Path(self.worker_config.results_path_folder) / Path( From 8808a05c8d6fa6ce01968e75ca6c857d7fd3ef6b Mon Sep 17 00:00:00 2001 From: C-Achard Date: Fri, 14 Jul 2023 15:42:10 +0200 Subject: [PATCH 013/250] Fix anisotropy calculation --- napari_cellseg3d/_tests/test_interface.py | 4 ++-- napari_cellseg3d/interface.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/napari_cellseg3d/_tests/test_interface.py b/napari_cellseg3d/_tests/test_interface.py index 08e0e675..40e831eb 100644 --- a/napari_cellseg3d/_tests/test_interface.py +++ b/napari_cellseg3d/_tests/test_interface.py @@ -15,6 +15,6 @@ def test_log(qtbot): def test_zoom_factor(): - resolution = [10.0, 10.0, 5.0] + resolution = [5.0, 10.0, 5.0] zoom = AnisotropyWidgets.anisotropy_zoom_factor(resolution) - assert zoom == [1, 1, 0.5] + assert zoom == [1, 0.5, 1] diff --git a/napari_cellseg3d/interface.py b/napari_cellseg3d/interface.py index 321f3705..de856909 100644 --- a/napari_cellseg3d/interface.py +++ b/napari_cellseg3d/interface.py @@ -738,8 +738,8 @@ def anisotropy_zoom_factor(aniso_res): """ - base = max(aniso_res) - return [res / base for res in aniso_res] + base = min(aniso_res) + return [base / res for res in aniso_res] def enabled(self): """Returns : whether anisotropy correction has been enabled or not""" From 0ed9545485408fd31a224ac1262031401098f0c4 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 17 Jul 2023 14:10:49 +0200 Subject: [PATCH 014/250] Normalization changes+ fix issues with default path --- napari_cellseg3d/_tests/test_base_plugin.py | 2 +- .../_tests/test_model_framework.py | 8 +-- napari_cellseg3d/_tests/test_training.py | 2 +- napari_cellseg3d/_tests/test_utils.py | 21 ++++++- .../code_models/instance_segmentation.py | 8 +-- napari_cellseg3d/code_models/workers.py | 34 +++++++++- napari_cellseg3d/code_plugins/plugin_base.py | 63 ++++++++++--------- .../code_plugins/plugin_convert.py | 31 +++++---- .../dev_scripts/correct_labels.py | 10 +-- napari_cellseg3d/utils.py | 49 ++++++++++++--- 10 files changed, 161 insertions(+), 67 deletions(-) diff --git a/napari_cellseg3d/_tests/test_base_plugin.py b/napari_cellseg3d/_tests/test_base_plugin.py index 98c35ce6..ff049657 100644 --- a/napari_cellseg3d/_tests/test_base_plugin.py +++ b/napari_cellseg3d/_tests/test_base_plugin.py @@ -15,5 +15,5 @@ def test_base_single_image(make_napari_viewer_proxy): assert plugin._check_results_path(str(test_folder)) plugin.image_path = test_image assert plugin._default_path[0] != test_image - plugin._update_default() + plugin._update_default_paths() assert plugin._default_path[0] == test_image diff --git a/napari_cellseg3d/_tests/test_model_framework.py b/napari_cellseg3d/_tests/test_model_framework.py index 8ed3d33a..497d97e8 100644 --- a/napari_cellseg3d/_tests/test_model_framework.py +++ b/napari_cellseg3d/_tests/test_model_framework.py @@ -15,9 +15,9 @@ def test_update_default(make_napari_viewer_proxy): widget.images_filepaths = [] widget.results_path = None - widget._update_default() + widget._update_default_paths() - assert widget._default_folders == [] + assert widget._default_path == [None, None, None] widget.images_filepaths = [ pth("C:/test/test/images.tif"), @@ -30,9 +30,9 @@ def test_update_default(make_napari_viewer_proxy): widget.results_path = pth("D:/dataset/res") # widget.model_path = None - widget._update_default() + widget._update_default_paths() - assert widget._default_folders == [ + assert widget._default_path == [ pth("C:/test/test"), pth("C:/dataset/labels"), pth("D:/dataset/res"), diff --git a/napari_cellseg3d/_tests/test_training.py b/napari_cellseg3d/_tests/test_training.py index cf327ef9..2378d193 100644 --- a/napari_cellseg3d/_tests/test_training.py +++ b/napari_cellseg3d/_tests/test_training.py @@ -90,7 +90,7 @@ def test_training(make_napari_viewer_proxy, qtbot): res = next(worker.train()) assert isinstance(res, TrainingReport) - assert res.epoch == 0 + assert res.epoch == 1 widget.worker = worker res.show_plot = True diff --git a/napari_cellseg3d/_tests/test_utils.py b/napari_cellseg3d/_tests/test_utils.py index b1342afe..5e7cbeb2 100644 --- a/napari_cellseg3d/_tests/test_utils.py +++ b/napari_cellseg3d/_tests/test_utils.py @@ -210,16 +210,31 @@ def test_parse_default_path(): user_path = Path().home() assert utils.parse_default_path([None]) == str(user_path) - test_path = "C:/test/test" + test_path = "C:/test/test/test/test" path = [test_path, None, None] - assert utils.parse_default_path(path) == test_path + assert utils.parse_default_path(path, check_existence=False) == test_path + + test_path = "C:/test/does/not/exist" + path = [test_path, None, None] + assert utils.parse_default_path(path, check_existence=True) == str( + Path.home() + ) long_path = "D:/very/long/path/what/a/bore/ifonlytherewas/something/tohelpmenotsearchit/allthetime" path = [test_path, None, None, long_path, ""] - assert utils.parse_default_path(path) == long_path + assert utils.parse_default_path(path, check_existence=False) == long_path def test_thread_test(make_napari_viewer_proxy): viewer = make_napari_viewer_proxy() w = thread_test.create_connected_widget(viewer) viewer.window.add_dock_widget(w) + + +def test_quantile_norm(): + array = rand_gen.random(size=(100, 100, 100)) + low_quantile = np.quantile(array, 0.01) + high_quantile = np.quantile(array, 0.99) + array_norm = utils.quantile_normalization(array) + assert array_norm.min() >= low_quantile + assert array_norm.max() <= high_quantile diff --git a/napari_cellseg3d/code_models/instance_segmentation.py b/napari_cellseg3d/code_models/instance_segmentation.py index 9ea4cbef..0e2c0871 100644 --- a/napari_cellseg3d/code_models/instance_segmentation.py +++ b/napari_cellseg3d/code_models/instance_segmentation.py @@ -251,10 +251,10 @@ def binary_watershed( rem_seed_thres (int): threshold for small seeds removal. Default : 3 """ - logger.debug( - f"Running watershed segmentation with thres_objects={thres_objects}, thres_seeding={thres_seeding}," - f" thres_small={thres_small} and rem_seed_thres={rem_seed_thres}" - ) + # logger.debug( + # f"Running watershed segmentation with thres_objects={thres_objects}, thres_seeding={thres_seeding}," + # f" thres_small={thres_small} and rem_seed_thres={rem_seed_thres}" + # ) semantic = np.squeeze(volume) seed_map = semantic > thres_seeding foreground = semantic > thres_objects diff --git a/napari_cellseg3d/code_models/workers.py b/napari_cellseg3d/code_models/workers.py index 1b1210d7..26044806 100644 --- a/napari_cellseg3d/code_models/workers.py +++ b/napari_cellseg3d/code_models/workers.py @@ -27,6 +27,7 @@ EnsureType, EnsureTyped, LoadImaged, + # NormalizeIntensityd, Orientationd, Rand3DElasticd, RandAffined, @@ -390,6 +391,7 @@ def load_folder(self): EnsureChannelFirstd(keys=["image"]), # Orientationd(keys=["image"], axcodes="PLI"), # anisotropic_transform, + # NormalizeIntensityd(keys=["image"]), # TODO normalize EnsureTyped(keys=["image"]), ] ) @@ -399,6 +401,7 @@ def load_folder(self): LoadImaged(keys=["image"]), # AddChanneld(keys=["image"]), #already done EnsureChannelFirstd(keys=["image"]), + # NormalizeIntensityd(keys=["image"]), # TODO normalize # Orientationd(keys=["image"], axcodes="PLI"), # anisotropic_transform, SpatialPadd(keys=["image"], spatial_size=pad), @@ -1201,8 +1204,9 @@ def train(self): size = self.config.sample_size if do_sampling else check + PADDING = utils.get_padding_dim(size) model = model_class( # FIXME check if correct - input_img_size=utils.get_padding_dim(size), use_checkpoint=True + input_img_size=PADDING, use_checkpoint=True ) model = model.to(self.config.device) @@ -1284,6 +1288,7 @@ def get_loader_func(num_samples): [ LoadImaged(keys=["image", "label"]), EnsureChannelFirstd(keys=["image", "label"]), + # NormalizeIntensityd(keys=["image"]), # TODO normalize RandSpatialCropSamplesd( keys=["image", "label"], roi_size=( @@ -1356,9 +1361,10 @@ def get_loader_func(num_samples): ), EnsureChannelFirstd(keys=["image", "label"]), Orientationd(keys=["image", "label"], axcodes="PLI"), + # NormalizeIntensityd(keys=["image"]), # TODO normalize SpatialPadd( keys=["image", "label"], - spatial_size=(utils.get_padding_dim(check)), + spatial_size=PADDING, ), EnsureTyped(keys=["image", "label"]), ] @@ -1411,6 +1417,13 @@ def get_loader_func(num_samples): best_metric = -1 best_metric_epoch = -1 + def normalize_batch(batch): # TODO(refactor as a transform) + batch_dim = batch.shape[0] + channels_dim = batch.shape[1] + for i in range(batch_dim): + for j in range(channels_dim): + batch[i][j] = utils.quantile_normalization(batch[i][j]) + # time = utils.get_date_time() logger.debug("Weights") @@ -1479,6 +1492,19 @@ def get_loader_func(num_samples): batch_data["image"].to(device), batch_data["label"].to(device), ) + + normalize_batch(inputs) + # images = [im.cpu().numpy() for im in inputs] + # [images.append(l.cpu().numpy()) for l in labels] + # + # yield TrainingReport( + # show_plot=True, + # epoch=1, + # images=images, + # ) + # + # break + optimizer.zero_grad() outputs = model(inputs) # self.log(f"Output dimensions : {outputs.shape}") @@ -1500,6 +1526,8 @@ def get_loader_func(num_samples): show_plot=False, weights=model.state_dict() ) + # return + epoch_loss /= step epoch_loss_values.append(epoch_loss) self.log(f"Epoch: {epoch + 1}, Average loss: {epoch_loss:.4f}") @@ -1527,6 +1555,8 @@ def get_loader_func(num_samples): val_data["image"].to(device), val_data["label"].to(device), ) + + normalize_batch(val_inputs) try: with torch.no_grad(): val_outputs = sliding_window_inference( diff --git a/napari_cellseg3d/code_plugins/plugin_base.py b/napari_cellseg3d/code_plugins/plugin_base.py index b406ee20..7ea5aa11 100644 --- a/napari_cellseg3d/code_plugins/plugin_base.py +++ b/napari_cellseg3d/code_plugins/plugin_base.py @@ -242,6 +242,7 @@ def _show_file_dialog(self): choice = str(f_name[0]) self.filetype = str(Path(choice).suffix) logger.debug(f"Filetype set to {self.filetype}") + self._update_default_paths() return choice def _show_dialog_images(self): @@ -251,7 +252,7 @@ def _show_dialog_images(self): self.image_path = f_name logger.debug(f"Image path set to {self.image_path}") self.image_filewidget.text_field.setText(self.image_path) - self._update_default() + self._update_default_paths() def _show_dialog_labels(self): """Show file dialog and set label path""" @@ -260,13 +261,18 @@ def _show_dialog_labels(self): self.label_path = f_name logger.debug(f"Label path set to {self.label_path}") self.labels_filewidget.text_field.setText(self.label_path) - self._update_default() + self._update_default_paths() def _check_results_path(self, folder: str): + """Check if results folder exists, create it if not""" + logger.debug(f"Checking results folder : {folder}") if folder != "" and isinstance(folder, str): if not Path(folder).is_dir(): Path(folder).mkdir(parents=True, exist_ok=True) if not Path(folder).is_dir(): + logger.info( + f"Could not create missing results folder : {folder}" + ) return False logger.info(f"Created missing results folder : {folder}") return True @@ -276,15 +282,16 @@ def _check_results_path(self, folder: str): def _load_results_path(self): """Show file dialog to set :py:attr:`~results_path`""" + self._update_default_paths() folder = ui.open_folder_dialog(self, self._default_path) if self._check_results_path(folder): - self.results_path = folder - # logger.debug(f"Results path : {self.results_path}") + self.results_path = str(Path(folder).resolve()) + logger.debug(f"Results path : {self.results_path}") self.results_filewidget.text_field.setText(self.results_path) - self._update_default() + self._update_default_paths() - def _update_default(self): + def _update_default_paths(self): """Updates default path for smoother navigation when opening file dialogs""" self._default_path = [ self.image_path, @@ -363,7 +370,7 @@ def __init__( self.results_path = None """str: path to output folder,to save results in""" - self._default_folders = [None] + self._default_path = [None] """Update defaults from PluginBaseFolder with model_path""" self.docked_widgets = [] @@ -404,7 +411,7 @@ def load_dataset_paths(self): array(str): all loaded file paths """ # filetype = self.filetype_choice.currentText() - directory = ui.open_folder_dialog(self, self._default_folders) + directory = ui.open_folder_dialog(self, self._default_path) file_paths = sorted(Path(directory).glob("*" + ".tif")) if len(file_paths) == 0: @@ -424,7 +431,7 @@ def load_image_dataset(self): path = str(Path(filenames[0]).parent) self.image_filewidget.text_field.setText(path) self.image_filewidget.check_ready() - self._update_default() + self._update_default_paths(path) def load_label_dataset(self): """Show file dialog to set :py:attr:`~labels_filepaths`""" @@ -435,26 +442,26 @@ def load_label_dataset(self): path = str(Path(filenames[0]).parent) self.labels_filewidget.text_field.setText(path) self.labels_filewidget.check_ready() - self._update_default() + self._update_default_paths(path) - def _update_default(self): + def _update_default_paths(self, path=None): """Update default path for smoother file dialogs""" - if len(self.images_filepaths) != 0: - from_images = str(Path(self.images_filepaths[0]).parent) - else: - from_images = None - - if len(self.labels_filepaths) != 0: - from_labels = str(Path(self.labels_filepaths[0]).parent) - else: - from_labels = None - - self._default_folders = [ - path - for path in [ - from_images, - from_labels, + logger.debug(f"Updating default paths with {path}") + if path is None: + self._default_path = [ + self.extract_dataset_paths(self.images_filepaths), + self.extract_dataset_paths(self.labels_filepaths), self.results_path, ] - if (path != [] and path is not None) - ] + return + if Path(path).is_dir(): + self._default_path.append(path) + + @staticmethod + def extract_dataset_paths(paths): + """Gets the parent folder name of the first image and label paths""" + if len(paths) == 0: + return None + if paths[0] is None: + return None + return str(Path(paths[0]).parent) diff --git a/napari_cellseg3d/code_plugins/plugin_convert.py b/napari_cellseg3d/code_plugins/plugin_convert.py index 4357e51e..aa70bc73 100644 --- a/napari_cellseg3d/code_plugins/plugin_convert.py +++ b/napari_cellseg3d/code_plugins/plugin_convert.py @@ -179,7 +179,7 @@ def _start(self): utils.mkdir_from_str(self.results_path) remove_size = self.size_for_removal_counter.value() - if self.layer_choice: + if self.layer_choice.isChecked(): if self.image_layer_loader.layer_data() is not None: layer = self.image_layer_loader.layer() @@ -233,8 +233,10 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): self.start_btn = ui.Button("Start", self._start) - self.results_path = Path.home() / Path("cellseg3d/threshold") - self.results_filewidget.text_field.setText(str(self.results_path)) + self.results_path = str( + Path.home() / Path("cellseg3d/instance_labels") + ) + self.results_filewidget.text_field.setText(self.results_path) self.results_filewidget.check_ready() self._build() @@ -260,8 +262,13 @@ def _build(self): def _start(self): Path(self.results_path).mkdir(exist_ok=True, parents=True) + logger.debug(f"Running on layer : {self.layer_choice.isChecked()}") + logger.debug(f"Running on folder : {self.folder_choice.isChecked()}") + logger.debug(f"Images : {self.labels_filepaths}") - if self.layer_choice: + self.results_filewidget.check_ready() + + if self.layer_choice.isChecked(): if self.label_layer_loader.layer_data() is not None: layer = self.label_layer_loader.layer() @@ -277,18 +284,20 @@ def _start(self): self._viewer, layer, semantic, f"semantic_{layer.name}" ) elif ( - self.folder_choice.isChecked() and len(self.images_filepaths) != 0 + self.folder_choice.isChecked() and len(self.labels_filepaths) != 0 ): images = [ to_semantic(file, is_file_path=True) - for file in self.images_filepaths + for file in self.labels_filepaths ] utils.save_folder( self.results_path, f"semantic_results_{utils.get_date_time()}", images, - self.images_filepaths, + self.labels_filepaths, ) + else: + logger.warning("Please specify a layer or a folder") class ToInstanceUtils(BasePluginFolder): @@ -347,7 +356,7 @@ def _build(self): def _start(self): utils.mkdir_from_str(self.results_path) - if self.layer_choice: + if self.layer_choice.isChecked(): if self.label_layer_loader.layer_data() is not None: layer = self.label_layer_loader.layer() @@ -408,8 +417,8 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): text_label="Remove all smaller than (value):", ) - self.results_path = Path.home() / Path("cellseg3d/threshold") - self.results_filewidget.text_field.setText(str(self.results_path)) + self.results_path = str(Path.home() / Path("cellseg3d/threshold")) + self.results_filewidget.text_field.setText(self.results_path) self.results_filewidget.check_ready() self.container = self._build() @@ -443,7 +452,7 @@ def _start(self): utils.mkdir_from_str(self.results_path) remove_size = self.binarize_counter.value() - if self.layer_choice: + if self.layer_choice.isChecked(): if self.image_layer_loader.layer_data() is not None: layer = self.image_layer_loader.layer() diff --git a/napari_cellseg3d/dev_scripts/correct_labels.py b/napari_cellseg3d/dev_scripts/correct_labels.py index f413812d..4f529abe 100644 --- a/napari_cellseg3d/dev_scripts/correct_labels.py +++ b/napari_cellseg3d/dev_scripts/correct_labels.py @@ -364,8 +364,10 @@ def relabel_non_unique_i_folder(folder_path, end_of_new_name="relabeled"): if __name__ == "__main__": - im_path = Path("C:/Users/Cyril/Desktop/Proj_bachelor/data/somatomotor") + im_path = Path.home() / "Desktop/Code/CELLSEG_BENCHMARK/TPH2_DATA/visual" - image_path = str(im_path / "volumes/c1images.tif") - gt_labels_path = str(im_path / "labels/c1labels.tif") - relabel(image_path, gt_labels_path, check_for_unicity=False, go_fast=False) + # i = 4 + # im_id = i+1 + image_path = str(im_path / "visual.tif") + gt_labels_path = str(im_path / "visual_gt.tif") + relabel(image_path, gt_labels_path, check_for_unicity=True, go_fast=False) diff --git a/napari_cellseg3d/utils.py b/napari_cellseg3d/utils.py index 0f9eee69..193e0b21 100644 --- a/napari_cellseg3d/utils.py +++ b/napari_cellseg3d/utils.py @@ -2,16 +2,14 @@ import math from datetime import datetime from pathlib import Path -from typing import TYPE_CHECKING, Union +from typing import Union import napari import numpy as np +import torch from monai.transforms import Zoom from tifffile import imread, imwrite -if TYPE_CHECKING: - import torch - LOGGER = logging.getLogger(__name__) ############### # Global logging level setting @@ -391,7 +389,7 @@ def fill_list_in_between(lst, n, fill_value): return None -def parse_default_path(possible_paths): +def parse_default_path(possible_paths, check_existence=True): """Returns a default path based on a vector of paths, some of which might be empty. Args: @@ -401,14 +399,18 @@ def parse_default_path(possible_paths): """ default_paths = [] + LOGGER.debug(f"Utils : possible paths are {possible_paths}") if any(path is not None for path in possible_paths): - default_paths = [ - p for p in possible_paths if p is not None and len(p) > 2 - ] + for p in possible_paths: + if p is not None and (Path(p).exists() or not check_existence): + default_paths.append(str(Path(p).resolve().as_posix())) + # default_paths = [ + # str(Path(p).resolve()) for p in possible_paths if (Path(p).exists()) + # ] # default_paths = [ # path for path in default_paths if path is not None and path != [] # ] - print(default_paths) + LOGGER.debug(f"Utils : default paths are {default_paths}") if len(default_paths) == 0: return str(Path().home()) default_path = max(default_paths, key=len) @@ -468,3 +470,32 @@ def load_images(dir_or_path, filetype="", as_folder: bool = False): # "Loading as folder not implemented yet. Use napari to load as folder" # images_original = dask_imread(filename_pattern_original) # ) + + +def quantile_normalization( + image: Union[np.ndarray, torch.Tensor], + quantile_high=0.99, + quantile_low=0.01, +): + """Normalizes an image using the quantiles""" + if quantile_high < quantile_low: + raise ValueError( + f"quantile_high must be greater than quantile_low, got {quantile_high} and {quantile_low}" + ) + + if isinstance(image, torch.Tensor): + qtl = torch.quantile + where = torch.where + elif isinstance(image, np.ndarray): + qtl = np.quantile + where = np.where + else: + raise TypeError("image needs to be torch tensor or numpy array") + + shape = image.shape + image = image.flatten() + high_quantile_value = qtl(image, quantile_high) + low_quantile_value = qtl(image, quantile_low) + image = where(image > high_quantile_value, high_quantile_value, image) + image = where(image < low_quantile_value, low_quantile_value, image) + return image.reshape(shape) From be816ba3fdd0588ebd35bc2f9c73ed4853c4db21 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 17 Jul 2023 14:11:04 +0200 Subject: [PATCH 015/250] Create crop_data.py --- napari_cellseg3d/dev_scripts/crop_data.py | 61 +++++++++++++++++++++++ 1 file changed, 61 insertions(+) create mode 100644 napari_cellseg3d/dev_scripts/crop_data.py diff --git a/napari_cellseg3d/dev_scripts/crop_data.py b/napari_cellseg3d/dev_scripts/crop_data.py new file mode 100644 index 00000000..2f24e619 --- /dev/null +++ b/napari_cellseg3d/dev_scripts/crop_data.py @@ -0,0 +1,61 @@ +from pathlib import Path + +import numpy as np +from tifffile import imread, imwrite + + +def crop_3d_image(image, roi_size): + """Crops a 3d image by extracting all regions of size roi_size. + If the edge of the array is reached, the cropped region is overlapped with the previous cropped region. + """ + image_size = image.shape + cropped_images = [] + for i in range(0, image_size[0], roi_size[0]): + for j in range(0, image_size[1], roi_size[1]): + for k in range(0, image_size[2], roi_size[2]): + if i + roi_size[0] >= image_size[0]: + crop_location_i = image_size[0] - roi_size[0] + else: + crop_location_i = i + if j + roi_size[1] >= image_size[1]: + crop_location_j = image_size[1] - roi_size[1] + else: + crop_location_j = j + if k + roi_size[2] >= image_size[2]: + crop_location_k = image_size[2] - roi_size[2] + else: + crop_location_k = k + cropped_images.append( + image[ + crop_location_i : crop_location_i + roi_size[0], + crop_location_j : crop_location_j + roi_size[1], + crop_location_k : crop_location_k + roi_size[2], + ] + ) + return cropped_images + + +if __name__ == "__main__": + image_path = ( + Path().home() + # / "Desktop/Code/CELLSEG_BENCHMARK/TPH2_DATA/somatomotor_iso" + / "Desktop/Code/CELLSEG_BENCHMARK/TPH2_DATA/somatomotor_iso/labels/semantic" + ) + assert image_path.exists() + image_list = image_path.glob("*.tif") + for j in image_list: + print(j) + image = imread(str(j)) + crops = crop_3d_image(image, (64, 64, 64)) + # viewer = napari.Viewer() + if not (image_path / "cropped").exists(): + (image_path / "cropped").mkdir(exist_ok=False) + for i, im in enumerate(crops): + print(im.shape) + # viewer.add_image(im) + imwrite( + str(image_path / f"cropped/{j.stem}_{i}_crop.tif"), + im.astype(np.uint16), + dtype="uint16", + ) + # napari.run() From 7a458f116f2a753f8488a50426c061385f92999d Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 17 Jul 2023 15:15:07 +0200 Subject: [PATCH 016/250] Added proper transform for normalization --- napari_cellseg3d/code_models/workers.py | 54 ++++++++++--------- .../code_plugins/plugin_model_training.py | 2 +- 2 files changed, 29 insertions(+), 27 deletions(-) diff --git a/napari_cellseg3d/code_models/workers.py b/napari_cellseg3d/code_models/workers.py index 26044806..5708827f 100644 --- a/napari_cellseg3d/code_models/workers.py +++ b/napari_cellseg3d/code_models/workers.py @@ -27,6 +27,7 @@ EnsureType, EnsureTyped, LoadImaged, + MapTransform, # NormalizeIntensityd, Orientationd, Rand3DElasticd, @@ -236,6 +237,28 @@ def to(self, device): pass +class QuantileNormalizationd(MapTransform): + def __init__(self, keys, allow_missing_keys: bool = False): + super().__init__(keys, allow_missing_keys) + + def __call__(self, data): + d = dict(data) + for key in self.keys: + d[key] = self.normalizer(d[key]) + return d + + def normalizer(self, image: torch.Tensor): + """Normalize each image in a batch individually by quantile normalization.""" + if image.ndim == 4: + for i in range(image.shape[0]): + image[i] = utils.quantile_normalization(image[i]) + else: + raise NotImplementedError( + "QuantileNormalizationd only supports 2D and 3D tensors with NCHWD format" + ) + return image + + @dataclass class InferenceResult: """Class to record results of a segmentation job""" @@ -391,7 +414,7 @@ def load_folder(self): EnsureChannelFirstd(keys=["image"]), # Orientationd(keys=["image"], axcodes="PLI"), # anisotropic_transform, - # NormalizeIntensityd(keys=["image"]), # TODO normalize + QuantileNormalizationd(keys=["image"]), EnsureTyped(keys=["image"]), ] ) @@ -567,9 +590,7 @@ def create_inference_result( if crf_results is not None: crf_results = utils.correct_rotation(crf_results) if instance_labels is not None: - instance_labels = utils.correct_rotation( - instance_labels - ) # TODO(cyril) check if correct + instance_labels = utils.correct_rotation(instance_labels) return InferenceResult( image_id=i + 1, @@ -765,6 +786,7 @@ def inference_on_layer(self, image, model, post_process_transforms): self.log("Inference started on layer...") image = image.type(torch.FloatTensor) + image = utils.quantile_normalization(image) out = self.model_output( image, @@ -1288,7 +1310,7 @@ def get_loader_func(num_samples): [ LoadImaged(keys=["image", "label"]), EnsureChannelFirstd(keys=["image", "label"]), - # NormalizeIntensityd(keys=["image"]), # TODO normalize + QuantileNormalizationd(keys=["image"]), RandSpatialCropSamplesd( keys=["image", "label"], roi_size=( @@ -1361,7 +1383,7 @@ def get_loader_func(num_samples): ), EnsureChannelFirstd(keys=["image", "label"]), Orientationd(keys=["image", "label"], axcodes="PLI"), - # NormalizeIntensityd(keys=["image"]), # TODO normalize + QuantileNormalizationd(keys=["image"]), SpatialPadd( keys=["image", "label"], spatial_size=PADDING, @@ -1417,13 +1439,6 @@ def get_loader_func(num_samples): best_metric = -1 best_metric_epoch = -1 - def normalize_batch(batch): # TODO(refactor as a transform) - batch_dim = batch.shape[0] - channels_dim = batch.shape[1] - for i in range(batch_dim): - for j in range(channels_dim): - batch[i][j] = utils.quantile_normalization(batch[i][j]) - # time = utils.get_date_time() logger.debug("Weights") @@ -1493,18 +1508,6 @@ def normalize_batch(batch): # TODO(refactor as a transform) batch_data["label"].to(device), ) - normalize_batch(inputs) - # images = [im.cpu().numpy() for im in inputs] - # [images.append(l.cpu().numpy()) for l in labels] - # - # yield TrainingReport( - # show_plot=True, - # epoch=1, - # images=images, - # ) - # - # break - optimizer.zero_grad() outputs = model(inputs) # self.log(f"Output dimensions : {outputs.shape}") @@ -1556,7 +1559,6 @@ def normalize_batch(batch): # TODO(refactor as a transform) val_data["label"].to(device), ) - normalize_batch(val_inputs) try: with torch.no_grad(): val_outputs = sliding_window_inference( diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 676ab78c..50b6e54f 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -996,7 +996,7 @@ def on_yield(self, report: TrainingReport): else: for i in rge: if layer_name + str(i) not in [ - layer.name for layer in self.result_layers + layer.name for layer in self._viewer.layers ]: new_layer = self._viewer.add_image( report.images[i], From 19758e1b5bdc0e18fcc202945dd7c373012e6a66 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 17 Jul 2023 15:41:46 +0200 Subject: [PATCH 017/250] Training improvments --- napari_cellseg3d/code_models/workers.py | 17 +++++++++++++++++ .../code_plugins/plugin_model_training.py | 2 +- 2 files changed, 18 insertions(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/workers.py b/napari_cellseg3d/code_models/workers.py index 5708827f..d2fa1e41 100644 --- a/napari_cellseg3d/code_models/workers.py +++ b/napari_cellseg3d/code_models/workers.py @@ -1653,7 +1653,24 @@ def get_loader_func(num_samples): f"Train completed, best_metric: {best_metric:.4f} " f"at epoch: {best_metric_epoch}" ) + # Save last checkpoint + weights_filename = f"{model_name}_last_epoch_{epoch + 1}.pth" + self.log("Saving last model") + torch.save( + model.state_dict(), + Path(self.config.results_path_folder) / Path(weights_filename), + ) + self.log("Saving complete, exiting") model.to("cpu") + # clear (V)RAM + val_ds = None + train_ds = None + val_loader = None + train_loader = None + torch.cuda.empty_cache() + import gc + + gc.collect() except Exception as e: self.raise_error(e, "Error in training") diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 50b6e54f..9a8b6195 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -980,7 +980,7 @@ def on_yield(self, report: TrainingReport): if report.show_plot: try: - layer_name = "Training_checkpoint_" + layer_name = "Validation output n°" rge = range(len(report.images)) self.log.print_and_log(len(report.images)) From 62e4438b4d60560022ccae94f7f9e4ac7ec70f43 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 17 Jul 2023 16:34:10 +0200 Subject: [PATCH 018/250] Fix VNet training --- napari_cellseg3d/code_models/models/model_SwinUNetR.py | 5 +++++ napari_cellseg3d/code_models/models/model_VNet.py | 7 +++++-- 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/code_models/models/model_SwinUNetR.py b/napari_cellseg3d/code_models/models/model_SwinUNetR.py index 2d7b5ef6..4a0bc35a 100644 --- a/napari_cellseg3d/code_models/models/model_SwinUNetR.py +++ b/napari_cellseg3d/code_models/models/model_SwinUNetR.py @@ -1,4 +1,5 @@ from monai.networks.nets import SwinUNETR +from torch import softmax from napari_cellseg3d.utils import LOGGER @@ -36,6 +37,10 @@ def __init__( use_checkpoint=use_checkpoint, ) + def forward(self, x_in): + y = super().forward(x_in) + return softmax(y, dim=1) + # def get_output(self, input): # out = self(input) # return torch.sigmoid(out) diff --git a/napari_cellseg3d/code_models/models/model_VNet.py b/napari_cellseg3d/code_models/models/model_VNet.py index b082ccab..4e375a11 100644 --- a/napari_cellseg3d/code_models/models/model_VNet.py +++ b/napari_cellseg3d/code_models/models/model_VNet.py @@ -8,9 +8,12 @@ class VNet_(VNet): def __init__(self, in_channels=1, out_channels=1, **kwargs): try: super().__init__( - in_channels=in_channels, out_channels=out_channels, **kwargs + in_channels=in_channels, + out_channels=out_channels, + bias=True, + **kwargs, ) except TypeError: super().__init__( - in_channels=in_channels, out_channels=out_channels + in_channels=in_channels, out_channels=out_channels, bias=True ) From 704c38d6279ff364e457e8f71018b704d0e75096 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Tue, 18 Jul 2023 10:15:35 +0200 Subject: [PATCH 019/250] Split workers file in smaller ones --- .../{test_workers.py => test_inference.py} | 14 +- napari_cellseg3d/_tests/test_models.py | 2 +- .../_tests/test_plugin_inference.py | 2 +- napari_cellseg3d/_tests/test_training.py | 4 +- .../_tests/test_weight_download.py | 2 +- .../code_models/models/model_SwinUNetR.py | 5 +- .../code_models/models/model_TRAILMAP_MS.py | 2 +- .../code_models/models/model_WNet.py | 2 - .../code_models/models/wnet/model.py | 2 - .../code_models/worker_inference.py | 776 ++++++++ .../code_models/worker_training.py | 731 +++++++ napari_cellseg3d/code_models/workers.py | 1679 ----------------- napari_cellseg3d/code_models/workers_utils.py | 221 +++ .../code_plugins/plugin_model_inference.py | 21 +- .../code_plugins/plugin_model_training.py | 9 +- napari_cellseg3d/utils.py | 11 +- 16 files changed, 1768 insertions(+), 1715 deletions(-) rename napari_cellseg3d/_tests/{test_workers.py => test_inference.py} (92%) create mode 100644 napari_cellseg3d/code_models/worker_inference.py create mode 100644 napari_cellseg3d/code_models/worker_training.py delete mode 100644 napari_cellseg3d/code_models/workers.py create mode 100644 napari_cellseg3d/code_models/workers_utils.py diff --git a/napari_cellseg3d/_tests/test_workers.py b/napari_cellseg3d/_tests/test_inference.py similarity index 92% rename from napari_cellseg3d/_tests/test_workers.py rename to napari_cellseg3d/_tests/test_inference.py index 30ebae08..336630f5 100644 --- a/napari_cellseg3d/_tests/test_workers.py +++ b/napari_cellseg3d/_tests/test_inference.py @@ -5,20 +5,16 @@ import pytest import torch from monai.data import DataLoader -from numpy.random import PCG64, Generator -from napari_cellseg3d.code_models.workers import ( +from napari_cellseg3d.code_models.worker_inference import InferenceWorker +from napari_cellseg3d.code_models.workers_utils import ( + PRETRAINED_WEIGHTS_DIR, InferenceResult, - InferenceWorker, ONNXModelWrapper, WeightsDownloader, ) -from napari_cellseg3d.config import ( - PRETRAINED_WEIGHTS_DIR, - InferenceWorkerConfig, -) - -rand_gen = Generator(PCG64(12345)) +from napari_cellseg3d.config import InferenceWorkerConfig +from napari_cellseg3d.utils import rand_gen def test_onnx_inference(make_napari_viewer_proxy): diff --git a/napari_cellseg3d/_tests/test_models.py b/napari_cellseg3d/_tests/test_models.py index 898b4e4c..d32ff62b 100644 --- a/napari_cellseg3d/_tests/test_models.py +++ b/napari_cellseg3d/_tests/test_models.py @@ -111,7 +111,7 @@ def on_yield(result): def test_pretrained_weights_compatibility(): - from napari_cellseg3d.code_models.workers import WeightsDownloader + from napari_cellseg3d.code_models.workers_utils import WeightsDownloader from napari_cellseg3d.config import MODEL_LIST, PRETRAINED_WEIGHTS_DIR for model_name in MODEL_LIST: diff --git a/napari_cellseg3d/_tests/test_plugin_inference.py b/napari_cellseg3d/_tests/test_plugin_inference.py index 309c5ab7..de5c78ae 100644 --- a/napari_cellseg3d/_tests/test_plugin_inference.py +++ b/napari_cellseg3d/_tests/test_plugin_inference.py @@ -9,8 +9,8 @@ volume_stats, ) from napari_cellseg3d.code_models.models.model_test import TestModel +from napari_cellseg3d.code_models.workers_utils import InferenceResult from napari_cellseg3d.code_plugins.plugin_model_inference import ( - InferenceResult, Inferer, ) from napari_cellseg3d.config import MODEL_LIST diff --git a/napari_cellseg3d/_tests/test_training.py b/napari_cellseg3d/_tests/test_training.py index 2378d193..f76b6d23 100644 --- a/napari_cellseg3d/_tests/test_training.py +++ b/napari_cellseg3d/_tests/test_training.py @@ -3,9 +3,9 @@ from napari_cellseg3d import config from napari_cellseg3d._tests.fixtures import LogFixture from napari_cellseg3d.code_models.models.model_test import TestModel +from napari_cellseg3d.code_models.workers_utils import TrainingReport from napari_cellseg3d.code_plugins.plugin_model_training import ( Trainer, - TrainingReport, ) from napari_cellseg3d.config import MODEL_LIST @@ -90,7 +90,7 @@ def test_training(make_napari_viewer_proxy, qtbot): res = next(worker.train()) assert isinstance(res, TrainingReport) - assert res.epoch == 1 + assert res.epoch == 0 widget.worker = worker res.show_plot = True diff --git a/napari_cellseg3d/_tests/test_weight_download.py b/napari_cellseg3d/_tests/test_weight_download.py index a00ab1de..e1392436 100644 --- a/napari_cellseg3d/_tests/test_weight_download.py +++ b/napari_cellseg3d/_tests/test_weight_download.py @@ -1,4 +1,4 @@ -from napari_cellseg3d.code_models.workers import ( +from napari_cellseg3d.code_models.workers_utils import ( PRETRAINED_WEIGHTS_DIR, WeightsDownloader, ) diff --git a/napari_cellseg3d/code_models/models/model_SwinUNetR.py b/napari_cellseg3d/code_models/models/model_SwinUNetR.py index 4a0bc35a..621ae0b3 100644 --- a/napari_cellseg3d/code_models/models/model_SwinUNetR.py +++ b/napari_cellseg3d/code_models/models/model_SwinUNetR.py @@ -1,5 +1,5 @@ from monai.networks.nets import SwinUNETR -from torch import softmax +from torch import sigmoid from napari_cellseg3d.utils import LOGGER @@ -39,7 +39,8 @@ def __init__( def forward(self, x_in): y = super().forward(x_in) - return softmax(y, dim=1) + # return softmax(y, dim=1) + return sigmoid(y) # def get_output(self, input): # out = self(input) diff --git a/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py b/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py index baf8635d..8f1a493a 100644 --- a/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py +++ b/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py @@ -8,7 +8,7 @@ class TRAILMAP_MS_(UNet3D): use_default_training = True weights_file = "TRAILMAP_MS_best_metric_epoch_26.pth" - # original model from Liqun Luo lab, transferred to pytorch and trained on mesoSPIM-acquired data (mostly cFOS as of July 2022) + # original model from Liqun Luo lab, transferred to pytorch and trained on mesoSPIM-acquired data (mostly TPH2 as of July 2022) def __init__(self, in_channels=1, out_channels=1, **kwargs): try: diff --git a/napari_cellseg3d/code_models/models/model_WNet.py b/napari_cellseg3d/code_models/models/model_WNet.py index 4058b603..37768616 100644 --- a/napari_cellseg3d/code_models/models/model_WNet.py +++ b/napari_cellseg3d/code_models/models/model_WNet.py @@ -11,11 +11,9 @@ def __init__( in_channels=1, out_channels=2, # num_classes=2, - device="cpu", **kwargs, ): super().__init__( - device=device, in_channels=in_channels, out_channels=out_channels, # num_classes=num_classes, diff --git a/napari_cellseg3d/code_models/models/wnet/model.py b/napari_cellseg3d/code_models/models/wnet/model.py index 5ef726b6..060242a1 100644 --- a/napari_cellseg3d/code_models/models/wnet/model.py +++ b/napari_cellseg3d/code_models/models/wnet/model.py @@ -23,13 +23,11 @@ class WNet_encoder(nn.Module): def __init__( self, - device, in_channels=1, out_channels=2 # num_classes=2 ): super().__init__() - self.device = device self.encoder = UNet( in_channels=in_channels, out_channels=out_channels, diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py new file mode 100644 index 00000000..c4221e7b --- /dev/null +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -0,0 +1,776 @@ +import platform +from pathlib import Path + +import numpy as np +import torch + +# MONAI +from monai.data import DataLoader, Dataset +from monai.inferers import sliding_window_inference +from monai.transforms import ( + AddChannel, + AsDiscrete, + Compose, + EnsureChannelFirstd, + EnsureType, + EnsureTyped, + LoadImaged, + SpatialPad, + SpatialPadd, + ToTensor, + Zoom, +) +from napari._qt.qthreading import GeneratorWorker +from tifffile import imwrite + +# local +from napari_cellseg3d import config, utils +from napari_cellseg3d.code_models.crf import crf_with_config +from napari_cellseg3d.code_models.instance_segmentation import volume_stats +from napari_cellseg3d.code_models.workers_utils import ( + PRETRAINED_WEIGHTS_DIR, + InferenceResult, + LogSignal, + ONNXModelWrapper, + QuantileNormalizationd, + WeightsDownloader, +) +from napari_cellseg3d.utils import LOGGER as logger + +""" +Writing something to log messages from outside the main thread needs specific care, +Following the instructions in the guides below to have a worker with custom signals, +a custom worker function was implemented. +""" + +# https://python-forum.io/thread-31349.html +# https://www.pythoncentral.io/pysidepyqt-tutorial-creating-your-own-signals-and-slots/ +# https://napari-staging-site.github.io/guides/stable/threading.html + + +class InferenceWorker(GeneratorWorker): + """A custom worker to run inference jobs in. + Inherits from :py:class:`napari.qt.threading.GeneratorWorker`""" + + def __init__( + self, + worker_config: config.InferenceWorkerConfig, + ): + """Initializes a worker for inference with the arguments needed by the :py:func:`~inference` function. + + The config contains the following attributes: + * device: cuda or cpu device to use for torch + * model_dict: the :py:attr:`~self.models_dict` dictionary to obtain the model name, class and instance + * weights_dict: dict with "custom" : bool to use custom weights or not; "path" : the path to weights if custom or name of the file if not custom + * results_path: the path to save the results to + * filetype: the file extension to use when saving, + * transforms: a dict containing transforms to perform at various times. + * instance: a dict containing parameters regarding instance segmentation + * use_window: use window inference with specific size or whole image + * window_infer_size: size of window if use_window is True + * keep_on_cpu: keep images on CPU or no + * stats_csv: compute stats on cells and save them to a csv file + * images_filepaths: the paths to the images of the dataset + * layer: the layer to run inference on + + Args: + * worker_config (config.InferenceWorkerConfig): dataclass containing the proper configuration elements + + Note: See :py:func:`~self.inference` + """ + + super().__init__(self.inference) + self._signals = LogSignal() # add custom signals + self.log_signal = self._signals.log_signal + self.warn_signal = self._signals.warn_signal + self.error_signal = self._signals.error_signal + + self.config = worker_config + + """These attributes are all arguments of :py:func:~inference, please see that for reference""" + + self.downloader = WeightsDownloader() + """Download utility""" + + @staticmethod + def create_inference_dict(images_filepaths): + """Create a dict for MONAI with "image" keys with all image paths in :py:attr:`~self.images_filepaths` + + Returns: + dict: list of image paths from loaded folder""" + return [{"image": image_name} for image_name in images_filepaths] + + def set_download_log(self, widget): + self.downloader.log_widget = widget + + def log(self, text): + """Sends a signal that ``text`` should be logged + + Args: + text (str): text to logged + """ + self.log_signal.emit(text) + + def warn(self, warning): + """Sends a warning to main thread""" + self.warn_signal.emit(warning) + + def raise_error(self, exception, msg): + """Raises an error in main thread""" + logger.error(msg, exc_info=True) + logger.error(exception, exc_info=True) + + self.log_signal.emit("!" * 20) + self.log_signal.emit("Error occured") + # self.log_signal.emit(msg) + # self.log_signal.emit(str(exception)) + + self.error_signal.emit(exception, msg) + self.errored.emit(exception) + yield exception + # self.quit() + + def log_parameters(self): + config = self.config + + self.log("-" * 20) + self.log("\nParameters summary :") + + self.log(f"Model is : {config.model_info.name}") + if config.post_process_config.thresholding.enabled: + self.log( + f"Thresholding is enabled at {config.post_process_config.thresholding.threshold_value}" + ) + + status = ( + "enabled" + if config.sliding_window_config.is_enabled() + else "disabled" + ) + + self.log(f"Window inference is {status}\n") + if status == "enabled": + self.log( + f"Window size is {self.config.sliding_window_config.window_size}" + ) + self.log( + f"Window overlap is {self.config.sliding_window_config.window_overlap}" + ) + + if config.keep_on_cpu: + self.log("Dataset loaded to CPU") + else: + self.log(f"Dataset loaded on {config.device} device") + + if config.post_process_config.zoom.enabled: + self.log( + f"Scaling factor : {config.post_process_config.zoom.zoom_values} (x,y,z)" + ) + + instance_config = config.post_process_config.instance + if instance_config.enabled: + self.log( + f"Instance segmentation enabled, method : {instance_config.method.name}\n" + ) + self.log("-" * 20) + + def load_folder(self): + images_dict = self.create_inference_dict(self.config.images_filepaths) + + # TODO : better solution than loading first image always ? + data_check = LoadImaged(keys=["image"])(images_dict[0]) + check = data_check["image"].shape + pad = utils.get_padding_dim(check) + + if self.config.sliding_window_config.is_enabled(): + load_transforms = Compose( + [ + LoadImaged(keys=["image"]), + # AddChanneld(keys=["image"]), #already done + EnsureChannelFirstd(keys=["image"]), + # Orientationd(keys=["image"], axcodes="PLI"), + # anisotropic_transform, + QuantileNormalizationd(keys=["image"]), + EnsureTyped(keys=["image"]), + ] + ) + else: + load_transforms = Compose( + [ + LoadImaged(keys=["image"]), + # AddChanneld(keys=["image"]), #already done + EnsureChannelFirstd(keys=["image"]), + # NormalizeIntensityd(keys=["image"]), # TODO normalize + # Orientationd(keys=["image"], axcodes="PLI"), + # anisotropic_transform, + SpatialPadd(keys=["image"], spatial_size=pad), + EnsureTyped(keys=["image"]), + ] + ) + + self.log("\nLoading dataset...") + inference_ds = Dataset(data=images_dict, transform=load_transforms) + inference_loader = DataLoader( + inference_ds, batch_size=1, num_workers=2 + ) + self.log("Done") + return inference_loader + + def load_layer(self): + self.log("\nLoading layer\n") + data = np.squeeze(self.config.layer.data) + + volume = np.array(data, dtype=np.int16) + + volume_dims = len(volume.shape) + if volume_dims != 3: + raise ValueError( + f"Data array is not 3-dimensional but {volume_dims}-dimensional," + f" please check for extra channel/batch dimensions" + ) + + volume = np.swapaxes( + volume, 0, 2 + ) # for anisotropy to be monai-like, i.e. zyx # FIXME rotation not always correct + + dims_check = volume.shape + + # logger.debug(volume.shape) + # logger.debug(volume.dtype) + if self.config.sliding_window_config.is_enabled(): + load_transforms = Compose( + [ + ToTensor(), + # anisotropic_transform, + AddChannel(), + # SpatialPad(spatial_size=pad), + AddChannel(), + EnsureType(), + ], + map_items=False, + log_stats=True, + ) + else: + self.log("Checking dimensions...") + pad = utils.get_padding_dim(dims_check) + load_transforms = Compose( + [ + ToTensor(), + # anisotropic_transform, + AddChannel(), + SpatialPad(spatial_size=pad), + AddChannel(), + EnsureType(), + ], + map_items=False, + log_stats=True, + ) + + input_image = load_transforms(volume) + self.log("Done") + return input_image + + def model_output( + self, + inputs, + model, + post_process_transforms, + post_process=True, + aniso_transform=None, + ): + inputs = inputs.to("cpu") + + # def model_output(inputs): + # return post_process_transforms( + # self.config.model_info.get_model().get_output(model, inputs) + # ) + + dataset_device = ( + "cpu" if self.config.keep_on_cpu else self.config.device + ) + + if self.config.sliding_window_config.is_enabled(): + window_size = self.config.sliding_window_config.window_size + window_size = [window_size, window_size, window_size] + window_overlap = self.config.sliding_window_config.window_overlap + else: + window_size = None + window_overlap = 0 + try: + # logger.debug(f"model : {model}") + logger.debug(f"inputs shape : {inputs.shape}") + logger.debug(f"inputs type : {inputs.dtype}") + try: + # outputs = model(inputs) + inputs = utils.remap_image(inputs) + + def model_output_wrapper(inputs): + result = model(inputs) + return post_process_transforms(result) + + with torch.no_grad(): + outputs = sliding_window_inference( + inputs, + roi_size=window_size, + sw_batch_size=1, # TODO add param + predictor=model_output_wrapper, + sw_device=self.config.device, + device=dataset_device, + overlap=window_overlap, + mode="gaussian", + sigma_scale=0.01, + progress=True, + ) + except Exception as e: + logger.exception(e) + logger.debug("failed to run sliding window inference") + self.raise_error(e, "Error during sliding window inference") + logger.debug(f"Inference output shape: {outputs.shape}") + self.log("Post-processing...") + out = outputs.detach().cpu().numpy() + if aniso_transform is not None: + out = aniso_transform(out) + if post_process: + out = np.array(out).astype(np.float32) + out = np.squeeze(out) + return out + except Exception as e: + logger.exception(e) + self.raise_error(e, "Error during sliding window inference") + # sys.stdout = old_stdout + # sys.stderr = old_stderr + + def create_inference_result( + self, + semantic_labels, + instance_labels, + crf_results=None, + from_layer: bool = False, + original=None, + stats=None, + i=0, + ) -> InferenceResult: + if not from_layer and original is None: + raise ValueError( + "If the image is not from a layer, an original should always be available" + ) + + if from_layer: + if i != 0: + raise ValueError( + "A layer's ID should always be 0 (default value)" + ) + + if semantic_labels is not None: + semantic_labels = utils.correct_rotation(semantic_labels) + if crf_results is not None: + crf_results = utils.correct_rotation(crf_results) + if instance_labels is not None: + instance_labels = utils.correct_rotation(instance_labels) + + return InferenceResult( + image_id=i + 1, + original=original, + instance_labels=instance_labels, + crf_results=crf_results, + stats=stats, + result=semantic_labels, + model_name=self.config.model_info.name, + ) + + def get_original_filename(self, i): + return Path(self.config.images_filepaths[i]).stem + + def get_instance_result(self, semantic_labels, from_layer=False, i=-1): + if not from_layer and i == -1: + raise ValueError( + "An ID should be provided when running from a file" + ) + + if self.config.post_process_config.instance.enabled: + instance_labels = self.instance_seg( + semantic_labels, + i + 1, + ) + data_dict = self.stats_csv(instance_labels) + else: + instance_labels = None + data_dict = None + return instance_labels, data_dict + + def save_image( + self, + image, + from_layer=False, + i=0, + additional_info="", + ): + if not from_layer: + original_filename = "_" + self.get_original_filename(i) + "_" + filetype = self.config.filetype + else: + original_filename = "_" + filetype = ".tif" + + time = utils.get_date_time() + + file_path = ( + self.config.results_path + + "/" + + f"{additional_info}" + + f"Prediction_{i+1}" + + original_filename + + self.config.model_info.name + + f"_{time}" + + filetype + ) + try: + imwrite(file_path, image) + except ValueError as e: + self.raise_error(e, "Error during image saving") + filename = Path(file_path).stem + + if from_layer: + self.log(f"\nLayer prediction saved as : {filename}") + else: + self.log(f"\nFile n°{i+1} saved as : {filename}") + + def aniso_transform(self, image): + if self.config.post_process_config.zoom.enabled: + zoom = self.config.post_process_config.zoom.zoom_values + anisotropic_transform = Zoom( + zoom=zoom, + keep_size=False, + padding_mode="empty", + ) + return anisotropic_transform(image[0]) + return image + + def instance_seg( + self, semantic_labels, image_id=0, original_filename="layer" + ): + if image_id is not None: + self.log(f"\nRunning instance segmentation for image n°{image_id}") + + method = self.config.post_process_config.instance.method + instance_labels = method.run_method_on_channels(semantic_labels) + self.log(f"DEBUG instance results shape : {instance_labels.shape}") + + filetype = ( + ".tif" + if self.config.filetype == "" + else "_" + self.config.filetype + ) + + instance_filepath = ( + self.config.results_path + + "/" + + f"Instance_seg_labels_{image_id}_" + + original_filename + + "_" + + self.config.model_info.name + + f"_{utils.get_date_time()}" + + filetype + ) + + imwrite(instance_filepath, instance_labels) + self.log( + f"Instance segmentation results for image n°{image_id} have been saved as:" + ) + self.log(Path(instance_filepath).name) + return instance_labels + + def inference_on_folder(self, inf_data, i, model, post_process_transforms): + self.log("-" * 10) + self.log(f"Inference started on image n°{i + 1}...") + + inputs = inf_data["image"] + + out = self.model_output( + inputs, + model, + post_process_transforms, + aniso_transform=self.aniso_transform, + ) + + self.save_image(out, i=i) + instance_labels, stats = self.get_instance_result(out, i=i) + if self.config.use_crf: + crf_in = inputs.detach().cpu().numpy() + try: + crf_results = self.run_crf( + crf_in, + out, + aniso_transform=self.aniso_transform, + image_id=i, + ) + + except ValueError as e: + self.log(f"Error occurred during CRF : {e}") + crf_results = None + else: + crf_results = None + + original = np.array(inf_data["image"]).astype(np.float32) + + self.log(f"Inference completed on image n°{i+1}") + + return self.create_inference_result( + out, + instance_labels, + crf_results, + from_layer=False, + original=original, + stats=stats, + i=i, + ) + + def run_crf(self, image, labels, aniso_transform, image_id=0): + try: + if aniso_transform is not None: + image = aniso_transform(image) + crf_results = crf_with_config( + image, labels, config=self.config.crf_config, log=self.log + ) + self.save_image( + crf_results, + i=image_id, + additional_info="CRF_", + from_layer=True, + ) + return crf_results + except ValueError as e: + self.log(f"Error occurred during CRF : {e}") + return None + + def stats_csv(self, instance_labels): + try: + if self.config.compute_stats: + if len(instance_labels.shape) == 4: + stats = [volume_stats(c) for c in instance_labels] + else: + stats = [volume_stats(instance_labels)] + else: + stats = None + return stats + except ValueError as e: + self.log(f"Error occurred during stats computing : {e}") + return None + + def inference_on_layer(self, image, model, post_process_transforms): + self.log("-" * 10) + self.log("Inference started on layer...") + + image = image.type(torch.FloatTensor) + image = utils.quantile_normalization(image) + + out = self.model_output( + image, + model, + post_process_transforms, + aniso_transform=self.aniso_transform, + ) + + self.save_image(out, from_layer=True) + + instance_labels, stats = self.get_instance_result( + semantic_labels=out, from_layer=True + ) + + crf_results = ( + self.run_crf(image, out, aniso_transform=self.aniso_transform) + if self.config.use_crf + else None + ) + + return self.create_inference_result( + semantic_labels=out, + instance_labels=instance_labels, + crf_results=crf_results, + from_layer=True, + stats=stats, + ) + + # @thread_worker(connect={"errored": self.raise_error}) + def inference(self): + """ + Requires: + * device: cuda or cpu device to use for torch + + * model_dict: the :py:attr:`~self.models_dict` dictionary to obtain the model name, class and instance + + * weights: the loaded weights from the model + + * images_filepaths: the paths to the images of the dataset + + * results_path: the path to save the results to + + * filetype: the file extension to use when saving, + + * transforms: a dict containing transforms to perform at various times. + + * use_window: use window inference with specific size or whole image + + * window_infer_size: size of window if use_window is True + + * keep_on_cpu: keep images on CPU or no + + * stats_csv: compute stats on cells and save them to a csv file + + Yields: + dict: contains : + * "image_id" : index of the returned image + + * "original" : original volume used for inference + + * "result" : inference result + + """ + sys = platform.system() + logger.debug(f"OS is {sys}") + if sys == "Darwin": + torch.set_num_threads(1) # required for threading on macOS ? + self.log("Number of threads has been set to 1 for macOS") + + try: + dims = self.config.model_info.model_input_size + self.log(f"MODEL DIMS : {dims}") + model_name = self.config.model_info.name + model_class = self.config.model_info.get_model() + self.log(f"Model name : {model_name}") + + weights_config = self.config.weights_config + post_process_config = self.config.post_process_config + if Path(weights_config.path).suffix == ".pt": + self.log("Instantiating PyTorch jit model...") + model = torch.jit.load(weights_config.path) + # try: + elif Path(weights_config.path).suffix == ".onnx": + self.log("Instantiating ONNX model...") + model = ONNXModelWrapper(weights_config.path) + else: # assume is .pth + self.log("Instantiating model...") + model = model_class( + input_img_size=[dims, dims, dims], + # device=self.config.device, + num_classes=self.config.model_info.num_classes, + ) + # try: + model = model.to(self.config.device) + # except Exception as e: + # self.raise_error(e, "Issue loading model to device") + # logger.debug(f"model : {model}") + if model is None: + raise ValueError("Model is None") + # try: + self.log("\nLoading weights...") + if weights_config.custom: + weights = weights_config.path + else: + self.downloader.download_weights( + model_name, + model_class.weights_file, + ) + weights = str( + PRETRAINED_WEIGHTS_DIR / Path(model_class.weights_file) + ) + + model.load_state_dict( # note that this is redefined in WNet_ + torch.load( + weights, + map_location=self.config.device, + ) + ) + self.log("Done") + # except Exception as e: + # self.raise_error(e, "Issue loading weights") + # except Exception as e: + # self.raise_error(e, "Issue instantiating model") + + # if model_name == "SegResNet": + # model = model_class( + # input_image_size=[ + # dims, + # dims, + # dims, + # ], + # ) + # elif model_name == "SwinUNetR": + # model = model_class( + # img_size=[dims, dims, dims], + # use_checkpoint=False, + # ) + # else: + # model = model_class.get_net() + + self.log_parameters() + + # load_transforms = Compose( + # [ + # LoadImaged(keys=["image"]), + # # AddChanneld(keys=["image"]), #already done + # EnsureChannelFirstd(keys=["image"]), + # # Orientationd(keys=["image"], axcodes="PLI"), + # # anisotropic_transform, + # SpatialPadd(keys=["image"], spatial_size=pad), + # EnsureTyped(keys=["image"]), + # ] + # ) + + if not post_process_config.thresholding.enabled: + post_process_transforms = EnsureType() + else: + t = post_process_config.thresholding.threshold_value + post_process_transforms = Compose( + AsDiscrete(threshold=t), EnsureType() + ) + + is_folder = self.config.images_filepaths is not None + is_layer = self.config.layer is not None + + if is_layer and is_folder: + raise ValueError( + "Both a layer and a folder have been specified, please specify only one of the two. Aborting." + ) + elif is_folder: + inference_loader = self.load_folder() + ################## + ################## + # DEBUG + # from monai.utils import first + # + # check_data = first(inference_loader) + # image = check_data[0][0] + # logger.debug(image.shape) + ################## + ################## + elif is_layer: + input_image = self.load_layer() + else: + raise ValueError("No data has been provided. Aborting.") + + if model is None: + raise ValueError("Model is None") + + model.eval() + with torch.no_grad(): + ################################ + ################################ + ################################ + if is_folder: + for i, inf_data in enumerate(inference_loader): + yield self.inference_on_folder( + inf_data, i, model, post_process_transforms + ) + elif is_layer: + yield self.inference_on_layer( + input_image, model, post_process_transforms + ) + model.to("cpu") + # self.quit() + except Exception as e: + logger.exception(e) + self.raise_error(e, "Inference failed") + self.quit() + finally: + self.quit() diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py new file mode 100644 index 00000000..03e82ed5 --- /dev/null +++ b/napari_cellseg3d/code_models/worker_training.py @@ -0,0 +1,731 @@ +import platform +import time +from math import ceil +from pathlib import Path + +import torch + +# MONAI +from monai.data import ( + CacheDataset, + DataLoader, + PatchDataset, + decollate_batch, + pad_list_data_collate, +) +from monai.inferers import sliding_window_inference +from monai.metrics import DiceMetric +from monai.transforms import ( + AsDiscrete, + Compose, + EnsureChannelFirstd, + EnsureType, + EnsureTyped, + LoadImaged, + # NormalizeIntensityd, + Orientationd, + Rand3DElasticd, + RandAffined, + RandFlipd, + RandRotate90d, + RandShiftIntensityd, + RandSpatialCropSamplesd, + SpatialPadd, +) +from monai.utils import set_determinism + +# Qt +from napari.qt.threading import GeneratorWorker + +# local +from napari_cellseg3d import config, utils +from napari_cellseg3d.code_models.workers_utils import ( + PRETRAINED_WEIGHTS_DIR, + LogSignal, + QuantileNormalizationd, + TrainingReport, + WeightsDownloader, +) + +logger = utils.LOGGER +VERBOSE_SCHEDULER = True +logger.debug(f"PRETRAINED WEIGHT DIR LOCATION : {PRETRAINED_WEIGHTS_DIR}") + +""" +Writing something to log messages from outside the main thread needs specific care, +Following the instructions in the guides below to have a worker with custom signals, +a custom worker function was implemented. +""" + +# https://python-forum.io/thread-31349.html +# https://www.pythoncentral.io/pysidepyqt-tutorial-creating-your-own-signals-and-slots/ +# https://napari-staging-site.github.io/guides/stable/threading.html + + +class TrainingWorker(GeneratorWorker): + """A custom worker to run training jobs in. + Inherits from :py:class:`napari.qt.threading.GeneratorWorker`""" + + def __init__( + self, + worker_config: config.TrainingWorkerConfig, + ): + """Initializes a worker for inference with the arguments needed by the :py:func:`~train` function. Note: See :py:func:`~train` + + Args: + * device : device to train on, cuda or cpu + + * model_dict : dict containing the model's "name" and "class" + + * weights_path : path to weights files if transfer learning is to be used + + * data_dicts : dict from :py:func:`Trainer.create_train_dataset_dict` + + * validation_percent : percentage of images to use as validation + + * max_epochs : the amout of epochs to train for + + * loss_function : the loss function to use for training + + * learning_rate : the learning rate of the optimizer + + * val_interval : the interval at which to perform validation (e.g. if 2 will validate once every 2 epochs.) Also determines frequency of saving, depending on whether the metric is better or not + + * batch_size : the batch size to use for training + + * results_path : the path to save results in + + * sampling : whether to extract patches from images or not + + * num_samples : the number of samples to extract from an image for training + + * sample_size : the size of the patches to extract when sampling + + * do_augmentation : whether to perform data augmentation or not + + * deterministic : dict with "use deterministic" : bool, whether to use deterministic training, "seed": seed for RNG + + + """ + super().__init__(self.train) + self._signals = LogSignal() + self.log_signal = self._signals.log_signal + self.warn_signal = self._signals.warn_signal + self.error_signal = self._signals.error_signal + + self._weight_error = False + ############################################# + self.config = worker_config + + self.train_files = [] + self.val_files = [] + ####################################### + self.downloader = WeightsDownloader() + + def set_download_log(self, widget): + self.downloader.log_widget = widget + + def log(self, text): + """Sends a signal that ``text`` should be logged + + Args: + text (str): text to logged + """ + self.log_signal.emit(text) + + def warn(self, warning): + """Sends a warning to main thread""" + self.warn_signal.emit(warning) + + def raise_error(self, exception, msg): + """Sends an error to main thread""" + logger.error(msg, exc_info=True) + logger.error(exception, exc_info=True) + self.error_signal.emit(exception, msg) + self.errored.emit(exception) + self.quit() + + def log_parameters(self): + self.log("-" * 20) + self.log("Parameters summary :\n") + + self.log( + f"Percentage of dataset used for validation : {self.config.validation_percent * 100}%" + ) + + self.log("-" * 10) + self.log("Training files :\n") + [ + self.log(f"{Path(train_file['image']).name}\n") + for train_file in self.train_files + ] + self.log("-" * 10) + self.log("Validation files :\n") + [ + self.log(f"{Path(val_file['image']).name}\n") + for val_file in self.val_files + ] + self.log("-" * 10) + + if self.config.deterministic_config.enabled: + self.log("Deterministic training is enabled") + self.log(f"Seed is {self.config.deterministic_config.seed}") + + self.log(f"Training for {self.config.max_epochs} epochs") + self.log(f"Loss function is : {str(self.config.loss_function)}") + self.log( + f"Validation is performed every {self.config.validation_interval} epochs" + ) + self.log(f"Batch size is {self.config.batch_size}") + self.log(f"Learning rate is {self.config.learning_rate}") + + if self.config.sampling: + self.log( + f"Extracting {self.config.num_samples} patches of size {self.config.sample_size}" + ) + else: + self.log("Using whole images as dataset") + + if self.config.do_augmentation: + self.log("Data augmentation is enabled") + + if not self.config.weights_info.use_pretrained: + self.log(f"Using weights from : {self.config.weights_info.path}") + if self._weight_error: + self.log( + ">>>>>>>>>>>>>>>>>\n" + "WARNING:\nChosen weights were incompatible with the model,\n" + "the model will be trained from random weights\n" + "<<<<<<<<<<<<<<<<<\n" + ) + + # self.log("\n") + self.log("-" * 20) + + def train(self): + """Trains the PyTorch model for the given number of epochs, with the selected model and data, + using the chosen batch size, validation interval, loss function, and number of samples. + Will perform validation once every :py:obj:`val_interval` and save results if the mean dice is better + + Requires: + + * device : device to train on, cuda or cpu + + * model_dict : dict containing the model's "name" and "class" + + * weights_path : path to weights files if transfer learning is to be used + + * data_dicts : dict from :py:func:`Trainer.create_train_dataset_dict` + + * validation_percent : percentage of images to use as validation + + * max_epochs : the amount of epochs to train for + + * loss_function : the loss function to use for training + + * learning rate : the learning rate of the optimizer + + * val_interval : the interval at which to perform validation (e.g. if 2 will validate once every 2 epochs.) Also determines frequency of saving, depending on whether the metric is better or not + + * batch_size : the batch size to use for training + + * results_path : the path to save results in + + * sampling : whether to extract patches from images or not + + * num_samples : the number of samples to extract from an image for training + + * sample_size : the size of the patches to extract when sampling + + * do_augmentation : whether to perform data augmentation or not + + * deterministic : dict with "use deterministic" : bool, whether to use deterministic training, "seed": seed for RNG + """ + + ######################### + # error_log = open(results_path +"/error_log.log" % multiprocessing.current_process().name, 'x') + # faulthandler.enable(file=error_log, all_threads=True) + ######################### + model_config = self.config.model_info + weights_config = self.config.weights_info + deterministic_config = self.config.deterministic_config + + start_time = time.time() + + try: + if deterministic_config.enabled: + set_determinism( + seed=deterministic_config.seed + ) # use_deterministic_algorithms = True causes cuda error + + sys = platform.system() + logger.debug(sys) + if sys == "Darwin": # required for macOS ? + torch.set_num_threads(1) + self.log("Number of threads has been set to 1 for macOS") + + self.log(f"config model : {self.config.model_info.name}") + model_name = model_config.name + model_class = model_config.get_model() + + if not self.config.sampling: + data_check = LoadImaged(keys=["image"])( + self.config.train_data_dict[0] + ) + check = data_check["image"].shape + + do_sampling = self.config.sampling + + size = self.config.sample_size if do_sampling else check + + PADDING = utils.get_padding_dim(size) + model = model_class( # FIXME check if correct + input_img_size=PADDING, use_checkpoint=True + ) + model = model.to(self.config.device) + + epoch_loss_values = [] + val_metric_values = [] + + if len(self.config.train_data_dict) > 1: + self.train_files, self.val_files = ( + self.config.train_data_dict[ + 0 : int( + len(self.config.train_data_dict) + * self.config.validation_percent + ) + ], + self.config.train_data_dict[ + int( + len(self.config.train_data_dict) + * self.config.validation_percent + ) : + ], + ) + else: + self.train_files = self.val_files = self.config.train_data_dict + msg = f"Only one image file was provided : {self.config.train_data_dict[0]['image']}.\n" + + logger.debug(f"SAMPLING is {self.config.sampling}") + if not self.config.sampling: + msg += "Sampling is not in use, the only image provided will be used as the validation file." + self.warn(msg) + else: + msg += "Samples for validation will be cropped for the same only volume that is being used for training" + + logger.warning(msg) + + logger.debug( + f"Data dict from config is {self.config.train_data_dict}" + ) + logger.debug(f"Train files : {self.train_files}") + logger.debug(f"Val. files : {self.val_files}") + + if len(self.train_files) == 0: + raise ValueError("Training dataset is empty") + if len(self.val_files) == 0: + raise ValueError("Validation dataset is empty") + + if self.config.do_augmentation: + train_transforms = ( + Compose( # TODO : figure out which ones and values ? + [ + RandShiftIntensityd(keys=["image"], offsets=0.7), + Rand3DElasticd( + keys=["image", "label"], + sigma_range=(0.3, 0.7), + magnitude_range=(0.3, 0.7), + ), + RandFlipd(keys=["image", "label"]), + RandRotate90d(keys=["image", "label"]), + RandAffined( + keys=["image", "label"], + ), + EnsureTyped(keys=["image", "label"]), + ] + ) + ) + else: + train_transforms = EnsureTyped(keys=["image", "label"]) + + val_transforms = Compose( + [ + # LoadImaged(keys=["image", "label"]), + # EnsureChannelFirstd(keys=["image", "label"]), + EnsureTyped(keys=["image", "label"]), + ] + ) + + # self.log("Loading dataset...\n") + def get_loader_func(num_samples): + return Compose( + [ + LoadImaged(keys=["image", "label"]), + EnsureChannelFirstd(keys=["image", "label"]), + QuantileNormalizationd(keys=["image"]), + RandSpatialCropSamplesd( + keys=["image", "label"], + roi_size=( + self.config.sample_size + ), # multiply by axis_stretch_factor if anisotropy + # max_roi_size=(120, 120, 120), + random_size=False, + num_samples=num_samples, + ), + Orientationd(keys=["image", "label"], axcodes="PLI"), + SpatialPadd( + keys=["image", "label"], + spatial_size=( + utils.get_padding_dim(self.config.sample_size) + ), + ), + EnsureTyped(keys=["image", "label"]), + ] + ) + + if do_sampling: + # if there is only one volume, split samples + # TODO(cyril) : maybe implement something in user config to toggle this behavior + if len(self.config.train_data_dict) < 2: + num_train_samples = ceil( + self.config.num_samples + * self.config.validation_percent + ) + num_val_samples = ceil( + self.config.num_samples + * (1 - self.config.validation_percent) + ) + sample_loader_train = get_loader_func(num_train_samples) + sample_loader_eval = get_loader_func(num_val_samples) + else: + num_train_samples = ( + num_val_samples + ) = self.config.num_samples + + sample_loader_train = get_loader_func(num_train_samples) + sample_loader_eval = get_loader_func(num_val_samples) + + logger.debug(f"AMOUNT of train samples : {num_train_samples}") + logger.debug( + f"AMOUNT of validation samples : {num_val_samples}" + ) + + logger.debug("train_ds") + train_ds = PatchDataset( + data=self.train_files, + transform=train_transforms, + patch_func=sample_loader_train, + samples_per_image=num_train_samples, + ) + logger.debug("val_ds") + val_ds = PatchDataset( + data=self.val_files, + transform=val_transforms, + patch_func=sample_loader_eval, + samples_per_image=num_val_samples, + ) + + else: + load_whole_images = Compose( + [ + LoadImaged( + keys=["image", "label"], + # image_only=True, + # reader=WSIReader(backend="tifffile") + ), + EnsureChannelFirstd(keys=["image", "label"]), + Orientationd(keys=["image", "label"], axcodes="PLI"), + QuantileNormalizationd(keys=["image"]), + SpatialPadd( + keys=["image", "label"], + spatial_size=PADDING, + ), + EnsureTyped(keys=["image", "label"]), + ] + ) + logger.debug("Cache dataset : train") + train_ds = CacheDataset( + data=self.train_files, + transform=Compose(load_whole_images, train_transforms), + ) + logger.debug("Cache dataset : val") + val_ds = CacheDataset( + data=self.val_files, transform=load_whole_images + ) + logger.debug("Dataloader") + train_loader = DataLoader( + train_ds, + batch_size=self.config.batch_size, + shuffle=True, + num_workers=2, + collate_fn=pad_list_data_collate, + ) + + val_loader = DataLoader( + val_ds, batch_size=self.config.batch_size, num_workers=2 + ) + logger.info("\nDone") + + logger.debug("Optimizer") + optimizer = torch.optim.Adam( + model.parameters(), self.config.learning_rate + ) + + factor = self.config.scheduler_factor + if factor >= 1.0: + self.log(f"Warning : scheduler factor is {factor} >= 1.0") + self.log("Setting it to 0.5") + factor = 0.5 + + scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + optimizer=optimizer, + mode="min", + factor=factor, + patience=self.config.scheduler_patience, + verbose=VERBOSE_SCHEDULER, + ) + dice_metric = DiceMetric( + include_background=False, reduction="mean" + ) + + best_metric = -1 + best_metric_epoch = -1 + + # time = utils.get_date_time() + logger.debug("Weights") + + if weights_config.custom: + if weights_config.use_pretrained: + weights_file = model_class.weights_file + self.downloader.download_weights(model_name, weights_file) + weights = PRETRAINED_WEIGHTS_DIR / Path(weights_file) + weights_config.path = weights + else: + weights = str(Path(weights_config.path)) + + try: + model.load_state_dict( + torch.load( + weights, + map_location=self.config.device, + ) + ) + except RuntimeError as e: + logger.error(f"Error when loading weights : {e}") + logger.exception(e) + warn = ( + "WARNING:\nIt'd seem that the weights were incompatible with the model,\n" + "the model will be trained from random weights" + ) + self.log(warn) + self.warn(warn) + self._weight_error = True + + if self.config.device.type == "cuda": + self.log("\nUsing GPU :") + self.log(torch.cuda.get_device_name(0)) + else: + self.log("Using CPU") + + self.log_parameters() + + device = self.config.device + + # if model_name == "test": + # self.quit() + # yield TrainingReport(False) + + for epoch in range(self.config.max_epochs): + # self.log("\n") + self.log("-" * 10) + self.log(f"Epoch {epoch + 1}/{self.config.max_epochs}") + if device.type == "cuda": + self.log("Memory Usage:") + alloc_mem = round( + torch.cuda.memory_allocated(0) / 1024**3, 1 + ) + reserved_mem = round( + torch.cuda.memory_reserved(0) / 1024**3, 1 + ) + self.log(f"Allocated: {alloc_mem}GB") + self.log(f"Cached: {reserved_mem}GB") + + model.train() + epoch_loss = 0 + step = 0 + for batch_data in train_loader: + step += 1 + inputs, labels = ( + batch_data["image"].to(device), + batch_data["label"].to(device), + ) + + optimizer.zero_grad() + outputs = model(inputs) + # self.log(f"Output dimensions : {outputs.shape}") + if outputs.shape[1] > 1: + outputs = outputs[ + :, 1:, :, : + ] # FIXME fix channel number + if len(outputs.shape) < 4: + outputs = outputs.unsqueeze(0) + loss = self.config.loss_function(outputs, labels) + loss.backward() + optimizer.step() + epoch_loss += loss.detach().item() + self.log( + f"* {step}/{len(train_ds) // train_loader.batch_size}, " + f"Train loss: {loss.detach().item():.4f}" + ) + yield TrainingReport( + show_plot=False, weights=model.state_dict() + ) + + # return + + epoch_loss /= step + epoch_loss_values.append(epoch_loss) + self.log(f"Epoch: {epoch + 1}, Average loss: {epoch_loss:.4f}") + + self.log("Updating scheduler...") + scheduler.step(epoch_loss) + + checkpoint_output = [] + eta = ( + (time.time() - start_time) + * (self.config.max_epochs / (epoch + 1) - 1) + / 60 + ) + self.log("ETA: " + f"{eta:.2f}" + " minutes") + + if ( + (epoch + 1) % self.config.validation_interval == 0 + or epoch + 1 == self.config.max_epochs + ): + model.eval() + self.log("Performing validation...") + with torch.no_grad(): + for val_data in val_loader: + val_inputs, val_labels = ( + val_data["image"].to(device), + val_data["label"].to(device), + ) + + try: + with torch.no_grad(): + val_outputs = sliding_window_inference( + val_inputs, + roi_size=size, + sw_batch_size=self.config.batch_size, + predictor=model, + overlap=0.25, + sw_device=self.config.device, + device=self.config.device, + progress=False, + ) + except Exception as e: + self.raise_error(e, "Error during validation") + logger.debug( + f"val_outputs shape : {val_outputs.shape}" + ) + # val_outputs = model(val_inputs) + + pred = decollate_batch(val_outputs) + + labs = decollate_batch(val_labels) + + # TODO : more parameters/flexibility + post_pred = Compose( + AsDiscrete(threshold=0.6), EnsureType() + ) # + post_label = EnsureType() + + val_outputs = [ + post_pred(res_tensor) for res_tensor in pred + ] + + val_labels = [ + post_label(res_tensor) for res_tensor in labs + ] + + # logger.debug(len(val_outputs)) + # logger.debug(len(val_labels)) + + dice_metric(y_pred=val_outputs, y=val_labels) + checkpoint_output.append( + [res.detach().cpu() for res in val_outputs] + ) + + checkpoint_output = [ + item.numpy() + for batch in checkpoint_output + for item in batch + ] + + metric = dice_metric.aggregate().detach().item() + dice_metric.reset() + + val_metric_values.append(metric) + + train_report = TrainingReport( + show_plot=True, + epoch=epoch, + loss_values=epoch_loss_values, + validation_metric=val_metric_values, + weights=model.state_dict(), + images=checkpoint_output, + ) + self.log("Validation completed") + yield train_report + + weights_filename = ( + f"{model_name}_best_metric" + + f"_epoch_{epoch + 1}.pth" + ) + + if metric > best_metric: + best_metric = metric + best_metric_epoch = epoch + 1 + self.log("Saving best metric model") + torch.save( + model.state_dict(), + Path(self.config.results_path_folder) + / Path( + weights_filename, + ), + ) + self.log("Saving complete") + self.log( + f"Current epoch: {epoch + 1}, Current mean dice: {metric:.4f}" + f"\nBest mean dice: {best_metric:.4f} " + f"at epoch: {best_metric_epoch}" + ) + self.log("=" * 10) + self.log( + f"Train completed, best_metric: {best_metric:.4f} " + f"at epoch: {best_metric_epoch}" + ) + # Save last checkpoint + weights_filename = f"{model_name}_last_epoch_{epoch + 1}.pth" + self.log("Saving last model") + torch.save( + model.state_dict(), + Path(self.config.results_path_folder) / Path(weights_filename), + ) + self.log("Saving complete, exiting") + model.to("cpu") + # clear (V)RAM + val_ds = None + train_ds = None + val_loader = None + train_loader = None + torch.cuda.empty_cache() + import gc + + gc.collect() + + except Exception as e: + self.raise_error(e, "Error in training") + self.quit() + finally: + self.quit() diff --git a/napari_cellseg3d/code_models/workers.py b/napari_cellseg3d/code_models/workers.py deleted file mode 100644 index d2fa1e41..00000000 --- a/napari_cellseg3d/code_models/workers.py +++ /dev/null @@ -1,1679 +0,0 @@ -import platform -import time -import typing as t -from dataclasses import dataclass -from math import ceil -from pathlib import Path - -import numpy as np -import torch - -# MONAI -from monai.data import ( - CacheDataset, - DataLoader, - Dataset, - PatchDataset, - decollate_batch, - pad_list_data_collate, -) -from monai.inferers import sliding_window_inference -from monai.metrics import DiceMetric -from monai.transforms import ( - AddChannel, - AsDiscrete, - Compose, - EnsureChannelFirstd, - EnsureType, - EnsureTyped, - LoadImaged, - MapTransform, - # NormalizeIntensityd, - Orientationd, - Rand3DElasticd, - RandAffined, - RandFlipd, - RandRotate90d, - RandShiftIntensityd, - RandSpatialCropSamplesd, - SpatialPad, - SpatialPadd, - ToTensor, - Zoom, -) -from monai.utils import set_determinism - -# from napari.qt.threading import thread_worker -# threads -from napari.qt.threading import GeneratorWorker, WorkerBaseSignals - -# Qt -from qtpy.QtCore import Signal -from tifffile import imwrite -from tqdm import tqdm - -# local -from napari_cellseg3d import config, utils -from napari_cellseg3d import interface as ui -from napari_cellseg3d.code_models.crf import crf_with_config -from napari_cellseg3d.code_models.instance_segmentation import ( - ImageStats, - volume_stats, -) - -logger = utils.LOGGER - -PRETRAINED_WEIGHTS_DIR = Path(__file__).parent.resolve() / Path( - "models/pretrained" -) -VERBOSE_SCHEDULER = True -logger.debug(f"PRETRAINED WEIGHT DIR LOCATION : {PRETRAINED_WEIGHTS_DIR}") - - -class WeightsDownloader: - """A utility class the downloads the weights of a model when needed.""" - - def __init__(self, log_widget: t.Optional[ui.Log] = None): - """ - Creates a WeightsDownloader, optionally with a log widget to display the progress. - - Args: - log_widget (log_utility.Log): a Log to display the progress bar in. If None, uses logger.info() - """ - self.log_widget = log_widget - - def download_weights(self, model_name: str, model_weights_filename: str): - """ - Downloads a specific pretrained model. - This code is adapted from DeepLabCut with permission from MWMathis. - - Args: - model_name (str): name of the model to download - model_weights_filename (str): name of the .pth file expected for the model - """ - import json - import tarfile - import urllib.request - - def show_progress(_, block_size, __): # count, block_size, total_size - pbar.update(block_size) - - logger.info("*" * 20) - pretrained_folder_path = PRETRAINED_WEIGHTS_DIR - json_path = pretrained_folder_path / Path("pretrained_model_urls.json") - - check_path = pretrained_folder_path / Path(model_weights_filename) - - if Path(check_path).is_file(): - message = f"Weight file {model_weights_filename} already exists, skipping download" - if self.log_widget is not None: - self.log_widget.print_and_log(message, printing=False) - logger.info(message) - return - - with Path.open(json_path) as f: - neturls = json.load(f) - if model_name in neturls: - url = neturls[model_name] - response = urllib.request.urlopen(url) - - start_message = f"Downloading the model from HuggingFace {url}...." - total_size = int(response.getheader("Content-Length")) - if self.log_widget is None: - logger.info(start_message) - pbar = tqdm(unit="B", total=total_size, position=0) - else: - self.log_widget.print_and_log(start_message) - pbar = tqdm( - unit="B", - total=total_size, - position=0, - file=self.log_widget, - ) - - filename, _ = urllib.request.urlretrieve( - url, reporthook=show_progress - ) - with tarfile.open(filename, mode="r:gz") as tar: - - def is_within_directory(directory, target): - abs_directory = Path(directory).resolve() - abs_target = Path(target).resolve() - # prefix = os.path.commonprefix([abs_directory, abs_target]) - logger.debug(abs_directory) - logger.debug(abs_target) - logger.debug(abs_directory.parents) - - return abs_directory in abs_target.parents - - def safe_extract( - tar, path=".", members=None, *, numeric_owner=False - ): - for member in tar.getmembers(): - member_path = str(Path(path) / member.name) - if not is_within_directory(path, member_path): - raise Exception( - "Attempted Path Traversal in Tar File" - ) - - tar.extractall(path, members, numeric_owner=numeric_owner) - - safe_extract(tar, pretrained_folder_path) - # tar.extractall(pretrained_folder_path) - else: - raise ValueError( - f"Unknown model: {model_name}. Should be one of {', '.join(neturls)}" - ) - - -""" -Writing something to log messages from outside the main thread needs specific care, -Following the instructions in the guides below to have a worker with custom signals, -a custom worker function was implemented. -""" - -# https://python-forum.io/thread-31349.html -# https://www.pythoncentral.io/pysidepyqt-tutorial-creating-your-own-signals-and-slots/ -# https://napari-staging-site.github.io/guides/stable/threading.html - - -class LogSignal(WorkerBaseSignals): - """Signal to send messages to be logged from another thread. - - Separate from Worker instances as indicated `on this post`_ - - .. _on this post: https://stackoverflow.com/questions/2970312/pyqt4-qtcore-pyqtsignal-object-has-no-attribute-connect - """ # TODO link ? - - log_signal = Signal(str) - """qtpy.QtCore.Signal: signal to be sent when some text should be logged""" - warn_signal = Signal(str) - """qtpy.QtCore.Signal: signal to be sent when some warning should be emitted in main thread""" - error_signal = Signal(Exception, str) - """qtpy.QtCore.Signal: signal to be sent when some error should be emitted in main thread""" - - # Should not be an instance variable but a class variable, not defined in __init__, see - # https://stackoverflow.com/questions/2970312/pyqt4-qtcore-pyqtsignal-object-has-no-attribute-connect - - def __init__(self): - super().__init__() - - -# TODO(cyril): move inference and training workers to separate files - - -class ONNXModelWrapper(torch.nn.Module): - """Class to replace torch model by ONNX Runtime session""" - - def __init__(self, file_location): - super().__init__() - try: - import onnxruntime as ort - except ImportError as e: - logger.error("ONNX is not installed but ONNX model was loaded") - logger.error(e) - msg = "PLEASE INSTALL ONNX CPU OR GPU USING pip install napari-cellseg3d[onnx-cpu] OR napari-cellseg3d[onnx-gpu]" - logger.error(msg) - raise ImportError(msg) from e - - self.ort_session = ort.InferenceSession( - file_location, - providers=["CUDAExecutionProvider", "CPUExecutionProvider"], - ) - - def forward(self, modeL_input): - """Wraps ONNX output in a torch tensor""" - outputs = self.ort_session.run( - None, {"input": modeL_input.cpu().numpy()} - ) - return torch.tensor(outputs[0]) - - def eval(self): - """Dummy function to replace model.eval()""" - pass - - def to(self, device): - """Dummy function to replace model.to(device)""" - pass - - -class QuantileNormalizationd(MapTransform): - def __init__(self, keys, allow_missing_keys: bool = False): - super().__init__(keys, allow_missing_keys) - - def __call__(self, data): - d = dict(data) - for key in self.keys: - d[key] = self.normalizer(d[key]) - return d - - def normalizer(self, image: torch.Tensor): - """Normalize each image in a batch individually by quantile normalization.""" - if image.ndim == 4: - for i in range(image.shape[0]): - image[i] = utils.quantile_normalization(image[i]) - else: - raise NotImplementedError( - "QuantileNormalizationd only supports 2D and 3D tensors with NCHWD format" - ) - return image - - -@dataclass -class InferenceResult: - """Class to record results of a segmentation job""" - - image_id: int = 0 - original: np.array = None - instance_labels: np.array = None - crf_results: np.array = None - stats: "np.array[ImageStats]" = None - result: np.array = None - model_name: str = None - - -class InferenceWorker(GeneratorWorker): - """A custom worker to run inference jobs in. - Inherits from :py:class:`napari.qt.threading.GeneratorWorker`""" - - def __init__( - self, - worker_config: config.InferenceWorkerConfig, - ): - """Initializes a worker for inference with the arguments needed by the :py:func:`~inference` function. - - The config contains the following attributes: - * device: cuda or cpu device to use for torch - * model_dict: the :py:attr:`~self.models_dict` dictionary to obtain the model name, class and instance - * weights_dict: dict with "custom" : bool to use custom weights or not; "path" : the path to weights if custom or name of the file if not custom - * results_path: the path to save the results to - * filetype: the file extension to use when saving, - * transforms: a dict containing transforms to perform at various times. - * instance: a dict containing parameters regarding instance segmentation - * use_window: use window inference with specific size or whole image - * window_infer_size: size of window if use_window is True - * keep_on_cpu: keep images on CPU or no - * stats_csv: compute stats on cells and save them to a csv file - * images_filepaths: the paths to the images of the dataset - * layer: the layer to run inference on - - Args: - * worker_config (config.InferenceWorkerConfig): dataclass containing the proper configuration elements - - Note: See :py:func:`~self.inference` - """ - - super().__init__(self.inference) - self._signals = LogSignal() # add custom signals - self.log_signal = self._signals.log_signal - self.warn_signal = self._signals.warn_signal - self.error_signal = self._signals.error_signal - - self.config = worker_config - - """These attributes are all arguments of :py:func:~inference, please see that for reference""" - - self.downloader = WeightsDownloader() - """Download utility""" - - @staticmethod - def create_inference_dict(images_filepaths): - """Create a dict for MONAI with "image" keys with all image paths in :py:attr:`~self.images_filepaths` - - Returns: - dict: list of image paths from loaded folder""" - return [{"image": image_name} for image_name in images_filepaths] - - def set_download_log(self, widget): - self.downloader.log_widget = widget - - def log(self, text): - """Sends a signal that ``text`` should be logged - - Args: - text (str): text to logged - """ - self.log_signal.emit(text) - - def warn(self, warning): - """Sends a warning to main thread""" - self.warn_signal.emit(warning) - - def raise_error(self, exception, msg): - """Raises an error in main thread""" - logger.error(msg, exc_info=True) - logger.error(exception, exc_info=True) - - self.log_signal.emit("!" * 20) - self.log_signal.emit("Error occured") - # self.log_signal.emit(msg) - # self.log_signal.emit(str(exception)) - - self.error_signal.emit(exception, msg) - self.errored.emit(exception) - yield exception - # self.quit() - - def log_parameters(self): - config = self.config - - self.log("-" * 20) - self.log("\nParameters summary :") - - self.log(f"Model is : {config.model_info.name}") - if config.post_process_config.thresholding.enabled: - self.log( - f"Thresholding is enabled at {config.post_process_config.thresholding.threshold_value}" - ) - - status = ( - "enabled" - if config.sliding_window_config.is_enabled() - else "disabled" - ) - - self.log(f"Window inference is {status}\n") - if status == "enabled": - self.log( - f"Window size is {self.config.sliding_window_config.window_size}" - ) - self.log( - f"Window overlap is {self.config.sliding_window_config.window_overlap}" - ) - - if config.keep_on_cpu: - self.log("Dataset loaded to CPU") - else: - self.log(f"Dataset loaded on {config.device} device") - - if config.post_process_config.zoom.enabled: - self.log( - f"Scaling factor : {config.post_process_config.zoom.zoom_values} (x,y,z)" - ) - - instance_config = config.post_process_config.instance - if instance_config.enabled: - self.log( - f"Instance segmentation enabled, method : {instance_config.method.name}\n" - ) - self.log("-" * 20) - - def load_folder(self): - images_dict = self.create_inference_dict(self.config.images_filepaths) - - # TODO : better solution than loading first image always ? - data_check = LoadImaged(keys=["image"])(images_dict[0]) - check = data_check["image"].shape - pad = utils.get_padding_dim(check) - - if self.config.sliding_window_config.is_enabled(): - load_transforms = Compose( - [ - LoadImaged(keys=["image"]), - # AddChanneld(keys=["image"]), #already done - EnsureChannelFirstd(keys=["image"]), - # Orientationd(keys=["image"], axcodes="PLI"), - # anisotropic_transform, - QuantileNormalizationd(keys=["image"]), - EnsureTyped(keys=["image"]), - ] - ) - else: - load_transforms = Compose( - [ - LoadImaged(keys=["image"]), - # AddChanneld(keys=["image"]), #already done - EnsureChannelFirstd(keys=["image"]), - # NormalizeIntensityd(keys=["image"]), # TODO normalize - # Orientationd(keys=["image"], axcodes="PLI"), - # anisotropic_transform, - SpatialPadd(keys=["image"], spatial_size=pad), - EnsureTyped(keys=["image"]), - ] - ) - - self.log("\nLoading dataset...") - inference_ds = Dataset(data=images_dict, transform=load_transforms) - inference_loader = DataLoader( - inference_ds, batch_size=1, num_workers=2 - ) - self.log("Done") - return inference_loader - - def load_layer(self): - self.log("\nLoading layer\n") - data = np.squeeze(self.config.layer.data) - - volume = np.array(data, dtype=np.int16) - - volume_dims = len(volume.shape) - if volume_dims != 3: - raise ValueError( - f"Data array is not 3-dimensional but {volume_dims}-dimensional," - f" please check for extra channel/batch dimensions" - ) - - volume = np.swapaxes( - volume, 0, 2 - ) # for anisotropy to be monai-like, i.e. zyx # FIXME rotation not always correct - - dims_check = volume.shape - self.log("Checking dimensions...") - pad = utils.get_padding_dim(dims_check) - - # logger.debug(volume.shape) - # logger.debug(volume.dtype) - if self.config.sliding_window_config.is_enabled(): - load_transforms = Compose( - [ - ToTensor(), - # anisotropic_transform, - AddChannel(), - # SpatialPad(spatial_size=pad), - AddChannel(), - EnsureType(), - ], - map_items=False, - log_stats=True, - ) - else: - load_transforms = Compose( - [ - ToTensor(), - # anisotropic_transform, - AddChannel(), - SpatialPad(spatial_size=pad), - AddChannel(), - EnsureType(), - ], - map_items=False, - log_stats=True, - ) - - input_image = load_transforms(volume) - self.log("Done") - return input_image - - def model_output( - self, - inputs, - model, - post_process_transforms, - post_process=True, - aniso_transform=None, - ): - inputs = inputs.to("cpu") - - # def model_output(inputs): - # return post_process_transforms( - # self.config.model_info.get_model().get_output(model, inputs) - # ) - - dataset_device = ( - "cpu" if self.config.keep_on_cpu else self.config.device - ) - - if self.config.sliding_window_config.is_enabled(): - window_size = self.config.sliding_window_config.window_size - window_size = [window_size, window_size, window_size] - window_overlap = self.config.sliding_window_config.window_overlap - else: - window_size = None - window_overlap = 0 - try: - # logger.debug(f"model : {model}") - logger.debug(f"inputs shape : {inputs.shape}") - logger.debug(f"inputs type : {inputs.dtype}") - try: - # outputs = model(inputs) - inputs = utils.remap_image(inputs) - - def model_output_wrapper(inputs): - result = model(inputs) - return post_process_transforms(result) - - with torch.no_grad(): - outputs = sliding_window_inference( - inputs, - roi_size=window_size, - sw_batch_size=1, # TODO add param - predictor=model_output_wrapper, - sw_device=self.config.device, - device=dataset_device, - overlap=window_overlap, - mode="gaussian", - sigma_scale=0.01, - progress=True, - ) - except Exception as e: - logger.exception(e) - logger.debug("failed to run sliding window inference") - self.raise_error(e, "Error during sliding window inference") - logger.debug(f"Inference output shape: {outputs.shape}") - self.log("Post-processing...") - out = outputs.detach().cpu().numpy() - if aniso_transform is not None: - out = aniso_transform(out) - if post_process: - out = np.array(out).astype(np.float32) - out = np.squeeze(out) - return out - except Exception as e: - logger.exception(e) - self.raise_error(e, "Error during sliding window inference") - # sys.stdout = old_stdout - # sys.stderr = old_stderr - - def create_inference_result( - self, - semantic_labels, - instance_labels, - crf_results=None, - from_layer: bool = False, - original=None, - stats=None, - i=0, - ) -> InferenceResult: - if not from_layer and original is None: - raise ValueError( - "If the image is not from a layer, an original should always be available" - ) - - if from_layer: - if i != 0: - raise ValueError( - "A layer's ID should always be 0 (default value)" - ) - - if semantic_labels is not None: - semantic_labels = utils.correct_rotation(semantic_labels) - if crf_results is not None: - crf_results = utils.correct_rotation(crf_results) - if instance_labels is not None: - instance_labels = utils.correct_rotation(instance_labels) - - return InferenceResult( - image_id=i + 1, - original=original, - instance_labels=instance_labels, - crf_results=crf_results, - stats=stats, - result=semantic_labels, - model_name=self.config.model_info.name, - ) - - def get_original_filename(self, i): - return Path(self.config.images_filepaths[i]).stem - - def get_instance_result(self, semantic_labels, from_layer=False, i=-1): - if not from_layer and i == -1: - raise ValueError( - "An ID should be provided when running from a file" - ) - - if self.config.post_process_config.instance.enabled: - instance_labels = self.instance_seg( - semantic_labels, - i + 1, - ) - data_dict = self.stats_csv(instance_labels) - else: - instance_labels = None - data_dict = None - return instance_labels, data_dict - - def save_image( - self, - image, - from_layer=False, - i=0, - additional_info="", - ): - if not from_layer: - original_filename = "_" + self.get_original_filename(i) + "_" - filetype = self.config.filetype - else: - original_filename = "_" - filetype = ".tif" - - time = utils.get_date_time() - - file_path = ( - self.config.results_path - + "/" - + f"{additional_info}" - + f"Prediction_{i+1}" - + original_filename - + self.config.model_info.name - + f"_{time}" - + filetype - ) - try: - imwrite(file_path, image) - except ValueError as e: - self.raise_error(e, "Error during image saving") - filename = Path(file_path).stem - - if from_layer: - self.log(f"\nLayer prediction saved as : {filename}") - else: - self.log(f"\nFile n°{i+1} saved as : {filename}") - - def aniso_transform(self, image): - if self.config.post_process_config.zoom.enabled: - zoom = self.config.post_process_config.zoom.zoom_values - anisotropic_transform = Zoom( - zoom=zoom, - keep_size=False, - padding_mode="empty", - ) - return anisotropic_transform(image[0]) - return image - - def instance_seg( - self, semantic_labels, image_id=0, original_filename="layer" - ): - if image_id is not None: - self.log(f"\nRunning instance segmentation for image n°{image_id}") - - method = self.config.post_process_config.instance.method - instance_labels = method.run_method_on_channels(semantic_labels) - self.log(f"DEBUG instance results shape : {instance_labels.shape}") - - filetype = ( - ".tif" - if self.config.filetype == "" - else "_" + self.config.filetype - ) - - instance_filepath = ( - self.config.results_path - + "/" - + f"Instance_seg_labels_{image_id}_" - + original_filename - + "_" - + self.config.model_info.name - + f"_{utils.get_date_time()}" - + filetype - ) - - imwrite(instance_filepath, instance_labels) - self.log( - f"Instance segmentation results for image n°{image_id} have been saved as:" - ) - self.log(Path(instance_filepath).name) - return instance_labels - - def inference_on_folder(self, inf_data, i, model, post_process_transforms): - self.log("-" * 10) - self.log(f"Inference started on image n°{i + 1}...") - - inputs = inf_data["image"] - - out = self.model_output( - inputs, - model, - post_process_transforms, - aniso_transform=self.aniso_transform, - ) - - self.save_image(out, i=i) - instance_labels, stats = self.get_instance_result(out, i=i) - if self.config.use_crf: - crf_in = inputs.detach().cpu().numpy() - try: - crf_results = self.run_crf( - crf_in, - out, - aniso_transform=self.aniso_transform, - image_id=i, - ) - - except ValueError as e: - self.log(f"Error occurred during CRF : {e}") - crf_results = None - else: - crf_results = None - - original = np.array(inf_data["image"]).astype(np.float32) - - self.log(f"Inference completed on image n°{i+1}") - - return self.create_inference_result( - out, - instance_labels, - crf_results, - from_layer=False, - original=original, - stats=stats, - i=i, - ) - - def run_crf(self, image, labels, aniso_transform, image_id=0): - try: - if aniso_transform is not None: - image = aniso_transform(image) - crf_results = crf_with_config( - image, labels, config=self.config.crf_config, log=self.log - ) - self.save_image( - crf_results, - i=image_id, - additional_info="CRF_", - from_layer=True, - ) - return crf_results - except ValueError as e: - self.log(f"Error occurred during CRF : {e}") - return None - - def stats_csv(self, instance_labels): - try: - if self.config.compute_stats: - if len(instance_labels.shape) == 4: - stats = [volume_stats(c) for c in instance_labels] - else: - stats = [volume_stats(instance_labels)] - else: - stats = None - return stats - except ValueError as e: - self.log(f"Error occurred during stats computing : {e}") - return None - - def inference_on_layer(self, image, model, post_process_transforms): - self.log("-" * 10) - self.log("Inference started on layer...") - - image = image.type(torch.FloatTensor) - image = utils.quantile_normalization(image) - - out = self.model_output( - image, - model, - post_process_transforms, - aniso_transform=self.aniso_transform, - ) - - self.save_image(out, from_layer=True) - - instance_labels, stats = self.get_instance_result( - semantic_labels=out, from_layer=True - ) - - crf_results = ( - self.run_crf(image, out, aniso_transform=self.aniso_transform) - if self.config.use_crf - else None - ) - - return self.create_inference_result( - semantic_labels=out, - instance_labels=instance_labels, - crf_results=crf_results, - from_layer=True, - stats=stats, - ) - - # @thread_worker(connect={"errored": self.raise_error}) - def inference(self): - """ - Requires: - * device: cuda or cpu device to use for torch - - * model_dict: the :py:attr:`~self.models_dict` dictionary to obtain the model name, class and instance - - * weights: the loaded weights from the model - - * images_filepaths: the paths to the images of the dataset - - * results_path: the path to save the results to - - * filetype: the file extension to use when saving, - - * transforms: a dict containing transforms to perform at various times. - - * use_window: use window inference with specific size or whole image - - * window_infer_size: size of window if use_window is True - - * keep_on_cpu: keep images on CPU or no - - * stats_csv: compute stats on cells and save them to a csv file - - Yields: - dict: contains : - * "image_id" : index of the returned image - - * "original" : original volume used for inference - - * "result" : inference result - - """ - sys = platform.system() - logger.debug(f"OS is {sys}") - if sys == "Darwin": - torch.set_num_threads(1) # required for threading on macOS ? - self.log("Number of threads has been set to 1 for macOS") - - try: - dims = self.config.model_info.model_input_size - self.log(f"MODEL DIMS : {dims}") - model_name = self.config.model_info.name - model_class = self.config.model_info.get_model() - self.log(f"Model name : {model_name}") - - weights_config = self.config.weights_config - post_process_config = self.config.post_process_config - if Path(weights_config.path).suffix == ".pt": - self.log("Instantiating PyTorch jit model...") - model = torch.jit.load(weights_config.path) - # try: - elif Path(weights_config.path).suffix == ".onnx": - self.log("Instantiating ONNX model...") - model = ONNXModelWrapper(weights_config.path) - else: # assume is .pth - self.log("Instantiating model...") - model = model_class( # FIXME test if works - input_img_size=[dims, dims, dims], - device=self.config.device, - num_classes=self.config.model_info.num_classes, - ) - # try: - model = model.to(self.config.device) - # except Exception as e: - # self.raise_error(e, "Issue loading model to device") - # logger.debug(f"model : {model}") - if model is None: - raise ValueError("Model is None") - # try: - self.log("\nLoading weights...") - if weights_config.custom: - weights = weights_config.path - else: - self.downloader.download_weights( - model_name, - model_class.weights_file, - ) - weights = str( - PRETRAINED_WEIGHTS_DIR / Path(model_class.weights_file) - ) - - model.load_state_dict( # note that this is redefined in WNet_ - torch.load( - weights, - map_location=self.config.device, - ) - ) - self.log("Done") - # except Exception as e: - # self.raise_error(e, "Issue loading weights") - # except Exception as e: - # self.raise_error(e, "Issue instantiating model") - - # if model_name == "SegResNet": - # model = model_class( - # input_image_size=[ - # dims, - # dims, - # dims, - # ], - # ) - # elif model_name == "SwinUNetR": - # model = model_class( - # img_size=[dims, dims, dims], - # use_checkpoint=False, - # ) - # else: - # model = model_class.get_net() - - self.log_parameters() - - # load_transforms = Compose( - # [ - # LoadImaged(keys=["image"]), - # # AddChanneld(keys=["image"]), #already done - # EnsureChannelFirstd(keys=["image"]), - # # Orientationd(keys=["image"], axcodes="PLI"), - # # anisotropic_transform, - # SpatialPadd(keys=["image"], spatial_size=pad), - # EnsureTyped(keys=["image"]), - # ] - # ) - - if not post_process_config.thresholding.enabled: - post_process_transforms = EnsureType() - else: - t = post_process_config.thresholding.threshold_value - post_process_transforms = Compose( - AsDiscrete(threshold=t), EnsureType() - ) - - is_folder = self.config.images_filepaths is not None - is_layer = self.config.layer is not None - - if is_layer and is_folder: - raise ValueError( - "Both a layer and a folder have been specified, please specify only one of the two. Aborting." - ) - elif is_folder: - inference_loader = self.load_folder() - ################## - ################## - # DEBUG - # from monai.utils import first - # - # check_data = first(inference_loader) - # image = check_data[0][0] - # logger.debug(image.shape) - ################## - ################## - elif is_layer: - input_image = self.load_layer() - else: - raise ValueError("No data has been provided. Aborting.") - - if model is None: - raise ValueError("Model is None") - - model.eval() - with torch.no_grad(): - ################################ - ################################ - ################################ - if is_folder: - for i, inf_data in enumerate(inference_loader): - yield self.inference_on_folder( - inf_data, i, model, post_process_transforms - ) - elif is_layer: - yield self.inference_on_layer( - input_image, model, post_process_transforms - ) - model.to("cpu") - # self.quit() - except Exception as e: - logger.exception(e) - self.raise_error(e, "Inference failed") - self.quit() - finally: - self.quit() - - -@dataclass -class TrainingReport: - show_plot: bool = True - epoch: int = 0 - loss_values: t.Dict = None # TODO(cyril) : change to dict and unpack different losses for e.g. WNet with several losses - validation_metric: t.List = None - weights: np.array = None - images: t.List[np.array] = None - - -class TrainingWorker(GeneratorWorker): - """A custom worker to run training jobs in. - Inherits from :py:class:`napari.qt.threading.GeneratorWorker`""" - - def __init__( - self, - worker_config: config.TrainingWorkerConfig, - ): - """Initializes a worker for inference with the arguments needed by the :py:func:`~train` function. Note: See :py:func:`~train` - - Args: - * device : device to train on, cuda or cpu - - * model_dict : dict containing the model's "name" and "class" - - * weights_path : path to weights files if transfer learning is to be used - - * data_dicts : dict from :py:func:`Trainer.create_train_dataset_dict` - - * validation_percent : percentage of images to use as validation - - * max_epochs : the amout of epochs to train for - - * loss_function : the loss function to use for training - - * learning_rate : the learning rate of the optimizer - - * val_interval : the interval at which to perform validation (e.g. if 2 will validate once every 2 epochs.) Also determines frequency of saving, depending on whether the metric is better or not - - * batch_size : the batch size to use for training - - * results_path : the path to save results in - - * sampling : whether to extract patches from images or not - - * num_samples : the number of samples to extract from an image for training - - * sample_size : the size of the patches to extract when sampling - - * do_augmentation : whether to perform data augmentation or not - - * deterministic : dict with "use deterministic" : bool, whether to use deterministic training, "seed": seed for RNG - - - """ - super().__init__(self.train) - self._signals = LogSignal() - self.log_signal = self._signals.log_signal - self.warn_signal = self._signals.warn_signal - self.error_signal = self._signals.error_signal - - self._weight_error = False - ############################################# - self.config = worker_config - - self.train_files = [] - self.val_files = [] - ####################################### - self.downloader = WeightsDownloader() - - def set_download_log(self, widget): - self.downloader.log_widget = widget - - def log(self, text): - """Sends a signal that ``text`` should be logged - - Args: - text (str): text to logged - """ - self.log_signal.emit(text) - - def warn(self, warning): - """Sends a warning to main thread""" - self.warn_signal.emit(warning) - - def raise_error(self, exception, msg): - """Sends an error to main thread""" - logger.error(msg, exc_info=True) - logger.error(exception, exc_info=True) - self.error_signal.emit(exception, msg) - self.errored.emit(exception) - self.quit() - - def log_parameters(self): - self.log("-" * 20) - self.log("Parameters summary :\n") - - self.log( - f"Percentage of dataset used for validation : {self.config.validation_percent * 100}%" - ) - - self.log("-" * 10) - self.log("Training files :\n") - [ - self.log(f"{Path(train_file['image']).name}\n") - for train_file in self.train_files - ] - self.log("-" * 10) - self.log("Validation files :\n") - [ - self.log(f"{Path(val_file['image']).name}\n") - for val_file in self.val_files - ] - self.log("-" * 10) - - if self.config.deterministic_config.enabled: - self.log("Deterministic training is enabled") - self.log(f"Seed is {self.config.deterministic_config.seed}") - - self.log(f"Training for {self.config.max_epochs} epochs") - self.log(f"Loss function is : {str(self.config.loss_function)}") - self.log( - f"Validation is performed every {self.config.validation_interval} epochs" - ) - self.log(f"Batch size is {self.config.batch_size}") - self.log(f"Learning rate is {self.config.learning_rate}") - - if self.config.sampling: - self.log( - f"Extracting {self.config.num_samples} patches of size {self.config.sample_size}" - ) - else: - self.log("Using whole images as dataset") - - if self.config.do_augmentation: - self.log("Data augmentation is enabled") - - if not self.config.weights_info.use_pretrained: - self.log(f"Using weights from : {self.config.weights_info.path}") - if self._weight_error: - self.log( - ">>>>>>>>>>>>>>>>>\n" - "WARNING:\nChosen weights were incompatible with the model,\n" - "the model will be trained from random weights\n" - "<<<<<<<<<<<<<<<<<\n" - ) - - # self.log("\n") - self.log("-" * 20) - - def train(self): - """Trains the PyTorch model for the given number of epochs, with the selected model and data, - using the chosen batch size, validation interval, loss function, and number of samples. - Will perform validation once every :py:obj:`val_interval` and save results if the mean dice is better - - Requires: - - * device : device to train on, cuda or cpu - - * model_dict : dict containing the model's "name" and "class" - - * weights_path : path to weights files if transfer learning is to be used - - * data_dicts : dict from :py:func:`Trainer.create_train_dataset_dict` - - * validation_percent : percentage of images to use as validation - - * max_epochs : the amount of epochs to train for - - * loss_function : the loss function to use for training - - * learning rate : the learning rate of the optimizer - - * val_interval : the interval at which to perform validation (e.g. if 2 will validate once every 2 epochs.) Also determines frequency of saving, depending on whether the metric is better or not - - * batch_size : the batch size to use for training - - * results_path : the path to save results in - - * sampling : whether to extract patches from images or not - - * num_samples : the number of samples to extract from an image for training - - * sample_size : the size of the patches to extract when sampling - - * do_augmentation : whether to perform data augmentation or not - - * deterministic : dict with "use deterministic" : bool, whether to use deterministic training, "seed": seed for RNG - """ - - ######################### - # error_log = open(results_path +"/error_log.log" % multiprocessing.current_process().name, 'x') - # faulthandler.enable(file=error_log, all_threads=True) - ######################### - model_config = self.config.model_info - weights_config = self.config.weights_info - deterministic_config = self.config.deterministic_config - - start_time = time.time() - - try: - if deterministic_config.enabled: - set_determinism( - seed=deterministic_config.seed - ) # use_deterministic_algorithms = True causes cuda error - - sys = platform.system() - logger.debug(sys) - if sys == "Darwin": # required for macOS ? - torch.set_num_threads(1) - self.log("Number of threads has been set to 1 for macOS") - - self.log(f"config model : {self.config.model_info.name}") - model_name = model_config.name - model_class = model_config.get_model() - - if not self.config.sampling: - data_check = LoadImaged(keys=["image"])( - self.config.train_data_dict[0] - ) - check = data_check["image"].shape - - do_sampling = self.config.sampling - - size = self.config.sample_size if do_sampling else check - - PADDING = utils.get_padding_dim(size) - model = model_class( # FIXME check if correct - input_img_size=PADDING, use_checkpoint=True - ) - model = model.to(self.config.device) - - epoch_loss_values = [] - val_metric_values = [] - - if len(self.config.train_data_dict) > 1: - self.train_files, self.val_files = ( - self.config.train_data_dict[ - 0 : int( - len(self.config.train_data_dict) - * self.config.validation_percent - ) - ], - self.config.train_data_dict[ - int( - len(self.config.train_data_dict) - * self.config.validation_percent - ) : - ], - ) - else: - self.train_files = self.val_files = self.config.train_data_dict - msg = f"Only one image file was provided : {self.config.train_data_dict[0]['image']}.\n" - - logger.debug(f"SAMPLING is {self.config.sampling}") - if not self.config.sampling: - msg += "Sampling is not in use, the only image provided will be used as the validation file." - self.warn(msg) - else: - msg += "Samples for validation will be cropped for the same only volume that is being used for training" - - logger.warning(msg) - - logger.debug( - f"Data dict from config is {self.config.train_data_dict}" - ) - logger.debug(f"Train files : {self.train_files}") - logger.debug(f"Val. files : {self.val_files}") - - if len(self.train_files) == 0: - raise ValueError("Training dataset is empty") - if len(self.val_files) == 0: - raise ValueError("Validation dataset is empty") - - if self.config.do_augmentation: - train_transforms = ( - Compose( # TODO : figure out which ones and values ? - [ - RandShiftIntensityd(keys=["image"], offsets=0.7), - Rand3DElasticd( - keys=["image", "label"], - sigma_range=(0.3, 0.7), - magnitude_range=(0.3, 0.7), - ), - RandFlipd(keys=["image", "label"]), - RandRotate90d(keys=["image", "label"]), - RandAffined( - keys=["image", "label"], - ), - EnsureTyped(keys=["image", "label"]), - ] - ) - ) - else: - train_transforms = EnsureTyped(keys=["image", "label"]) - - val_transforms = Compose( - [ - # LoadImaged(keys=["image", "label"]), - # EnsureChannelFirstd(keys=["image", "label"]), - EnsureTyped(keys=["image", "label"]), - ] - ) - - # self.log("Loading dataset...\n") - def get_loader_func(num_samples): - return Compose( - [ - LoadImaged(keys=["image", "label"]), - EnsureChannelFirstd(keys=["image", "label"]), - QuantileNormalizationd(keys=["image"]), - RandSpatialCropSamplesd( - keys=["image", "label"], - roi_size=( - self.config.sample_size - ), # multiply by axis_stretch_factor if anisotropy - # max_roi_size=(120, 120, 120), - random_size=False, - num_samples=num_samples, - ), - Orientationd(keys=["image", "label"], axcodes="PLI"), - SpatialPadd( - keys=["image", "label"], - spatial_size=( - utils.get_padding_dim(self.config.sample_size) - ), - ), - EnsureTyped(keys=["image", "label"]), - ] - ) - - if do_sampling: - # if there is only one volume, split samples - # TODO(cyril) : maybe implement something in user config to toggle this behavior - if len(self.config.train_data_dict) < 2: - num_train_samples = ceil( - self.config.num_samples - * self.config.validation_percent - ) - num_val_samples = ceil( - self.config.num_samples - * (1 - self.config.validation_percent) - ) - sample_loader_train = get_loader_func(num_train_samples) - sample_loader_eval = get_loader_func(num_val_samples) - else: - num_train_samples = ( - num_val_samples - ) = self.config.num_samples - - sample_loader_train = get_loader_func(num_train_samples) - sample_loader_eval = get_loader_func(num_val_samples) - - logger.debug(f"AMOUNT of train samples : {num_train_samples}") - logger.debug( - f"AMOUNT of validation samples : {num_val_samples}" - ) - - logger.debug("train_ds") - train_ds = PatchDataset( - data=self.train_files, - transform=train_transforms, - patch_func=sample_loader_train, - samples_per_image=num_train_samples, - ) - logger.debug("val_ds") - val_ds = PatchDataset( - data=self.val_files, - transform=val_transforms, - patch_func=sample_loader_eval, - samples_per_image=num_val_samples, - ) - - else: - load_whole_images = Compose( - [ - LoadImaged( - keys=["image", "label"], - # image_only=True, - # reader=WSIReader(backend="tifffile") - ), - EnsureChannelFirstd(keys=["image", "label"]), - Orientationd(keys=["image", "label"], axcodes="PLI"), - QuantileNormalizationd(keys=["image"]), - SpatialPadd( - keys=["image", "label"], - spatial_size=PADDING, - ), - EnsureTyped(keys=["image", "label"]), - ] - ) - logger.debug("Cache dataset : train") - train_ds = CacheDataset( - data=self.train_files, - transform=Compose(load_whole_images, train_transforms), - ) - logger.debug("Cache dataset : val") - val_ds = CacheDataset( - data=self.val_files, transform=load_whole_images - ) - logger.debug("Dataloader") - train_loader = DataLoader( - train_ds, - batch_size=self.config.batch_size, - shuffle=True, - num_workers=2, - collate_fn=pad_list_data_collate, - ) - - val_loader = DataLoader( - val_ds, batch_size=self.config.batch_size, num_workers=2 - ) - logger.info("\nDone") - - logger.debug("Optimizer") - optimizer = torch.optim.Adam( - model.parameters(), self.config.learning_rate - ) - - factor = self.config.scheduler_factor - if factor >= 1.0: - self.log(f"Warning : scheduler factor is {factor} >= 1.0") - self.log("Setting it to 0.5") - factor = 0.5 - - scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - optimizer=optimizer, - mode="min", - factor=factor, - patience=self.config.scheduler_patience, - verbose=VERBOSE_SCHEDULER, - ) - dice_metric = DiceMetric( - include_background=False, reduction="mean" - ) - - best_metric = -1 - best_metric_epoch = -1 - - # time = utils.get_date_time() - logger.debug("Weights") - - if weights_config.custom: - if weights_config.use_pretrained: - weights_file = model_class.weights_file - self.downloader.download_weights(model_name, weights_file) - weights = PRETRAINED_WEIGHTS_DIR / Path(weights_file) - weights_config.path = weights - else: - weights = str(Path(weights_config.path)) - - try: - model.load_state_dict( - torch.load( - weights, - map_location=self.config.device, - ) - ) - except RuntimeError as e: - logger.error(f"Error when loading weights : {e}") - logger.exception(e) - warn = ( - "WARNING:\nIt'd seem that the weights were incompatible with the model,\n" - "the model will be trained from random weights" - ) - self.log(warn) - self.warn(warn) - self._weight_error = True - - if self.config.device.type == "cuda": - self.log("\nUsing GPU :") - self.log(torch.cuda.get_device_name(0)) - else: - self.log("Using CPU") - - self.log_parameters() - - device = self.config.device - - # if model_name == "test": - # self.quit() - # yield TrainingReport(False) - - for epoch in range(self.config.max_epochs): - # self.log("\n") - self.log("-" * 10) - self.log(f"Epoch {epoch + 1}/{self.config.max_epochs}") - if device.type == "cuda": - self.log("Memory Usage:") - alloc_mem = round( - torch.cuda.memory_allocated(0) / 1024**3, 1 - ) - reserved_mem = round( - torch.cuda.memory_reserved(0) / 1024**3, 1 - ) - self.log(f"Allocated: {alloc_mem}GB") - self.log(f"Cached: {reserved_mem}GB") - - model.train() - epoch_loss = 0 - step = 0 - for batch_data in train_loader: - step += 1 - inputs, labels = ( - batch_data["image"].to(device), - batch_data["label"].to(device), - ) - - optimizer.zero_grad() - outputs = model(inputs) - # self.log(f"Output dimensions : {outputs.shape}") - if outputs.shape[1] > 1: - outputs = outputs[ - :, 1:, :, : - ] # FIXME fix channel number - if len(outputs.shape) < 4: - outputs = outputs.unsqueeze(0) - loss = self.config.loss_function(outputs, labels) - loss.backward() - optimizer.step() - epoch_loss += loss.detach().item() - self.log( - f"* {step}/{len(train_ds) // train_loader.batch_size}, " - f"Train loss: {loss.detach().item():.4f}" - ) - yield TrainingReport( - show_plot=False, weights=model.state_dict() - ) - - # return - - epoch_loss /= step - epoch_loss_values.append(epoch_loss) - self.log(f"Epoch: {epoch + 1}, Average loss: {epoch_loss:.4f}") - - self.log("Updating scheduler...") - scheduler.step(epoch_loss) - - checkpoint_output = [] - eta = ( - (time.time() - start_time) - * (self.config.max_epochs / (epoch + 1) - 1) - / 60 - ) - self.log("ETA: " + f"{eta:.2f}" + " minutes") - - if ( - (epoch + 1) % self.config.validation_interval == 0 - or epoch + 1 == self.config.max_epochs - ): - model.eval() - self.log("Performing validation...") - with torch.no_grad(): - for val_data in val_loader: - val_inputs, val_labels = ( - val_data["image"].to(device), - val_data["label"].to(device), - ) - - try: - with torch.no_grad(): - val_outputs = sliding_window_inference( - val_inputs, - roi_size=size, - sw_batch_size=self.config.batch_size, - predictor=model, - overlap=0.25, - sw_device=self.config.device, - device=self.config.device, - progress=False, - ) - except Exception as e: - self.raise_error(e, "Error during validation") - logger.debug( - f"val_outputs shape : {val_outputs.shape}" - ) - # val_outputs = model(val_inputs) - - pred = decollate_batch(val_outputs) - - labs = decollate_batch(val_labels) - - # TODO : more parameters/flexibility - post_pred = Compose( - AsDiscrete(threshold=0.6), EnsureType() - ) # - post_label = EnsureType() - - val_outputs = [ - post_pred(res_tensor) for res_tensor in pred - ] - - val_labels = [ - post_label(res_tensor) for res_tensor in labs - ] - - # logger.debug(len(val_outputs)) - # logger.debug(len(val_labels)) - - dice_metric(y_pred=val_outputs, y=val_labels) - checkpoint_output.append( - [res.detach().cpu() for res in val_outputs] - ) - - checkpoint_output = [ - item.numpy() - for batch in checkpoint_output - for item in batch - ] - - metric = dice_metric.aggregate().detach().item() - dice_metric.reset() - - val_metric_values.append(metric) - - train_report = TrainingReport( - show_plot=True, - epoch=epoch, - loss_values=epoch_loss_values, - validation_metric=val_metric_values, - weights=model.state_dict(), - images=checkpoint_output, - ) - self.log("Validation completed") - yield train_report - - weights_filename = ( - f"{model_name}_best_metric" - + f"_epoch_{epoch + 1}.pth" - ) - - if metric > best_metric: - best_metric = metric - best_metric_epoch = epoch + 1 - self.log("Saving best metric model") - torch.save( - model.state_dict(), - Path(self.config.results_path_folder) - / Path( - weights_filename, - ), - ) - self.log("Saving complete") - self.log( - f"Current epoch: {epoch + 1}, Current mean dice: {metric:.4f}" - f"\nBest mean dice: {best_metric:.4f} " - f"at epoch: {best_metric_epoch}" - ) - self.log("=" * 10) - self.log( - f"Train completed, best_metric: {best_metric:.4f} " - f"at epoch: {best_metric_epoch}" - ) - # Save last checkpoint - weights_filename = f"{model_name}_last_epoch_{epoch + 1}.pth" - self.log("Saving last model") - torch.save( - model.state_dict(), - Path(self.config.results_path_folder) / Path(weights_filename), - ) - self.log("Saving complete, exiting") - model.to("cpu") - # clear (V)RAM - val_ds = None - train_ds = None - val_loader = None - train_loader = None - torch.cuda.empty_cache() - import gc - - gc.collect() - - except Exception as e: - self.raise_error(e, "Error in training") - self.quit() - finally: - self.quit() diff --git a/napari_cellseg3d/code_models/workers_utils.py b/napari_cellseg3d/code_models/workers_utils.py new file mode 100644 index 00000000..f377fd22 --- /dev/null +++ b/napari_cellseg3d/code_models/workers_utils.py @@ -0,0 +1,221 @@ +import typing as t +from dataclasses import dataclass +from pathlib import Path +from typing import TYPE_CHECKING + +import numpy as np +import torch +from monai.transforms import MapTransform +from qtpy.QtCore import Signal +from superqt.utils._qthreading import WorkerBaseSignals +from tqdm import tqdm + +# local +from napari_cellseg3d import interface as ui +from napari_cellseg3d import utils +from napari_cellseg3d.utils import LOGGER as logger + +if TYPE_CHECKING: + from napari_cellseg3d.code_models.instance_segmentation import ImageStats + +PRETRAINED_WEIGHTS_DIR = Path(__file__).parent.resolve() / Path( + "models/pretrained" +) + + +class WeightsDownloader: + """A utility class the downloads the weights of a model when needed.""" + + def __init__(self, log_widget: t.Optional[ui.Log] = None): + """ + Creates a WeightsDownloader, optionally with a log widget to display the progress. + + Args: + log_widget (log_utility.Log): a Log to display the progress bar in. If None, uses logger.info() + """ + self.log_widget = log_widget + + def download_weights(self, model_name: str, model_weights_filename: str): + """ + Downloads a specific pretrained model. + This code is adapted from DeepLabCut with permission from MWMathis. + + Args: + model_name (str): name of the model to download + model_weights_filename (str): name of the .pth file expected for the model + """ + import json + import tarfile + import urllib.request + + def show_progress(_, block_size, __): # count, block_size, total_size + pbar.update(block_size) + + logger.info("*" * 20) + pretrained_folder_path = PRETRAINED_WEIGHTS_DIR + json_path = pretrained_folder_path / Path("pretrained_model_urls.json") + + check_path = pretrained_folder_path / Path(model_weights_filename) + + if Path(check_path).is_file(): + message = f"Weight file {model_weights_filename} already exists, skipping download" + if self.log_widget is not None: + self.log_widget.print_and_log(message, printing=False) + logger.info(message) + return + + with Path.open(json_path) as f: + neturls = json.load(f) + if model_name in neturls: + url = neturls[model_name] + response = urllib.request.urlopen(url) + + start_message = f"Downloading the model from HuggingFace {url}...." + total_size = int(response.getheader("Content-Length")) + if self.log_widget is None: + logger.info(start_message) + pbar = tqdm(unit="B", total=total_size, position=0) + else: + self.log_widget.print_and_log(start_message) + pbar = tqdm( + unit="B", + total=total_size, + position=0, + file=self.log_widget, + ) + + filename, _ = urllib.request.urlretrieve( + url, reporthook=show_progress + ) + with tarfile.open(filename, mode="r:gz") as tar: + + def is_within_directory(directory, target): + abs_directory = Path(directory).resolve() + abs_target = Path(target).resolve() + # prefix = os.path.commonprefix([abs_directory, abs_target]) + logger.debug(abs_directory) + logger.debug(abs_target) + logger.debug(abs_directory.parents) + + return abs_directory in abs_target.parents + + def safe_extract( + tar, path=".", members=None, *, numeric_owner=False + ): + for member in tar.getmembers(): + member_path = str(Path(path) / member.name) + if not is_within_directory(path, member_path): + raise Exception( + "Attempted Path Traversal in Tar File" + ) + + tar.extractall(path, members, numeric_owner=numeric_owner) + + safe_extract(tar, pretrained_folder_path) + # tar.extractall(pretrained_folder_path) + else: + raise ValueError( + f"Unknown model: {model_name}. Should be one of {', '.join(neturls)}" + ) + + +class LogSignal(WorkerBaseSignals): + """Signal to send messages to be logged from another thread. + + Separate from Worker instances as indicated `on this post`_ + + .. _on this post: https://stackoverflow.com/questions/2970312/pyqt4-qtcore-pyqtsignal-object-has-no-attribute-connect + """ # TODO link ? + + log_signal = Signal(str) + """qtpy.QtCore.Signal: signal to be sent when some text should be logged""" + warn_signal = Signal(str) + """qtpy.QtCore.Signal: signal to be sent when some warning should be emitted in main thread""" + error_signal = Signal(Exception, str) + """qtpy.QtCore.Signal: signal to be sent when some error should be emitted in main thread""" + + # Should not be an instance variable but a class variable, not defined in __init__, see + # https://stackoverflow.com/questions/2970312/pyqt4-qtcore-pyqtsignal-object-has-no-attribute-connect + + def __init__(self): + super().__init__() + + +class ONNXModelWrapper(torch.nn.Module): + """Class to replace torch model by ONNX Runtime session""" + + def __init__(self, file_location): + super().__init__() + try: + import onnxruntime as ort + except ImportError as e: + logger.error("ONNX is not installed but ONNX model was loaded") + logger.error(e) + msg = "PLEASE INSTALL ONNX CPU OR GPU USING pip install napari-cellseg3d[onnx-cpu] OR napari-cellseg3d[onnx-gpu]" + logger.error(msg) + raise ImportError(msg) from e + + self.ort_session = ort.InferenceSession( + file_location, + providers=["CUDAExecutionProvider", "CPUExecutionProvider"], + ) + + def forward(self, modeL_input): + """Wraps ONNX output in a torch tensor""" + outputs = self.ort_session.run( + None, {"input": modeL_input.cpu().numpy()} + ) + return torch.tensor(outputs[0]) + + def eval(self): + """Dummy function to replace model.eval()""" + pass + + def to(self, device): + """Dummy function to replace model.to(device)""" + pass + + +class QuantileNormalizationd(MapTransform): + def __init__(self, keys, allow_missing_keys: bool = False): + super().__init__(keys, allow_missing_keys) + + def __call__(self, data): + d = dict(data) + for key in self.keys: + d[key] = self.normalizer(d[key]) + return d + + def normalizer(self, image: torch.Tensor): + """Normalize each image in a batch individually by quantile normalization.""" + if image.ndim == 4: + for i in range(image.shape[0]): + image[i] = utils.quantile_normalization(image[i]) + else: + raise NotImplementedError( + "QuantileNormalizationd only supports 2D and 3D tensors with NCHWD format" + ) + return image + + +@dataclass +class InferenceResult: + """Class to record results of a segmentation job""" + + image_id: int = 0 + original: np.array = None + instance_labels: np.array = None + crf_results: np.array = None + stats: "np.array[ImageStats]" = None + result: np.array = None + model_name: str = None + + +@dataclass +class TrainingReport: + show_plot: bool = True + epoch: int = 0 + loss_values: t.Dict = None # TODO(cyril) : change to dict and unpack different losses for e.g. WNet with several losses + validation_metric: t.List = None + weights: np.array = None + images: t.List[np.array] = None diff --git a/napari_cellseg3d/code_plugins/plugin_model_inference.py b/napari_cellseg3d/code_plugins/plugin_model_inference.py index 560cef3d..baa0791d 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_inference.py +++ b/napari_cellseg3d/code_plugins/plugin_model_inference.py @@ -15,10 +15,8 @@ InstanceWidgets, ) from napari_cellseg3d.code_models.model_framework import ModelFramework -from napari_cellseg3d.code_models.workers import ( - InferenceResult, - InferenceWorker, -) +from napari_cellseg3d.code_models.worker_inference import InferenceWorker +from napari_cellseg3d.code_models.workers_utils import InferenceResult from napari_cellseg3d.code_plugins.plugin_crf import CRFParamsWidget logger = utils.LOGGER @@ -242,6 +240,12 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): self._remove_unused() + def _toggle_display_crf(self): + if self.model_choice.currentText() == "WNet": + self.use_crf.setVisible(True) + else: + self.use_crf.setVisible(False) + def _set_tooltips(self): ################## ################## @@ -306,6 +310,7 @@ def check_ready(self): def _restrict_window_size_for_model(self): """Sets the window size to a value that is compatible with the chosen model""" + window_enabled = self.window_infer_box.isChecked() if self.model_choice.currentText() == "WNet": self.window_size_choice.setCurrentIndex(self._default_window_size) self.window_size_choice.setDisabled(True) @@ -314,8 +319,11 @@ def _restrict_window_size_for_model(self): else: self.window_size_choice.setDisabled(False) self.window_infer_box.setDisabled(False) - self.window_infer_box.setChecked(False) - self.window_size_choice.setCurrentIndex(self._default_window_size) + self.window_infer_box.setChecked(window_enabled) + if not window_enabled: + self.window_size_choice.setCurrentIndex( + self._default_window_size + ) def _toggle_display_model_input_size(self): if ( @@ -477,6 +485,7 @@ def _build(self): # self.instance_param_container, # instance segmentation ], ) + self.model_choice.currentIndexChanged.connect(self._toggle_display_crf) ModelFramework._show_io_element( self.save_stats_to_csv_box, self.use_instance_choice ) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 9a8b6195..4ec2aa04 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -32,14 +32,13 @@ from napari_cellseg3d import config, utils from napari_cellseg3d import interface as ui from napari_cellseg3d.code_models.model_framework import ModelFramework -from napari_cellseg3d.code_models.workers import ( - TrainingReport, +from napari_cellseg3d.code_models.worker_training import ( TrainingWorker, ) +from napari_cellseg3d.code_models.workers_utils import TrainingReport -NUMBER_TABS = 3 -DEFAULT_PATCH_SIZE = 64 - +NUMBER_TABS = 3 # how many tabs in the widget +DEFAULT_PATCH_SIZE = 64 # default patch size for training logger = utils.LOGGER diff --git a/napari_cellseg3d/utils.py b/napari_cellseg3d/utils.py index 193e0b21..c6a8bbac 100644 --- a/napari_cellseg3d/utils.py +++ b/napari_cellseg3d/utils.py @@ -8,6 +8,7 @@ import numpy as np import torch from monai.transforms import Zoom +from numpy.random import PCG64, Generator from tifffile import imread, imwrite LOGGER = logging.getLogger(__name__) @@ -22,6 +23,8 @@ Definitions of utility functions, classes, and variables """ +rand_gen = Generator(PCG64(12345)) + #################### # viewer utils @@ -314,11 +317,11 @@ def get_padding_dim(image_shape, anisotropy_factor=None): padding = [] dims = len(image_shape) - print(f"Dimension of data for padding : {dims}D") - print(f"Image shape is {image_shape}") + LOGGER.debug(f"Data is {dims}D") + LOGGER.debug(f"Image shape is {image_shape}") if dims != 2 and dims != 3: error = "Please check the dimensions of the input, only 2 or 3-dimensional data is supported currently" - print(error) + LOGGER.error(error) raise ValueError(error) for i in range(dims): @@ -346,7 +349,7 @@ def get_padding_dim(image_shape, anisotropy_factor=None): padding.append(pad) - print(f"Padding sizes are {padding}") + LOGGER.debug(f"Padding sizes are {padding}") return padding From 339db36c12ba195b948175f047405a8a78456515 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Tue, 18 Jul 2023 10:18:41 +0200 Subject: [PATCH 020/250] Centralized rand_gen --- napari_cellseg3d/_tests/test_models.py | 4 +--- napari_cellseg3d/_tests/test_plugin_inference.py | 4 +--- napari_cellseg3d/_tests/test_plugin_utils.py | 4 +--- napari_cellseg3d/_tests/test_utils.py | 3 +-- napari_cellseg3d/dev_scripts/thread_test.py | 3 +-- 5 files changed, 5 insertions(+), 13 deletions(-) diff --git a/napari_cellseg3d/_tests/test_models.py b/napari_cellseg3d/_tests/test_models.py index d32ff62b..a9176fa4 100644 --- a/napari_cellseg3d/_tests/test_models.py +++ b/napari_cellseg3d/_tests/test_models.py @@ -3,7 +3,6 @@ import numpy as np import pytest import torch -from numpy.random import PCG64, Generator from napari_cellseg3d.code_models.crf import ( CRFWorker, @@ -14,8 +13,7 @@ from napari_cellseg3d.code_models.models.model_TRAILMAP_MS import TRAILMAP_MS_ from napari_cellseg3d.code_models.models.wnet.soft_Ncuts import SoftNCutsLoss from napari_cellseg3d.config import MODEL_LIST, CRFConfig - -rand_gen = Generator(PCG64(12345)) +from napari_cellseg3d.utils import rand_gen def test_correct_shape_for_crf(): diff --git a/napari_cellseg3d/_tests/test_plugin_inference.py b/napari_cellseg3d/_tests/test_plugin_inference.py index de5c78ae..efa9d9fb 100644 --- a/napari_cellseg3d/_tests/test_plugin_inference.py +++ b/napari_cellseg3d/_tests/test_plugin_inference.py @@ -1,6 +1,5 @@ from pathlib import Path -from numpy.random import PCG64, Generator from tifffile import imread from napari_cellseg3d._tests.fixtures import LogFixture @@ -14,8 +13,7 @@ Inferer, ) from napari_cellseg3d.config import MODEL_LIST - -rand_gen = Generator(PCG64(12345)) +from napari_cellseg3d.utils import rand_gen def test_inference(make_napari_viewer_proxy, qtbot): diff --git a/napari_cellseg3d/_tests/test_plugin_utils.py b/napari_cellseg3d/_tests/test_plugin_utils.py index 1427021a..fe6fe2e8 100644 --- a/napari_cellseg3d/_tests/test_plugin_utils.py +++ b/napari_cellseg3d/_tests/test_plugin_utils.py @@ -1,13 +1,11 @@ import numpy as np -from numpy.random import PCG64, Generator from napari_cellseg3d.code_plugins.plugin_crop import Cropping from napari_cellseg3d.code_plugins.plugin_utilities import ( UTILITIES_WIDGETS, Utilities, ) - -rand_gen = Generator(PCG64(12345)) +from napari_cellseg3d.utils import rand_gen def test_utils_plugin(make_napari_viewer_proxy): diff --git a/napari_cellseg3d/_tests/test_utils.py b/napari_cellseg3d/_tests/test_utils.py index 5e7cbeb2..a5ac7fdb 100644 --- a/napari_cellseg3d/_tests/test_utils.py +++ b/napari_cellseg3d/_tests/test_utils.py @@ -5,12 +5,11 @@ import numpy as np import pytest import torch -from numpy.random import PCG64, Generator from napari_cellseg3d import utils from napari_cellseg3d.dev_scripts import thread_test -rand_gen = Generator(PCG64(12345)) +rand_gen = utils.rand_gen def test_singleton_class(): diff --git a/napari_cellseg3d/dev_scripts/thread_test.py b/napari_cellseg3d/dev_scripts/thread_test.py index 112ec0ce..82782ef8 100644 --- a/napari_cellseg3d/dev_scripts/thread_test.py +++ b/napari_cellseg3d/dev_scripts/thread_test.py @@ -2,7 +2,6 @@ import napari from napari.qt.threading import thread_worker -from numpy.random import PCG64, Generator from qtpy.QtWidgets import ( QGridLayout, QLabel, @@ -13,7 +12,7 @@ QWidget, ) -rand_gen = Generator(PCG64(12345)) +from napari_cellseg3d.utils import rand_gen #################################### # Tutorial code from napari forums # From 47a67fda13e6f516af9f7041f322ab91a18f72e0 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Tue, 18 Jul 2023 11:34:52 +0200 Subject: [PATCH 021/250] Train and inference tweaks --- .../code_models/worker_inference.py | 43 ++++++++++--------- .../code_models/worker_training.py | 6 ++- .../code_plugins/plugin_model_inference.py | 12 ++---- .../code_plugins/plugin_model_training.py | 12 ++++-- 4 files changed, 39 insertions(+), 34 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index c4221e7b..107f0d88 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -35,7 +35,8 @@ QuantileNormalizationd, WeightsDownloader, ) -from napari_cellseg3d.utils import LOGGER as logger + +logger = utils.LOGGER """ Writing something to log messages from outside the main thread needs specific care, @@ -308,6 +309,7 @@ def model_output_wrapper(inputs): result = model(inputs) return post_process_transforms(result) + model.eval() with torch.no_grad(): outputs = sliding_window_inference( inputs, @@ -561,7 +563,7 @@ def inference_on_layer(self, image, model, post_process_transforms): self.log("-" * 10) self.log("Inference started on layer...") - image = image.type(torch.FloatTensor) + # image = image.type(torch.FloatTensor) image = utils.quantile_normalization(image) out = self.model_output( @@ -674,13 +676,17 @@ def inference(self): weights = str( PRETRAINED_WEIGHTS_DIR / Path(model_class.weights_file) ) - - model.load_state_dict( # note that this is redefined in WNet_ - torch.load( - weights, - map_location=self.config.device, + try: + missing = model.load_state_dict( # note that this is redefined in WNet_ + torch.load( + weights, + map_location=self.config.device, + ), + strict=True, ) - ) + self.log(f"Weights status : {missing}") + except RuntimeError as e: + self.raise_error(e, "Error when loading weights") self.log("Done") # except Exception as e: # self.raise_error(e, "Issue loading weights") @@ -752,20 +758,15 @@ def inference(self): if model is None: raise ValueError("Model is None") - model.eval() - with torch.no_grad(): - ################################ - ################################ - ################################ - if is_folder: - for i, inf_data in enumerate(inference_loader): - yield self.inference_on_folder( - inf_data, i, model, post_process_transforms - ) - elif is_layer: - yield self.inference_on_layer( - input_image, model, post_process_transforms + if is_folder: + for i, inf_data in enumerate(inference_loader): + yield self.inference_on_folder( + inf_data, i, model, post_process_transforms ) + elif is_layer: + yield self.inference_on_layer( + input_image, model, post_process_transforms + ) model.to("cpu") # self.quit() except Exception as e: diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 03e82ed5..ed3f08ae 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -653,7 +653,11 @@ def get_loader_func(num_samples): dice_metric(y_pred=val_outputs, y=val_labels) checkpoint_output.append( - [res.detach().cpu() for res in val_outputs] + [ + val_outputs[0].detach().cpu(), + val_inputs[0].detach().cpu(), + val_labels[0].detach().cpu(), + ] ) checkpoint_output = [ diff --git a/napari_cellseg3d/code_plugins/plugin_model_inference.py b/napari_cellseg3d/code_plugins/plugin_model_inference.py index baa0791d..59c24c2c 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_inference.py +++ b/napari_cellseg3d/code_plugins/plugin_model_inference.py @@ -240,7 +240,7 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): self._remove_unused() - def _toggle_display_crf(self): + def _toggle_crf_choice(self): if self.model_choice.currentText() == "WNet": self.use_crf.setVisible(True) else: @@ -310,7 +310,6 @@ def check_ready(self): def _restrict_window_size_for_model(self): """Sets the window size to a value that is compatible with the chosen model""" - window_enabled = self.window_infer_box.isChecked() if self.model_choice.currentText() == "WNet": self.window_size_choice.setCurrentIndex(self._default_window_size) self.window_size_choice.setDisabled(True) @@ -319,11 +318,8 @@ def _restrict_window_size_for_model(self): else: self.window_size_choice.setDisabled(False) self.window_infer_box.setDisabled(False) - self.window_infer_box.setChecked(window_enabled) - if not window_enabled: - self.window_size_choice.setCurrentIndex( - self._default_window_size - ) + self.window_infer_box.setChecked(True) + self.window_size_choice.setCurrentIndex(self._default_window_size) def _toggle_display_model_input_size(self): if ( @@ -485,7 +481,7 @@ def _build(self): # self.instance_param_container, # instance segmentation ], ) - self.model_choice.currentIndexChanged.connect(self._toggle_display_crf) + self.model_choice.currentIndexChanged.connect(self._toggle_crf_choice) ModelFramework._show_io_element( self.save_stats_to_csv_box, self.use_instance_choice ) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 4ec2aa04..d2de8d0f 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -979,7 +979,11 @@ def on_yield(self, report: TrainingReport): if report.show_plot: try: - layer_name = "Validation output n°" + layer_names = [ + "Validation output", + "Validation image", + "Validation labels", + ] rge = range(len(report.images)) self.log.print_and_log(len(report.images)) @@ -988,18 +992,18 @@ def on_yield(self, report: TrainingReport): for i in rge: layer = self._viewer.add_image( report.images[i], - name=layer_name + str(i), + name=layer_names[i] + str(i), colormap="viridis", ) self.result_layers.append(layer) else: for i in rge: - if layer_name + str(i) not in [ + if layer_names[i] + str(i) not in [ layer.name for layer in self._viewer.layers ]: new_layer = self._viewer.add_image( report.images[i], - name=layer_name + str(i), + name=layer_names[i] + str(i), colormap="viridis", ) self.result_layers.append(new_layer) From c804dccc0195efeb5ddaad4f4565626625e16d9a Mon Sep 17 00:00:00 2001 From: C-Achard Date: Tue, 18 Jul 2023 14:20:31 +0200 Subject: [PATCH 022/250] Working on dtype issues --- .../code_models/models/model_TRAILMAP_MS.py | 4 +--- .../code_models/worker_inference.py | 18 +++++++++--------- 2 files changed, 10 insertions(+), 12 deletions(-) diff --git a/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py b/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py index 8f1a493a..bc8e43d5 100644 --- a/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py +++ b/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py @@ -1,7 +1,5 @@ from napari_cellseg3d.code_models.models.unet.model import UNet3D -from napari_cellseg3d.utils import LOGGER - -logger = LOGGER +from napari_cellseg3d.utils import LOGGER as logger class TRAILMAP_MS_(UNet3D): diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 107f0d88..cba91f50 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -201,7 +201,7 @@ def load_folder(self): LoadImaged(keys=["image"]), # AddChanneld(keys=["image"]), #already done EnsureChannelFirstd(keys=["image"]), - # NormalizeIntensityd(keys=["image"]), # TODO normalize + QuantileNormalizationd(keys=["image"]), # Orientationd(keys=["image"], axcodes="PLI"), # anisotropic_transform, SpatialPadd(keys=["image"], spatial_size=pad), @@ -219,9 +219,9 @@ def load_folder(self): def load_layer(self): self.log("\nLoading layer\n") - data = np.squeeze(self.config.layer.data) - - volume = np.array(data, dtype=np.int16) + image = np.squeeze(self.config.layer.data) + volume = image.astype(np.float32) + self.log(f"Image type : {str(image.dtype)}") volume_dims = len(volume.shape) if volume_dims != 3: @@ -229,15 +229,14 @@ def load_layer(self): f"Data array is not 3-dimensional but {volume_dims}-dimensional," f" please check for extra channel/batch dimensions" ) - volume = np.swapaxes( volume, 0, 2 - ) # for anisotropy to be monai-like, i.e. zyx # FIXME rotation not always correct + ) # for anisotropy to be monai-like, i.e. zyx dims_check = volume.shape - # logger.debug(volume.shape) - # logger.debug(volume.dtype) + logger.debug(volume.shape) + logger.debug(volume.dtype) if self.config.sliding_window_config.is_enabled(): load_transforms = Compose( [ @@ -563,8 +562,9 @@ def inference_on_layer(self, image, model, post_process_transforms): self.log("-" * 10) self.log("Inference started on layer...") - # image = image.type(torch.FloatTensor) image = utils.quantile_normalization(image) + self.log(f"Image type after normalization : {str(image.dtype)}") + self.log(f"Image shape after normalization : {str(image.shape)}") out = self.model_output( image, From 006df7bc1b76fda2ab6065b82570342a3efa2a33 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Tue, 18 Jul 2023 15:43:28 +0200 Subject: [PATCH 023/250] Fixed issues with remapping Remapping was done in inference always, which was breaking some models outputs. This is now done in the correct place, i.e. the WNet_model file --- .../code_models/models/model_WNet.py | 12 ++-- .../code_models/models/wnet/train_wnet.py | 15 +---- .../code_models/worker_inference.py | 49 ++++++--------- .../code_models/worker_training.py | 38 ++++++------ napari_cellseg3d/code_models/workers_utils.py | 7 ++- .../code_plugins/plugin_model_inference.py | 1 + .../code_plugins/plugin_model_training.py | 62 ++++++++++++------- napari_cellseg3d/dev_scripts/crop_data.py | 13 ++-- 8 files changed, 100 insertions(+), 97 deletions(-) diff --git a/napari_cellseg3d/code_models/models/model_WNet.py b/napari_cellseg3d/code_models/models/model_WNet.py index 37768616..e50a58a1 100644 --- a/napari_cellseg3d/code_models/models/model_WNet.py +++ b/napari_cellseg3d/code_models/models/model_WNet.py @@ -1,5 +1,6 @@ # local from napari_cellseg3d.code_models.models.wnet.model import WNet_encoder +from napari_cellseg3d.utils import remap_image class WNet_(WNet_encoder): @@ -22,13 +23,10 @@ def __init__( # def train(self: T, mode: bool = True) -> T: # raise NotImplementedError("Training not implemented for WNet") - # def forward(self, x): - # """Forward ENCODER pass of the W-Net model. - # Done this way to allow inference on the encoder only when called by sliding_window_inference. - # """ - # return self.forward_encoder(x) - # # enc = self.forward_encoder(x) - # # return self.forward_decoder(enc) + def forward(self, x): + """Forward pass of the W-Net model.""" + norm_x = remap_image(x) + return super().forward(norm_x) def load_state_dict(self, state_dict, strict=True): """Load the model state dict for inference, without the decoder weights.""" diff --git a/napari_cellseg3d/code_models/models/wnet/train_wnet.py b/napari_cellseg3d/code_models/models/wnet/train_wnet.py index 15d0e8ac..3b2ad353 100644 --- a/napari_cellseg3d/code_models/models/wnet/train_wnet.py +++ b/napari_cellseg3d/code_models/models/wnet/train_wnet.py @@ -5,7 +5,6 @@ import glob import time from pathlib import Path -from typing import Union from warnings import warn import numpy as np @@ -44,7 +43,7 @@ from napari_cellseg3d.code_models.models.wnet.model import WNet from napari_cellseg3d.code_models.models.wnet.soft_Ncuts import SoftNCutsLoss from napari_cellseg3d.utils import LOGGER as logger -from napari_cellseg3d.utils import dice_coeff, get_padding_dim +from napari_cellseg3d.utils import dice_coeff, get_padding_dim, remap_image try: import wandb @@ -115,18 +114,6 @@ def create_dataset_dict_no_labs(volume_directory): return [{"image": image_name} for image_name in images_filepaths] -def remap_image( - image: Union[np.ndarray, torch.Tensor], new_max=100, new_min=0 -): - """Normalizes a numpy array or Tensor using the max and min value""" - shape = image.shape - image = image.flatten() - image = (image - image.min()) / (image.max() - image.min()) - image = image * (new_max - new_min) + new_min - # image = set_quantile_to_value(image) - return image.reshape(shape) - - ################################ # Config & WANDB # ################################ diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index cba91f50..f6ad6fbe 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -32,6 +32,7 @@ InferenceResult, LogSignal, ONNXModelWrapper, + QuantileNormalization, QuantileNormalizationd, WeightsDownloader, ) @@ -135,7 +136,7 @@ def log_parameters(self): config = self.config self.log("-" * 20) - self.log("\nParameters summary :") + self.log("Parameters summary :") self.log(f"Model is : {config.model_info.name}") if config.post_process_config.thresholding.enabled: @@ -149,7 +150,7 @@ def log_parameters(self): else "disabled" ) - self.log(f"Window inference is {status}\n") + self.log(f"Window inference is {status}") if status == "enabled": self.log( f"Window size is {self.config.sliding_window_config.window_size}" @@ -171,7 +172,7 @@ def log_parameters(self): instance_config = config.post_process_config.instance if instance_config.enabled: self.log( - f"Instance segmentation enabled, method : {instance_config.method.name}\n" + f"Instance segmentation enabled, method : {instance_config.method.name}" ) self.log("-" * 20) @@ -209,7 +210,7 @@ def load_folder(self): ] ) - self.log("\nLoading dataset...") + self.log("Loading dataset...") inference_ds = Dataset(data=images_dict, transform=load_transforms) inference_loader = DataLoader( inference_ds, batch_size=1, num_workers=2 @@ -218,10 +219,10 @@ def load_folder(self): return inference_loader def load_layer(self): - self.log("\nLoading layer\n") + self.log("Loading layer") image = np.squeeze(self.config.layer.data) volume = image.astype(np.float32) - self.log(f"Image type : {str(image.dtype)}") + # self.log(f"Image type : {str(image.dtype)}") volume_dims = len(volume.shape) if volume_dims != 3: @@ -231,12 +232,13 @@ def load_layer(self): ) volume = np.swapaxes( volume, 0, 2 - ) # for anisotropy to be monai-like, i.e. zyx + ) # for dims to be monai-like, i.e. xyz, from napari zyx dims_check = volume.shape logger.debug(volume.shape) logger.debug(volume.dtype) + if self.config.sliding_window_config.is_enabled(): load_transforms = Compose( [ @@ -244,6 +246,7 @@ def load_layer(self): # anisotropic_transform, AddChannel(), # SpatialPad(spatial_size=pad), + QuantileNormalization(), AddChannel(), EnsureType(), ], @@ -258,6 +261,7 @@ def load_layer(self): ToTensor(), # anisotropic_transform, AddChannel(), + QuantileNormalization(), SpatialPad(spatial_size=pad), AddChannel(), EnsureType(), @@ -267,6 +271,8 @@ def load_layer(self): ) input_image = load_transforms(volume) + logger.debug(f"INPUT IMAGE SHAPE : {input_image.shape}") + logger.debug(f"INPUT IMAGE TYPE : {input_image.dtype}") self.log("Done") return input_image @@ -275,16 +281,9 @@ def model_output( inputs, model, post_process_transforms, - post_process=True, aniso_transform=None, ): inputs = inputs.to("cpu") - - # def model_output(inputs): - # return post_process_transforms( - # self.config.model_info.get_model().get_output(model, inputs) - # ) - dataset_device = ( "cpu" if self.config.keep_on_cpu else self.config.device ) @@ -302,7 +301,6 @@ def model_output( logger.debug(f"inputs type : {inputs.dtype}") try: # outputs = model(inputs) - inputs = utils.remap_image(inputs) def model_output_wrapper(inputs): result = model(inputs) @@ -327,13 +325,13 @@ def model_output_wrapper(inputs): logger.debug("failed to run sliding window inference") self.raise_error(e, "Error during sliding window inference") logger.debug(f"Inference output shape: {outputs.shape}") + self.log("Post-processing...") out = outputs.detach().cpu().numpy() if aniso_transform is not None: out = aniso_transform(out) - if post_process: - out = np.array(out).astype(np.float32) - out = np.squeeze(out) + out = np.array(out).astype(np.float32) + out = np.squeeze(out) return out except Exception as e: logger.exception(e) @@ -432,9 +430,9 @@ def save_image( filename = Path(file_path).stem if from_layer: - self.log(f"\nLayer prediction saved as : {filename}") + self.log(f"Layer prediction saved as : {filename}") else: - self.log(f"\nFile n°{i+1} saved as : {filename}") + self.log(f"File n°{i+1} saved as : {filename}") def aniso_transform(self, image): if self.config.post_process_config.zoom.enabled: @@ -451,7 +449,7 @@ def instance_seg( self, semantic_labels, image_id=0, original_filename="layer" ): if image_id is not None: - self.log(f"\nRunning instance segmentation for image n°{image_id}") + self.log(f"Running instance segmentation for image n°{image_id}") method = self.config.post_process_config.instance.method instance_labels = method.run_method_on_channels(semantic_labels) @@ -513,7 +511,6 @@ def inference_on_folder(self, inf_data, i, model, post_process_transforms): crf_results = None original = np.array(inf_data["image"]).astype(np.float32) - self.log(f"Inference completed on image n°{i+1}") return self.create_inference_result( @@ -562,17 +559,12 @@ def inference_on_layer(self, image, model, post_process_transforms): self.log("-" * 10) self.log("Inference started on layer...") - image = utils.quantile_normalization(image) - self.log(f"Image type after normalization : {str(image.dtype)}") - self.log(f"Image shape after normalization : {str(image.shape)}") - out = self.model_output( image, model, post_process_transforms, aniso_transform=self.aniso_transform, ) - self.save_image(out, from_layer=True) instance_labels, stats = self.get_instance_result( @@ -584,7 +576,6 @@ def inference_on_layer(self, image, model, post_process_transforms): if self.config.use_crf else None ) - return self.create_inference_result( semantic_labels=out, instance_labels=instance_labels, @@ -665,7 +656,7 @@ def inference(self): if model is None: raise ValueError("Model is None") # try: - self.log("\nLoading weights...") + self.log("Loading weights...") if weights_config.custom: weights = weights_config.path else: diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index ed3f08ae..b8d522c8 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -16,7 +16,6 @@ from monai.inferers import sliding_window_inference from monai.metrics import DiceMetric from monai.transforms import ( - AsDiscrete, Compose, EnsureChannelFirstd, EnsureType, @@ -636,7 +635,8 @@ def get_loader_func(num_samples): # TODO : more parameters/flexibility post_pred = Compose( - AsDiscrete(threshold=0.6), EnsureType() + # AsDiscrete(threshold=0.6), # needed ? + EnsureType() ) # post_label = EnsureType() @@ -652,13 +652,14 @@ def get_loader_func(num_samples): # logger.debug(len(val_labels)) dice_metric(y_pred=val_outputs, y=val_labels) - checkpoint_output.append( - [ - val_outputs[0].detach().cpu(), - val_inputs[0].detach().cpu(), - val_labels[0].detach().cpu(), - ] - ) + + checkpoint_output.append( + [ + val_outputs[0].detach().cpu(), + val_inputs[0].detach().cpu(), + val_labels[0].detach().cpu(), + ] + ) checkpoint_output = [ item.numpy() @@ -668,7 +669,6 @@ def get_loader_func(num_samples): metric = dice_metric.aggregate().detach().item() dice_metric.reset() - val_metric_values.append(metric) train_report = TrainingReport( @@ -684,7 +684,8 @@ def get_loader_func(num_samples): weights_filename = ( f"{model_name}_best_metric" - + f"_epoch_{epoch + 1}.pth" + # + f"_epoch_{epoch + 1}" # avoid saving per epoch + + ".pth" ) if metric > best_metric: @@ -710,7 +711,7 @@ def get_loader_func(num_samples): f"at epoch: {best_metric_epoch}" ) # Save last checkpoint - weights_filename = f"{model_name}_last_epoch_{epoch + 1}.pth" + weights_filename = f"{model_name}_latest.pth" self.log("Saving last model") torch.save( model.state_dict(), @@ -719,14 +720,11 @@ def get_loader_func(num_samples): self.log("Saving complete, exiting") model.to("cpu") # clear (V)RAM - val_ds = None - train_ds = None - val_loader = None - train_loader = None - torch.cuda.empty_cache() - import gc - - gc.collect() + # val_ds = None + # train_ds = None + # val_loader = None + # train_loader = None + # torch.cuda.empty_cache() except Exception as e: self.raise_error(e, "Error in training") diff --git a/napari_cellseg3d/code_models/workers_utils.py b/napari_cellseg3d/code_models/workers_utils.py index f377fd22..923bc727 100644 --- a/napari_cellseg3d/code_models/workers_utils.py +++ b/napari_cellseg3d/code_models/workers_utils.py @@ -5,7 +5,7 @@ import numpy as np import torch -from monai.transforms import MapTransform +from monai.transforms import MapTransform, Transform from qtpy.QtCore import Signal from superqt.utils._qthreading import WorkerBaseSignals from tqdm import tqdm @@ -198,6 +198,11 @@ def normalizer(self, image: torch.Tensor): return image +class QuantileNormalization(Transform): + def __call__(self, img): + return utils.quantile_normalization(img) + + @dataclass class InferenceResult: """Class to record results of a segmentation job""" diff --git a/napari_cellseg3d/code_plugins/plugin_model_inference.py b/napari_cellseg3d/code_plugins/plugin_model_inference.py index 59c24c2c..471c02ac 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_inference.py +++ b/napari_cellseg3d/code_plugins/plugin_model_inference.py @@ -481,6 +481,7 @@ def _build(self): # self.instance_param_container, # instance segmentation ], ) + self._toggle_crf_choice() self.model_choice.currentIndexChanged.connect(self._toggle_crf_choice) ModelFramework._show_io_element( self.save_stats_to_csv_box, self.use_instance_choice diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index d2de8d0f..d77d0c28 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -969,6 +969,42 @@ def on_error(self): # self.empty_cuda_cache() # self.clean_cache() + def _display_results(self, images, names, complete_missing=False): + if not complete_missing: + layer_output = self._viewer.add_image( + data=images[0], name=names[0], colormap="viridis" + ) + layer_image = self._viewer.add_image( + data=images[1], name=names[1], colormap="inferno" + ) + layer_labels = self._viewer.add_labels( + data=images[2], name=names[2] + ) + self.result_layers += [layer_output, layer_image, layer_labels] + else: + # add only the missing layers + for i in range(3): + if names[i] not in [ + layer.name for layer in self._viewer.layers + ]: + if i == 0: + layer_output = self._viewer.add_image( + data=images[i], name=names[i], colormap="viridis" + ) + self.result_layers[0] = layer_output + elif i == 1: + layer_image = self._viewer.add_image( + data=images[i], name=names[i], colormap="inferno" + ) + self.result_layers[1] = layer_image + else: + layer_labels = self._viewer.add_labels( + data=images[i], name=names[i] + ) + self.result_layers[2] = layer_labels + self.result_layers[i].data = images[i] + self.result_layers[i].refresh() + def on_yield(self, report: TrainingReport): # logger.info( # f"\nCatching results : for epoch {data['epoch']}, @@ -984,31 +1020,15 @@ def on_yield(self, report: TrainingReport): "Validation image", "Validation labels", ] - rge = range(len(report.images)) - + range(len(report.images)) self.log.print_and_log(len(report.images)) if report.epoch + 1 == self.worker_config.validation_interval: - for i in rge: - layer = self._viewer.add_image( - report.images[i], - name=layer_names[i] + str(i), - colormap="viridis", - ) - self.result_layers.append(layer) + self._display_results(report.images, layer_names) else: - for i in rge: - if layer_names[i] + str(i) not in [ - layer.name for layer in self._viewer.layers - ]: - new_layer = self._viewer.add_image( - report.images[i], - name=layer_names[i] + str(i), - colormap="viridis", - ) - self.result_layers.append(new_layer) - self.result_layers[i].data = report.images[i] - self.result_layers[i].refresh() + self._display_results( + report.images, layer_names, complete_missing=True + ) except Exception as e: logger.exception(e) diff --git a/napari_cellseg3d/dev_scripts/crop_data.py b/napari_cellseg3d/dev_scripts/crop_data.py index 2f24e619..d8be9cad 100644 --- a/napari_cellseg3d/dev_scripts/crop_data.py +++ b/napari_cellseg3d/dev_scripts/crop_data.py @@ -39,14 +39,17 @@ def crop_3d_image(image, roi_size): image_path = ( Path().home() # / "Desktop/Code/CELLSEG_BENCHMARK/TPH2_DATA/somatomotor_iso" - / "Desktop/Code/CELLSEG_BENCHMARK/TPH2_DATA/somatomotor_iso/labels/semantic" + # / "Desktop/Code/CELLSEG_BENCHMARK/TPH2_DATA/somatomotor_iso/labels/semantic" + / "Desktop/Code/CELLSEG_BENCHMARK/TPH2_mesospim/BENCHMARK/visual_small" ) - assert image_path.exists() + if not image_path.exists() or not image_path.is_dir(): + raise ValueError(f"Image path {image_path} does not exist") image_list = image_path.glob("*.tif") for j in image_list: print(j) image = imread(str(j)) - crops = crop_3d_image(image, (64, 64, 64)) + # crops = crop_3d_image(image, (64, 64, 64)) + crops = [image] # viewer = napari.Viewer() if not (image_path / "cropped").exists(): (image_path / "cropped").mkdir(exist_ok=False) @@ -55,7 +58,7 @@ def crop_3d_image(image, roi_size): # viewer.add_image(im) imwrite( str(image_path / f"cropped/{j.stem}_{i}_crop.tif"), - im.astype(np.uint16), - dtype="uint16", + im.astype(np.float32), + dtype="float32", ) # napari.run() From d5f7d161dda577a9427da684df04dc82910cf716 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 08:59:55 +0200 Subject: [PATCH 024/250] Docs + train WIP --- docs/res/guides/detailed_walkthrough.rst | 14 +--------- docs/res/guides/training_module_guide.rst | 7 +++-- .../code_models/models/model_TRAILMAP_MS.py | 2 +- .../code_plugins/plugin_model_training.py | 27 +++++++++++-------- 4 files changed, 23 insertions(+), 27 deletions(-) diff --git a/docs/res/guides/detailed_walkthrough.rst b/docs/res/guides/detailed_walkthrough.rst index 9415d447..56ef54ed 100644 --- a/docs/res/guides/detailed_walkthrough.rst +++ b/docs/res/guides/detailed_walkthrough.rst @@ -239,26 +239,14 @@ Scoring, review, analysis ---------------------------- -Model performance : Metrics utility -**************************************** - - -Using the metrics utility module, you can compare the model's predictions to any ground truth +.. Using the metrics utility module, you can compare the model's predictions to any ground truth labels you might have. - Simply provide your prediction and ground truth labels, and compute the results. A Dice metric of 1 indicates perfect matching, whereas a score of 0 indicates complete mismatch. - Select which score **you consider as sub-optimal**, and all results below this will be **shown in napari**. - - If at any time the **orientation of your prediction labels changed compared to the ground truth**, check the "Find best orientation" option to compensate for it. -.. note:: - Using inference on the images you used for training might yield to higher scores due to the fitting. - If you wish to know how well the model performs on more general datasets, use different images for inference and scoring. - Labels review ************************ diff --git a/docs/res/guides/training_module_guide.rst b/docs/res/guides/training_module_guide.rst index c398ca94..d910bc50 100644 --- a/docs/res/guides/training_module_guide.rst +++ b/docs/res/guides/training_module_guide.rst @@ -94,20 +94,23 @@ The available loss functions are : Function Reference ======================== ================================================================================================ Dice loss `Dice Loss from MONAI`_ with ``sigmoid=true`` -Focal loss `Focal Loss from MONAI`_ -Dice-Focal loss `Dice-focal Loss from MONAI`_ with ``sigmoid=true`` and ``lambda_dice = 0.5`` Generalized Dice loss `Generalized dice Loss from MONAI`_ with ``sigmoid=true`` Dice-CE loss `Dice-CE Loss from MONAI`_ with ``sigmoid=true`` Tversky loss `Tversky Loss from MONAI`_ with ``sigmoid=true`` ======================== ================================================================================================ +.. Binary cross-entropy `Binary cross entropy (BCE) loss from PyTorch`_ + BCE with logits `BCE loss with logits from PyTorch`_ + .. _Dice Loss from MONAI: https://docs.monai.io/en/stable/losses.html#diceloss .. _Focal Loss from MONAI: https://docs.monai.io/en/stable/losses.html#focalloss .. _Dice-focal Loss from MONAI: https://docs.monai.io/en/stable/losses.html#dicefocalloss .. _Generalized dice Loss from MONAI: https://docs.monai.io/en/stable/losses.html#generalizeddiceloss .. _Dice-CE Loss from MONAI: https://docs.monai.io/en/stable/losses.html#diceceloss .. _Tversky Loss from MONAI: https://docs.monai.io/en/stable/losses.html#tverskyloss +.. _Binary cross entropy (BCE) loss from PyTorch: +.. _BCE loss with logits from PyTorch: Once you are ready, press the Start button to begin training. The module will automatically load your dataset, perform data augmentation if you chose to, select a CUDA device if one is present, and train the model. diff --git a/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py b/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py index bc8e43d5..dbdd171d 100644 --- a/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py +++ b/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py @@ -8,7 +8,7 @@ class TRAILMAP_MS_(UNet3D): # original model from Liqun Luo lab, transferred to pytorch and trained on mesoSPIM-acquired data (mostly TPH2 as of July 2022) - def __init__(self, in_channels=1, out_channels=1, **kwargs): + def __init__(self, in_channels=1, out_channels=2, **kwargs): try: super().__init__( in_channels=in_channels, out_channels=out_channels, **kwargs diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index d77d0c28..9593d986 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -18,9 +18,7 @@ # MONAI from monai.losses import ( DiceCELoss, - DiceFocalLoss, DiceLoss, - FocalLoss, GeneralizedDiceLoss, TverskyLoss, ) @@ -142,13 +140,14 @@ def __init__( self.start_time = None self.loss_dict = { - "Dice loss": DiceLoss(sigmoid=True), - "Focal loss": FocalLoss(), - # "BCELoss":nn.BCELoss(), - "Dice-Focal loss": DiceFocalLoss(sigmoid=True, lambda_dice=0.5), - "Generalized Dice loss": GeneralizedDiceLoss(sigmoid=True), - "DiceCELoss": DiceCELoss(sigmoid=True, lambda_ce=0.5), - "Tversky loss": TverskyLoss(sigmoid=True), + "Dice": DiceLoss(sigmoid=True), + # "BCELoss": torch.nn.BCELoss(), # dev + # "BCELogits": torch.nn.BCEWithLogitsLoss(), + "Generalized Dice": GeneralizedDiceLoss(sigmoid=True), + "DiceCE": DiceCELoss(sigmoid=True, lambda_ce=0.5), + "Tversky": TverskyLoss(sigmoid=True), + # "Focal loss": FocalLoss(), + # "Dice-Focal loss": DiceFocalLoss(sigmoid=True, lambda_dice=0.5), } """Dict of loss functions""" @@ -168,6 +167,7 @@ def __init__( self.validation_values = [] # self.model_choice.setCurrentIndex(0) + ################### # TODO(cyril) : disable if we implement WNet training wnet_index = self.model_choice.findText("WNet") self.model_choice.removeItem(wnet_index) @@ -192,7 +192,11 @@ def __init__( ) self.loss_choice = ui.DropdownMenu( - sorted(self.loss_dict.keys()), text_label="Loss function" + # sorted( + list( + self.loss_dict.keys(), + ), + text_label="Loss function", ) self.lbl_loss_choice = self.loss_choice.label self.loss_choice.setCurrentIndex(0) @@ -310,7 +314,7 @@ def set_tooltips(): self.validation_percent_choice.tooltips = "Choose the proportion of images to retain for training.\nThe remaining images will be used for validation" self.epoch_choice.tooltips = "The number of epochs to train for.\nThe more you train, the better the model will fit the training data" self.loss_choice.setToolTip( - "The loss function to use for training.\nSee the list in the inference guide for more info" + "The loss function to use for training.\nSee the list in the training guide for more info" ) self.sample_choice_slider.tooltips = ( "The number of samples to extract per image" @@ -400,6 +404,7 @@ def _toggle_transfer_param(self): self.custom_weights_choice.setVisible(True) else: self.custom_weights_choice.setVisible(False) + self.weights_filewidget.setVisible(False) def _toggle_deterministic_param(self): if self.use_deterministic_choice.isChecked(): From 77e54c89a7845af9f6630f10edbc9906ae8d7340 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 09:11:10 +0200 Subject: [PATCH 025/250] Update plugin_model_training.py Fix dtype issue for label layer --- napari_cellseg3d/code_plugins/plugin_model_training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 9593d986..ec1c842e 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -983,7 +983,7 @@ def _display_results(self, images, names, complete_missing=False): data=images[1], name=names[1], colormap="inferno" ) layer_labels = self._viewer.add_labels( - data=images[2], name=names[2] + data=images[2].astype(np.uint16), name=names[2] ) self.result_layers += [layer_output, layer_image, layer_labels] else: From 35b4a4e2e10c01aabb8ad867be810c61dcfe6d68 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 09:17:30 +0200 Subject: [PATCH 026/250] Update plugin_model_training.py Better interrupt --- napari_cellseg3d/code_plugins/plugin_model_training.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index ec1c842e..69d1c4d1 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -974,6 +974,11 @@ def on_error(self): # self.empty_cuda_cache() # self.clean_cache() + def on_stop(self): + self.worker = None + self.btn_start.setText("Start") + [btn.setVisible(True) for btn in self.close_buttons] + def _display_results(self, images, names, complete_missing=False): if not complete_missing: layer_output = self._viewer.add_image( @@ -1057,6 +1062,7 @@ def on_yield(self, report: TrainingReport): ), ) self.log.print_and_log("Saving complete") + self.on_stop() self.stop_requested = False # def clean_cache(self): From 3dcb2f54b93d8fdb2404d6720b126b61d63f7499 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 09:18:11 +0200 Subject: [PATCH 027/250] Update model_TRAILMAP_MS.py --- napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py b/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py index dbdd171d..bc8e43d5 100644 --- a/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py +++ b/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py @@ -8,7 +8,7 @@ class TRAILMAP_MS_(UNet3D): # original model from Liqun Luo lab, transferred to pytorch and trained on mesoSPIM-acquired data (mostly TPH2 as of July 2022) - def __init__(self, in_channels=1, out_channels=2, **kwargs): + def __init__(self, in_channels=1, out_channels=1, **kwargs): try: super().__init__( in_channels=in_channels, out_channels=out_channels, **kwargs From 41636586b98d60b75be07c629215f56a3c9bdc8a Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 09:48:07 +0200 Subject: [PATCH 028/250] Training and inference QoL Colormap changes --- .../code_plugins/plugin_model_inference.py | 6 +++--- .../code_plugins/plugin_model_training.py | 15 ++++++++++++--- 2 files changed, 15 insertions(+), 6 deletions(-) diff --git a/napari_cellseg3d/code_plugins/plugin_model_inference.py b/napari_cellseg3d/code_plugins/plugin_model_inference.py index 471c02ac..c9dfbc10 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_inference.py +++ b/napari_cellseg3d/code_plugins/plugin_model_inference.py @@ -577,9 +577,9 @@ def _display_results(self, result: InferenceResult): opacity=0.7, ) - out_colormap = "twilight" - if self.worker_config.post_process_config.thresholding.enabled: - out_colormap = "turbo" + out_colormap = "turbo" + # if self.worker_config.post_process_config.thresholding.enabled: + # out_colormap = "twilight" viewer.add_image( result.result, diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 69d1c4d1..ed24b92e 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -975,14 +975,20 @@ def on_error(self): # self.clean_cache() def on_stop(self): + self._remove_result_layers() self.worker = None self.btn_start.setText("Start") [btn.setVisible(True) for btn in self.close_buttons] + def _remove_result_layers(self): + for layer in self.result_layers: + self._viewer.layers.remove(layer) + self.result_layers = [] + def _display_results(self, images, names, complete_missing=False): if not complete_missing: layer_output = self._viewer.add_image( - data=images[0], name=names[0], colormap="viridis" + data=images[0], name=names[0], colormap="turbo" ) layer_image = self._viewer.add_image( data=images[1], name=names[1], colormap="inferno" @@ -991,6 +997,9 @@ def _display_results(self, images, names, complete_missing=False): data=images[2].astype(np.uint16), name=names[2] ) self.result_layers += [layer_output, layer_image, layer_labels] + self._viewer.grid.enabled = True + self._viewer.dims.ndisplay = 3 + self._viewer.reset_view() else: # add only the missing layers for i in range(3): @@ -999,7 +1008,7 @@ def _display_results(self, images, names, complete_missing=False): ]: if i == 0: layer_output = self._viewer.add_image( - data=images[i], name=names[i], colormap="viridis" + data=images[i], name=names[i], colormap="turbo" ) self.result_layers[0] = layer_output elif i == 1: @@ -1009,7 +1018,7 @@ def _display_results(self, images, names, complete_missing=False): self.result_layers[1] = layer_image else: layer_labels = self._viewer.add_labels( - data=images[i], name=names[i] + data=images[i].astype(np.uint16), name=names[i] ) self.result_layers[2] = layer_labels self.result_layers[i].data = images[i] From 7c9a501d88e8dc804b8f17055de3c14c536d49fe Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 10:06:15 +0200 Subject: [PATCH 029/250] Fix dtype for val labels and SegRes output --- .../code_models/models/model_SwinUNetR.py | 9 ++++----- .../code_models/worker_training.py | 18 +++++++++--------- .../code_plugins/plugin_model_training.py | 4 ++-- 3 files changed, 15 insertions(+), 16 deletions(-) diff --git a/napari_cellseg3d/code_models/models/model_SwinUNetR.py b/napari_cellseg3d/code_models/models/model_SwinUNetR.py index 621ae0b3..34502a12 100644 --- a/napari_cellseg3d/code_models/models/model_SwinUNetR.py +++ b/napari_cellseg3d/code_models/models/model_SwinUNetR.py @@ -1,5 +1,4 @@ from monai.networks.nets import SwinUNETR -from torch import sigmoid from napari_cellseg3d.utils import LOGGER @@ -37,10 +36,10 @@ def __init__( use_checkpoint=use_checkpoint, ) - def forward(self, x_in): - y = super().forward(x_in) - # return softmax(y, dim=1) - return sigmoid(y) + # def forward(self, x_in): + # y = super().forward(x_in) + # return softmax(y, dim=1) + # return sigmoid(y) # def get_output(self, input): # out = self(input) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index b8d522c8..e7495f8c 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -3,6 +3,7 @@ from math import ceil from pathlib import Path +import numpy as np import torch # MONAI @@ -655,17 +656,16 @@ def get_loader_func(num_samples): checkpoint_output.append( [ - val_outputs[0].detach().cpu(), - val_inputs[0].detach().cpu(), - val_labels[0].detach().cpu(), + val_outputs[0].detach().cpu().numpy(), + val_inputs[0].detach().cpu().numpy(), + val_labels[0] + .detach() + .cpu() + .numpy() + .astype(np.uint16), ] ) - - checkpoint_output = [ - item.numpy() - for batch in checkpoint_output - for item in batch - ] + [np.squeeze(vol) for vol in checkpoint_output] metric = dice_metric.aggregate().detach().item() dice_metric.reset() diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index ed24b92e..3efbdeca 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -994,7 +994,7 @@ def _display_results(self, images, names, complete_missing=False): data=images[1], name=names[1], colormap="inferno" ) layer_labels = self._viewer.add_labels( - data=images[2].astype(np.uint16), name=names[2] + data=images[2], name=names[2] ) self.result_layers += [layer_output, layer_image, layer_labels] self._viewer.grid.enabled = True @@ -1018,7 +1018,7 @@ def _display_results(self, images, names, complete_missing=False): self.result_layers[1] = layer_image else: layer_labels = self._viewer.add_labels( - data=images[i].astype(np.uint16), name=names[i] + data=images[i], name=names[i] ) self.result_layers[2] = layer_labels self.result_layers[i].data = images[i] From 563bd8c5f9a3d0d451ab3e9c6806799d56cb2bb9 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 10:27:45 +0200 Subject: [PATCH 030/250] Choose device UI --- .../code_models/model_framework.py | 26 ++++++++++++++++++- .../code_plugins/plugin_model_inference.py | 4 ++- .../code_plugins/plugin_model_training.py | 10 +++++-- 3 files changed, 36 insertions(+), 4 deletions(-) diff --git a/napari_cellseg3d/code_models/model_framework.py b/napari_cellseg3d/code_models/model_framework.py index ee9834c2..9a3b6d39 100644 --- a/napari_cellseg3d/code_models/model_framework.py +++ b/napari_cellseg3d/code_models/model_framework.py @@ -90,6 +90,16 @@ def __init__( "Load custom weights", self._toggle_weights_path, self ) + available_devices = ["CPU"] + [ + f"GPU {i}" for i in range(torch.cuda.device_count()) + ] + self.device_choice = ui.DropdownMenu( + available_devices, + parent=self, + text_label="Device", + ) + self.device_choice.tooltips = "Choose the device to use for training.\nIf you have a GPU, it is recommended to use it" + ################################################### # status report docked widget @@ -298,9 +308,23 @@ def _load_weights_path(self): ) self._update_weights_path(file) + def check_device_choice(self): + choice = self.device_choice.currentText() + if choice == "CPU": + device = "cpu" + elif "GPU" in choice: + i = int(choice.split(" ")[1]) + device = f"cuda:{i}" + else: + device = self.get_device() + logger.debug(f"DEVICE choice : {device}") + return device + @staticmethod def get_device(show=True): - """Automatically discovers any cuda device and uses it for tensor operations. + """ + Tries to use the device specified by user and uses it for tensor operations. + If not available, automatically discovers any cuda device. If none is available (CUDA not installed), uses cpu instead.""" device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if show: diff --git a/napari_cellseg3d/code_plugins/plugin_model_inference.py b/napari_cellseg3d/code_plugins/plugin_model_inference.py index c9dfbc10..7b97f07b 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_inference.py +++ b/napari_cellseg3d/code_plugins/plugin_model_inference.py @@ -447,6 +447,8 @@ def _build(self): self.window_infer_box, self.window_infer_params, self.keep_data_on_cpu_box, + self.device_choice.label, + self.device_choice, ], ) self.window_infer_params.setVisible(False) @@ -786,7 +788,7 @@ def _set_worker_config(self) -> config.InferenceWorkerConfig: window_config = config.SlidingWindowConfig() self.worker_config = config.InferenceWorkerConfig( - device=self.get_device(), + device=self.check_device_choice(), model_info=self.model_info, weights_config=self.weights_config, results_path=self.results_path, diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 3efbdeca..ff1c00a0 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -536,6 +536,11 @@ def _build(self): self.validation_percent_choice.container, data_tab.layout, ) + ui.GroupedWidget.create_single_widget_group( + "Device", + self.device_choice, + data_tab.layout, + ) ################ ui.add_blank(self, data_tab.layout) ################ @@ -692,7 +697,8 @@ def _build(self): ui.LEFT_AL, ) - self.container_seed.setVisible(False) + # self.container_seed.setVisible(False) + self.use_deterministic_choice.setChecked(True) seed_w.setLayout(seed_l) train_tab.layout.addWidget(seed_w) @@ -883,7 +889,7 @@ def _set_worker_config(self) -> config.TrainingWorkerConfig: logger.debug("Loading config...") self.worker_config = config.TrainingWorkerConfig( - device=self.get_device(), + device=self.check_device_choice(), model_info=model_config, weights_info=self.weights_config, train_data_dict=self.data, From 94e22cad75d929769c8910507e84d31f2613277f Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 10:41:11 +0200 Subject: [PATCH 031/250] Update worker_training.py Fixed device --- napari_cellseg3d/code_models/worker_training.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index e7495f8c..b2d1554b 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -521,9 +521,10 @@ def get_loader_func(num_samples): self.warn(warn) self._weight_error = True - if self.config.device.type == "cuda": + if "cuda" in self.config.device: + device_id = self.config.device.split(":")[-1] self.log("\nUsing GPU :") - self.log(torch.cuda.get_device_name(0)) + self.log(torch.cuda.get_device_name(int(device_id))) else: self.log("Using CPU") From 5f283e3006b7b5acbb5711986585f4f51b86a95b Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 10:43:36 +0200 Subject: [PATCH 032/250] Update interface.py Update name of utils when called by quick menu --- napari_cellseg3d/interface.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/interface.py b/napari_cellseg3d/interface.py index de856909..30d6f0de 100644 --- a/napari_cellseg3d/interface.py +++ b/napari_cellseg3d/interface.py @@ -203,7 +203,7 @@ def show_utils_menu(self, widget, event): if action == possible_action: text = possible_action.text().split(": ")[1] widget = UTILITIES_WIDGETS[text](widget._viewer) - widget._viewer.window.add_dock_widget(widget) + widget._viewer.window.add_dock_widget(widget, name=text) ############## From 7d4871f9eef5199c2d99c02613600b98e2e6a721 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 10:47:54 +0200 Subject: [PATCH 033/250] Update model_framework.py --- napari_cellseg3d/code_models/model_framework.py | 1 + 1 file changed, 1 insertion(+) diff --git a/napari_cellseg3d/code_models/model_framework.py b/napari_cellseg3d/code_models/model_framework.py index 9a3b6d39..4e0cdfe4 100644 --- a/napari_cellseg3d/code_models/model_framework.py +++ b/napari_cellseg3d/code_models/model_framework.py @@ -99,6 +99,7 @@ def __init__( text_label="Device", ) self.device_choice.tooltips = "Choose the device to use for training.\nIf you have a GPU, it is recommended to use it" + self.device_choice.setCurrentIndex(len(available_devices) - 1) ################################################### # status report docked widget From e0dfc5afde3af4199d744d4742fd4e3412338171 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 10:48:58 +0200 Subject: [PATCH 034/250] Update model_SwinUNetR.py --- napari_cellseg3d/code_models/models/model_SwinUNetR.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/models/model_SwinUNetR.py b/napari_cellseg3d/code_models/models/model_SwinUNetR.py index 34502a12..dbe3075d 100644 --- a/napari_cellseg3d/code_models/models/model_SwinUNetR.py +++ b/napari_cellseg3d/code_models/models/model_SwinUNetR.py @@ -12,7 +12,7 @@ class SwinUNETR_(SwinUNETR): def __init__( self, in_channels=1, - out_channels=1, + out_channels=2, input_img_size=128, use_checkpoint=True, **kwargs, From 932a843fc450b567fdcaae536f5277b5ebf418c0 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 10:51:15 +0200 Subject: [PATCH 035/250] Update worker_training.py --- napari_cellseg3d/code_models/worker_training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index b2d1554b..fcae2aaa 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -530,7 +530,7 @@ def get_loader_func(num_samples): self.log_parameters() - device = self.config.device + device = torch.device(self.config.device) # if model_name == "test": # self.quit() From 899b3bd119086dbbab9df56b745eafb0d348e6e0 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 11:02:46 +0200 Subject: [PATCH 036/250] Update model_SwinUNetR.py --- napari_cellseg3d/code_models/models/model_SwinUNetR.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/code_models/models/model_SwinUNetR.py b/napari_cellseg3d/code_models/models/model_SwinUNetR.py index dbe3075d..0dbf0be5 100644 --- a/napari_cellseg3d/code_models/models/model_SwinUNetR.py +++ b/napari_cellseg3d/code_models/models/model_SwinUNetR.py @@ -12,8 +12,8 @@ class SwinUNETR_(SwinUNETR): def __init__( self, in_channels=1, - out_channels=2, - input_img_size=128, + out_channels=1, + input_img_size=(64, 64, 64), use_checkpoint=True, **kwargs, ): @@ -24,6 +24,9 @@ def __init__( out_channels=out_channels, feature_size=48, use_checkpoint=use_checkpoint, + drop_rate=0.5, + attn_drop_rate=0.5, + use_v2=True, **kwargs, ) except TypeError as e: @@ -34,6 +37,9 @@ def __init__( out_channels=1, feature_size=48, use_checkpoint=use_checkpoint, + drop_rate=0.5, + attn_drop_rate=0.5, + use_v2=True, ) # def forward(self, x_in): From f109889e0037a5c4f4b256b5875c2d50346358f7 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 11:07:38 +0200 Subject: [PATCH 037/250] Update worker_training.py --- napari_cellseg3d/code_models/worker_training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index fcae2aaa..2720a348 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -666,7 +666,7 @@ def get_loader_func(num_samples): .astype(np.uint16), ] ) - [np.squeeze(vol) for vol in checkpoint_output] + # [np.squeeze(vol) for vol in checkpoint_output] metric = dice_metric.aggregate().detach().item() dice_metric.reset() From 2eb0624f147c2e1242535b3e98a6464f0fb31343 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 11:18:44 +0200 Subject: [PATCH 038/250] Fix training volumes --- .../code_models/worker_training.py | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 2720a348..58550fb5 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -657,16 +657,19 @@ def get_loader_func(num_samples): checkpoint_output.append( [ - val_outputs[0].detach().cpu().numpy(), - val_inputs[0].detach().cpu().numpy(), - val_labels[0] - .detach() - .cpu() - .numpy() - .astype(np.uint16), + val_outputs[0].detach().cpu(), + val_inputs[0].detach().cpu(), + val_labels[0].detach().cpu(), ] ) - # [np.squeeze(vol) for vol in checkpoint_output] + checkpoint_output = [ + item.numpy() + for batch in checkpoint_output + for item in batch + ] + checkpoint_output[2] = checkpoint_output[2].astype( + np.uint16 + ) metric = dice_metric.aggregate().detach().item() dice_metric.reset() From 30d333636e2503e8a43844d8f254d978c159c065 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 11:46:45 +0200 Subject: [PATCH 039/250] Dice update --- napari_cellseg3d/code_models/worker_training.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 58550fb5..9a438a22 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -484,9 +484,7 @@ def get_loader_func(num_samples): patience=self.config.scheduler_patience, verbose=VERBOSE_SCHEDULER, ) - dice_metric = DiceMetric( - include_background=False, reduction="mean" - ) + dice_metric = DiceMetric(include_background=True, reduction="mean") best_metric = -1 best_metric_epoch = -1 @@ -664,8 +662,8 @@ def get_loader_func(num_samples): ) checkpoint_output = [ item.numpy() - for batch in checkpoint_output - for item in batch + for channel in checkpoint_output + for item in channel ] checkpoint_output[2] = checkpoint_output[2].astype( np.uint16 From 649a96620f9cc8bdb5a79e97eb55e93ba9d18fc6 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 11:49:18 +0200 Subject: [PATCH 040/250] Disable checkpoint plot save --- .../code_plugins/plugin_model_training.py | 26 +++++++++---------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index ff1c00a0..528d06e7 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -1169,19 +1169,19 @@ def plot_loss(self, loss, dice_metric): ) self.canvas.draw_idle() - plot_path = self.worker_config.results_path_folder / Path( - "../Loss_plots" - ) - Path(plot_path).mkdir(parents=True, exist_ok=True) - - if self.canvas is not None: - self.canvas.figure.savefig( - str( - plot_path - / f"checkpoint_metric_plots_{utils.get_date_time()}.png" - ), - format="png", - ) + # plot_path = self.worker_config.results_path_folder / Path( + # "../Loss_plots" + # ) + # Path(plot_path).mkdir(parents=True, exist_ok=True) + # + # if self.canvas is not None: + # self.canvas.figure.savefig( + # str( + # plot_path + # / f"checkpoint_metric_plots_{utils.get_date_time()}.png" + # ), + # format="png", + # ) def update_loss_plot(self, loss, metric): """ From e232a30668ab08cc952a54b1bbb739fbef4d67ec Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 11:54:49 +0200 Subject: [PATCH 041/250] Update worker_training.py --- napari_cellseg3d/code_models/worker_training.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 9a438a22..b7b581af 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -484,7 +484,9 @@ def get_loader_func(num_samples): patience=self.config.scheduler_patience, verbose=VERBOSE_SCHEDULER, ) - dice_metric = DiceMetric(include_background=True, reduction="mean") + dice_metric = DiceMetric( + include_background=True, reduction="mean", ignore_empty=False + ) best_metric = -1 best_metric_epoch = -1 @@ -650,6 +652,13 @@ def get_loader_func(num_samples): # logger.debug(len(val_outputs)) # logger.debug(len(val_labels)) + dice_test = [ + utils.dice_coeff(i, j) + for i, j in zip(val_outputs, val_labels) + ] + logger.debug( + f"TEST VALIDATION Dice score : {dice_test}" + ) dice_metric(y_pred=val_outputs, y=val_labels) From e55f2ecd776a270505dce62e93be80bf3af0881a Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 11:57:06 +0200 Subject: [PATCH 042/250] Restore as_discrete --- napari_cellseg3d/code_models/worker_training.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index b7b581af..6e9bc85b 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -17,6 +17,7 @@ from monai.inferers import sliding_window_inference from monai.metrics import DiceMetric from monai.transforms import ( + AsDiscrete, Compose, EnsureChannelFirstd, EnsureType, @@ -637,8 +638,8 @@ def get_loader_func(num_samples): # TODO : more parameters/flexibility post_pred = Compose( - # AsDiscrete(threshold=0.6), # needed ? - EnsureType() + AsDiscrete(threshold=0.5), # needed ? + EnsureType(), ) # post_label = EnsureType() From aab474a0c7679ec3dcdea972f2001054ecf76310 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 12:00:26 +0200 Subject: [PATCH 043/250] Update worker_training.py --- napari_cellseg3d/code_models/worker_training.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 6e9bc85b..96df980c 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -653,12 +653,14 @@ def get_loader_func(num_samples): # logger.debug(len(val_outputs)) # logger.debug(len(val_labels)) - dice_test = [ - utils.dice_coeff(i, j) - for i, j in zip(val_outputs, val_labels) - ] + dice_test = np.array( + [ + utils.dice_coeff(i, j) + for i, j in zip(val_outputs, val_labels) + ] + ) logger.debug( - f"TEST VALIDATION Dice score : {dice_test}" + f"TEST VALIDATION Dice score : {dice_test.mean()}" ) dice_metric(y_pred=val_outputs, y=val_labels) From da1f1493852386e315f73e0f73d17062ed86acbd Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 12:01:49 +0200 Subject: [PATCH 044/250] Update worker_training.py --- napari_cellseg3d/code_models/worker_training.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 96df980c..1a1661ad 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -643,6 +643,7 @@ def get_loader_func(num_samples): ) # post_label = EnsureType() + output_raw = [t for t in pred] val_outputs = [ post_pred(res_tensor) for res_tensor in pred ] @@ -667,7 +668,7 @@ def get_loader_func(num_samples): checkpoint_output.append( [ - val_outputs[0].detach().cpu(), + output_raw[0].detach().cpu(), val_inputs[0].detach().cpu(), val_labels[0].detach().cpu(), ] From 10e5991515e74bd0513b7318e592d9684fc7f9ff Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 12:12:54 +0200 Subject: [PATCH 045/250] Enforce tensor range [0,1] --- napari_cellseg3d/code_models/worker_inference.py | 14 ++++++++++++-- napari_cellseg3d/code_models/worker_training.py | 10 +++++++--- napari_cellseg3d/code_models/workers_utils.py | 10 ++++++++++ 3 files changed, 29 insertions(+), 5 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index f6ad6fbe..10b72da4 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -34,6 +34,7 @@ ONNXModelWrapper, QuantileNormalization, QuantileNormalizationd, + RemapTensor, WeightsDownloader, ) @@ -715,11 +716,20 @@ def inference(self): # ) if not post_process_config.thresholding.enabled: - post_process_transforms = EnsureType() + post_process_transforms = Compose( + [ + RemapTensor(new_max=1.0, new_min=0.0), + EnsureType(), + ] + ) else: t = post_process_config.thresholding.threshold_value post_process_transforms = Compose( - AsDiscrete(threshold=t), EnsureType() + [ + RemapTensor(new_max=1.0, new_min=0.0), + AsDiscrete(threshold=t), + EnsureType(), + ] ) is_folder = self.config.images_filepaths is not None diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 1a1661ad..139503ef 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -44,6 +44,7 @@ PRETRAINED_WEIGHTS_DIR, LogSignal, QuantileNormalizationd, + RemapTensor, TrainingReport, WeightsDownloader, ) @@ -638,12 +639,15 @@ def get_loader_func(num_samples): # TODO : more parameters/flexibility post_pred = Compose( - AsDiscrete(threshold=0.5), # needed ? - EnsureType(), + [ + RemapTensor(new_max=1, new_min=0), + AsDiscrete(threshold=0.5), # needed ? + EnsureType(), + ] ) # post_label = EnsureType() - output_raw = [t for t in pred] + output_raw = [RemapTensor(0, 1)(t) for t in pred] val_outputs = [ post_pred(res_tensor) for res_tensor in pred ] diff --git a/napari_cellseg3d/code_models/workers_utils.py b/napari_cellseg3d/code_models/workers_utils.py index 923bc727..8427d683 100644 --- a/napari_cellseg3d/code_models/workers_utils.py +++ b/napari_cellseg3d/code_models/workers_utils.py @@ -203,6 +203,16 @@ def __call__(self, img): return utils.quantile_normalization(img) +class RemapTensor(Transform): + def __init__(self, new_max, new_min): + super().__init__() + self.max = new_max + self.min = new_min + + def __call__(self, img): + return utils.remap_image(img, new_max=self.max, new_min=self.min) + + @dataclass class InferenceResult: """Class to record results of a segmentation job""" From 6a18d29667eea685c9181a90dc3050a1a7527e8e Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 12:22:32 +0200 Subject: [PATCH 046/250] Add model output and labels to training view --- .../code_models/worker_training.py | 12 +++++---- .../code_plugins/plugin_model_training.py | 26 +++++++++++++++---- 2 files changed, 28 insertions(+), 10 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 139503ef..9b767afe 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -44,7 +44,6 @@ PRETRAINED_WEIGHTS_DIR, LogSignal, QuantileNormalizationd, - RemapTensor, TrainingReport, WeightsDownloader, ) @@ -640,14 +639,16 @@ def get_loader_func(num_samples): # TODO : more parameters/flexibility post_pred = Compose( [ - RemapTensor(new_max=1, new_min=0), + # RemapTensor(new_max=1, new_min=0), AsDiscrete(threshold=0.5), # needed ? EnsureType(), ] ) # post_label = EnsureType() - output_raw = [RemapTensor(0, 1)(t) for t in pred] + # output_raw = [RemapTensor(0, 1)(t) for t in pred] + output_raw = pred + val_outputs = [ post_pred(res_tensor) for res_tensor in pred ] @@ -658,7 +659,7 @@ def get_loader_func(num_samples): # logger.debug(len(val_outputs)) # logger.debug(len(val_labels)) - dice_test = np.array( + dice_test = np.array( # TODO(cyril): remove [ utils.dice_coeff(i, j) for i, j in zip(val_outputs, val_labels) @@ -673,6 +674,7 @@ def get_loader_func(num_samples): checkpoint_output.append( [ output_raw[0].detach().cpu(), + val_outputs[0].detach().cpu(), val_inputs[0].detach().cpu(), val_labels[0].detach().cpu(), ] @@ -682,7 +684,7 @@ def get_loader_func(num_samples): for channel in checkpoint_output for item in channel ] - checkpoint_output[2] = checkpoint_output[2].astype( + checkpoint_output[3] = checkpoint_output[2].astype( np.uint16 ) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 528d06e7..c0a17226 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -996,13 +996,21 @@ def _display_results(self, images, names, complete_missing=False): layer_output = self._viewer.add_image( data=images[0], name=names[0], colormap="turbo" ) + layer_output_discrete = self._viewer.add_image( + data=images[1], name=names[1], colormap="bop orange" + ) layer_image = self._viewer.add_image( - data=images[1], name=names[1], colormap="inferno" + data=images[2], name=names[2], colormap="inferno" ) layer_labels = self._viewer.add_labels( - data=images[2], name=names[2] + data=images[3], name=names[3] ) - self.result_layers += [layer_output, layer_image, layer_labels] + self.result_layers += [ + layer_output, + layer_output_discrete, + layer_image, + layer_labels, + ] self._viewer.grid.enabled = True self._viewer.dims.ndisplay = 3 self._viewer.reset_view() @@ -1018,15 +1026,22 @@ def _display_results(self, images, names, complete_missing=False): ) self.result_layers[0] = layer_output elif i == 1: + layer_output_discrete = self._viewer.add_image( + data=images[i], + name=names[i], + colormap="bop orange", + ) + self.result_layers[1] = layer_output_discrete + elif i == 2: layer_image = self._viewer.add_image( data=images[i], name=names[i], colormap="inferno" ) - self.result_layers[1] = layer_image + self.result_layers[2] = layer_image else: layer_labels = self._viewer.add_labels( data=images[i], name=names[i] ) - self.result_layers[2] = layer_labels + self.result_layers[3] = layer_labels self.result_layers[i].data = images[i] self.result_layers[i].refresh() @@ -1042,6 +1057,7 @@ def on_yield(self, report: TrainingReport): try: layer_names = [ "Validation output", + "Validation output (discrete)", "Validation image", "Validation labels", ] From 4490b019686b8e37ecfafdea80164e56a1e39bf1 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 12:23:57 +0200 Subject: [PATCH 047/250] Update worker_training.py --- napari_cellseg3d/code_models/worker_training.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 9b767afe..e11cb1d7 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -44,6 +44,7 @@ PRETRAINED_WEIGHTS_DIR, LogSignal, QuantileNormalizationd, + RemapTensor, TrainingReport, WeightsDownloader, ) @@ -639,7 +640,7 @@ def get_loader_func(num_samples): # TODO : more parameters/flexibility post_pred = Compose( [ - # RemapTensor(new_max=1, new_min=0), + RemapTensor(new_max=1, new_min=0), AsDiscrete(threshold=0.5), # needed ? EnsureType(), ] From 7e09af2a48b73815f7dedd82c26db21ff357ca7f Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 12:29:30 +0200 Subject: [PATCH 048/250] Update worker_training.py --- napari_cellseg3d/code_models/worker_training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index e11cb1d7..7f24f906 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -685,7 +685,7 @@ def get_loader_func(num_samples): for channel in checkpoint_output for item in channel ] - checkpoint_output[3] = checkpoint_output[2].astype( + checkpoint_output[3] = checkpoint_output[3].astype( np.uint16 ) From 4f176303ded09e72232f1cbd92d02d88ea628622 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 13:49:52 +0200 Subject: [PATCH 049/250] Config + disable remap in inference --- napari_cellseg3d/code_models/worker_inference.py | 5 ++--- napari_cellseg3d/code_plugins/plugin_model_training.py | 2 +- napari_cellseg3d/config.py | 2 +- 3 files changed, 4 insertions(+), 5 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 10b72da4..31399ef9 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -34,7 +34,6 @@ ONNXModelWrapper, QuantileNormalization, QuantileNormalizationd, - RemapTensor, WeightsDownloader, ) @@ -718,7 +717,7 @@ def inference(self): if not post_process_config.thresholding.enabled: post_process_transforms = Compose( [ - RemapTensor(new_max=1.0, new_min=0.0), + # RemapTensor(new_max=1.0, new_min=0.0), EnsureType(), ] ) @@ -726,7 +725,7 @@ def inference(self): t = post_process_config.thresholding.threshold_value post_process_transforms = Compose( [ - RemapTensor(new_max=1.0, new_min=0.0), + # RemapTensor(new_max=1.0, new_min=0.0), AsDiscrete(threshold=t), EnsureType(), ] diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index c0a17226..b22b3f52 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -186,7 +186,7 @@ def __init__( self.epoch_choice = ui.IntIncrementCounter( lower=2, - upper=200, + upper=999, default=self.default_config.max_epochs, text_label="Number of epochs : ", ) diff --git a/napari_cellseg3d/config.py b/napari_cellseg3d/config.py index af42d779..b05f7ac7 100644 --- a/napari_cellseg3d/config.py +++ b/napari_cellseg3d/config.py @@ -247,7 +247,7 @@ class TrainingWorkerConfig: weights_info: WeightsInfo = None train_data_dict: dict = None validation_percent: float = 0.8 - max_epochs: int = 5 + max_epochs: int = 50 loss_function: callable = None learning_rate: np.float64 = 1e-3 scheduler_patience: int = 10 From d3315ddb9ba2310e5872b51f9a769210f37180cc Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 14:21:21 +0200 Subject: [PATCH 050/250] fix remap --- napari_cellseg3d/code_models/worker_inference.py | 5 +++-- napari_cellseg3d/code_models/worker_training.py | 9 ++++++--- 2 files changed, 9 insertions(+), 5 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 31399ef9..10b72da4 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -34,6 +34,7 @@ ONNXModelWrapper, QuantileNormalization, QuantileNormalizationd, + RemapTensor, WeightsDownloader, ) @@ -717,7 +718,7 @@ def inference(self): if not post_process_config.thresholding.enabled: post_process_transforms = Compose( [ - # RemapTensor(new_max=1.0, new_min=0.0), + RemapTensor(new_max=1.0, new_min=0.0), EnsureType(), ] ) @@ -725,7 +726,7 @@ def inference(self): t = post_process_config.thresholding.threshold_value post_process_transforms = Compose( [ - # RemapTensor(new_max=1.0, new_min=0.0), + RemapTensor(new_max=1.0, new_min=0.0), AsDiscrete(threshold=t), EnsureType(), ] diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 7f24f906..8baddb7c 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -641,14 +641,17 @@ def get_loader_func(num_samples): post_pred = Compose( [ RemapTensor(new_max=1, new_min=0), - AsDiscrete(threshold=0.5), # needed ? + AsDiscrete(threshold=0.25), # needed ? EnsureType(), ] ) # post_label = EnsureType() - # output_raw = [RemapTensor(0, 1)(t) for t in pred] - output_raw = pred + output_raw = [ + RemapTensor(new_max=1, new_min=0)(t) + for t in pred + ] + # output_raw = pred val_outputs = [ post_pred(res_tensor) for res_tensor in pred From 7b398d558a8621346b06f89daf8f601997952651 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 14:32:06 +0200 Subject: [PATCH 051/250] Usage QoL improvements --- napari_cellseg3d/code_plugins/plugin_crop.py | 1 + napari_cellseg3d/code_plugins/plugin_model_training.py | 3 ++- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_plugins/plugin_crop.py b/napari_cellseg3d/code_plugins/plugin_crop.py index df65ac5e..3ff46261 100644 --- a/napari_cellseg3d/code_plugins/plugin_crop.py +++ b/napari_cellseg3d/code_plugins/plugin_crop.py @@ -312,6 +312,7 @@ def _start(self): vw = self._viewer vw.dims.ndisplay = 3 + vw.grid.enabled = False vw.scale_bar.visible = True if self.aniso_widgets.enabled(): diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index b22b3f52..f155ad7a 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -916,6 +916,7 @@ def on_start(self): self.remove_docked_widgets() self.display_status_report() + self._remove_result_layers() self.log.print_and_log(f"Worker started at {utils.get_time()}") self.log.print_and_log("\nWorker is running...") @@ -997,7 +998,7 @@ def _display_results(self, images, names, complete_missing=False): data=images[0], name=names[0], colormap="turbo" ) layer_output_discrete = self._viewer.add_image( - data=images[1], name=names[1], colormap="bop orange" + data=images[1], name=names[1], colormap="bop blue" ) layer_image = self._viewer.add_image( data=images[2], name=names[2], colormap="inferno" From bf7c7161d7112fa64e81434c596327e0394dbf6f Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 15:08:37 +0200 Subject: [PATCH 052/250] Save w/ loss name and epochs --- napari_cellseg3d/code_plugins/plugin_model_training.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index f155ad7a..ff2c8631 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -879,7 +879,11 @@ def _set_worker_config(self) -> config.TrainingWorkerConfig: validation_percent = self.validation_percent_choice.slider_value / 100 results_path_folder = Path( - self.results_path + f"/{model_config.name}_{utils.get_date_time()}" + self.results_path + + f"/{model_config.name}_" + + f"{self.loss_choice.currentText()}_" + + f"{self.epoch_choice.value()}e_" + + f"{utils.get_date_time()}" ) Path(results_path_folder).mkdir( parents=True, exist_ok=False From 496d25622bfb4e2d28e74c4af7abf02d414765d1 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 15:26:33 +0200 Subject: [PATCH 053/250] Update worker_inference.py --- napari_cellseg3d/code_models/worker_inference.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 10b72da4..0244e4c5 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -130,7 +130,7 @@ def raise_error(self, exception, msg): self.error_signal.emit(exception, msg) self.errored.emit(exception) - yield exception + raise exception # self.quit() def log_parameters(self): From ccc693f820477ab0ea1f24edffb552058c460ec6 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 15:28:47 +0200 Subject: [PATCH 054/250] Update worker_inference.py --- napari_cellseg3d/code_models/worker_inference.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 0244e4c5..10b72da4 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -130,7 +130,7 @@ def raise_error(self, exception, msg): self.error_signal.emit(exception, msg) self.errored.emit(exception) - raise exception + yield exception # self.quit() def log_parameters(self): From e283bafed3dd2bd174f74e08d25cb38ebc73975d Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 15:40:47 +0200 Subject: [PATCH 055/250] Change threshold for Swin --- napari_cellseg3d/code_models/worker_inference.py | 6 ++++-- napari_cellseg3d/code_models/worker_training.py | 6 ++++-- napari_cellseg3d/code_models/workers_utils.py | 9 +++++++++ 3 files changed, 17 insertions(+), 4 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 10b72da4..6522755a 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -9,7 +9,7 @@ from monai.inferers import sliding_window_inference from monai.transforms import ( AddChannel, - AsDiscrete, + # AsDiscrete, Compose, EnsureChannelFirstd, EnsureType, @@ -35,6 +35,7 @@ QuantileNormalization, QuantileNormalizationd, RemapTensor, + Threshold, WeightsDownloader, ) @@ -727,7 +728,8 @@ def inference(self): post_process_transforms = Compose( [ RemapTensor(new_max=1.0, new_min=0.0), - AsDiscrete(threshold=t), + # AsDiscrete(threshold=t), + Threshold(threshold=t), EnsureType(), ] ) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 8baddb7c..c558a085 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -17,7 +17,7 @@ from monai.inferers import sliding_window_inference from monai.metrics import DiceMetric from monai.transforms import ( - AsDiscrete, + # AsDiscrete, Compose, EnsureChannelFirstd, EnsureType, @@ -45,6 +45,7 @@ LogSignal, QuantileNormalizationd, RemapTensor, + Threshold, TrainingReport, WeightsDownloader, ) @@ -638,10 +639,11 @@ def get_loader_func(num_samples): labs = decollate_batch(val_labels) # TODO : more parameters/flexibility + post_pred = Compose( [ RemapTensor(new_max=1, new_min=0), - AsDiscrete(threshold=0.25), # needed ? + Threshold(threshold=0.5), EnsureType(), ] ) # diff --git a/napari_cellseg3d/code_models/workers_utils.py b/napari_cellseg3d/code_models/workers_utils.py index 8427d683..5efb93a0 100644 --- a/napari_cellseg3d/code_models/workers_utils.py +++ b/napari_cellseg3d/code_models/workers_utils.py @@ -213,6 +213,15 @@ def __call__(self, img): return utils.remap_image(img, new_max=self.max, new_min=self.min) +class Threshold(Transform): + def __init__(self, threshold=0.5): + super().__init__() + self.threshold = threshold + + def __call__(self, img): + return torch.where(img > self.threshold, 1, 0) + + @dataclass class InferenceResult: """Class to record results of a segmentation job""" From 5b6e049f9f0f0fb4c9381f0c18747eb3ccf84a2f Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 16:03:35 +0200 Subject: [PATCH 056/250] Docs + change norm in inference Normalize each window instead of whole image for memory issues --- docs/res/guides/training_module_guide.rst | 10 ++-------- napari_cellseg3d/code_models/worker_inference.py | 11 ++++++----- 2 files changed, 8 insertions(+), 13 deletions(-) diff --git a/docs/res/guides/training_module_guide.rst b/docs/res/guides/training_module_guide.rst index d910bc50..0a577b86 100644 --- a/docs/res/guides/training_module_guide.rst +++ b/docs/res/guides/training_module_guide.rst @@ -95,22 +95,16 @@ Function Reference ======================== ================================================================================================ Dice loss `Dice Loss from MONAI`_ with ``sigmoid=true`` Generalized Dice loss `Generalized dice Loss from MONAI`_ with ``sigmoid=true`` -Dice-CE loss `Dice-CE Loss from MONAI`_ with ``sigmoid=true`` +Dice-CE loss `Dice-CrossEntropy Loss from MONAI`_ with ``sigmoid=true`` Tversky loss `Tversky Loss from MONAI`_ with ``sigmoid=true`` ======================== ================================================================================================ - -.. Binary cross-entropy `Binary cross entropy (BCE) loss from PyTorch`_ - BCE with logits `BCE loss with logits from PyTorch`_ - .. _Dice Loss from MONAI: https://docs.monai.io/en/stable/losses.html#diceloss .. _Focal Loss from MONAI: https://docs.monai.io/en/stable/losses.html#focalloss .. _Dice-focal Loss from MONAI: https://docs.monai.io/en/stable/losses.html#dicefocalloss .. _Generalized dice Loss from MONAI: https://docs.monai.io/en/stable/losses.html#generalizeddiceloss -.. _Dice-CE Loss from MONAI: https://docs.monai.io/en/stable/losses.html#diceceloss +.. _Dice-CrossEntropy Loss from MONAI: https://docs.monai.io/en/stable/losses.html#diceceloss .. _Tversky Loss from MONAI: https://docs.monai.io/en/stable/losses.html#tverskyloss -.. _Binary cross entropy (BCE) loss from PyTorch: -.. _BCE loss with logits from PyTorch: Once you are ready, press the Start button to begin training. The module will automatically load your dataset, perform data augmentation if you chose to, select a CUDA device if one is present, and train the model. diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 6522755a..9ed0e4d2 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -33,7 +33,6 @@ LogSignal, ONNXModelWrapper, QuantileNormalization, - QuantileNormalizationd, RemapTensor, Threshold, WeightsDownloader, @@ -194,7 +193,7 @@ def load_folder(self): EnsureChannelFirstd(keys=["image"]), # Orientationd(keys=["image"], axcodes="PLI"), # anisotropic_transform, - QuantileNormalizationd(keys=["image"]), + # QuantileNormalizationd(keys=["image"]), EnsureTyped(keys=["image"]), ] ) @@ -204,7 +203,7 @@ def load_folder(self): LoadImaged(keys=["image"]), # AddChanneld(keys=["image"]), #already done EnsureChannelFirstd(keys=["image"]), - QuantileNormalizationd(keys=["image"]), + # QuantileNormalizationd(keys=["image"]), # Orientationd(keys=["image"], axcodes="PLI"), # anisotropic_transform, SpatialPadd(keys=["image"], spatial_size=pad), @@ -248,7 +247,7 @@ def load_layer(self): # anisotropic_transform, AddChannel(), # SpatialPad(spatial_size=pad), - QuantileNormalization(), + # QuantileNormalization(), AddChannel(), EnsureType(), ], @@ -263,7 +262,7 @@ def load_layer(self): ToTensor(), # anisotropic_transform, AddChannel(), - QuantileNormalization(), + # QuantileNormalization(), SpatialPad(spatial_size=pad), AddChannel(), EnsureType(), @@ -301,10 +300,12 @@ def model_output( # logger.debug(f"model : {model}") logger.debug(f"inputs shape : {inputs.shape}") logger.debug(f"inputs type : {inputs.dtype}") + normalizazion = QuantileNormalization() try: # outputs = model(inputs) def model_output_wrapper(inputs): + inputs = normalizazion(inputs) result = model(inputs) return post_process_transforms(result) From 7696f187db6f508b6d96b637d298045497bcc4ab Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 16:07:50 +0200 Subject: [PATCH 057/250] Update worker_inference.py --- napari_cellseg3d/code_models/worker_inference.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 9ed0e4d2..1790feeb 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -33,6 +33,7 @@ LogSignal, ONNXModelWrapper, QuantileNormalization, + QuantileNormalizationd, RemapTensor, Threshold, WeightsDownloader, @@ -193,7 +194,7 @@ def load_folder(self): EnsureChannelFirstd(keys=["image"]), # Orientationd(keys=["image"], axcodes="PLI"), # anisotropic_transform, - # QuantileNormalizationd(keys=["image"]), + QuantileNormalizationd(keys=["image"]), EnsureTyped(keys=["image"]), ] ) @@ -203,7 +204,7 @@ def load_folder(self): LoadImaged(keys=["image"]), # AddChanneld(keys=["image"]), #already done EnsureChannelFirstd(keys=["image"]), - # QuantileNormalizationd(keys=["image"]), + QuantileNormalizationd(keys=["image"]), # Orientationd(keys=["image"], axcodes="PLI"), # anisotropic_transform, SpatialPadd(keys=["image"], spatial_size=pad), @@ -247,7 +248,7 @@ def load_layer(self): # anisotropic_transform, AddChannel(), # SpatialPad(spatial_size=pad), - # QuantileNormalization(), + QuantileNormalization(), AddChannel(), EnsureType(), ], @@ -262,7 +263,7 @@ def load_layer(self): ToTensor(), # anisotropic_transform, AddChannel(), - # QuantileNormalization(), + QuantileNormalization(), SpatialPad(spatial_size=pad), AddChannel(), EnsureType(), @@ -300,12 +301,12 @@ def model_output( # logger.debug(f"model : {model}") logger.debug(f"inputs shape : {inputs.shape}") logger.debug(f"inputs type : {inputs.dtype}") - normalizazion = QuantileNormalization() + # normalization = QuantileNormalization() try: # outputs = model(inputs) def model_output_wrapper(inputs): - inputs = normalizazion(inputs) + # inputs = normalization(inputs) result = model(inputs) return post_process_transforms(result) From 4bdad2f67a3b0fea48c7c4370709980ac81c4d2b Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 16:13:30 +0200 Subject: [PATCH 058/250] Normalize before ToTensor to avoid OOM from torch --- napari_cellseg3d/code_models/worker_inference.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 1790feeb..0738ecd2 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -244,11 +244,11 @@ def load_layer(self): if self.config.sliding_window_config.is_enabled(): load_transforms = Compose( [ + QuantileNormalization(), ToTensor(), # anisotropic_transform, AddChannel(), # SpatialPad(spatial_size=pad), - QuantileNormalization(), AddChannel(), EnsureType(), ], @@ -260,10 +260,10 @@ def load_layer(self): pad = utils.get_padding_dim(dims_check) load_transforms = Compose( [ + QuantileNormalization(), ToTensor(), # anisotropic_transform, AddChannel(), - QuantileNormalization(), SpatialPad(spatial_size=pad), AddChannel(), EnsureType(), From abe207a88040c1effd93f363d758fb10fed8be32 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 16:37:53 +0200 Subject: [PATCH 059/250] Improve error handling for csv when aborting --- napari_cellseg3d/code_plugins/plugin_model_training.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index ff2c8631..29698785 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -949,8 +949,10 @@ def on_finish(self): self.log.print_and_log("Done") self.log.print_and_log("*" * 10) - - self._make_csv() + try: + self._make_csv() + except ValueError as e: + logger.warning(f"Error while saving CSV report: {e}") self.btn_start.setText("Start") [btn.setVisible(True) for btn in self.close_buttons] From 0dff8e2f5a84dd881f165385d08197cdb17a0d65 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 16:56:59 +0200 Subject: [PATCH 060/250] Show LR from scheduler and fix memory device print --- napari_cellseg3d/code_models/worker_training.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index c558a085..26013795 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -546,10 +546,10 @@ def get_loader_func(num_samples): if device.type == "cuda": self.log("Memory Usage:") alloc_mem = round( - torch.cuda.memory_allocated(0) / 1024**3, 1 + torch.cuda.memory_allocated(device_id) / 1024**3, 1 ) reserved_mem = round( - torch.cuda.memory_reserved(0) / 1024**3, 1 + torch.cuda.memory_reserved(device_id) / 1024**3, 1 ) self.log(f"Allocated: {alloc_mem}GB") self.log(f"Cached: {reserved_mem}GB") @@ -593,6 +593,9 @@ def get_loader_func(num_samples): self.log("Updating scheduler...") scheduler.step(epoch_loss) + self.log( + f"Current learning rate: {optimizer.param_groups[0]['lr']}" + ) checkpoint_output = [] eta = ( From 09cc3401a0795229c5a59ec3d4bca4c1e85dd88a Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 16:58:55 +0200 Subject: [PATCH 061/250] Update worker_training.py --- napari_cellseg3d/code_models/worker_training.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 26013795..64381749 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -546,10 +546,10 @@ def get_loader_func(num_samples): if device.type == "cuda": self.log("Memory Usage:") alloc_mem = round( - torch.cuda.memory_allocated(device_id) / 1024**3, 1 + torch.cuda.memory_allocated(device) / 1024**3, 1 ) reserved_mem = round( - torch.cuda.memory_reserved(device_id) / 1024**3, 1 + torch.cuda.memory_reserved(device) / 1024**3, 1 ) self.log(f"Allocated: {alloc_mem}GB") self.log(f"Cached: {reserved_mem}GB") From c3e671722cd9779b4794e132007d17a215dc501c Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 18:19:51 +0200 Subject: [PATCH 062/250] TODO cleanup --- .../code_models/instance_segmentation.py | 2 +- .../code_models/worker_training.py | 28 ++++++++----------- napari_cellseg3d/code_plugins/plugin_base.py | 7 ----- napari_cellseg3d/code_plugins/plugin_crop.py | 7 ++--- .../code_plugins/plugin_model_inference.py | 6 ++-- .../code_plugins/plugin_model_training.py | 6 ++-- .../code_plugins/plugin_review.py | 4 +-- napari_cellseg3d/interface.py | 2 +- 8 files changed, 23 insertions(+), 39 deletions(-) diff --git a/napari_cellseg3d/code_models/instance_segmentation.py b/napari_cellseg3d/code_models/instance_segmentation.py index 0e2c0871..3ffbdbb9 100644 --- a/napari_cellseg3d/code_models/instance_segmentation.py +++ b/napari_cellseg3d/code_models/instance_segmentation.py @@ -99,7 +99,7 @@ def run_method(self, image): def _make_list_from_channels( self, image - ): # TODO(cyril) : adapt to batch dimension + ): # TODO(cyril) : adapt to batch dimension (needed ?) if len(image.shape) > 4: raise ValueError( f"Image has {len(image.shape)} dimensions, but should have at most 4 dimensions (CHWD)" diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 64381749..047df9c1 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -282,9 +282,7 @@ def train(self): size = self.config.sample_size if do_sampling else check PADDING = utils.get_padding_dim(size) - model = model_class( # FIXME check if correct - input_img_size=PADDING, use_checkpoint=True - ) + model = model_class(input_img_size=PADDING, use_checkpoint=True) model = model.to(self.config.device) epoch_loss_values = [] @@ -570,7 +568,7 @@ def get_loader_func(num_samples): if outputs.shape[1] > 1: outputs = outputs[ :, 1:, :, : - ] # FIXME fix channel number + ] # TODO(cyril): adapt if additional channels if len(outputs.shape) < 4: outputs = outputs.unsqueeze(0) loss = self.config.loss_function(outputs, labels) @@ -632,17 +630,15 @@ def get_loader_func(num_samples): ) except Exception as e: self.raise_error(e, "Error during validation") + logger.debug( f"val_outputs shape : {val_outputs.shape}" ) # val_outputs = model(val_inputs) pred = decollate_batch(val_outputs) - labs = decollate_batch(val_labels) - # TODO : more parameters/flexibility - post_pred = Compose( [ RemapTensor(new_max=1, new_min=0), @@ -668,15 +664,15 @@ def get_loader_func(num_samples): # logger.debug(len(val_outputs)) # logger.debug(len(val_labels)) - dice_test = np.array( # TODO(cyril): remove - [ - utils.dice_coeff(i, j) - for i, j in zip(val_outputs, val_labels) - ] - ) - logger.debug( - f"TEST VALIDATION Dice score : {dice_test.mean()}" - ) + # dice_test = np.array( + # [ + # utils.dice_coeff(i, j) + # for i, j in zip(val_outputs, val_labels) + # ] + # ) + # logger.debug( + # f"TEST VALIDATION Dice score : {dice_test.mean()}" + # ) dice_metric(y_pred=val_outputs, y=val_labels) diff --git a/napari_cellseg3d/code_plugins/plugin_base.py b/napari_cellseg3d/code_plugins/plugin_base.py index 7ea5aa11..f369320b 100644 --- a/napari_cellseg3d/code_plugins/plugin_base.py +++ b/napari_cellseg3d/code_plugins/plugin_base.py @@ -220,13 +220,6 @@ def _build(self): """Method to be defined by children classes""" raise NotImplementedError("To be defined in child classes") - # def _show_filetype_choice(self): # TODO(cyril): remove? - # """Method to show/hide the filetype choice when "loading as folder" is (de)selected""" - # show = self.load_as_stack_choice.isChecked() - # if show is not None: - # self.filetype_choice.setVisible(show) - # # self.lbl_ft.setVisible(show) - def _show_file_dialog(self): """Open file dialog and process path for a single file""" # if self.load_as_stack_choice.isChecked(): diff --git a/napari_cellseg3d/code_plugins/plugin_crop.py b/napari_cellseg3d/code_plugins/plugin_crop.py index 3ff46261..c6e822d4 100644 --- a/napari_cellseg3d/code_plugins/plugin_crop.py +++ b/napari_cellseg3d/code_plugins/plugin_crop.py @@ -37,7 +37,7 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): """ super().__init__(viewer) - self.docked_widgets = [] # TODO add remove on close + self.docked_widgets = [] self.results_path = Path.home() / Path("cellseg3d/cropped") self.btn_start = ui.Button("Start", self._start) @@ -266,9 +266,6 @@ def _start(self): """Launches cropping process by loading the files from the chosen folders, and adds control widgets to the napari Viewer for moving the cropped volume. """ - # TODO maybe implement proper reset function so multiple runs can be done without closing napari - # maybe use singletons or make docked widgets attributes that are hidden upon opening - if not self._check_ready(): logger.warning("Please select at least one valid layer !") return @@ -355,7 +352,7 @@ def add_isotropic_layer( self, layer, colormap="inferno", - contrast_lim=(200, 1000), # TODO generalize ? + contrast_lim=(200, 1000), opacity=0.7, visible=True, ): diff --git a/napari_cellseg3d/code_plugins/plugin_model_inference.py b/napari_cellseg3d/code_plugins/plugin_model_inference.py index 7b97f07b..134e22f4 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_inference.py +++ b/napari_cellseg3d/code_plugins/plugin_model_inference.py @@ -124,9 +124,9 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): self.anisotropy_wdgt = ui.AnisotropyWidgets( self, - default_x=1.5, - default_y=1.5, - default_z=5, # TODO change default + default_x=1, + default_y=1, + default_z=1, ) # self.worker_config.post_process_config.zoom.zoom_values = [ diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 29698785..5c5185b2 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -167,10 +167,10 @@ def __init__( self.validation_values = [] # self.model_choice.setCurrentIndex(0) - ################### # TODO(cyril) : disable if we implement WNet training + ################### + # TODO(cyril) : disable if we implement WNet training wnet_index = self.model_choice.findText("WNet") self.model_choice.removeItem(wnet_index) - ################################ # interface @@ -911,7 +911,7 @@ def _set_worker_config(self) -> config.TrainingWorkerConfig: sample_size=patch_size, do_augmentation=self.augment_choice.isChecked(), deterministic_config=deterministic_config, - ) # TODO(cyril) continue to put params in config + ) return self.worker_config diff --git a/napari_cellseg3d/code_plugins/plugin_review.py b/napari_cellseg3d/code_plugins/plugin_review.py index 4e1a7e66..d3216436 100644 --- a/napari_cellseg3d/code_plugins/plugin_review.py +++ b/napari_cellseg3d/code_plugins/plugin_review.py @@ -144,9 +144,7 @@ def _build(self): # self._show_io_element(self.results_filewidget) self.results_filewidget.text_field.setText( - str( - Path.home() / Path("cellseg3d/review") - ) # TODO(cyril) : check proper behaviour + str(Path.home() / Path("cellseg3d/review")) ) csv_param_w.setLayout(csv_param_l) diff --git a/napari_cellseg3d/interface.py b/napari_cellseg3d/interface.py index 30d6f0de..de605cf6 100644 --- a/napari_cellseg3d/interface.py +++ b/napari_cellseg3d/interface.py @@ -1360,7 +1360,7 @@ def combine_blocks( # TODO FIXME PLEASE this is a horrible design b=11, ): """Combines two QWidget objects and puts them side by side (first on the left/top and second on the right/bottom depending on "horizontal") - Weird argument names due the initial implementation of it. # TODO maybe fix arg names + Weird argument names due the initial implementation of it. # TODO maybe fix arg names or refactor Args: left_or_above (QWidget): First widget, to be added on the left/above of "second" From d29cdce9b87f22dd9cad94f4db54a103eb932509 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 22:12:21 +0200 Subject: [PATCH 063/250] Disable normalization for WNet --- napari_cellseg3d/code_models/worker_inference.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 0738ecd2..1ad24899 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -241,10 +241,16 @@ def load_layer(self): logger.debug(volume.shape) logger.debug(volume.dtype) + normalization = ( + QuantileNormalization() + if self.config.model_info.name != "WNet" + else lambda x: x + ) if self.config.sliding_window_config.is_enabled(): load_transforms = Compose( [ - QuantileNormalization(), + # QuantileNormalization(), + normalization, ToTensor(), # anisotropic_transform, AddChannel(), @@ -260,7 +266,8 @@ def load_layer(self): pad = utils.get_padding_dim(dims_check) load_transforms = Compose( [ - QuantileNormalization(), + # QuantileNormalization(), + normalization, ToTensor(), # anisotropic_transform, AddChannel(), From f33ccd8c432e00357ae134d1f134a2e6782dcf4d Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 20 Jul 2023 09:49:11 +0200 Subject: [PATCH 064/250] Fix loss function instantiation --- napari_cellseg3d/_tests/test_training.py | 11 ++++-- .../code_models/worker_training.py | 28 ++++++++++++- .../code_plugins/plugin_model_training.py | 39 ++++++------------- 3 files changed, 46 insertions(+), 32 deletions(-) diff --git a/napari_cellseg3d/_tests/test_training.py b/napari_cellseg3d/_tests/test_training.py index f76b6d23..0c54d36a 100644 --- a/napari_cellseg3d/_tests/test_training.py +++ b/napari_cellseg3d/_tests/test_training.py @@ -53,6 +53,14 @@ def test_update_loss_plot(make_napari_viewer_proxy): assert widget.train_loss_plot is not None +def test_check_matching_losses(): + plugin = Trainer(None) + config = plugin._set_worker_config() + worker = plugin._create_worker_from_config(config) + + assert plugin.loss_list == list(worker.loss_dict.keys()) + + def test_training(make_napari_viewer_proxy, qtbot): im_path = str(Path(__file__).resolve().parent / "res/test.tif") @@ -73,9 +81,6 @@ def test_training(make_napari_viewer_proxy, qtbot): assert widget.check_ready() - ################# - # Training is too long to test properly this way. Do not use on Github - ################# MODEL_LIST["test"] = TestModel widget.model_choice.addItem("test") widget.model_choice.setCurrentText("test") diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 047df9c1..20a98f9b 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -15,6 +15,12 @@ pad_list_data_collate, ) from monai.inferers import sliding_window_inference +from monai.losses import ( + DiceCELoss, + DiceLoss, + GeneralizedDiceLoss, + TverskyLoss, +) from monai.metrics import DiceMetric from monai.transforms import ( # AsDiscrete, @@ -125,6 +131,25 @@ def __init__( ####################################### self.downloader = WeightsDownloader() + self.loss_dict = { + "Dice": DiceLoss(sigmoid=True), + # "BCELoss": torch.nn.BCELoss(), # dev + # "BCELogits": torch.nn.BCEWithLogitsLoss(), + "Generalized Dice": GeneralizedDiceLoss(sigmoid=True), + "DiceCE": DiceCELoss(sigmoid=True, lambda_ce=0.5), + "Tversky": TverskyLoss(sigmoid=True), + # "Focal loss": FocalLoss(), + # "Dice-Focal loss": DiceFocalLoss(sigmoid=True, lambda_dice=0.5), + } + self.loss_function = None + + def set_loss_from_config(self): + try: + self.loss_function = self.loss_dict[self.config.loss_function] + except KeyError as e: + self.raise_error(e, "Loss function not found, aborting job") + return self.loss_function + def set_download_log(self, widget): self.downloader.log_widget = widget @@ -532,6 +557,7 @@ def get_loader_func(num_samples): self.log_parameters() device = torch.device(self.config.device) + self.set_loss_from_config() # if model_name == "test": # self.quit() @@ -571,7 +597,7 @@ def get_loader_func(num_samples): ] # TODO(cyril): adapt if additional channels if len(outputs.shape) < 4: outputs = outputs.unsqueeze(0) - loss = self.config.loss_function(outputs, labels) + loss = self.loss_function(outputs, labels) loss.backward() optimizer.step() epoch_loss += loss.detach().item() diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 5c5185b2..944b0402 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -15,14 +15,6 @@ if TYPE_CHECKING: import napari -# MONAI -from monai.losses import ( - DiceCELoss, - DiceLoss, - GeneralizedDiceLoss, - TverskyLoss, -) - # Qt from qtpy.QtWidgets import QSizePolicy @@ -139,17 +131,15 @@ def __init__( """Whether the worker should stop or not""" self.start_time = None - self.loss_dict = { - "Dice": DiceLoss(sigmoid=True), - # "BCELoss": torch.nn.BCELoss(), # dev - # "BCELogits": torch.nn.BCEWithLogitsLoss(), - "Generalized Dice": GeneralizedDiceLoss(sigmoid=True), - "DiceCE": DiceCELoss(sigmoid=True, lambda_ce=0.5), - "Tversky": TverskyLoss(sigmoid=True), - # "Focal loss": FocalLoss(), - # "Dice-Focal loss": DiceFocalLoss(sigmoid=True, lambda_dice=0.5), - } - """Dict of loss functions""" + self.loss_list = [ # MUST BE MATCHED WITH THE LOSS FUNCTIONS IN THE TRAINING WORKER DICT + "Dice", + "Generalized Dice", + "DiceCE", + "Tversky", + # "Focal loss", + # "Dice-Focal loss", + ] + """List of loss functions""" self.canvas = None """Canvas to plot loss and dice metric in""" @@ -192,10 +182,7 @@ def __init__( ) self.loss_choice = ui.DropdownMenu( - # sorted( - list( - self.loss_dict.keys(), - ), + self.loss_list, text_label="Loss function", ) self.lbl_loss_choice = self.loss_choice.label @@ -383,10 +370,6 @@ def _update_validation_choice(self): elif validation.maximum() < max_epoch: validation.setMaximum(max_epoch) - def get_loss(self, key): - """Getter for loss function selected by user""" - return self.loss_dict[key] - def _toggle_patch_dims(self): if self.patch_choice.isChecked(): [w.setVisible(True) for w in self.patch_size_widgets] @@ -899,7 +882,7 @@ def _set_worker_config(self) -> config.TrainingWorkerConfig: train_data_dict=self.data, validation_percent=validation_percent, max_epochs=self.epoch_choice.value(), - loss_function=self.get_loss(self.loss_choice.currentText()), + loss_function=self.loss_choice.currentText(), learning_rate=float(self.learning_rate_choice.currentText()), scheduler_patience=self.scheduler_patience_choice.value(), scheduler_factor=self.scheduler_factor_choice.slider_value, From 1ba48dab06a3c50afa0e2bd64acc9076b50817ff Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 20 Jul 2023 09:54:29 +0200 Subject: [PATCH 065/250] Update worker_inference.py --- napari_cellseg3d/code_models/worker_inference.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 1ad24899..279e04bf 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -241,11 +241,10 @@ def load_layer(self): logger.debug(volume.shape) logger.debug(volume.dtype) - normalization = ( - QuantileNormalization() - if self.config.model_info.name != "WNet" - else lambda x: x - ) + normalization = QuantileNormalization() + # if self.config.model_info.name != "WNet" + # else lambda x: x + # ) if self.config.sliding_window_config.is_enabled(): load_transforms = Compose( [ From 0f81dde58dd75f92b6e75d189f6cc30fc7a9ea1f Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 20 Jul 2023 09:55:35 +0200 Subject: [PATCH 066/250] Revert "Update worker_inference.py" This reverts commit 1ba48dab06a3c50afa0e2bd64acc9076b50817ff. --- napari_cellseg3d/code_models/worker_inference.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 279e04bf..1ad24899 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -241,10 +241,11 @@ def load_layer(self): logger.debug(volume.shape) logger.debug(volume.dtype) - normalization = QuantileNormalization() - # if self.config.model_info.name != "WNet" - # else lambda x: x - # ) + normalization = ( + QuantileNormalization() + if self.config.model_info.name != "WNet" + else lambda x: x + ) if self.config.sliding_window_config.is_enabled(): load_transforms = Compose( [ From 792b002f3cd4c3aa21117bf85f6effc8d3da4298 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 20 Jul 2023 11:22:55 +0200 Subject: [PATCH 067/250] Tooltip update --- napari_cellseg3d/code_models/model_framework.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/model_framework.py b/napari_cellseg3d/code_models/model_framework.py index 4e0cdfe4..585c53a0 100644 --- a/napari_cellseg3d/code_models/model_framework.py +++ b/napari_cellseg3d/code_models/model_framework.py @@ -337,7 +337,7 @@ def get_device(show=True): def empty_cuda_cache(self): """Empties the cuda cache if the device is a cuda device""" if self.get_device(show=False).type == "cuda": - logger.info("Attempting to empty cache...") + logger.info("Emptying cache...") torch.cuda.empty_cache() logger.info("Attempt complete : Cache emptied") From d2adae662c52a508e33062ff02d79c182e9fd7b5 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 20 Jul 2023 11:23:10 +0200 Subject: [PATCH 068/250] Test disabling smoothing --- napari_cellseg3d/code_models/worker_training.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 20a98f9b..774b4435 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -132,12 +132,12 @@ def __init__( self.downloader = WeightsDownloader() self.loss_dict = { - "Dice": DiceLoss(sigmoid=True), + "Dice": DiceLoss(sigmoid=True, smooth_nr=0.0), # "BCELoss": torch.nn.BCELoss(), # dev # "BCELogits": torch.nn.BCEWithLogitsLoss(), "Generalized Dice": GeneralizedDiceLoss(sigmoid=True), "DiceCE": DiceCELoss(sigmoid=True, lambda_ce=0.5), - "Tversky": TverskyLoss(sigmoid=True), + "Tversky": TverskyLoss(sigmoid=True, smooth_nr=0.0), # "Focal loss": FocalLoss(), # "Dice-Focal loss": DiceFocalLoss(sigmoid=True, lambda_dice=0.5), } From 41e3da7a0a62306741f83f23f6c4f2b59f54843f Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 20 Jul 2023 11:56:55 +0200 Subject: [PATCH 069/250] Remove smooth_nr --- napari_cellseg3d/code_models/worker_training.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 774b4435..20a98f9b 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -132,12 +132,12 @@ def __init__( self.downloader = WeightsDownloader() self.loss_dict = { - "Dice": DiceLoss(sigmoid=True, smooth_nr=0.0), + "Dice": DiceLoss(sigmoid=True), # "BCELoss": torch.nn.BCELoss(), # dev # "BCELogits": torch.nn.BCEWithLogitsLoss(), "Generalized Dice": GeneralizedDiceLoss(sigmoid=True), "DiceCE": DiceCELoss(sigmoid=True, lambda_ce=0.5), - "Tversky": TverskyLoss(sigmoid=True, smooth_nr=0.0), + "Tversky": TverskyLoss(sigmoid=True), # "Focal loss": FocalLoss(), # "Dice-Focal loss": DiceFocalLoss(sigmoid=True, lambda_dice=0.5), } From 6bb71bb39a3399b4ad7868b183943fbcf781a619 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Fri, 21 Jul 2023 15:57:02 +0200 Subject: [PATCH 070/250] Fix shape label update --- napari_cellseg3d/interface.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/napari_cellseg3d/interface.py b/napari_cellseg3d/interface.py index de605cf6..22f6a4a3 100644 --- a/napari_cellseg3d/interface.py +++ b/napari_cellseg3d/interface.py @@ -757,8 +757,6 @@ def __init__( ): super().__init__(parent=parent, fixed=False) self._viewer = viewer - - self.image = None self.layer_type = layer_type self.layer_list = DropdownMenu( @@ -789,11 +787,14 @@ def _update_tooltip(self): self.layer_list.setToolTip(self.layer_list.currentText()) def _update_description(self): - if self.layer_list.currentText() != "": - self.layer_description.setVisible(True) - shape_desc = f"Shape : {self.layer_data().shape}" - self.layer_description.setText(shape_desc) - else: + try: + if self.layer_list.currentText() != "": + self.layer_description.setVisible(True) + shape_desc = f"Shape : {self.layer_data().shape}" + self.layer_description.setText(shape_desc) + else: + self.layer_description.setVisible(False) + except KeyError: self.layer_description.setVisible(False) def _add_layer(self, event): From b1b7c81ada98e8adf7378bdb989b7453f5e9124c Mon Sep 17 00:00:00 2001 From: C-Achard Date: Fri, 21 Jul 2023 16:07:07 +0200 Subject: [PATCH 071/250] Update test_plugin_inference.py --- napari_cellseg3d/_tests/test_plugin_inference.py | 14 +++++--------- 1 file changed, 5 insertions(+), 9 deletions(-) diff --git a/napari_cellseg3d/_tests/test_plugin_inference.py b/napari_cellseg3d/_tests/test_plugin_inference.py index efa9d9fb..eb4254e1 100644 --- a/napari_cellseg3d/_tests/test_plugin_inference.py +++ b/napari_cellseg3d/_tests/test_plugin_inference.py @@ -1,7 +1,3 @@ -from pathlib import Path - -from tifffile import imread - from napari_cellseg3d._tests.fixtures import LogFixture from napari_cellseg3d.code_models.instance_segmentation import ( INSTANCE_SEGMENTATION_METHOD_LIST, @@ -17,10 +13,9 @@ def test_inference(make_napari_viewer_proxy, qtbot): - im_path = str(Path(__file__).resolve().parent / "res/test.tif") - image = imread(im_path) - - assert image.shape == (6, 6, 6) + dims = 6 + image = rand_gen.random(size=(dims, dims, dims)) + # assert image.shape == (dims, dims, dims) viewer = make_napari_viewer_proxy() widget = Inferer(viewer) @@ -46,14 +41,15 @@ def test_inference(make_napari_viewer_proxy, qtbot): widget.model_choice.addItem(test_model_name) widget.model_choice.setCurrentText(test_model_name) + widget.window_infer_box.setChecked(False) widget.worker_config = widget._set_worker_config() assert widget.worker_config is not None assert widget.model_info is not None - widget.window_infer_box.setChecked(False) worker = widget._create_worker_from_config(widget.worker_config) assert worker.config is not None assert worker.config.model_info is not None + assert worker.config.sliding_window_config.is_enabled() is False worker.config.layer = viewer.layers[0].data worker.config.post_process_config.instance.enabled = True worker.config.post_process_config.instance.method = ( From eb99f2ceb084cbfd58d02ebc35bed005e667f3c7 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Fri, 21 Jul 2023 18:46:01 +0200 Subject: [PATCH 072/250] Trying to fix silent error when loading incompatible weights --- .../code_models/worker_inference.py | 35 ++++++++++--------- .../code_plugins/plugin_model_inference.py | 2 ++ .../code_plugins/plugin_model_training.py | 1 + 3 files changed, 22 insertions(+), 16 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 1ad24899..b66647c3 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -119,7 +119,7 @@ def warn(self, warning): """Sends a warning to main thread""" self.warn_signal.emit(warning) - def raise_error(self, exception, msg): + def _raise_error(self, exception, msg): """Raises an error in main thread""" logger.error(msg, exc_info=True) logger.error(exception, exc_info=True) @@ -131,8 +131,8 @@ def raise_error(self, exception, msg): self.error_signal.emit(exception, msg) self.errored.emit(exception) + self.quit() yield exception - # self.quit() def log_parameters(self): config = self.config @@ -334,7 +334,8 @@ def model_output_wrapper(inputs): except Exception as e: logger.exception(e) logger.debug("failed to run sliding window inference") - self.raise_error(e, "Error during sliding window inference") + self._raise_error(e, "Error during sliding window inference") + # raise e logger.debug(f"Inference output shape: {outputs.shape}") self.log("Post-processing...") @@ -346,7 +347,8 @@ def model_output_wrapper(inputs): return out except Exception as e: logger.exception(e) - self.raise_error(e, "Error during sliding window inference") + self._raise_error(e, "Error during sliding window inference") + # raise e # sys.stdout = old_stdout # sys.stderr = old_stderr @@ -437,7 +439,7 @@ def save_image( try: imwrite(file_path, image) except ValueError as e: - self.raise_error(e, "Error during image saving") + raise e filename = Path(file_path).stem if from_layer: @@ -595,7 +597,7 @@ def inference_on_layer(self, image, model, post_process_transforms): stats=stats, ) - # @thread_worker(connect={"errored": self.raise_error}) + # @thread_worker(connect={"errored": self._raise_error}) def inference(self): """ Requires: @@ -659,10 +661,10 @@ def inference(self): # device=self.config.device, num_classes=self.config.model_info.num_classes, ) - # try: - model = model.to(self.config.device) - # except Exception as e: - # self.raise_error(e, "Issue loading model to device") + try: + model = model.to(self.config.device) + except RuntimeError as e: + self._raise_error(e, "Issue loading model to device") # logger.debug(f"model : {model}") if model is None: raise ValueError("Model is None") @@ -687,13 +689,14 @@ def inference(self): strict=True, ) self.log(f"Weights status : {missing}") - except RuntimeError as e: - self.raise_error(e, "Error when loading weights") + except Exception as e: + self._raise_error(e, "Error when loading weights") + return None self.log("Done") # except Exception as e: - # self.raise_error(e, "Issue loading weights") + # self._raise_error(e, "Issue loading weights") # except Exception as e: - # self.raise_error(e, "Issue instantiating model") + # self._raise_error(e, "Issue instantiating model") # if model_name == "SegResNet": # model = model_class( @@ -779,11 +782,11 @@ def inference(self): yield self.inference_on_layer( input_image, model, post_process_transforms ) + model.to("cpu") # self.quit() except Exception as e: logger.exception(e) - self.raise_error(e, "Inference failed") - self.quit() + self._raise_error(e, "Inference failed") finally: self.quit() diff --git a/napari_cellseg3d/code_plugins/plugin_model_inference.py b/napari_cellseg3d/code_plugins/plugin_model_inference.py index 134e22f4..4bb20231 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_inference.py +++ b/napari_cellseg3d/code_plugins/plugin_model_inference.py @@ -843,6 +843,8 @@ def on_yield(self, result: InferenceResult): if isinstance(result, Exception): self.on_error(result) # raise result + if result is None: + self.on_error("Worker yielded None") # viewer, progress, show_res, show_res_number, zoon, show_original # check that viewer checkbox is on and that max number of displays has not been reached. diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 944b0402..80767396 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -903,6 +903,7 @@ def on_start(self): self.remove_docked_widgets() self.display_status_report() + self.log.clear() self._remove_result_layers() self.log.print_and_log(f"Worker started at {utils.get_time()}") From 1971d8dfe59a06597a0b0400d9240f4fc45d0c7c Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 24 Jul 2023 14:41:45 +0200 Subject: [PATCH 073/250] Set training to load weights w/ strict --- napari_cellseg3d/code_models/worker_training.py | 3 ++- napari_cellseg3d/dev_scripts/crop_data.py | 8 ++++---- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 20a98f9b..d7a49fd9 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -534,7 +534,8 @@ def get_loader_func(num_samples): torch.load( weights, map_location=self.config.device, - ) + ), + strict=True, ) except RuntimeError as e: logger.error(f"Error when loading weights : {e}") diff --git a/napari_cellseg3d/dev_scripts/crop_data.py b/napari_cellseg3d/dev_scripts/crop_data.py index d8be9cad..a8734d29 100644 --- a/napari_cellseg3d/dev_scripts/crop_data.py +++ b/napari_cellseg3d/dev_scripts/crop_data.py @@ -40,7 +40,7 @@ def crop_3d_image(image, roi_size): Path().home() # / "Desktop/Code/CELLSEG_BENCHMARK/TPH2_DATA/somatomotor_iso" # / "Desktop/Code/CELLSEG_BENCHMARK/TPH2_DATA/somatomotor_iso/labels/semantic" - / "Desktop/Code/CELLSEG_BENCHMARK/TPH2_mesospim/BENCHMARK/visual_small" + / "Desktop/Code/CELLSEG_BENCHMARK/TPH2_mesospim/visual_iso/labels/semantic" ) if not image_path.exists() or not image_path.is_dir(): raise ValueError(f"Image path {image_path} does not exist") @@ -48,8 +48,8 @@ def crop_3d_image(image, roi_size): for j in image_list: print(j) image = imread(str(j)) - # crops = crop_3d_image(image, (64, 64, 64)) - crops = [image] + crops = crop_3d_image(image, (64, 64, 64)) + # crops = [image] # viewer = napari.Viewer() if not (image_path / "cropped").exists(): (image_path / "cropped").mkdir(exist_ok=False) @@ -57,7 +57,7 @@ def crop_3d_image(image, roi_size): print(im.shape) # viewer.add_image(im) imwrite( - str(image_path / f"cropped/{j.stem}_{i}_crop.tif"), + str(image_path / f"cropped/a_{j.stem}_{i}_crop.tif"), im.astype(np.float32), dtype="float32", ) From 3d8069e09f0e9833ee000a9b859445350e1a2c7b Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 24 Jul 2023 16:36:37 +0200 Subject: [PATCH 074/250] Add LayerNorm --- .../code_models/models/wnet/model.py | 19 +- .../code_models/worker_inference.py | 2 +- .../dev_scripts/test_new_evaluation.ipynb | 245 ++++++++++++++++++ 3 files changed, 259 insertions(+), 7 deletions(-) create mode 100644 napari_cellseg3d/dev_scripts/test_new_evaluation.ipynb diff --git a/napari_cellseg3d/code_models/models/wnet/model.py b/napari_cellseg3d/code_models/models/wnet/model.py index 060242a1..0a833fa1 100644 --- a/napari_cellseg3d/code_models/models/wnet/model.py +++ b/napari_cellseg3d/code_models/models/wnet/model.py @@ -16,6 +16,7 @@ "Xide Xia", "Brian Kulis", ] +NUM_GROUPS = 8 class WNet_encoder(nn.Module): @@ -179,11 +180,13 @@ def __init__(self, in_channels, out_channels, dropout=0.65): nn.Conv3d(in_channels, out_channels, 3, padding=1), nn.ReLU(), nn.Dropout(p=dropout), - nn.BatchNorm3d(out_channels), + # nn.BatchNorm3d(out_channels), + nn.GroupNorm(num_groups=NUM_GROUPS, num_channels=out_channels), nn.Conv3d(out_channels, out_channels, 3, padding=1), nn.ReLU(), nn.Dropout(p=dropout), - nn.BatchNorm3d(out_channels), + # nn.BatchNorm3d(out_channels), + nn.GroupNorm(num_groups=NUM_GROUPS, num_channels=out_channels), ) def forward(self, x): @@ -202,12 +205,14 @@ def __init__(self, in_channels, out_channels, dropout=0.65): nn.Conv3d(in_channels, out_channels, 1), nn.ReLU(), nn.Dropout(p=dropout), - nn.BatchNorm3d(out_channels), + # nn.BatchNorm3d(out_channels), + nn.GroupNorm(num_groups=NUM_GROUPS, num_channels=out_channels), nn.Conv3d(out_channels, out_channels, 3, padding=1), nn.Conv3d(out_channels, out_channels, 1), nn.ReLU(), nn.Dropout(p=dropout), - nn.BatchNorm3d(out_channels), + # nn.BatchNorm3d(out_channels), + nn.GroupNorm(num_groups=NUM_GROUPS, num_channels=out_channels), ) def forward(self, x): @@ -225,11 +230,13 @@ def __init__(self, in_channels, out_channels, dropout=0.65): nn.Conv3d(in_channels, 64, 3, padding=1), nn.ReLU(), nn.Dropout(p=dropout), - nn.BatchNorm3d(64), + # nn.BatchNorm3d(64), + nn.GroupNorm(num_groups=NUM_GROUPS, num_channels=64), nn.Conv3d(64, 64, 3, padding=1), nn.ReLU(), nn.Dropout(p=dropout), - nn.BatchNorm3d(64), + # nn.BatchNorm3d(64), + nn.GroupNorm(num_groups=NUM_GROUPS, num_channels=64), nn.Conv3d(64, out_channels, 1), ) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index b66647c3..ceedac53 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -686,7 +686,7 @@ def inference(self): weights, map_location=self.config.device, ), - strict=True, + strict=False, # True, # TODO(cyril): change to True ) self.log(f"Weights status : {missing}") except Exception as e: diff --git a/napari_cellseg3d/dev_scripts/test_new_evaluation.ipynb b/napari_cellseg3d/dev_scripts/test_new_evaluation.ipynb new file mode 100644 index 00000000..12707e9b --- /dev/null +++ b/napari_cellseg3d/dev_scripts/test_new_evaluation.ipynb @@ -0,0 +1,245 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import evaluate_labels as evl\n", + "from tifffile import imread\n", + "import time\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from importlib import reload\n", + "reload(evl)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "path_true_labels=Path.home() / \"Desktop/Code/CELLSEG_BENCHMARK/RESULTS/full data/LABELS/relabel_gt.tif\"" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "ename": "IndexError", + "evalue": "too many indices for array: array is 1-dimensional, but 2 were indexed", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[16], line 4\u001b[0m\n\u001b[0;32m 2\u001b[0m labels \u001b[38;5;241m=\u001b[39m imread(path_model_label)\n\u001b[0;32m 3\u001b[0m \u001b[38;5;66;03m# labels.shape\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mevl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mevaluate_model_performance\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath_true_labels\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43mvisualize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_graphical_summary\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43mplot_according_to_gt_label\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\Desktop\\Code\\CellSeg3d\\napari_cellseg3d\\dev_scripts\\evaluate_labels.py:58\u001b[0m, in \u001b[0;36mevaluate_model_performance\u001b[1;34m(labels, model_labels, threshold_correct, print_details, visualize, return_graphical_summary, plot_according_to_gt_label)\u001b[0m\n\u001b[0;32m 20\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Evaluate the model performance.\u001b[39;00m\n\u001b[0;32m 21\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[0;32m 22\u001b[0m \u001b[38;5;124;03m----------\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 55\u001b[0m \u001b[38;5;124;03mgraph_true_positive_ratio_model: ndarray\u001b[39;00m\n\u001b[0;32m 56\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 57\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMapping labels...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 58\u001b[0m tmp \u001b[38;5;241m=\u001b[39m \u001b[43mmap_labels\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 59\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 60\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel_labels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 61\u001b[0m \u001b[43m \u001b[49m\u001b[43mthreshold_correct\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 62\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_total_number_gt_labels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 63\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_dict_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 64\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_graphical_summary\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_graphical_summary\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 65\u001b[0m \u001b[43m \u001b[49m\u001b[43mplot_according_to_gt_labels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mplot_according_to_gt_label\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 66\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 67\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_graphical_summary:\n\u001b[0;32m 68\u001b[0m (\n\u001b[0;32m 69\u001b[0m map_labels_existing,\n\u001b[0;32m 70\u001b[0m map_fused_neurons,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 75\u001b[0m graph_true_positive_ratio_model,\n\u001b[0;32m 76\u001b[0m ) \u001b[38;5;241m=\u001b[39m tmp\n", + "File \u001b[1;32m~\\Desktop\\Code\\CellSeg3d\\napari_cellseg3d\\dev_scripts\\evaluate_labels.py:422\u001b[0m, in \u001b[0;36mmap_labels\u001b[1;34m(gt_labels, model_labels, threshold_correct, return_total_number_gt_labels, return_dict_map, accuracy_function, return_graphical_summary, plot_according_to_gt_labels)\u001b[0m\n\u001b[0;32m 419\u001b[0m \u001b[38;5;66;03m# remove from new_labels the labels that are in map_labels_existing\u001b[39;00m\n\u001b[0;32m 420\u001b[0m new_labels \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(new_labels)\n\u001b[0;32m 421\u001b[0m i_new_labels \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39misin(\n\u001b[1;32m--> 422\u001b[0m \u001b[43mnew_labels\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdict_map\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmodel_label\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m,\n\u001b[0;32m 423\u001b[0m map_labels_existing[:, dict_map[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_label\u001b[39m\u001b[38;5;124m\"\u001b[39m]],\n\u001b[0;32m 424\u001b[0m invert\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m 425\u001b[0m )\n\u001b[0;32m 426\u001b[0m new_labels \u001b[38;5;241m=\u001b[39m new_labels[i_new_labels, :]\n\u001b[0;32m 427\u001b[0m \u001b[38;5;66;03m# find the fused neurons: multiple gt labels are mapped to the same model label\u001b[39;00m\n", + "\u001b[1;31mIndexError\u001b[0m: too many indices for array: array is 1-dimensional, but 2 were indexed" + ] + } + ], + "source": [ + "path_model_label=Path.home() / \"Desktop/Code/CELLSEG_BENCHMARK/RESULTS/full data/instance/isotropic_visual_cp_masks(1).tif\"\n", + "labels = imread(path_model_label)\n", + "# labels.shape\n", + "res = evl.evaluate_model_performance(imread(path_true_labels), labels,visualize=False, return_graphical_summary=True,plot_according_to_gt_label=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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envjuu++ksmnTpgkjIyNx69YtufnHjh0rtLW1RVxcXI4xvH79OlvZwIEDhaGhoXj79q0QQoj09HTh5OQkypUrJ168eCFX9+N92ahRI2FiYiJ3zH5aJyAgQJQrVy7bOidNmpTtPQcgtLS0xPXr13ON+927d6Jq1aqiadOmUtnt27eFlpaW6NSpk8jIyFAYU0ZGhihTpozo0aOH3PT58+cLmUwm7t27l23dH6/TxsZGVK1aVbx580Yq3717twAgJk6cKJVlHX9nz55Vurwsc+fOFQBETExMtmkAhK6urrhz545UdvnyZQFALFmyRCrr16+fsLe3F0+fPpWbv2fPnsLMzEzh+/4xRdP9/PxE+fLlpdd//vlnrts0fPhwYWpqKtLT05XWCQ4OFgDEiRMnpLKXL18KJycn4ejoKL13v/zyiwAg5s+fn20ZHx9jn54jFG1LZGSkACDWr18vleX0efTx8RE+Pj7S64ULFwoA4tdff5XK3r17Jzw9PYWxsbFISUkRQvzv/FayZEnx/Plzqe7OnTsFAPHXX38p3S9C/O+48fDwEO/evZPK58yZIwCInTt3CiE+7C9zc3MxYMAAufkTEhKEmZmZXLmy82vW9t+4cUMIIcSuXbuEnp6eaN++vdzno1q1aqJTp07Sa1WPtQ0bNggtLS2591kIIVasWCEAiIiICKlM1eNckbNnz2b7Tsni4+OT7X1PS0sTdnZ2okuXLlJZXmJVJGs9mzZtkspu3rwpndNOnTolle/fvz9bvKp+r2V9dk6fPi2VPX78WJiZmcmdQ/JyfCg6F39KlWsMRRRdByj6fIaEhAiZTCb3XaLqtcaJEycEALFx40a5ZYaFhWUr//Rz3aFDB1GlSpU8b1d6erpIS0uTK3vx4oWwtbUV33zzjVSWl/NBjRo1hL29vdy1yYEDBwQAhd+hn6pSpYrctmVR9VooL8eMIkOHDhUymUxcvHhRKnv27JmwtLTM9v2mLFZl8nuOV8f59NPPy4IFCwQA6XjML5W7JGVkZGD//v3o2LEjypYtK5W7ubnBz88v1/nNzc1x/fp13L59W9VVZtOlSxe5XzVz8/FQj1m/NL17905hU2tBycjIwIEDB9CxY0eUL19eKre3t8dXX32FkydPIiUlRW6eoKAgua4v3t7eyMjIwP3795WuJz4+HpcuXUJgYCAsLS2l8mrVqqF58+bYu3dvAW7V/1SuXFn6hQz48CtzpUqVcO/ePalsy5Yt8Pb2hoWFBZ4+fSr9+fr6IiMjA8ePH89xHR//EvXy5Us8ffoU3t7eeP36NW7evAngQ9N4TEwMgoODs/XBz9qXT548wfHjx/HNN9/IHbMf18kPHx8fVK5cOce4X7x4geTkZHh7e+PChQtS+Y4dO5CZmYmJEydmGxQgKyYtLS34+/tj165dePnypTR948aNaNCgAZycnJTGdu7cOTx+/BiDBg2S6zffpk0buLq6KuxSUxB8fX3lfk2vVq0aTE1NpeNCCIFt27ahXbt2EELIHRd+fn5ITk6W20+KfLx/k5OT8fTpU/j4+ODevXtITk4G8L/7MXbv3q2wS1dWnVevXuXYVWHv3r2oW7cuGjZsKJUZGxsjKCgIsbGxuHHjBgBg27ZtsLKywtChQ7MtI6dj7ONtef/+PZ49e4YKFSrA3Nw81/2QU8x2dnbo1auXVKajo4Nhw4YhNTUVx44dk6vfo0cPWFhYSK+zPtcff5ZzEhQUJPer6LfffosSJUpI556DBw8iKSkJvXr1knu/tbW1Ua9evWxdIxTJiinrnHHixAnUqVMHzZs3x4kTJwB86P547do1qW5ejrUtW7bAzc0Nrq6ucvWaNm0KANlizO04zy9jY2N8/fXX0mtdXV3UrVs323k1L7EqW0/Pnj2l15UqVYK5uTnc3NzkWr6z/p+1/rx8r+3duxf169dH3bp1pXrW1tbw9/eXi6Ugjo+PFcQ1RpaPP5+vXr3C06dP0aBBAwghcPHixWz1c7vW2LJlC8zMzNC8eXO5bfXw8ICxsXGO22pubo6HDx/mqbsgAGhra0v3OWVmZuL58+dIT09H7dq1FZ5jcjsfZF1zBAQEwMzMTKrXvHlzhd+H+ZHbtdC/PWbCwsLg6ekp17piaWmZ7dgsCHk9x6vzfJr1vbhz506VuoUro3LC8OTJE7x58wYuLi7ZpqnSp3rq1KlISkpCxYoV4e7ujlGjRsn1QVVFThdKn9LS0pI7sQFAxYoVAUCtQxQ+efIEr1+/VrhP3NzckJmZiQcPHsiVf3oxm/WhzalvY9YHSNl6nj59ilevXuU5/tx8GivwId6PY719+zbCwsJgbW0t95c1Ek/WzbbKXL9+HZ06dYKZmRlMTU1hbW0tfZlmXRhm9Q/PafzyrBNdQY9xruw43L17N+rXrw99fX1YWlrC2toay5cvl2IGPsStpaWV6wm2T58+ePPmjdQ/NDo6GufPn0fv3r1znC+n48LV1TXHJPTfyO24ePLkCZKSkrBq1apsx0Xfvn0B5H5cREREwNfXV7pfx9raWuqDmbWPfXx80KVLF0yZMgVWVlbo0KEDQkND5fp4Dxo0CBUrVkSrVq1QpkwZfPPNN9n6od+/f1/pZytrOvDh/axUqVKeR0B68+YNJk6cKN3jY2VlBWtrayQlJckdL3lx//59uLi4ZEtEP405S37OOx/79LvA2NgY9vb20vk168KtadOm2d7zAwcO5Pp+Ax+6Abi4uEjJwYkTJ+Dt7Y1GjRrhn3/+wb179xAREYHMzEzpAicvx9rt27dx/fr1bPWyvis+jVGV819+lClTJluCqei8mpdYVV2PmZkZHBwcspUBkPv8qvq9lnUcfurTeQvi+PhYQVxjZImLi5N+jDM2Noa1tbV0L+Cnn09VrjVu376N5ORk2NjYZNvW1NTUHLd1zJgxMDY2Rt26deHi4oLBgwer3JVk3bp1qFatmnRPh7W1Nfbs2aPwHJPb+SDr/JHfa0BV5BbDvz1m7t+/jwoVKmQrV1T2b+X1HK/O82mPHj3g5eWF/v37w9bWFj179sQff/yR5+Sh0MYCbdSoEe7evYudO3fiwIEDWLNmDRYsWIAVK1bkOtRnFkV9IP8NZb8AKrq5Vp20tbUVlotPbpoqClSJNTMzE82bN8fo0aMV1s06mSqSlJQEHx8fmJqaYurUqXB2doa+vj4uXLiAMWPG/KvsWJm8HgeKjsMTJ06gffv2aNSoEX766SfY29tDR0cHoaGhud6cpkjlypXh4eGBX3/9FX369MGvv/4KXV1ddO/ePc/LKgy5HRdZ79vXX38t3Wz9qaz+6IrcvXsXzZo1g6urK+bPnw8HBwfo6upi7969WLBggbR8mUyGrVu34tSpU/jrr7+wf/9+fPPNN5g3bx5OnToFY2Nj2NjY4NKlS9i/fz/27duHffv2ITQ0FH369Ml2g7C6DB06FKGhoQgODoanpyfMzMwgk8nQs2dPtRzjiqj7vJO1HRs2bICdnV226aomWQ0bNsThw4fx5s0bnD9/HhMnTkTVqlVhbm6OEydOICoqCsbGxqhZs6bcelU51jIzM+Hu7o758+crrPfphbS69pmq59W8xJqX9WjiO6igjo8sBXGNAXw47zdv3hzPnz/HmDFj4OrqCiMjIzx69AiBgYH5+nxmZmbCxsYGGzduVDg9p54Tbm5uiI6Oxu7duxEWFoZt27bhp59+wsSJEzFlyhSl8/36668IDAxEx44dMWrUKNjY2EBbWxshISFyAzJkKQrXIap+jxTUMaNOBX2O/zfbbmBggOPHj+Po0aPYs2cPwsLC8Pvvv6Np06Y4cOCA0v2ebR2qBps1woyi5r7o6GiVlmFpaYm+ffuib9++SE1NRaNGjTB58mTpw1yQT6bLzMzEvXv35C5Ob926BQDSjSFZ2eunD6NS9CusqrFZW1vD0NBQ4T65efMmtLS0VDqx5ybrwWvK1mNlZaX0IVw5KYj3wNnZGampqbmO7a9IeHg4nj17hu3bt6NRo0ZSeUxMTLZ1AMC1a9eUrifrV59r167luE4LCwuFDyTLy6/x27Ztg76+Pvbv3y93A1VoaGi2uDMzM3Hjxo1cx2Pv06cPRo4cifj4eGzatAlt2rSRazJW5OPjIqurQpbo6GilD+zLzb89LqytrWFiYoKMjIx8HRd//fUX0tLSsGvXLrlfoZQ1w9avXx/169fHjBkzsGnTJvj7+2Pz5s3SuUZXVxft2rVDu3btkJmZiUGDBmHlypWYMGECKlSogHLlyin9bAH/28/Ozs44ffo03r9/n6dnwWzduhUBAQGYN2+eVPb27dtsx2Fe9nu5cuVw5coVZGZmyrUyfBpzQbl9+zaaNGkivU5NTUV8fDxat24N4H+fURsbm1zf85y209vbG6Ghodi8eTMyMjLQoEEDaGlpoWHDhlLC0KBBA+lLLy/HmrOzMy5fvoxmzZqp7UnCQMGdVwsjVkXy8r1Wrlw5la4T8nJ8qCq3awxVXL16Fbdu3cK6devQp08fqVxZF0ZVrjWcnZ1x6NAheHl55euHTyMjI/To0QM9evTAu3fv0LlzZ8yYMQPjxo1TOmTz1q1bUb58eWzfvl3ueMlt0Axlss4f/+Ya8N8et//2mClXrhzu3LmTrVxR2b+NVdVzfJaCPJ8qoqWlhWbNmqFZs2aYP38+Zs6ciR9++AFHjx5VeXkqd0nS1taGn58fduzYgbi4OKk8KioK+/fvz3X+Z8+eyb02NjZGhQoV5LoKZF3gFtTTZJcuXSr9XwiBpUuXQkdHB82aNQPw4eDR1tbO1qf+p59+yrYsVWPT1tZGixYtsHPnTrmuT4mJidi0aRMaNmwIU1PTfG7R/9jb26NGjRpYt26dXEzXrl3DgQMHpIMsrwriPejevTsiIyMVHhdJSUlIT09XOm/Wl/7Hv2q8e/cu23tSq1YtODk5YeHChdlizZrX2toajRo1wi+//CJ3zH66fGdnZyQnJ8s1X8fHx6s8XFxW3DKZTK5VIjY2NtvIJR07doSWlhamTp2a7VeGT3/J6dWrF2QyGYYPH4579+7J9XFWpnbt2rCxscGKFSvkPlv79u1DVFSU3GhNefFvjwttbW106dIF27ZtU5jA5TaMsKLjIjk5OVtC9uLFi2z7MSsxy9ofn56LtLS0pF+cs+q0bt0aZ86cQWRkpFTv1atXWLVqFRwdHaUuZV26dMHTp0/lzjVZcvplTltbO9v0JUuWZGvVyst+b926NRISEuRGFElPT8eSJUtgbGwsdakoKKtWrZK7T2T58uVIT0+XRvXx8/ODqakpZs6cqfB+ko/f85y2M6ur0ezZs1GtWjWpu4y3tzcOHz6Mc+fOyd1XlZdjrXv37nj06BFWr16drd6bN28KrFtnQZ1XCyNWRfLyvda6dWucOnUKZ86ckeo9efIk26/reTk+VKHKNYYqFJ1rhBDZhl3+WG7XGt27d0dGRgamTZuWbd709PQcj4tPt0tXVxeVK1eGEELpfVrKtuP06dNy57S8+Pia4+MuNQcPHpTu6cqNkZHRv/oM/Ntjxs/PD5GRkXJPXH/+/LnClp9/G6uq5/gsBXk+/dTz58+zlX36vaiKPLXfTJkyBWFhYfD29sagQYOkL6MqVark2lewcuXKaNy4MTw8PGBpaYlz585h69atcjcLeXh4AACGDRsGPz8/aGtry92glRf6+voICwtDQEAA6tWrh3379mHPnj3473//KzX/mZmZoVu3bliyZAlkMhmcnZ2xe/duhX3B8hLb9OnTpTFvBw0ahBIlSmDlypVIS0tTOLZ2fs2dOxetWrWCp6cn+vXrJw2ramZmlu8nptaoUQPa2tqYPXs2kpOToaenJ419r6pRo0Zh165daNu2rTTk6qtXr3D16lVs3boVsbGx0tCcn2rQoAEsLCwQEBCAYcOGQSaTYcOGDdk+eFpaWli+fDnatWuHGjVqoG/fvrC3t8fNmzdx/fp1KVlZvHgxGjZsiFq1aiEoKAhOTk6IjY3Fnj17pJNGz549MWbMGHTq1AnDhg2ThiqrWLGiyjegtmnTBvPnz0fLli3x1Vdf4fHjx1i2bBkqVKgg99moUKECfvjhB0ybNg3e3t7o3Lkz9PT0cPbsWZQqVQohISFSXWtra7Rs2RJbtmyBubm5Shf7Ojo6mD17Nvr27QsfHx/06tVLGlbV0dERI0aMUGl7PpV1/P/www/o2bMndHR00K5duzy1Ys2aNQtHjx5FvXr1MGDAAFSuXBnPnz/HhQsXcOjQIYUntSwtWrSQWgUGDhyI1NRUrF69GjY2NoiPj5fqrVu3Dj/99BM6deoEZ2dnvHz5EqtXr4apqamURPfv3x/Pnz9H06ZNUaZMGdy/fx9LlixBjRo1pP7+Y8eOxW+//YZWrVph2LBhsLS0xLp16xATE4Nt27ZJv+D36dMH69evx8iRI3HmzBl4e3vj1atXOHToEAYNGoQOHToo3J62bdtiw4YNMDMzQ+XKlREZGYlDhw5lG5ozL5/HoKAgrFy5EoGBgTh//jwcHR2xdetWREREYOHChdLwmAXl3bt3aNasGbp3747o6Gj89NNPaNiwIdq3bw8AMDU1xfLly9G7d2/UqlULPXv2hLW1NeLi4rBnzx54eXlJF1o5nV8rVKgAOzs7REdHy91c3qhRI4wZMwYA5BIGQPVjrXfv3vjjjz/wn//8B0ePHoWXlxcyMjJw8+ZN/PHHH9i/fz9q1679r/eVs7MzzM3NsWLFCpiYmMDIyAj16tXL0315hRWrMqp+r40ePRobNmxAy5YtMXz4cGlY1awWsCx5OT5Uoco1hipcXV3h7OyM77//Ho8ePYKpqSm2bdum9D4VVa41fHx8MHDgQISEhODSpUto0aIFdHR0cPv2bWzZsgWLFi1C165dFS6/RYsWsLOzg5eXF2xtbREVFYWlS5eiTZs2OX6m27Zti+3bt6NTp05o06YNYmJisGLFClSuXBmpqal52idZQkJC0KZNGzRs2BDffPMNnj9/Ll0DqrJMDw8PLF++HNOnT0eFChVgY2OTrSU8J//2mBk9ejR+/fVXNG/eHEOHDpWGVS1btiyeP38u16rwb2NV9RyfpSDPp5+aOnUqjh8/jjZt2qBcuXJ4/PgxfvrpJ5QpU0ZuYI9c5XVYpWPHjgkPDw+hq6srypcvL1asWKFwyLNPh1WdPn26qFu3rjA3NxcGBgbC1dVVzJgxQ24YqfT0dDF06FBhbW0tZDKZtMysYb/mzp2bLR5lw6oaGRmJu3fvihYtWghDQ0Nha2srJk2alG0oyydPnoguXboIQ0NDYWFhIQYOHCiuXbuWbZnKYhMi+3BaQghx4cIF4efnJ4yNjYWhoaFo0qSJ+Pvvv+XqKBvWUtlwr4ocOnRIeHl5CQMDA2FqairatWsnDUH46fJUGVZVCCFWr14typcvLw2XmxVHuXLlFA5b9+kwcEJ8GAJs3LhxokKFCkJXV1dYWVmJBg0aiB9//FHuPVckIiJC1K9fXxgYGIhSpUqJ0aNHS8P8fbpPTp48KZo3by5MTEyEkZGRqFatWrYhDq9duyY6deokzM3Nhb6+vqhUqZKYMGGCXJ0DBw6IqlWrCl1dXVGpUiXx66+/Kh1WdfDgwQrj/vnnn4WLi4vQ09MTrq6uIjQ0VOlwgL/88ouoWbOm0NPTExYWFsLHx0ccPHgwW70//vhDABBBQUE57rNP/f7779LyLS0thb+/v3j48KFcnbwMqyrEh+FyS5cuLbS0tOSGoFO2Tz49BwghRGJiohg8eLBwcHAQOjo6ws7OTjRr1kysWrUq1/Xv2rVLVKtWTejr6wtHR0cxe/ZsaVjTrFguXLggevXqJcqWLSv09PSEjY2NaNu2rTh37py0nK1bt4oWLVoIGxsboaurK8qWLSsGDhwo4uPj5dZ39+5d0bVrV+m4qVu3rti9e3e2uF6/fi1++OEH4eTkJG1T165d5Yaf/PQc8eLFC9G3b19hZWUljI2NhZ+fn7h586bCfabs86joc5eYmCgtV1dXV7i7u2cbyjOn86mic9mnso6bY8eOiaCgIGFhYSGMjY2Fv7+/3BDPWY4ePSr8/PyEmZmZ0NfXF87OziIwMFDuPcnp/CqEEN26dRMAxO+//y6VvXv3ThgaGgpdXV25IYQ/3heqHGvv3r0Ts2fPFlWqVJE+jx4eHmLKlCkiOTlZbt+oepwrsnPnTlG5cmVRokQJue8XHx8fhcNmKhruWdVYFVG2HmXndUXbq8r3mhBCXLlyRfj4+Ah9fX1RunRpMW3aNPHzzz9nG7pSCNWOD1WGVVXlGkMRRd+3N27cEL6+vsLY2FhYWVmJAQMGSEPo5vdaQwghVq1aJTw8PISBgYEwMTER7u7uYvTo0eKff/6R6nz6uV65cqVo1KiRKFmypNDT0xPOzs5i1KhRub7fmZmZYubMmaJcuXJCT09P1KxZU+zevTvbcZXX88G2bduEm5ub0NPTE5UrVxbbt29XOjT5pxISEkSbNm2EiYmJACBtZ16vhVQ5ZpS5ePGi8Pb2Fnp6eqJMmTIiJCRELF68WAAQCQkJucaqTH7P8eo4n376eTl8+LDo0KGDKFWqlNDV1RWlSpUSvXr1yjb0fW5k/7+hRFQE7dy5Ex07dsTx48ez/YpKRERE/05wcDBWrlyJ1NRUlW8A/hIxYSAqwtq2bYuoqCjcuXOn0G90JCIi+py8efNG7sbzZ8+eoWLFiqhVq1aOz+ahQhxWlYhUt3nzZly5cgV79uzBokWLmCwQERH9S56enmjcuDHc3NyQmJiIn3/+GSkpKZgwYYKmQyvy2MJAVATJZDIYGxujR48eWLFiRZEaX5qIiKg4+u9//4utW7fi4cOHkMlkqFWrFiZNmlRgQ/t+zpgwEBERERGRUio/h4GIiIiIiL48TBiIiIiIiEgpdoymPMvMzMQ///wDExMT3oxLRERUTAgh8PLlS5QqVUp6ACWRKpgwUJ79888/cHBw0HQYRERElA8PHjxAmTJlNB0GFSNMGCjPsh5H/+DBA5iammo4GiIiIlJFSkoKHBwcpO9xIlUxYaA8y+qGZGpqyoSBiIiomGF3YsordmAjIiIiIiKlmDAQEREREZFSTBiIiIiIiEgp3sNARJ+tjIwMvH//XtNhEBEVCh0dHWhra2s6DPoMMWEgos+OEAIJCQlISkrSdChERIXK3NwcdnZ2vLGZChQTBiL67GQlCzY2NjA0NOQXJxF99oQQeP36NR4/fgwAsLe313BE9DlhwkBEn5WMjAwpWShZsqSmwyEiKjQGBgYAgMePH8PGxobdk6jA8KZnIvqsZN2zYGhoqOFIiIgKX9a5j/dvUUFiwkBEnyV2QyKiLxHPfaQOTBiIiIiIiEgpJgxEREVE48aNERwcrOkwirS1a9fC3Ny8yCwnN69fv0aXLl1gamoKmUxW7Ebumjx5MmrUqKHpMJTKz/sok8mwY8eOAll/Xj+z4eHhBXIcODo6YuHChf9qGUR5wZueieiL4Th2T6GuL3ZWm0JdX27Cw8PRpEkTvHjxolAultWhR48eaN26dZ7mcXR0RHBwsNyFXX6Wkx/r1q3DiRMn8Pfff8PKygpmZmZqXyfljUwmQ0xMDBwdHTUdClGRxYSBiIiKDQMDA2kkmKKwnNzcvXsXbm5uqFq1ar6XkZGRAZlMBi0tdgogIs3g2YeIqAhJT0/HkCFDYGZmBisrK0yYMAFCCGl6Wloavv/+e5QuXRpGRkaoV68ewsPDpen3799Hu3btYGFhASMjI1SpUgV79+5FbGwsmjRpAgCwsLCATCZDYGCgwhiePXuGXr16oXTp0jA0NIS7uzt+++03uTpbt26Fu7s7DAwMULJkSfj6+uLVq1cAPrRk1K1bF0ZGRjA3N4eXlxfu378vzbt8+XI4OztDV1cXlSpVwoYNG+SWnZSUhIEDB8LW1hb6+vqoWrUqdu/eDSB7F5S7d++iQ4cOsLW1hbGxMerUqYNDhw5J0xs3boz79+9jxIgRkMlk0g2hirqy5BaXTCbDmjVr0KlTJxgaGsLFxQW7du1SuA+z1j1v3jwcP34cMpkMjRs3BgC8ePECffr0gYWFBQwNDdGqVSvcvn1bmi8rtl27dqFy5crQ09NDXFycwnVcu3YNrVq1grGxMWxtbdG7d288ffpUmh4WFoaGDRvC3NwcJUuWRNu2bXH37l25ZTx8+BC9evWCpaUljIyMULt2bZw+fVquzoYNG+Do6AgzMzP07NkTL1++VLrdWfHv3r0blSpVgqGhIbp27YrXr19j3bp1cHR0hIWFBYYNG4aMjAxpvtz2S9ayy5YtC0NDQ3Tq1AnPnj3Ltv6dO3eiVq1a0NfXR/ny5TFlyhSkp6crjfdjL168gL+/P6ytrWFgYAAXFxeEhoaqNC/wYT/Vrl0bJiYmsLOzw1dffSU9F+FjERERqFatGvT19VG/fn1cu3ZNbvrJkyfh7e0NAwMDODg4YNiwYdLni0gTmDAQERUh69atQ4kSJXDmzBksWrQI8+fPx5o1a6TpQ4YMQWRkJDZv3owrV66gW7duaNmypXRhNXjwYKSlpeH48eO4evUqZs+eDWNjYzg4OGDbtm0AgOjoaMTHx2PRokUKY3j79i08PDywZ88eXLt2DUFBQejduzfOnDkDAIiPj0evXr3wzTffICoqCuHh4ejcuTOEEEhPT0fHjh3h4+ODK1euIDIyEkFBQdKF+p9//onhw4fju+++w7Vr1zBw4ED07dsXR48eBQBkZmaiVatWiIiIwK+//oobN25g1qxZSseTT01NRevWrXH48GFcvHgRLVu2RLt27aQL7O3bt6NMmTKYOnUq4uPjER8fr3A5ucWVZcqUKejevTuuXLmC1q1bw9/fH8+fP1e4zO3bt2PAgAHw9PREfHw8tm/fDgAIDAzEuXPnsGvXLkRGRkIIgdatW8sNg/n69WvMnj0ba9aswfXr12FjY5Nt+UlJSWjatClq1qyJc+fOISwsDImJiejevbtU59WrVxg5ciTOnTuHw4cPQ0tLC506dUJmZqa0/3x8fPDo0SPs2rULly9fxujRo6XpwIekbMeOHdi9ezd2796NY8eOYdasWQq3+eP4Fy9ejM2bNyMsLAzh4eHo1KkT9u7di71792LDhg1YuXIltm7dKs2T2345ffo0+vXrhyFDhuDSpUto0qQJpk+fLrfeEydOoE+fPhg+fDhu3LiBlStXYu3atZgxY0aO8WaZMGECbty4gX379iEqKgrLly+HlZWVSvMCH4YynTZtGi5fvowdO3YgNjZWYWI+atQozJs3D2fPnoW1tTXatWsnbefdu3fRsmVLdOnSBVeuXMHvv/+OkydPYsiQISrHQVTgBFEeJScnCwAiOTlZ06EQZfPmzRtx48YN8ebNm2zTyo3ZXah/eeXj4yPc3NxEZmamVDZmzBjh5uYmhBDi/v37QltbWzx69EhuvmbNmolx48YJIYRwd3cXkydPVrj8o0ePCgDixYsXeY6tTZs24rvvvhNCCHH+/HkBQMTGxmar9+zZMwFAhIeHK1xOgwYNxIABA+TKunXrJlq3bi2EEGL//v1CS0tLREdHK5w/NDRUmJmZ5RhrlSpVxJIlS6TX5cqVEwsWLMhxObnFJYQQAMT48eOl16mpqQKA2Ldvn9JYhg8fLnx8fKTXt27dEgBERESEVPb06VNhYGAg/vjjDyk2AOLSpUs5bue0adNEixYt5MoePHggACjdf0+ePBEAxNWrV4UQQqxcuVKYmJiIZ8+eKaw/adIkYWhoKFJSUqSyUaNGiXr16imNKyv+O3fuSGUDBw4UhoaG4uXLl1KZn5+fGDhwoBBCtf3Sq1cvufdDCCF69Ogh9z42a9ZMzJw5U67Ohg0bhL29vfQagPjzzz8Vxt6uXTvRt29fpdv2KR8fHzF8+HCl08+ePSsASNud9RlcP3eueH31qnh99ap4ePKkMNDXFxv+vyygc2fxTdeucss5ceKE0NLSks5rio7pLDmdA/n9TfnFFgYioiKkfv36cuOoe3p64vbt28jIyMDVq1eRkZGBihUrwtjYWPo7duyY1M1k2LBhmD59Ory8vDBp0iRcuXIlzzFkZGRg2rRpcHd3h6WlJYyNjbF//37pV/vq1aujWbNmcHd3R7du3bB69Wq8ePECAGBpaYnAwED4+fmhXbt2WLRokdyv+lFRUfDy8pJbn5eXF6KiogAAly5dQpkyZVCxYkWVYk1NTcX3338PNzc3mJubw9jYGFFRUUq78CiTW1xZqlWrJv3fyMgIpqamCruc5LSeEiVKoF69elJZyZIlUalSJbl16erqyq1LkcuXL+Po0aNyx4KrqysASMfD7du30atXL5QvXx6mpqbSjb1Z++fSpUuoWbMmLC0tla7H0dERJiYm0mt7e/tct9nQ0BDOzs7Sa1tbWzg6OsLY2FiuLGs5quyXqKgouenAh8/Hp/tk6tSpcvtkwIABiI+Px+vXr3OMGQC+/fZbbN68GTVq1MDo0aPx999/5zrPx86fP4927dqhbNmyMDExgY+PDwBkOx7rVa8u/d/SzAwujo64GRMDALgaHY1fd+6U2wY/Pz9kZmYi5v/rEBU23vRMRFRMpKamQltbG+fPn8/WRSfrQqx///7w8/PDnj17cODAAYSEhGDevHkYOnSoyuuZO3cuFi1ahIULF8Ld3R1GRkYIDg7Gu3fvAADa2to4ePAg/v77bxw4cABLlizBDz/8gNOnT8PJyQmhoaEYNmwYwsLC8Pvvv2P8+PE4ePAg6tevn+u683oj8vfff4+DBw/ixx9/RIUKFWBgYICuXbtKsRY0HR0dudcymUyu+05BMTAwyPUBXKmpqWjXrh1mz56dbZq9vT0AoF27dihXrhxWr16NUqVKITMzE1WrVpX2jyr7Oz/brGiewth3qampmDJlCjp37pxtmr6+fq7zt2rVCvfv38fevXtx8OBBNGvWDIMHD8aPP/6Y67yvXr2Cn58f/Pz8sHHjRlhbWyMuLg5+fn55Oh5fvX6Nft26YeSUKdmmlS1bVuXlEBUktjAQERUhn95seurUKbi4uEBbWxs1a9ZERkYGHj9+jAoVKsj92dnZSfM4ODjgP//5D7Zv347vvvsOq1evBvDhV2sAcjeaKhIREYEOHTrg66+/RvXq1VG+fHncunVLro5MJoOXlxemTJmCixcvQldXF3/++ac0vWbNmhg3bhz+/vtvVK1aFZs2bQIAuLm5ISIiItv6KleuDODDL/gPHz7Mtr6cYg0MDESnTp3g7u4OOzs7xMbGytXR1dXNdZtzi6uguLm5IT09Xe59fvbsGaKjo/O8rlq1auH69etwdHTMdjwYGRlJyx0/fjyaNWsGNzc3qSUoS7Vq1XDp0iWl92EUFlX2i5ubm8LPx8dq1aqF6OjobPujQoUKKo8yZW1tjYCAAPz6669YuHAhVq1apdJ8N2/exLNnzzBr1ix4e3vD1dVVaUvMmcuXpf+/SE7Gnfv34erkBACo4eaGm3fvKtyGrM8wUWFjwkBEVITExcVh5MiRiI6Oxm+//YYlS5Zg+PDhAICKFSvC398fffr0wfbt2xETE4MzZ84gJCQEe/Z8eMZEcHAw9u/fj5iYGFy4cAFHjx6Fm5sbAKBcuXKQyWTYvXs3njx5gtTUVIUxuLi4SC0IUVFRGDhwIBITE6Xpp0+fxsyZM3Hu3DnExcVh+/btePLkCdzc3BATE4Nx48YhMjIS9+/fx4EDB3D79m0phlGjRmHt2rVYvnw5bt++jfnz52P79u34/vvvAQA+Pj5o1KgRunTpgoMHDyImJgb79u1DWFiY0li3b9+OS5cu4fLly/jqq6+y/Wrt6OiI48eP49GjR3IjCH0st7gKiouLCzp06IABAwbg5MmTuHz5Mr7++muULl0aHTp0yNOyBg8ejOfPn6NXr144e/Ys7t69i/3796Nv377IyMiAhYUFSpYsiVWrVuHOnTs4cuQIRo4cKbeMXr16wc7ODh07dkRERATu3buHbdu2ITIysiA3O1eq7JesVqsff/wRt2/fxtKlS7MdFxMnTsT69esxZcoUXL9+HVFRUdi8eTPGjx+vUhwTJ07Ezp07cefOHVy/fh27d++Wjt3clC1bFrq6uliyZAnu3buHXbt2Ydq0aQrrhqxciaOnTuH67dsIGj8eJc3N0a5ZMwDAyG++wanLl6Wbu2/fvo2dO3fypmfSKCYMRERFSJ8+ffDmzRvUrVsXgwcPxvDhwxEUFCRNDw0NRZ8+ffDdd9+hUqVK6NixI86ePSt1VcjIyMDgwYPh5uaGli1bomLFivjpp58AAKVLl8aUKVMwduxY2NraKr0AGT9+PGrVqgU/Pz80btxYuqDMYmpqiuPHj6N169aoWLEixo8fj3nz5qFVq1YwNDTEzZs30aVLF1SsWBFBQUEYPHgwBg4cCADo2LEjFi1ahB9//BFVqlTBypUrERoaKg05CgDbtm1DnTp10KtXL1SuXBmjR49W2kIwf/58WFhYoEGDBmjXrh38/PxQq1YtuTpTp05FbGwsnJ2dYW1trXA5qsRVUEJDQ+Hh4YG2bdvC09MTQgjs3bs3W5ed3JQqVQoRERHIyMhAixYt4O7ujuDgYJibm0NLSwtaWlrYvHkzzp8/j6pVq2LEiBGYO3eu3DJ0dXVx4MAB2NjYoHXr1nB3d89xVCp1ym2/1K9fH6tXr8aiRYtQvXp1HDhwIFsi4Ofnh927d+PAgQOoU6cO6tevjwULFqBcuXIqxaCrq4tx48ahWrVqaNSoEbS1tbF582aV5rW2tsbatWuxZcsWVK5cGbNmzVLalWlqcDBGzZ4Nrx49kPjsGbYuWQLd/99O90qVsD80FLdu3YK3tzdq1qyJiRMnolSpUirFQaQOMiE+GuCbSAUpKSkwMzNDcnIyTE1NNR0OkZy3b98iJiYGTk5OKvVZJiIqbG8+ee6CIgb5fNhfTudAfn9TfrGFgYiIiIiIlGLCQERERERESjFhICIiIiIipZgwEBERERGRUkwYiIiIiIhIKSYMxczx48fRrl07lCpVCjKZDDt27JCmvX//HmPGjJGezFqqVCn06dMH//zzj9wynj9/Dn9/f5iamsLc3Bz9+vVTOh47EREREX3ZmDAUM69evUL16tWxbNmybNNev36NCxcuYMKECbhw4QK2b9+O6OhotG/fXq6ev78/rl+/joMHD2L37t04fvy43DjvRERERERZSmg6AMqbVq1aoVWrVgqnmZmZ4eDBg3JlS5cuRd26dREXF4eyZcsiKioKYWFhOHv2LGrXrg0AWLJkCVq3bo0ff/yRD4YhIiIiIjlsYfjMJScnQyaTwdzcHAAQGRkJc3NzKVkAAF9fX2hpaeH06dMKl5GWloaUlBS5PyIiIiL6MjBh+Iy9ffsWY8aMQa9evaQnOiYkJMDGxkauXokSJWBpaYmEhASFywkJCYGZmZn05+DgoPbYib5EQggEBQXB0tISMpkMly5dynWe2NhYlesWVY0bN0ZwcHCOddauXSv98EFERIWLXZI+U+/fv0f37t0hhMDy5cv/1bLGjRuHkSNHSq9TUlKYNFDxNNmskNeXnKfqYWFhWLt2LcLDw1G+fHlYWVmpKbCiZfv27dDR0ZFeOzo6Ijg4WC6J6NGjB1q3bq2B6IiIiAnDZygrWbh//z6OHDkitS4AgJ2dHR4/fixXPz09Hc+fP4ednZ3C5enp6UFPT0+tMRMRcPfuXdjb26NBgwaaDqVQWVpa5lrHwMAABgYGhRANERF9il2SPjNZycLt27dx6NAhlCxZUm66p6cnkpKScP78eansyJEjyMzMRL169Qo7XCL6f4GBgRg6dCji4uIgk8ng6OgI4EOrQ8OGDWFubo6SJUuibdu2uHv3rtLlvHjxAv7+/rC2toaBgQFcXFwQGhoqTX/w4AG6d+8Oc3NzWFpaokOHDoiNjVW6vPDwcMhkMuzZswfVqlWDvr4+6tevj2vXrsnV27ZtG6pUqQI9PT04Ojpi3rx5ctN/+uknuLi4QF9fH7a2tujatas07eMuSY0bN8b9+/cxYsQIyGQyyGQyAPJdkm7dugWZTIabN2/KrWPBggVwdnaWXl+7dg2tWrWCsbExbG1t0bt3bzx9+lTpthIRkWJMGIqZ1NRUXLp0SeqvHBMTg0uXLiEuLg7v379H165dce7cOWzcuBEZGRlISEhAQkIC3r17BwBwc3NDy5YtMWDAAJw5cwYREREYMmQIevbsyRGSiDRo0aJFmDp1KsqUKYP4+HicPXsWwIehlEeOHIlz587h8OHD0NLSQqdOnZCZmalwORMmTMCNGzewb98+REVFYfny5VLXpvfv38PPzw8mJiY4ceIEIiIiYGxsjJYtW0rnCGVGjRqFefPm4ezZs7C2tka7du3w/v17AMD58+fRvXt39OzZE1evXsXkyZMxYcIErF27FgBw7tw5DBs2DFOnTkV0dDTCwsLQqFEjhevZvn07ypQpg6lTpyI+Ph7x8fHZ6lSsWBG1a9fGxo0b5co3btyIr776CgCQlJSEpk2bombNmjh37hzCwsKQmJiI7t2757idRESUHbskFTPnzp1DkyZNpNdZ9xYEBARg8uTJ2LVrFwCgRo0acvMdPXoUjRs3BvDhS3XIkCFo1qwZtLS00KVLFyxevLhQ4icixczMzGBiYgJtbW257oFdunSRq/fLL7/A2toaN27cQNWqVbMtJy4uDjVr1pRGQstqqQCA33//HZmZmVizZo30y31oaCjMzc0RHh6OFi1aKI1v0qRJaN68OQBg3bp1KFOmDP788090794d8+fPR7NmzTBhwgQAHy7ob9y4gblz5yIwMBBxcXEwMjJC27ZtYWJignLlyqFmzZoK12NpaQltbW2YmJgo7SYJfHiezNKlSzFt2jQAH1odzp8/j19//RXAhyGla9asiZkzZ8rtOwcHB9y6dQsVK1ZUumwiIpLHhKGYady4MYQQSqfnNC2LpaUlNm3aVJBhEZGa3L59GxMnTsTp06fx9OlTqWUhLi5OYcLw7bffokuXLrhw4QJatGiBjh07SvdEXL58GXfu3IGJiYncPG/fvs2xmxPwoTtjFktLS1SqVAlRUVEAgKioKHTo0EGuvpeXFxYuXIiMjAw0b94c5cqVQ/ny5dGyZUu0bNkSnTp1gqGhYd53yP/r2bMnvv/+e5w6dQr169fHxo0bUatWLbi6ukrbevToURgbG2eb9+7du0wYiIjygAkDEVER1q5dO5QrVw6rV69GqVKlkJmZiapVqyrtQtSqVSvcv38fe/fuxcGDB9GsWTMMHjwYP/74I1JTU+Hh4ZGtKw8AWFtbq20bTExMcOHCBYSHh+PAgQOYOHEiJk+ejLNnz+Z7qFQ7Ozs0bdoUmzZtQv369bFp0yZ8++230vTU1FS0a9cOs2fPzjavvb19fjeFiOiLxHsYiIiKqGfPniE6Ohrjx49Hs2bN4ObmhhcvXuQ6n7W1NQICAvDrr79i4cKFWLVqFQCgVq1auH37NmxsbFChQgW5PzOznIecPXXqlPT/Fy9e4NatW3BzcwPw4d6oiIgIufoRERGoWLEitLW1AXx43ouvry/mzJmDK1euIDY2FkeOHFG4Ll1dXWRkZOS6nf7+/vj9998RGRmJe/fuoWfPntK0WrVq4fr163B0dMy2rUZGRrkum4iI/ocJAxFREWVhYYGSJUti1apVuHPnDo4cOSL3TBRFJk6ciJ07d+LOnTu4fv06du/eLV3Y+/v7w8rKCh06dMCJEycQExOD8PBwDBs2DA8fPsxxuVOnTsXhw4dx7do1BAYGwsrKCh07dgQAfPfddzh8+DCmTZuGW7duYd26dVi6dCm+//57AMDu3buxePFiXLp0Cffv38f69euRmZmJSpUqKVyXo6Mjjh8/jkePHuU4qlHnzp3x8uVLfPvtt2jSpIncwA2DBw/G8+fP0atXL5w9exZ3797F/v370bdvX5WSESIi+h8mDERERZSWlhY2b96M8+fPo2rVqhgxYgTmzp2b4zy6uroYN24cqlWrhkaNGkFbWxubN28GABgaGuL48eMoW7YsOnfuDDc3N/Tr1w9v376Ve16LIrNmzcLw4cPh4eGBhIQE/PXXX9DV1QXw4df8P/74A5s3b0bVqlUxceJETJ06FYGBgQAAc3NzbN++HU2bNoWbmxtWrFiB3377DVWqVFG4rqlTpyI2NhbOzs45dpUyMTFBu3btcPnyZfj7+8tNK1WqFCIiIpCRkYEWLVrA3d0dwcHBMDc3h5YWv/qIiPJCJlS5S5boIykpKTAzM0NycnKuFxlEhe3t27eIiYmBk5MT9PX1NR1OsRceHo4mTZrgxYsX+b7fgIjkvfnkOSaKGCgY1EAVOZ0D+f1N+cWfWYiIiIiISCkmDEREREREpBSHVSUiIqVye/YLERF9/tjCQERERERESjFhICIiIiIipZgwEBERERGRUkwYiIiIiIhIKSYMRERERESkFBMGIiIiIiJSigkDEREVmrVr16r0xGiZTIYdO3aoPR4iIsodn8NARF8M93Xuhbq+qwFX81S/cePGqFGjBhYuXKiegIqAHj16oHXr1tLryZMnY8eOHbh06ZJcvfj4eFhYWBRydEREpAgTBiKiYkQIgYyMDJQoUTxP3wYGBjAwMMi1np2dXSFEQ0REqmCXJCKiIiAwMBDHjh3DokWLIJPJIJPJEBsbi/DwcMhkMuzbtw8eHh7Q09PDyZMnERgYiI4dO8otIzg4GI0bN5ZeZ2ZmIiQkBE5OTjAwMED16tWxdevWHONwdHTEtGnT0KtXLxgZGaF06dJYtmyZXJ24uDh06NABxsbGMDU1Rffu3ZGYmChNv3z5Mpo0aQITExOYmprCw8MD586dAyDfJWnt2rWYMmUKLl++LG3z2rVrAch3SWrQoAHGjBkjF8OTJ0+go6OD48ePAwDS0tLw/fffo3Tp0jAyMkK9evUQHh6uwp4nIqLcMGEgIioCFi1aBE9PTwwYMADx8fGIj4+Hg4ODNH3s2LGYNWsWoqKiUK1aNZWWGRISgvXr12PFihW4fv06RowYga+//hrHjh3Lcb65c+eievXquHjxIsaOHYvhw4fj4MGDAD4kIR06dMDz589x7NgxHDx4EPfu3UOPHj2k+f39/VGmTBmcPXsW58+fx9ixY6Gjo5NtPT169MB3332HKlWqSNv88XI+Xt7mzZshhJDKfv/9d5QqVQre3t4AgCFDhiAyMhKbN2/GlStX0K1bN7Rs2RK3b99WaV8REZFyxbNNm4joM2NmZgZdXV0YGhoq7I4zdepUNG/eXOXlpaWlYebMmTh06BA8PT0BAOXLl8fJkyexcuVK+Pj4KJ3Xy8sLY8eOBQBUrFgRERERWLBgAZo3b47Dhw/j6tWriImJkRKa9evXo0qVKjh79izq1KmDuLg4jBo1Cq6urgAAFxcXhesxMDCAsbExSpQokWMXpO7duyM4OBgnT56UEoRNmzahV69ekMlkiIuLQ2hoKOLi4lCqVCkAwPfff4+wsDCEhoZi5syZKu83IiLKji0MRETFQO3atfNU/86dO3j9+jWaN28OY2Nj6W/9+vW4e/dujvNmJRgfv46KigIAREVFwcHBQa71o3LlyjA3N5fqjBw5Ev3794evry9mzZqV6/pyY21tjRYtWmDjxo0AgJiYGERGRsLf3x8AcPXqVWRkZKBixYpy23rs2LF/vW4iImILAxFRsWBkZCT3WktLS66LDgC8f/9e+n9qaioAYM+ePShdurRcPT09PTVF+cHkyZPx1VdfYc+ePdi3bx8mTZqEzZs3o1OnTvlepr+/P4YNG4YlS5Zg06ZNcHd3h7v7h1GvUlNToa2tjfPnz0NbW1tuPmNj43+1LURExISBiKjI0NXVRUZGhkp1ra2tce3aNbmyS5cuSfcKVK5cGXp6eoiLi8ux+5Eip06dyvbazc0NAODm5oYHDx7gwYMHUivDjRs3kJSUhMqVK0vzVKxYERUrVsSIESPQq1cvhIaGKkwYVN3mDh06ICgoCGFhYdi0aRP69OkjTatZsyYyMjLw+PFjqcsSEREVHHZJIiIqIhwdHXH69GnExsbi6dOnyMzMVFq3adOmOHfuHNavX4/bt29j0qRJcgmEiYkJvv/+e4wYMQLr1q3D3bt3ceHCBSxZsgTr1q3LMY6IiAjMmTMHt27dwrJly7BlyxYMHz4cAODr6wt3d3f4+/vjwoULOHPmDPr06QMfHx/Url0bb968wZAhQxAeHo779+8jIiICZ8+elRIORdscExODS5cu4enTp0hLS1NYz8jICB07dsSECRMQFRWFXr16SdMqVqwIf39/9OnTB9u3b0dMTAzOnDmDkJAQ7NmzJ8dtJSKi3DFhICIqIr7//ntoa2ujcuXKsLa2RlxcnNK6fn5+mDBhAkaPHo06derg5cuXcr+6A8C0adMwYcIEhISEwM3NDS1btsSePXvg5OSUYxzfffcdzp07h5o1a2L69OmYP38+/Pz8AHwY7nTnzp2wsLBAo0aN4Ovri/Lly+P3338HAGhra+PZs2fo06cPKlasiO7du6NVq1aYMmWKwnV16dIFLVu2RJMmTWBtbY3ffvtNaVz+/v64fPkyvL29UbZsWblpoaGh6NOnD7777jtUqlQJHTt2xNmzZ7PVIyoK7tnJcv0jKkpk4tNOsES5SElJgZmZGZKTk2FqaqrpcIjkvH37FjExMXBycoK+vr6mwyl2HB0dERwcjODgYE2HQvTZuv70eq51qlhVydeyczoH8vub8ostDEREREREpBQTBiIiIiIiUoqjJBERkSQ2NlbTIRARURHDFgYiIiIiIlKKCQMRERERESnFhIGIiIiIiJRiwkBEREREREoxYSAiIiIiIqWYMBARERERkVJMGIiISCWTJ09GjRo1VK4fGxsLmUyGS5cuKa3j6OiIhQsXqrzMtWvXwtzcXOX6yshkMuzYsUPpdCEEgoKCYGlpmes2FCWq7M+8vo8A0Lhx4yLz9O/w8HDIZDIkJSWpPE9BxF9Qxx5RccTnMBDRFyPK1a1Q1+d2MypP9Y8fP465c+fi/PnziI+Px59//omOHTuqJzjKUVhYGNauXYvw8HCUL18eVlZWmg4pX2QyWbbj6Pvvv8fQoUM1F5QSsbGxcHJywsWLF/Oc0BCRerGFgYioiHj16hWqV6+OZcuWaTqUL97du3dhb2+PBg0awM7ODiVK5P33NSEE0tPT1RDdv2NsbIySJUtqOgwiKkaYMBARFRGtWrXC9OnT0alTJ5Xnyepe8ssvv6Bs2bIwNjbGoEGDkJGRgTlz5sDOzg42NjaYMWOG3HxxcXHo0KEDjI2NYWpqiu7duyMxMVGuzqxZs2BrawsTExP069cPb9++zbb+NWvWwM3NDfr6+nB1dcVPP/2Uv43/f/Pnz4e7uzuMjIzg4OCAQYMGITU1NVu9HTt2wMXFBfr6+vDz88ODBw/kpu/cuRO1atWCvr4+ypcvjylTpqh88R4YGIihQ4ciLi4OMpkMjo6OAIC0tDQMGzYMNjY20NfXR8OGDXH27FlpvqyuMvv27YOHhwf09PRw8uTJbMvP6qr1xx9/wNvbGwYGBqhTpw5u3bqFs2fPonbt2jA2NkarVq3w5MkTaT5F3Wo6duyIwMBAhduRFXenTp3ktuPTLkmBgYHo2LEjpkyZAmtra5iamuI///kP3r17p3QfpaWl4fvvv0fp0qVhZGSEevXqITw8XGl9ALh58yYaNmwIfX19VK5cGYcOHZLrGubk5AQAqFmzJmQyGRo3bpzj8rI8e/YMvXr1QunSpWFoaAh3d3f89ttv2eqlp6djyJAhMDMzg5WVFSZMmAAhRL636fLly2jSpAlMTExgamoKDw8PnDt3TqWYiYobJgxERMXc3bt3sW/fPoSFheG3337Dzz//jDZt2uDhw4c4duwYZs+ejfHjx+P06dMAgMzMTHTo0AHPnz/HsWPHcPDgQdy7dw89evSQlvnHH39g8uTJmDlzJs6dOwd7e/tsycDGjRsxceJEzJgxA1FRUZg5cyYmTJiAdevW5XtbtLS0sHjxYly/fh3r1q3DkSNHMHr0aLk6r1+/xowZM7B+/XpEREQgKSkJPXv2lKafOHECffr0wfDhw3Hjxg2sXLkSa9euzZY0KbNo0SJMnToVZcqUQXx8vJQUjB49Gtu2bcO6detw4cIFVKhQAX5+fnj+/Lnc/GPHjsWsWbMQFRWFatWqKV3PpEmTMH78eFy4cAElSpTAV199hdGjR2PRokU4ceIE7ty5g4kTJ6q667LJijs0NFRuOxQ5fPgwoqKiEB4ejt9++w3bt2/HlClTlNYfMmQIIiMjsXnzZly5cgXdunVDy5Ytcfv2bYX1MzIy0LFjRxgaGuL06dNYtWoVfvjhB7k6Z86cAQAcOnQI8fHx2L59u0rb+fbtW3h4eGDPnj24du0agoKC0Lt3b2l5WdatW4cSJUrgzJkzWLRoEebPn481a9bke5v8/f1RpkwZnD17FufPn8fYsWOho6OjUsxExY4gyqPk5GQBQCQnJ2s6FKJs3rx5I27cuCHevHmTbdqNSq6F+vdvABB//vlnrvUmTZokDA0NRUpKilTm5+cnHB0dRUZGhlRWqVIlERISIoQQ4sCBA0JbW1vExcVJ069fvy4AiDNnzgghhPD09BSDBg2SW1e9evVE9erVpdfOzs5i06ZNcnWmTZsmPD09hRBCxMTECADi4sWLSuMvV66cWLBggdLpW7ZsESVLlpReh4aGCgDi1KlTUllUVJQAIE6fPi2EEKJZs2Zi5syZcsvZsGGDsLe3l17ntn8XLFggypUrJ71OTU0VOjo6YuPGjVLZu3fvRKlSpcScOXOEEEIcPXpUABA7duxQulwh/rdf1qxZI5X99ttvAoA4fPiwVBYSEiIqVaokvfbx8RHDhw+XW1aHDh1EQECA9PrT/aloOydNmiT3PgYEBAhLS0vx6tUrqWz58uXC2NhYOoY+Xvf9+/eFtra2ePTokdxymzVrJsaNG6dwm/ft2ydKlCgh4uPjpbKDBw/KxafK8SLE//bzixcvlNZp06aN+O6776TXPj4+ws3NTWRmZkplY8aMEW5ubipvU2hoqDAzM5OmmZiYiLVr1+YYqzLXnlzL9S+/cjoH8vub8os3PRMRFXOOjo4wMTGRXtva2kJbWxtaWlpyZY8fPwYAREVFwcHBAQ4ODtL0ypUrw9zcHFFRUahTpw6ioqLwn//8R249np6eOHr0KIAP91vcvXsX/fr1w4ABA6Q66enpMDMzy/e2HDp0CCEhIbh58yZSUlKQnp6Ot2/f4vXr1zA0NAQAlChRAnXq1JHmcXV1lWKvW7cuLl++jIiICLkWhYyMjGzLyYu7d+/i/fv38PLyksp0dHRQt25dREXJ39xeu3ZtlZb5ceuDra0tAMDd3V2uLOs9U7fq1avL7RdPT0+kpqbiwYMHKFeunFzdq1evIiMjAxUrVpQrT0tLU3pvRHR0NBwcHGBnZyeV1a1bt0Biz8jIwMyZM/HHH3/g0aNHePfuHdLS0rK9z/Xr14dMJpNee3p6Yt68ecjIyMjXNo0cORL9+/fHhg0b4Ovri27dusHZ2blAtomoqGHCQERUzH3aDUImkyksy8zMLLB1Zt1XsHr1atSrV09umra2dr6WGRsbi7Zt2+Lbb7/FjBkzYGlpiZMnT6Jfv3549+6dyhf6qampmDJlCjp37pxtmr6+fr5iywsjIyOV6n38HmVdyH5a9vF7pqWlJdfnHgDev3//b0LNl9TUVGhra+P8+fPZ3mtjY+NCj2fu3LlYtGgRFi5cKN3/EhwcnOM9GJ/KzzZNnjwZX331Ffbs2YN9+/Zh0qRJ2Lx5c57uQSIqLpgwEBF9Ydzc3PDgwQM8ePBAamW4ceMGkpKSULlyZanO6dOn0adPH2m+U6dOSf+3tbVFqVKlcO/ePfj7+xdIXOfPn0dmZibmzZsntY788ccf2eqlp6fj3Llz0i/U0dHRSEpKgpvbh2Fza9WqhejoaFSoUKFA4gIAZ2dn6OrqIiIiQvrF/f379zh79myhPZ/A2toa8fHx0uuMjAxcu3YNTZo0UTqPjo4OMjIycl325cuX8ebNGxgYGAD48F4bGxvLtUJlqVmzJjIyMvD48WN4e3urFHulSpXw4MEDJCYmSq0pn95ToaurK21XXkRERKBDhw74+uuvAXy4R+fWrVvSsZwl6x6eLKdOnYKLiwu0tbXztU0AULFiRVSsWBEjRoxAr169EBoayoSBPktMGIiIiojU1FTcuXNHeh0TE4NLly7B0tISZcuWLbD1+Pr6wt3dHf7+/li4cCHS09MxaNAg+Pj4SN1phg8fjsDAQNSuXRteXl7YuHEjrl+/jvLly0vLmTJlCoYNGwYzMzO0bNkSaWlpOHfuHF68eIGRI0fmOa4KFSrg/fv3WLJkCdq1a4eIiAisWLEiWz0dHR0MHToUixcvRokSJTBkyBDUr19fSiAmTpyItm3bomzZsujatSu0tLRw+fJlXLt2DdOnT8/XPjMyMsK3336LUaNGSe/HnDlz8Pr1a/Tr1y9fy8yrpk2bYuTIkdizZw+cnZ0xf/78XB9e5ujoiMOHD8PLywt6enqwsLBQWO/du3fo168fxo8fj9jYWEyaNAlDhgyR69aWpWLFivD390efPn0wb9481KxZE0+ePMHhw4dRrVo1tGnTJts8zZs3h7OzMwICAjBnzhy8fPkS48ePB/C/1hUbGxsYGBggLCwMZcqUgb6+vkrd21xcXLB161b8/fffsLCwwPz585GYmJgtYYiLi8PIkSMxcOBAXLhwAUuWLMG8efPytU1v3rzBqFGj0LVrVzg5OeHhw4c4e/YsunTpkmu8RMURE4ZiJrcHOwkhMGnSJKxevRpJSUnw8vLC8uXL4eLiItV5/vw5hg4dir/++gtaWlro0qULFi1apJGmZKLClNcHqRW2c+fOyf1anHXRHRAQgLVr1xbYemQyGXbu3ImhQ4eiUaNG0NLSQsuWLbFkyRKpTo8ePXD37l2MHj0ab9++RZcuXfDtt99i//79Up3+/fvD0NAQc+fOxahRo2BkZAR3d/d8/+JevXp1zJ8/H7Nnz8a4cePQqFEjhISEyLVyAIChoSHGjBmDr776Co8ePYK3tzd+/vlnabqfnx92796NqVOnYvbs2dDR0YGrqyv69++fr7iyzJo1C5mZmejduzdevnyJ2rVrY//+/UovwgvaN998g8uXL6NPnz4oUaIERowYkWPrAgDMmzcPI0eOxOrVq1G6dGnExsYqrNesWTO4uLigUaNGSEtLQ69evTB58mSlyw0NDcX06dPx3Xff4dGjR7CyskL9+vXRtm1bhfW1tbWxY8cO9O/fH3Xq1EH58uUxd+5ctGvXTuomVqJECSxevBhTp07FxIkT4e3tnetQrQAwfvx43Lt3D35+fjA0NERQUBA6duyI5ORkuXp9+vTBmzdvULduXWhra2P48OEICgrK1zZpa2vj2bNn6NOnDxITE2FlZYXOnTvnOLIUUXEmE592iKQibd++fYiIiICHhwc6d+6cLWGYPXs2QkJCsG7dOjg5OWHChAm4evUqbty4IZ2UW7Vqhfj4eKxcuRLv379H3759UadOHWzatEmlGFJSUmBmZobk5GSYmpqqYzOJ8u3t27eIiYmBk5NTofRXJyruAgMDkZSUJD0PobBERESgYcOGuHPnzhd3s/D1p9dzrVPFqkq+lp3TOZDf35RfbGEoZlq1aoVWrVopnCaEwMKFCzF+/Hh06NABALB+/XrY2tpix44d6NmzJ6KiohAWFiY9HAgAlixZgtatW+PHH39EqVKlCm1biIjoy/Hnn3/C2NgYLi4uuHPnDoYPHw4vL68vLlkgKo744LbPSExMDBISEuDr6yuVmZmZoV69eoiMjAQAREZGwtzcXG7YP19fX2hpaWW7ISxLWloaUlJS5P6IiIjy4uXLlxg8eDBcXV0RGBiIOnXqYOfOnZoOi4hUwBaGz0hCQgKA/43nncXW1laalpCQABsbG7npJUqUgKWlpVTnUyEhIeyXSUT0mSrI+2Ny0qdPn2z3oxBR8cAWBsrVuHHjkJycLP09ePBA0yERERERUSFhwvAZyXqCZmJiolx5YmKiNM3Ozi7bk0PT09Px/PlzuSdwfkxPTw+mpqZyf0RFHcdzIKIvEc99pA5MGD4jTk5OsLOzw+HDh6WylJQUnD59Gp6engAAT09PJCUl4fz581KdI0eOIDMzM9vTWomKo6wn5b5+/VrDkRARFb6sc9+nT3sn+jd4D0Mxk9uDnYKDgzF9+nS4uLhIw6qWKlVKGnrVzc0NLVu2xIABA7BixQq8f/8eQ4YMQc+ePTlCEn0WtLW1YW5uLrWkGRoaSg+GIiIqCjLfZ+Za5+3bt3laphACr1+/xuPHj2Fubg5tbe38hkeUDROGYia3BzuNHj0ar169QlBQEJKSktCwYUOEhYXJjcW8ceNGDBkyBM2aNZMe3LZ48eJC3xYidcnqXvdp9zsioqLgcWru56YSSfm7RDM3N1faxZgov/jgNsozPviFiouMjAy8f/9e02EQEclp/2f7XOvs6rQrz8vV0dHJsWWB39+UX2xhIKLPlra2NpvliajIiX8Xn2sdPqmeihLe9FzIMjIycOnSJbx48ULToRARERER5YoJg5oFBwfj559/BvAhWfDx8UGtWrXg4OCA8PBwzQZHRERERJQLJgxqtnXrVlSvXh0A8NdffyEmJgY3b97EiBEj8MMPP2g4OiIiIiKinDFhULOnT59KoxXs3bsX3bp1Q8WKFfHNN9/g6tWrGo6OiIiIiChnTBjUzNbWFjdu3EBGRgbCwsLQvHlzAB8erMKbMYmIiIioqOMoSWrWt29fdO/eHfb29pDJZPD19QUAnD59Gq6urhqOjoiIiIgoZ0wY1Gzy5MmoWrUqHjx4gG7dukFPTw/Ah+Eex44dq+HoiIiIiIhyxoShEHTt2jVbWUBAgAYiISIiIiLKGyYMarB48WKV6w4bNkyNkRARERER/TtMGNRgwYIFKtWTyWRMGIiIiIioSGPCoAYxMTGaDoGIiIiIqEBwWNVC8u7dO0RHRyM9PV3ToRARERERqYwJg5q9fv0a/fr1g6GhIapUqYK4uDgAwNChQzFr1iwNR0dERERElDMmDGo2btw4XL58GeHh4dDX15fKfX198fvvv2swMiIiIiKi3PEeBjXbsWMHfv/9d9SvXx8ymUwqr1KlCu7evavByIiIiIiIcscWBjV78uQJbGxsspW/evVKLoEgIiIiIiqKmDCoWe3atbFnzx7pdVaSsGbNGnh6emoqLCIiIiIilbBLkprNnDkTrVq1wo0bN5Ceno5Fixbhxo0b+Pvvv3Hs2DFNh0dERERElCO2MKhZw4YNcenSJaSnp8Pd3R0HDhyAjY0NIiMj4eHhoenwiIiIiIhyxBaGQuDs7IzVq1drOgwiIiIiojxjwlAIMjIy8OeffyIqKgoAULlyZXTo0AElSnD3ExEREVHRxitWNbt+/Trat2+PhIQEVKpUCQAwe/ZsWFtb46+//kLVqlU1HCERERERkXK8h0HN+vfvjypVquDhw4e4cOECLly4gAcPHqBatWoICgrSdHhERERERDliC4OaXbp0CefOnYOFhYVUZmFhgRkzZqBOnToajIyIiIiIKHdsYVCzihUrIjExMVv548ePUaFCBQ1ERERERESkOiYMapCSkiL9hYSEYNiwYdi6dSsePnyIhw8fYuvWrQgODsbs2bM1HSoRERERUY7YJUkNzM3NpSc6A4AQAt27d5fKhBAAgHbt2iEjI0MjMRIRERERqYIJgxocPXpU0yEQERERERUIJgxq4OPjo+kQiIiIiIgKBBOGQvL69WvExcXh3bt3cuXVqlXTUERERERERLljwqBmT548Qd++fbFv3z6F03kPAxEREREVZRwlSc2Cg4ORlJSE06dPw8DAAGFhYVi3bh1cXFywa9cuTYdHRERERJQjtjCo2ZEjR7Bz507Url0bWlpaKFeuHJo3bw5TU1OEhISgTZs2mg6RiIiIiEgptjCo2atXr2BjYwPgwxOenzx5AgBwd3fHhQsXNBkaEREREVGumDCoWaVKlRAdHQ0AqF69OlauXIlHjx5hxYoVsLe313B0REREREQ5Y5ckNRs+fDji4+MBAJMmTULLli2xceNG6OrqYu3atZoNjoiIiIgoF0wY1Ozrr7+W/u/h4YH79+/j5s2bKFu2LKysrDQYGRERERFR7pgwFDJDQ0PUqlVL02EQEREREamECYMajBw5UuW68+fPV2MkRERERET/DhMGNbh48aJK9WQymZojISIiIiL6d5gwqMHRo0c1HQIRERERUYHgsKpERERERKQUEwYiIiIiIlKKCQMRERERESnFhOEzk5GRgQkTJsDJyQkGBgZwdnbGtGnTIISQ6gghMHHiRNjb28PAwAC+vr64ffu2BqMmIiIioqKKCcNnZvbs2Vi+fDmWLl2KqKgozJ49G3PmzMGSJUukOnPmzMHixYuxYsUKnD59GkZGRvDz88Pbt281GDkRERERFUVMGNRs3bp12LNnj/R69OjRMDc3R4MGDXD//v0CX9/ff/+NDh06oE2bNnB0dETXrl3RokULnDlzBsCH1oWFCxdi/Pjx6NChA6pVq4b169fjn3/+wY4dOwo8HiIiIiIq3pgwqNnMmTNhYGAAAIiMjMSyZcswZ84cWFlZYcSIEQW+vgYNGuDw4cO4desWAODy5cs4efIkWrVqBQCIiYlBQkICfH19pXnMzMxQr149REZGKlxmWloaUlJS5P6IiIiI6MvA5zCo2YMHD1ChQgUAwI4dO9ClSxcEBQXBy8sLjRs3LvD1jR07FikpKXB1dYW2tjYyMjIwY8YM+Pv7AwASEhIAALa2tnLz2draStM+FRISgilTphR4rERERERU9LGFQc2MjY3x7NkzAMCBAwfQvHlzAIC+vj7evHlT4Ov7448/sHHjRmzatAkXLlzAunXr8OOPP2LdunX5Xua4ceOQnJws/T148KAAIyYiIiKioowtDGrWvHlz9O/fHzVr1sStW7fQunVrAMD169fh6OhY4OsbNWoUxo4di549ewIA3N3dcf/+fYSEhCAgIAB2dnYAgMTERNjb20vzJSYmokaNGgqXqaenBz09vQKPlYiIiIiKPrYwqNmyZcvg6emJJ0+eYNu2bShZsiQA4Pz58+jVq1eBr+/169fQ0pJ/W7W1tZGZmQkAcHJygp2dHQ4fPixNT0lJwenTp+Hp6Vng8RARERFR8cYWBjUzNzfH0qVLs5Wr656Adu3aYcaMGShbtiyqVKmCixcvYv78+fjmm28AADKZDMHBwZg+fTpcXFzg5OSECRMmoFSpUujYsaNaYiIiIiKi4osJg5rExcXJvS5btmyhrHfJkiWYMGECBg0ahMePH6NUqVIYOHAgJk6cKNUZPXo0Xr16haCgICQlJaFhw4YICwuDvr5+ocRIRERERMWHTHz8CGAqMFpaWpDJZBBCQCaTISMjQ9MhFZiUlBSYmZkhOTkZpqammg6HiIioWHFf555rnasBVwt8vfz+pvxiC4OaZN0zQERERERUnPGmZyIiIiIiUootDGqwa9culeu2b99ejZEQEREREf07TBjUQNXRhj63exuIiIiI6PPDhEENeP8CEREREX0ueA9DIXr79q2mQyAiIiIiyhMmDGqWkZGBadOmoXTp0jA2Nsa9e/cAABMmTMDPP/+s4eiIiIiIiHLGhEHNZsyYgbVr12LOnDnQ1dWVyqtWrYo1a9ZoMDIiIiIiotwxYVCz9evXY9WqVfD394e2trZUXr16ddy8eVODkRERERER5Y4Jg5o9evQIFSpUyFaemZmJ9+/fayAiIiIiIiLVMWFQs8qVK+PEiRPZyrdu3YqaNWtqICIiIiIiItVxWFU1mzhxIgICAvDo0SNkZmZi+/btiI6Oxvr167F7925Nh0dERERElCO2MKhZhw4d8Ndff+HQoUMwMjLCxIkTERUVhb/++gvNmzfXdHhERERERDliC0Mh8Pb2xsGDBzUdBhERERFRnrGFgYiIiIiIlGILgxpYWFhAJpOpVPf58+dqjoaIiIiIKP+YMKjBwoULpf8/e/YM06dPh5+fHzw9PQEAkZGR2L9/PyZMmKChCImIiIiIVCMTQghNB/E569KlC5o0aYIhQ4bIlS9duhSHDh3Cjh07NBPYv5CSkgIzMzMkJyfD1NRU0+EQEREVK+7r3HOtczXgaoGvl9/flF+8h0HN9u/fj5YtW2Yrb9myJQ4dOqSBiIiIiIiIVMeEQc1KliyJnTt3ZivfuXMnSpYsqYGIiIiIiIhUx3sY1GzKlCno378/wsPDUa9ePQDA6dOnERYWhtWrV2s4OiIiIiKinDFhULPAwEC4ublh8eLF2L59OwDAzc0NJ0+elBIIIiIiIqKiiglDIahXrx42btyo6TCIiIiIiPKMCUMhyMjIwI4dOxAVFQUAqFKlCtq3bw9tbW0NR0ZERERElDMmDGp2584dtGnTBg8fPkSlSpUAACEhIXBwcMCePXvg7Oys4QiJiIiIiJTjKElqNmzYMJQvXx4PHjzAhQsXcOHCBcTFxcHJyQnDhg3TdHhERERERDliC4OaHTt2DKdOnYKlpaVUVrJkScyaNQteXl4ajIyIiIiIKHdsYVAzPT09vHz5Mlt5amoqdHV1NRAREREREZHqmDCoWdu2bREUFITTp09DCAEhBE6dOoX//Oc/aN++vabDIyIiIiLKERMGNVu8eDGcnZ3h6ekJfX196Ovrw8vLCxUqVMCiRYs0HR4RERERUY54D4OamZubY+fOnbh9+zZu3rwJ4MOD2ypUqKDhyIiIiIiIcseEoZC4uLjAxcVF02EQEREREeUJEwY1E0Jg69atOHr0KB4/fozMzEy56du3b9dQZEREREREuWPCoGbBwcFYuXIlmjRpAltbW8hkMk2HRERERESkMiYMarZhwwZs374drVu31nQoRERERER5xlGS1MzMzAzly5fXdBhERERERPnChEHNJk+ejClTpuDNmzeaDoWIiIiIKM/YJUnNunfvjt9++w02NjZwdHSEjo6O3PQLFy5oKDIiIiIiotwxYVCzgIAAnD9/Hl9//TVveiYiIiKiYocJg5rt2bMH+/fvR8OGDTUdChERERFRnvEeBjVzcHCAqamppsMgIiIiIsoXJgxqNm/ePIwePRqxsbGaDoWIiIiIKM/YJUnNvv76a7x+/RrOzs4wNDTMdtPz8+fPNRQZEREREVHumDCo2cKFCzUdAhERERFRvjFhULOAgABNh0BERERElG+8h+Ez9OjRI3z99dcoWbIkDAwM4O7ujnPnzknThRCYOHEi7O3tYWBgAF9fX9y+fVuDERMRERFRUcWE4TPz4sULeHl5QUdHB/v27cONGzcwb948WFhYSHXmzJmDxYsXY8WKFTh9+jSMjIzg5+eHt2/fajByIiIiIiqK2CXpMzN79mw4ODggNDRUKnNycpL+L4TAwoULMX78eHTo0AEAsH79etja2mLHjh3o2bNnocdMREREREUXWxjU4MqVK8jMzNTIunft2oXatWujW7dusLGxQc2aNbF69WppekxMDBISEuDr6yuVmZmZoV69eoiMjFS4zLS0NKSkpMj9EREREdGXgQmDGtSsWRNPnz4FAJQvXx7Pnj0rtHXfu3cPy5cvh4uLC/bv349vv/0Ww4YNw7p16wAACQkJAABbW1u5+WxtbaVpnwoJCYGZmZn05+DgoN6NICIiIqIigwmDGpibmyMmJgYAEBsbW6itDZmZmahVqxZmzpyJmjVrIigoCAMGDMCKFSvyvcxx48YhOTlZ+nvw4EEBRkxERERERRnvYVCDLl26wMfHB/b29pDJZKhduza0tbUV1r13716Brtve3h6VK1eWK3Nzc8O2bdsAAHZ2dgCAxMRE2NvbS3USExNRo0YNhcvU09ODnp5egcZJRERERMUDEwY1WLVqFTp37ow7d+5g2LBhGDBgAExMTApl3V5eXoiOjpYru3XrFsqVKwfgww3QdnZ2OHz4sJQgpKSk4PTp0/j2228LJUYiIiIiKj6YMKhJy5YtAQDnz5/H8OHDCy1hGDFiBBo0aICZM2eie/fuOHPmDFatWoVVq1YBAGQyGYKDgzF9+nS4uLjAyckJEyZMQKlSpdCxY8dCiZGIiIiIig8mDGr28fCmDx8+BACUKVNGbeurU6cO/vzzT4wbNw5Tp06Fk5MTFi5cCH9/f6nO6NGj8erVKwQFBSEpKQkNGzZEWFgY9PX11RYXERERERVPMiGE0HQQn7PMzExMnz4d8+bNQ2pqKgDAxMQE3333HX744QdoaRW/+85TUlJgZmaG5ORkmJqaajocIiKiYsV9nXuuda4GXC3w9fL7m/KLLQxq9sMPP+Dnn3/GrFmz4OXlBQA4efIkJk+ejLdv32LGjBkajpCIiIiISDkmDGq2bt06rFmzBu3bt5fKqlWrhtKlS2PQoEFMGIiIiIioSCt+/WGKmefPn8PV1TVbuaurK54/f66BiIiIiIiIVMeEQc2qV6+OpUuXZitfunQpqlevroGIiIiIiIhUxy5JajZnzhy0adMGhw4dgqenJwAgMjISDx48wN69ezUcHRERERFRztjCoGY+Pj64desWOnXqhKSkJCQlJaFz586Ijo6Gt7e3psMjIiIiIsoRWxgKQalSpXhzMxEREREVS2xhICIiIiIipZgwEBERERGRUkwYiIiIiIhIKSYMaiSEQFxcHN6+favpUIiIiIiI8oUJgxoJIVChQgU8ePBA06EQEREREeULEwY10tLSgouLC549e6bpUIiIiIiI8oUJg5rNmjULo0aNwrVr1zQdChERERFRnvE5DGrWp08fvH79GtWrV4euri4MDAzkpj9//lxDkRERERER5Y4Jg5otXLhQ0yEQEREREeUbEwY1CwgI0HQIRERERET5xnsYCsHdu3cxfvx49OrVC48fPwYA7Nu3D9evX9dwZEREREREOWPCoGbHjh2Du7s7Tp8+je3btyM1NRUAcPnyZUyaNEnD0RERERER5YwJg5qNHTsW06dPx8GDB6GrqyuVN23aFKdOndJgZEREREREuWPCoGZXr15Fp06dspXb2Njg6dOnGoiIiIiIiEh1TBjUzNzcHPHx8dnKL168iNKlS2sgIiIiIiIi1TFhULOePXtizJgxSEhIgEwmQ2ZmJiIiIvD999+jT58+mg6PiIiIiChHTBjUbObMmXB1dYWDgwNSU1NRuXJlNGrUCA0aNMD48eM1HR4RERERUY74HAY109XVxerVqzFhwgRcu3YNqampqFmzJlxcXDQdGhERERFRrpgwFJKyZcvCwcEBACCTyTQcDRERERGRatglqRD8/PPPqFq1KvT19aGvr4+qVatizZo1mg6LiIiIiChXbGFQs4kTJ2L+/PkYOnQoPD09AQCRkZEYMWIE4uLiMHXqVA1HSERERESkHBMGNVu+fDlWr16NXr16SWXt27dHtWrVMHToUCYMRERERFSksUuSmr1//x61a9fOVu7h4YH09HQNREREREREpDomDGrWu3dvLF++PFv5qlWr4O/vr4GIiIiIiIhUxy5JajBy5Ejp/zKZDGvWrMGBAwdQv359AMDp06cRFxfHB7cRERERUZHHhEENLl68KPfaw8MDAHD37l0AgJWVFaysrHD9+vVCj42IiIiIKC+YMKjB0aNHNR0CEREREVGB4D0MRERERESkFFsY1Ozt27dYsmQJjh49isePHyMzM1Nu+oULFzQUGRERERFR7pgwqFm/fv1w4MABdO3aFXXr1oVMJtN0SEREREREKmPCoGa7d+/G3r174eXlpelQiIiIiIjyjPcwqFnp0qVhYmKi6TCIiIiIiPKFCYOazZs3D2PGjMH9+/c1HQoRERERUZ6xS5Ka1a5dG2/fvkX58uVhaGgIHR0duenPnz/XUGRERERERLljwqBmvXr1wqNHjzBz5kzY2trypmciIiIiKlaYMKjZ33//jcjISFSvXl3ToRARERER5RnvYVAzV1dXvHnzRtNhEBERERHlCxMGNZs1axa+++47hIeH49mzZ0hJSZH7IyIiIiIqypgwqFnLli0RGRmJZs2awcbGBhYWFrCwsIC5uTksLCzUvv5Zs2ZBJpMhODhYKnv79i0GDx6MkiVLwtjYGF26dEFiYqLaYyEiIiKi4of3MKjZ0aNHNbbus2fPYuXKlahWrZpc+YgRI7Bnzx5s2bIFZmZmGDJkCDp37oyIiAgNRUpERERERRUTBjXz8fHRyHpTU1Ph7++P1atXY/r06VJ5cnIyfv75Z2zatAlNmzYFAISGhsLNzQ2nTp1C/fr1NRIvERERERVNTBjU7Pjx4zlOb9SokVrWO3jwYLRp0wa+vr5yCcP58+fx/v17+Pr6SmWurq4oW7YsIiMjFSYMaWlpSEtLk17z3gsiIiKiLwcTBjVr3LhxtrKPn8WQkZFR4OvcvHkzLly4gLNnz2ablpCQAF1dXZibm8uV29raIiEhQeHyQkJCMGXKlAKPk4iIiIiKPt70rGYvXryQ+3v8+DHCwsJQp04dHDhwoMDX9+DBAwwfPhwbN26Evr5+gSxz3LhxSE5Olv4ePHhQIMslIiIioqKPLQxqZmZmlq2sefPm0NXVxciRI3H+/PkCXd/58+fx+PFj1KpVSyrLyMjA8ePHsXTpUuzfvx/v3r1DUlKSXCtDYmIi7OzsFC5TT08Penp6BRonERERERUPTBg0xNbWFtHR0QW+3GbNmuHq1atyZX379oWrqyvGjBkDBwcH6Ojo4PDhw+jSpQsAIDo6GnFxcfD09CzweIiIiL4kUa5uuVcax8svKl54xKrZlStX5F4LIRAfH49Zs2ahRo0aBb4+ExMTVK1aVa7MyMgIJUuWlMr79euHkSNHwtLSEqamphg6dCg8PT05QhIRERERZcOEQc1q1KgBmUwGIYRcef369fHLL79oJKYFCxZAS0sLXbp0QVpaGvz8/PDTTz9pJBYiIiIiKtqYMKhZTEyM3GstLS1YW1sX2A3JqggPD5d7ra+vj2XLlmHZsmWFFgMRERERFU9MGNSsXLlymg6BiIiIiCjfmDAUgsOHD+Pw4cN4/PgxMjMz5aZpqlsSEREREZEqmDCo2ZQpUzB16lTUrl0b9vb2cg9tIyIiIiIq6pgwqNmKFSuwdu1a9O7dW9OhEBERERHlGZ/0rGbv3r1DgwYNNB0GEREREVG+MGFQs/79+2PTpk2aDoOIiIiIKF/YJUnN3r59i1WrVuHQoUOoVq0adHR05KbPnz9fQ5EREREREeWOCYOaXblyRXqi87Vr1+Sm8QZoIiIiIirqmDCo2dGjRzUdAhERERFRvvEeBiIiIiIiUootDEREREQqiHJ1y7VO93G8tKLPD1sYiIiIiIhIKSYMRERERESkFBMGIiIiIiJSigkDEREREREpxTtziIiI6Iunyg3NRF8qtjAQEREREZFSTBiIiIiIiEgpdkkiIiKiLx6fn0CkHFsYiIiIiIhIKSYMRERERESkFBMGIiIiIiJSigkDEREREREpxYSBiIiIiIiUYsJARERERERKMWEgIiIiIiKlmDAQEREREZFSTBiIiIiIiEgpJgxERERERKQUEwYiIiIiIlKKCQMRERERESnFhIGIiIiIiJRiwkBEREREREoxYSAiIiIiIqWYMBARERERkVJMGIiIiIiISCkmDEREREREpBQTBiIiIiIiUqqEpgMgIiIi+lSUq1uuddxuRhVCJETEFgYiIiIiIlKKCQMRERERESnFhIGIiIiIiJRiwkBERERERErxpmciIiIqcrqPy/0S5WohxEFEbGEgIiIiIqIcMGH4zISEhKBOnTowMTGBjY0NOnbsiOjoaLk6b9++xeDBg1GyZEkYGxujS5cuSExM1FDERERERFSUMWH4zBw7dgyDBw/GqVOncPDgQbx//x4tWrTAq1evpDojRozAX3/9hS1btuDYsWP4559/0LlzZw1GTURERERFFe9h+MyEhYXJvV67di1sbGxw/vx5NGrUCMnJyfj555+xadMmNG3aFAAQGhoKNzc3nDp1CvXr19dE2ERERERURLGF4TOXnJwMALC0tAQAnD9/Hu/fv4evr69Ux9XVFWXLlkVkZKTCZaSlpSElJUXuj4iIiIi+DEwYPmOZmZkIDg6Gl5cXqlatCgBISEiArq4uzM3N5era2toiISFB4XJCQkJgZmYm/Tk4OKg7dCIiIiIqIpgwfMYGDx6Ma9euYfPmzf9qOePGjUNycrL09+DBgwKKkIiIiIiKOt7D8JkaMmQIdu/ejePHj6NMmTJSuZ2dHd69e4ekpCS5VobExETY2dkpXJaenh709PTUHTIRERERFUFsYfjMCCEwZMgQ/Pnnnzhy5AicnJzkpnt4eEBHRweHDx+WyqKjoxEXFwdPT8/CDpeIiIiIiji2MHxmBg8ejE2bNmHnzp0wMTGR7kswMzODgYEBzMzM0K9fP4wcORKWlpYwNTXF0KFD4enpyRGSiIiIiCgbJgyfmeXLlwMAGjduLFceGhqKwMBAAMCCBQugpaWFLl26IC0tDX5+fvjpp58KOVIiIiL1i3J1U63iOF4SESnDT8dnRgiRax19fX0sW7YMy5YtK4SIiIiIiKg44z0MRERERESkFBMGIiIiIiJSigkDEREREREpxYSBiIiIiIiU4k3PRERE9NnqztGPiP41tjAQEREREZFSTBiIiIiIiEgpJgxERERERKQUEwYiIiIiIlKKCQMRERERESnFoQOIiIioUEW5uuVeiaMbERUZbGEgIiIiIiKlmL4TERFRoeKzEYiKF7YwEBERERGRUkwYiIiIiIhIKbYJEhERkUo3IrvdjMq1jvs694IIh4iKELYwEBERERGRUkwYiIiIiIhIKXZJIiIiomKJ3Z+ICgdbGIiIiIiISCkmDEREREREpBS7JBEREZFqD1NjFyCiLxJbGIiIiIiISCkmDEREREREpBS7JBERERWwgnoIGhFRUcAWBiIiIiIiUootDFT0TDZToU6y+uMgIsonVW4gvlpA62JrBhGpG1sYiIiIiIhIKSYMRERERESkFLskERHRF8FdhWcIXA0oqI5Chacwuz8R0ZeJLQxERERERKQUEwYiIiIiIlKKXZKIiEhjilo3oaIWjyojIEGFLklERP8GWxiIiIiIiEgpJgxERERERKQU2zGJiIiKKFVGQFKFKl2tiIiUYQsDEREREREpxYSBiIiIiIiUYpckIiINcxy7J9c6sbPaFLvlfK4KqnsPuwkRUXHBFgYiIiIiIlKKLQxERArwV/acqfJ8gIK6YZeIiDSLLQxERERERKQUEwYiIiIiIlKK7cVEuSiOXVOKY8xUvBRmdyN2fyIi0iy2MHzBli1bBkdHR+jr66NevXo4c+aMpkMiIiIioiKGCcMX6vfff8fIkSMxadIkXLhwAdWrV4efnx8eP36s6dCIiIiIqAiRCSGEpoOgwlevXj3UqVMHS5cuBQBkZmbCwcEBQ4cOxdixY3OcNyUlBWZmZkhOToapqWnBBzfZTIU6yblWUaVbTkEpzO49RW67CvH9itX/qkDWpRJVtkul5agQTxE75k3ccj4HEJH6XQ24WuDLVPv3N3222MLwBXr37h3Onz8PX19fqUxLSwu+vr6IjIzUYGREREREVNTwLrEv0NOnT5GRkQFbW1u5cltbW9y8eTNb/bS0NKSlpUmvk5M//NKZkpKingDTVGj0UmHdmWmvCyAY1ahtXyhQ5LarEN+vFFnBrEslqmyXKorYPlRFxpuMAlkOEeWfOr5XspbJziWUV0wYKFchISGYMmVKtnIHBwcNRPP/ZhVQd5ECYrZQ0xGoR4FtVwG9XyotpYgdGwUWT1HbLiJSK7Nv1feZf/nyJczMeE4h1TFh+AJZWVlBW1sbiYmJcuWJiYmws7PLVn/cuHEYOXKk9DozMxPPnz9HyZIlIZPJCjS2lJQUODg44MGDB+xfqUbcz4WD+7lwcD8XDu7nwqOufS2EwMuXL1GqVKkCWyZ9GZgwfIF0dXXh4eGBw4cPo2PHjgA+JAGHDx/GkCFDstXX09ODnp6eXJm5ublaYzQ1NeUXUiHgfi4c3M+Fg/u5cHA/Fx517Gu2LFB+MGH4Qo0cORIBAQGoXbs26tati4ULF+LVq1fo27evpkMjIiIioiKECcMXqkePHnjy5AkmTpyIhIQE1KhRA2FhYdluhCYiIiKiLxsThi/YkCFDFHZB0iQ9PT1MmjQpWxcoKljcz4WD+7lwcD8XDu7nwsN9TUUNH9xGRERERERK8cFtRERERESkFBMGIiIiIiJSigkDEREREREpxYSBiIiIiIiUYsJAhW7ZsmVwdHSEvr4+6tWrhzNnzuRYf8uWLXB1dYW+vj7c3d2xd+/eQoq0eMvLfl69ejW8vb1hYWEBCwsL+Pr65vq+0Ad5PZ6zbN68GTKZTHp4IuUsr/s5KSkJgwcPhr29PfT09FCxYkWeO1SQ1/28cOFCVKpUCQYGBnBwcMCIESPw9u3bQoq2eDp+/DjatWuHUqVKQSaTYceOHbnOEx4ejlq1akFPTw8VKlTA2rVr1R4nkRxBVIg2b94sdHV1xS+//CKuX78uBgwYIMzNzUViYqLC+hEREUJbW1vMmTNH3LhxQ4wfP17o6OiIq1evFnLkxUte9/NXX30lli1bJi5evCiioqJEYGCgMDMzEw8fPizkyIuXvO7nLDExMaJ06dLC29tbdOjQoXCCLcbyup/T0tJE7dq1RevWrcXJkydFTEyMCA8PF5cuXSrkyIuXvO7njRs3Cj09PbFx40YRExMj9u/fL+zt7cWIESMKOfLiZe/eveKHH34Q27dvFwDEn3/+mWP9e/fuCUNDQzFy5Ehx48YNsWTJEqGtrS3CwsIKJ2AiIQQTBipUdevWFYMHD5ZeZ2RkiFKlSomQkBCF9bt37y7atGkjV1avXj0xcOBAtcZZ3OV1P38qPT1dmJiYiHXr1qkrxM9CfvZzenq6aNCggVizZo0ICAhgwqCCvO7n5cuXi/Lly4t3794VVoifhbzu58GDB4umTZvKlY0cOVJ4eXmpNc7PiSoJw+jRo0WVKlXkynr06CH8/PzUGBmRPHZJokLz7t07nD9/Hr6+vlKZlpYWfH19ERkZqXCeyMhIufoA4Ofnp7Q+5W8/f+r169d4//49LC0t1RVmsZff/Tx16lTY2NigX79+hRFmsZef/bxr1y54enpi8ODBsLW1RdWqVTFz5kxkZGQUVtjFTn72c4MGDXD+/Hmp29K9e/ewd+9etG7dulBi/lLwe5CKAj7pmQrN06dPkZGRAVtbW7lyW1tb3Lx5U+E8CQkJCusnJCSoLc7iLj/7+VNjxoxBqVKlsn1J0f/kZz+fPHkSP//8My5dulQIEX4e8rOf7927hyNHjsDf3x979+7FnTt3MGjQILx//x6TJk0qjLCLnfzs56+++gpPnz5Fw4YNIYRAeno6/vOf/+C///1vYYT8xVD2PZiSkoI3b97AwMBAQ5HRl4QtDEQkZ9asWdi8eTP+/PNP6Ovrazqcz8bLly/Ru3dvrF69GlZWVpoO57OWmZkJGxsbrFq1Ch4eHujRowd++OEHrFixQtOhfVbCw8Mxc+ZM/PTTT7hw4QK2b9+OPXv2YNq0aZoOjYgKGFsYqNBYWVlBW1sbiYmJcuWJiYmws7NTOI+dnV2e6lP+9nOWH3/8EbNmzcKhQ4dQrVo1dYZZ7OV1P9+9exexsbFo166dVJaZmQkAKFGiBKKjo+Hs7KzeoIuh/BzP9vb20NHRgba2tlTm5uaGhIQEvHv3Drq6umqNuTjKz36eMGECevfujf79+wMA3N3d8erVKwQFBeGHH36AlhZ/kywIyr4HTU1N2bpAhYafZio0urq68PDwwOHDh6WyzMxMHD58GJ6engrn8fT0lKsPAAcPHlRan/K3nwFgzpw5mDZtGsLCwlC7du3CCLVYy+t+dnV1xdWrV3Hp0iXpr3379mjSpAkuXboEBweHwgy/2MjP8ezl5YU7d+5ICRkA3Lp1C/b29kwWlMjPfn79+nW2pCArSRNCqC/YLwy/B6lI0PRd1/Rl2bx5s9DT0xNr164VN27cEEFBQcLc3FwkJCQIIYTo3bu3GDt2rFQ/IiJClChRQvz4448iKipKTJo0icOqqiCv+3nWrFlCV1dXbN26VcTHx0t/L1++1NQmFAt53c+f4ihJqsnrfo6LixMmJiZiyJAhIjo6WuzevVvY2NiI6dOna2oTioW87udJkyYJExMT8dtvv4l79+6JAwcOCGdnZ9G9e3dNbUKx8PLlS3Hx4kVx8eJFAUDMnz9fXLx4Udy/f18IIcTYsWNF7969pfpZw6qOGjVKREVFiWXLlnFYVSp0TBio0C1ZskSULVtW6Orqirp164pTp05J03x8fERAQIBc/T/++ENUrFhR6OrqiipVqog9e/YUcsTFU172c7ly5QSAbH+TJk0q/MCLmbwezx9jwqC6vO7nv//+W9SrV0/o6emJ8uXLixkzZoj09PRCjrr4yct+fv/+vZg8ebJwdnYW/9fOvYVE1e5xHP+NlY1OhGl2UMyaEhIcNZu6iEIZLwQx0i6KiSQJqYjAMrECbS4isCww8EKSMsK0iCgMoQPW1dhBS0NpKLMGMaLD5EUHPNDMvtjsYfvm0l7qVTf7+7lb83+e9fx5LgZ+PGsts9kciIuLC+zduzcwMDAw+Y3/D7l///6Y/7f/2dsdO3YE0tPTf5qTmpoaCA0NDVit1kBdXd2k943/b6ZAgHNDAAAAAGPjHQYAAAAAhggMAAAAAAwRGAAAAAAYIjAAAAAAMERgAAAAAGCIwAAAAADAEIEBAAAAgCECAwAAAABDBAYAAAAAhggMAIBfNjIyMtUtAAAmGYEBAKaxW7duaf369YqIiFBUVJRycnLU29sbrPf398vpdCoyMlIWi0V2u12PHj0K1m/evKk1a9bIbDZr/vz5ysvLC9ZMJpNu3Lgxar2IiAhduHBBkuT1emUymXTlyhWlp6fLbDbr0qVL8vl8cjqdio2NVXh4uGw2mxobG0fdx+/36+TJk1qxYoVmz56tJUuW6Pjx45Ikh8Ohffv2jRr/8eNHhYaGqqWl5U9sGwDgDyIwAMA09u3bNxUXF6u9vV0tLS0KCQlRXl6e/H6/vn79qvT0dL19+1ZNTU169uyZSktL5ff7JUnNzc3Ky8tTdna2Ojo61NLSorVr1/7tHg4fPqyioiJ5PB5lZWVpcHBQq1evVnNzs7q7u7Vr1y7l5+fr8ePHwTlHjhxRRUWFysvL9fz5czU0NGjhwoWSpMLCQjU0NGhoaCg4vr6+XrGxsXI4HL+5YwCAP80UCAQCU90EAODXfPr0SdHR0erq6lJra6tKSkrk9XoVGRn509h169bJarWqvr5+zHuZTCZdv35dubm5wd8iIiJUVVWlgoICeb1eLVu2TFVVVSoqKhq3r5ycHK1cuVKnTp3Sly9fFB0drerqahUWFv40dnBwUDExMaqpqdGWLVskSSkpKdq8ebNcLtff2A0AwGTghAEAprGenh45nU5ZrVbNnTtXS5culST19fWps7NTq1atGjMsSFJnZ6cyMzN/uwe73T7q+sePHzp27JhsNpsiIyM1Z84c3b59W319fZIkj8ejoaEhw7XNZrPy8/N1/vx5SdLTp0/V3d2tgoKC3+4VAPDnzZzqBgAAxjZu3Kj4+HjV1tYqJiZGfr9fSUlJGh4eVlhY2LhzJ6qbTCb99ZB5rJeaLRbLqOvKykqdOXNGVVVVstlsslgs2r9/v4aHh39pXenfjyWlpqaqv79fdXV1cjgcio+Pn3AeAGDyccIAANOUz+fTixcvVFZWpszMTCUmJmpgYCBYT05OVmdnpz5//jzm/OTk5HFfIo6Ojta7d++C1z09Pfr+/fuEfbndbm3atEnbt29XSkqKrFarXr58GawnJCQoLCxs3LVtNpvsdrtqa2vV0NCgnTt3TrguAGBqEBgAYJqaN2+eoqKidPbsWb169Ur37t1TcXFxsO50OrVo0SLl5ubK7Xbr9evXunbtmh48eCBJcrlcamxslMvlksfjUVdXl06cOBGc73A4VF1drY6ODrW3t2vPnj2aNWvWhH0lJCTo7t27am1tlcfj0e7du/X+/ftg3Ww269ChQyotLdXFixfV29urhw8f6ty5c6PuU1hYqIqKCgUCgVFfbwIATC8EBgCYpkJCQnT58mU9efJESUlJOnDggCorK4P10NBQ3blzRwsWLFB2drZsNpsqKio0Y8YMSVJGRoauXr2qpqYmpaamyuFwjPqS0enTpxUXF6cNGzZo27ZtKikpUXh4+IR9lZWVKS0tTVlZWcrIyAiGlv9WXl6ugwcP6ujRo0pMTNTWrVv14cOHUWOcTqdmzpwpp9Mps9n8GzsFAPgn8ZUkAMCU8Hq9Wr58udra2pSWljbV7QAADBAYAACTamRkRD6fTyUlJXrz5o3cbvdUtwQAGAePJAEAJpXb7dbixYvV1tammpqaqW4HADABThgAAAAAGOKEAQAAAIAhAgMAAAAAQwQGAAAAAIYIDAAAAAAMERgAAAAAGCIwAAAAADBEYAAAAABgiMAAAAAAwBCBAQAAAIChfwHkjzRZw4CAhQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "path_model_label=Path.home() / \"Desktop/Code/CELLSEG_BENCHMARK/RESULTS/full data/instance/instance_pred_WNet.tif\"\n", + "res = evl.evaluate_model_performance(imread(path_true_labels), imread(path_model_label),visualize=False, return_graphical_summary=True,plot_according_to_gt_label=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "path_model_label=Path.home() / \"Desktop/Code/CELLSEG_BENCHMARK/RESULTS/full data/instance/stardist_labels.tif\"\n", + "res = evl.evaluate_model_performance(imread(path_true_labels), imread(path_model_label),visualize=False, return_graphical_summary=True,plot_according_to_gt_label=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "path_model_label=Path.home() / \"Desktop/Code/CELLSEG_BENCHMARK/RESULTS/full data/instance/instance_threshold_pred_Swin_Generalized_latest(1).tif\"\n", + "res = evl.evaluate_model_performance(imread(path_true_labels), imread(path_model_label),visualize=False, return_graphical_summary=True,plot_according_to_gt_label=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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mSWWzZ88WhoaG4vr163Lzf/PNN0JbW1skJibmGUN6enqushEjRggDAwPx6tUrIYQQmZmZonLlysLBwUE8e/ZMru67+7JVq1bC2NhY7ph9v46vr69wcHDItc5Zs2bles8BCC0tLREdHZ1v3K9fvxZ16tQRbdu2lcpu3LghtLS0RK9evURWVpbCmLKyskTFihXFgAED5KYvWrRIyGQycevWrVzrfnedVlZWok6dOuLly5dS+a5duwQAMXPmTKks5/g7c+aM0uXlWLBggQAg4uPjc00DIHR0dMTNmzelsosXLwoAYunSpVLZ0KFDha2trXj8+LHc/AMHDhSmpqYK3/d3KZru6ekpqlSpIr3etm1bvts0btw4YWJiIjIzM5XWGT9+vAAgjh07JpU9f/5cVK5cWTg6Okrv3W+//SYAiEWLFuVaxrvH2PvnCEXbEhkZKQCI9evXS2V5fR49PDyEh4eH9Hrx4sUCgPj999+lstevX4tmzZoJIyMjkZqaKoT4v/Nb+fLlxdOnT6W6O3bsEADEP//8o3S/CPF/x42bm5t4/fq1VD5//nwBQOzYsUMI8XZ/mZmZieHDh8vNf//+fWFqaipXruz8mrP9V69eFUIIsXPnTqGrqyu6d+8u9/lwdXUVvXr1kl6reqxt2LBBaGlpyb3PQgixYsUKAUBERERIZaoe54qcOXMm13dKDg8Pj1zve0ZGhrCxsRF9+vSRygoSqyI569m0aZNUdu3aNemcdvLkSal83759ueJV9Xst57Nz6tQpqezhw4fC1NRU7hxSkOND0bn4fapcYyii6DpA0eczODhYyGQyue8SVa81jh07JgCIjRs3yi0zLCwsV/n7n+sePXqI2rVrF3i7MjMzRUZGhlzZs2fPhLW1tfjiiy+ksoKcD+rVqydsbW3lrk32798vACj8Dn1f7dq15bYth6rXQgU5ZhQZM2aMkMlk4sKFC1LZkydPhLm5ea7vN2WxKlPYc7w6zqfvf15+/PFHAUA6HgtL5S5JWVlZ2LdvH3r27IlKlSpJ5c7OzvD09Mx3fjMzM0RHR+PGjRuqrjKXPn36yP2qmZ93h3rM+aXp9evXCptai0pWVhb279+Pnj17okqVKlK5ra0tBg0ahOPHjyM1NVVuHn9/f7muL+7u7sjKysLt27eVricpKQlRUVHw8/ODubm5VO7q6ooOHTpgz549RbhV/6dWrVrSL2TA21+Za9SogVu3bkllW7Zsgbu7O8qVK4fHjx9Lf+3bt0dWVhaOHj2a5zre/SXq+fPnePz4Mdzd3ZGeno5r164BeNs0Hh8fj/Hjx+fqg5+zLx89eoSjR4/iiy++kDtm361TGB4eHqhVq1aecT979gwpKSlwd3fH+fPnpfLt27cjOzsbM2fOzDUoQE5MWlpa8PHxwc6dO/H8+XNp+saNG9G8eXNUrlxZaWxnz57Fw4cPMXLkSLl+8127dkXNmjUVdqkpCu3bt5f7Nd3V1RUmJibScSGEwN9//w0vLy8IIeSOC09PT6SkpMjtJ0Xe3b8pKSl4/PgxPDw8cOvWLaSkpAD4v/sxdu3apbBLV06dFy9e5NlVYc+ePWjcuDFatmwplRkZGcHf3x8JCQm4evUqAODvv/+GhYUFxowZk2sZeR1j727Lmzdv8OTJE1StWhVmZmb57oe8YraxsYG3t7dUVrZsWYwdOxZpaWk4cuSIXP0BAwagXLly0uucz/W7n+W8+Pv7y/0q+tVXX6FMmTLSuefAgQNITk6Gt7e33Putra2NJk2a5OoaoUhOTDnnjGPHjqFRo0bo0KEDjh07BuBt98crV65IdQtyrG3ZsgXOzs6oWbOmXL22bdsCQK4Y8zvOC8vIyAifffaZ9FpHRweNGzfOdV4tSKzK1jNw4EDpdY0aNWBmZgZnZ2e5lu+c/+esvyDfa3v27EHTpk3RuHFjqZ6lpSV8fHzkYimK4+NdRXGNkePdz+eLFy/w+PFjNG/eHEIIXLhwIVf9/K41tmzZAlNTU3To0EFuW93c3GBkZJTntpqZmeHu3bsF6i4IANra2tJ9TtnZ2Xj69CkyMzPRsGFDheeY/M4HOdccvr6+MDU1lep16NBB4fdhYeR3LfShx0xYWBiaNWsm17pibm6e69gsCgU9x6vzfJrzvbhjxw6VuoUro3LC8OjRI7x8+RLVqlXLNU2VPtVBQUFITk5G9erV4eLigq+//lquD6oq8rpQep+WlpbciQ0AqlevDgBqHaLw0aNHSE9PV7hPnJ2dkZ2djTt37siVv38xm/OhzatvY84HSNl6Hj9+jBcvXhQ4/vy8HyvwNt53Y71x4wbCwsJgaWkp95czEk/OzbbKREdHo1evXjA1NYWJiQksLS2lL9OcC8Oc/uF5jV+ec6Ir6jHOlR2Hu3btQtOmTaGnpwdzc3NYWlpi+fLlUszA27i1tLTyPcEOHjwYL1++lPqHxsbG4ty5c/j888/znC+v46JmzZp5JqEfIr/j4tGjR0hOTsaqVatyHRdDhgwBkP9xERERgfbt20v361haWkp9MHP2sYeHB/r06YPAwEBYWFigR48eCAkJkevjPXLkSFSvXh2dO3dGxYoV8cUXX+Tqh3779m2ln62c6cDb97NGjRoFHgHp5cuXmDlzpnSPj4WFBSwtLZGcnCx3vBTE7du3Ua1atVyJ6Psx5yjMeedd738XGBkZwdbWVjq/5ly4tW3bNtd7vn///nzfb+BtN4Bq1apJycGxY8fg7u6OVq1a4b///sOtW7cQERGB7Oxs6QKnIMfajRs3EB0dnateznfF+zGqcv4rjIoVK+ZKMBWdVwsSq6rrMTU1hb29fa4yAHKfX1W/13KOw/e9P29RHB/vKoprjByJiYnSj3FGRkawtLSU7gV8//OpyrXGjRs3kJKSAisrq1zbmpaWlue2Tp06FUZGRmjcuDGqVauGUaNGqdyVZN26dXB1dZXu6bC0tMTu3bsVnmPyOx/knD8Kew2oivxi+NBj5vbt26hatWquckVlH6qg53h1nk8HDBiAFi1aYNiwYbC2tsbAgQPx119/FTh5KLaxQFu1aoW4uDjs2LED+/fvx5o1a/Djjz9ixYoV+Q71mUNRH8gPoewXQEU316qTtra2wnLx3k1TJYEqsWZnZ6NDhw6YMmWKwro5J1NFkpOT4eHhARMTEwQFBcHJyQl6eno4f/48pk6d+kHZsTIFPQ4UHYfHjh1D9+7d0apVK/zyyy+wtbVF2bJlERISku/NaYrUqlULbm5u+P333zF48GD8/vvv0NHRQf/+/Qu8rOKQ33GR87599tln0s3W78vpj65IXFwc2rVrh5o1a2LRokWwt7eHjo4O9uzZgx9//FFavkwmw9atW3Hy5En8888/2LdvH7744gssXLgQJ0+ehJGREaysrBAVFYV9+/Zh79692Lt3L0JCQjB48OBcNwiry5gxYxASEoLx48ejWbNmMDU1hUwmw8CBA9VyjCui7vNOznZs2LABNjY2uaarmmS1bNkShw4dwsuXL3Hu3DnMnDkTderUgZmZGY4dO4aYmBgYGRmhfv36cutV5VjLzs6Gi4sLFi1apLDe+xfS6tpnqp5XCxJrQdajie+gojo+chTFNQbw9rzfoUMHPH36FFOnTkXNmjVhaGiIe/fuwc/Pr1Cfz+zsbFhZWWHjxo0Kp+fVc8LZ2RmxsbHYtWsXwsLC8Pfff+OXX37BzJkzERgYqHS+33//HX5+fujZsye+/vprWFlZQVtbG8HBwXIDMuQoCdchqn6PFNUxo05FfY7/kG3X19fH0aNHcfjwYezevRthYWH4888/0bZtW+zfv1/pfs+1DlWDzRlhRlFzX2xsrErLMDc3x5AhQzBkyBCkpaWhVatWCAgIkD7MRflkuuzsbNy6dUvu4vT69esAIN0YkpO9vv8wKkW/wqoam6WlJQwMDBTuk2vXrkFLS0ulE3t+ch68pmw9FhYWSh/ClZeieA+cnJyQlpaW79j+ioSHh+PJkycIDQ1Fq1atpPL4+Phc6wCAK1euKF1Pzq8+V65cyXOd5cqVU/hAsoL8Gv/3339DT08P+/btk7uBKiQkJFfc2dnZuHr1ar7jsQ8ePBgTJ05EUlISNm3ahK5du8o1GSvy7nGR01UhR2xsrNIH9uXnQ48LS0tLGBsbIysrq1DHxT///IOMjAzs3LlT7lcoZc2wTZs2RdOmTfH9999j06ZN8PHxwebNm6VzjY6ODry8vODl5YXs7GyMHDkSK1euxIwZM1C1alU4ODgo/WwB/7efnZyccOrUKbx586ZAz4LZunUrfH19sXDhQqns1atXuY7Dgux3BwcHXLp0CdnZ2XKtDO/HXFRu3LiBNm3aSK/T0tKQlJSELl26APi/z6iVlVW+73le2+nu7o6QkBBs3rwZWVlZaN68ObS0tNCyZUspYWjevLn0pVeQY83JyQkXL15Eu3bt1PYkYaDozqvFEasiBflec3BwUOk6oSDHh6ryu8ZQxeXLl3H9+nWsW7cOgwcPlsqVdWFU5VrDyckJBw8eRIsWLQr1w6ehoSEGDBiAAQMG4PXr1+jduze+//57TJs2TemQzVu3bkWVKlUQGhoqd7zkN2iGMjnnjw+5BvzQ4/ZDjxkHBwfcvHkzV7misg+NVdVzfI6iPJ8qoqWlhXbt2qFdu3ZYtGgR5syZg2+//RaHDx9WeXkqd0nS1taGp6cntm/fjsTERKk8JiYG+/bty3f+J0+eyL02MjJC1apV5boK5FzgFtXTZH/++Wfp/0II/PzzzyhbtizatWsH4O3Bo62tnatP/S+//JJrWarGpq2tjY4dO2LHjh1yXZ8ePHiATZs2oWXLljAxMSnkFv0fW1tb1KtXD+vWrZOL6cqVK9i/f790kBVUUbwH/fv3R2RkpMLjIjk5GZmZmUrnzfnSf/dXjdevX+d6Txo0aIDKlStj8eLFuWLNmdfS0hKtWrXCb7/9JnfMvr98JycnpKSkyDVfJyUlqTxcXE7cMplMrlUiISEh18glPXv2hJaWFoKCgnL9yvD+Lzne3t6QyWQYN24cbt26JdfHWZmGDRvCysoKK1askPts7d27FzExMXKjNRXEhx4X2tra6NOnD/7++2+FCVx+wwgrOi5SUlJyJWTPnj3LtR9zErOc/fH+uUhLS0v6xTmnTpcuXXD69GlERkZK9V68eIFVq1bB0dFR6lLWp08fPH78WO5ckyOvX+a0tbVzTV+6dGmuVq2C7PcuXbrg/v37ciOKZGZmYunSpTAyMpK6VBSVVatWyd0nsnz5cmRmZkqj+nh6esLExARz5sxReD/Ju+95XtuZ09Vo3rx5cHV1lbrLuLu749ChQzh79qzcfVUFOdb69++Pe/fuYfXq1bnqvXz5ssi6dRbVebU4YlWkIN9rXbp0wcmTJ3H69Gmp3qNHj3L9ul6Q40MVqlxjqELRuUYIkWvY5Xfld63Rv39/ZGVlYfbs2bnmzczMzPO4eH+7dHR0UKtWLQghlN6npWw7Tp06JXdOK4h3rzne7VJz4MAB6Z6u/BgaGn7QZ+BDjxlPT09ERkbKPXH96dOnClt+PjRWVc/xOYryfPq+p0+f5ip7/3tRFQVqvwkMDERYWBjc3d0xcuRI6cuodu3a+fYVrFWrFlq3bg03NzeYm5vj7Nmz2Lp1q9zNQm5ubgCAsWPHwtPTE9ra2nI3aBWEnp4ewsLC4OvriyZNmmDv3r3YvXs3/ve//0nNf6ampujXrx+WLl0KmUwGJycn7Nq1S2FfsILE9t1330lj3o4cORJlypTBypUrkZGRoXBs7cJasGABOnfujGbNmmHo0KHSsKqmpqaFfmJqvXr1oK2tjXnz5iElJQW6urrS2Peq+vrrr7Fz505069ZNGnL1xYsXuHz5MrZu3YqEhARpaM73NW/eHOXKlYOvry/Gjh0LmUyGDRs25PrgaWlpYfny5fDy8kK9evUwZMgQ2Nra4tq1a4iOjpaSlZ9++gktW7ZEgwYN4O/vj8qVKyMhIQG7d++WThoDBw7E1KlT0atXL4wdO1Yaqqx69eoq34DatWtXLFq0CJ06dcKgQYPw8OFDLFu2DFWrVpX7bFStWhXffvstZs+eDXd3d/Tu3Ru6uro4c+YM7OzsEBwcLNW1tLREp06dsGXLFpiZmal0sV+2bFnMmzcPQ4YMgYeHB7y9vaVhVR0dHTFhwgSVtud9Ocf/t99+i4EDB6Js2bLw8vIqUCvW3LlzcfjwYTRp0gTDhw9HrVq18PTpU5w/fx4HDx5UeFLL0bFjR6lVYMSIEUhLS8Pq1athZWWFpKQkqd66devwyy+/oFevXnBycsLz58+xevVqmJiYSEn0sGHD8PTpU7Rt2xYVK1bE7du3sXTpUtSrV0/q7//NN9/gjz/+QOfOnTF27FiYm5tj3bp1iI+Px99//y39gj948GCsX78eEydOxOnTp+Hu7o4XL17g4MGDGDlyJHr06KFwe7p164YNGzbA1NQUtWrVQmRkJA4ePJhraM6CfB79/f2xcuVK+Pn54dy5c3B0dMTWrVsRERGBxYsXS8NjFpXXr1+jXbt26N+/P2JjY/HLL7+gZcuW6N69OwDAxMQEy5cvx+eff44GDRpg4MCBsLS0RGJiInbv3o0WLVpIF1p5nV+rVq0KGxsbxMbGyt1c3qpVK0ydOhUA5BIGQPVj7fPPP8dff/2FL7/8EocPH0aLFi2QlZWFa9eu4a+//sK+ffvQsGHDD95XTk5OMDMzw4oVK2BsbAxDQ0M0adKkQPflFVesyqj6vTZlyhRs2LABnTp1wrhx46RhVXNawHIU5PhQhSrXGKqoWbMmnJycMHnyZNy7dw8mJib4+++/ld6nosq1hoeHB0aMGIHg4GBERUWhY8eOKFu2LG7cuIEtW7ZgyZIl6Nu3r8Lld+zYETY2NmjRogWsra0RExODn3/+GV27ds3zM92tWzeEhoaiV69e6Nq1K+Lj47FixQrUqlULaWlpBdonOYKDg9G1a1e0bNkSX3zxBZ4+fSpdA6qyTDc3NyxfvhzfffcdqlatCisrq1wt4Xn50GNmypQp+P3339GhQweMGTNGGla1UqVKePr0qVyrwofGquo5PkdRnk/fFxQUhKNHj6Jr165wcHDAw4cP8csvv6BixYpyA3vkq6DDKh05ckS4ubkJHR0dUaVKFbFixQqFQ569P6zqd999Jxo3bizMzMyEvr6+qFmzpvj+++/lhpHKzMwUY8aMEZaWlkImk0nLzBn2a8GCBbniUTasqqGhoYiLixMdO3YUBgYGwtraWsyaNSvXUJaPHj0Sffr0EQYGBqJcuXJixIgR4sqVK7mWqSw2IXIPpyWEEOfPnxeenp7CyMhIGBgYiDZt2ogTJ07I1VE2rKWy4V4VOXjwoGjRooXQ19cXJiYmwsvLSxqC8P3lqTKsqhBCrF69WlSpUkUaLjcnDgcHB4XD1r0/DJwQb4cAmzZtmqhatarQ0dERFhYWonnz5uKHH36Qe88ViYiIEE2bNhX6+vrCzs5OTJkyRRrm7/19cvz4cdGhQwdhbGwsDA0Nhaura64hDq9cuSJ69eolzMzMhJ6enqhRo4aYMWOGXJ39+/eLOnXqCB0dHVGjRg3x+++/Kx1WddSoUQrj/vXXX0W1atWErq6uqFmzpggJCVE6HOBvv/0m6tevL3R1dUW5cuWEh4eHOHDgQK56f/31lwAg/P3989xn7/vzzz+l5ZubmwsfHx9x9+5duToFGVZViLfD5VaoUEFoaWnJDUGnbJ+8fw4QQogHDx6IUaNGCXt7e1G2bFlhY2Mj2rVrJ1atWpXv+nfu3ClcXV2Fnp6ecHR0FPPmzZOGNc2J5fz588Lb21tUqlRJ6OrqCisrK9GtWzdx9uxZaTlbt24VHTt2FFZWVkJHR0dUqlRJjBgxQiQlJcmtLy4uTvTt21c6bho3bix27dqVK6709HTx7bffisqVK0vb1LdvX7nhJ98/Rzx79kwMGTJEWFhYCCMjI+Hp6SmuXbumcJ8p+zwq+tw9ePBAWq6Ojo5wcXHJNZRnXudTReey9+UcN0eOHBH+/v6iXLlywsjISPj4+MgN8Zzj8OHDwtPTU5iamgo9PT3h5OQk/Pz85N6TvM6vQgjRr18/AUD8+eefUtnr16+FgYGB0NHRkRtC+N19ocqx9vr1azFv3jxRu3Zt6fPo5uYmAgMDRUpKity+UfU4V2THjh2iVq1aokyZMnLfLx4eHgqHzVQ03LOqsSqibD3KzuuKtleV7zUhhLh06ZLw8PAQenp6okKFCmL27Nni119/zTV0pRCqHR+qDKuqyjWGIoq+b69evSrat28vjIyMhIWFhRg+fLg0hG5hrzWEEGLVqlXCzc1N6OvrC2NjY+Hi4iKmTJki/vvvP6nO+5/rlStXilatWony5csLXV1d4eTkJL7++ut83+/s7GwxZ84c4eDgIHR1dUX9+vXFrl27ch1XBT0f/P3338LZ2Vno6uqKWrVqidDQUKVDk7/v/v37omvXrsLY2FgAkLazoNdCqhwzyly4cEG4u7sLXV1dUbFiRREcHCx++uknAUDcv38/31iVKew5Xh3n0/c/L4cOHRI9evQQdnZ2QkdHR9jZ2Qlvb+9cQ9/nR/b/N5SISqAdO3agZ8+eOHr0aK5fUYmIiOjDjB8/HitXrkRaWprKNwB/ipgwEJVg3bp1Q0xMDG7evFnsNzoSERF9TF6+fCl34/mTJ09QvXp1NGjQIM9n81AxDqtKRKrbvHkzLl26hN27d2PJkiVMFoiIiD5Qs2bN0Lp1azg7O+PBgwf49ddfkZqaihkzZmg6tBKPLQxEJZBMJoORkREGDBiAFStWlKjxpYmIiEqj//3vf9i6dSvu3r0LmUyGBg0aYNasWUU2tO/HjAkDEREREREppfJzGIiIiIiI6NPDhIGIiIiIiJRix2gqsOzsbPz3338wNjbmzbhERESlhBACz58/h52dnfQASiJVMGGgAvvvv/9gb2+v6TCIiIioEO7cuYOKFStqOgwqRZgwUIHlPI7+zp07MDEx0XA0REREpIrU1FTY29tL3+NEqmLCQAWW0w3JxMSECQMREVEpw+7EVFDswEZEREREREoxYSAiIiIiIqWYMBARERERkVK8h4GIPlpZWVl48+aNpsMgIioWZcuWhba2tqbDoI8QEwYi+ugIIXD//n0kJydrOhQiomJlZmYGGxsb3thMRYoJAxF9dHKSBSsrKxgYGPCLk4g+ekIIpKen4+HDhwAAW1tbDUdEHxMmDET0UcnKypKShfLly2s6HCKiYqOvrw8AePjwIaysrNg9iYoMb3omoo9Kzj0LBgYGGo6EiKj45Zz7eP8WFSUmDET0UWI3JCL6FPHcR+rAhIGIiIiIiJRiwkBEVEK0bt0a48eP13QYJdratWthZmZWYpaTn/T0dPTp0wcmJiaQyWSlbuSugIAA1KtXT9NhKFWY91Emk2H79u1Fsv6CfmbDw8OL5DhwdHTE4sWLP2gZRAXBm56J6JPh+M3uYl1fwtyuxbq+/ISHh6NNmzZ49uxZsVwsq8OAAQPQpUuXAs3j6OiI8ePHy13YFWY5hbFu3TocO3YMJ06cgIWFBUxNTdW+TioYmUyG+Ph4ODo6ajoUohKLCQMREZUa+vr60kgwJWE5+YmLi4OzszPq1KlT6GVkZWVBJpNBS4udAohIM3j2ISIqQTIzMzF69GiYmprCwsICM2bMgBBCmp6RkYHJkyejQoUKMDQ0RJMmTRAeHi5Nv337Nry8vFCuXDkYGhqidu3a2LNnDxISEtCmTRsAQLly5SCTyeDn56cwhidPnsDb2xsVKlSAgYEBXFxc8Mcff8jV2bp1K1xcXKCvr4/y5cujffv2ePHiBYC3LRmNGzeGoaEhzMzM0KJFC9y+fVuad/ny5XBycoKOjg5q1KiBDRs2yC07OTkZI0aMgLW1NfT09FCnTh3s2rULQO4uKHFxcejRowesra1hZGSERo0a4eDBg9L01q1b4/bt25gwYQJkMpl0Q6iiriz5xSWTybBmzRr06tULBgYGqFatGnbu3KlwH+ase+HChTh69ChkMhlat24NAHj27BkGDx6McuXKwcDAAJ07d8aNGzek+XJi27lzJ2rVqgVdXV0kJiYqXMeVK1fQuXNnGBkZwdraGp9//jkeP34sTQ8LC0PLli1hZmaG8uXLo1u3boiLi5Nbxt27d+Ht7Q1zc3MYGhqiYcOGOHXqlFydDRs2wNHREaamphg4cCCeP3+udLtz4t+1axdq1KgBAwMD9O3bF+np6Vi3bh0cHR1Rrlw5jB07FllZWdJ8+e2XnGVXqlQJBgYG6NWrF548eZJr/Tt27ECDBg2gp6eHKlWqIDAwEJmZmUrjfdezZ8/g4+MDS0tL6Ovro1q1aggJCVFpXuDtfmrYsCGMjY1hY2ODQYMGSc9FeFdERARcXV2hp6eHpk2b4sqVK3LTjx8/Dnd3d+jr68Pe3h5jx46VPl9EmsCEgYioBFm3bh3KlCmD06dPY8mSJVi0aBHWrFkjTR89ejQiIyOxefNmXLp0Cf369UOnTp2kC6tRo0YhIyMDR48exeXLlzFv3jwYGRnB3t4ef//9NwAgNjYWSUlJWLJkicIYXr16BTc3N+zevRtXrlyBv78/Pv/8c5w+fRoAkJSUBG9vb3zxxReIiYlBeHg4evfuDSEEMjMz0bNnT3h4eODSpUuIjIyEv7+/dKG+bds2jBs3DpMmTcKVK1cwYsQIDBkyBIcPHwYAZGdno3PnzoiIiMDvv/+Oq1evYu7cuUrHk09LS0OXLl1w6NAhXLhwAZ06dYKXl5d0gR0aGoqKFSsiKCgISUlJSEpKUric/OLKERgYiP79++PSpUvo0qULfHx88PTpU4XLDA0NxfDhw9GsWTMkJSUhNDQUAODn54ezZ89i586diIyMhBACXbp0kRsGMz09HfPmzcOaNWsQHR0NKyurXMtPTk5G27ZtUb9+fZw9exZhYWF48OAB+vfvL9V58eIFJk6ciLNnz+LQoUPQ0tJCr169kJ2dLe0/Dw8P3Lt3Dzt37sTFixcxZcoUaTrwNinbvn07du3ahV27duHIkSOYO3euwm1+N/6ffvoJmzdvRlhYGMLDw9GrVy/s2bMHe/bswYYNG7By5Ups3bpVmie//XLq1CkMHToUo0ePRlRUFNq0aYPvvvtObr3Hjh3D4MGDMW7cOFy9ehUrV67E2rVr8f333+cZb44ZM2bg6tWr2Lt3L2JiYrB8+XJYWFioNC/wdijT2bNn4+LFi9i+fTsSEhIUJuZff/01Fi5ciDNnzsDS0hJeXl7SdsbFxaFTp07o06cPLl26hD///BPHjx/H6NGjVY6DqMgJogJKSUkRAERKSoqmQyHK5eXLl+Lq1avi5cuXuaY5TN1VrH8F5eHhIZydnUV2drZUNnXqVOHs7CyEEOL27dtCW1tb3Lt3T26+du3aiWnTpgkhhHBxcREBAQEKl3/48GEBQDx79qzAsXXt2lVMmjRJCCHEuXPnBACRkJCQq96TJ08EABEeHq5wOc2bNxfDhw+XK+vXr5/o0qWLEEKIffv2CS0tLREbG6tw/pCQEGFqappnrLVr1xZLly6VXjs4OIgff/wxz+XkF5cQQgAQ06dPl16npaUJAGLv3r1KYxk3bpzw8PCQXl+/fl0AEBEREVLZ48ePhb6+vvjrr7+k2ACIqKioPLdz9uzZomPHjnJld+7cEQCU7r9Hjx4JAOLy5ctCCCFWrlwpjI2NxZMnTxTWnzVrljAwMBCpqalS2ddffy2aNGmiNK6c+G/evCmVjRgxQhgYGIjnz59LZZ6enmLEiBFCCNX2i7e3t9z7IYQQAwYMkHsf27VrJ+bMmSNXZ8OGDcLW1lZ6DUBs27ZNYexeXl5iyJAhSrftfR4eHmLcuHFKp585c0YAkLY75zO4fsECkX75ski/fFncPX5c6OvpiQ3/v8y3d2/xRd++css5duyY0NLSks5rio7pHHmdA/n9TYXFFgYiohKkadOmcuOoN2vWDDdu3EBWVhYuX76MrKwsVK9eHUZGRtLfkSNHpG4mY8eOxXfffYcWLVpg1qxZuHTpUoFjyMrKwuzZs+Hi4gJzc3MYGRlh37590q/2devWRbt27eDi4oJ+/fph9erVePbsGQDA3Nwcfn5+8PT0hJeXF5YsWSL3q35MTAxatGght74WLVogJiYGABAVFYWKFSuievXqKsWalpaGyZMnw9nZGWZmZjAyMkJMTIzSLjzK5BdXDldXV+n/hoaGMDExUdjlJK/1lClTBk2aNJHKypcvjxo1asitS0dHR25dily8eBGHDx+WOxZq1qwJANLxcOPGDXh7e6NKlSowMTGRbuzN2T9RUVGoX78+zM3Nla7H0dERxsbG0mtbW9t8t9nAwABOTk7Sa2trazg6OsLIyEiuLGc5quyXmJgYuenA28/H+/skKChIbp8MHz4cSUlJSE9PzzNmAPjqq6+wefNm1KtXD1OmTMGJEyfynedd586dg5eXFypVqgRjY2N4eHgAQK7jsUndutL/zU1NUc3REdfi4wEAl2Nj8fuOHXLb4OnpiezsbMT//zpExY03PRMRlRJpaWnQ1tbGuXPncnXRybkQGzZsGDw9PbF7927s378fwcHBWLhwIcaMGaPyehYsWIAlS5Zg8eLFcHFxgaGhIcaPH4/Xr18DALS1tXHgwAGcOHEC+/fvx9KlS/Htt9/i1KlTqFy5MkJCQjB27FiEhYXhzz//xPTp03HgwAE0bdo033UX9EbkyZMn48CBA/jhhx9QtWpV6Ovro2/fvlKsRa1s2bJyr2UymVz3naKir6+f7wO40tLS4OXlhXnz5uWaZmtrCwDw8vKCg4MDVq9eDTs7O2RnZ6NOnTrS/lFlfxdmmxXNUxz7Li0tDYGBgejdu3euaXp6evnO37lzZ9y+fRt79uzBgQMH0K5dO4waNQo//PBDvvO+ePECnp6e8PT0xMaNG2FpaYnExER4enoW6Hh8kZ6Oof36YWJgYK5plSpVUnk5REWJLQxERCXI+zebnjx5EtWqVYO2tjbq16+PrKwsPHz4EFWrVpX7s7Gxkeaxt7fHl19+idDQUEyaNAmrV68G8PZXawByN5oqEhERgR49euCzzz5D3bp1UaVKFVy/fl2ujkwmQ4sWLRAYGIgLFy5AR0cH27Ztk6bXr18f06ZNw4kTJ1CnTh1s2rQJAODs7IyIiIhc66tVqxaAt7/g3717N9f68orVz88PvXr1gouLC2xsbJCQkCBXR0dHJ99tzi+uouLs7IzMzEy59/nJkyeIjY0t8LoaNGiA6OhoODo65joeDA0NpeVOnz4d7dq1g7Ozs9QSlMPV1RVRUVFK78MoLqrsF2dnZ4Wfj3c1aNAAsbGxufZH1apVVR5lytLSEr6+vvj999+xePFirFq1SqX5rl27hidPnmDu3Llwd3dHzZo1lbbEnL54Ufr/s5QU3Lx9GzUrVwYA1HN2xrW4OIXbkPMZJipuTBiIiEqQxMRETJw4EbGxsfjjjz+wdOlSjBs3DgBQvXp1+Pj4YPDgwQgNDUV8fDxOnz6N4OBg7N799hkT48ePx759+xAfH4/z58/j8OHDcHZ2BgA4ODhAJpNh165dePToEdLS0hTGUK1aNakFISYmBiNGjMCDBw+k6adOncKcOXNw9uxZJCYmIjQ0FI8ePYKzszPi4+Mxbdo0REZG4vbt29i/fz9u3LghxfD1119j7dq1WL58OW7cuIFFixYhNDQUkydPBgB4eHigVatW6NOnDw4cOID4+Hjs3bsXYWFhSmMNDQ1FVFQULl68iEGDBuX61drR0RFHjx7FvXv35EYQeld+cRWVatWqoUePHhg+fDiOHz+Oixcv4rPPPkOFChXQo0ePAi1r1KhRePr0Kby9vXHmzBnExcVh3759GDJkCLKyslCuXDmUL18eq1atws2bN/Hvv/9i4sSJcsvw9vaGjY0NevbsiYiICNy6dQt///03IiMji3Kz86XKfslptfrhhx9w48YN/Pzzz7mOi5kzZ2L9+vUIDAxEdHQ0YmJisHnzZkyfPl2lOGbOnIkdO3bg5s2biI6Oxq5du6RjNz+VKlWCjo4Oli5dilu3bmHnzp2YPXu2wrrBK1fi8MmTiL5xA/7Tp6O8mRm82rUDAEz84gucvHhRurn7xo0b2LFjB296Jo1iwkBEVIIMHjwYL1++ROPGjTFq1CiMGzcO/v7+0vSQkBAMHjwYkyZNQo0aNdCzZ0+cOXNG6qqQlZWFUaNGwdnZGZ06dUL16tXxyy+/AAAqVKiAwMBAfPPNN7C2tlZ6ATJ9+nQ0aNAAnp6eaN26tXRBmcPExARHjx5Fly5dUL16dUyfPh0LFy5E586dYWBggGvXrqFPnz6oXr06/P39MWrUKIwYMQIA0LNnTyxZsgQ//PADateujZUrVyIkJEQachQA/v77bzRq1Aje3t6oVasWpkyZorSFYNGiRShXrhyaN28OLy8veHp6okGDBnJ1goKCkJCQACcnJ1haWipcjipxFZWQkBC4ubmhW7duaNasGYQQ2LNnT64uO/mxs7NDREQEsrKy0LFjR7i4uGD8+PEwMzODlpYWtLS0sHnzZpw7dw516tTBhAkTsGDBArll6OjoYP/+/bCyskKXLl3g4uKS56hU6pTffmnatClWr16NJUuWoG7duti/f3+uRMDT0xO7du3C/v370ahRIzRt2hQ//vgjHBwcVIpBR0cH06ZNg6urK1q1agVtbW1s3rxZpXktLS2xdu1abNmyBbVq1cLcuXOVdmUKGj8eX8+bhxYDBuDBkyfYunQpdP7/drrUqIF9ISG4fv063N3dUb9+fcycORN2dnYqxUGkDjIh3hngm0gFqampMDU1RUpKCkxMTDQdDpGcV69eIT4+HpUrV1apzzIRUXF7+d5zFxTRL+TD/vI6B/L7mwqLLQxERERERKQUEwYiIiIiIlKKCQMRERERESnFhIGIiIiIiJRiwkBEREREREoxYSAiIiIiIqWYMBARERERkVJMGIiIiIiISCkmDEREREREpBQTBiKiEkIIAX9/f5ibm0MmkyEqKirfeRISElSuW1K1bt0a48ePz7PO2rVrYWZmVizxEBGRvDKaDoCIqNgEmBbz+lIKVD0sLAxr165FeHg4qlSpAgsLCzUFVrKEhoaibNmy0mtHR0eMHz9eLokYMGAAunTpooHoiIiICQMRUQkRFxcHW1tbNG/eXNOhFCtzc/N86+jr60NfX78YoiEiovexSxIRUQng5+eHMWPGIDExETKZDI6OjgDetjq0bNkSZmZmKF++PLp164a4uDily3n27Bl8fHxgaWkJfX19VKtWDSEhIdL0O3fuoH///jAzM4O5uTl69OiBhIQEpcsLDw+HTCbD7t274erqCj09PTRt2hRXrlyRq/f333+jdu3a0NXVhaOjIxYuXCg3/ZdffkG1atWgp6cHa2tr9O3bV5r2bpek1q1b4/bt25gwYQJkMhlkMhkA+S5J169fh0wmw7Vr1+TW8eOPP8LJyUl6feXKFXTu3BlGRkawtrbG559/jsePHyvdViIiUowJAxFRCbBkyRIEBQWhYsWKSEpKwpkzZwAAL168wMSJE3H27FkcOnQIWlpa6NWrF7KzsxUuZ8aMGbh69Sr27t2LmJgYLF++XOra9ObNG3h6esLY2BjHjh1DREQEjIyM0KlTJ7x+/TrP+L7++mssXLgQZ86cgaWlJby8vPDmzRsAwLlz59C/f38MHDgQly9fRkBAAGbMmIG1a9cCAM6ePYuxY8ciKCgIsbGxCAsLQ6tWrRSuJzQ0FBUrVkRQUBCSkpKQlJSUq0716tXRsGFDbNy4Ua5848aNGDRoEAAgOTkZbdu2Rf369XH27FmEhYXhwYMH6N+/f57bSVQcbtnI8v0jKknYJYmIqAQwNTWFsbExtLW1YWNjI5X36dNHrt5vv/0GS0tLXL16FXXq1Mm1nMTERNSvXx8NGzYEAKmlAgD+/PNPZGdnY82aNdIv9yEhITAzM0N4eDg6duyoNL5Zs2ahQ4cOAIB169ahYsWK2LZtG/r3749FixahXbt2mDFjBoC3F/RXr17FggUL4Ofnh8TERBgaGqJbt24wNjaGg4MD6tevr3A95ubm0NbWhrGxsdx+eJ+Pjw9+/vlnzJ49G8DbVodz587h999/BwD8/PPPqF+/PubMmSO37+zt7XH9+nVUr15d6bKJiEgeWxiIiEqwGzduwNvbG1WqVIGJiYmUACQmJiqs/9VXX2Hz5s2oV68epkyZghMnTkjTLl68iJs3b8LY2BhGRkYwMjKCubk5Xr16lWc3JwBo1qyZ9H9zc3PUqFEDMTExAICYmBi0aNFCrn6LFi1w48YNZGVloUOHDnBwcECVKlXw+eefY+PGjUhPTy/M7pAMHDgQCQkJOHnyJIC3rQsNGjRAzZo1pW09fPiwtJ1GRkbStPy2lYiI5DFhKGWOHj0KLy8v2NnZQSaTYfv27XLTc/r8vv+3YMECqY6jo2Ou6XPnzi3mLSEiVXh5eeHp06dYvXo1Tp06hVOnTgGA0i5EnTt3lu4B+O+//9CuXTtMnjwZAJCWlgY3NzdERUXJ/V2/fl3qyqMOxsbGOH/+PP744w/Y2tpi5syZqFu3LpKTkwu9TBsbG7Rt2xabNm0CAGzatAk+Pj7S9LS0NHh5eeXa1hs3bijtDkVERIoxYShlXrx4gbp162LZsmUKp+f0+c35++233yCTyXJ1a3i3f3BSUhLGjBlTHOETUQE8efIEsbGxmD59Otq1awdnZ2c8e/Ys3/ksLS3h6+uL33//HYsXL8aqVasAAA0aNMCNGzdgZWWFqlWryv2ZmuY95GzOL/nA2xurr1+/DmdnZwCAs7MzIiIi5OpHRESgevXq0NbWBgCUKVMG7du3x/z583Hp0iUkJCTg33//VbguHR0dZGVl5budPj4++PPPPxEZGYlbt25h4MCB0rQGDRogOjoajo6OubbV0NAw32UTEdH/YcJQynTu3BnfffcdevXqpXC6jY2N3N+OHTvQpk0bVKlSRa5eTv/gnD9+gRKVPOXKlUP58uWxatUq3Lx5E//++y8mTpyY5zwzZ87Ejh07cPPmTURHR2PXrl3Shb2Pjw8sLCzQo0cPHDt2DPHx8QgPD8fYsWNx9+7dPJcbFBSEQ4cO4cqVK/Dz84OFhQV69uwJAJg0aRIOHTqE2bNn4/r161i3bh1+/vlnqWVj165d+OmnnxAVFYXbt29j/fr1yM7ORo0aNRSuy9HREUePHsW9e/fyHNWod+/eeP78Ob766iu0adMGdnZ20rRRo0bh6dOn8Pb2xpkzZxAXF4d9+/ZhyJAhKiUjRET0f5gwfMQePHiA3bt3Y+jQobmmzZ07F+XLl0f9+vWxYMECZGZmaiBCIsqLlpYWNm/ejHPnzqFOnTqYMGGCXPdCRXR0dDBt2jS4urqiVatW0NbWxubNmwEABgYGOHr0KCpVqoTevXvD2dkZQ4cOxatXr2BiYpLncufOnYtx48bBzc0N9+/fxz///AMdHR0Ab3/N/+uvv7B582bUqVMHM2fORFBQEPz8/AAAZmZmCA0NRdu2beHs7IwVK1bgjz/+QO3atRWuKygoCAkJCXBycoKlpaXSmIyNjeHl5YWLFy/KdUcCADs7O0RERCArKwsdO3aEi4sLxo8fDzMzM2hp8auPiKggZEIIoekgqHBkMhm2bdsm/cr3vvnz52Pu3Ln477//oKenJ5UvWrQIDRo0gLm5OU6cOIFp06ZhyJAhWLRokcLlZGRkICMjQ3qdmpoKe3t7pKSk5HuRQVTcXr16hfj4eFSuXFnuuKfCCQ8PR5s2bfDs2TPpOQhE9GGiH0fnW6e2heKEOj95nQNTU1NhamrK728qMA6r+hH77bff4OPjk+uE8W6XBldXV+jo6GDEiBEIDg6Grq5uruUEBwcjMDBQ7fESERERUcnDdtmP1LFjxxAbG4thw4blW7dJkybIzMxU+rTXadOmISUlRfq7c+dOEUdLRERERCUVWxg+Ur/++ivc3NxQt27dfOtGRUVBS0sLVlZWCqfr6uoqbHkgoo9f69atwZ6rRESfNiYMpUxaWhpu3rwpvY6Pj0dUVBTMzc1RqVIlAG/7KG7ZsgULFy7MNX9kZCROnTqFNm3awNjYGJGRkZgwYQI+++wzlCtXrti2g4iIiIhKByYMpczZs2fRpk0b6XXO/Qi+vr5Yu3YtAGDz5s0QQsDb2zvX/Lq6uti8eTMCAgKQkZGBypUrY8KECfkO1UhEREREnyaOkkQFxlEWqCTjKElEVNJxlCQqbXjTMxERERERKcWEgYiIiIiIlGLCQERERERESjFhICKiYrN27VqVnhgtk8mwfft2tcdDRET54yhJRPTJcFnnUqzru+x7uUD1W7dujXr16mHx4sXqCagEGDBgALp06SK9DggIwPbt2xEVFSVXLykpiUM9ExGVEEwYiIhKESEEsrKyUKZM6Tx96+vrQ19fP996NjY2xRANERGpgl2SiIhKAD8/Pxw5cgRLliyBTCaDTCZDQkICwsPDIZPJsHfvXri5uUFXVxfHjx+Hn58fevbsKbeM8ePHo3Xr1tLr7OxsBAcHo3LlytDX10fdunWxdevWPONwdHTE7Nmz4e3tDUNDQ1SoUAHLli2Tq5OYmIgePXrAyMgIJiYm6N+/Px48eCBNv3jxovRwSBMTE7i5ueHs2bMA5LskrV27FoGBgbh48aK0zTnPk3m3S1Lz5s0xdepUuRgePXqEsmXL4ujRowCAjIwMTJ48GRUqVIChoSGaNGmC8PBwFfY8ERHlhwkDEVEJsGTJEjRr1gzDhw9HUlISkpKSYG9vL03/5ptvMHfuXMTExMDV1VWlZQYHB2P9+vVYsWIFoqOjpae6HzlyJM/5FixYgLp16+LChQv45ptvMG7cOBw4cADA2ySkR48eePr0KY4cOYIDBw7g1q1bGDBggDS/j48PKlasiDNnzuDcuXP45ptvULZs2VzrGTBgACZNmoTatWtL2/zuct5dXs4DKXP8+eefsLOzg7u7OwBg9OjRiIyMxObNm3Hp0iX069cPnTp1wo0bN1TaV0REpFzpbNMmIvrImJqaQkdHBwYGBgq74wQFBaFDhw4qLy8jIwNz5szBwYMH0axZMwBAlSpVcPz4caxcuRIeHh5K523RogW++eYbAED16tURERGBH3/8ER06dMChQ4dw+fJlxMfHSwnN+vXrUbt2bZw5cwaNGjVCYmIivv76a9SsWRMAUK1aNYXr0dfXh5GREcqUKZNnF6T+/ftj/PjxOH78uJQgbNq0Cd7e3pDJZEhMTERISAgSExNhZ2cHAJg8eTLCwsIQEhKCOXPmqLzfiIgoN7YwEBGVAg0bNixQ/Zs3byI9PR0dOnSAkZGR9Ld+/XrExcXlOW9OgvHu65iYGABATEwM7O3t5Vo/atWqBTMzM6nOxIkTMWzYMLRv3x5z587Nd335sbS0RMeOHbFx40YAQHx8PCIjI+Hj4wMAuHz5MrKyslC9enW5bT1y5MgHr5uIiNjCQERUKhgaGsq91tLSkuuiAwBv3ryR/p+WlgYA2L17NypUqCBXT1dXV01RvhUQEIBBgwZh9+7d2Lt3L2bNmoXNmzejV69ehV6mj48Pxo4di6VLl2LTpk1wcXGBi8vbUa/S0tKgra2Nc+fOQVtbW24+IyOjD9oWIiJiwkBEVGLo6OggKytLpbqWlpa4cuWKXFlUVJR0r0CtWrWgq6uLxMTEPLsfKXLy5Mlcr52dnQEAzs7OuHPnDu7cuSO1Mly9ehXJycmoVauWNE/16tVRvXp1TJgwAd7e3ggJCVGYMKi6zT169IC/vz/CwsKwadMmDB48WJpWv359ZGVl4eHDh1KXJSIiKjrskkREVEI4Ojri1KlTSEhIwOPHj5Gdna20btu2bXH27FmsX78eN27cwKxZs+QSCGNjY0yePBkTJkzAunXrEBcXh/Pnz2Pp0qVYt25dnnFERERg/vz5uH79OpYtW4YtW7Zg3LhxAID27dvDxcUFPj4+OH/+PE6fPo3BgwfDw8MDDRs2xMuXLzF69GiEh4fj9u3biIiIwJkzZ6SEQ9E2x8fHIyoqCo8fP0ZGRobCeoaGhujZsydmzJiBmJgYeHt7S9OqV68OHx8fDB48GKGhoYiPj8fp06cRHByM3bt357mtRESUPyYMREQlxOTJk6GtrY1atWrB0tISiYmJSut6enpixowZmDJlCho1aoTnz5/L/eoOALNnz8aMGTMQHBwMZ2dndOrUCbt370blypXzjGPSpEk4e/Ys6tevj++++w6LFi2Cp6cngLfDne7YsQPlypVDq1at0L59e1SpUgV//vknAEBbWxtPnjzB4MGDUb16dfTv3x+dO3dGYGCgwnX16dMHnTp1Qps2bWBpaYk//vhDaVw+Pj64ePEi3N3dUalSJblpISEhGDx4MCZNmoQaNWqgZ8+eOHPmTK56RERUcDLxfidYonykpqbC1NQUKSkpMDEx0XQ4RHJevXqF+Ph4VK5cGXp6epoOp9RxdHTE+PHjMX78eE2HQvTRin4cnW+d2ha1C7XsvM6B/P6mwmILAxERERERKcWEgYiIiIiIlOIoSUREJElISNB0CEREVMKwhYGIiIiIiJRiwkBEREREREoxYSAiIiIiIqWYMBARERERkVJMGIiIiIiISCkmDEREREREpBQTBiIiUklAQADq1auncv2EhATIZDJERUUprePo6IjFixervMy1a9fCzMxM5frKyGQybN++Xel0IQT8/f1hbm6e7zaUJKrsz4K+jwDQunXrEvP07/DwcMhkMiQnJ6s8T1HEX1THHlFpxOcwENEnI6amc7Guz/laTIHqHz16FAsWLMC5c+eQlJSEbdu2oWfPnuoJjvIUFhaGtWvXIjw8HFWqVIGFhYWmQyoUmUyW6ziaPHkyxowZo7mglEhISEDlypVx4cKFAic0RKRebGEgIiohXrx4gbp162LZsmWaDuWTFxcXB1tbWzRv3hw2NjYoU6bgv68JIZCZmamG6D6MkZERypcvr+kwiKgUYcJARFRCdO7cGd999x169eql8jw53Ut+++03VKpUCUZGRhg5ciSysrIwf/582NjYwMrKCt9//73cfImJiejRoweMjIxgYmKC/v3748GDB3J15s6dC2traxgbG2Po0KF49epVrvWvWbMGzs7O0NPTQ82aNfHLL78UbuP/v0WLFsHFxQWGhoawt7fHyJEjkZaWlqve9u3bUa1aNejp6cHT0xN37tyRm75jxw40aNAAenp6qFKlCgIDA1W+ePfz88OYMWOQmJgImUwGR0dHAEBGRgbGjh0LKysr6OnpoWXLljhz5ow0X05Xmb1798LNzQ26uro4fvx4ruXndNX666+/4O7uDn19fTRq1AjXr1/HmTNn0LBhQxgZGaFz58549OiRNJ+ibjU9e/aEn5+fwu3IibtXr15y2/F+lyQ/Pz/07NkTgYGBsLS0hImJCb788ku8fv1a6T7KyMjA5MmTUaFCBRgaGqJJkyYIDw9XWh8Arl27hpYtW0JPTw+1atXCwYMH5bqGVa5cGQBQv359yGQytG7dOs/l5Xjy5Am8vb1RoUIFGBgYwMXFBX/88UeuepmZmRg9ejRMTU1hYWGBGTNmQAhR6G26ePEi2rRpA2NjY5iYmMDNzQ1nz55VKWai0oYJAxFRKRcXF4e9e/ciLCwMf/zxB3799Vd07doVd+/exZEjRzBv3jxMnz4dp06dAgBkZ2ejR48eePr0KY4cOYIDBw7g1q1bGDBggLTMv/76CwEBAZgzZw7Onj0LW1vbXMnAxo0bMXPmTHz//feIiYnBnDlzMGPGDKxbt67Q26KlpYWffvoJ0dHRWLduHf79919MmTJFrk56ejq+//57rF+/HhEREUhOTsbAgQOl6ceOHcPgwYMxbtw4XL16FStXrsTatWtzJU3KLFmyBEFBQahYsSKSkpKkpGDKlCn4+++/sW7dOpw/fx5Vq1aFp6cnnj59Kjf/N998g7lz5yImJgaurq5K1zNr1ixMnz4d58+fR5kyZTBo0CBMmTIFS5YswbFjx3Dz5k3MnDlT1V2XS07cISEhctuhyKFDhxATE4Pw8HD88ccfCA0NRWBgoNL6o0ePRmRkJDZv3oxLly6hX79+6NSpE27cuKGwflZWFnr27AkDAwOcOnUKq1atwrfffitX5/Tp0wCAgwcPIikpCaGhoSpt56tXr+Dm5obdu3fjypUr8Pf3x+effy4tL8e6detQpkwZnD59GkuWLMGiRYuwZs2aQm+Tj48PKlasiDNnzuDcuXP45ptvULZsWZViJip1BFEBpaSkCAAiJSVF06EQ5fLy5Utx9epV8fLly1zTrtaoWax/HwKA2LZtW771Zs2aJQwMDERqaqpU5unpKRwdHUVWVpZUVqNGDREcHCyEEGL//v1CW1tbJCYmStOjo6MFAHH69GkhhBDNmjUTI0eOlFtXkyZNRN26daXXTk5OYtOmTXJ1Zs+eLZo1ayaEECI+Pl4AEBcuXFAav4ODg/jxxx+VTt+yZYsoX7689DokJEQAECdPnpTKYmJiBABx6tQpIYQQ7dq1E3PmzJFbzoYNG4Stra30Or/9++OPPwoHBwfpdVpamihbtqzYuHGjVPb69WthZ2cn5s+fL4QQ4vDhwwKA2L59u9LlCvF/+2XNmjVS2R9//CEAiEOHDkllwcHBokaNGtJrDw8PMW7cOLll9ejRQ/j6+kqv39+firZz1qxZcu+jr6+vMDc3Fy9evJDKli9fLoyMjKRj6N113759W2hra4t79+7JLbddu3Zi2rRpCrd57969okyZMiIpKUkqO3DggFx8qhwvQvzffn727JnSOl27dhWTJk2SXnt4eAhnZ2eRnZ0tlU2dOlU4OzurvE0hISHC1NRUmmZsbCzWrl2bZ6zKXHl0Jd+/wsrrHMjvbyos3vRMRFTKOTo6wtjYWHptbW0NbW1taGlpyZU9fPgQABATEwN7e3vY29tL02vVqgUzMzPExMSgUaNGiImJwZdffim3nmbNmuHw4cMA3t5vERcXh6FDh2L48OFSnczMTJiamhZ6Ww4ePIjg4GBcu3YNqampyMzMxKtXr5Ceng4DAwMAQJkyZdCoUSNpnpo1a0qxN27cGBcvXkRERIRci0JWVlau5RREXFwc3rx5gxYtWkhlZcuWRePGjRETI39ze8OGDVVa5rutD9bW1gAAFxcXubKc90zd6tatK7dfmjVrhrS0NNy5cwcODg5ydS9fvoysrCxUr15drjwjI0PpvRGxsbGwt7eHjY2NVNa4ceMiiT0rKwtz5szBX3/9hXv37uH169fIyMjI9T43bdoUMplMet2sWTMsXLgQWVlZhdqmiRMnYtiwYdiwYQPat2+Pfv36wcnJqUi2iaikYcJARFTKvd8NQiaTKSzLzs4usnXm3FewevVqNGnSRG6atrZ2oZaZkJCAbt264auvvsL3338Pc3NzHD9+HEOHDsXr169VvtBPS0tDYGAgevfunWuanp5eoWIrCENDQ5Xqvfse5VzIvl/27numpaUl1+ceAN68efMhoRZKWloatLW1ce7cuVzvtZGRUbHHs2DBAixZsgSLFy+W7n8ZP358nvdgvK8w2xQQEIBBgwZh9+7d2Lt3L2bNmoXNmzcX6B4kotKCCQMR0SfG2dkZd+7cwZ07d6RWhqtXryI5ORm1atWS6pw6dQqDBw+W5jt58qT0f2tra9jZ2eHWrVvw8fEpkrjOnTuH7OxsLFy4UGod+euvv3LVy8zMxNmzZ6VfqGNjY5GcnAxn57fD5jZo0ACxsbGoWrVqkcQFAE5OTtDR0UFERIT0i/ubN29w5syZYns+gaWlJZKSkqTXWVlZuHLlCtq0aaN0nrJlyyIrKyvfZV+8eBEvX76Evr4+gLfvtZGRkVwrVI769esjKysLDx8+hLu7u0qx16hRA3fu3MGDBw+k1pT376nQ0dGRtqsgIiIi0KNHD3z22WcA3t6jc/36delYzpFzD0+OkydPolq1atDW1i7UNgFA9erVUb16dUyYMAHe3t4ICQlhwkAfJSYMREQlRFpaGm7evCm9jo+PR1RUFMzNzVGpUqUiW0/79u3h4uICHx8fLF68GJmZmRg5ciQ8PDyk7jTjxo2Dn58fGjZsiBYtWmDjxo2Ijo5GlSpVpOUEBgZi7NixMDU1RadOnZCRkYGzZ8/i2bNnmDhxYoHjqlq1Kt68eYOlS5fCy8sLERERWLFiRa56ZcuWxZgxY/DTTz+hTJkyGD16NJo2bSolEDNnzkS3bt1QqVIl9O3bF1paWrh48SKuXLmC7777rlD7zNDQEF999RW+/vpr6f2YP38+0tPTMXTo0EIts6Datm2LiRMnYvfu3XBycsKiRYvyfXiZo6MjDh06hBYtWkBXVxflypVTWO/169cYOnQopk+fjoSEBMyaNQujR4+W69aWo3r16vDx8cHgwYOxcOFC1K9fH48ePcKhQ4fg6uqKrl275pqnQ4cOcHJygq+vL+bPn4/nz59j+vTpAP6vdcXKygr6+voICwtDxYoVoaenp1L3tmrVqmHr1q04ceIEypUrh0WLFuHBgwe5EobExERMnDgRI0aMwPnz57F06VIsXLiwUNv08uVLfP311+jbty8qV66Mu3fv4syZM+jTp0++8RKVRkwYiOiTUdAHqRW3s2fPyv1anHPR7evri7Vr1xbZemQyGXbs2IExY8agVatW0NLSQqdOnbB06VKpzoABAxAXF4cpU6bg1atX6NOnD7766ivs27dPqjNs2DAYGBhgwYIF+Prrr2FoaAgXF5dC/+Jet25dLFq0CPPmzcO0adPQqlUrBAcHy7VyAICBgQGmTp2KQYMG4d69e3B3d8evv/4qTff09MSuXbsQFBSEefPmoWzZsqhZsyaGDRtWqLhyzJ07F9nZ2fj888/x/PlzNGzYEPv27VN6EV7UvvjiC1y8eBGDBw9GmTJlMGHChDxbFwBg4cKFmDhxIlavXo0KFSogISFBYb127dqhWrVqaNWqFTIyMuDt7Y2AgAClyw0JCcF3332HSZMm4d69e7CwsEDTpk3RrVs3hfW1tbWxfft2DBs2DI0aNUKVKlWwYMECeHl5Sd3EypQpg59++glBQUGYOXMm3N3d8x2qFQCmT5+OW7duwdPTEwYGBvD390fPnj2RkpIiV2/w4MF4+fIlGjduDG1tbYwbNw7+/v6F2iZtbW08efIEgwcPxoMHD2BhYYHevXvnObIUUWkmE+93iCTKR2pqKkxNTZGSkgITExNNh0Mk59WrV4iPj0flypWLpb86UWnn5+eH5ORk6XkIxSUiIgItW7bEzZs3P7mbhaMfR+dbp7ZF7UItO69zIL+/qbD4HIZS5ujRo/Dy8oKdnZ3cA29y+Pn5QSaTyf116tRJrs7Tp0/h4+MDExMTmJmZYejQoQofjERERFRUtm3bhgMHDiAhIQEHDx6Ev78/WrRo8cklC0SlEROGUubFixeoW7culi1bprROp06dkJSUJP29/8RLHx8fREdH48CBA9i1axeOHj0q1yxLRERU1J4/f45Ro0ahZs2a8PPzQ6NGjbBjxw5Nh0VEKuA9DKVM586d0blz5zzr6Orqyo11/a6YmBiEhYXhzJkz0s2NS5cuRZcuXfDDDz/Azs6uyGMmIqKSqyjvj8nL4MGDc92PQkSlA1sYPkLh4eGwsrJCjRo18NVXX+HJkyfStMjISJiZmck9WKh9+/bQ0tLKNeRcjoyMDKSmpsr9EREREdGngQnDR6ZTp05Yv349Dh06hHnz5uHIkSPo3LmzNK71/fv3YWVlJTdPmTJlYG5ujvv37ytcZnBwMExNTaU/ReNyE5U0HM+BiD5FPPeROrBL0kdm4MCB0v9dXFzg6uoKJycnhIeHo127doVa5rRp0+TGVE9NTWXSQCVWzpNy09PTpYdQERF9KtLT0wHkfgI80YdgwvCRq1KlCiwsLHDz5k20a9cONjY2ePjwoVydzMxMPH36VOl9D7q6utDV1S2OcIk+mLa2NszMzKTj3MDAQHowFBFRSZD9JjvfOq9evSrQMoUQSE9Px8OHD2FmZgZtbe3ChkeUCxOGj9zdu3fx5MkT2NraAgCaNWuG5ORknDt3Dm5ubgCAf//9F9nZ2WjSpIkmQyUqMjnJ7/vJMRFRSfAwLf9zU5nkwl2imZmZKf0BkKiwmDCUMmlpabh586b0Oj4+HlFRUTA3N4e5uTkCAwPRp08f2NjYSE9prVq1Kjw9PQEAzs7O6NSpE4YPH44VK1bgzZs3GD16NAYOHMgRkuijIZPJYGtrCysrK7x580bT4RARyRm3bVy+dXb22lng5ZYtW5YtC6QWTBhKmbNnz6JNmzbS65x7C3x9fbF8+XJcunQJ69atQ3JyMuzs7NCxY0fMnj1brkvRxo0bMXr0aLRr1w5aWlro06cPfvrpp2LfFiJ109bW5pcnEZU4Sa+T8q3DJ9VTSSITvJ2+WGVlZeHy5ctwcHBAuXLlNB1OofDR8kRERIXnss4l3zqXfS8X+Xr5/U2FxWFV1Wz8+PH49ddfAbxNFjw8PNCgQQPY29sjPDxcs8EREREREeWDCYOabd26FXXr1gUA/PPPP4iPj8e1a9cwYcIEfPvttxqOjoiIiIgob0wY1Ozx48fSaAV79uxBv379UL16dXzxxRe4fLnomxuJiIiIiIoSEwY1s7a2xtWrV5GVlYWwsDB06NABwNsHq/BmTCIiIiIq6ThKkpoNGTIE/fv3h62tLWQyGdq3bw8AOHXqFGrWrKnh6IiIiIiI8saEQc0CAgJQp04d3LlzB/369ZOGN9XW1sY333yj4eiIiIiIiPLGhKEY9O3bN1eZr6+vBiIhIiIiIioYJgxqUJCHoI0dO1aNkRARERERfRgmDGrw448/qlRPJpMxYSAiIiKiEo0JgxrEx8drOgQiIiIioiLBYVWLyevXrxEbG4vMzExNh0JEREREpDImDGqWnp6OoUOHwsDAALVr10ZiYiIAYMyYMZg7d66GoyMiIiIiyhsTBjWbNm0aLl68iPDwcOjp6Unl7du3x59//qnByIiIiIiI8sd7GNRs+/bt+PPPP9G0aVPIZDKpvHbt2oiLi9NgZERERERE+WMLg5o9evQIVlZWucpfvHghl0AQEREREZVETBjUrGHDhti9e7f0OidJWLNmDZo1a6apsIiIiIiIVMIuSWo2Z84cdO7cGVevXkVmZiaWLFmCq1ev4sSJEzhy5IimwyMiIiIiyhNbGNSsZcuWiIqKQmZmJlxcXLB//35YWVkhMjISbm5umg6PiIiIiChPbGEoBk5OTli9erWmwyAiIiIiKjAmDMUgKysL27ZtQ0xMDACgVq1a6NGjB8qU4e4nIiIiopKNV6xqFh0dje7du+P+/fuoUaMGAGDevHmwtLTEP//8gzp16mg4QiIiIiIi5XgPg5oNGzYMtWvXxt27d3H+/HmcP38ed+7cgaurK/z9/TUdHhERERFRntjCoGZRUVE4e/YsypUrJ5WVK1cO33//PRo1aqTByIiIiIiI8scWBjWrXr06Hjx4kKv84cOHqFq1qgYiIiIiIiJSHRMGNUhNTZX+goODMXbsWGzduhV3797F3bt3sXXrVowfPx7z5s3TdKhERERERHlilyQ1MDMzk57oDABCCPTv318qE0IAALy8vJCVlaWRGImIiIiIVMGEQQ0OHz6s6RCIiIiIiIoEEwY18PDw0HQIRERERERFgglDMUlPT0diYiJev34tV+7q6qqhiIiIiIiI8seEQc0ePXqEIUOGYO/evQqn8x4GIiIiIirJOEqSmo0fPx7Jyck4deoU9PX1ERYWhnXr1qFatWrYuXOnpsMjIiIiIsoTWxjU7N9//8WOHTvQsGFDaGlpwcHBAR06dICJiQmCg4PRtWtXTYdIRERERKQUWxjU7MWLF7CysgLw9gnPjx49AgC4uLjg/PnzmgyNiIiIiChfTBjUrEaNGoiNjQUA1K1bFytXrsS9e/ewYsUK2Nraajg6IiIiIqK8sUuSmo0bNw5JSUkAgFmzZqFTp07YuHEjdHR0sHbtWs0GR0RERESUD7YwqNlnn30GPz8/AICbmxtu376NM2fO4M6dOxgwYECBl3f06FF4eXnBzs4OMpkM27dvl6a9efMGU6dOhYuLCwwNDWFnZ4fBgwfjv//+k1uGo6MjZDKZ3N/cuXM/ZDOJiIiI6CPFhKGYGRgYoEGDBrCwsCjU/C9evEDdunWxbNmyXNPS09Nx/vx5zJgxA+fPn0doaChiY2PRvXv3XHWDgoKQlJQk/Y0ZM6ZQ8RARERHRx41dktRg4sSJKtddtGhRgZbduXNndO7cWeE0U1NTHDhwQK7s559/RuPGjZGYmIhKlSpJ5cbGxrCxsSnQuomIiIjo08OEQQ0uXLigUj2ZTKbmSICUlBTIZDKYmZnJlc+dOxezZ89GpUqVMGjQIEyYMAFlyig+HDIyMpCRkSG9Tk1NVWfIRERERFSCMGFQg8OHD2s6BADAq1evMHXqVHh7e8PExEQqHzt2LBo0aABzc3OcOHEC06ZNQ1JSktLWjuDgYAQGBhZX2ERERERUgsiEEELTQVDhyGQybNu2DT179sw17c2bN+jTpw/u3r2L8PBwuYThfb/99htGjBiBtLQ06Orq5pquqIXB3t4eKSkpeS6XiIiIcnNZ55Jvncu+l4t8vampqTA1NeX3NxUYWxg+Qm/evEH//v1x+/Zt/Pvvv/meFJo0aYLMzEwkJCSgRo0auabr6uoqTCSIiIiI6OPHhOEjk5Ms3LhxA4cPH0b58uXznScqKgpaWlrSE6mJiIiIiHIwYShl0tLScPPmTel1fHw8oqKiYG5uDltbW/Tt2xfnz5/Hrl27kJWVhfv37wMAzM3NoaOjg8jISJw6dQpt2rSBsbExIiMjMWHCBHz22WcoV66cpjaLiIiIiEooJgylzNmzZ9GmTRvpdc4Qrr6+vggICMDOnTsBAPXq1ZOb7/Dhw2jdujV0dXWxefNmBAQEICMjA5UrV8aECRMKNBQsEREREX06mDCo2bp162BhYYGuXbsCAKZMmYJVq1ahVq1a+OOPP+Dg4FCg5bVu3Rp53aee3z3sDRo0wMmTJwu0TiIiIiL6dPFJz2o2Z84c6OvrAwAiIyOxbNkyzJ8/HxYWFpgwYYKGoyMiIiIiyhtbGNTszp07qFq1KgBg+/bt6NOnD/z9/dGiRQu0bt1as8EREREREeWDLQxqZmRkhCdPngAA9u/fjw4dOgAA9PT08PLlS02GRkRERESUL7YwqFmHDh0wbNgw1K9fH9evX0eXLl0AANHR0XB0dNRscERERERE+WALg5otW7YMzZo1w6NHj/D3339Lz0U4d+4cvL29NRwdEREREVHeZCK/YXWI3sNHyxMRERWeyzqXfOtc9r1c5Ovl9zcVFrskqUliYqLc60qVKmkoEiIiIiKiwmPCoCaOjo6QyWQQQkAmkyErK0vTIRERERERFRgTBjXJzs7WdAhERERERB+MNz0TEREREZFSbGFQg507d6pct3v37mqMhIiIiIjowzBhUIOePXuqVI/3NhARERFRSceEQQ14/wIRERERfSx4D0MxevXqlaZDICIiIiIqECYMapaVlYXZs2ejQoUKMDIywq1btwAAM2bMwK+//qrh6IiIiIiI8saEQc2+//57rF27FvPnz4eOjo5UXqdOHaxZs0aDkRERERER5Y8Jg5qtX78eq1atgo+PD7S1taXyunXr4tq1axqMjIiIiIgof0wY1OzevXuoWrVqrvLs7Gy8efNGAxEREREREamOCYOa1apVC8eOHctVvnXrVtSvX18DERERERERqY7DqqrZzJkz4evri3v37iE7OxuhoaGIjY3F+vXrsWvXLk2HR0RERESUJ7YwqFmPHj3wzz//4ODBgzA0NMTMmTMRExODf/75Bx06dNB0eEREREREeWILQzFwd3fHgQMHNB0GEREREVGBsYWBiIiIiIiUYguDGpQrVw4ymUyluk+fPlVzNEREREREhceEQQ0WL14s/f/Jkyf47rvv4OnpiWbNmgEAIiMjsW/fPsyYMUNDERIRERERqUYmhBCaDuJj1qdPH7Rp0wajR4+WK//5559x8OBBbN++XTOBfYDU1FSYmpoiJSUFJiYmmg6HiIioVHFZ55Jvncu+l4t8vfz+psLiPQxqtm/fPnTq1ClXeadOnXDw4EENREREREREpDomDGpWvnx57NixI1f5jh07UL58eQ1ERERERESkOt7DoGaBgYEYNmwYwsPD0aRJEwDAqVOnEBYWhtWrV2s4OiIiIiKivDFhUDM/Pz84Ozvjp59+QmhoKADA2dkZx48flxIIIiIiIqKSiglDMWjSpAk2btyo6TCIiIiIiAqMCUMxyMrKwvbt2xETEwMAqF27Nrp37w5tbW0NR0ZERERElDcmDGp28+ZNdO3aFXfv3kWNGjUAAMHBwbC3t8fu3bvh5OSk4QiJiIiIiJTjKElqNnbsWFSpUgV37tzB+fPncf78eSQmJqJy5coYO3aspsMjIiIiIsoTWxjU7MiRIzh58iTMzc2lsvLly2Pu3Llo0aKFBiMjIiIiIsofWxjUTFdXF8+fP89VnpaWBh0dnQIv7+jRo/Dy8oKdnR1kMlmuJ0ULITBz5kzY2tpCX18f7du3x40bN+TqPH36FD4+PjAxMYGZmRmGDh2KtLS0AsdCRERERB8/Jgxq1q1bN/j7++PUqVMQQkAIgZMnT+LLL79E9+7dC7y8Fy9eoG7duli2bJnC6fPnz8dPP/2EFStW4NSpUzA0NISnpydevXol1fHx8UF0dDQOHDiAXbt24ejRo/D39y/0NhIRERHRx0smhBCaDuJjlpycDF9fX/zzzz8oW7YsACAzMxPdu3fH2rVrYWpqWuhly2QybNu2DT179gTwtnXBzs4OkyZNwuTJkwEAKSkpsLa2xtq1azFw4EDExMSgVq1aOHPmDBo2bAgACAsLQ5cuXXD37l3Y2dnlu97U1FSYmpoiJSUFJiYmhY6fiIjoU+SyziXfOpd9Lxf5evn9TYXFexjUzMzMDDt27MCNGzdw7do1AG8f3Fa1atUiX1d8fDzu37+P9u3bS2WmpqZo0qQJIiMjMXDgQERGRsLMzExKFgCgffv20NLSwqlTp9CrV69cy83IyEBGRob0OjU1tchjJyIiIqKSiQlDMalWrRqqVaum1nXcv38fAGBtbS1Xbm1tLU27f/8+rKys5KaXKVMG5ubmUp33BQcHIzAwUA0RExEREVFJx4RBzYQQ2Lp1Kw4fPoyHDx8iOztbbnpoaKiGIlPdtGnTMHHiROl1amoq7O3tNRgRERERERUXJgxqNn78eKxcuRJt2rSBtbU1ZDKZ2tZlY2MDAHjw4AFsbW2l8gcPHqBevXpSnYcPH8rNl5mZiadPn0rzv09XVxe6urrqCZqIiIiISjQmDGq2YcMGhIaGokuXLmpfV+XKlWFjY4NDhw5JCUJqaipOnTqFr776CgDQrFkzJCcn49y5c3BzcwMA/Pvvv8jOzkaTJk3UHiMRERERlS5MGNTM1NQUVapUKbLlpaWl4ebNm9Lr+Ph4REVFwdzcHJUqVcL48ePx3XffoVq1aqhcuTJmzJgBOzs7aSQlZ2dndOrUCcOHD8eKFSvw5s0bjB49GgMHDlRphCQiIiIi+rTwOQxqFhAQgMDAQLx8+bJIlnf27FnUr18f9evXBwBMnDgR9evXx8yZMwEAU6ZMwZgxY+Dv749GjRohLS0NYWFh0NPTk5axceNG1KxZE+3atUOXLl3QsmVLrFq1qkjiIyIiIqKPC5/DoGYvX75Er169EBERAUdHR+lZDDnOnz+vocgKj+M4ExERFR6fw0ClDbskqZmvry/OnTuHzz77TO03PRMRERERFTUmDGq2e/du7Nu3Dy1bttR0KEREREREBcZ7GNTM3t6ezX5EREREVGoxYVCzhQsXYsqUKUhISNB0KEREREREBcYuSWr22WefIT09HU5OTjAwMMh10/PTp081FBkRERERUf6YMKjZ4sWLNR0CEREREVGhMWFQM19fX02HQERERERUaLyHgYiIiIiIlGLCQERERERESjFhICIiIiIipZgwqMGlS5eQnZ2t6TCIiIiIiD4YEwY1qF+/Ph4/fgwAqFKlCp48eaLhiIiIiIiICocJgxqYmZkhPj4eAJCQkMDWBiIiIiIqtTisqhr06dMHHh4esLW1hUwmQ8OGDaGtra2w7q1bt4o5OiIiIiIi1TFhUINVq1ahd+/euHnzJsaOHYvhw4fD2NhY02ERERERERUYEwY16dSpEwDg3LlzGDduHBMGIiIiIiqVmDCoWUhIiPT/u3fvAgAqVqyoqXCIiIiIiAqENz2rWXZ2NoKCgmBqagoHBwc4ODjAzMwMs2fP5s3QRERERFTisYVBzb799lv8+uuvmDt3Llq0aAEAOH78OAICAvDq1St8//33Go6QiIiIiEg5Jgxqtm7dOqxZswbdu3eXylxdXVGhQgWMHDmSCQMRERERlWjskqRmT58+Rc2aNXOV16xZE0+fPtVAREREREREqmPCoGZ169bFzz//nKv8559/Rt26dTUQERERERGR6tglSc3mz5+Prl274uDBg2jWrBkAIDIyEnfu3MGePXs0HB0RERERUd7YwqBmHh4euH79Onr16oXk5GQkJyejd+/eiI2Nhbu7u6bDIyIiIiLKE1sYioGdnR1vbiYiIiKiUoktDEREREREpBQTBiIiIiIiUooJAxERERERKcWEQY2EEEhMTMSrV680HQoRERERUaEwYVAjIQSqVq2KO3fuaDoUIiIiIqJCYcKgRlpaWqhWrRqePHmi6VCIiIiIiAqFCYOazZ07F19//TWuXLmi6VCIiIiIiAqMz2FQs8GDByM9PR1169aFjo4O9PX15aY/ffpUQ5EREREREeWPCYOaLV68WNMhEBEREREVGhMGNfP19dV0CEREREREhcZ7GIpBXFwcpk+fDm9vbzx8+BAAsHfvXkRHRxf5uhwdHSGTyXL9jRo1CgDQunXrXNO+/PLLIo+DiIiIiD4OTBjU7MiRI3BxccGpU6cQGhqKtLQ0AMDFixcxa9asIl/fmTNnkJSUJP0dOHAAANCvXz+pzvDhw+XqzJ8/v8jjICIiIqKPAxMGNfvmm2/w3Xff4cCBA9DR0ZHK27Zti5MnTxb5+iwtLWFjYyP97dq1C05OTvDw8JDqGBgYyNUxMTEp8jiIiIiI6OPAhEHNLl++jF69euUqt7KywuPHj9W67tevX+P333/HF198AZlMJpVv3LgRFhYWqFOnDqZNm4b09HS1xkFEREREpRdvelYzMzMzJCUloXLlynLlFy5cQIUKFdS67u3btyM5ORl+fn5S2aBBg+Dg4AA7OztcunQJU6dORWxsLEJDQ5UuJyMjAxkZGdLr1NRUdYZNRERERCUIEwY1GzhwIKZOnYotW7ZAJpMhOzsbERERmDx5MgYPHqzWdf/666/o3Lkz7OzspDJ/f3/p/y4uLrC1tUW7du0QFxcHJycnhcsJDg5GYGCgWmMlIiIiopKJXZLUbM6cOahZsybs7e2RlpaGWrVqoVWrVmjevDmmT5+utvXevn0bBw8exLBhw/Ks16RJEwDAzZs3ldaZNm0aUlJSpL87d+4UaaxEREREVHKxhUHNdHR0sHr1asyYMQNXrlxBWloa6tevj2rVqql1vSEhIbCyskLXrl3zrBcVFQUAsLW1VVpHV1cXurq6RRkeEREREZUSTBiKSaVKlWBvbw8Acjcgq0N2djZCQkLg6+uLMmX+7y2Oi4vDpk2b0KVLF5QvXx6XLl3ChAkT0KpVK7i6uqo1JiIiIiIqndglqRj8+uuvqFOnDvT09KCnp4c6depgzZo1alvfwYMHkZiYiC+++EKuXEdHBwcPHkTHjh1Rs2ZNTJo0CX369ME///yjtliIiIiIqHRjC4OazZw5E4sWLcKYMWPQrFkzAEBkZCQmTJiAxMREBAUFFfk6O3bsCCFErnJ7e3scOXKkyNdHRERERB8vJgxqtnz5cqxevRre3t5SWffu3eHq6ooxY8aoJWEgIiIiIioq7JKkZm/evEHDhg1zlbu5uSEzM1MDERERERERqY4Jg5p9/vnnWL58ea7yVatWwcfHRwMRERERERGpjl2S1GDixInS/2UyGdasWYP9+/ejadOmAIBTp04hMTFR7Q9uIyIiIiL6UEwY1ODChQtyr93c3AC8HdYUACwsLGBhYYHo6Ohij42IiIiIqCCYMKjB4cOHNR0CEREREVGR4D0MRERERESkFFsY1OzVq1dYunQpDh8+jIcPHyI7O1tu+vnz5zUUGRERERFR/pgwqNnQoUOxf/9+9O3bF40bN4ZMJtN0SEREREREKmPCoGa7du3Cnj170KJFC02HQkRERERUYLyHQc0qVKgAY2NjTYdBRERERFQoTBjUbOHChZg6dSpu376t6VCIiIiIiAqMXZLUrGHDhnj16hWqVKkCAwMDlC1bVm7606dPNRQZEREREVH+mDCombe3N+7du4c5c+bA2tqaNz0TERERUanChEHNTpw4gcjISNStW1fToRARERERFRjvYVCzmjVr4uXLl5oOg4iIiIioUJgwqNncuXMxadIkhIeH48mTJ0hNTZX7IyIiIiIqydglSc06deoEAGjXrp1cuRACMpkMWVlZmgiLiIiIiEglTBjU7PDhw5oOgYiIiIio0JgwqJmHh4emQyAiIiIiKjQmDGp29OjRPKe3atWqmCIhIiIiIio4Jgxq1rp161xl7z6LgfcwEBEREVFJxlGS1OzZs2dyfw8fPkRYWBgaNWqE/fv3azo8IiIiIqI8sYVBzUxNTXOVdejQATo6Opg4cSLOnTungaiIiIiIiFTDFgYNsba2RmxsrKbDICIiIiLKE1sY1OzSpUtyr4UQSEpKwty5c1GvXj3NBEVEREREpCImDGpWr149yGQyCCHkyps2bYrffvtNQ1EREREREamGCYOaxcfHy73W0tKCpaUl9PT0NBQREREREZHqmDComYODg6ZDICIiIiIqNCYMxeDQoUM4dOgQHj58iOzsbLlp7JZERERERCUZEwY1CwwMRFBQEBo2bAhbW1u5h7YREREREZV0TBjUbMWKFVi7di0+//xzTYdCRERERFRgfA6Dmr1+/RrNmzfXdBhERERERIXChEHNhg0bhk2bNmk6DCIiIiKiQmGXJDV79eoVVq1ahYMHD8LV1RVly5aVm75o0SINRUZERERElD8mDGp26dIl6YnOV65ckZvGG6CJiIiIqKRjwqBmhw8fLtb1BQQEIDAwUK6sRo0auHbtGoC3LR6TJk3C5s2bkZGRAU9PT/zyyy+wtrYu1jiJiIiIqHTgPQwfodq1ayMpKUn6O378uDRtwoQJ+Oeff7BlyxYcOXIE//33H3r37q3BaImIiIioJGMLw0eoTJkysLGxyVWekpKCX3/9FZs2bULbtm0BACEhIXB2dsbJkyfRtGnT4g5VsQBTFeqkqD8OIiIiImILw8foxo0bsLOzQ5UqVeDj44PExEQAwLlz5/DmzRu0b99eqluzZk1UqlQJkZGRSpeXkZGB1NRUuT8iIiIi+jQwYfjINGnSBGvXrkVYWBiWL1+O+Ph4uLu74/nz57h//z50dHRgZmYmN4+1tTXu37+vdJnBwcEwNTWV/uzt7dW8FURERERUUrBL0kemc+fO0v9dXV3RpEkTODg44K+//oK+vn6hljlt2jRMnDhRep2amsqkgYiIiOgTwRaGj5yZmRmqV6+OmzdvwsbGBq9fv0ZycrJcnQcPHii85yGHrq4uTExM5P6IiIiI6NPAhOEjl5aWhri4ONja2sLNzQ1ly5bFoUOHpOmxsbFITExEs2bNNBglEREREZVU7JL0kZk8eTK8vLzg4OCA//77D7NmzYK2tja8vb1hamqKoUOHYuLEiTA3N4eJiQnGjBmDZs2alZwRkoiIiIioRGHC8JG5e/cuvL298eTJE1haWqJly5Y4efIkLC0tAQA//vgjtLS00KdPH7kHtxERERERKcKE4SOzefPmPKfr6elh2bJlWLZsWTFFRERERESlGe9hICIiIiIipZgwEBERERGRUuySRERERFREYmo6519pGi+/qHRhCwMRERERESnFhIGIiIiIiJRiwkBEREREREoxYSAiIiIiIqWYMBARERERkVK8TZ+IiIg+eaqMbuR8LaYYIiEqedjCQERERERESrGFgYiIiD55/VV4NsLlYoiDqCRiCwMRERERESnFhIGIiIiIiJRiwkBEREREREoxYSAiIiIiIqV40zMRERFREVHl5mmi0oYtDEREREREpBQTBiIiIiIiUooJAxERERERKcWEgYiIiIiIlGLCQERERERESjFhICIiIiIipZgwEBERERGRUkwYiIiIiIhIKSYMRERERESkFBMGIiIiIiJSigkDEREREREpxYSBiIiIiIiUKqPpAIiIiIhKg5iazvlXmsZLK/r4sIWBiIiIiIiUYsJARERERERKMWEgIiIiIiKlmDAQEREREZFSTBiIiIiIiEgpJgxERERERKQUE4aPTHBwMBo1agRjY2NYWVmhZ8+eiI2NlavTunVryGQyub8vv/xSQxETERERUUnGwYI/MkeOHMGoUaPQqFEjZGZm4n//+x86duyIq1evwtDQUKo3fPhwBAUFSa8NDAw0ES4REVGp0Z/PWKBPFI/8j0xYWJjc67Vr18LKygrnzp1Dq1atpHIDAwPY2NgUd3hEREREVMowYfjIpaSkAADMzc3lyjdu3Ijff/8dNjY28PLywowZM5S2MmRkZCAjI0N6nZqaqr6AiYiIVKTKk5edr8UUQyREHzcmDB+x7OxsjB8/Hi1atECdOnWk8kGDBsHBwQF2dna4dOkSpk6ditjYWISGhipcTnBwMAIDA4srbCIioiKjSlIBAGB3IyKl+On4iI0aNQpXrlzB8ePH5cr9/f2l/7u4uMDW1hbt2rVDXFwcnJycci1n2rRpmDhxovQ6NTUV9vb26guciIiIiEoMJgwfqdGjR2PXrl04evQoKlasmGfdJk2aAABu3rypMGHQ1dWFrq6uWuIkIiIiopKNCcNHRgiBMWPGYNu2bQgPD0flypXznScqKgoAYGtrq+boiIiIiKi0YcLwkRk1ahQ2bdqEHTt2wNjYGPfv3wcAmJqaQl9fH3Fxcdi0aRO6dOmC8uXL49KlS5gwYQJatWoFV1dXDUdPRESkOlWGOf0rOLMYIiH6uDFh+MgsX74cwNuHs70rJCQEfn5+0NHRwcGDB7F48WK8ePEC9vb26NOnD6ZPn66BaImIiIiopGPC8JERQuQ53d7eHkeOHCmmaIiIiDSLD1sj+nBamg6AiIiIiIhKLiYMRERERESkFBMGIiIiIiJSigkDEREREREpxYSBiIiIiIiUYsJARERERERKcawxIiIiKnFiajrnX4lDphIVC7YwEBERERGRUkzNiYiIqMThA9eISg62MBARERERkVJMGIiIiIiISCkmDEREREREpBQTBiIiIiIiUooJAxERERERKcWEgYiIiIiIlGLCQERERERESjFhICIiIiIipZgwEBERERGRUkwYiIiIiIhIKSYMRERERESkFBMGIiIiIiJSigkDEREREREpxYSBiIiIiIiUKqPpAIiIiOjTElPTOf9K03iJQlRSsIWBiIiIiIiUYvpOREREKlGlZcD5WkwxREJExYktDEREREREpBRbGIiIiD5yRdUy0F+F+wouqxCPKsshopKDLQxERERERKQUU3wiIjVy/GZ3vnUS5nYthkhUVxpj/pSpNOIQEdEHYMJARPSR4IV+8SjOG39VWVdRde9RpSsREX2a2CWJiIiIiIiUYgsDEREVWFH9yu6yziXfOpd9S9Zv30V1429RraukUeU9JaLShS0MRERERESkVOn76YKIqBgU5/0ApfHeg+L8lb2ofrEuzpaK0viAM7YMEJEybGH4hC1btgyOjo7Q09NDkyZNcPr0aU2HREREREQlDFsYPlF//vknJk6ciBUrVqBJkyZYvHgxPD09ERsbCysrK02HR6RQUf0Sr8pySpqiilmV5Rg7f1Mk6/qUf7EuzhYYIiJ1YwvDJ2rRokUYPnw4hgwZglq1amHFihUwMDDAb7/9punQiIiIiKgEYQvDJ+j169c4d+4cpk2bJpVpaWmhffv2iIyM1GBkBRBgqkKdFPXHUQAlrZ96Uf1iXZz9+Eua0hjzp6yktXiUtHiIiJRhwvAJevz4MbKysmBtbS1Xbm1tjWvXruWqn5GRgYyMDOl1SsrbC/HU1FT1BJghimY56oqvkLIz0vOto7Z9qoAq8aiiqGIuzniKal0fq6yXWZoOgeiTp47vg5xlClFE37P0yWDCQPkKDg5GYGBgrnJ7e3sNRFMAc1VohShhTBdrOoKCK2kxl7R4iIgKw/Qr9X2HPX/+HKampe87kjSHCcMnyMLCAtra2njw4IFc+YMHD2BjY5Or/rRp0zBx4kTpdXZ2Np4+fYry5ctDJpMVaWypqamwt7fHnTt3YGJiUqTLpv/D/Vw8uJ+LB/dz8eB+Lj7q2tdCCDx//hx2dnZFtkz6NDBh+ATp6OjAzc0Nhw4dQs+ePQG8TQIOHTqE0aNH56qvq6sLXV1duTIzMzO1xmhiYsIvpGLA/Vw8uJ+LB/dz8eB+Lj7q2NdsWaDCYMLwiZo4cSJ8fX3RsGFDNG7cGIsXL8aLFy8wZMgQTYdGRERERCUIE4ZP1IABA/Do0SPMnDkT9+/fR7169RAWFpbrRmgiIiIi+rQxYfiEjR49WmEXJE3S1dXFrFmzcnWBoqLF/Vw8uJ+LB/dz8eB+Lj7c11TSyATH1iIiIiIiIiX4pGciIiIiIlKKCQMRERERESnFhIGIiIiIiJRiwkBEREREREoxYaBit2zZMjg6OkJPTw9NmjTB6dOn86y/ZcsW1KxZE3p6enBxccGePXuKKdLSrSD7efXq1XB3d0e5cuVQrlw5tG/fPt/3hd4q6PGcY/PmzZDJZNLDEylvBd3PycnJGDVqFGxtbaGrq4vq1avz3KGCgu7nxYsXo0aNGtDX14e9vT0mTJiAV69eFVO0pdPRo0fh5eUFOzs7yGQybN++Pd95wsPD0aBBA+jq6qJq1apYu3at2uMkkiOIitHmzZuFjo6O+O2330R0dLQYPny4MDMzEw8ePFBYPyIiQmhra4v58+eLq1eviunTp4uyZcuKy5cvF3PkpUtB9/OgQYPEsmXLxIULF0RMTIzw8/MTpqam4u7du8UceelS0P2cIz4+XlSoUEG4u7uLHj16FE+wpVhB93NGRoZo2LCh6NKlizh+/LiIj48X4eHhIioqqpgjL10Kup83btwodHV1xcaNG0V8fLzYt2+fsLW1FRMmTCjmyEuXPXv2iG+//VaEhoYKAGLbtm151r9165YwMDAQEydOFFevXhVLly4V2traIiwsrHgCJhJCMGGgYtW4cWMxatQo6XVWVpaws7MTwcHBCuv3799fdO3aVa6sSZMmYsSIEWqNs7Qr6H5+X2ZmpjA2Nhbr1q1TV4gfhcLs58zMTNG8eXOxZs0a4evry4RBBQXdz8uXLxdVqlQRr1+/Lq4QPwoF3c+jRo0Sbdu2lSubOHGiaNGihVrj/JiokjBMmTJF1K5dW65swIABwtPTU42REcljlyQqNq9fv8a5c+fQvn17qUxLSwvt27dHZGSkwnkiIyPl6gOAp6en0vpUuP38vvT0dLx58wbm5ubqCrPUK+x+DgoKgpWVFYYOHVocYZZ6hdnPO3fuRLNmzTBq1ChYW1ujTp06mDNnDrKysoor7FKnMPu5efPmOHfunNRt6datW9izZw+6dOlSLDF/Kvg9SCUBn/RMxebx48fIysqCtbW1XLm1tTWuXbumcJ779+8rrH///n21xVnaFWY/v2/q1Kmws7PL9SVF/6cw+/n48eP49ddfERUVVQwRfhwKs59v3bqFf//9Fz4+PtizZw9u3ryJkSNH4s2bN5g1a1ZxhF3qFGY/Dxo0CI8fP0bLli0hhEBmZia+/PJL/O9//yuOkD8Zyr4HU1NT8fLlS+jr62soMvqUsIWBiOTMnTsXmzdvxrZt26Cnp6fpcD4az58/x+eff47Vq1fDwsJC0+F81LKzs2FlZYVVq1bBzc0NAwYMwLfffosVK1ZoOrSPSnh4OObMmYNffvkF58+fR2hoKHbv3o3Zs2drOjQiKmJsYaBiY2FhAW1tbTx48ECu/MGDB7CxsVE4j42NTYHqU+H2c44ffvgBc+fOxcGDB+Hq6qrOMEu9gu7nuLg4JCQkwMvLSyrLzs4GAJQpUwaxsbFwcnJSb9ClUGGOZ1tbW5QtWxba2tpSmbOzM+7fv4/Xr19DR0dHrTGXRoXZzzNmzMDnn3+OYcOGAQBcXFzw4sUL+Pv749tvv4WWFn+TLArKvgdNTEzYukDFhp9mKjY6Ojpwc3PDoUOHpLLs7GwcOnQIzZo1UzhPs2bN5OoDwIEDB5TWp8LtZwCYP38+Zs+ejbCwMDRs2LA4Qi3VCrqfa9asicuXLyMqKkr66969O9q0aYOoqCjY29sXZ/ilRmGO5xYtWuDmzZtSQgb8v3buLqTJ9o8D+Hdmc2o+LE1LRa2ZkeTUzAyimMwDQYy0A2PRUEIsQrDMzEBbUMJKCyMDSUoJ014pLKEM62hmac1QHGraEsPezANLfKldz8GfxuOTS/3Xsyl9P7CD2+u67vt3/xDly737Arq6uuDr68uwYMP/0+eRkZEfQsH3kCaE+O+K/cPw/yDNCY5+65r+LFeuXBEuLi6isrJSdHR0iIyMDCGXy8Xbt2+FEEJotVqRl5dnnW8wGISzs7MoLi4WJpNJ6HQ6bqs6A7Pts16vF1KpVNy4cUMMDAxYP8PDw466hXlhtn3+N+6SNDOz7XNfX5/w8PAQmZmZorOzU9y9e1f4+PiI48ePO+oW5oXZ9lmn0wkPDw9RU1Mjent7RX19vQgODhYpKSmOuoV5YXh4WBiNRmE0GgUAcfr0aWE0GsXr16+FEELk5eUJrVZrnf99W9WDBw8Kk8kkzp07x21Vye4YGMjuzp49KwIDA4VUKhUxMTGiqanJOqZSqURqauqk+deuXROrVq0SUqlUrFmzRtTV1dm54vlpNn0OCgoSAH746HQ6+xc+z8z29/mfGBhmbrZ9bmxsFBs2bBAuLi5CoVCIwsJC8fXrVztXPf/Mps8TExPi6NGjIjg4WMhkMhEQECD27t0rhoaG7F/4PPLo0aMp/95+721qaqpQqVQ/rImMjBRSqVQoFApRUVFh97rpzyYRgs8NiYiIiIhoanyHgYiIiIiIbGJgICIiIiIimxgYiIiIiIjIJgYGIiIiIiKyiYGBiIiIiIhsYmAgIiIiIiKbGBiIiIiIiMgmBgYiIiIiIrKJgYGIiIiIiGxiYCAiohmbmJhwdAlERGRnDAxERHPYvXv3sGnTJsjlcnh5eSExMRE9PT3W8f7+fmg0Gnh6esLd3R3R0dF48uSJdfzOnTtYv349ZDIZlixZguTkZOuYRCLB7du3J11PLpejsrISAGA2myGRSHD16lWoVCrIZDJcvnwZg4OD0Gg08Pf3h5ubG5RKJWpqaiadx2Kx4OTJk1i5ciVcXFwQGBiIwsJCAIBarUZmZuak+R8+fIBUKkVDQ8PvaBsREf1GDAxERHPYly9fkJ2djZaWFjQ0NMDJyQnJycmwWCz4/PkzVCoV3rx5g9raWrx48QK5ubmwWCwAgLq6OiQnJyMhIQFGoxENDQ2IiYmZdQ15eXnIysqCyWRCfHw8RkdHsW7dOtTV1aG9vR0ZGRnQarV4+vSpdc3hw4eh1+tRUFCAjo4OVFdXY+nSpQCA9PR0VFdXY2xszDq/qqoK/v7+UKvVv9gxIiL63SRCCOHoIoiIaGY+fvwIb29vtLW1obGxETk5OTCbzfD09Pxh7saNG6FQKFBVVTXluSQSCW7duoWkpCTrz+RyOUpKSpCWlgaz2YwVK1agpKQEWVlZP60rMTERq1evRnFxMYaHh+Ht7Y3S0lKkp6f/MHd0dBR+fn4oKytDSkoKACAiIgLbtm2DTqebRTeIiMge+ISBiGgO6+7uhkajgUKhwF9//YXly5cDAPr6+tDa2oq1a9dOGRYAoLW1FXFxcb9cQ3R09KTjb9++4dixY1AqlfD09MSiRYtw//599PX1AQBMJhPGxsZsXlsmk0Gr1eLixYsAgOfPn6O9vR1paWm/XCsREf1+zo4ugIiIbNuyZQuCgoJQXl4OPz8/WCwWhIWFYXx8HK6urj9dO924RCLBvx8yT/VSs7u7+6TjoqIinDlzBiUlJVAqlXB3d8e+ffswPj4+o+sC//taUmRkJPr7+1FRUQG1Wo2goKBp1xERkf3xCQMR0Rw1ODiIzs5O5OfnIy4uDqGhoRgaGrKOh4eHo7W1FZ8+fZpyfXh4+E9fIvb29sbAwID1uLu7GyMjI9PWZTAYsHXrVuzcuRMRERFQKBTo6uqyjoeEhMDV1fWn11YqlYiOjkZ5eTmqq6uxa9euaa9LRESOwcBARDRHLV68GF5eXjh//jxevnyJhw8fIjs72zqu0WiwbNkyJCUlwWAwoLe3Fzdv3sTjx48BADqdDjU1NdDpdDCZTGhra8OJEyes69VqNUpLS2E0GtHS0oI9e/Zg4cKF09YVEhKCBw8eoLGxESaTCbt378a7d++s4zKZDIcOHUJubi4uXbqEnp4eNDU14cKFC5POk56eDr1eDyHEpN2biIhobmFgICKao5ycnHDlyhU8e/YMYWFh2L9/P4qKiqzjUqkU9fX18PHxQUJCApRKJfR6PRYsWAAAiI2NxfXr11FbW4vIyEio1epJOxmdOnUKAQEB2Lx5M3bs2IGcnBy4ublNW1d+fj6ioqIQHx+P2NhYa2j5p4KCAhw4cABHjhxBaGgotm/fjvfv30+ao9Fo4OzsDI1GA5lM9gudIiKi/xJ3SSIiIocwm80IDg5Gc3MzoqKiHF0OERHZwMBARER2NTExgcHBQeTk5ODVq1cwGAyOLomIiH6CX0kiIiK7MhgM8PX1RXNzM8rKyhxdDhERTYNPGIiIiIiIyCY+YSAiIiIiIpsYGIiIiIiIyCYGBiIiIiIisomBgYiIiIiIbGJgICIiIiIimxgYiIiIiIjIJgYGIiIiIiKyiYGBiIiIiIhsYmAgIiIiIiKb/gY59atwiG7GygAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "path_model_label=Path.home() / \"Desktop/Code/CELLSEG_BENCHMARK/RESULTS/full data/instance/instance_threshold_pred_TRAILMAP_DiceCE_best_metric(1).tif\"\n", + "res = evl.evaluate_model_performance(imread(path_true_labels), imread(path_model_label),visualize=False, return_graphical_summary=True,plot_according_to_gt_label=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "path_model_label=Path.home() / \"Desktop/Code/CELLSEG_BENCHMARK/RESULTS/full data/instance/instance_threshold_pred_VNet_Generalized_latest(1).tif\"\n", + "res = evl.evaluate_model_performance(imread(path_true_labels), imread(path_model_label),visualize=False, return_graphical_summary=True,plot_according_to_gt_label=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "path_model_label=Path.home() / \"Desktop/Code/CELLSEG_BENCHMARK/RESULTS/full data/instance/instance_threshold_pred_SegResNet_Generalized_latest.tif\"\n", + "res = evl.evaluate_model_performance(imread(path_true_labels), imread(path_model_label),visualize=False, return_graphical_summary=True,plot_according_to_gt_label=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 20cba1f83604c305c09408b5cd91b021640bbb79 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Tue, 25 Jul 2023 15:23:14 +0200 Subject: [PATCH 075/250] Change softmax arg --- napari_cellseg3d/code_models/models/wnet/model.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/napari_cellseg3d/code_models/models/wnet/model.py b/napari_cellseg3d/code_models/models/wnet/model.py index 0a833fa1..4746ebea 100644 --- a/napari_cellseg3d/code_models/models/wnet/model.py +++ b/napari_cellseg3d/code_models/models/wnet/model.py @@ -32,7 +32,7 @@ def __init__( self.encoder = UNet( in_channels=in_channels, out_channels=out_channels, - encoder=True, + softmax=False, ) def forward(self, x): @@ -55,10 +55,10 @@ def __init__( ): super(WNet, self).__init__() self.encoder = UNet( - in_channels, num_classes, encoder=True, dropout=dropout + in_channels, num_classes, softmax=True, dropout=dropout ) self.decoder = UNet( - num_classes, out_channels, encoder=False, dropout=dropout + num_classes, out_channels, softmax=False, dropout=dropout ) def forward(self, x): @@ -84,7 +84,7 @@ def __init__( in_channels: int, out_channels: int, channels: List[int] = None, - encoder: bool = True, + softmax: bool = True, dropout: float = 0.65, ): if channels is None: @@ -120,7 +120,7 @@ def __init__( ) self.sm = nn.Softmax(dim=1) - self.encoder = encoder + self.softmax = softmax def forward(self, x): """Forward pass of the U-Net model.""" @@ -165,7 +165,7 @@ def forward(self, x): dim=1, ) ) - if self.encoder: + if self.softmax: x = self.sm(x) return x From f85a6052ec08335b887f45b35ff205bd7e9b0487 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Tue, 25 Jul 2023 15:27:59 +0200 Subject: [PATCH 076/250] Num group 2 --- napari_cellseg3d/code_models/models/wnet/model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/models/wnet/model.py b/napari_cellseg3d/code_models/models/wnet/model.py index 4746ebea..c0fe8900 100644 --- a/napari_cellseg3d/code_models/models/wnet/model.py +++ b/napari_cellseg3d/code_models/models/wnet/model.py @@ -16,7 +16,7 @@ "Xide Xia", "Brian Kulis", ] -NUM_GROUPS = 8 +NUM_GROUPS = 2 class WNet_encoder(nn.Module): From ea07ad4e60c8ca2a9150ab58b591a461576f688f Mon Sep 17 00:00:00 2001 From: C-Achard Date: Tue, 25 Jul 2023 15:31:41 +0200 Subject: [PATCH 077/250] Update model.py --- napari_cellseg3d/code_models/models/wnet/model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/models/wnet/model.py b/napari_cellseg3d/code_models/models/wnet/model.py index c0fe8900..fc44a0a6 100644 --- a/napari_cellseg3d/code_models/models/wnet/model.py +++ b/napari_cellseg3d/code_models/models/wnet/model.py @@ -16,7 +16,7 @@ "Xide Xia", "Brian Kulis", ] -NUM_GROUPS = 2 +NUM_GROUPS = 4 class WNet_encoder(nn.Module): From 83d14e8eeeb1103435a6d8922f9a259dc56d7012 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Tue, 25 Jul 2023 15:33:06 +0200 Subject: [PATCH 078/250] Update model.py --- napari_cellseg3d/code_models/models/wnet/model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/models/wnet/model.py b/napari_cellseg3d/code_models/models/wnet/model.py index fc44a0a6..b0690ce0 100644 --- a/napari_cellseg3d/code_models/models/wnet/model.py +++ b/napari_cellseg3d/code_models/models/wnet/model.py @@ -16,7 +16,7 @@ "Xide Xia", "Brian Kulis", ] -NUM_GROUPS = 4 +NUM_GROUPS = 16 class WNet_encoder(nn.Module): From aaf174e7b3c58f22a51232499097e1a98e616b02 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Tue, 25 Jul 2023 18:46:03 +0200 Subject: [PATCH 079/250] Reduce depth of WNet --- .../code_models/models/wnet/model.py | 42 ++++++++++--------- 1 file changed, 22 insertions(+), 20 deletions(-) diff --git a/napari_cellseg3d/code_models/models/wnet/model.py b/napari_cellseg3d/code_models/models/wnet/model.py index b0690ce0..cd2bcb16 100644 --- a/napari_cellseg3d/code_models/models/wnet/model.py +++ b/napari_cellseg3d/code_models/models/wnet/model.py @@ -16,7 +16,7 @@ "Xide Xia", "Brian Kulis", ] -NUM_GROUPS = 16 +NUM_GROUPS = 4 class WNet_encoder(nn.Module): @@ -100,21 +100,22 @@ def __init__( self.in_b = InBlock(in_channels, self.channels[0], dropout=dropout) self.conv1 = Block(channels[0], self.channels[1], dropout=dropout) self.conv2 = Block(channels[1], self.channels[2], dropout=dropout) - self.conv3 = Block(channels[2], self.channels[3], dropout=dropout) - self.bot = Block(channels[3], self.channels[4], dropout=dropout) - self.deconv1 = Block(channels[4], self.channels[3], dropout=dropout) - self.conv_trans1 = nn.ConvTranspose3d( - self.channels[4], self.channels[3], 2, stride=2 - ) + # self.conv3 = Block(channels[2], self.channels[3], dropout=dropout) + # self.bot = Block(channels[3], self.channels[4], dropout=dropout) + self.bot = Block(channels[2], self.channels[3], dropout=dropout) + # self.deconv1 = Block(channels[4], self.channels[3], dropout=dropout) self.deconv2 = Block(channels[3], self.channels[2], dropout=dropout) + self.deconv3 = Block(channels[2], self.channels[1], dropout=dropout) + self.out_b = OutBlock(channels[1], out_channels, dropout=dropout) + # self.conv_trans1 = nn.ConvTranspose3d( + # self.channels[4], self.channels[3], 2, stride=2 + # ) self.conv_trans2 = nn.ConvTranspose3d( self.channels[3], self.channels[2], 2, stride=2 ) - self.deconv3 = Block(channels[2], self.channels[1], dropout=dropout) self.conv_trans3 = nn.ConvTranspose3d( self.channels[2], self.channels[1], 2, stride=2 ) - self.out_b = OutBlock(channels[1], out_channels, dropout=dropout) self.conv_trans_out = nn.ConvTranspose3d( self.channels[1], self.channels[0], 2, stride=2 ) @@ -127,17 +128,18 @@ def forward(self, x): in_b = self.in_b(x) c1 = self.conv1(self.max_pool(in_b)) c2 = self.conv2(self.max_pool(c1)) - c3 = self.conv3(self.max_pool(c2)) - x = self.bot(self.max_pool(c3)) - x = self.deconv1( - torch.cat( - [ - c3, - self.conv_trans1(x), - ], - dim=1, - ) - ) + # c3 = self.conv3(self.max_pool(c2)) + # x = self.bot(self.max_pool(c3)) + x = self.bot(self.max_pool(c2)) + # x = self.deconv1( + # torch.cat( + # [ + # c3, + # self.conv_trans1(x), + # ], + # dim=1, + # ) + # ) x = self.deconv2( torch.cat( [ From 622e9b31ffdcbe20241d24890b308a221d70d28e Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 26 Jul 2023 16:49:41 +0200 Subject: [PATCH 080/250] Started WNet training UI --- napari_cellseg3d/_tests/test_training.py | 10 +- .../code_models/models/wnet/train_wnet.py | 45 +- .../code_models/worker_training.py | 794 +++++++++++++++++- .../code_plugins/plugin_model_training.py | 477 +++++++---- napari_cellseg3d/config.py | 88 +- napari_cellseg3d/interface.py | 99 ++- 6 files changed, 1246 insertions(+), 267 deletions(-) diff --git a/napari_cellseg3d/_tests/test_training.py b/napari_cellseg3d/_tests/test_training.py index 0c54d36a..ac5d32a7 100644 --- a/napari_cellseg3d/_tests/test_training.py +++ b/napari_cellseg3d/_tests/test_training.py @@ -14,7 +14,7 @@ def test_update_loss_plot(make_napari_viewer_proxy): view = make_napari_viewer_proxy() widget = Trainer(view) - widget.worker_config = config.TrainingWorkerConfig() + widget.worker_config = config.SupervisedTrainingWorkerConfig() widget.worker_config.validation_interval = 1 widget.worker_config.results_path_folder = "." @@ -55,8 +55,8 @@ def test_update_loss_plot(make_napari_viewer_proxy): def test_check_matching_losses(): plugin = Trainer(None) - config = plugin._set_worker_config() - worker = plugin._create_worker_from_config(config) + config = plugin._set_supervised_worker_config() + worker = plugin._create_supervised_worker_from_config(config) assert plugin.loss_list == list(worker.loss_dict.keys()) @@ -84,9 +84,9 @@ def test_training(make_napari_viewer_proxy, qtbot): MODEL_LIST["test"] = TestModel widget.model_choice.addItem("test") widget.model_choice.setCurrentText("test") - worker_config = widget._set_worker_config() + worker_config = widget._set_supervised_worker_config() assert worker_config.model_info.name == "test" - worker = widget._create_worker_from_config(worker_config) + worker = widget._create_supervised_worker_from_config(worker_config) worker.config.train_data_dict = [{"image": im_path, "label": im_path}] worker.config.val_data_dict = [{"image": im_path, "label": im_path}] worker.config.max_epochs = 1 diff --git a/napari_cellseg3d/code_models/models/wnet/train_wnet.py b/napari_cellseg3d/code_models/models/wnet/train_wnet.py index 3b2ad353..7207fe35 100644 --- a/napari_cellseg3d/code_models/models/wnet/train_wnet.py +++ b/napari_cellseg3d/code_models/models/wnet/train_wnet.py @@ -115,11 +115,11 @@ def create_dataset_dict_no_labs(volume_directory): ################################ -# Config & WANDB # +# WNet: Config & WANDB # ################################ -class Config: +class WNetTrainingWorkerConfig: def __init__(self): # WNet self.in_channels = 1 @@ -144,29 +144,20 @@ def __init__(self): self.num_epochs = 100 self.val_interval = 5 self.batch_size = 2 - self.num_workers = 4 - - # CRF - self.sa = 50 # 10 - self.sb = 20 - self.sg = 1 - self.w1 = 50 # 10 - self.w2 = 20 - self.n_iter = 5 # Data - self.train_volume_directory = "./../dataset/VIP_full" - self.eval_volume_directory = "./../dataset/VIP_cropped/eval/" + # self.train_volume_directory = "./../dataset/VIP_full" + # self.eval_volume_directory = "./../dataset/VIP_cropped/eval/" self.normalize_input = True self.normalizing_function = remap_image # normalize_quantile - self.use_patch = False - self.patch_size = (64, 64, 64) - self.num_patches = 30 - self.eval_num_patches = 20 - self.do_augmentation = True - self.parallel = False - - self.save_model = True + # self.use_patch = False + # self.patch_size = (64, 64, 64) + # self.num_patches = 30 + # self.eval_num_patches = 20 + # self.do_augmentation = True + # self.parallel = False + + # self.save_model = True self.save_model_path = ( r"./../results/new_model/wnet_new_model_all_data_3class.pth" ) @@ -177,7 +168,7 @@ def __init__(self): self.weights_path = None -c = Config() +c = WNetTrainingWorkerConfig() ############### # Scheduler config ############### @@ -283,9 +274,9 @@ def __init__(self): def train(weights_path=None, train_config=None): if train_config is None: - config = Config() + config = WNetTrainingWorkerConfig() ############## - # disable metadata tracking + # disable metadata tracking in MONAI set_track_meta(False) ############## if WANDB_INSTALLED: @@ -698,7 +689,7 @@ def get_dataset(config): """Creates a Dataset from the original data using the tifffile library Args: - config (Config): The configuration object + config (WNetTrainingWorkerConfig): The configuration object Returns: (tuple): A tuple containing the shape of the data and the dataset @@ -776,7 +767,7 @@ def get_patch_dataset(config): """Creates a Dataset from the original data using the tifffile library Args: - config (Config): The configuration object + config (WNetTrainingWorkerConfig): The configuration object Returns: (tuple): A tuple containing the shape of the data and the dataset @@ -885,7 +876,7 @@ def get_dataset_monai(config): """Creates a Dataset applying some transforms/augmentation on the data using the MONAI library Args: - config (Config): The configuration object + config (WNetTrainingWorkerConfig): The configuration object Returns: (tuple): A tuple containing the shape of the data and the dataset diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index d7a49fd9..a1850e91 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -1,10 +1,12 @@ import platform import time +from abc import abstractmethod from math import ceil from pathlib import Path import numpy as np import torch +import torch.nn as nn # MONAI from monai.data import ( @@ -14,6 +16,7 @@ decollate_batch, pad_list_data_collate, ) +from monai.data.meta_obj import set_track_meta from monai.inferers import sliding_window_inference from monai.losses import ( DiceCELoss, @@ -23,8 +26,9 @@ ) from monai.metrics import DiceMetric from monai.transforms import ( - # AsDiscrete, + AsDiscrete, Compose, + EnsureChannelFirst, EnsureChannelFirstd, EnsureType, EnsureTyped, @@ -37,7 +41,9 @@ RandRotate90d, RandShiftIntensityd, RandSpatialCropSamplesd, + ScaleIntensityRanged, SpatialPadd, + ToTensor, ) from monai.utils import set_determinism @@ -46,6 +52,8 @@ # local from napari_cellseg3d import config, utils +from napari_cellseg3d.code_models.models.wnet.model import WNet +from napari_cellseg3d.code_models.models.wnet.soft_Ncuts import SoftNCutsLoss from napari_cellseg3d.code_models.workers_utils import ( PRETRAINED_WEIGHTS_DIR, LogSignal, @@ -60,6 +68,17 @@ VERBOSE_SCHEDULER = True logger.debug(f"PRETRAINED WEIGHT DIR LOCATION : {PRETRAINED_WEIGHTS_DIR}") +try: + import wandb + + WANDB_INSTALLED = True +except ImportError: + logger.warning( + "wandb not installed, wandb config will not be taken into account", + stacklevel=1, + ) + WANDB_INSTALLED = False + """ Writing something to log messages from outside the main thread needs specific care, Following the instructions in the guides below to have a worker with custom signals, @@ -70,14 +89,742 @@ # https://www.pythoncentral.io/pysidepyqt-tutorial-creating-your-own-signals-and-slots/ # https://napari-staging-site.github.io/guides/stable/threading.html +# TODO list for WNet training : +# 1. Create a custom base worker for training to avoid code duplication +# 2. Create a custom worker for WNet training +# 3. Adapt UI for WNet training (Advanced tab + model choice on first tab) +# 4. Adapt plots and TrainingReport for WNet training + + +class TrainingWorkerBase(GeneratorWorker): + """A basic worker abstract class, to run training jobs in. + Contains the minimal common elements required for training models.""" + + def __init__(self): + super().__init__(self.train) + self._signals = LogSignal() + self.log_signal = self._signals.log_signal + self.warn_signal = self._signals.warn_signal + self.error_signal = self._signals.error_signal + self.downloader = WeightsDownloader() + self.train_files = [] + self.val_files = [] + self.config = None + + self._weight_error = False + ################################ + + def set_download_log(self, widget): + """Sets the log widget for the downloader to output to""" + self.downloader.log_widget = widget -class TrainingWorker(GeneratorWorker): - """A custom worker to run training jobs in. - Inherits from :py:class:`napari.qt.threading.GeneratorWorker`""" + def log(self, text): + """Sends a signal that ``text`` should be logged + Goes in a Log object, defined in :py:mod:`napari_cellseg3d.interface + Sends a signal to the main thread to log the text. + Signal is defined in napari_cellseg3d.workers_utils.LogSignal + + Args: + text (str): text to logged + """ + self.log_signal.emit(text) + + def warn(self, warning): + """Sends a warning to main thread""" + self.warn_signal.emit(warning) + + def raise_error(self, exception, msg): + """Sends an error to main thread""" + logger.error(msg, exc_info=True) + logger.error(exception, exc_info=True) + self.error_signal.emit(exception, msg) + self.errored.emit(exception) + self.quit() + + @abstractmethod + def log_parameters(self): + """Logs the parameters of the training""" + raise NotImplementedError + + @abstractmethod + def train(self): + """Starts a training job""" + raise NotImplementedError + + +class WNetTrainingWorker(TrainingWorkerBase): + """A custom worker to run WNet (unsupervised) training jobs in. + Inherits from :py:class:`napari.qt.threading.GeneratorWorker` via :py:class:`TrainingWorkerBase` + """ def __init__( self, - worker_config: config.TrainingWorkerConfig, + worker_config: config.WNetTrainingWorkerConfig, + ): + super().__init__() + self.config = worker_config + + @staticmethod + def create_dataset_dict_no_labs(volume_directory): + """Creates unsupervised data dictionary for MONAI transforms and training.""" + images_filepaths = sorted( + Path.glob(str(Path(volume_directory) / "*.tif")) + ) + if len(images_filepaths) == 0: + raise ValueError(f"Data folder {volume_directory} is empty") + + logger.info("Images :") + for file in images_filepaths: + logger.info(Path(file).stem) + logger.info("*" * 10) + return [{"image": image_name} for image_name in images_filepaths] + + @staticmethod + def create_dataset_dict(volume_directory, label_directory): + """Creates data dictionary for MONAI transforms and training.""" + images_filepaths = sorted( + [str(file) for file in Path(volume_directory).glob("*.tif")] + ) + + labels_filepaths = sorted( + [str(file) for file in Path(label_directory).glob("*.tif")] + ) + if len(images_filepaths) == 0 or len(labels_filepaths) == 0: + raise ValueError( + f"Data folders are empty \n{volume_directory} \n{label_directory}" + ) + + logger.info("Images :") + for file in images_filepaths: + logger.info(Path(file).stem) + logger.info("*" * 10) + logger.info("Labels :") + for file in labels_filepaths: + logger.info(Path(file).stem) + try: + data_dicts = [ + {"image": image_name, "label": label_name} + for image_name, label_name in zip( + images_filepaths, labels_filepaths + ) + ] + except ValueError as e: + raise ValueError( + f"Number of images and labels does not match : \n{volume_directory} \n{label_directory}" + ) from e + # self.log(f"Loaded eval image: {data_dicts}") + return data_dicts + + def get_patch_dataset(self, volume_directory): + """Creates a Dataset from the original data using the tifffile library + + Args: + volume_directory (str): Path to the directory containing the data + + Returns: + (tuple): A tuple containing the shape of the data and the dataset + """ + + train_files = self.create_dataset_dict_no_labs( + volume_directory=volume_directory + ) + + patch_func = Compose( + [ + LoadImaged(keys=["image"], image_only=True), + EnsureChannelFirstd(keys=["image"], channel_dim="no_channel"), + RandSpatialCropSamplesd( + keys=["image"], + roi_size=( + self.config.sample_size + ), # multiply by axis_stretch_factor if anisotropy + # max_roi_size=(120, 120, 120), + random_size=False, + num_samples=self.config.num_samples, + ), + Orientationd(keys=["image"], axcodes="PLI"), + SpatialPadd( + keys=["image"], + spatial_size=( + utils.get_padding_dim(self.config.sample_size) + ), + ), + EnsureTyped(keys=["image"]), + ] + ) + + train_transforms = Compose( + [ + ScaleIntensityRanged( + keys=["image"], + a_min=0, + a_max=2000, + b_min=0.0, + b_max=1.0, + clip=True, + ), + RandShiftIntensityd(keys=["image"], offsets=0.1, prob=0.5), + RandFlipd(keys=["image"], spatial_axis=[1], prob=0.5), + RandFlipd(keys=["image"], spatial_axis=[2], prob=0.5), + RandRotate90d(keys=["image"], prob=0.1, max_k=3), + EnsureTyped(keys=["image"]), + ] + ) + + dataset = PatchDataset( + data=train_files, + samples_per_image=self.config.num_samples, + patch_func=patch_func, + transform=train_transforms, + ) + + return self.config.sample_size, dataset + + def get_patch_eval_dataset(self, volume_directory): + eval_files = self.create_dataset_dict( + volume_directory=volume_directory + "/vol", + label_directory=volume_directory + "/lab", + ) + + patch_func = Compose( + [ + LoadImaged(keys=["image", "label"], image_only=True), + EnsureChannelFirstd( + keys=["image", "label"], channel_dim="no_channel" + ), + # NormalizeIntensityd(keys=["image"]) if config.normalize_input else lambda x: x, + RandSpatialCropSamplesd( + keys=["image", "label"], + roi_size=( + self.config.sample_size + ), # multiply by axis_stretch_factor if anisotropy + # max_roi_size=(120, 120, 120), + random_size=False, + num_samples=self.config.eval_num_patches, + ), + Orientationd(keys=["image", "label"], axcodes="PLI"), + SpatialPadd( + keys=["image", "label"], + spatial_size=( + utils.get_padding_dim(self.config.sample_size) + ), + ), + EnsureTyped(keys=["image", "label"]), + ] + ) + + eval_transforms = Compose( + [ + EnsureTyped(keys=["image", "label"]), + ] + ) + + return PatchDataset( + data=eval_files, + samples_per_image=self.config.eval_num_patches, + patch_func=patch_func, + transform=eval_transforms, + ) + + def get_dataset_monai(self): + """Creates a Dataset applying some transforms/augmentation on the data using the MONAI library + + Args: + config (WNetTrainingWorkerConfig): The configuration object + + Returns: + (tuple): A tuple containing the shape of the data and the dataset + """ + # train_files = self.create_dataset_dict_no_labs( + # volume_directory=self.config.train_volume_directory + # ) + # self.log(train_files) + # self.log(len(train_files)) + # self.log(train_files[0]) + train_files = self.config.train_data_dict + + first_volume = LoadImaged(keys=["image"])(train_files[0]) + first_volume_shape = first_volume["image"].shape + + # Transforms to be applied to each volume + load_single_images = Compose( + [ + LoadImaged(keys=["image"]), + EnsureChannelFirstd(keys=["image"]), + Orientationd(keys=["image"], axcodes="PLI"), + SpatialPadd( + keys=["image"], + spatial_size=(utils.get_padding_dim(first_volume_shape)), + ), + EnsureTyped(keys=["image"]), + ] + ) + + if self.config.do_augmentation: + train_transforms = Compose( + [ + ScaleIntensityRanged( + keys=["image"], + a_min=0, + a_max=2000, + b_min=0.0, + b_max=1.0, + clip=True, + ), + RandShiftIntensityd(keys=["image"], offsets=0.1, prob=0.5), + RandFlipd(keys=["image"], spatial_axis=[1], prob=0.5), + RandFlipd(keys=["image"], spatial_axis=[2], prob=0.5), + RandRotate90d(keys=["image"], prob=0.1, max_k=3), + EnsureTyped(keys=["image"]), + ] + ) + else: + train_transforms = EnsureTyped(keys=["image"]) + + # Create the dataset + dataset = CacheDataset( + data=train_files, + transform=Compose([load_single_images, train_transforms]), + ) + + return first_volume_shape, dataset + + # def get_scheduler(self, optimizer, verbose=False): + # scheduler_name = self.config.scheduler + # if scheduler_name == "None": + # scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( + # optimizer, + # T_max=100, + # eta_min=config.lr - 1e-6, + # verbose=verbose, + # ) + # + # elif scheduler_name == "ReduceLROnPlateau": + # scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + # optimizer, + # mode="min", + # factor=schedulers["ReduceLROnPlateau"]["factor"], + # patience=schedulers["ReduceLROnPlateau"]["patience"], + # verbose=verbose, + # ) + # elif scheduler_name == "CosineAnnealingLR": + # scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( + # optimizer, + # T_max=schedulers["CosineAnnealingLR"]["T_max"], + # eta_min=schedulers["CosineAnnealingLR"]["eta_min"], + # verbose=verbose, + # ) + # elif scheduler_name == "CosineAnnealingWarmRestarts": + # scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts( + # optimizer, + # T_0=schedulers["CosineAnnealingWarmRestarts"]["T_0"], + # eta_min=schedulers["CosineAnnealingWarmRestarts"]["eta_min"], + # T_mult=schedulers["CosineAnnealingWarmRestarts"]["T_mult"], + # verbose=verbose, + # ) + # elif scheduler_name == "CyclicLR": + # scheduler = torch.optim.lr_scheduler.CyclicLR( + # optimizer, + # base_lr=schedulers["CyclicLR"]["base_lr"], + # max_lr=schedulers["CyclicLR"]["max_lr"], + # step_size_up=schedulers["CyclicLR"]["step_size_up"], + # mode=schedulers["CyclicLR"]["mode"], + # cycle_momentum=False, + # ) + # else: + # raise ValueError(f"Scheduler {scheduler_name} not provided") + # return scheduler + def train(self): + if self.config is None: + self.config = config.WNetTrainingWorkerConfig() + ############## + # disable metadata tracking in MONAI + set_track_meta(False) + ############## + # if WANDB_INSTALLED: + # wandb.init( + # config=WANDB_CONFIG, project="WNet-benchmark", mode=WANDB_MODE + # ) + + set_determinism( + seed=self.config.deterministic_config.seed + ) # use default seed from NP_MAX + torch.use_deterministic_algorithms(True, warn_only=True) + + normalize_function = self.config.normalizing_function + CUDA = torch.cuda.is_available() + device = torch.device("cuda" if CUDA else "cpu") + + self.log(f"Using device: {device}") + + self.log("Config:") + [self.log(str(a)) for a in self.config.__dict__.items()] + + self.log("Initializing training...") + self.log("Getting the data") + + if self.config.sampling: + (data_shape, dataset) = self.get_patch_dataset(self.config) + else: + (data_shape, dataset) = self.get_dataset(self.config) + transform = Compose( + [ + ToTensor(), + EnsureChannelFirst(channel_dim=0), + ] + ) + dataset = [transform(im) for im in dataset] + for data in dataset: + self.log(f"Data shape: {data.shape}") + break + + dataloader = DataLoader( + dataset, + batch_size=self.config.batch_size, + shuffle=True, + num_workers=self.config.num_workers, + collate_fn=pad_list_data_collate, + ) + + if self.config.eval_volume_dict is not None: + eval_dataset = self.get_patch_eval_dataset( + self.config.eval_volume_dict + ) # FIXME + + eval_dataloader = DataLoader( + eval_dataset, + batch_size=self.config.batch_size, + shuffle=False, + num_workers=self.config.num_workers, + collate_fn=pad_list_data_collate, + ) + + dice_metric = DiceMetric( + include_background=False, reduction="mean", get_not_nans=False + ) + ################################################### + # Training the model # + ################################################### + self.log("Initializing the model:") + + self.log("- getting the model") + # Initialize the model + model = WNet( + in_channels=self.config.in_channels, + out_channels=self.config.out_channels, + num_classes=self.config.num_classes, + dropout=self.config.dropout, + ) + model = ( + nn.DataParallel(model).cuda() + if CUDA and self.config.parallel + else model + ) + model.to(device) + + if self.config.use_clipping: + for p in model.parameters(): + p.register_hook( + lambda grad: torch.clamp( + grad, + min=-self.config.clipping, + max=self.config.clipping, + ) + ) + + if WANDB_INSTALLED: + wandb.watch(model, log_freq=100) + + if self.config.weights_info.path is not None: + model.load_state_dict( + torch.load(self.config.weights_info.path, map_location=device) + ) + + self.log("- getting the optimizers") + # Initialize the optimizers + if self.config.weight_decay is not None: + decay = self.config.weight_decay + optimizer = torch.optim.Adam( + model.parameters(), lr=self.config.lr, weight_decay=decay + ) + else: + optimizer = torch.optim.Adam(model.parameters(), lr=self.config.lr) + + self.log("- getting the loss functions") + # Initialize the Ncuts loss function + criterionE = SoftNCutsLoss( + data_shape=data_shape, + device=device, + intensity_sigma=self.config.intensity_sigma, + spatial_sigma=self.config.spatial_sigma, + radius=self.config.radius, + ) + + if self.config.reconstruction_loss == "MSE": + criterionW = nn.MSELoss() + elif self.config.reconstruction_loss == "BCE": + criterionW = nn.BCELoss() + else: + raise ValueError( + f"Unknown reconstruction loss : {self.config.reconstruction_loss} not supported" + ) + + self.log("- getting the learning rate schedulers") + # Initialize the learning rate schedulers + # scheduler = get_scheduler(self.config, optimizer) + # scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + # optimizer, mode="min", factor=0.5, patience=10, verbose=True + # ) + model.train() + + self.log("Ready") + self.log("Training the model") + self.log("*" * 50) + + startTime = time.time() + ncuts_losses = [] + rec_losses = [] + total_losses = [] + best_dice = -1 + + # Train the model + for epoch in range(self.config.num_epochs): + self.log(f"Epoch {epoch + 1} of {self.config.num_epochs}") + + epoch_ncuts_loss = 0 + epoch_rec_loss = 0 + epoch_loss = 0 + + for _i, batch in enumerate(dataloader): + # raise NotImplementedError("testing") + if self.config.sampling: + image = batch["image"].to(device) + else: + image = batch.to(device) + if self.config.batch_size == 1: + image = image.unsqueeze(0) + else: + image = image.unsqueeze(0) + image = torch.swapaxes(image, 0, 1) + + # Forward pass + enc = model.forward_encoder(image) + # Compute the Ncuts loss + Ncuts = criterionE(enc, image) + epoch_ncuts_loss += Ncuts.item() + # if WANDB_INSTALLED: + # wandb.log({"Ncuts loss": Ncuts.item()}) + + # Forward pass + enc, dec = model(image) + + # Compute the reconstruction loss + if isinstance(criterionW, nn.MSELoss): + reconstruction_loss = criterionW(dec, image) + elif isinstance(criterionW, nn.BCELoss): + reconstruction_loss = criterionW( + torch.sigmoid(dec), + utils.remap_image(image, new_max=1), + ) + + epoch_rec_loss += reconstruction_loss.item() + if WANDB_INSTALLED: + wandb.log( + {"Reconstruction loss": reconstruction_loss.item()} + ) + + # Backward pass for the reconstruction loss + optimizer.zero_grad() + alpha = self.config.n_cuts_weight + beta = self.config.rec_loss_weight + + loss = alpha * Ncuts + beta * reconstruction_loss + epoch_loss += loss.item() + # if WANDB_INSTALLED: + # wandb.log({"Sum of losses": loss.item()}) + loss.backward(loss) + optimizer.step() + + # if self.config.scheduler == "CosineAnnealingWarmRestarts": + # scheduler.step(epoch + _i / len(dataloader)) + # if ( + # self.config.scheduler == "CosineAnnealingLR" + # or self.config.scheduler == "CyclicLR" + # ): + # scheduler.step() + + ncuts_losses.append(epoch_ncuts_loss / len(dataloader)) + rec_losses.append(epoch_rec_loss / len(dataloader)) + total_losses.append(epoch_loss / len(dataloader)) + + # if WANDB_INSTALLED: + # wandb.log({"Ncuts loss_epoch": ncuts_losses[-1]}) + # wandb.log({"Reconstruction loss_epoch": rec_losses[-1]}) + # wandb.log({"Sum of losses_epoch": total_losses[-1]}) + # wandb.log({"epoch": epoch}) + # wandb.log({"learning_rate model": optimizerW.param_groups[0]["lr"]}) + # wandb.log({"learning_rate encoder": optimizerE.param_groups[0]["lr"]}) + # wandb.log({"learning_rate model": optimizer.param_groups[0]["lr"]}) + + self.log("Ncuts loss: " + str(ncuts_losses[-1])) + if epoch > 0: + self.log( + "Ncuts loss difference: " + + str(ncuts_losses[-1] - ncuts_losses[-2]) + ) + self.log("Reconstruction loss: " + str(rec_losses[-1])) + if epoch > 0: + self.log( + "Reconstruction loss difference: " + + str(rec_losses[-1] - rec_losses[-2]) + ) + self.log("Sum of losses: " + str(total_losses[-1])) + if epoch > 0: + self.log( + "Sum of losses difference: " + + str(total_losses[-1] - total_losses[-2]), + ) + + # Update the learning rate + # if self.config.scheduler == "ReduceLROnPlateau": + # # schedulerE.step(epoch_ncuts_loss) + # # schedulerW.step(epoch_rec_loss) + # scheduler.step(epoch_rec_loss) + if ( + self.config.eval_volume_directory is not None + and (epoch + 1) % self.config.val_interval == 0 + ): + model.eval() + self.log("Validating...") + with torch.no_grad(): + for _k, val_data in enumerate(eval_dataloader): + val_inputs, val_labels = ( + val_data["image"].to(device), + val_data["label"].to(device), + ) + + # normalize val_inputs across channels + for i in range(val_inputs.shape[0]): + for j in range(val_inputs.shape[1]): + val_inputs[i][j] = normalize_function( + val_inputs[i][j] + ) + + val_outputs = model.forward_encoder(val_inputs) + val_outputs = AsDiscrete(threshold=0.5)(val_outputs) + + # compute metric for current iteration + for channel in range(val_outputs.shape[1]): + max_dice_channel = torch.argmax( + torch.Tensor( + [ + utils.dice_coeff( + y_pred=val_outputs[ + :, + channel : (channel + 1), + :, + :, + :, + ], + y_true=val_labels, + ) + ] + ) + ) + + dice_metric( + y_pred=val_outputs[ + :, + max_dice_channel : (max_dice_channel + 1), + :, + :, + :, + ], + y=val_labels, + ) + + # aggregate the final mean dice result + metric = dice_metric.aggregate().item() + self.log("Validation Dice score: ", metric) + if best_dice < metric < 2: + best_dice = metric + epoch + 1 + if self.config.save_model: + save_best_path = Path( + self.config.save_model_path + ).parents[0] + save_best_path.mkdir(parents=True, exist_ok=True) + save_best_name = Path( + self.config.save_model_path + ).stem + save_path = ( + str(save_best_path / save_best_name) + + "_best_metric.pth" + ) + self.log(f"Saving new best model to {save_path}") + torch.save(model.state_dict(), save_path) + + if WANDB_INSTALLED: + # log validation dice score for each validation round + wandb.log({"val/dice_metric": metric}) + + # reset the status for next validation round + dice_metric.reset() + + eta = ( + (time.time() - startTime) + * (self.config.num_epochs / (epoch + 1) - 1) + / 60 + ) + self.log( + f"ETA: {eta} minutes", + ) + self.log("-" * 20) + + # Save the model # FIXME + if self.config.save_model and epoch % self.config.save_every == 0: + torch.save(model.state_dict(), self.config.save_model_path) + # with open(self.config.save_losses_path, "wb") as f: + # pickle.dump((ncuts_losses, rec_losses), f) + + self.log("Training finished") + self.log(f"Best dice metric : {best_dice}") + # if WANDB_INSTALLED and self.config.eval_volume_directory is not None: + # wandb.log( + # { + # "best_dice_metric": best_dice, + # "best_metric_epoch": best_dice_epoch, + # } + # ) + self.log("*" * 50) + + # Save the model FIXME + if self.config.save_model: + print("Saving the model to: ", self.config.save_model_path) + torch.save(model.state_dict(), self.config.save_model_path) + # with open(self.config.save_losses_path, "wb") as f: + # pickle.dump((ncuts_losses, rec_losses), f) + # if WANDB_INSTALLED: + # model_artifact = wandb.Artifact( + # "WNet", + # type="model", + # description="WNet benchmark", + # metadata=dict(WANDB_CONFIG), + # ) + # model_artifact.add_file(self.config.save_model_path) + # wandb.log_artifact(model_artifact) + + return ncuts_losses, rec_losses, model + + +class TrainingWorker(TrainingWorkerBase): + """A custom worker to run supervised training jobs in. + Inherits from :py:class:`napari.qt.threading.GeneratorWorker` via :py:class:`TrainingWorkerBase` + """ + + def __init__( + self, + worker_config: config.SupervisedTrainingWorkerConfig, ): """Initializes a worker for inference with the arguments needed by the :py:func:`~train` function. Note: See :py:func:`~train` @@ -116,21 +863,9 @@ def __init__( """ - super().__init__(self.train) - self._signals = LogSignal() - self.log_signal = self._signals.log_signal - self.warn_signal = self._signals.warn_signal - self.error_signal = self._signals.error_signal - - self._weight_error = False - ############################################# + super().__init__() # worker function is self.train in parent class self.config = worker_config - - self.train_files = [] - self.val_files = [] ####################################### - self.downloader = WeightsDownloader() - self.loss_dict = { "Dice": DiceLoss(sigmoid=True), # "BCELoss": torch.nn.BCELoss(), # dev @@ -150,29 +885,6 @@ def set_loss_from_config(self): self.raise_error(e, "Loss function not found, aborting job") return self.loss_function - def set_download_log(self, widget): - self.downloader.log_widget = widget - - def log(self, text): - """Sends a signal that ``text`` should be logged - - Args: - text (str): text to logged - """ - self.log_signal.emit(text) - - def warn(self, warning): - """Sends a warning to main thread""" - self.warn_signal.emit(warning) - - def raise_error(self, exception, msg): - """Sends an error to main thread""" - logger.error(msg, exc_info=True) - logger.error(exception, exc_info=True) - self.error_signal.emit(exception, msg) - self.errored.emit(exception) - self.quit() - def log_parameters(self): self.log("-" * 20) self.log("Parameters summary :\n") diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 80767396..e71f82cc 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -27,7 +27,7 @@ ) from napari_cellseg3d.code_models.workers_utils import TrainingReport -NUMBER_TABS = 3 # how many tabs in the widget +NUMBER_TABS = 4 # how many tabs in the widget DEFAULT_PATCH_SIZE = 64 # default patch size for training logger = utils.LOGGER @@ -37,7 +37,7 @@ class Trainer(ModelFramework, metaclass=ui.QWidgetSingleton): Features parameter selection for training, dynamic loss plotting and automatic saving of the best weights during training through validation.""" - default_config = config.TrainingWorkerConfig() + default_config = config.SupervisedTrainingWorkerConfig() def __init__( self, @@ -159,8 +159,8 @@ def __init__( # self.model_choice.setCurrentIndex(0) ################### # TODO(cyril) : disable if we implement WNet training - wnet_index = self.model_choice.findText("WNet") - self.model_choice.removeItem(wnet_index) + # wnet_index = self.model_choice.findText("WNet") + # self.model_choice.removeItem(wnet_index) ################################ # interface @@ -275,7 +275,7 @@ def __init__( "Deterministic training", func=self._toggle_deterministic_param ) self.box_seed = ui.IntIncrementCounter( - upper=10000000, + upper=1000000000, default=self.default_config.deterministic_config.seed, ) self.lbl_seed = ui.make_label("Seed", self) @@ -286,68 +286,85 @@ def __init__( self.progress.setVisible(False) """Dock widget containing the progress bar""" - self.btn_start = ui.Button("Start training", self.start) - - # self.btn_model_path.setVisible(False) - # self.lbl_model_path.setVisible(False) - + self.start_button_supervised = None # button created later and only shown if supervised model is selected + self.loss_group = None # group box created later and only shown if supervised model is selected ############################ ############################ - def set_tooltips(): - # tooltips - self.zip_choice.setToolTip( - "Checking this will save a copy of the results as a zip folder" - ) - self.validation_percent_choice.tooltips = "Choose the proportion of images to retain for training.\nThe remaining images will be used for validation" - self.epoch_choice.tooltips = "The number of epochs to train for.\nThe more you train, the better the model will fit the training data" - self.loss_choice.setToolTip( - "The loss function to use for training.\nSee the list in the training guide for more info" - ) - self.sample_choice_slider.tooltips = ( - "The number of samples to extract per image" - ) - self.batch_choice.tooltips = ( - "The batch size to use for training.\n A larger value will feed more images per iteration to the model,\n" - " which is faster and possibly improves performance, but uses more memory" - ) - self.val_interval_choice.tooltips = ( - "The number of epochs to perform before validating data.\n " - "The lower the value, the more often the score of the model will be computed and the more often the weights will be saved." - ) - self.learning_rate_choice.setToolTip( - "The learning rate to use in the optimizer. \nUse a lower value if you're using pre-trained weights" - ) - self.scheduler_factor_choice.setToolTip( - "The factor by which to reduce the learning rate once the loss reaches a plateau" - ) - self.scheduler_patience_choice.setToolTip( - "The amount of epochs to wait for before reducing the learning rate" - ) - self.augment_choice.setToolTip( - "Check this to enable data augmentation, which will randomly deform, flip and shift the intensity in images" - " to provide a more general dataset. \nUse this if you're extracting more than 10 samples per image" - ) - [ - w.setToolTip("Size of the sample to extract") - for w in self.patch_size_widgets - ] - self.patch_choice.setToolTip( - "Check this to automatically crop your images in smaller, cubic images for training." - "\nShould be used if you have a small dataset (and large images)" - ) - self.use_deterministic_choice.setToolTip( - "Enable deterministic training for reproducibility." - "Using the same seed with all other parameters being similar should yield the exact same results between two runs." - ) - self.use_transfer_choice.setToolTip( - "Use this you want to initialize the model with pre-trained weights or use your own weights." - ) - self.box_seed.setToolTip("Seed to use for RNG") - + # WNet parameters + self.wnet_widgets = ( + None # widgets created later and only shown if WNet is selected + ) + self.advanced_next_button = ( + None # button created later and only shown if WNet is selected + ) + self.start_button_unsupervised = ( + None # button created later and only shown if WNet is selected + ) + ############################ + # self.btn_model_path.setVisible(False) + # self.lbl_model_path.setVisible(False) ############################ ############################ - set_tooltips() + self._set_tooltips() self._build() + self.model_choice.currentTextChanged.connect( + self._toggle_unsupervised_mode + ) + self._toggle_unsupervised_mode() + + def _set_tooltips(self): + # tooltips + self.zip_choice.setToolTip( + "Checking this will save a copy of the results as a zip folder" + ) + self.validation_percent_choice.tooltips = "Choose the proportion of images to retain for training.\nThe remaining images will be used for validation" + self.epoch_choice.tooltips = "The number of epochs to train for.\nThe more you train, the better the model will fit the training data" + self.loss_choice.setToolTip( + "The loss function to use for training.\nSee the list in the training guide for more info" + ) + self.sample_choice_slider.tooltips = ( + "The number of samples to extract per image" + ) + self.batch_choice.tooltips = ( + "The batch size to use for training.\n A larger value will feed more images per iteration to the model,\n" + " which is faster and possibly improves performance, but uses more memory" + ) + self.val_interval_choice.tooltips = ( + "The number of epochs to perform before validating data.\n " + "The lower the value, the more often the score of the model will be computed and the more often the weights will be saved." + ) + self.learning_rate_choice.setToolTip( + "The learning rate to use in the optimizer. \nUse a lower value if you're using pre-trained weights" + ) + self.scheduler_factor_choice.setToolTip( + "The factor by which to reduce the learning rate once the loss reaches a plateau" + ) + self.scheduler_patience_choice.setToolTip( + "The amount of epochs to wait for before reducing the learning rate" + ) + self.augment_choice.setToolTip( + "Check this to enable data augmentation, which will randomly deform, flip and shift the intensity in images" + " to provide a more general dataset. \nUse this if you're extracting more than 10 samples per image" + ) + [ + w.setToolTip("Size of the sample to extract") + for w in self.patch_size_widgets + ] + self.patch_choice.setToolTip( + "Check this to automatically crop your images in smaller, cubic images for training." + "\nShould be used if you have a small dataset (and large images)" + ) + self.use_deterministic_choice.setToolTip( + "Enable deterministic training for reproducibility." + "Using the same seed with all other parameters being similar should yield the exact same results between two runs." + ) + self.use_transfer_choice.setToolTip( + "Use this you want to initialize the model with pre-trained weights or use your own weights." + ) + self.box_seed.setToolTip("Seed to use for RNG") + + def _make_start_button(self): + return ui.Button("Start training", self.start, parent=self) def _hide_unused(self): [ @@ -411,6 +428,33 @@ def check_ready(self): return False return True + def _toggle_unsupervised_mode(self): + """Change all the UI elements needed for unsupervised learning mode""" + if self.model_choice.currentText() == "WNet": + self.setTabVisible(3, True) + self.setTabEnabled(3, True) + self.start_button_unsupervised.setVisible(True) + self.start_button_supervised.setVisible(False) + self.advanced_next_button.setVisible(True) + self.start_btn = self.start_button_unsupervised + # loss + # self.loss_choice.setVisible(False) + self.loss_group.setVisible(False) + self.scheduler_factor_choice.setVisible(False) + self.scheduler_patience_choice.setVisible(False) + else: + self.setTabVisible(3, False) + self.setTabEnabled(3, False) + self.start_button_unsupervised.setVisible(False) + self.start_button_supervised.setVisible(True) + self.advanced_next_button.setVisible(False) + self.start_btn = self.start_button_supervised + # loss + # self.loss_choice.setVisible(True) + self.loss_group.setVisible(True) + self.scheduler_factor_choice.setVisible(True) + self.scheduler_patience_choice.setVisible(True) + def _build(self): """Builds the layout of the widget and creates the following tabs and prompts: @@ -453,48 +497,16 @@ def _build(self): ######## ################ ######################## - # first tab : model and dataset choices - data_tab = ui.ContainerWidget() - ################ - # first group : Data - data_group, data_layout = ui.make_group("Data") - - ui.add_widgets( - data_layout, - [ - # ui.combine_blocks( - # self.filetype_choice, self.filetype_choice.label - # ), # file extension - self.image_filewidget, - self.labels_filewidget, - self.results_filewidget, - # ui.combine_blocks(self.model_choice, self.model_choice.label), # model choice - # TODO : add custom model choice - self.zip_choice, # save as zip - ], - ) - - for w in [ - self.image_filewidget, - self.labels_filewidget, - self.results_filewidget, - ]: - w.check_ready() - - if self.data_path is not None: - self.image_filewidget.text_field.setText(self.data_path) - - if self.label_path is not None: - self.labels_filewidget.text_field.setText(self.label_path) - - if self.results_path is not None: - self.results_filewidget.text_field.setText(self.results_path) - - data_group.setLayout(data_layout) - data_tab.layout.addWidget(data_group, alignment=ui.LEFT_AL) - # end of first group : Data + # first tab : model, weights and device choices + model_tab = ui.ContainerWidget() ################ - ui.add_blank(widget=data_tab, layout=data_tab.layout) + ui.GroupedWidget.create_single_widget_group( + "Model", + self.model_choice, + model_tab.layout, + ) # model choice + self.model_choice.label.setVisible(False) + ui.add_blank(model_tab, model_tab.layout) ################ transfer_group_w, transfer_group_l = ui.make_group("Transfer learning") @@ -510,26 +522,21 @@ def _build(self): self.weights_filewidget.setVisible(False) transfer_group_w.setLayout(transfer_group_l) - data_tab.layout.addWidget(transfer_group_w, alignment=ui.LEFT_AL) + model_tab.layout.addWidget(transfer_group_w, alignment=ui.LEFT_AL) ################ - ui.add_blank(self, data_tab.layout) + ui.add_blank(self, model_tab.layout) ################ - ui.GroupedWidget.create_single_widget_group( - "Validation (%)", - self.validation_percent_choice.container, - data_tab.layout, - ) ui.GroupedWidget.create_single_widget_group( "Device", self.device_choice, - data_tab.layout, + model_tab.layout, ) ################ - ui.add_blank(self, data_tab.layout) + ui.add_blank(self, model_tab.layout) ################ # buttons ui.add_widgets( - data_tab.layout, + model_tab.layout, [ self._make_next_button(), # next ui.add_blank(self), @@ -539,13 +546,54 @@ def _build(self): ################## ############ ###### - # second tab : image sizes, data augmentation, patches size and behaviour + # Second tab : image sizes, data augmentation, patches size and behaviour ###### ############ ################## - augment_tab_w = ui.ContainerWidget() - augment_tab_l = augment_tab_w.layout + data_tab_w = ui.ContainerWidget() + data_tab_l = data_tab_w.layout ################## + ################ + # group : Data + data_group, data_layout = ui.make_group("Data") + + ui.add_widgets( + data_layout, + [ + # ui.combine_blocks( + # self.filetype_choice, self.filetype_choice.label + # ), # file extension + self.image_filewidget, + self.labels_filewidget, + self.results_filewidget, + # ui.combine_blocks(self.model_choice, self.model_choice.label), # model choice + # TODO : add custom model choice + self.zip_choice, # save as zip + ], + ) + + for w in [ + self.image_filewidget, + self.labels_filewidget, + self.results_filewidget, + ]: + w.check_ready() + + if self.data_path is not None: + self.image_filewidget.text_field.setText(self.data_path) + + if self.label_path is not None: + self.labels_filewidget.text_field.setText(self.label_path) + + if self.results_path is not None: + self.results_filewidget.text_field.setText(self.results_path) + + data_group.setLayout(data_layout) + data_tab_l.addWidget(data_group, alignment=ui.LEFT_AL) + # end of first group : Data + ################ + ui.add_blank(widget=data_tab_w, layout=data_tab_l) + ################ # extract patches or not patch_size_w = ui.ContainerWidget() @@ -579,27 +627,36 @@ def _build(self): horizontal=False, ) ui.GroupedWidget.create_single_widget_group( - "Sampling", sampling, augment_tab_l, b=0, t=11 + "Sampling", sampling, data_tab_l, b=0, t=11 ) ####################### ####################### - ui.add_blank(augment_tab_w, augment_tab_l) + ui.add_blank(data_tab_w, data_tab_l) ####################### ####################### ui.GroupedWidget.create_single_widget_group( "Augmentation", self.augment_choice, - augment_tab_l, + data_tab_l, ) # augment data toggle self.augment_choice.toggle() ####################### + ui.add_blank(data_tab_w, data_tab_l) ####################### - ui.add_blank(augment_tab_w, augment_tab_l) + ui.GroupedWidget.create_single_widget_group( + "Validation (%)", + self.validation_percent_choice.container, + data_tab_l, + ) + ####################### ####################### - augment_tab_l.addWidget( + ui.add_blank(self, data_tab_l) + ####################### + ####################### + data_tab_l.addWidget( ui.combine_blocks( left_or_above=self._make_prev_button(), right_or_below=self._make_next_button(), @@ -608,40 +665,29 @@ def _build(self): alignment=ui.LEFT_AL, ) - augment_tab_l.addWidget(self.close_buttons[1], alignment=ui.LEFT_AL) + data_tab_l.addWidget(self.close_buttons[1], alignment=ui.LEFT_AL) ################## ############ ###### - # third tab : training parameters + # Third tab : training parameters ###### ############ ################## train_tab = ui.ContainerWidget() ################## - # solo groups for loss and model ui.add_blank(train_tab, train_tab.layout) - - ui.GroupedWidget.create_single_widget_group( - "Model", - self.model_choice, - train_tab.layout, - ) # model choice - self.model_choice.label.setVisible(False) - - ui.add_blank(train_tab, train_tab.layout) - ui.GroupedWidget.create_single_widget_group( + ################## + self.loss_group = ui.GroupedWidget.create_single_widget_group( "Loss", self.loss_choice, train_tab.layout, ) # loss choice self.lbl_loss_choice.setVisible(False) - - # end of solo groups for loss and model + # end of solo groups for loss ################## ui.add_blank(train_tab, train_tab.layout) ################## # training params group - train_param_group_w, train_param_group_l = ui.make_group( "Training parameters", r=1, b=5, t=11 ) @@ -679,24 +725,29 @@ def _build(self): [self.use_deterministic_choice, self.container_seed], ui.LEFT_AL, ) - # self.container_seed.setVisible(False) self.use_deterministic_choice.setChecked(True) - seed_w.setLayout(seed_l) train_tab.layout.addWidget(seed_w) - # end of deterministic choice group ################## # buttons ui.add_blank(self, train_tab.layout) + self.advanced_next_button = self._make_next_button() + self.advanced_next_button.setVisible(False) + self.start_button_supervised = self._make_start_button() + ui.add_widgets( train_tab.layout, [ - self._make_prev_button(), # previous - self.btn_start, # start + ui.combine_blocks( + left_or_above=self._make_prev_button(), # previous + right_or_below=self.advanced_next_button, # next (only if unsupervised) + l=1, + ), + self.start_button_supervised, # start ui.add_blank(self), self.close_buttons[2], ], @@ -704,17 +755,105 @@ def _build(self): ################## ############ ###### - # end of tab layouts + # Fourth tab : advanced parameters (unsupervised only) + ###### + ############ + ################## + advanced_tab = ui.ContainerWidget(parent=self) + self.wnet_widgets = ui.WNetWidgets(parent=advanced_tab) + ui.add_blank(advanced_tab, advanced_tab.layout) + ################## + model_params_group_w, model_params_group_l = ui.make_group( + "WNet parameters", r=20, b=5, t=11 + ) + ui.add_widgets( + model_params_group_l, + [ + self.wnet_widgets.num_classes_choice.label, + self.wnet_widgets.num_classes_choice, + self.wnet_widgets.loss_choice.label, + self.wnet_widgets.loss_choice, + ], + ) + model_params_group_w.setLayout(model_params_group_l) + advanced_tab.layout.addWidget(model_params_group_w) + ################## + ui.add_blank(advanced_tab, advanced_tab.layout) + ################## + ncuts_loss_params_group_w, ncuts_loss_params_group_l = ui.make_group( + "NCuts loss parameters", r=35, b=5, t=11 + ) + ui.add_widgets( + ncuts_loss_params_group_l, + [ + self.wnet_widgets.intensity_sigma_choice.label, + self.wnet_widgets.intensity_sigma_choice, + self.wnet_widgets.spatial_sigma_choice.label, + self.wnet_widgets.spatial_sigma_choice, + self.wnet_widgets.radius_choice.label, + self.wnet_widgets.radius_choice, + ], + ) + ncuts_loss_params_group_w.setLayout(ncuts_loss_params_group_l) + advanced_tab.layout.addWidget(ncuts_loss_params_group_w) + ################## + ui.add_blank(advanced_tab, advanced_tab.layout) + ################## + losses_weights_group_w, losses_weights_group_l = ui.make_group( + "Losses weights", r=1, b=5, t=11 + ) + + # container for reconstruction weight and divide factor + reconstruction_weight_container = ui.ContainerWidget( + vertical=False, parent=losses_weights_group_w + ) + ui.add_widgets( + reconstruction_weight_container.layout, + [ + self.wnet_widgets.reconstruction_weight_choice, + ui.make_label(" / "), + self.wnet_widgets.reconstruction_weight_divide_factor_choice, + ], + ) + ui.add_widgets( + losses_weights_group_l, + [ + self.wnet_widgets.ncuts_weight_choice.label, + self.wnet_widgets.ncuts_weight_choice, + self.wnet_widgets.reconstruction_weight_choice.label, + reconstruction_weight_container, + ], + ) + losses_weights_group_w.setLayout(losses_weights_group_l) + advanced_tab.layout.addWidget(losses_weights_group_w) + ################## + ui.add_blank(advanced_tab, advanced_tab.layout) + ################## + # buttons + self.start_button_unsupervised = self._make_start_button() + ui.add_widgets( + advanced_tab.layout, + [ + self._make_prev_button(), # previous + self.start_button_unsupervised, # start + ui.add_blank(self), + self.close_buttons[3], + ], + ) + ################## + ############ + ###### + # end of tab layouts ui.ScrollArea.make_scrollable( - contained_layout=data_tab.layout, - parent=data_tab, + contained_layout=model_tab.layout, + parent=model_tab, min_wh=[200, 300], ) # , max_wh=[200,1000]) ui.ScrollArea.make_scrollable( - contained_layout=augment_tab_l, - parent=augment_tab_w, + contained_layout=data_tab_l, + parent=data_tab_w, min_wh=[200, 300], ) @@ -723,30 +862,28 @@ def _build(self): parent=train_tab, min_wh=[200, 300], ) - self.addTab(data_tab, "Data") - self.addTab(augment_tab_w, "Augmentation") + ui.ScrollArea.make_scrollable( + contained_layout=advanced_tab.layout, + parent=advanced_tab, + min_wh=[200, 300], + ) + + self.addTab(model_tab, "Model") + self.addTab(data_tab_w, "Data") self.addTab(train_tab, "Training") + self.addTab(advanced_tab, "Advanced") self.setMinimumSize(220, 100) self._hide_unused() default_results_path = ( - config.TrainingWorkerConfig().results_path_folder + config.SupervisedTrainingWorkerConfig().results_path_folder ) self.results_filewidget.text_field.setText(default_results_path) self.results_filewidget.check_ready() self._check_results_path(default_results_path) self.results_path = default_results_path - # def _show_dialog_lab(self): - # """Shows the dialog to load label files in a path, loads them (see :doc:model_framework) and changes the widget - # label :py:attr:`self.label_filewidget.text_field` accordingly""" - # folder = ui.open_folder_dialog(self, self._default_path) - # - # if folder: - # self.label_path = folder - # self.labels_filewidget.text_field.setText(self.label_path) - def send_log(self, text): """Sends a message via the Log attribute""" self.log.print_and_log(text) @@ -790,7 +927,7 @@ def start(self): pass else: self.worker.start() - self.btn_start.setText("Running... Click to stop") + self.start_btn.setText("Running... Click to stop") else: # starting a new job goes here self.log.print_and_log("Starting...") self.log.print_and_log("*" * 20) @@ -806,7 +943,7 @@ def start(self): self.config = config.TrainerConfig( save_as_zip=self.zip_choice.isChecked() ) - self._set_worker_config() + self._set_supervised_worker_config() self.worker = TrainingWorker(worker_config=self.worker_config) self.worker.set_download_log(self.log) @@ -829,15 +966,15 @@ def start(self): f"Stop requested at {utils.get_time()}. \nWaiting for next yielding step..." ) self.stop_requested = True - self.btn_start.setText("Stopping... Please wait") + self.start_btn.setText("Stopping... Please wait") self.log.print_and_log("*" * 20) self.worker.quit() else: self.worker.start() - self.btn_start.setText("Running... Click to stop") + self.start_btn.setText("Running... Click to stop") - def _create_worker_from_config( - self, worker_config: config.TrainingWorkerConfig + def _create_supervised_worker_from_config( + self, worker_config: config.SupervisedTrainingWorkerConfig ): if isinstance(config, config.TrainerConfig): raise TypeError( @@ -845,7 +982,9 @@ def _create_worker_from_config( ) return TrainingWorker(worker_config=worker_config) - def _set_worker_config(self) -> config.TrainingWorkerConfig: + def _set_supervised_worker_config( + self, + ) -> config.SupervisedTrainingWorkerConfig: model_config = config.ModelInfo(name=self.model_choice.currentText()) self.weights_config.path = self.weights_config.path @@ -875,7 +1014,7 @@ def _set_worker_config(self) -> config.TrainingWorkerConfig: patch_size = [w.value() for w in self.patch_size_widgets] logger.debug("Loading config...") - self.worker_config = config.TrainingWorkerConfig( + self.worker_config = config.SupervisedTrainingWorkerConfig( device=self.check_device_choice(), model_info=model_config, weights_info=self.weights_config, @@ -938,7 +1077,7 @@ def on_finish(self): except ValueError as e: logger.warning(f"Error while saving CSV report: {e}") - self.btn_start.setText("Start") + self.start_btn.setText("Start") [btn.setVisible(True) for btn in self.close_buttons] # del self.worker @@ -974,7 +1113,7 @@ def on_error(self): def on_stop(self): self._remove_result_layers() self.worker = None - self.btn_start.setText("Start") + self.start_btn.setText("Start") [btn.setVisible(True) for btn in self.close_buttons] def _remove_result_layers(self): diff --git a/napari_cellseg3d/config.py b/napari_cellseg3d/config.py index b05f7ac7..84f6468c 100644 --- a/napari_cellseg3d/config.py +++ b/napari_cellseg3d/config.py @@ -14,7 +14,7 @@ from napari_cellseg3d.code_models.models.model_TRAILMAP_MS import TRAILMAP_MS_ from napari_cellseg3d.code_models.models.model_VNet import VNet_ from napari_cellseg3d.code_models.models.model_WNet import WNet_ -from napari_cellseg3d.utils import LOGGER +from napari_cellseg3d.utils import LOGGER, remap_image logger = LOGGER @@ -37,7 +37,8 @@ ################ -# Review +# Review # +################ @dataclass @@ -61,13 +62,14 @@ class ReviewSession: time_taken: datetime.timedelta -################ -# Model & weights +################### +# Model & weights # +################### @dataclass class ModelInfo: - """Dataclass recording model info + """Dataclass recording supervised models info Args: name (str): name of the model model_input_size (Optional[List[int]]): input size of the model @@ -102,8 +104,9 @@ class WeightsInfo: use_pretrained: Optional[bool] = False -################ -# Post processing & instance segmentation +############################################# +# Post processing & instance segmentation # +############################################# @dataclass @@ -153,8 +156,9 @@ class CRFConfig: n_iters: int = 5 -################ -# Inference configs +##################### +# Inference configs # +##################### @dataclass @@ -219,8 +223,9 @@ class InferenceWorkerConfig: layer: napari.layers.Layer = None -################ -# Training configs +#################### +# Training configs # +#################### @dataclass @@ -228,7 +233,7 @@ class DeterministicConfig: """Class to record deterministic config""" enabled: bool = False - seed: int = 23498 + seed: int = 34936339 # default seed from NP_MAX @dataclass @@ -240,26 +245,65 @@ class TrainerConfig: @dataclass class TrainingWorkerConfig: - """Class to record config for Trainer plugin""" + """General class to record config for training""" + # model params device: str = "cpu" - model_info: ModelInfo = None - weights_info: WeightsInfo = None - train_data_dict: dict = None - validation_percent: float = 0.8 max_epochs: int = 50 - loss_function: callable = None learning_rate: np.float64 = 1e-3 - scheduler_patience: int = 10 - scheduler_factor: float = 0.5 validation_interval: int = 2 batch_size: int = 1 + deterministic_config: DeterministicConfig = DeterministicConfig() + scheduler_factor: float = 0.5 + scheduler_patience: int = 10 + weights_info: WeightsInfo = None + # data params results_path_folder: str = str(Path.home() / Path("cellseg3d/training")) sampling: bool = False num_samples: int = 2 sample_size: List[int] = None do_augmentation: bool = True - deterministic_config: DeterministicConfig = DeterministicConfig() + num_workers: int = 4 + train_data_dict: dict = None + + +@dataclass +class SupervisedTrainingWorkerConfig(TrainingWorkerConfig): + """Class to record config for Trainer plugin""" + + model_info: ModelInfo = None + loss_function: callable = None + validation_percent: float = 0.8 + + +@dataclass +class WNetTrainingWorkerConfig(TrainingWorkerConfig): + """Class to record config for WNet worker""" + + # model params + in_channels: int = 1 # encoder input channels + out_channels: int = 1 # decoder (reconstruction) output channels + num_classes: int = 2 # encoder output channels + dropout: float = 0.65 + use_clipping: bool = False # use gradient clipping + clipping: float = 1.0 # clipping value + # NCuts loss params + intensity_sigma: float = 1.0 + spatial_sigma: float = 4.0 + radius: int = 2 # pixel radius for loss computation; might be overriden depending on data shape + # reconstruction loss params + reconstruction_loss: str = "MSE" # or "BCE" + # summed losses weights + n_cuts_weight: float = 0.5 + rec_loss_weight: float = ( + 0.5 / 100 + ) # must be adjusted depending on images; compare to NCuts loss value + # normalization params + normalizing_function: callable = remap_image + # data params + train_data_dict: dict = None + eval_volume_dict: str = None + eval_num_patches: int = 10 ################ @@ -269,7 +313,7 @@ class TrainingWorkerConfig: @dataclass class WNetCRFConfig: - "Class to store parameters of WNet CRF post processing" + """Class to store parameters of WNet CRF post-processing""" # CRF sa = 10 # 50 diff --git a/napari_cellseg3d/interface.py b/napari_cellseg3d/interface.py index 22f6a4a3..e5f448f3 100644 --- a/napari_cellseg3d/interface.py +++ b/napari_cellseg3d/interface.py @@ -8,8 +8,6 @@ # Qt # from qtpy.QtCore import QtWarningMsg from qtpy import QtCore - -# from qtpy.QtCore import QtWarningMsg from qtpy.QtCore import QObject, Qt, QUrl from qtpy.QtGui import QCursor, QDesktopServices, QTextCursor from qtpy.QtWidgets import ( @@ -38,6 +36,7 @@ # Local from napari_cellseg3d import utils +from napari_cellseg3d.config import WNetTrainingWorkerConfig """ User interface functions and aliases""" @@ -1329,9 +1328,10 @@ def create_single_widget_group( alignment=LEFT_AL, ): group = cls(title, l, t, r, b) - group.layout.addWidget(widget) + group.layout.addWidget(widget, alignment=alignment) group.setLayout(group.layout) layout.addWidget(group, alignment=alignment) + return group def add_widgets(layout, widgets, alignment=LEFT_AL): @@ -1417,3 +1417,96 @@ def open_url(url): url (str): Url to be opened """ QDesktopServices.openUrl(QUrl(url, QUrl.TolerantMode)) + + +class WNetWidgets: + """A collection of widgets for the WNet training GUI""" + + default_config = WNetTrainingWorkerConfig() + + def __init__(self, parent): + self.num_classes_choice = DropdownMenu( + entries=["2", "3", "4"], + parent=parent, + text_label="Number of classes", + ) + self.intensity_sigma_choice = DoubleIncrementCounter( + lower=1.0, + upper=100.0, + default=self.default_config.intensity_sigma, + parent=parent, + text_label="Intensity sigma", + ) + self.intensity_sigma_choice.setMaximumWidth(20) + self.spatial_sigma_choice = DoubleIncrementCounter( + lower=1.0, + upper=100.0, + default=self.default_config.spatial_sigma, + parent=parent, + text_label="Spatial sigma", + ) + self.spatial_sigma_choice.setMaximumWidth(20) + self.radius_choice = IntIncrementCounter( + lower=1, + upper=5, + default=self.default_config.radius, + parent=parent, + text_label="Radius", + ) + self.radius_choice.setMaximumWidth(20) + self.loss_choice = DropdownMenu( + entries=["MSE", "BCE"], parent=parent, text_label="Loss function" + ) + self.ncuts_weight_choice = DoubleIncrementCounter( + lower=0.1, + upper=1.0, + default=self.default_config.n_cuts_weight, + parent=parent, + text_label="NCuts weight", + ) + self.reconstruction_weight_choice = DoubleIncrementCounter( + lower=0.1, + upper=1.0, + default=0.5, + parent=parent, + text_label="Reconstruction weight", + ) + self.reconstruction_weight_choice.setMaximumWidth(20) + self.reconstruction_weight_divide_factor_choice = IntIncrementCounter( + lower=1, + upper=10000, + default=100, + parent=parent, + text_label="Reconstruction weight divide factor", + ) + self.reconstruction_weight_divide_factor_choice.setMaximumWidth(20) + self.evaluation_patches_choice = Slider( + lower=1, + upper=100, + default=self.default_config.eval_num_patches, + parent=parent, + text_label="Number of patches for evaluation", + ) + + self._set_tooltips() + + def _set_tooltips(self): + self.num_classes_choice.setToolTip("Number of classes to segment") + self.intensity_sigma_choice.setToolTip( + "Intensity sigma for the NCuts loss" + ) + self.spatial_sigma_choice.setToolTip( + "Spatial sigma for the NCuts loss" + ) + self.radius_choice.setToolTip("Radius of NCuts loss region") + self.loss_choice.setToolTip("Loss function to use for reconstruction") + self.ncuts_weight_choice.setToolTip("Weight of the NCuts loss") + self.reconstruction_weight_choice.setToolTip( + "Weight of the reconstruction loss" + ) + self.reconstruction_weight_divide_factor_choice.setToolTip( + "Divide factor for the reconstruction loss.\nThis might have to be changed depending on your images.\nIf you notice that the reconstruction loss is too high, raise this factor until the\nreconstruction loss is in the same order of magnitude as the NCuts loss." + ) + self.evaluation_patches_choice.setToolTip( + "Number of patches to use for evaluation" + ) From fdcf797fc9c3fcbf4131aa0ae004c4ff54bfc7e0 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 27 Jul 2023 17:23:16 +0200 Subject: [PATCH 081/250] Workable WNet training prototype --- napari_cellseg3d/_tests/test_inference.py | 23 +- .../_tests/test_model_framework.py | 3 +- napari_cellseg3d/_tests/test_training.py | 135 +- .../code_models/model_framework.py | 26 + .../code_models/models/wnet/model.py | 66 +- .../code_models/models/wnet/train_wnet.py | 1983 +++++++++-------- .../code_models/worker_training.py | 973 ++++---- napari_cellseg3d/code_models/workers_utils.py | 10 +- napari_cellseg3d/code_plugins/plugin_base.py | 37 +- .../code_plugins/plugin_model_training.py | 704 +++--- napari_cellseg3d/config.py | 12 +- napari_cellseg3d/interface.py | 111 +- 12 files changed, 2175 insertions(+), 1908 deletions(-) diff --git a/napari_cellseg3d/_tests/test_inference.py b/napari_cellseg3d/_tests/test_inference.py index 336630f5..f5a89b14 100644 --- a/napari_cellseg3d/_tests/test_inference.py +++ b/napari_cellseg3d/_tests/test_inference.py @@ -23,7 +23,7 @@ def test_onnx_inference(make_napari_viewer_proxy): path = str(Path(PRETRAINED_WEIGHTS_DIR).resolve() / "wnet.onnx") assert Path(path).is_file() dims = 64 - batch = 2 + batch = 1 x = torch.randn(size=(batch, 1, dims, dims, dims)) worker = ONNXModelWrapper(file_location=path) assert worker.eval() is None @@ -66,16 +66,27 @@ def test_inference_on_folder(): config.images_filepaths = [ str(Path(__file__).resolve().parent / "res/test.tif") ] + config.sliding_window_config.window_size = 8 - def mock_work(x): - return x + class mock_work: + @staticmethod + def eval(): + return True + + def __call__(self, x): + return torch.Tensor(x) worker = InferenceWorker(worker_config=config) - worker.aniso_transform = mock_work + worker.aniso_transform = mock_work() - image = torch.Tensor(rand_gen.random((1, 1, 64, 64, 64))) + image = torch.Tensor(rand_gen.random(size=(1, 1, 8, 8, 8))) + assert image.shape == (1, 1, 8, 8, 8) + assert image.dtype == torch.float32 res = worker.inference_on_folder( - {"image": image}, 0, model=mock_work, post_process_transforms=mock_work + {"image": image}, + 0, + model=mock_work(), + post_process_transforms=mock_work(), ) assert isinstance(res, InferenceResult) diff --git a/napari_cellseg3d/_tests/test_model_framework.py b/napari_cellseg3d/_tests/test_model_framework.py index 497d97e8..0a078273 100644 --- a/napari_cellseg3d/_tests/test_model_framework.py +++ b/napari_cellseg3d/_tests/test_model_framework.py @@ -17,7 +17,7 @@ def test_update_default(make_napari_viewer_proxy): widget._update_default_paths() - assert widget._default_path == [None, None, None] + assert widget._default_path == [None, None, None, None] widget.images_filepaths = [ pth("C:/test/test/images.tif"), @@ -36,6 +36,7 @@ def test_update_default(make_napari_viewer_proxy): pth("C:/test/test"), pth("C:/dataset/labels"), pth("D:/dataset/res"), + None, ] diff --git a/napari_cellseg3d/_tests/test_training.py b/napari_cellseg3d/_tests/test_training.py index ac5d32a7..c5737f11 100644 --- a/napari_cellseg3d/_tests/test_training.py +++ b/napari_cellseg3d/_tests/test_training.py @@ -9,61 +9,87 @@ ) from napari_cellseg3d.config import MODEL_LIST +im_path = Path(__file__).resolve().parent / "res/test.tif" +im_path_str = str(im_path) + + +def test_create_supervised_worker_from_config(make_napari_viewer_proxy): + widget = Trainer(make_napari_viewer_proxy()) + worker = widget._create_worker() + default_config = config.SupervisedTrainingWorkerConfig() + excluded = [ + "results_path_folder", + "loss_function", + "model_info", + "sample_size", + "weights_info", + ] + for attr in dir(default_config): + if not attr.startswith("__") and attr not in excluded: + assert getattr(default_config, attr) == getattr( + worker.config, attr + ) + + +def test_create_unspervised_worker_from_config(make_napari_viewer_proxy): + widget = Trainer(make_napari_viewer_proxy()) + widget.model_choice.setCurrentText("WNet") + widget._toggle_unsupervised_mode(enabled=True) + default_config = config.WNetTrainingWorkerConfig() + worker = widget._create_worker() + excluded = ["results_path_folder", "sample_size", "weights_info"] + for attr in dir(default_config): + if not attr.startswith("__") and attr not in excluded: + assert getattr(default_config, attr) == getattr( + worker.config, attr + ) + def test_update_loss_plot(make_napari_viewer_proxy): view = make_napari_viewer_proxy() widget = Trainer(view) widget.worker_config = config.SupervisedTrainingWorkerConfig() + assert widget._is_current_job_supervised() is True widget.worker_config.validation_interval = 1 widget.worker_config.results_path_folder = "." - epoch_loss_values = [1] + epoch_loss_values = {"loss": [1]} metric_values = [] - widget.update_loss_plot(epoch_loss_values, metric_values) - - assert widget.dice_metric_plot is None - assert widget.train_loss_plot is None + assert widget.plot_2 is None + assert widget.plot_1 is None widget.worker_config.validation_interval = 2 - epoch_loss_values = [0, 1] + epoch_loss_values = {"loss": [0, 1]} metric_values = [0.2] - widget.update_loss_plot(epoch_loss_values, metric_values) + assert widget.plot_2 is None + assert widget.plot_1 is None - assert widget.dice_metric_plot is None - assert widget.train_loss_plot is None - - epoch_loss_values = [0, 1, 0.5, 0.7] - metric_values = [0.2, 0.3] - + epoch_loss_values = {"loss": [0, 1, 0.5, 0.7]} + metric_values = [0.1, 0.2] widget.update_loss_plot(epoch_loss_values, metric_values) + assert widget.plot_2 is not None + assert widget.plot_1 is not None - assert widget.dice_metric_plot is not None - assert widget.train_loss_plot is not None - - epoch_loss_values = [0, 1, 0.5, 0.7, 0.5, 0.7] + epoch_loss_values = {"loss": [0, 1, 0.5, 0.7, 0.5, 0.7]} metric_values = [0.2, 0.3, 0.5, 0.7] - widget.update_loss_plot(epoch_loss_values, metric_values) - - assert widget.dice_metric_plot is not None - assert widget.train_loss_plot is not None + assert widget.plot_2 is not None + assert widget.plot_1 is not None def test_check_matching_losses(): plugin = Trainer(None) - config = plugin._set_supervised_worker_config() + config = plugin._set_worker_config() worker = plugin._create_supervised_worker_from_config(config) assert plugin.loss_list == list(worker.loss_dict.keys()) def test_training(make_napari_viewer_proxy, qtbot): - im_path = str(Path(__file__).resolve().parent / "res/test.tif") - viewer = make_napari_viewer_proxy() widget = Trainer(viewer) widget.log = LogFixture() @@ -74,8 +100,8 @@ def test_training(make_napari_viewer_proxy, qtbot): assert not widget.check_ready() - widget.images_filepaths = [im_path] - widget.labels_filepaths = [im_path] + widget.images_filepaths = [im_path_str] + widget.labels_filepaths = [im_path_str] widget.epoch_choice.setValue(1) widget.val_interval_choice.setValue(1) @@ -84,11 +110,16 @@ def test_training(make_napari_viewer_proxy, qtbot): MODEL_LIST["test"] = TestModel widget.model_choice.addItem("test") widget.model_choice.setCurrentText("test") - worker_config = widget._set_supervised_worker_config() + widget.unsupervised_mode = False + worker_config = widget._set_worker_config() assert worker_config.model_info.name == "test" worker = widget._create_supervised_worker_from_config(worker_config) - worker.config.train_data_dict = [{"image": im_path, "label": im_path}] - worker.config.val_data_dict = [{"image": im_path, "label": im_path}] + worker.config.train_data_dict = [ + {"image": im_path_str, "label": im_path_str} + ] + worker.config.val_data_dict = [ + {"image": im_path_str, "label": im_path_str} + ] worker.config.max_epochs = 1 worker.config.validation_interval = 2 worker.log_parameters() @@ -99,20 +130,34 @@ def test_training(make_napari_viewer_proxy, qtbot): widget.worker = worker res.show_plot = True - res.loss_values = [1, 1, 1, 1] - res.validation_metric = [1, 1, 1, 1] + res.loss_1_values = {"loss": [1, 1, 1, 1]} + res.loss_2_values = [1, 1, 1, 1] widget.on_yield(res) - assert widget.loss_values == [1, 1, 1, 1] - assert widget.validation_values == [1, 1, 1, 1] - - # def on_error(e): - # print(e) - # assert False - # - # with qtbot.waitSignal( - # signal=widget.worker.finished, timeout=10000, raising=True - # ) as blocker: - # blocker.connect(widget.worker.errored) - # widget.worker.error_signal.connect(on_error) - # widget.worker.train() - # assert widget.worker is not None + assert widget.loss_1_values["loss"] == [1, 1, 1, 1] + assert widget.loss_2_values == [1, 1, 1, 1] + + +def test_unsupervised_worker(make_napari_viewer_proxy): + viewer = make_napari_viewer_proxy() + widget = Trainer(viewer) + + widget.model_choice.setCurrentText("WNet") + widget._toggle_unsupervised_mode(enabled=True) + + widget.unsupervised_images_filewidget.text_field.setText( + str(im_path.parent) + ) + widget.data = widget.create_dataset_dict_no_labs() + worker = widget._create_worker() + dataloader, eval_dataloader, data_shape = worker._get_data() + assert eval_dataloader is None + assert data_shape == (6, 6, 6) + + widget.images_filepaths = [str(im_path.parent)] + widget.labels_filepaths = [str(im_path.parent)] + widget.unsupervised_eval_data = widget.create_train_dataset_dict() + assert widget.unsupervised_eval_data is not None + worker = widget._create_worker() + dataloader, eval_dataloader, data_shape = worker._get_data() + assert eval_dataloader is not None + assert data_shape == (6, 6, 6) diff --git a/napari_cellseg3d/code_models/model_framework.py b/napari_cellseg3d/code_models/model_framework.py index 585c53a0..9bcd67a6 100644 --- a/napari_cellseg3d/code_models/model_framework.py +++ b/napari_cellseg3d/code_models/model_framework.py @@ -245,6 +245,24 @@ def _toggle_weights_path(self): self.custom_weights_choice, self.weights_filewidget ) + def create_dataset_dict_no_labs(self): + """Creates unsupervised data dictionary for MONAI transforms and training.""" + volume_directory = Path( + self.unsupervised_images_filewidget.text_field.text() + ) + if not volume_directory.exists(): + raise ValueError(f"Data folder {volume_directory} does not exist") + images_filepaths = sorted(Path.glob(volume_directory, "*.tif")) + if len(images_filepaths) == 0: + raise ValueError(f"Data folder {volume_directory} is empty") + + logger.info("Images :") + for file in images_filepaths: + logger.info(Path(file).stem) + logger.info("*" * 10) + + return [{"image": str(image_name)} for image_name in images_filepaths] + def create_train_dataset_dict(self): """Creates data dictionary for MONAI transforms and training. @@ -255,9 +273,17 @@ def create_train_dataset_dict(self): * "label" : corresponding label """ + logger.debug(f"Images : {self.images_filepaths}") + logger.debug(f"Labels : {self.labels_filepaths}") + if len(self.images_filepaths) == 0 or len(self.labels_filepaths) == 0: raise ValueError("Data folders are empty") + if not Path(self.images_filepaths[0]).parent.exists(): + raise ValueError("Images folder does not exist") + if not Path(self.labels_filepaths[0]).parent.exists(): + raise ValueError("Labels folder does not exist") + logger.info("Images :\n") for file in self.images_filepaths: logger.info(Path(file).name) diff --git a/napari_cellseg3d/code_models/models/wnet/model.py b/napari_cellseg3d/code_models/models/wnet/model.py index cd2bcb16..0f9822cd 100644 --- a/napari_cellseg3d/code_models/models/wnet/model.py +++ b/napari_cellseg3d/code_models/models/wnet/model.py @@ -98,24 +98,25 @@ def __init__( self.channels = channels self.max_pool = nn.MaxPool3d(2) self.in_b = InBlock(in_channels, self.channels[0], dropout=dropout) - self.conv1 = Block(channels[0], self.channels[1], dropout=dropout) - self.conv2 = Block(channels[1], self.channels[2], dropout=dropout) + # self.conv1 = Block(channels[0], self.channels[1], dropout=dropout) + # self.conv2 = Block(channels[1], self.channels[2], dropout=dropout) # self.conv3 = Block(channels[2], self.channels[3], dropout=dropout) # self.bot = Block(channels[3], self.channels[4], dropout=dropout) - self.bot = Block(channels[2], self.channels[3], dropout=dropout) + # self.bot = Block(channels[2], self.channels[3], dropout=dropout) + self.bot = Block(channels[0], self.channels[1], dropout=dropout) # self.deconv1 = Block(channels[4], self.channels[3], dropout=dropout) - self.deconv2 = Block(channels[3], self.channels[2], dropout=dropout) - self.deconv3 = Block(channels[2], self.channels[1], dropout=dropout) + # self.deconv2 = Block(channels[3], self.channels[2], dropout=dropout) + # self.deconv3 = Block(channels[2], self.channels[1], dropout=dropout) self.out_b = OutBlock(channels[1], out_channels, dropout=dropout) # self.conv_trans1 = nn.ConvTranspose3d( # self.channels[4], self.channels[3], 2, stride=2 # ) - self.conv_trans2 = nn.ConvTranspose3d( - self.channels[3], self.channels[2], 2, stride=2 - ) - self.conv_trans3 = nn.ConvTranspose3d( - self.channels[2], self.channels[1], 2, stride=2 - ) + # self.conv_trans2 = nn.ConvTranspose3d( + # self.channels[3], self.channels[2], 2, stride=2 + # ) + # self.conv_trans3 = nn.ConvTranspose3d( + # self.channels[2], self.channels[1], 2, stride=2 + # ) self.conv_trans_out = nn.ConvTranspose3d( self.channels[1], self.channels[0], 2, stride=2 ) @@ -126,11 +127,12 @@ def __init__( def forward(self, x): """Forward pass of the U-Net model.""" in_b = self.in_b(x) - c1 = self.conv1(self.max_pool(in_b)) - c2 = self.conv2(self.max_pool(c1)) + # c1 = self.conv1(self.max_pool(in_b)) + # c2 = self.conv2(self.max_pool(c1)) # c3 = self.conv3(self.max_pool(c2)) # x = self.bot(self.max_pool(c3)) - x = self.bot(self.max_pool(c2)) + # x = self.bot(self.max_pool(c2)) + x = self.bot(self.max_pool(in_b)) # x = self.deconv1( # torch.cat( # [ @@ -140,24 +142,24 @@ def forward(self, x): # dim=1, # ) # ) - x = self.deconv2( - torch.cat( - [ - c2, - self.conv_trans2(x), - ], - dim=1, - ) - ) - x = self.deconv3( - torch.cat( - [ - c1, - self.conv_trans3(x), - ], - dim=1, - ) - ) + # x = self.deconv2( + # torch.cat( + # [ + # c2, + # self.conv_trans2(x), + # ], + # dim=1, + # ) + # ) + # x = self.deconv3( + # torch.cat( + # [ + # c1, + # self.conv_trans3(x), + # ], + # dim=1, + # ) + # ) x = self.out_b( torch.cat( [ diff --git a/napari_cellseg3d/code_models/models/wnet/train_wnet.py b/napari_cellseg3d/code_models/models/wnet/train_wnet.py index 7207fe35..d999fc17 100644 --- a/napari_cellseg3d/code_models/models/wnet/train_wnet.py +++ b/napari_cellseg3d/code_models/models/wnet/train_wnet.py @@ -1,991 +1,992 @@ -""" -This file contains the code to train the WNet model. -""" -# import napari -import glob -import time -from pathlib import Path -from warnings import warn - -import numpy as np -import tifffile as tiff -import torch -import torch.nn as nn - -# MONAI -from monai.data import ( - CacheDataset, - DataLoader, - PatchDataset, - pad_list_data_collate, -) -from monai.data.meta_obj import set_track_meta -from monai.metrics import DiceMetric -from monai.transforms import ( - AsDiscrete, - Compose, - EnsureChannelFirst, - EnsureChannelFirstd, - EnsureTyped, - LoadImaged, - Orientationd, - RandFlipd, - RandRotate90d, - RandShiftIntensityd, - RandSpatialCropSamplesd, - ScaleIntensityRanged, - SpatialPadd, - ToTensor, -) -from monai.utils.misc import set_determinism - -# local -from napari_cellseg3d.code_models.models.wnet.model import WNet -from napari_cellseg3d.code_models.models.wnet.soft_Ncuts import SoftNCutsLoss -from napari_cellseg3d.utils import LOGGER as logger -from napari_cellseg3d.utils import dice_coeff, get_padding_dim, remap_image - -try: - import wandb - - WANDB_INSTALLED = True -except ImportError: - warn( - "wandb not installed, wandb config will not be taken into account", - stacklevel=1, - ) - WANDB_INSTALLED = False - -__author__ = "Yves Paychère, Colin Hofmann, Cyril Achard" - - -########################## -# Utils functions # -########################## - - -def create_dataset_dict(volume_directory, label_directory): - """Creates data dictionary for MONAI transforms and training.""" - images_filepaths = sorted( - [str(file) for file in Path(volume_directory).glob("*.tif")] - ) - - labels_filepaths = sorted( - [str(file) for file in Path(label_directory).glob("*.tif")] - ) - if len(images_filepaths) == 0 or len(labels_filepaths) == 0: - raise ValueError( - f"Data folders are empty \n{volume_directory} \n{label_directory}" - ) - - logger.info("Images :") - for file in images_filepaths: - logger.info(Path(file).stem) - logger.info("*" * 10) - logger.info("Labels :") - for file in labels_filepaths: - logger.info(Path(file).stem) - try: - data_dicts = [ - {"image": image_name, "label": label_name} - for image_name, label_name in zip( - images_filepaths, labels_filepaths - ) - ] - except ValueError as e: - raise ValueError( - f"Number of images and labels does not match : \n{volume_directory} \n{label_directory}" - ) from e - # print(f"Loaded eval image: {data_dicts}") - return data_dicts - - -def create_dataset_dict_no_labs(volume_directory): - """Creates unsupervised data dictionary for MONAI transforms and training.""" - images_filepaths = sorted(glob.glob(str(Path(volume_directory) / "*.tif"))) - if len(images_filepaths) == 0: - raise ValueError(f"Data folder {volume_directory} is empty") - - logger.info("Images :") - for file in images_filepaths: - logger.info(Path(file).stem) - logger.info("*" * 10) - - return [{"image": image_name} for image_name in images_filepaths] - - -################################ -# WNet: Config & WANDB # -################################ - - -class WNetTrainingWorkerConfig: - def __init__(self): - # WNet - self.in_channels = 1 - self.out_channels = 1 - self.num_classes = 2 - self.dropout = 0.65 - self.use_clipping = False - self.clipping = 1 - - self.lr = 1e-6 - self.scheduler = "None" # "CosineAnnealingLR" # "ReduceLROnPlateau" - self.weight_decay = 0.01 # None - - self.intensity_sigma = 1 - self.spatial_sigma = 4 - self.radius = 2 # yields to a radius depending on the data shape - - self.n_cuts_weight = 0.5 - self.reconstruction_loss = "MSE" # "BCE" - self.rec_loss_weight = 0.5 / 100 - - self.num_epochs = 100 - self.val_interval = 5 - self.batch_size = 2 - - # Data - # self.train_volume_directory = "./../dataset/VIP_full" - # self.eval_volume_directory = "./../dataset/VIP_cropped/eval/" - self.normalize_input = True - self.normalizing_function = remap_image # normalize_quantile - # self.use_patch = False - # self.patch_size = (64, 64, 64) - # self.num_patches = 30 - # self.eval_num_patches = 20 - # self.do_augmentation = True - # self.parallel = False - - # self.save_model = True - self.save_model_path = ( - r"./../results/new_model/wnet_new_model_all_data_3class.pth" - ) - # self.save_losses_path = ( - # r"./../results/new_model/wnet_new_model_all_data_3class.pkl" - # ) - self.save_every = 5 - self.weights_path = None - - -c = WNetTrainingWorkerConfig() -############### -# Scheduler config -############### -schedulers = { - "ReduceLROnPlateau": { - "factor": 0.5, - "patience": 50, - }, - "CosineAnnealingLR": { - "T_max": 25000, - "eta_min": 1e-8, - }, - "CosineAnnealingWarmRestarts": { - "T_0": 50000, - "eta_min": 1e-8, - "T_mult": 1, - }, - "CyclicLR": { - "base_lr": 2e-7, - "max_lr": 2e-4, - "step_size_up": 250, - "mode": "triangular", - }, -} - -############### -# WANDB_CONFIG -############### -WANDB_MODE = "disabled" -# WANDB_MODE = "online" - -WANDB_CONFIG = { - # data setting - "num_workers": c.num_workers, - "normalize": c.normalize_input, - "use_patch": c.use_patch, - "patch_size": c.patch_size, - "num_patches": c.num_patches, - "eval_num_patches": c.eval_num_patches, - "do_augmentation": c.do_augmentation, - "model_save_path": c.save_model_path, - # train setting - "batch_size": c.batch_size, - "learning_rate": c.lr, - "weight_decay": c.weight_decay, - "scheduler": { - "name": c.scheduler, - "ReduceLROnPlateau_config": { - "factor": schedulers["ReduceLROnPlateau"]["factor"], - "patience": schedulers["ReduceLROnPlateau"]["patience"], - }, - "CosineAnnealingLR_config": { - "T_max": schedulers["CosineAnnealingLR"]["T_max"], - "eta_min": schedulers["CosineAnnealingLR"]["eta_min"], - }, - "CosineAnnealingWarmRestarts_config": { - "T_0": schedulers["CosineAnnealingWarmRestarts"]["T_0"], - "eta_min": schedulers["CosineAnnealingWarmRestarts"]["eta_min"], - "T_mult": schedulers["CosineAnnealingWarmRestarts"]["T_mult"], - }, - "CyclicLR_config": { - "base_lr": schedulers["CyclicLR"]["base_lr"], - "max_lr": schedulers["CyclicLR"]["max_lr"], - "step_size_up": schedulers["CyclicLR"]["step_size_up"], - "mode": schedulers["CyclicLR"]["mode"], - }, - }, - "max_epochs": c.num_epochs, - "save_every": c.save_every, - "val_interval": c.val_interval, - # loss - "reconstruction_loss": c.reconstruction_loss, - "loss weights": { - "n_cuts_weight": c.n_cuts_weight, - "rec_loss_weight": c.rec_loss_weight, - }, - "loss_params": { - "intensity_sigma": c.intensity_sigma, - "spatial_sigma": c.spatial_sigma, - "radius": c.radius, - }, - # model - "model_type": "wnet", - "model_params": { - "in_channels": c.in_channels, - "out_channels": c.out_channels, - "num_classes": c.num_classes, - "dropout": c.dropout, - "use_clipping": c.use_clipping, - "clipping_value": c.clipping, - }, - # CRF - "crf_params": { - "sa": c.sa, - "sb": c.sb, - "sg": c.sg, - "w1": c.w1, - "w2": c.w2, - "n_iter": c.n_iter, - }, -} - - -def train(weights_path=None, train_config=None): - if train_config is None: - config = WNetTrainingWorkerConfig() - ############## - # disable metadata tracking in MONAI - set_track_meta(False) - ############## - if WANDB_INSTALLED: - wandb.init( - config=WANDB_CONFIG, project="WNet-benchmark", mode=WANDB_MODE - ) - - set_determinism(seed=34936339) # use default seed from NP_MAX - torch.use_deterministic_algorithms(True, warn_only=True) - - config = train_config - normalize_function = config.normalizing_function - CUDA = torch.cuda.is_available() - device = torch.device("cuda" if CUDA else "cpu") - - print(f"Using device: {device}") - - print("Config:") - [print(a) for a in config.__dict__.items()] - - print("Initializing training...") - print("Getting the data") - - if config.use_patch: - (data_shape, dataset) = get_patch_dataset(config) - else: - (data_shape, dataset) = get_dataset(config) - transform = Compose( - [ - ToTensor(), - EnsureChannelFirst(channel_dim=0), - ] - ) - dataset = [transform(im) for im in dataset] - for data in dataset: - print(f"data shape: {data.shape}") - break - - dataloader = DataLoader( - dataset, - batch_size=config.batch_size, - shuffle=True, - num_workers=config.num_workers, - collate_fn=pad_list_data_collate, - ) - - if config.eval_volume_directory is not None: - eval_dataset = get_patch_eval_dataset(config) - - eval_dataloader = DataLoader( - eval_dataset, - batch_size=config.batch_size, - shuffle=False, - num_workers=config.num_workers, - collate_fn=pad_list_data_collate, - ) - - dice_metric = DiceMetric( - include_background=False, reduction="mean", get_not_nans=False - ) - ################################################### - # Training the model # - ################################################### - print("Initializing the model:") - - print("- getting the model") - # Initialize the model - model = WNet( - in_channels=config.in_channels, - out_channels=config.out_channels, - num_classes=config.num_classes, - dropout=config.dropout, - ) - model = ( - nn.DataParallel(model).cuda() if CUDA and config.parallel else model - ) - model.to(device) - - if config.use_clipping: - for p in model.parameters(): - p.register_hook( - lambda grad: torch.clamp( - grad, min=-config.clipping, max=config.clipping - ) - ) - - if WANDB_INSTALLED: - wandb.watch(model, log_freq=100) - - if weights_path is not None: - model.load_state_dict(torch.load(weights_path, map_location=device)) - - print("- getting the optimizers") - # Initialize the optimizers - if config.weight_decay is not None: - decay = config.weight_decay - optimizer = torch.optim.Adam( - model.parameters(), lr=config.lr, weight_decay=decay - ) - else: - optimizer = torch.optim.Adam(model.parameters(), lr=config.lr) - - print("- getting the loss functions") - # Initialize the Ncuts loss function - criterionE = SoftNCutsLoss( - data_shape=data_shape, - device=device, - intensity_sigma=config.intensity_sigma, - spatial_sigma=config.spatial_sigma, - radius=config.radius, - ) - - if config.reconstruction_loss == "MSE": - criterionW = nn.MSELoss() - elif config.reconstruction_loss == "BCE": - criterionW = nn.BCELoss() - else: - raise ValueError( - f"Unknown reconstruction loss : {config.reconstruction_loss} not supported" - ) - - print("- getting the learning rate schedulers") - # Initialize the learning rate schedulers - scheduler = get_scheduler(config, optimizer) - # scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - # optimizer, mode="min", factor=0.5, patience=10, verbose=True - # ) - model.train() - - print("Ready") - print("Training the model") - print("*" * 50) - - startTime = time.time() - ncuts_losses = [] - rec_losses = [] - total_losses = [] - best_dice = -1 - best_dice_epoch = -1 - - # Train the model - for epoch in range(config.num_epochs): - print(f"Epoch {epoch + 1} of {config.num_epochs}") - - epoch_ncuts_loss = 0 - epoch_rec_loss = 0 - epoch_loss = 0 - - for _i, batch in enumerate(dataloader): - # raise NotImplementedError("testing") - if config.use_patch: - image = batch["image"].to(device) - else: - image = batch.to(device) - if config.batch_size == 1: - image = image.unsqueeze(0) - else: - image = image.unsqueeze(0) - image = torch.swapaxes(image, 0, 1) - - # Forward pass - enc = model.forward_encoder(image) - # out = model.forward(image) - - # Compute the Ncuts loss - Ncuts = criterionE(enc, image) - epoch_ncuts_loss += Ncuts.item() - if WANDB_INSTALLED: - wandb.log({"Ncuts loss": Ncuts.item()}) - - # Forward pass - enc, dec = model(image) - - # Compute the reconstruction loss - if isinstance(criterionW, nn.MSELoss): - reconstruction_loss = criterionW(dec, image) - elif isinstance(criterionW, nn.BCELoss): - reconstruction_loss = criterionW( - torch.sigmoid(dec), - remap_image(image, new_max=1), - ) - - epoch_rec_loss += reconstruction_loss.item() - if WANDB_INSTALLED: - wandb.log({"Reconstruction loss": reconstruction_loss.item()}) - - # Backward pass for the reconstruction loss - optimizer.zero_grad() - alpha = config.n_cuts_weight - beta = config.rec_loss_weight - - loss = alpha * Ncuts + beta * reconstruction_loss - epoch_loss += loss.item() - if WANDB_INSTALLED: - wandb.log({"Sum of losses": loss.item()}) - loss.backward(loss) - optimizer.step() - - if config.scheduler == "CosineAnnealingWarmRestarts": - scheduler.step(epoch + _i / len(dataloader)) - if ( - config.scheduler == "CosineAnnealingLR" - or config.scheduler == "CyclicLR" - ): - scheduler.step() - - ncuts_losses.append(epoch_ncuts_loss / len(dataloader)) - rec_losses.append(epoch_rec_loss / len(dataloader)) - total_losses.append(epoch_loss / len(dataloader)) - - if WANDB_INSTALLED: - wandb.log({"Ncuts loss_epoch": ncuts_losses[-1]}) - wandb.log({"Reconstruction loss_epoch": rec_losses[-1]}) - wandb.log({"Sum of losses_epoch": total_losses[-1]}) - # wandb.log({"epoch": epoch}) - # wandb.log({"learning_rate model": optimizerW.param_groups[0]["lr"]}) - # wandb.log({"learning_rate encoder": optimizerE.param_groups[0]["lr"]}) - wandb.log({"learning_rate model": optimizer.param_groups[0]["lr"]}) - - print("Ncuts loss: ", ncuts_losses[-1]) - if epoch > 0: - print( - "Ncuts loss difference: ", - ncuts_losses[-1] - ncuts_losses[-2], - ) - print("Reconstruction loss: ", rec_losses[-1]) - if epoch > 0: - print( - "Reconstruction loss difference: ", - rec_losses[-1] - rec_losses[-2], - ) - print("Sum of losses: ", total_losses[-1]) - if epoch > 0: - print( - "Sum of losses difference: ", - total_losses[-1] - total_losses[-2], - ) - - # Update the learning rate - if config.scheduler == "ReduceLROnPlateau": - # schedulerE.step(epoch_ncuts_loss) - # schedulerW.step(epoch_rec_loss) - scheduler.step(epoch_rec_loss) - if ( - config.eval_volume_directory is not None - and (epoch + 1) % config.val_interval == 0 - ): - model.eval() - print("Validating...") - with torch.no_grad(): - for _k, val_data in enumerate(eval_dataloader): - val_inputs, val_labels = ( - val_data["image"].to(device), - val_data["label"].to(device), - ) - - # normalize val_inputs across channels - if config.normalize_input: - for i in range(val_inputs.shape[0]): - for j in range(val_inputs.shape[1]): - val_inputs[i][j] = normalize_function( - val_inputs[i][j] - ) - - val_outputs = model.forward_encoder(val_inputs) - val_outputs = AsDiscrete(threshold=0.5)(val_outputs) - - # compute metric for current iteration - for channel in range(val_outputs.shape[1]): - max_dice_channel = torch.argmax( - torch.Tensor( - [ - dice_coeff( - y_pred=val_outputs[ - :, - channel : (channel + 1), - :, - :, - :, - ], - y_true=val_labels, - ) - ] - ) - ) - - dice_metric( - y_pred=val_outputs[ - :, - max_dice_channel : (max_dice_channel + 1), - :, - :, - :, - ], - y=val_labels, - ) - # if plot_val_input: # only once - # logged_image = val_inputs.detach().cpu().numpy() - # logged_image = np.swapaxes(logged_image, 2, 4) - # logged_image = logged_image[0, :, 32, :, :] - # images = wandb.Image( - # logged_image, caption="Validation input" - # ) - # - # wandb.log({"val/input": images}) - # plot_val_input = False - - # if k == 2 and (30 <= epoch <= 50 or epoch % 100 == 0): - # logged_image = val_outputs.detach().cpu().numpy() - # logged_image = np.swapaxes(logged_image, 2, 4) - # logged_image = logged_image[ - # 0, max_dice_channel, 32, :, : - # ] - # images = wandb.Image( - # logged_image, caption="Validation output" - # ) - # - # wandb.log({"val/output": images}) - # dice_metric(y_pred=val_outputs[:, 2:, :,:,:], y=val_labels) - # dice_metric(y_pred=val_outputs[:, 1:, :, :, :], y=val_labels) - - # import napari - # view = napari.Viewer() - # view.add_image(val_inputs.cpu().numpy(), name="input") - # view.add_image(val_labels.cpu().numpy(), name="label") - # vis_out = np.array( - # [i.detach().cpu().numpy() for i in val_outputs], - # dtype=np.float32, - # ) - # crf_out = np.array( - # [i.detach().cpu().numpy() for i in crf_outputs], - # dtype=np.float32, - # ) - # view.add_image(vis_out, name="output") - # view.add_image(crf_out, name="crf_output") - # napari.run() - - # aggregate the final mean dice result - metric = dice_metric.aggregate().item() - print("Validation Dice score: ", metric) - if best_dice < metric < 2: - best_dice = metric - best_dice_epoch = epoch + 1 - if config.save_model: - save_best_path = Path(config.save_model_path).parents[ - 0 - ] - save_best_path.mkdir(parents=True, exist_ok=True) - save_best_name = Path(config.save_model_path).stem - save_path = ( - str(save_best_path / save_best_name) - + "_best_metric.pth" - ) - print(f"Saving new best model to {save_path}") - torch.save(model.state_dict(), save_path) - - if WANDB_INSTALLED: - # log validation dice score for each validation round - wandb.log({"val/dice_metric": metric}) - - # reset the status for next validation round - dice_metric.reset() - - print( - "ETA: ", - (time.time() - startTime) - * (config.num_epochs / (epoch + 1) - 1) - / 60, - "minutes", - ) - print("-" * 20) - - # Save the model - if config.save_model and epoch % config.save_every == 0: - torch.save(model.state_dict(), config.save_model_path) - # with open(config.save_losses_path, "wb") as f: - # pickle.dump((ncuts_losses, rec_losses), f) - - print("Training finished") - print(f"Best dice metric : {best_dice}") - if WANDB_INSTALLED and config.eval_volume_directory is not None: - wandb.log( - { - "best_dice_metric": best_dice, - "best_metric_epoch": best_dice_epoch, - } - ) - print("*" * 50) - - # Save the model - if config.save_model: - print("Saving the model to: ", config.save_model_path) - torch.save(model.state_dict(), config.save_model_path) - # with open(config.save_losses_path, "wb") as f: - # pickle.dump((ncuts_losses, rec_losses), f) - if WANDB_INSTALLED: - model_artifact = wandb.Artifact( - "WNet", - type="model", - description="WNet benchmark", - metadata=dict(WANDB_CONFIG), - ) - model_artifact.add_file(config.save_model_path) - wandb.log_artifact(model_artifact) - - return ncuts_losses, rec_losses, model - - -def get_dataset(config): - """Creates a Dataset from the original data using the tifffile library - - Args: - config (WNetTrainingWorkerConfig): The configuration object - - Returns: - (tuple): A tuple containing the shape of the data and the dataset - """ - train_files = create_dataset_dict_no_labs( - volume_directory=config.train_volume_directory - ) - train_files = [d.get("image") for d in train_files] - # logger.debug(f"train_files: {train_files}") - volumes = tiff.imread(train_files).astype(np.float32) - volume_shape = volumes.shape - # logger.debug(f"volume_shape: {volume_shape}") - - if len(volume_shape) == 3: - volumes = np.expand_dims(volumes, axis=0) - - if config.normalize_input: - volumes = np.array( - [ - # mad_normalization(volume) - config.normalizing_function(volume) - for volume in volumes - ] - ) - # mean = volumes.mean(axis=0) - # std = volumes.std(axis=0) - # volumes = (volumes - mean) / std - # print("NORMALIZED VOLUMES") - # print(volumes.shape) - # [print("MIN MAX", volume.flatten().min(), volume.flatten().max()) for volume in volumes] - # print(volumes.mean(axis=0), volumes.std(axis=0)) - - dataset = CacheDataset(data=volumes) - - return (volume_shape, dataset) - - # train_files = create_dataset_dict_no_labs( - # volume_directory=config.train_volume_directory - # ) - # train_files = [d.get("image") for d in train_files] - # volumes = [] - # for file in train_files: - # image = tiff.imread(file).astype(np.float32) - # image = np.expand_dims(image, axis=0) # add channel dimension - # volumes.append(image) - # # volumes = tiff.imread(train_files).astype(np.float32) - # volume_shape = volumes[0].shape - # # print(volume_shape) - # - # if config.do_augmentation: - # augmentation = Compose( - # [ - # ScaleIntensityRange( - # a_min=0, - # a_max=2000, - # b_min=0.0, - # b_max=1.0, - # clip=True, - # ), - # RandShiftIntensity(offsets=0.1, prob=0.5), - # RandFlip(spatial_axis=[1], prob=0.5), - # RandFlip(spatial_axis=[2], prob=0.5), - # RandRotate90(prob=0.1, max_k=3), - # ] - # ) - # else: - # augmentation = None - # - # dataset = CacheDataset(data=np.array(volumes), transform=augmentation) - # - # return (volume_shape, dataset) - - -def get_patch_dataset(config): - """Creates a Dataset from the original data using the tifffile library - - Args: - config (WNetTrainingWorkerConfig): The configuration object - - Returns: - (tuple): A tuple containing the shape of the data and the dataset - """ - - train_files = create_dataset_dict_no_labs( - volume_directory=config.train_volume_directory - ) - - patch_func = Compose( - [ - LoadImaged(keys=["image"], image_only=True), - EnsureChannelFirstd(keys=["image"], channel_dim="no_channel"), - RandSpatialCropSamplesd( - keys=["image"], - roi_size=( - config.patch_size - ), # multiply by axis_stretch_factor if anisotropy - # max_roi_size=(120, 120, 120), - random_size=False, - num_samples=config.num_patches, - ), - Orientationd(keys=["image"], axcodes="PLI"), - SpatialPadd( - keys=["image"], - spatial_size=(get_padding_dim(config.patch_size)), - ), - EnsureTyped(keys=["image"]), - ] - ) - - train_transforms = Compose( - [ - ScaleIntensityRanged( - keys=["image"], - a_min=0, - a_max=2000, - b_min=0.0, - b_max=1.0, - clip=True, - ), - RandShiftIntensityd(keys=["image"], offsets=0.1, prob=0.5), - RandFlipd(keys=["image"], spatial_axis=[1], prob=0.5), - RandFlipd(keys=["image"], spatial_axis=[2], prob=0.5), - RandRotate90d(keys=["image"], prob=0.1, max_k=3), - EnsureTyped(keys=["image"]), - ] - ) - - dataset = PatchDataset( - data=train_files, - samples_per_image=config.num_patches, - patch_func=patch_func, - transform=train_transforms, - ) - - return config.patch_size, dataset - - -def get_patch_eval_dataset(config): - eval_files = create_dataset_dict( - volume_directory=config.eval_volume_directory + "/vol", - label_directory=config.eval_volume_directory + "/lab", - ) - - patch_func = Compose( - [ - LoadImaged(keys=["image", "label"], image_only=True), - EnsureChannelFirstd( - keys=["image", "label"], channel_dim="no_channel" - ), - # NormalizeIntensityd(keys=["image"]) if config.normalize_input else lambda x: x, - RandSpatialCropSamplesd( - keys=["image", "label"], - roi_size=( - config.patch_size - ), # multiply by axis_stretch_factor if anisotropy - # max_roi_size=(120, 120, 120), - random_size=False, - num_samples=config.eval_num_patches, - ), - Orientationd(keys=["image", "label"], axcodes="PLI"), - SpatialPadd( - keys=["image", "label"], - spatial_size=(get_padding_dim(config.patch_size)), - ), - EnsureTyped(keys=["image", "label"]), - ] - ) - - eval_transforms = Compose( - [ - EnsureTyped(keys=["image", "label"]), - ] - ) - - return PatchDataset( - data=eval_files, - samples_per_image=config.eval_num_patches, - patch_func=patch_func, - transform=eval_transforms, - ) - - -def get_dataset_monai(config): - """Creates a Dataset applying some transforms/augmentation on the data using the MONAI library - - Args: - config (WNetTrainingWorkerConfig): The configuration object - - Returns: - (tuple): A tuple containing the shape of the data and the dataset - """ - train_files = create_dataset_dict_no_labs( - volume_directory=config.train_volume_directory - ) - # print(train_files) - # print(len(train_files)) - # print(train_files[0]) - first_volume = LoadImaged(keys=["image"])(train_files[0]) - first_volume_shape = first_volume["image"].shape - - # Transforms to be applied to each volume - load_single_images = Compose( - [ - LoadImaged(keys=["image"]), - EnsureChannelFirstd(keys=["image"]), - Orientationd(keys=["image"], axcodes="PLI"), - SpatialPadd( - keys=["image"], - spatial_size=(get_padding_dim(first_volume_shape)), - ), - EnsureTyped(keys=["image"]), - ] - ) - - if config.do_augmentation: - train_transforms = Compose( - [ - ScaleIntensityRanged( - keys=["image"], - a_min=0, - a_max=2000, - b_min=0.0, - b_max=1.0, - clip=True, - ), - RandShiftIntensityd(keys=["image"], offsets=0.1, prob=0.5), - RandFlipd(keys=["image"], spatial_axis=[1], prob=0.5), - RandFlipd(keys=["image"], spatial_axis=[2], prob=0.5), - RandRotate90d(keys=["image"], prob=0.1, max_k=3), - EnsureTyped(keys=["image"]), - ] - ) - else: - train_transforms = EnsureTyped(keys=["image"]) - - # Create the dataset - dataset = CacheDataset( - data=train_files, - transform=Compose(load_single_images, train_transforms), - ) - - return first_volume_shape, dataset - - -def get_scheduler(config, optimizer, verbose=False): - scheduler_name = config.scheduler - if scheduler_name == "None": - scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( - optimizer, - T_max=100, - eta_min=config.lr - 1e-6, - verbose=verbose, - ) - - elif scheduler_name == "ReduceLROnPlateau": - scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - optimizer, - mode="min", - factor=schedulers["ReduceLROnPlateau"]["factor"], - patience=schedulers["ReduceLROnPlateau"]["patience"], - verbose=verbose, - ) - elif scheduler_name == "CosineAnnealingLR": - scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( - optimizer, - T_max=schedulers["CosineAnnealingLR"]["T_max"], - eta_min=schedulers["CosineAnnealingLR"]["eta_min"], - verbose=verbose, - ) - elif scheduler_name == "CosineAnnealingWarmRestarts": - scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts( - optimizer, - T_0=schedulers["CosineAnnealingWarmRestarts"]["T_0"], - eta_min=schedulers["CosineAnnealingWarmRestarts"]["eta_min"], - T_mult=schedulers["CosineAnnealingWarmRestarts"]["T_mult"], - verbose=verbose, - ) - elif scheduler_name == "CyclicLR": - scheduler = torch.optim.lr_scheduler.CyclicLR( - optimizer, - base_lr=schedulers["CyclicLR"]["base_lr"], - max_lr=schedulers["CyclicLR"]["max_lr"], - step_size_up=schedulers["CyclicLR"]["step_size_up"], - mode=schedulers["CyclicLR"]["mode"], - cycle_momentum=False, - ) - else: - raise ValueError(f"Scheduler {scheduler_name} not provided") - return scheduler - - -if __name__ == "__main__": - weights_location = str( - # Path(__file__).resolve().parent / "../weights/wnet.pth" - # "../wnet_SUM_MSE_DAPI_rad2_best_metric.pth" - ) - train( - # weights_location - ) +# """ +# This file contains the code to train the WNet model. +# """ +# # import napari +# import glob +# import time +# from pathlib import Path +# from warnings import warn +# +# import numpy as np +# import tifffile as tiff +# import torch +# import torch.nn as nn +# +# # MONAI +# from monai.data import ( +# CacheDataset, +# DataLoader, +# PatchDataset, +# pad_list_data_collate, +# ) +# from monai.data.meta_obj import set_track_meta +# from monai.metrics import DiceMetric +# from monai.transforms import ( +# AsDiscrete, +# Compose, +# EnsureChannelFirst, +# EnsureChannelFirstd, +# EnsureTyped, +# LoadImaged, +# Orientationd, +# RandFlipd, +# RandRotate90d, +# RandShiftIntensityd, +# RandSpatialCropSamplesd, +# ScaleIntensityRanged, +# SpatialPadd, +# ToTensor, +# ) +# from monai.utils.misc import set_determinism +# +# # local +# from napari_cellseg3d.code_models.models.wnet.model import WNet +# from napari_cellseg3d.code_models.models.wnet.soft_Ncuts import SoftNCutsLoss +# from napari_cellseg3d.utils import LOGGER as logger +# from napari_cellseg3d.utils import dice_coeff, get_padding_dim, remap_image +# +# try: +# import wandb +# +# WANDB_INSTALLED = True +# except ImportError: +# warn( +# "wandb not installed, wandb config will not be taken into account", +# stacklevel=1, +# ) +# WANDB_INSTALLED = False +# +# __author__ = "Yves Paychère, Colin Hofmann, Cyril Achard" +# +# +# ########################## +# # Utils functions # +# ########################## +# +# +# # def create_dataset_dict(volume_directory, label_directory): +# # """Creates data dictionary for MONAI transforms and training.""" +# # images_filepaths = sorted( +# # [str(file) for file in Path(volume_directory).glob("*.tif")] +# # ) +# # +# # labels_filepaths = sorted( +# # [str(file) for file in Path(label_directory).glob("*.tif")] +# # ) +# # if len(images_filepaths) == 0 or len(labels_filepaths) == 0: +# # raise ValueError( +# # f"Data folders are empty \n{volume_directory} \n{label_directory}" +# # ) +# # +# # logger.info("Images :") +# # for file in images_filepaths: +# # logger.info(Path(file).stem) +# # logger.info("*" * 10) +# # logger.info("Labels :") +# # for file in labels_filepaths: +# # logger.info(Path(file).stem) +# # try: +# # data_dicts = [ +# # {"image": image_name, "label": label_name} +# # for image_name, label_name in zip( +# # images_filepaths, labels_filepaths +# # ) +# # ] +# # except ValueError as e: +# # raise ValueError( +# # f"Number of images and labels does not match : \n{volume_directory} \n{label_directory}" +# # ) from e +# # # print(f"Loaded eval image: {data_dicts}") +# # return data_dicts +# +# +# def create_dataset_dict_no_labs(volume_directory): +# """Creates unsupervised data dictionary for MONAI transforms and training.""" +# images_filepaths = sorted(glob.glob(str(Path(volume_directory) / "*.tif"))) +# if len(images_filepaths) == 0: +# raise ValueError(f"Data folder {volume_directory} is empty") +# +# logger.info("Images :") +# for file in images_filepaths: +# logger.info(Path(file).stem) +# logger.info("*" * 10) +# +# return [{"image": image_name} for image_name in images_filepaths] +# +# +# ################################ +# # WNet: Config & WANDB # +# ################################ +# +# +# class WNetTrainingWorkerConfig: +# def __init__(self): +# # WNet +# self.in_channels = 1 +# self.out_channels = 1 +# self.num_classes = 2 +# self.dropout = 0.65 +# self.use_clipping = False +# self.clipping = 1 +# +# self.lr = 1e-6 +# self.scheduler = "None" # "CosineAnnealingLR" # "ReduceLROnPlateau" +# self.weight_decay = 0.01 # None +# +# self.intensity_sigma = 1 +# self.spatial_sigma = 4 +# self.radius = 2 # yields to a radius depending on the data shape +# +# self.n_cuts_weight = 0.5 +# self.reconstruction_loss = "MSE" # "BCE" +# self.rec_loss_weight = 0.5 / 100 +# +# self.num_epochs = 100 +# self.val_interval = 5 +# self.batch_size = 2 +# +# # Data +# # self.train_volume_directory = "./../dataset/VIP_full" +# # self.eval_volume_directory = "./../dataset/VIP_cropped/eval/" +# self.normalize_input = True +# self.normalizing_function = remap_image # normalize_quantile +# # self.use_patch = False +# # self.patch_size = (64, 64, 64) +# # self.num_patches = 30 +# # self.eval_num_patches = 20 +# # self.do_augmentation = True +# # self.parallel = False +# +# # self.save_model = True +# self.save_model_path = ( +# r"./../results/new_model/wnet_new_model_all_data_3class.pth" +# ) +# # self.save_losses_path = ( +# # r"./../results/new_model/wnet_new_model_all_data_3class.pkl" +# # ) +# self.save_every = 5 +# self.weights_path = None +# +# +# c = WNetTrainingWorkerConfig() +# ############### +# # Scheduler config +# ############### +# schedulers = { +# "ReduceLROnPlateau": { +# "factor": 0.5, +# "patience": 50, +# }, +# "CosineAnnealingLR": { +# "T_max": 25000, +# "eta_min": 1e-8, +# }, +# "CosineAnnealingWarmRestarts": { +# "T_0": 50000, +# "eta_min": 1e-8, +# "T_mult": 1, +# }, +# "CyclicLR": { +# "base_lr": 2e-7, +# "max_lr": 2e-4, +# "step_size_up": 250, +# "mode": "triangular", +# }, +# } +# +# ############### +# # WANDB_CONFIG +# ############### +# WANDB_MODE = "disabled" +# # WANDB_MODE = "online" +# +# WANDB_CONFIG = { +# # data setting +# "num_workers": c.num_workers, +# "normalize": c.normalize_input, +# "use_patch": c.use_patch, +# "patch_size": c.patch_size, +# "num_patches": c.num_patches, +# "eval_num_patches": c.eval_num_patches, +# "do_augmentation": c.do_augmentation, +# "model_save_path": c.save_model_path, +# # train setting +# "batch_size": c.batch_size, +# "learning_rate": c.lr, +# "weight_decay": c.weight_decay, +# "scheduler": { +# "name": c.scheduler, +# "ReduceLROnPlateau_config": { +# "factor": schedulers["ReduceLROnPlateau"]["factor"], +# "patience": schedulers["ReduceLROnPlateau"]["patience"], +# }, +# "CosineAnnealingLR_config": { +# "T_max": schedulers["CosineAnnealingLR"]["T_max"], +# "eta_min": schedulers["CosineAnnealingLR"]["eta_min"], +# }, +# "CosineAnnealingWarmRestarts_config": { +# "T_0": schedulers["CosineAnnealingWarmRestarts"]["T_0"], +# "eta_min": schedulers["CosineAnnealingWarmRestarts"]["eta_min"], +# "T_mult": schedulers["CosineAnnealingWarmRestarts"]["T_mult"], +# }, +# "CyclicLR_config": { +# "base_lr": schedulers["CyclicLR"]["base_lr"], +# "max_lr": schedulers["CyclicLR"]["max_lr"], +# "step_size_up": schedulers["CyclicLR"]["step_size_up"], +# "mode": schedulers["CyclicLR"]["mode"], +# }, +# }, +# "max_epochs": c.num_epochs, +# "save_every": c.save_every, +# "val_interval": c.val_interval, +# # loss +# "reconstruction_loss": c.reconstruction_loss, +# "loss weights": { +# "n_cuts_weight": c.n_cuts_weight, +# "rec_loss_weight": c.rec_loss_weight, +# }, +# "loss_params": { +# "intensity_sigma": c.intensity_sigma, +# "spatial_sigma": c.spatial_sigma, +# "radius": c.radius, +# }, +# # model +# "model_type": "wnet", +# "model_params": { +# "in_channels": c.in_channels, +# "out_channels": c.out_channels, +# "num_classes": c.num_classes, +# "dropout": c.dropout, +# "use_clipping": c.use_clipping, +# "clipping_value": c.clipping, +# }, +# # CRF +# "crf_params": { +# "sa": c.sa, +# "sb": c.sb, +# "sg": c.sg, +# "w1": c.w1, +# "w2": c.w2, +# "n_iter": c.n_iter, +# }, +# } +# +# +# def train(weights_path=None, train_config=None): +# if train_config is None: +# config = WNetTrainingWorkerConfig() +# ############## +# # disable metadata tracking in MONAI +# set_track_meta(False) +# ############## +# if WANDB_INSTALLED: +# wandb.init( +# config=WANDB_CONFIG, project="WNet-benchmark", mode=WANDB_MODE +# ) +# +# set_determinism(seed=34936339) # use default seed from NP_MAX +# torch.use_deterministic_algorithms(True, warn_only=True) +# +# config = train_config +# normalize_function = config.normalizing_function +# CUDA = torch.cuda.is_available() +# device = torch.device("cuda" if CUDA else "cpu") +# +# print(f"Using device: {device}") +# +# print("Config:") +# [print(a) for a in config.__dict__.items()] +# +# print("Initializing training...") +# print("Getting the data") +# +# if config.use_patch: +# (data_shape, dataset) = get_patch_dataset(config) +# else: +# (data_shape, dataset) = get_dataset(config) +# transform = Compose( +# [ +# ToTensor(), +# EnsureChannelFirst(channel_dim=0), +# ] +# ) +# dataset = [transform(im) for im in dataset] +# for data in dataset: +# print(f"data shape: {data.shape}") +# break +# +# dataloader = DataLoader( +# dataset, +# batch_size=config.batch_size, +# shuffle=True, +# num_workers=config.num_workers, +# collate_fn=pad_list_data_collate, +# ) +# +# if config.eval_volume_directory is not None: +# # eval_dataset = get_patch_eval_dataset(config) +# eval_dataset = None +# +# eval_dataloader = DataLoader( +# eval_dataset, +# batch_size=config.batch_size, +# shuffle=False, +# num_workers=config.num_workers, +# collate_fn=pad_list_data_collate, +# ) +# +# dice_metric = DiceMetric( +# include_background=False, reduction="mean", get_not_nans=False +# ) +# ################################################### +# # Training the model # +# ################################################### +# print("Initializing the model:") +# +# print("- getting the model") +# # Initialize the model +# model = WNet( +# in_channels=config.in_channels, +# out_channels=config.out_channels, +# num_classes=config.num_classes, +# dropout=config.dropout, +# ) +# model = ( +# nn.DataParallel(model).cuda() if CUDA and config.parallel else model +# ) +# model.to(device) +# +# if config.use_clipping: +# for p in model.parameters(): +# p.register_hook( +# lambda grad: torch.clamp( +# grad, min=-config.clipping, max=config.clipping +# ) +# ) +# +# if WANDB_INSTALLED: +# wandb.watch(model, log_freq=100) +# +# if weights_path is not None: +# model.load_state_dict(torch.load(weights_path, map_location=device)) +# +# print("- getting the optimizers") +# # Initialize the optimizers +# if config.weight_decay is not None: +# decay = config.weight_decay +# optimizer = torch.optim.Adam( +# model.parameters(), lr=config.lr, weight_decay=decay +# ) +# else: +# optimizer = torch.optim.Adam(model.parameters(), lr=config.lr) +# +# print("- getting the loss functions") +# # Initialize the Ncuts loss function +# criterionE = SoftNCutsLoss( +# data_shape=data_shape, +# device=device, +# intensity_sigma=config.intensity_sigma, +# spatial_sigma=config.spatial_sigma, +# radius=config.radius, +# ) +# +# if config.reconstruction_loss == "MSE": +# criterionW = nn.MSELoss() +# elif config.reconstruction_loss == "BCE": +# criterionW = nn.BCELoss() +# else: +# raise ValueError( +# f"Unknown reconstruction loss : {config.reconstruction_loss} not supported" +# ) +# +# print("- getting the learning rate schedulers") +# # Initialize the learning rate schedulers +# scheduler = get_scheduler(config, optimizer) +# # scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( +# # optimizer, mode="min", factor=0.5, patience=10, verbose=True +# # ) +# model.train() +# +# print("Ready") +# print("Training the model") +# print("*" * 50) +# +# startTime = time.time() +# ncuts_losses = [] +# rec_losses = [] +# total_losses = [] +# best_dice = -1 +# best_dice_epoch = -1 +# +# # Train the model +# for epoch in range(config.num_epochs): +# print(f"Epoch {epoch + 1} of {config.num_epochs}") +# +# epoch_ncuts_loss = 0 +# epoch_rec_loss = 0 +# epoch_loss = 0 +# +# for _i, batch in enumerate(dataloader): +# # raise NotImplementedError("testing") +# if config.use_patch: +# image = batch["image"].to(device) +# else: +# image = batch.to(device) +# if config.batch_size == 1: +# image = image.unsqueeze(0) +# else: +# image = image.unsqueeze(0) +# image = torch.swapaxes(image, 0, 1) +# +# # Forward pass +# enc = model.forward_encoder(image) +# # out = model.forward(image) +# +# # Compute the Ncuts loss +# Ncuts = criterionE(enc, image) +# epoch_ncuts_loss += Ncuts.item() +# if WANDB_INSTALLED: +# wandb.log({"Ncuts loss": Ncuts.item()}) +# +# # Forward pass +# enc, dec = model(image) +# +# # Compute the reconstruction loss +# if isinstance(criterionW, nn.MSELoss): +# reconstruction_loss = criterionW(dec, image) +# elif isinstance(criterionW, nn.BCELoss): +# reconstruction_loss = criterionW( +# torch.sigmoid(dec), +# remap_image(image, new_max=1), +# ) +# +# epoch_rec_loss += reconstruction_loss.item() +# if WANDB_INSTALLED: +# wandb.log({"Reconstruction loss": reconstruction_loss.item()}) +# +# # Backward pass for the reconstruction loss +# optimizer.zero_grad() +# alpha = config.n_cuts_weight +# beta = config.rec_loss_weight +# +# loss = alpha * Ncuts + beta * reconstruction_loss +# epoch_loss += loss.item() +# if WANDB_INSTALLED: +# wandb.log({"Sum of losses": loss.item()}) +# loss.backward(loss) +# optimizer.step() +# +# if config.scheduler == "CosineAnnealingWarmRestarts": +# scheduler.step(epoch + _i / len(dataloader)) +# if ( +# config.scheduler == "CosineAnnealingLR" +# or config.scheduler == "CyclicLR" +# ): +# scheduler.step() +# +# ncuts_losses.append(epoch_ncuts_loss / len(dataloader)) +# rec_losses.append(epoch_rec_loss / len(dataloader)) +# total_losses.append(epoch_loss / len(dataloader)) +# +# if WANDB_INSTALLED: +# wandb.log({"Ncuts loss_epoch": ncuts_losses[-1]}) +# wandb.log({"Reconstruction loss_epoch": rec_losses[-1]}) +# wandb.log({"Sum of losses_epoch": total_losses[-1]}) +# # wandb.log({"epoch": epoch}) +# # wandb.log({"learning_rate model": optimizerW.param_groups[0]["lr"]}) +# # wandb.log({"learning_rate encoder": optimizerE.param_groups[0]["lr"]}) +# wandb.log({"learning_rate model": optimizer.param_groups[0]["lr"]}) +# +# print("Ncuts loss: ", ncuts_losses[-1]) +# if epoch > 0: +# print( +# "Ncuts loss difference: ", +# ncuts_losses[-1] - ncuts_losses[-2], +# ) +# print("Reconstruction loss: ", rec_losses[-1]) +# if epoch > 0: +# print( +# "Reconstruction loss difference: ", +# rec_losses[-1] - rec_losses[-2], +# ) +# print("Sum of losses: ", total_losses[-1]) +# if epoch > 0: +# print( +# "Sum of losses difference: ", +# total_losses[-1] - total_losses[-2], +# ) +# +# # Update the learning rate +# if config.scheduler == "ReduceLROnPlateau": +# # schedulerE.step(epoch_ncuts_loss) +# # schedulerW.step(epoch_rec_loss) +# scheduler.step(epoch_rec_loss) +# if ( +# config.eval_volume_directory is not None +# and (epoch + 1) % config.val_interval == 0 +# ): +# model.eval() +# print("Validating...") +# with torch.no_grad(): +# for _k, val_data in enumerate(eval_dataloader): +# val_inputs, val_labels = ( +# val_data["image"].to(device), +# val_data["label"].to(device), +# ) +# +# # normalize val_inputs across channels +# if config.normalize_input: +# for i in range(val_inputs.shape[0]): +# for j in range(val_inputs.shape[1]): +# val_inputs[i][j] = normalize_function( +# val_inputs[i][j] +# ) +# +# val_outputs = model.forward_encoder(val_inputs) +# val_outputs = AsDiscrete(threshold=0.5)(val_outputs) +# +# # compute metric for current iteration +# for channel in range(val_outputs.shape[1]): +# max_dice_channel = torch.argmax( +# torch.Tensor( +# [ +# dice_coeff( +# y_pred=val_outputs[ +# :, +# channel : (channel + 1), +# :, +# :, +# :, +# ], +# y_true=val_labels, +# ) +# ] +# ) +# ) +# +# dice_metric( +# y_pred=val_outputs[ +# :, +# max_dice_channel : (max_dice_channel + 1), +# :, +# :, +# :, +# ], +# y=val_labels, +# ) +# # if plot_val_input: # only once +# # logged_image = val_inputs.detach().cpu().numpy() +# # logged_image = np.swapaxes(logged_image, 2, 4) +# # logged_image = logged_image[0, :, 32, :, :] +# # images = wandb.Image( +# # logged_image, caption="Validation input" +# # ) +# # +# # wandb.log({"val/input": images}) +# # plot_val_input = False +# +# # if k == 2 and (30 <= epoch <= 50 or epoch % 100 == 0): +# # logged_image = val_outputs.detach().cpu().numpy() +# # logged_image = np.swapaxes(logged_image, 2, 4) +# # logged_image = logged_image[ +# # 0, max_dice_channel, 32, :, : +# # ] +# # images = wandb.Image( +# # logged_image, caption="Validation output" +# # ) +# # +# # wandb.log({"val/output": images}) +# # dice_metric(y_pred=val_outputs[:, 2:, :,:,:], y=val_labels) +# # dice_metric(y_pred=val_outputs[:, 1:, :, :, :], y=val_labels) +# +# # import napari +# # view = napari.Viewer() +# # view.add_image(val_inputs.cpu().numpy(), name="input") +# # view.add_image(val_labels.cpu().numpy(), name="label") +# # vis_out = np.array( +# # [i.detach().cpu().numpy() for i in val_outputs], +# # dtype=np.float32, +# # ) +# # crf_out = np.array( +# # [i.detach().cpu().numpy() for i in crf_outputs], +# # dtype=np.float32, +# # ) +# # view.add_image(vis_out, name="output") +# # view.add_image(crf_out, name="crf_output") +# # napari.run() +# +# # aggregate the final mean dice result +# metric = dice_metric.aggregate().item() +# print("Validation Dice score: ", metric) +# if best_dice < metric < 2: +# best_dice = metric +# best_dice_epoch = epoch + 1 +# if config.save_model: +# save_best_path = Path(config.save_model_path).parents[ +# 0 +# ] +# save_best_path.mkdir(parents=True, exist_ok=True) +# save_best_name = Path(config.save_model_path).stem +# save_path = ( +# str(save_best_path / save_best_name) +# + "_best_metric.pth" +# ) +# print(f"Saving new best model to {save_path}") +# torch.save(model.state_dict(), save_path) +# +# if WANDB_INSTALLED: +# # log validation dice score for each validation round +# wandb.log({"val/dice_metric": metric}) +# +# # reset the status for next validation round +# dice_metric.reset() +# +# print( +# "ETA: ", +# (time.time() - startTime) +# * (config.num_epochs / (epoch + 1) - 1) +# / 60, +# "minutes", +# ) +# print("-" * 20) +# +# # Save the model +# if config.save_model and epoch % config.save_every == 0: +# torch.save(model.state_dict(), config.save_model_path) +# # with open(config.save_losses_path, "wb") as f: +# # pickle.dump((ncuts_losses, rec_losses), f) +# +# print("Training finished") +# print(f"Best dice metric : {best_dice}") +# if WANDB_INSTALLED and config.eval_volume_directory is not None: +# wandb.log( +# { +# "best_dice_metric": best_dice, +# "best_metric_epoch": best_dice_epoch, +# } +# ) +# print("*" * 50) +# +# # Save the model +# if config.save_model: +# print("Saving the model to: ", config.save_model_path) +# torch.save(model.state_dict(), config.save_model_path) +# # with open(config.save_losses_path, "wb") as f: +# # pickle.dump((ncuts_losses, rec_losses), f) +# if WANDB_INSTALLED: +# model_artifact = wandb.Artifact( +# "WNet", +# type="model", +# description="WNet benchmark", +# metadata=dict(WANDB_CONFIG), +# ) +# model_artifact.add_file(config.save_model_path) +# wandb.log_artifact(model_artifact) +# +# return ncuts_losses, rec_losses, model +# +# +# def get_dataset(config): +# """Creates a Dataset from the original data using the tifffile library +# +# Args: +# config (WNetTrainingWorkerConfig): The configuration object +# +# Returns: +# (tuple): A tuple containing the shape of the data and the dataset +# """ +# train_files = create_dataset_dict_no_labs( +# volume_directory=config.train_volume_directory +# ) +# train_files = [d.get("image") for d in train_files] +# # logger.debug(f"train_files: {train_files}") +# volumes = tiff.imread(train_files).astype(np.float32) +# volume_shape = volumes.shape +# # logger.debug(f"volume_shape: {volume_shape}") +# +# if len(volume_shape) == 3: +# volumes = np.expand_dims(volumes, axis=0) +# +# if config.normalize_input: +# volumes = np.array( +# [ +# # mad_normalization(volume) +# config.normalizing_function(volume) +# for volume in volumes +# ] +# ) +# # mean = volumes.mean(axis=0) +# # std = volumes.std(axis=0) +# # volumes = (volumes - mean) / std +# # print("NORMALIZED VOLUMES") +# # print(volumes.shape) +# # [print("MIN MAX", volume.flatten().min(), volume.flatten().max()) for volume in volumes] +# # print(volumes.mean(axis=0), volumes.std(axis=0)) +# +# dataset = CacheDataset(data=volumes) +# +# return (volume_shape, dataset) +# +# # train_files = create_dataset_dict_no_labs( +# # volume_directory=config.train_volume_directory +# # ) +# # train_files = [d.get("image") for d in train_files] +# # volumes = [] +# # for file in train_files: +# # image = tiff.imread(file).astype(np.float32) +# # image = np.expand_dims(image, axis=0) # add channel dimension +# # volumes.append(image) +# # # volumes = tiff.imread(train_files).astype(np.float32) +# # volume_shape = volumes[0].shape +# # # print(volume_shape) +# # +# # if config.do_augmentation: +# # augmentation = Compose( +# # [ +# # ScaleIntensityRange( +# # a_min=0, +# # a_max=2000, +# # b_min=0.0, +# # b_max=1.0, +# # clip=True, +# # ), +# # RandShiftIntensity(offsets=0.1, prob=0.5), +# # RandFlip(spatial_axis=[1], prob=0.5), +# # RandFlip(spatial_axis=[2], prob=0.5), +# # RandRotate90(prob=0.1, max_k=3), +# # ] +# # ) +# # else: +# # augmentation = None +# # +# # dataset = CacheDataset(data=np.array(volumes), transform=augmentation) +# # +# # return (volume_shape, dataset) +# +# +# def get_patch_dataset(config): +# """Creates a Dataset from the original data using the tifffile library +# +# Args: +# config (WNetTrainingWorkerConfig): The configuration object +# +# Returns: +# (tuple): A tuple containing the shape of the data and the dataset +# """ +# +# train_files = create_dataset_dict_no_labs( +# volume_directory=config.train_volume_directory +# ) +# +# patch_func = Compose( +# [ +# LoadImaged(keys=["image"], image_only=True), +# EnsureChannelFirstd(keys=["image"], channel_dim="no_channel"), +# RandSpatialCropSamplesd( +# keys=["image"], +# roi_size=( +# config.patch_size +# ), # multiply by axis_stretch_factor if anisotropy +# # max_roi_size=(120, 120, 120), +# random_size=False, +# num_samples=config.num_patches, +# ), +# Orientationd(keys=["image"], axcodes="PLI"), +# SpatialPadd( +# keys=["image"], +# spatial_size=(get_padding_dim(config.patch_size)), +# ), +# EnsureTyped(keys=["image"]), +# ] +# ) +# +# train_transforms = Compose( +# [ +# ScaleIntensityRanged( +# keys=["image"], +# a_min=0, +# a_max=2000, +# b_min=0.0, +# b_max=1.0, +# clip=True, +# ), +# RandShiftIntensityd(keys=["image"], offsets=0.1, prob=0.5), +# RandFlipd(keys=["image"], spatial_axis=[1], prob=0.5), +# RandFlipd(keys=["image"], spatial_axis=[2], prob=0.5), +# RandRotate90d(keys=["image"], prob=0.1, max_k=3), +# EnsureTyped(keys=["image"]), +# ] +# ) +# +# dataset = PatchDataset( +# data=train_files, +# samples_per_image=config.num_patches, +# patch_func=patch_func, +# transform=train_transforms, +# ) +# +# return config.patch_size, dataset +# +# +# # def get_patch_eval_dataset(config): +# # eval_files = create_dataset_dict( +# # volume_directory=config.eval_volume_directory + "/vol", +# # label_directory=config.eval_volume_directory + "/lab", +# # ) +# # +# # patch_func = Compose( +# # [ +# # LoadImaged(keys=["image", "label"], image_only=True), +# # EnsureChannelFirstd( +# # keys=["image", "label"], channel_dim="no_channel" +# # ), +# # # NormalizeIntensityd(keys=["image"]) if config.normalize_input else lambda x: x, +# # RandSpatialCropSamplesd( +# # keys=["image", "label"], +# # roi_size=( +# # config.patch_size +# # ), # multiply by axis_stretch_factor if anisotropy +# # # max_roi_size=(120, 120, 120), +# # random_size=False, +# # num_samples=config.eval_num_patches, +# # ), +# # Orientationd(keys=["image", "label"], axcodes="PLI"), +# # SpatialPadd( +# # keys=["image", "label"], +# # spatial_size=(get_padding_dim(config.patch_size)), +# # ), +# # EnsureTyped(keys=["image", "label"]), +# # ] +# # ) +# # +# # eval_transforms = Compose( +# # [ +# # EnsureTyped(keys=["image", "label"]), +# # ] +# # ) +# # +# # return PatchDataset( +# # data=eval_files, +# # samples_per_image=config.eval_num_patches, +# # patch_func=patch_func, +# # transform=eval_transforms, +# # ) +# +# +# def get_dataset_monai(config): +# """Creates a Dataset applying some transforms/augmentation on the data using the MONAI library +# +# Args: +# config (WNetTrainingWorkerConfig): The configuration object +# +# Returns: +# (tuple): A tuple containing the shape of the data and the dataset +# """ +# train_files = create_dataset_dict_no_labs( +# volume_directory=config.train_volume_directory +# ) +# # print(train_files) +# # print(len(train_files)) +# # print(train_files[0]) +# first_volume = LoadImaged(keys=["image"])(train_files[0]) +# first_volume_shape = first_volume["image"].shape +# +# # Transforms to be applied to each volume +# load_single_images = Compose( +# [ +# LoadImaged(keys=["image"]), +# EnsureChannelFirstd(keys=["image"]), +# Orientationd(keys=["image"], axcodes="PLI"), +# SpatialPadd( +# keys=["image"], +# spatial_size=(get_padding_dim(first_volume_shape)), +# ), +# EnsureTyped(keys=["image"]), +# ] +# ) +# +# if config.do_augmentation: +# train_transforms = Compose( +# [ +# ScaleIntensityRanged( +# keys=["image"], +# a_min=0, +# a_max=2000, +# b_min=0.0, +# b_max=1.0, +# clip=True, +# ), +# RandShiftIntensityd(keys=["image"], offsets=0.1, prob=0.5), +# RandFlipd(keys=["image"], spatial_axis=[1], prob=0.5), +# RandFlipd(keys=["image"], spatial_axis=[2], prob=0.5), +# RandRotate90d(keys=["image"], prob=0.1, max_k=3), +# EnsureTyped(keys=["image"]), +# ] +# ) +# else: +# train_transforms = EnsureTyped(keys=["image"]) +# +# # Create the dataset +# dataset = CacheDataset( +# data=train_files, +# transform=Compose(load_single_images, train_transforms), +# ) +# +# return first_volume_shape, dataset +# +# +# def get_scheduler(config, optimizer, verbose=False): +# scheduler_name = config.scheduler +# if scheduler_name == "None": +# scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( +# optimizer, +# T_max=100, +# eta_min=config.lr - 1e-6, +# verbose=verbose, +# ) +# +# elif scheduler_name == "ReduceLROnPlateau": +# scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( +# optimizer, +# mode="min", +# factor=schedulers["ReduceLROnPlateau"]["factor"], +# patience=schedulers["ReduceLROnPlateau"]["patience"], +# verbose=verbose, +# ) +# elif scheduler_name == "CosineAnnealingLR": +# scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( +# optimizer, +# T_max=schedulers["CosineAnnealingLR"]["T_max"], +# eta_min=schedulers["CosineAnnealingLR"]["eta_min"], +# verbose=verbose, +# ) +# elif scheduler_name == "CosineAnnealingWarmRestarts": +# scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts( +# optimizer, +# T_0=schedulers["CosineAnnealingWarmRestarts"]["T_0"], +# eta_min=schedulers["CosineAnnealingWarmRestarts"]["eta_min"], +# T_mult=schedulers["CosineAnnealingWarmRestarts"]["T_mult"], +# verbose=verbose, +# ) +# elif scheduler_name == "CyclicLR": +# scheduler = torch.optim.lr_scheduler.CyclicLR( +# optimizer, +# base_lr=schedulers["CyclicLR"]["base_lr"], +# max_lr=schedulers["CyclicLR"]["max_lr"], +# step_size_up=schedulers["CyclicLR"]["step_size_up"], +# mode=schedulers["CyclicLR"]["mode"], +# cycle_momentum=False, +# ) +# else: +# raise ValueError(f"Scheduler {scheduler_name} not provided") +# return scheduler +# +# +# if __name__ == "__main__": +# weights_location = str( +# # Path(__file__).resolve().parent / "../weights/wnet.pth" +# # "../wnet_SUM_MSE_DAPI_rad2_best_metric.pth" +# ) +# train( +# # weights_location +# ) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index a1850e91..125466f9 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -28,7 +28,6 @@ from monai.transforms import ( AsDiscrete, Compose, - EnsureChannelFirst, EnsureChannelFirstd, EnsureType, EnsureTyped, @@ -43,7 +42,6 @@ RandSpatialCropSamplesd, ScaleIntensityRanged, SpatialPadd, - ToTensor, ) from monai.utils import set_determinism @@ -164,71 +162,16 @@ def __init__( super().__init__() self.config = worker_config - @staticmethod - def create_dataset_dict_no_labs(volume_directory): - """Creates unsupervised data dictionary for MONAI transforms and training.""" - images_filepaths = sorted( - Path.glob(str(Path(volume_directory) / "*.tif")) - ) - if len(images_filepaths) == 0: - raise ValueError(f"Data folder {volume_directory} is empty") - - logger.info("Images :") - for file in images_filepaths: - logger.info(Path(file).stem) - logger.info("*" * 10) - return [{"image": image_name} for image_name in images_filepaths] - - @staticmethod - def create_dataset_dict(volume_directory, label_directory): - """Creates data dictionary for MONAI transforms and training.""" - images_filepaths = sorted( - [str(file) for file in Path(volume_directory).glob("*.tif")] - ) - - labels_filepaths = sorted( - [str(file) for file in Path(label_directory).glob("*.tif")] - ) - if len(images_filepaths) == 0 or len(labels_filepaths) == 0: - raise ValueError( - f"Data folders are empty \n{volume_directory} \n{label_directory}" - ) - - logger.info("Images :") - for file in images_filepaths: - logger.info(Path(file).stem) - logger.info("*" * 10) - logger.info("Labels :") - for file in labels_filepaths: - logger.info(Path(file).stem) - try: - data_dicts = [ - {"image": image_name, "label": label_name} - for image_name, label_name in zip( - images_filepaths, labels_filepaths - ) - ] - except ValueError as e: - raise ValueError( - f"Number of images and labels does not match : \n{volume_directory} \n{label_directory}" - ) from e - # self.log(f"Loaded eval image: {data_dicts}") - return data_dicts - - def get_patch_dataset(self, volume_directory): + def get_patch_dataset(self, train_transforms): """Creates a Dataset from the original data using the tifffile library Args: - volume_directory (str): Path to the directory containing the data + train_data_dict (dict): dict with the Paths to the directory containing the data Returns: (tuple): A tuple containing the shape of the data and the dataset """ - train_files = self.create_dataset_dict_no_labs( - volume_directory=volume_directory - ) - patch_func = Compose( [ LoadImaged(keys=["image"], image_only=True), @@ -252,27 +195,8 @@ def get_patch_dataset(self, volume_directory): EnsureTyped(keys=["image"]), ] ) - - train_transforms = Compose( - [ - ScaleIntensityRanged( - keys=["image"], - a_min=0, - a_max=2000, - b_min=0.0, - b_max=1.0, - clip=True, - ), - RandShiftIntensityd(keys=["image"], offsets=0.1, prob=0.5), - RandFlipd(keys=["image"], spatial_axis=[1], prob=0.5), - RandFlipd(keys=["image"], spatial_axis=[2], prob=0.5), - RandRotate90d(keys=["image"], prob=0.1, max_k=3), - EnsureTyped(keys=["image"]), - ] - ) - dataset = PatchDataset( - data=train_files, + data=self.config.train_data_dict, samples_per_image=self.config.num_samples, patch_func=patch_func, transform=train_transforms, @@ -280,53 +204,39 @@ def get_patch_dataset(self, volume_directory): return self.config.sample_size, dataset - def get_patch_eval_dataset(self, volume_directory): - eval_files = self.create_dataset_dict( - volume_directory=volume_directory + "/vol", - label_directory=volume_directory + "/lab", - ) - - patch_func = Compose( + def get_patch_dataset_eval(self, eval_dataset_dict): + eval_transforms = Compose( [ LoadImaged(keys=["image", "label"], image_only=True), EnsureChannelFirstd( keys=["image", "label"], channel_dim="no_channel" ), - # NormalizeIntensityd(keys=["image"]) if config.normalize_input else lambda x: x, - RandSpatialCropSamplesd( - keys=["image", "label"], - roi_size=( - self.config.sample_size - ), # multiply by axis_stretch_factor if anisotropy - # max_roi_size=(120, 120, 120), - random_size=False, - num_samples=self.config.eval_num_patches, - ), + # RandSpatialCropSamplesd( + # keys=["image", "label"], + # roi_size=( + # self.config.sample_size + # ), # multiply by axis_stretch_factor if anisotropy + # # max_roi_size=(120, 120, 120), + # random_size=False, + # num_samples=self.config.num_samples, + # ), Orientationd(keys=["image", "label"], axcodes="PLI"), - SpatialPadd( - keys=["image", "label"], - spatial_size=( - utils.get_padding_dim(self.config.sample_size) - ), - ), - EnsureTyped(keys=["image", "label"]), - ] - ) - - eval_transforms = Compose( - [ + # SpatialPadd( + # keys=["image", "label"], + # spatial_size=( + # utils.get_padding_dim(self.config.sample_size) + # ), + # ), EnsureTyped(keys=["image", "label"]), ] ) - return PatchDataset( - data=eval_files, - samples_per_image=self.config.eval_num_patches, - patch_func=patch_func, + return CacheDataset( + data=eval_dataset_dict, transform=eval_transforms, ) - def get_dataset_monai(self): + def get_dataset(self, train_transforms): """Creates a Dataset applying some transforms/augmentation on the data using the MONAI library Args: @@ -360,27 +270,6 @@ def get_dataset_monai(self): ] ) - if self.config.do_augmentation: - train_transforms = Compose( - [ - ScaleIntensityRanged( - keys=["image"], - a_min=0, - a_max=2000, - b_min=0.0, - b_max=1.0, - clip=True, - ), - RandShiftIntensityd(keys=["image"], offsets=0.1, prob=0.5), - RandFlipd(keys=["image"], spatial_axis=[1], prob=0.5), - RandFlipd(keys=["image"], spatial_axis=[2], prob=0.5), - RandRotate90d(keys=["image"], prob=0.1, max_k=3), - EnsureTyped(keys=["image"]), - ] - ) - else: - train_transforms = EnsureTyped(keys=["image"]) - # Create the dataset dataset = CacheDataset( data=train_files, @@ -434,50 +323,46 @@ def get_dataset_monai(self): # else: # raise ValueError(f"Scheduler {scheduler_name} not provided") # return scheduler - def train(self): - if self.config is None: - self.config = config.WNetTrainingWorkerConfig() - ############## - # disable metadata tracking in MONAI - set_track_meta(False) - ############## - # if WANDB_INSTALLED: - # wandb.init( - # config=WANDB_CONFIG, project="WNet-benchmark", mode=WANDB_MODE - # ) - - set_determinism( - seed=self.config.deterministic_config.seed - ) # use default seed from NP_MAX - torch.use_deterministic_algorithms(True, warn_only=True) - - normalize_function = self.config.normalizing_function - CUDA = torch.cuda.is_available() - device = torch.device("cuda" if CUDA else "cpu") - - self.log(f"Using device: {device}") - - self.log("Config:") - [self.log(str(a)) for a in self.config.__dict__.items()] - - self.log("Initializing training...") - self.log("Getting the data") - if self.config.sampling: - (data_shape, dataset) = self.get_patch_dataset(self.config) - else: - (data_shape, dataset) = self.get_dataset(self.config) - transform = Compose( + def _get_data(self): + if self.config.do_augmentation: + train_transforms = Compose( [ - ToTensor(), - EnsureChannelFirst(channel_dim=0), + ScaleIntensityRanged( + keys=["image"], + a_min=0, + a_max=2000, + b_min=0.0, + b_max=1.0, + clip=True, + ), + RandShiftIntensityd(keys=["image"], offsets=0.1, prob=0.5), + RandFlipd(keys=["image"], spatial_axis=[1], prob=0.5), + RandFlipd(keys=["image"], spatial_axis=[2], prob=0.5), + RandRotate90d(keys=["image"], prob=0.1, max_k=3), + EnsureTyped(keys=["image"]), ] ) - dataset = [transform(im) for im in dataset] - for data in dataset: - self.log(f"Data shape: {data.shape}") - break + else: + train_transforms = EnsureTyped(keys=["image"]) + if self.config.sampling: + self.log("Loading patch dataset") + (data_shape, dataset) = self.get_patch_dataset(train_transforms) + else: + self.log("Loading volume dataset") + (data_shape, dataset) = self.get_dataset(train_transforms) + # transform = Compose( + # [ + # ToTensor(), + # EnsureChannelFirst(channel_dim=0), + # ] + # ) + # dataset = [transform(im) for im in dataset] + # for data in dataset: + # self.log(f"Data shape: {data.shape}") + # break + logger.debug(f"Data shape : {data_shape}") dataloader = DataLoader( dataset, batch_size=self.config.batch_size, @@ -487,9 +372,7 @@ def train(self): ) if self.config.eval_volume_dict is not None: - eval_dataset = self.get_patch_eval_dataset( - self.config.eval_volume_dict - ) # FIXME + eval_dataset = self.get_dataset(train_transforms) eval_dataloader = DataLoader( eval_dataset, @@ -498,326 +381,469 @@ def train(self): num_workers=self.config.num_workers, collate_fn=pad_list_data_collate, ) + else: + eval_dataloader = None + return dataloader, eval_dataloader, data_shape - dice_metric = DiceMetric( - include_background=False, reduction="mean", get_not_nans=False - ) - ################################################### - # Training the model # - ################################################### - self.log("Initializing the model:") - - self.log("- getting the model") - # Initialize the model - model = WNet( - in_channels=self.config.in_channels, - out_channels=self.config.out_channels, - num_classes=self.config.num_classes, - dropout=self.config.dropout, - ) - model = ( - nn.DataParallel(model).cuda() - if CUDA and self.config.parallel - else model - ) - model.to(device) - - if self.config.use_clipping: - for p in model.parameters(): - p.register_hook( - lambda grad: torch.clamp( - grad, - min=-self.config.clipping, - max=self.config.clipping, - ) - ) + def train(self): + try: + if self.config is None: + self.config = config.WNetTrainingWorkerConfig() + ############## + # disable metadata tracking in MONAI + set_track_meta(False) + ############## + # if WANDB_INSTALLED: + # wandb.init( + # config=WANDB_CONFIG, project="WNet-benchmark", mode=WANDB_MODE + # ) - if WANDB_INSTALLED: - wandb.watch(model, log_freq=100) + set_determinism( + seed=self.config.deterministic_config.seed + ) # use default seed from NP_MAX + torch.use_deterministic_algorithms(True, warn_only=True) - if self.config.weights_info.path is not None: - model.load_state_dict( - torch.load(self.config.weights_info.path, map_location=device) - ) + normalize_function = utils.remap_image + device = self.config.device - self.log("- getting the optimizers") - # Initialize the optimizers - if self.config.weight_decay is not None: - decay = self.config.weight_decay - optimizer = torch.optim.Adam( - model.parameters(), lr=self.config.lr, weight_decay=decay - ) - else: - optimizer = torch.optim.Adam(model.parameters(), lr=self.config.lr) - - self.log("- getting the loss functions") - # Initialize the Ncuts loss function - criterionE = SoftNCutsLoss( - data_shape=data_shape, - device=device, - intensity_sigma=self.config.intensity_sigma, - spatial_sigma=self.config.spatial_sigma, - radius=self.config.radius, - ) + self.log(f"Using device: {device}") - if self.config.reconstruction_loss == "MSE": - criterionW = nn.MSELoss() - elif self.config.reconstruction_loss == "BCE": - criterionW = nn.BCELoss() - else: - raise ValueError( - f"Unknown reconstruction loss : {self.config.reconstruction_loss} not supported" + self.log("Config:") + [self.log(str(a)) for a in self.config.__dict__.items()] + + self.log("Initializing training...") + self.log("Getting the data") + + dataloader, eval_dataloader, data_shape = self._get_data() + + dice_metric = DiceMetric( + include_background=False, reduction="mean", get_not_nans=False + ) + ################################################### + # Training the model # + ################################################### + self.log("Initializing the model:") + + self.log("- Getting the model") + # Initialize the model + model = WNet( + in_channels=self.config.in_channels, + out_channels=self.config.out_channels, + num_classes=self.config.num_classes, + dropout=self.config.dropout, ) + model.to(device) + + if self.config.use_clipping: + for p in model.parameters(): + p.register_hook( + lambda grad: torch.clamp( + grad, + min=-self.config.clipping, + max=self.config.clipping, + ) + ) - self.log("- getting the learning rate schedulers") - # Initialize the learning rate schedulers - # scheduler = get_scheduler(self.config, optimizer) - # scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - # optimizer, mode="min", factor=0.5, patience=10, verbose=True - # ) - model.train() + if WANDB_INSTALLED: + wandb.watch(model, log_freq=100) - self.log("Ready") - self.log("Training the model") - self.log("*" * 50) + if self.config.weights_info.custom: + if self.config.weights_info.use_pretrained: + weights_file = "wnet.pth" + self.downloader.download_weights("WNet", weights_file) + weights = PRETRAINED_WEIGHTS_DIR / Path(weights_file) + self.config.weights_info.path = weights + else: + weights = str(Path(self.config.weights_info.path)) - startTime = time.time() - ncuts_losses = [] - rec_losses = [] - total_losses = [] - best_dice = -1 + try: + model.load_state_dict( + torch.load( + weights, + map_location=self.config.device, + ), + strict=True, + ) + except RuntimeError as e: + logger.error(f"Error when loading weights : {e}") + logger.exception(e) + warn = ( + "WARNING:\nIt'd seem that the weights were incompatible with the model,\n" + "the model will be trained from random weights" + ) + self.log(warn) + self.warn(warn) + self._weight_error = True + else: + self.log("Model will be trained from scratch") + self.log("- Getting the optimizer") + # Initialize the optimizers + if self.config.weight_decay is not None: + decay = self.config.weight_decay + optimizer = torch.optim.Adam( + model.parameters(), + lr=self.config.learning_rate, + weight_decay=decay, + ) + else: + optimizer = torch.optim.Adam( + model.parameters(), lr=self.config.learning_rate + ) - # Train the model - for epoch in range(self.config.num_epochs): - self.log(f"Epoch {epoch + 1} of {self.config.num_epochs}") + self.log("- Getting the loss functions") + # Initialize the Ncuts loss function + criterionE = SoftNCutsLoss( + data_shape=data_shape, + device=device, + intensity_sigma=self.config.intensity_sigma, + spatial_sigma=self.config.spatial_sigma, + radius=self.config.radius, + ) + + if self.config.reconstruction_loss == "MSE": + criterionW = nn.MSELoss() + elif self.config.reconstruction_loss == "BCE": + criterionW = nn.BCELoss() + else: + raise ValueError( + f"Unknown reconstruction loss : {self.config.reconstruction_loss} not supported" + ) - epoch_ncuts_loss = 0 - epoch_rec_loss = 0 - epoch_loss = 0 + # self.log("- getting the learning rate schedulers") + # Initialize the learning rate schedulers + # scheduler = get_scheduler(self.config, optimizer) + # scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + # optimizer, mode="min", factor=0.5, patience=10, verbose=True + # ) + model.train() + + self.log("Ready") + self.log("Training the model") + self.log("*" * 20) + + startTime = time.time() + ncuts_losses = [] + rec_losses = [] + total_losses = [] + best_dice = -1 + + # Train the model + for epoch in range(self.config.max_epochs): + self.log(f"Epoch {epoch + 1} of {self.config.max_epochs}") + + epoch_ncuts_loss = 0 + epoch_rec_loss = 0 + epoch_loss = 0 - for _i, batch in enumerate(dataloader): - # raise NotImplementedError("testing") - if self.config.sampling: + for _i, batch in enumerate(dataloader): + # raise NotImplementedError("testing") image = batch["image"].to(device) - else: - image = batch.to(device) - if self.config.batch_size == 1: - image = image.unsqueeze(0) - else: - image = image.unsqueeze(0) - image = torch.swapaxes(image, 0, 1) - - # Forward pass - enc = model.forward_encoder(image) - # Compute the Ncuts loss - Ncuts = criterionE(enc, image) - epoch_ncuts_loss += Ncuts.item() - # if WANDB_INSTALLED: - # wandb.log({"Ncuts loss": Ncuts.item()}) - - # Forward pass - enc, dec = model(image) - - # Compute the reconstruction loss - if isinstance(criterionW, nn.MSELoss): - reconstruction_loss = criterionW(dec, image) - elif isinstance(criterionW, nn.BCELoss): - reconstruction_loss = criterionW( - torch.sigmoid(dec), - utils.remap_image(image, new_max=1), - ) + # if self.config.batch_size == 1: + # image = image.unsqueeze(0) + # else: + # image = image.unsqueeze(0) + # image = torch.swapaxes(image, 0, 1) + + # Forward pass + enc = model.forward_encoder(image) + # Compute the Ncuts loss + Ncuts = criterionE(enc, image) + epoch_ncuts_loss += Ncuts.item() + # if WANDB_INSTALLED: + # wandb.log({"Ncuts loss": Ncuts.item()}) + + # Forward pass + enc, dec = model(image) + + # Compute the reconstruction loss + if isinstance(criterionW, nn.MSELoss): + reconstruction_loss = criterionW(dec, image) + elif isinstance(criterionW, nn.BCELoss): + reconstruction_loss = criterionW( + torch.sigmoid(dec), + utils.remap_image(image, new_max=1), + ) + + epoch_rec_loss += reconstruction_loss.item() + if WANDB_INSTALLED: + wandb.log( + {"Reconstruction loss": reconstruction_loss.item()} + ) + + # Backward pass for the reconstruction loss + optimizer.zero_grad() + alpha = self.config.n_cuts_weight + beta = self.config.rec_loss_weight + + loss = alpha * Ncuts + beta * reconstruction_loss + epoch_loss += loss.item() + # if WANDB_INSTALLED: + # wandb.log({"Sum of losses": loss.item()}) + loss.backward(loss) + optimizer.step() - epoch_rec_loss += reconstruction_loss.item() - if WANDB_INSTALLED: - wandb.log( - {"Reconstruction loss": reconstruction_loss.item()} + # if self.config.scheduler == "CosineAnnealingWarmRestarts": + # scheduler.step(epoch + _i / len(dataloader)) + # if ( + # self.config.scheduler == "CosineAnnealingLR" + # or self.config.scheduler == "CyclicLR" + # ): + # scheduler.step() + + yield TrainingReport( + show_plot=False, weights=model.state_dict() ) - # Backward pass for the reconstruction loss - optimizer.zero_grad() - alpha = self.config.n_cuts_weight - beta = self.config.rec_loss_weight + ncuts_losses.append(epoch_ncuts_loss / len(dataloader)) + rec_losses.append(epoch_rec_loss / len(dataloader)) + total_losses.append(epoch_loss / len(dataloader)) + + if eval_dataloader is None: + try: + enc_out = enc[0].detach().cpu().numpy() + dec_out = dec[0].detach().cpu().numpy() + image = image[0].detach().cpu().numpy() + + images_dict = { + "Encoder output": { + "data": enc_out, + "cmap": "turbo", + }, + "Encoder output (discrete)": { + "data": AsDiscrete(threshold=0.5)( + enc_out + ).numpy(), + "cmap": "turbo", + }, + "Decoder output": { + "data": dec_out, + "cmap": "gist_earth", + }, + "Input image": {"data": image, "cmap": "inferno"}, + } + + yield TrainingReport( + show_plot=True, + epoch=epoch, + loss_1_values={"SoftNCuts loss": ncuts_losses}, + loss_2_values=rec_losses, + weights=model.state_dict(), + images_dict=images_dict, + ) + except TypeError: + pass - loss = alpha * Ncuts + beta * reconstruction_loss - epoch_loss += loss.item() # if WANDB_INSTALLED: - # wandb.log({"Sum of losses": loss.item()}) - loss.backward(loss) - optimizer.step() - - # if self.config.scheduler == "CosineAnnealingWarmRestarts": - # scheduler.step(epoch + _i / len(dataloader)) - # if ( - # self.config.scheduler == "CosineAnnealingLR" - # or self.config.scheduler == "CyclicLR" - # ): - # scheduler.step() - - ncuts_losses.append(epoch_ncuts_loss / len(dataloader)) - rec_losses.append(epoch_rec_loss / len(dataloader)) - total_losses.append(epoch_loss / len(dataloader)) + # wandb.log({"Ncuts loss_epoch": ncuts_losses[-1]}) + # wandb.log({"Reconstruction loss_epoch": rec_losses[-1]}) + # wandb.log({"Sum of losses_epoch": total_losses[-1]}) + # wandb.log({"epoch": epoch}) + # wandb.log({"learning_rate model": optimizerW.param_groups[0]["lr"]}) + # wandb.log({"learning_rate encoder": optimizerE.param_groups[0]["lr"]}) + # wandb.log({"learning_rate model": optimizer.param_groups[0]["lr"]}) + + self.log("Ncuts loss: " + str(ncuts_losses[-1])) + if epoch > 0: + self.log( + "Ncuts loss difference: " + + str(ncuts_losses[-1] - ncuts_losses[-2]) + ) + self.log("Reconstruction loss: " + str(rec_losses[-1])) + if epoch > 0: + self.log( + "Reconstruction loss difference: " + + str(rec_losses[-1] - rec_losses[-2]) + ) + self.log("Sum of losses: " + str(total_losses[-1])) + if epoch > 0: + self.log( + "Sum of losses difference: " + + str(total_losses[-1] - total_losses[-2]), + ) - # if WANDB_INSTALLED: - # wandb.log({"Ncuts loss_epoch": ncuts_losses[-1]}) - # wandb.log({"Reconstruction loss_epoch": rec_losses[-1]}) - # wandb.log({"Sum of losses_epoch": total_losses[-1]}) - # wandb.log({"epoch": epoch}) - # wandb.log({"learning_rate model": optimizerW.param_groups[0]["lr"]}) - # wandb.log({"learning_rate encoder": optimizerE.param_groups[0]["lr"]}) - # wandb.log({"learning_rate model": optimizer.param_groups[0]["lr"]}) - - self.log("Ncuts loss: " + str(ncuts_losses[-1])) - if epoch > 0: - self.log( - "Ncuts loss difference: " - + str(ncuts_losses[-1] - ncuts_losses[-2]) - ) - self.log("Reconstruction loss: " + str(rec_losses[-1])) - if epoch > 0: - self.log( - "Reconstruction loss difference: " - + str(rec_losses[-1] - rec_losses[-2]) - ) - self.log("Sum of losses: " + str(total_losses[-1])) - if epoch > 0: - self.log( - "Sum of losses difference: " - + str(total_losses[-1] - total_losses[-2]), - ) + # Update the learning rate + # if self.config.scheduler == "ReduceLROnPlateau": + # # schedulerE.step(epoch_ncuts_loss) + # # schedulerW.step(epoch_rec_loss) + # scheduler.step(epoch_rec_loss) + if ( + eval_dataloader is not None + and (epoch + 1) % self.config.validation_interval == 0 + ): + model.eval() + self.log("Validating...") + with torch.no_grad(): + for _k, val_data in enumerate(eval_dataloader): + val_inputs, val_labels = ( + val_data["image"].to(device), + val_data["label"].to(device), + ) - # Update the learning rate - # if self.config.scheduler == "ReduceLROnPlateau": - # # schedulerE.step(epoch_ncuts_loss) - # # schedulerW.step(epoch_rec_loss) - # scheduler.step(epoch_rec_loss) - if ( - self.config.eval_volume_directory is not None - and (epoch + 1) % self.config.val_interval == 0 - ): - model.eval() - self.log("Validating...") - with torch.no_grad(): - for _k, val_data in enumerate(eval_dataloader): - val_inputs, val_labels = ( - val_data["image"].to(device), - val_data["label"].to(device), - ) + # normalize val_inputs across channels + for i in range(val_inputs.shape[0]): + for j in range(val_inputs.shape[1]): + val_inputs[i][j] = normalize_function( + val_inputs[i][j] + ) - # normalize val_inputs across channels - for i in range(val_inputs.shape[0]): - for j in range(val_inputs.shape[1]): - val_inputs[i][j] = normalize_function( - val_inputs[i][j] - ) + val_outputs = sliding_window_inference( + val_inputs, + roi_size=[64, 64, 64], + sw_batch_size=1, + predictor=model.forward_encoder, + overlap=0, + progress=True, + ) + val_outputs = AsDiscrete(threshold=0.5)( + val_outputs + ) + val_decoder_outputs = model.forward_decoder( + val_outputs + ) - val_outputs = model.forward_encoder(val_inputs) - val_outputs = AsDiscrete(threshold=0.5)(val_outputs) - - # compute metric for current iteration - for channel in range(val_outputs.shape[1]): - max_dice_channel = torch.argmax( - torch.Tensor( - [ - utils.dice_coeff( - y_pred=val_outputs[ - :, - channel : (channel + 1), - :, - :, - :, - ], - y_true=val_labels, - ) - ] + # compute metric for current iteration + for channel in range(val_outputs.shape[1]): + max_dice_channel = torch.argmax( + torch.Tensor( + [ + utils.dice_coeff( + y_pred=val_outputs[ + :, + channel : (channel + 1), + :, + :, + :, + ], + y_true=val_labels, + ) + ] + ) ) - ) - dice_metric( - y_pred=val_outputs[ - :, - max_dice_channel : (max_dice_channel + 1), - :, - :, - :, - ], - y=val_labels, - ) + dice_metric( + y_pred=val_outputs[ + :, + max_dice_channel : (max_dice_channel + 1), + :, + :, + :, + ], + y=val_labels, + ) - # aggregate the final mean dice result - metric = dice_metric.aggregate().item() - self.log("Validation Dice score: ", metric) - if best_dice < metric < 2: - best_dice = metric - epoch + 1 - if self.config.save_model: - save_best_path = Path( - self.config.save_model_path - ).parents[0] - save_best_path.mkdir(parents=True, exist_ok=True) - save_best_name = Path( - self.config.save_model_path - ).stem + # aggregate the final mean dice result + metric = dice_metric.aggregate().item() + self.log(f"Validation Dice score: {metric}") + if best_dice < metric <= 1: + best_dice = metric + # save the best model + save_best_path = self.config.results_path_folder + # save_best_path.mkdir(parents=True, exist_ok=True) + save_best_name = "wnet" save_path = ( - str(save_best_path / save_best_name) + str(Path(save_best_path) / save_best_name) + "_best_metric.pth" ) self.log(f"Saving new best model to {save_path}") torch.save(model.state_dict(), save_path) - if WANDB_INSTALLED: - # log validation dice score for each validation round - wandb.log({"val/dice_metric": metric}) + if WANDB_INSTALLED: + # log validation dice score for each validation round + wandb.log({"val/dice_metric": metric}) + + display_dict = { + "Decoder output": { + "data": val_decoder_outputs[0], + "cmap": "gist_earth", + }, + "Encoder output": { + "data": val_outputs[0], + "cmap": "turbo", + }, + "Labels": { + "data": val_labels[0], + "cmap": "bop blue", + }, + "Inputs": { + "data": val_inputs[0], + "cmap": "inferno", + }, + } + + yield TrainingReport( + epoch=epoch, + loss_1_values={ + "Ncuts loss": ncuts_losses, + "Dice metric": metric, + }, + loss_2_values=rec_losses, + weights=model.state_dict(), + images_dict=display_dict, + ) + + # reset the status for next validation round + dice_metric.reset() + + eta = ( + (time.time() - startTime) + * (self.config.max_epochs / (epoch + 1) - 1) + / 60 + ) + self.log( + f"ETA: {eta} minutes", + ) + self.log("-" * 20) - # reset the status for next validation round - dice_metric.reset() + # Save the model + if epoch % 5 == 0: + torch.save( + model.state_dict(), + self.config.results_path_folder + "/wnet_.pth", + ) - eta = ( - (time.time() - startTime) - * (self.config.num_epochs / (epoch + 1) - 1) - / 60 + self.log("Training finished") + if best_dice > -1: + self.log(f"Best dice metric : {best_dice}") + # if WANDB_INSTALLED and self.config.eval_volume_directory is not None: + # wandb.log( + # { + # "best_dice_metric": best_dice, + # "best_metric_epoch": best_dice_epoch, + # } + # ) + self.log("*" * 50) + + # Save the model + + print( + "Saving the model to: ", + self.config.results_path_folder + "/wnet.pth", ) - self.log( - f"ETA: {eta} minutes", + torch.save( + model.state_dict(), + self.config.results_path_folder + "/wnet.pth", ) - self.log("-" * 20) - - # Save the model # FIXME - if self.config.save_model and epoch % self.config.save_every == 0: - torch.save(model.state_dict(), self.config.save_model_path) - # with open(self.config.save_losses_path, "wb") as f: - # pickle.dump((ncuts_losses, rec_losses), f) - - self.log("Training finished") - self.log(f"Best dice metric : {best_dice}") - # if WANDB_INSTALLED and self.config.eval_volume_directory is not None: - # wandb.log( - # { - # "best_dice_metric": best_dice, - # "best_metric_epoch": best_dice_epoch, - # } - # ) - self.log("*" * 50) - - # Save the model FIXME - if self.config.save_model: - print("Saving the model to: ", self.config.save_model_path) - torch.save(model.state_dict(), self.config.save_model_path) - # with open(self.config.save_losses_path, "wb") as f: - # pickle.dump((ncuts_losses, rec_losses), f) - # if WANDB_INSTALLED: - # model_artifact = wandb.Artifact( - # "WNet", - # type="model", - # description="WNet benchmark", - # metadata=dict(WANDB_CONFIG), - # ) - # model_artifact.add_file(self.config.save_model_path) - # wandb.log_artifact(model_artifact) - - return ncuts_losses, rec_losses, model - - -class TrainingWorker(TrainingWorkerBase): + + # if WANDB_INSTALLED: + # model_artifact = wandb.Artifact( + # "WNet", + # type="model", + # description="WNet benchmark", + # metadata=dict(WANDB_CONFIG), + # ) + # model_artifact.add_file(self.config.save_model_path) + # wandb.log_artifact(model_artifact) + + return ncuts_losses, rec_losses, model + except Exception as e: + msg = f"Training failed with exception: {e}" + self.log(msg) + self.raise_error(e, msg) + self.quit() + raise e + + +class SupervisedTrainingWorker(TrainingWorkerBase): """A custom worker to run supervised training jobs in. Inherits from :py:class:`napari.qt.threading.GeneratorWorker` via :py:class:`TrainingWorkerBase` """ @@ -1436,13 +1462,32 @@ def get_loader_func(num_samples): dice_metric.reset() val_metric_values.append(metric) + images_dict = { + "Validation output": { + "data": checkpoint_output[0], + "cmap": "turbo", + }, + "Validation output (discrete)": { + "data": checkpoint_output[1], + "cmap": "bop blue", + }, + "Validation image": { + "data": checkpoint_output[2], + "cmap": "inferno", + }, + "Validation labels": { + "data": checkpoint_output[3], + "cmap": "green", + }, + } + train_report = TrainingReport( show_plot=True, epoch=epoch, - loss_values=epoch_loss_values, - validation_metric=val_metric_values, + loss_1_values={"Loss": epoch_loss_values}, + loss_2_values=val_metric_values, weights=model.state_dict(), - images=checkpoint_output, + images_dict=images_dict, ) self.log("Validation completed") yield train_report diff --git a/napari_cellseg3d/code_models/workers_utils.py b/napari_cellseg3d/code_models/workers_utils.py index 5efb93a0..b07e96c8 100644 --- a/napari_cellseg3d/code_models/workers_utils.py +++ b/napari_cellseg3d/code_models/workers_utils.py @@ -239,7 +239,11 @@ class InferenceResult: class TrainingReport: show_plot: bool = True epoch: int = 0 - loss_values: t.Dict = None # TODO(cyril) : change to dict and unpack different losses for e.g. WNet with several losses - validation_metric: t.List = None + loss_1_values: t.Dict = None # example : {"Loss" : [0.1, 0.2, 0.3]} + loss_2_values: t.List = None weights: np.array = None - images: t.List[np.array] = None + images_dict: t.Dict = ( + None # output, discrete output, target, target labels + ) + # OR decoder output, encoder output, target, target labels + # format : {"Layer name" : {"data" : np.array, "cmap" : "turbo"}} diff --git a/napari_cellseg3d/code_plugins/plugin_base.py b/napari_cellseg3d/code_plugins/plugin_base.py index f369320b..1da69bd0 100644 --- a/napari_cellseg3d/code_plugins/plugin_base.py +++ b/napari_cellseg3d/code_plugins/plugin_base.py @@ -360,6 +360,8 @@ def __init__( """array(str): paths to images for training or inference""" self.labels_filepaths = [] """array(str): paths to labels for training""" + self.validation_filepaths = [] + """array(str): paths to validation files (unsup. learning)""" self.results_path = None """str: path to output folder,to save results in""" @@ -372,24 +374,25 @@ def __init__( ####################################################### # interface - # self.image_filewidget = ui.FilePathWidget( - # "Images directory", self.load_image_dataset, self - # ) self.image_filewidget.text_field = "Images directory" self.image_filewidget.button.clicked.disconnect( self._show_dialog_images ) self.image_filewidget.button.clicked.connect(self.load_image_dataset) - # self.labels_filewidget = ui.FilePathWidget( - # "Labels directory", self.load_label_dataset, self - # ) self.labels_filewidget.text_field = "Labels directory" self.labels_filewidget.button.clicked.disconnect( self._show_dialog_labels ) self.labels_filewidget.button.clicked.connect(self.load_label_dataset) - + ################ + # Validation images widget + self.unsupervised_images_filewidget = ui.FilePathWidget( + description="Training directory", + file_function=self.load_validation_images_dataset, + parent=self, + ) + self.unsupervised_images_filewidget.setVisible(False) # self.filetype_choice = ui.DropdownMenu( # [".tif", ".tiff"], label="File format" # ) @@ -426,6 +429,19 @@ def load_image_dataset(self): self.image_filewidget.check_ready() self._update_default_paths(path) + def load_validation_images_dataset(self): + """Show file dialog to set :py:attr:`~val_images_filepaths`""" + filenames = self.load_dataset_paths() + logger.debug(f"val filenames : {filenames}") + if filenames: + self.validation_filepaths = [ + str(path) for path in sorted(filenames) + ] + path = str(Path(filenames[0]).parent) + self.unsupervised_images_filewidget.text_field.setText(path) + self.unsupervised_images_filewidget.check_ready() + self._update_default_paths(path) + def load_label_dataset(self): """Show file dialog to set :py:attr:`~labels_filepaths`""" filenames = self.load_dataset_paths() @@ -444,6 +460,7 @@ def _update_default_paths(self, path=None): self._default_path = [ self.extract_dataset_paths(self.images_filepaths), self.extract_dataset_paths(self.labels_filepaths), + self.extract_dataset_paths(self.validation_filepaths), self.results_path, ] return @@ -458,3 +475,9 @@ def extract_dataset_paths(paths): if paths[0] is None: return None return str(Path(paths[0]).parent) + + def _check_all_filepaths(self): + self.image_filewidget.check_ready() + self.labels_filewidget.check_ready() + self.results_filewidget.check_ready() + self.unsupervised_images_filewidget.check_ready() diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index e71f82cc..17ca7b11 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -23,7 +23,8 @@ from napari_cellseg3d import interface as ui from napari_cellseg3d.code_models.model_framework import ModelFramework from napari_cellseg3d.code_models.worker_training import ( - TrainingWorker, + SupervisedTrainingWorker, + WNetTrainingWorker, ) from napari_cellseg3d.code_models.workers_utils import TrainingReport @@ -80,10 +81,6 @@ def __init__( * A choice of using random or deterministic training - TODO training plugin: - * Custom model loading - - Args: viewer: napari viewer to display the widget in @@ -121,7 +118,7 @@ def __init__( self.config = config.TrainerConfig() - self.model = None # TODO : custom model loading ? + self.model = None self.worker = None """Training worker for multithreading, should be a TrainingWorker instance from :doc:model_workers.py""" self.worker_config = None @@ -130,6 +127,9 @@ def __init__( self.stop_requested = False """Whether the worker should stop or not""" self.start_time = None + """Start time of the latest job""" + self.unsupervised_mode = False + self.unsupervised_eval_data = None self.loss_list = [ # MUST BE MATCHED WITH THE LOSS FUNCTIONS IN THE TRAINING WORKER DICT "Dice", @@ -143,29 +143,45 @@ def __init__( self.canvas = None """Canvas to plot loss and dice metric in""" - self.train_loss_plot = None + self.plot_1 = None """Plot for loss""" - self.dice_metric_plot = None + self.plot_2 = None """Plot for dice metric""" self.plot_dock = None """Docked widget with plots""" self.result_layers = [] """Layers to display checkpoint""" + self.plot_1_labels = { + "title": { + "supervised": "Epoch average loss", + "unsupervised": "Metrics", + }, + "ylabel": { + "supervised": "Loss", + "unsupervised": "", + }, + } + self.plot_2_labels = { + "title": { + "supervised": "Epoch average dice metric", + "unsupervised": "Reconstruction loss", + }, + "ylabel": { + "supervised": "Metric", + "unsupervised": "Loss", + }, + } + self.df = None - self.loss_values = [] - self.validation_values = [] - - # self.model_choice.setCurrentIndex(0) - ################### - # TODO(cyril) : disable if we implement WNet training - # wnet_index = self.model_choice.findText("WNet") - # self.model_choice.removeItem(wnet_index) - ################################ + self.loss_1_values = [] + self.loss_2_values = [] + + ########### # interface + ########### self.zip_choice = ui.CheckBox("Compress results") - self.validation_percent_choice = ui.Slider( lower=10, upper=90, @@ -214,20 +230,10 @@ def __init__( self._update_validation_choice ) - learning_rate_vals = [ - "1e-2", - "1e-3", - "1e-4", - "1e-5", - "1e-6", - ] - - self.learning_rate_choice = ui.DropdownMenu( - learning_rate_vals, text_label="Learning rate" + self.learning_rate_choice = LearningRateWidget(parent=self) + self.lbl_learning_rate_choice = ( + self.learning_rate_choice.lr_value_choice.label ) - self.lbl_learning_rate_choice = self.learning_rate_choice.label - - self.learning_rate_choice.setCurrentIndex(1) self.scheduler_patience_choice = ui.IntIncrementCounter( 1, @@ -286,8 +292,10 @@ def __init__( self.progress.setVisible(False) """Dock widget containing the progress bar""" - self.start_button_supervised = None # button created later and only shown if supervised model is selected - self.loss_group = None # group box created later and only shown if supervised model is selected + # widgets created later and only shown if supervised model is selected + self.start_button_supervised = None + self.loss_group = None + self.validation_group = None ############################ ############################ # WNet parameters @@ -428,32 +436,42 @@ def check_ready(self): return False return True - def _toggle_unsupervised_mode(self): + def _toggle_unsupervised_mode(self, enabled=False): """Change all the UI elements needed for unsupervised learning mode""" - if self.model_choice.currentText() == "WNet": - self.setTabVisible(3, True) - self.setTabEnabled(3, True) - self.start_button_unsupervised.setVisible(True) - self.start_button_supervised.setVisible(False) - self.advanced_next_button.setVisible(True) + if self.model_choice.currentText() == "WNet" or enabled: + unsupervised = True self.start_btn = self.start_button_unsupervised - # loss - # self.loss_choice.setVisible(False) - self.loss_group.setVisible(False) - self.scheduler_factor_choice.setVisible(False) - self.scheduler_patience_choice.setVisible(False) + self.image_filewidget.text_field.setText("Validation images") + self.labels_filewidget.text_field.setText("Validation labels") else: - self.setTabVisible(3, False) - self.setTabEnabled(3, False) - self.start_button_unsupervised.setVisible(False) - self.start_button_supervised.setVisible(True) - self.advanced_next_button.setVisible(False) + unsupervised = False self.start_btn = self.start_button_supervised - # loss - # self.loss_choice.setVisible(True) - self.loss_group.setVisible(True) - self.scheduler_factor_choice.setVisible(True) - self.scheduler_patience_choice.setVisible(True) + self.image_filewidget.text_field.setText("Images directory") + self.labels_filewidget.text_field.setText("Labels directory") + + supervised = not unsupervised + self.unsupervised_mode = unsupervised + + self.setTabVisible(3, unsupervised) + self.setTabEnabled(3, unsupervised) + self.start_button_unsupervised.setVisible(unsupervised) + self.start_button_supervised.setVisible(supervised) + self.advanced_next_button.setVisible(unsupervised) + # loss + # self.loss_choice.setVisible(supervised) + self.loss_group.setVisible(supervised) + # scheduler + self.scheduler_factor_choice.container.setVisible(supervised) + self.scheduler_factor_choice.label.setVisible(supervised) + self.scheduler_patience_choice.setVisible(supervised) + self.scheduler_patience_choice.label.setVisible(supervised) + # data + self.unsupervised_images_filewidget.setVisible(unsupervised) + self.validation_group.setVisible(supervised) + self.image_filewidget.required = supervised + self.labels_filewidget.required = supervised + + self._check_all_filepaths() def _build(self): """Builds the layout of the widget and creates the following tabs and prompts: @@ -560,14 +578,11 @@ def _build(self): ui.add_widgets( data_layout, [ - # ui.combine_blocks( - # self.filetype_choice, self.filetype_choice.label - # ), # file extension + self.unsupervised_images_filewidget, self.image_filewidget, self.labels_filewidget, + ui.make_label("Results :", parent=self), self.results_filewidget, - # ui.combine_blocks(self.model_choice, self.model_choice.label), # model choice - # TODO : add custom model choice self.zip_choice, # save as zip ], ) @@ -645,12 +660,11 @@ def _build(self): ####################### ui.add_blank(data_tab_w, data_tab_l) ####################### - ui.GroupedWidget.create_single_widget_group( + self.validation_group = ui.GroupedWidget.create_single_widget_group( "Validation (%)", self.validation_percent_choice.container, data_tab_l, ) - ####################### ####################### ui.add_blank(self, data_tab_l) @@ -675,7 +689,7 @@ def _build(self): ################## train_tab = ui.ContainerWidget() ################## - ui.add_blank(train_tab, train_tab.layout) + # ui.add_blank(train_tab, train_tab.layout) ################## self.loss_group = ui.GroupedWidget.create_single_widget_group( "Loss", @@ -760,7 +774,7 @@ def _build(self): ############ ################## advanced_tab = ui.ContainerWidget(parent=self) - self.wnet_widgets = ui.WNetWidgets(parent=advanced_tab) + self.wnet_widgets = WNetWidgets(parent=advanced_tab) ui.add_blank(advanced_tab, advanced_tab.layout) ################## model_params_group_w, model_params_group_l = ui.make_group( @@ -934,18 +948,26 @@ def start(self): self._reset_loss_plot() - try: - self.data = self.create_train_dataset_dict() - except ValueError as err: - self.data = None - raise err - self.config = config.TrainerConfig( save_as_zip=self.zip_choice.isChecked() ) - self._set_supervised_worker_config() - self.worker = TrainingWorker(worker_config=self.worker_config) + if self.unsupervised_mode: + try: + self.data = self.create_dataset_dict_no_labs() + except ValueError as err: + self.data = None + raise err + else: + try: + self.data = self.create_train_dataset_dict() + except ValueError as err: + self.data = None + raise err + + # self._set_worker_config() + self.worker = self._create_worker() # calls _set_worker_config + self.worker.set_download_log(self.log) [btn.setVisible(False) for btn in self.close_buttons] @@ -978,13 +1000,27 @@ def _create_supervised_worker_from_config( ): if isinstance(config, config.TrainerConfig): raise TypeError( - "Expected a TrainingWorkerConfig, got a TrainerConfig" + "Expected a SupervisedTrainingWorkerConfig, got a TrainerConfig" ) - return TrainingWorker(worker_config=worker_config) + return SupervisedTrainingWorker(worker_config=worker_config) - def _set_supervised_worker_config( + def _create_unsupervised_worker_from_config( + self, worker_config: config.WNetTrainingWorkerConfig + ): + return WNetTrainingWorker(worker_config=worker_config) + + def _create_worker(self): + self._set_worker_config() + if self.unsupervised_mode: + return self._create_unsupervised_worker_from_config( + self.worker_config + ) + return self._create_supervised_worker_from_config(self.worker_config) + + def _set_worker_config( self, - ) -> config.SupervisedTrainingWorkerConfig: + ) -> config.TrainingWorkerConfig: + logger.debug("Loading config...") model_config = config.ModelInfo(name=self.model_choice.currentText()) self.weights_config.path = self.weights_config.path @@ -992,14 +1028,11 @@ def _set_supervised_worker_config( self.weights_config.use_pretrained = ( not self.use_transfer_choice.isChecked() ) - deterministic_config = config.DeterministicConfig( enabled=self.use_deterministic_choice.isChecked(), seed=self.box_seed.value(), ) - validation_percent = self.validation_percent_choice.slider_value / 100 - results_path_folder = Path( self.results_path + f"/{model_config.name}_" @@ -1010,10 +1043,36 @@ def _set_supervised_worker_config( Path(results_path_folder).mkdir( parents=True, exist_ok=False ) # avoid overwrite where possible - patch_size = [w.value() for w in self.patch_size_widgets] - logger.debug("Loading config...") + if self.unsupervised_mode: + try: + self.unsupervised_eval_data = self.create_train_dataset_dict() + except ValueError: + self.unsupervised_eval_data = None + self.worker_config = self._set_unsupervised_worker_config( + results_path_folder, + patch_size, + deterministic_config, + self.unsupervised_eval_data, + ) + else: + self.worker_config = self._set_supervised_worker_config( + model_config, + results_path_folder, + patch_size, + deterministic_config, + ) + return self.worker_config + + def _set_supervised_worker_config( + self, + model_config, + results_path_folder, + patch_size, + deterministic_config, + ): + validation_percent = self.validation_percent_choice.slider_value / 100 self.worker_config = config.SupervisedTrainingWorkerConfig( device=self.check_device_choice(), model_info=model_config, @@ -1022,7 +1081,7 @@ def _set_supervised_worker_config( validation_percent=validation_percent, max_epochs=self.epoch_choice.value(), loss_function=self.loss_choice.currentText(), - learning_rate=float(self.learning_rate_choice.currentText()), + learning_rate=self.learning_rate_choice.get_learning_rate(), scheduler_patience=self.scheduler_patience_choice.value(), scheduler_factor=self.scheduler_factor_choice.slider_value, validation_interval=self.val_interval_choice.value(), @@ -1037,6 +1096,43 @@ def _set_supervised_worker_config( return self.worker_config + def _set_unsupervised_worker_config( + self, + results_path_folder, + patch_size, + deterministic_config, + eval_volume_dict, + ) -> config.WNetTrainingWorkerConfig: + self.worker_config = config.WNetTrainingWorkerConfig( + device=self.check_device_choice(), + weights_info=self.weights_config, + train_data_dict=self.data, + max_epochs=self.epoch_choice.value(), + learning_rate=self.learning_rate_choice.get_learning_rate(), + validation_interval=self.val_interval_choice.value(), + batch_size=self.batch_choice.slider_value, + results_path_folder=str(results_path_folder), + sampling=self.patch_choice.isChecked(), + num_samples=self.sample_choice_slider.slider_value, + sample_size=patch_size, + do_augmentation=self.augment_choice.isChecked(), + deterministic_config=deterministic_config, + num_classes=int( + self.wnet_widgets.num_classes_choice.currentText() + ), + reconstruction_loss=self.wnet_widgets.loss_choice.currentText(), + n_cuts_weight=self.wnet_widgets.ncuts_weight_choice.value(), + rec_loss_weight=self.wnet_widgets.get_reconstruction_weight(), + eval_volume_dict=eval_volume_dict, + ) + + return self.worker_config + + def _is_current_job_supervised(self): + if isinstance(self.worker, WNetTrainingWorker): + return False + return True + def on_start(self): """Catches started signal from worker""" @@ -1121,61 +1217,41 @@ def _remove_result_layers(self): self._viewer.layers.remove(layer) self.result_layers = [] - def _display_results(self, images, names, complete_missing=False): + def _display_results(self, images_dict, complete_missing=False): + layer_list = [] if not complete_missing: - layer_output = self._viewer.add_image( - data=images[0], name=names[0], colormap="turbo" - ) - layer_output_discrete = self._viewer.add_image( - data=images[1], name=names[1], colormap="bop blue" - ) - layer_image = self._viewer.add_image( - data=images[2], name=names[2], colormap="inferno" - ) - layer_labels = self._viewer.add_labels( - data=images[3], name=names[3] - ) - self.result_layers += [ - layer_output, - layer_output_discrete, - layer_image, - layer_labels, - ] + for layer_name in list(images_dict.keys()): + logger.debug(f"Adding layer {layer_name}") + layer = self._viewer.add_image( + data=images_dict[layer_name]["data"], + name=layer_name, + colormap=images_dict[layer_name]["cmap"], + ) + layer_list.append(layer) + self.result_layers += layer_list self._viewer.grid.enabled = True self._viewer.dims.ndisplay = 3 self._viewer.reset_view() else: - # add only the missing layers - for i in range(3): - if names[i] not in [ + for i, layer_name in enumerate(list(images_dict.keys())): + if layer_name not in [ layer.name for layer in self._viewer.layers ]: - if i == 0: - layer_output = self._viewer.add_image( - data=images[i], name=names[i], colormap="turbo" - ) - self.result_layers[0] = layer_output - elif i == 1: - layer_output_discrete = self._viewer.add_image( - data=images[i], - name=names[i], - colormap="bop orange", - ) - self.result_layers[1] = layer_output_discrete - elif i == 2: - layer_image = self._viewer.add_image( - data=images[i], name=names[i], colormap="inferno" - ) - self.result_layers[2] = layer_image - else: - layer_labels = self._viewer.add_labels( - data=images[i], name=names[i] - ) - self.result_layers[3] = layer_labels - self.result_layers[i].data = images[i] - self.result_layers[i].refresh() - - def on_yield(self, report: TrainingReport): + logger.debug(f"Adding missing layer {layer_name}") + layer = self._viewer.add_image( + data=images_dict[layer_name]["data"], + name=layer_name, + colormap=images_dict[layer_name]["cmap"], + ) + layer_list[i] = layer + else: + logger.debug(f"Refreshing layer {layer_name}") + self.result_layers[i].data = images_dict[layer_name][ + "data" + ] + self.result_layers[i].refresh() + + def on_yield(self, report: TrainingReport): # TODO refactor for dict # logger.info( # f"\nCatching results : for epoch {data['epoch']}, # loss is {data['losses']} and validation is {data['val_metrics']}" @@ -1185,20 +1261,17 @@ def on_yield(self, report: TrainingReport): if report.show_plot: try: - layer_names = [ - "Validation output", - "Validation output (discrete)", - "Validation image", - "Validation labels", - ] - range(len(report.images)) - self.log.print_and_log(len(report.images)) - - if report.epoch + 1 == self.worker_config.validation_interval: - self._display_results(report.images, layer_names) + self.log.print_and_log(len(report.images_dict)) + + if ( + report.epoch == 0 + or report.epoch + 1 + == self.worker_config.validation_interval + ): + self._display_results(report.images_dict) else: self._display_results( - report.images, layer_names, complete_missing=True + report.images_dict, complete_missing=True ) except Exception as e: logger.exception(e) @@ -1207,9 +1280,9 @@ def on_yield(self, report: TrainingReport): 100 * (report.epoch + 1) // self.worker_config.max_epochs ) - self.update_loss_plot(report.loss_values, report.validation_metric) - self.loss_values = report.loss_values - self.validation_values = report.validation_metric + self.update_loss_plot(report.loss_1_values, report.loss_2_values) + self.loss_1_values = report.loss_1_values + self.loss_2_values = report.loss_2_values if self.stop_requested: self.log.print_and_log( @@ -1226,110 +1299,106 @@ def on_yield(self, report: TrainingReport): self.on_stop() self.stop_requested = False - # def clean_cache(self): - # """Attempts to clear memory after training""" - # # del self.worker - # self.worker = None - # # if self.model is not None: - # # del self.model - # # self.model = None - # - # # del self.data - # # self.close() - # # del self - # if self.get_device(show=False).type == "cuda": - # self.empty_cuda_cache() - def _make_csv(self): size_column = range(1, self.worker_config.max_epochs + 1) - if len(self.loss_values) == 0 or self.loss_values is None: + if len(self.loss_1_values) == 0 or self.loss_1_values is None: logger.warning("No loss values to add to csv !") return - val = utils.fill_list_in_between( - self.validation_values, - self.worker_config.validation_interval - 1, - "", - )[: len(size_column)] - - if len(val) != len(self.loss_values): - err = f"Validation and loss values don't have the same length ! Got {len(val)} and {len(self.loss_values)}" - logger.error(err) - # return None - raise ValueError(err) - - self.df = pd.DataFrame( - { - "epoch": size_column, - "loss": self.loss_values, - "validation": val, - } - ) + if self._is_current_job_supervised(): + val = utils.fill_list_in_between( + self.loss_2_values, + self.worker_config.validation_interval - 1, + "", + )[: len(size_column)] + self.df = pd.DataFrame( + { + "epoch": size_column, + "loss": self.loss_1_values, + "validation": val, + } + ) + if len(val) != len(self.loss_1_values): + err = f"Validation and loss values don't have the same length ! Got {len(val)} and {len(self.loss_1_values)}" + logger.error(err) + raise ValueError(err) + else: + self.df = pd.DataFrame( + { + "epoch": size_column, + "Ncuts loss": self.loss_1_values, + "Reconstruction loss": self.loss_2_values, + } + ) + path = Path(self.worker_config.results_path_folder) / Path( "training.csv" ) self.df.to_csv(path, index=False) - def plot_loss(self, loss, dice_metric): + def _plot_loss( + self, + loss_values_1: dict, + loss_values_2: list, + show_plot_2_max: bool = True, + ): """Creates two subplots to plot the training loss and validation metric""" + plot_key = ( + "supervised" + if self._is_current_job_supervised() + else "unsupervised" + ) with plt.style.context("dark_background"): # update loss - self.train_loss_plot.set_title("Epoch average loss") - self.train_loss_plot.set_xlabel("Epoch") - self.train_loss_plot.set_ylabel("Loss") - x = [i + 1 for i in range(len(loss))] - y = loss - self.train_loss_plot.plot(x, y) - # self.train_loss_plot.set_ylim(0, 1) - - # update metrics - x = [ - self.worker_config.validation_interval * (i + 1) - for i in range(len(dice_metric)) - ] - y = dice_metric - - epoch_min = ( - np.argmax(y) + 1 - ) * self.worker_config.validation_interval - dice_min = np.max(y) + self.plot_1.set_title(self.plot_1_labels["title"][plot_key]) + self.plot_1.set_xlabel("Epoch") + self.plot_1.set_ylabel(self.plot_2_labels["ylabel"][plot_key]) + + for metric_name in list(loss_values_1.keys()): + if metric_name == "Dice coefficient": + x = [ + self.worker_config.validation_interval * (i + 1) + for i in range(len(loss_values_1[metric_name])) + ] + else: + x = [i + 1 for i in range(len(loss_values_1[metric_name]))] + y = loss_values_1[metric_name] + self.plot_1.plot(x, y, label=metric_name) + self.plot_1.legend(loc="lower right") + + # update plot 2 + if self._is_current_job_supervised(): + x = [ + self.worker_config.validation_interval * (i + 1) + for i in range(len(loss_values_2)) + ] + else: + x = [i + 1 for i in range(len(loss_values_2))] + y = loss_values_2 - self.dice_metric_plot.plot(x, y, zorder=1) + self.plot_2.plot(x, y, zorder=1) # self.dice_metric_plot.set_ylim(0, 1) - self.dice_metric_plot.set_title( - "Validation metric : Mean Dice coefficient" - ) - self.dice_metric_plot.set_xlabel("Epoch") - self.dice_metric_plot.set_ylabel("Dice") - - self.dice_metric_plot.scatter( - epoch_min, - dice_min, - c="r", - label="Maximum Dice coeff.", - zorder=5, - ) - self.dice_metric_plot.legend( - facecolor=ui.napari_grey, loc="lower right" - ) + self.plot_2.set_title(self.plot_2_labels["title"][plot_key]) + self.plot_2.set_xlabel("Epoch") + self.plot_2.set_ylabel(self.plot_2_labels["ylabel"][plot_key]) + + if show_plot_2_max: + epoch_min = ( + np.argmax(y) + 1 + ) * self.worker_config.validation_interval + dice_min = np.max(y) + self.plot_2.scatter( + epoch_min, + dice_min, + c="r", + label="Maximum Dice coeff.", + zorder=5, + ) + self.plot_2.legend(facecolor=ui.napari_grey, loc="lower right") self.canvas.draw_idle() - # plot_path = self.worker_config.results_path_folder / Path( - # "../Loss_plots" - # ) - # Path(plot_path).mkdir(parents=True, exist_ok=True) - # - # if self.canvas is not None: - # self.canvas.figure.savefig( - # str( - # plot_path - # / f"checkpoint_metric_plots_{utils.get_date_time()}.png" - # ), - # format="png", - # ) - - def update_loss_plot(self, loss, metric): + def update_loss_plot(self, loss_1: dict, loss_2: list): """ Updates the plots on subsequent validation steps. Creates the plot on the second validation step (epoch == val_interval*2). @@ -1339,7 +1408,8 @@ def update_loss_plot(self, loss, metric): Returns: returns empty if the epoch is < than 2 * validation interval. """ - epoch = len(loss) + epoch = len(loss_1[list(loss_1.keys())[0]]) + logger.debug(f"Updating loss plot for epoch {epoch}") if epoch < self.worker_config.validation_interval * 2: return if epoch == self.worker_config.validation_interval * 2: @@ -1347,13 +1417,13 @@ def update_loss_plot(self, loss, metric): with plt.style.context("dark_background"): self.canvas = FigureCanvas(Figure(figsize=(10, 1.5))) # loss plot - self.train_loss_plot = self.canvas.figure.add_subplot(1, 2, 1) + self.plot_1 = self.canvas.figure.add_subplot(1, 2, 1) # dice metric validation plot - self.dice_metric_plot = self.canvas.figure.add_subplot(1, 2, 2) + self.plot_2 = self.canvas.figure.add_subplot(1, 2, 2) self.canvas.figure.set_facecolor(bckgrd_color) - self.dice_metric_plot.set_facecolor(bckgrd_color) - self.train_loss_plot.set_facecolor(bckgrd_color) + self.plot_2.set_facecolor(bckgrd_color) + self.plot_1.set_facecolor(bckgrd_color) # self.canvas.figure.tight_layout() @@ -1377,26 +1447,164 @@ def update_loss_plot(self, loss, metric): self.canvas, name="Loss plots", area="bottom" ) self.plot_dock._close_btn = False + self.docked_widgets.append(self.plot_dock) except AttributeError as e: logger.exception(e) logger.error( "Plot dock widget could not be added. Should occur in testing only" ) - - self.docked_widgets.append(self.plot_dock) - self.plot_loss(loss, metric) + self._plot_loss(loss_1, loss_2) else: with plt.style.context("dark_background"): - self.train_loss_plot.cla() - self.dice_metric_plot.cla() + self.plot_1.cla() + self.plot_2.cla() - self.plot_loss(loss, metric) + self._plot_loss(loss_1, loss_2) def _reset_loss_plot(self): - if ( - self.train_loss_plot is not None - and self.dice_metric_plot is not None - ): + if self.plot_1 is not None and self.plot_2 is not None: with plt.style.context("dark_background"): - self.train_loss_plot.cla() - self.dice_metric_plot.cla() + self.plot_1.cla() + self.plot_2.cla() + + +class LearningRateWidget(ui.ContainerWidget): + def __init__(self, parent=None): + super().__init__(vertical=False, parent=parent) + + self.lr_exponent_dict = { + "1e-2": 1e-2, + "1e-3": 1e-3, + "1e-4": 1e-4, + "1e-5": 1e-5, + "1e-6": 1e-6, + "1e-7": 1e-7, + "1e-8": 1e-8, + } + + self.lr_value_choice = ui.IntIncrementCounter( + lower=1, + upper=9, + default=1, + text_label="Learning rate : ", + parent=self, + fixed=False, + ) + self.lr_exponent_choice = ui.DropdownMenu( + list(self.lr_exponent_dict.keys()), + parent=self, + fixed=False, + ) + self._build() + + def _build(self): + self.lr_value_choice.setFixedWidth(20) + # self.lr_exponent_choice.setFixedWidth(100) + self.lr_exponent_choice.setCurrentIndex(1) + ui.add_widgets( + self.layout, + [ + self.lr_value_choice, + ui.make_label("x"), + self.lr_exponent_choice, + ], + ) + + def get_learning_rate(self) -> float: + return float( + self.lr_value_choice.value() + * self.lr_exponent_dict[self.lr_exponent_choice.currentText()] + ) + + +class WNetWidgets: + """A collection of widgets for the WNet training GUI""" + + default_config = config.WNetTrainingWorkerConfig() + + def __init__(self, parent): + self.num_classes_choice = ui.DropdownMenu( + entries=["2", "3", "4"], + parent=parent, + text_label="Number of classes", + ) + self.intensity_sigma_choice = ui.DoubleIncrementCounter( + lower=1.0, + upper=100.0, + default=self.default_config.intensity_sigma, + parent=parent, + text_label="Intensity sigma", + ) + self.intensity_sigma_choice.setMaximumWidth(20) + self.spatial_sigma_choice = ui.DoubleIncrementCounter( + lower=1.0, + upper=100.0, + default=self.default_config.spatial_sigma, + parent=parent, + text_label="Spatial sigma", + ) + self.spatial_sigma_choice.setMaximumWidth(20) + self.radius_choice = ui.IntIncrementCounter( + lower=1, + upper=5, + default=self.default_config.radius, + parent=parent, + text_label="Radius", + ) + self.radius_choice.setMaximumWidth(20) + self.loss_choice = ui.DropdownMenu( + entries=["MSE", "BCE"], + parent=parent, + text_label="Reconstruction loss", + ) + self.ncuts_weight_choice = ui.DoubleIncrementCounter( + lower=0.1, + upper=1.0, + default=self.default_config.n_cuts_weight, + parent=parent, + text_label="NCuts weight", + ) + self.reconstruction_weight_choice = ui.DoubleIncrementCounter( + lower=0.1, + upper=1.0, + default=0.5, + parent=parent, + text_label="Reconstruction weight", + ) + self.reconstruction_weight_choice.setMaximumWidth(20) + self.reconstruction_weight_divide_factor_choice = ( + ui.IntIncrementCounter( + lower=1, + upper=10000, + default=100, + parent=parent, + text_label="Reconstruction weight divide factor", + ) + ) + self.reconstruction_weight_divide_factor_choice.setMaximumWidth(20) + + self._set_tooltips() + + def _set_tooltips(self): + self.num_classes_choice.setToolTip("Number of classes to segment") + self.intensity_sigma_choice.setToolTip( + "Intensity sigma for the NCuts loss" + ) + self.spatial_sigma_choice.setToolTip( + "Spatial sigma for the NCuts loss" + ) + self.radius_choice.setToolTip("Radius of NCuts loss region") + self.loss_choice.setToolTip("Loss function to use for reconstruction") + self.ncuts_weight_choice.setToolTip("Weight of the NCuts loss") + self.reconstruction_weight_choice.setToolTip( + "Weight of the reconstruction loss" + ) + self.reconstruction_weight_divide_factor_choice.setToolTip( + "Divide factor for the reconstruction loss.\nThis might have to be changed depending on your images.\nIf you notice that the reconstruction loss is too high, raise this factor until the\nreconstruction loss is in the same order of magnitude as the NCuts loss." + ) + + def get_reconstruction_weight(self): + return float( + self.reconstruction_weight_choice.value() + / self.reconstruction_weight_divide_factor_choice.value() + ) diff --git a/napari_cellseg3d/config.py b/napari_cellseg3d/config.py index 84f6468c..72f8dfab 100644 --- a/napari_cellseg3d/config.py +++ b/napari_cellseg3d/config.py @@ -14,7 +14,7 @@ from napari_cellseg3d.code_models.models.model_TRAILMAP_MS import TRAILMAP_MS_ from napari_cellseg3d.code_models.models.model_VNet import VNet_ from napari_cellseg3d.code_models.models.model_WNet import WNet_ -from napari_cellseg3d.utils import LOGGER, remap_image +from napari_cellseg3d.utils import LOGGER logger = LOGGER @@ -24,10 +24,10 @@ MODEL_LIST = { "SegResNet": SegResNet_, "VNet": VNet_, - # "TRAILMAP": TRAILMAP, "TRAILMAP_MS": TRAILMAP_MS_, "SwinUNetR": SwinUNETR_, "WNet": WNet_, + # "TRAILMAP": TRAILMAP, # "test" : DO NOT USE, reserved for testing } @@ -232,7 +232,7 @@ class InferenceWorkerConfig: class DeterministicConfig: """Class to record deterministic config""" - enabled: bool = False + enabled: bool = True seed: int = 34936339 # default seed from NP_MAX @@ -256,7 +256,7 @@ class TrainingWorkerConfig: deterministic_config: DeterministicConfig = DeterministicConfig() scheduler_factor: float = 0.5 scheduler_patience: int = 10 - weights_info: WeightsInfo = None + weights_info: WeightsInfo = WeightsInfo() # data params results_path_folder: str = str(Path.home() / Path("cellseg3d/training")) sampling: bool = False @@ -287,6 +287,7 @@ class WNetTrainingWorkerConfig(TrainingWorkerConfig): dropout: float = 0.65 use_clipping: bool = False # use gradient clipping clipping: float = 1.0 # clipping value + weight_decay: float = 1e-5 # weight decay (used 0.01 historically) # NCuts loss params intensity_sigma: float = 1.0 spatial_sigma: float = 4.0 @@ -299,11 +300,10 @@ class WNetTrainingWorkerConfig(TrainingWorkerConfig): 0.5 / 100 ) # must be adjusted depending on images; compare to NCuts loss value # normalization params - normalizing_function: callable = remap_image + # normalizing_function: callable = remap_image # FIXME: call directly in worker, not a param # data params train_data_dict: dict = None eval_volume_dict: str = None - eval_num_patches: int = 10 ################ diff --git a/napari_cellseg3d/interface.py b/napari_cellseg3d/interface.py index e5f448f3..7d1ec7c5 100644 --- a/napari_cellseg3d/interface.py +++ b/napari_cellseg3d/interface.py @@ -1,4 +1,3 @@ -import contextlib import threading from functools import partial from typing import List, Optional @@ -36,7 +35,6 @@ # Local from napari_cellseg3d import utils -from napari_cellseg3d.config import WNetTrainingWorkerConfig """ User interface functions and aliases""" @@ -873,9 +871,7 @@ def __init__( self.build() self.check_ready() - - if self._required: - self._text_field.textChanged.connect(self.check_ready) + self.text_field.textChanged.connect(self.check_ready) def build(self): """Builds the layout of the widget""" @@ -914,11 +910,15 @@ def button(self): def check_ready(self): """Check if a path is correctly set""" - if self.text_field.text() in ["", self._initial_desc]: + if ( + self.text_field.text() in ["", self._initial_desc] + and self.required + ): self.update_field_color("indianred") self.text_field.setToolTip("Mandatory field !") return False self.update_field_color(f"{napari_param_darkgrey}") + self.text_field.setToolTip(f"{self.text_field.text()}") return True @property @@ -928,12 +928,6 @@ def required(self): @required.setter def required(self, is_required): """If set to True, will be colored red if incorrectly set""" - if is_required: - self.text_field.textChanged.connect(self.check_ready) - else: - with contextlib.suppress(TypeError): - self.text_field.textChanged.disconnect(self.check_ready) - self.check_ready() self._required = is_required @@ -1417,96 +1411,3 @@ def open_url(url): url (str): Url to be opened """ QDesktopServices.openUrl(QUrl(url, QUrl.TolerantMode)) - - -class WNetWidgets: - """A collection of widgets for the WNet training GUI""" - - default_config = WNetTrainingWorkerConfig() - - def __init__(self, parent): - self.num_classes_choice = DropdownMenu( - entries=["2", "3", "4"], - parent=parent, - text_label="Number of classes", - ) - self.intensity_sigma_choice = DoubleIncrementCounter( - lower=1.0, - upper=100.0, - default=self.default_config.intensity_sigma, - parent=parent, - text_label="Intensity sigma", - ) - self.intensity_sigma_choice.setMaximumWidth(20) - self.spatial_sigma_choice = DoubleIncrementCounter( - lower=1.0, - upper=100.0, - default=self.default_config.spatial_sigma, - parent=parent, - text_label="Spatial sigma", - ) - self.spatial_sigma_choice.setMaximumWidth(20) - self.radius_choice = IntIncrementCounter( - lower=1, - upper=5, - default=self.default_config.radius, - parent=parent, - text_label="Radius", - ) - self.radius_choice.setMaximumWidth(20) - self.loss_choice = DropdownMenu( - entries=["MSE", "BCE"], parent=parent, text_label="Loss function" - ) - self.ncuts_weight_choice = DoubleIncrementCounter( - lower=0.1, - upper=1.0, - default=self.default_config.n_cuts_weight, - parent=parent, - text_label="NCuts weight", - ) - self.reconstruction_weight_choice = DoubleIncrementCounter( - lower=0.1, - upper=1.0, - default=0.5, - parent=parent, - text_label="Reconstruction weight", - ) - self.reconstruction_weight_choice.setMaximumWidth(20) - self.reconstruction_weight_divide_factor_choice = IntIncrementCounter( - lower=1, - upper=10000, - default=100, - parent=parent, - text_label="Reconstruction weight divide factor", - ) - self.reconstruction_weight_divide_factor_choice.setMaximumWidth(20) - self.evaluation_patches_choice = Slider( - lower=1, - upper=100, - default=self.default_config.eval_num_patches, - parent=parent, - text_label="Number of patches for evaluation", - ) - - self._set_tooltips() - - def _set_tooltips(self): - self.num_classes_choice.setToolTip("Number of classes to segment") - self.intensity_sigma_choice.setToolTip( - "Intensity sigma for the NCuts loss" - ) - self.spatial_sigma_choice.setToolTip( - "Spatial sigma for the NCuts loss" - ) - self.radius_choice.setToolTip("Radius of NCuts loss region") - self.loss_choice.setToolTip("Loss function to use for reconstruction") - self.ncuts_weight_choice.setToolTip("Weight of the NCuts loss") - self.reconstruction_weight_choice.setToolTip( - "Weight of the reconstruction loss" - ) - self.reconstruction_weight_divide_factor_choice.setToolTip( - "Divide factor for the reconstruction loss.\nThis might have to be changed depending on your images.\nIf you notice that the reconstruction loss is too high, raise this factor until the\nreconstruction loss is in the same order of magnitude as the NCuts loss." - ) - self.evaluation_patches_choice.setToolTip( - "Number of patches to use for evaluation" - ) From ebaf5880b83304cfea9b82f8e1863ff7590a9ddc Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 27 Jul 2023 18:30:01 +0200 Subject: [PATCH 082/250] Fixes --- .../code_models/models/wnet/model.py | 68 +++++++++---------- .../code_models/worker_training.py | 5 +- .../code_plugins/plugin_model_training.py | 37 +++++++--- 3 files changed, 63 insertions(+), 47 deletions(-) diff --git a/napari_cellseg3d/code_models/models/wnet/model.py b/napari_cellseg3d/code_models/models/wnet/model.py index 0f9822cd..0bfe8851 100644 --- a/napari_cellseg3d/code_models/models/wnet/model.py +++ b/napari_cellseg3d/code_models/models/wnet/model.py @@ -98,25 +98,25 @@ def __init__( self.channels = channels self.max_pool = nn.MaxPool3d(2) self.in_b = InBlock(in_channels, self.channels[0], dropout=dropout) - # self.conv1 = Block(channels[0], self.channels[1], dropout=dropout) - # self.conv2 = Block(channels[1], self.channels[2], dropout=dropout) + self.conv1 = Block(channels[0], self.channels[1], dropout=dropout) + self.conv2 = Block(channels[1], self.channels[2], dropout=dropout) # self.conv3 = Block(channels[2], self.channels[3], dropout=dropout) # self.bot = Block(channels[3], self.channels[4], dropout=dropout) - # self.bot = Block(channels[2], self.channels[3], dropout=dropout) - self.bot = Block(channels[0], self.channels[1], dropout=dropout) + self.bot = Block(channels[2], self.channels[3], dropout=dropout) + # self.bot = Block(channels[0], self.channels[1], dropout=dropout) # self.deconv1 = Block(channels[4], self.channels[3], dropout=dropout) - # self.deconv2 = Block(channels[3], self.channels[2], dropout=dropout) - # self.deconv3 = Block(channels[2], self.channels[1], dropout=dropout) + self.deconv2 = Block(channels[3], self.channels[2], dropout=dropout) + self.deconv3 = Block(channels[2], self.channels[1], dropout=dropout) self.out_b = OutBlock(channels[1], out_channels, dropout=dropout) # self.conv_trans1 = nn.ConvTranspose3d( # self.channels[4], self.channels[3], 2, stride=2 # ) - # self.conv_trans2 = nn.ConvTranspose3d( - # self.channels[3], self.channels[2], 2, stride=2 - # ) - # self.conv_trans3 = nn.ConvTranspose3d( - # self.channels[2], self.channels[1], 2, stride=2 - # ) + self.conv_trans2 = nn.ConvTranspose3d( + self.channels[3], self.channels[2], 2, stride=2 + ) + self.conv_trans3 = nn.ConvTranspose3d( + self.channels[2], self.channels[1], 2, stride=2 + ) self.conv_trans_out = nn.ConvTranspose3d( self.channels[1], self.channels[0], 2, stride=2 ) @@ -127,12 +127,12 @@ def __init__( def forward(self, x): """Forward pass of the U-Net model.""" in_b = self.in_b(x) - # c1 = self.conv1(self.max_pool(in_b)) - # c2 = self.conv2(self.max_pool(c1)) + c1 = self.conv1(self.max_pool(in_b)) + c2 = self.conv2(self.max_pool(c1)) # c3 = self.conv3(self.max_pool(c2)) # x = self.bot(self.max_pool(c3)) - # x = self.bot(self.max_pool(c2)) - x = self.bot(self.max_pool(in_b)) + x = self.bot(self.max_pool(c2)) + # x = self.bot(self.max_pool(in_b)) # x = self.deconv1( # torch.cat( # [ @@ -142,24 +142,24 @@ def forward(self, x): # dim=1, # ) # ) - # x = self.deconv2( - # torch.cat( - # [ - # c2, - # self.conv_trans2(x), - # ], - # dim=1, - # ) - # ) - # x = self.deconv3( - # torch.cat( - # [ - # c1, - # self.conv_trans3(x), - # ], - # dim=1, - # ) - # ) + x = self.deconv2( + torch.cat( + [ + c2, + self.conv_trans2(x), + ], + dim=1, + ) + ) + x = self.deconv3( + torch.cat( + [ + c1, + self.conv_trans3(x), + ], + dim=1, + ) + ) x = self.out_b( torch.cat( [ diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 125466f9..144796dd 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -92,6 +92,7 @@ # 2. Create a custom worker for WNet training # 3. Adapt UI for WNet training (Advanced tab + model choice on first tab) # 4. Adapt plots and TrainingReport for WNet training +# 5. log_parameters function class TrainingWorkerBase(GeneratorWorker): @@ -408,7 +409,7 @@ def train(self): self.log(f"Using device: {device}") - self.log("Config:") + self.log("Config:") # FIXME log_parameters func instead [self.log(str(a)) for a in self.config.__dict__.items()] self.log("Initializing training...") @@ -773,7 +774,7 @@ def train(self): yield TrainingReport( epoch=epoch, loss_1_values={ - "Ncuts loss": ncuts_losses, + "SoftNcuts loss": ncuts_losses, "Dice metric": metric, }, loss_2_values=rec_losses, diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 17ca7b11..70e98d48 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -174,7 +174,7 @@ def __init__( } self.df = None - self.loss_1_values = [] + self.loss_1_values = {} self.loss_2_values = [] ########### @@ -1267,7 +1267,8 @@ def on_yield(self, report: TrainingReport): # TODO refactor for dict report.epoch == 0 or report.epoch + 1 == self.worker_config.validation_interval - ): + ) and len(self.result_layers) == 0: + self.result_layers = [] self._display_results(report.images_dict) else: self._display_results( @@ -1312,6 +1313,7 @@ def _make_csv(self): self.worker_config.validation_interval - 1, "", )[: len(size_column)] + self.df = pd.DataFrame( { "epoch": size_column, @@ -1324,13 +1326,25 @@ def _make_csv(self): logger.error(err) raise ValueError(err) else: - self.df = pd.DataFrame( - { - "epoch": size_column, - "Ncuts loss": self.loss_1_values, - "Reconstruction loss": self.loss_2_values, - } - ) + ncuts_loss = self.loss_1_values["SoftNCuts"] + try: + dice_metric = self.loss_1_values["Dice metric"] + self.df = pd.DataFrame( + { + "epoch": size_column, + "Ncuts loss": ncuts_loss, + "Dice metric": dice_metric, + "Reconstruction loss": self.loss_2_values, + } + ) + except KeyError: + self.df = pd.DataFrame( + { + "epoch": size_column, + "Ncuts loss": ncuts_loss, + "Reconstruction loss": self.loss_2_values, + } + ) path = Path(self.worker_config.results_path_folder) / Path( "training.csv" @@ -1410,6 +1424,7 @@ def update_loss_plot(self, loss_1: dict, loss_2: list): epoch = len(loss_1[list(loss_1.keys())[0]]) logger.debug(f"Updating loss plot for epoch {epoch}") + plot_max = self._is_current_job_supervised() if epoch < self.worker_config.validation_interval * 2: return if epoch == self.worker_config.validation_interval * 2: @@ -1453,13 +1468,13 @@ def update_loss_plot(self, loss_1: dict, loss_2: list): logger.error( "Plot dock widget could not be added. Should occur in testing only" ) - self._plot_loss(loss_1, loss_2) + self._plot_loss(loss_1, loss_2, show_plot_2_max=plot_max) else: with plt.style.context("dark_background"): self.plot_1.cla() self.plot_2.cla() - self._plot_loss(loss_1, loss_2) + self._plot_loss(loss_1, loss_2, show_plot_2_max=plot_max) def _reset_loss_plot(self): if self.plot_1 is not None and self.plot_2 is not None: From 2618ae15d7cb89ea6546c128595f7b4529db3129 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Fri, 28 Jul 2023 10:43:14 +0200 Subject: [PATCH 083/250] Test fixes --- .../_tests/test_model_framework.py | 2 +- ...raining.py => test_supervised_training.py} | 40 ---------------- .../_tests/test_unsup_training.py | 44 +++++++++++++++++ .../code_models/models/wnet/model.py | 36 +++++++------- .../code_models/worker_training.py | 41 +++++++++++----- .../code_plugins/plugin_model_training.py | 47 +++++++++++++++++-- 6 files changed, 136 insertions(+), 74 deletions(-) rename napari_cellseg3d/_tests/{test_training.py => test_supervised_training.py} (72%) create mode 100644 napari_cellseg3d/_tests/test_unsup_training.py diff --git a/napari_cellseg3d/_tests/test_model_framework.py b/napari_cellseg3d/_tests/test_model_framework.py index 0a078273..1cb86569 100644 --- a/napari_cellseg3d/_tests/test_model_framework.py +++ b/napari_cellseg3d/_tests/test_model_framework.py @@ -35,8 +35,8 @@ def test_update_default(make_napari_viewer_proxy): assert widget._default_path == [ pth("C:/test/test"), pth("C:/dataset/labels"), - pth("D:/dataset/res"), None, + pth("D:/dataset/res"), ] diff --git a/napari_cellseg3d/_tests/test_training.py b/napari_cellseg3d/_tests/test_supervised_training.py similarity index 72% rename from napari_cellseg3d/_tests/test_training.py rename to napari_cellseg3d/_tests/test_supervised_training.py index c5737f11..2ce1ee03 100644 --- a/napari_cellseg3d/_tests/test_training.py +++ b/napari_cellseg3d/_tests/test_supervised_training.py @@ -31,20 +31,6 @@ def test_create_supervised_worker_from_config(make_napari_viewer_proxy): ) -def test_create_unspervised_worker_from_config(make_napari_viewer_proxy): - widget = Trainer(make_napari_viewer_proxy()) - widget.model_choice.setCurrentText("WNet") - widget._toggle_unsupervised_mode(enabled=True) - default_config = config.WNetTrainingWorkerConfig() - worker = widget._create_worker() - excluded = ["results_path_folder", "sample_size", "weights_info"] - for attr in dir(default_config): - if not attr.startswith("__") and attr not in excluded: - assert getattr(default_config, attr) == getattr( - worker.config, attr - ) - - def test_update_loss_plot(make_napari_viewer_proxy): view = make_napari_viewer_proxy() widget = Trainer(view) @@ -135,29 +121,3 @@ def test_training(make_napari_viewer_proxy, qtbot): widget.on_yield(res) assert widget.loss_1_values["loss"] == [1, 1, 1, 1] assert widget.loss_2_values == [1, 1, 1, 1] - - -def test_unsupervised_worker(make_napari_viewer_proxy): - viewer = make_napari_viewer_proxy() - widget = Trainer(viewer) - - widget.model_choice.setCurrentText("WNet") - widget._toggle_unsupervised_mode(enabled=True) - - widget.unsupervised_images_filewidget.text_field.setText( - str(im_path.parent) - ) - widget.data = widget.create_dataset_dict_no_labs() - worker = widget._create_worker() - dataloader, eval_dataloader, data_shape = worker._get_data() - assert eval_dataloader is None - assert data_shape == (6, 6, 6) - - widget.images_filepaths = [str(im_path.parent)] - widget.labels_filepaths = [str(im_path.parent)] - widget.unsupervised_eval_data = widget.create_train_dataset_dict() - assert widget.unsupervised_eval_data is not None - worker = widget._create_worker() - dataloader, eval_dataloader, data_shape = worker._get_data() - assert eval_dataloader is not None - assert data_shape == (6, 6, 6) diff --git a/napari_cellseg3d/_tests/test_unsup_training.py b/napari_cellseg3d/_tests/test_unsup_training.py new file mode 100644 index 00000000..163b2a3d --- /dev/null +++ b/napari_cellseg3d/_tests/test_unsup_training.py @@ -0,0 +1,44 @@ +from pathlib import Path + +from napari_cellseg3d import config +from napari_cellseg3d.code_plugins.plugin_model_training import ( + Trainer, +) + +im_path = Path(__file__).resolve().parent / "res/test.tif" +im_path_str = str(im_path) + + +def test_unsupervised_worker(make_napari_viewer_proxy): + unsup_viewer = make_napari_viewer_proxy() + widget = Trainer(viewer=unsup_viewer) + + widget.model_choice.setCurrentText("WNet") + widget._toggle_unsupervised_mode(enabled=True) + + default_config = config.WNetTrainingWorkerConfig() + worker = widget._create_worker(additional_results_description="TEST_1") + excluded = ["results_path_folder", "sample_size", "weights_info"] + for attr in dir(default_config): + if not attr.startswith("__") and attr not in excluded: + assert getattr(default_config, attr) == getattr( + worker.config, attr + ) + + widget.unsupervised_images_filewidget.text_field.setText( + str(im_path.parent) + ) + widget.data = widget.create_dataset_dict_no_labs() + worker = widget._create_worker(additional_results_description="TEST_2") + dataloader, eval_dataloader, data_shape = worker._get_data() + assert eval_dataloader is None + assert data_shape == (6, 6, 6) + + widget.images_filepaths = [str(im_path.parent)] + widget.labels_filepaths = [str(im_path.parent)] + widget.unsupervised_eval_data = widget.create_train_dataset_dict() + assert widget.unsupervised_eval_data is not None + worker = widget._create_worker(additional_results_description="TEST_3") + dataloader, eval_dataloader, data_shape = worker._get_data() + assert eval_dataloader is not None + assert data_shape == (6, 6, 6) diff --git a/napari_cellseg3d/code_models/models/wnet/model.py b/napari_cellseg3d/code_models/models/wnet/model.py index 0bfe8851..989ae3b7 100644 --- a/napari_cellseg3d/code_models/models/wnet/model.py +++ b/napari_cellseg3d/code_models/models/wnet/model.py @@ -99,21 +99,22 @@ def __init__( self.max_pool = nn.MaxPool3d(2) self.in_b = InBlock(in_channels, self.channels[0], dropout=dropout) self.conv1 = Block(channels[0], self.channels[1], dropout=dropout) - self.conv2 = Block(channels[1], self.channels[2], dropout=dropout) + # self.conv2 = Block(channels[1], self.channels[2], dropout=dropout) # self.conv3 = Block(channels[2], self.channels[3], dropout=dropout) # self.bot = Block(channels[3], self.channels[4], dropout=dropout) - self.bot = Block(channels[2], self.channels[3], dropout=dropout) + # self.bot = Block(channels[2], self.channels[3], dropout=dropout) + self.bot = Block(channels[1], self.channels[2], dropout=dropout) # self.bot = Block(channels[0], self.channels[1], dropout=dropout) # self.deconv1 = Block(channels[4], self.channels[3], dropout=dropout) - self.deconv2 = Block(channels[3], self.channels[2], dropout=dropout) + # self.deconv2 = Block(channels[3], self.channels[2], dropout=dropout) self.deconv3 = Block(channels[2], self.channels[1], dropout=dropout) self.out_b = OutBlock(channels[1], out_channels, dropout=dropout) # self.conv_trans1 = nn.ConvTranspose3d( # self.channels[4], self.channels[3], 2, stride=2 # ) - self.conv_trans2 = nn.ConvTranspose3d( - self.channels[3], self.channels[2], 2, stride=2 - ) + # self.conv_trans2 = nn.ConvTranspose3d( + # self.channels[3], self.channels[2], 2, stride=2 + # ) self.conv_trans3 = nn.ConvTranspose3d( self.channels[2], self.channels[1], 2, stride=2 ) @@ -128,10 +129,11 @@ def forward(self, x): """Forward pass of the U-Net model.""" in_b = self.in_b(x) c1 = self.conv1(self.max_pool(in_b)) - c2 = self.conv2(self.max_pool(c1)) + # c2 = self.conv2(self.max_pool(c1)) # c3 = self.conv3(self.max_pool(c2)) # x = self.bot(self.max_pool(c3)) - x = self.bot(self.max_pool(c2)) + # x = self.bot(self.max_pool(c2)) + x = self.bot(self.max_pool(c1)) # x = self.bot(self.max_pool(in_b)) # x = self.deconv1( # torch.cat( @@ -142,15 +144,15 @@ def forward(self, x): # dim=1, # ) # ) - x = self.deconv2( - torch.cat( - [ - c2, - self.conv_trans2(x), - ], - dim=1, - ) - ) + # x = self.deconv2( + # torch.cat( + # [ + # c2, + # self.conv_trans2(x), + # ], + # dim=1, + # ) + # ) x = self.deconv3( torch.cat( [ diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 144796dd..d98c6ecf 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -643,23 +643,38 @@ def train(self): # wandb.log({"learning_rate encoder": optimizerE.param_groups[0]["lr"]}) # wandb.log({"learning_rate model": optimizer.param_groups[0]["lr"]}) - self.log("Ncuts loss: " + str(ncuts_losses[-1])) + # self.log("Ncuts loss: " + str(ncuts_losses[-1])) + # if epoch > 0: + # self.log( + # "Ncuts loss difference: " + # + str(ncuts_losses[-1] - ncuts_losses[-2]) + # ) + # self.log("Reconstruction loss: " + str(rec_losses[-1])) + # if epoch > 0: + # self.log( + # "Reconstruction loss difference: " + # + str(rec_losses[-1] - rec_losses[-2]) + # ) + # self.log("Sum of losses: " + str(total_losses[-1])) + # if epoch > 0: + # self.log( + # "Sum of losses difference: " + # + str(total_losses[-1] - total_losses[-2]), + # ) + + # show losses and differences with 5 points precision + self.log(f"Ncuts loss: {ncuts_losses[-1]:.5f}") + self.log(f"Reconstruction loss: {rec_losses[-1]:.5f}") + self.log(f"Sum of losses: {total_losses[-1]:.5f}") if epoch > 0: - self.log( - "Ncuts loss difference: " - + str(ncuts_losses[-1] - ncuts_losses[-2]) + self.lof( + f"Ncuts loss difference: {ncuts_losses[-1] - ncuts_losses[-2]:.5f}" ) - self.log("Reconstruction loss: " + str(rec_losses[-1])) - if epoch > 0: self.log( - "Reconstruction loss difference: " - + str(rec_losses[-1] - rec_losses[-2]) + f"Reconstruction loss difference: {rec_losses[-1] - rec_losses[-2]:.5f}" ) - self.log("Sum of losses: " + str(total_losses[-1])) - if epoch > 0: self.log( - "Sum of losses difference: " - + str(total_losses[-1] - total_losses[-2]), + f"Sum of losses difference: {total_losses[-1] - total_losses[-2]:.5f}" ) # Update the learning rate @@ -774,7 +789,7 @@ def train(self): yield TrainingReport( epoch=epoch, loss_1_values={ - "SoftNcuts loss": ncuts_losses, + "SoftNCuts loss": ncuts_losses, "Dice metric": metric, }, loss_2_values=rec_losses, diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 70e98d48..a956bd3c 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -1009,8 +1009,10 @@ def _create_unsupervised_worker_from_config( ): return WNetTrainingWorker(worker_config=worker_config) - def _create_worker(self): - self._set_worker_config() + def _create_worker(self, additional_results_description=None): + self._set_worker_config( + additional_description=additional_results_description + ) if self.unsupervised_mode: return self._create_unsupervised_worker_from_config( self.worker_config @@ -1019,7 +1021,15 @@ def _create_worker(self): def _set_worker_config( self, + additional_description=None, ) -> config.TrainingWorkerConfig: + """Creates a worker config for supervised or unsupervised training + Args: + additional_description: Additional description to add to the results folder name + + Returns: + A worker config + """ logger.debug("Loading config...") model_config = config.ModelInfo(name=self.model_choice.currentText()) @@ -1033,10 +1043,21 @@ def _set_worker_config( seed=self.box_seed.value(), ) + loss_name = ( + (f"{self.loss_choice.currentText()}_") + if not self.unsupervised_mode + else "" + ) + additional_description = ( + (f"{additional_description}_") + if additional_description is not None + else "" + ) results_path_folder = Path( self.results_path + f"/{model_config.name}_" - + f"{self.loss_choice.currentText()}_" + + additional_description + + loss_name + f"{self.epoch_choice.value()}e_" + f"{utils.get_date_time()}" ) @@ -1072,6 +1093,16 @@ def _set_supervised_worker_config( patch_size, deterministic_config, ): + """Sets the worker config for supervised training + Args: + model_config: Model config + results_path_folder: Path to results folder + patch_size: Patch size + deterministic_config: Deterministic config + + Returns: + A worker config + """ validation_percent = self.validation_percent_choice.slider_value / 100 self.worker_config = config.SupervisedTrainingWorkerConfig( device=self.check_device_choice(), @@ -1103,6 +1134,16 @@ def _set_unsupervised_worker_config( deterministic_config, eval_volume_dict, ) -> config.WNetTrainingWorkerConfig: + """Sets the worker config for unsupervised training + Args: + results_path_folder: Path to results folder + patch_size: Patch size + deterministic_config: Deterministic config + eval_volume_dict: Evaluation volume dictionary + + Returns: + A worker config + """ self.worker_config = config.WNetTrainingWorkerConfig( device=self.check_device_choice(), weights_info=self.weights_config, From a4dddb89a9f5e1bacb5ae81a8f3371e51edc79b5 Mon Sep 17 00:00:00 2001 From: Cyril Achard <94955160+C-Achard@users.noreply.github.com> Date: Fri, 28 Jul 2023 11:06:55 +0200 Subject: [PATCH 084/250] Temp fix for CRF (#46) --- .github/workflows/test_and_deploy.yml | 2 +- napari_cellseg3d/code_models/crf.py | 2 +- pyproject.toml | 4 ++-- tox.ini | 2 +- 4 files changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/test_and_deploy.yml b/.github/workflows/test_and_deploy.yml index 406bf4f5..fafb1719 100644 --- a/.github/workflows/test_and_deploy.yml +++ b/.github/workflows/test_and_deploy.yml @@ -51,7 +51,7 @@ jobs: run: | python -m pip install --upgrade pip python -m pip install setuptools tox tox-gh-actions -# pip install git+https://github.com/lucasb-eyer/pydensecrf.git@master#egg=pydensecrf +# pip install git+https://github.com/kodalli/pydensecrf.git@master#egg=pydensecrf # this runs the platform-specific tests declared in tox.ini - name: Test with tox diff --git a/napari_cellseg3d/code_models/crf.py b/napari_cellseg3d/code_models/crf.py index b362246a..79951fc5 100644 --- a/napari_cellseg3d/code_models/crf.py +++ b/napari_cellseg3d/code_models/crf.py @@ -7,7 +7,7 @@ Philipp Krähenbühl and Vladlen Koltun NIPS 2011 -Implemented using the pydense libary available at https://github.com/lucasb-eyer/pydensecrf. +Implemented using the pydense libary available at https://github.com/kodalli/pydensecrf. """ from warnings import warn diff --git a/pyproject.toml b/pyproject.toml index 40450f9b..5d5be93b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -120,7 +120,7 @@ line_length = 79 [project.optional-dependencies] crf = [ - "pydensecrf@git+https://github.com/lucasb-eyer/pydensecrf.git#egg=master", + "pydensecrf@git+https://github.com/kodalli/pydensecrf.git#egg=master", ] dev = [ "isort", @@ -142,7 +142,7 @@ test = [ "coverage", "tox", "twine", - "pydensecrf@git+https://github.com/lucasb-eyer/pydensecrf.git#egg=master", + "pydensecrf@git+https://github.com/kodalli/pydensecrf.git#egg=master", ] onnx-cpu = [ "onnx", diff --git a/tox.ini b/tox.ini index b8c76091..195b0dff 100644 --- a/tox.ini +++ b/tox.ini @@ -34,7 +34,7 @@ deps = magicgui pytest-qt qtpy - git+https://github.com/lucasb-eyer/pydensecrf.git@master#egg=pydensecrf + git+https://github.com/kodalli/pydensecrf.git@master#egg=pydensecrf onnx onnxruntime ; pyopencl[pocl] From 1041090cf3e50e72c675471ab4a7e68592103780 Mon Sep 17 00:00:00 2001 From: Cyril Achard <94955160+C-Achard@users.noreply.github.com> Date: Fri, 28 Jul 2023 11:06:55 +0200 Subject: [PATCH 085/250] Temp fix for CRF (#46) --- .github/workflows/test_and_deploy.yml | 2 +- napari_cellseg3d/code_models/crf.py | 2 +- pyproject.toml | 4 ++-- tox.ini | 2 +- 4 files changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/test_and_deploy.yml b/.github/workflows/test_and_deploy.yml index 406bf4f5..fafb1719 100644 --- a/.github/workflows/test_and_deploy.yml +++ b/.github/workflows/test_and_deploy.yml @@ -51,7 +51,7 @@ jobs: run: | python -m pip install --upgrade pip python -m pip install setuptools tox tox-gh-actions -# pip install git+https://github.com/lucasb-eyer/pydensecrf.git@master#egg=pydensecrf +# pip install git+https://github.com/kodalli/pydensecrf.git@master#egg=pydensecrf # this runs the platform-specific tests declared in tox.ini - name: Test with tox diff --git a/napari_cellseg3d/code_models/crf.py b/napari_cellseg3d/code_models/crf.py index b362246a..79951fc5 100644 --- a/napari_cellseg3d/code_models/crf.py +++ b/napari_cellseg3d/code_models/crf.py @@ -7,7 +7,7 @@ Philipp Krähenbühl and Vladlen Koltun NIPS 2011 -Implemented using the pydense libary available at https://github.com/lucasb-eyer/pydensecrf. +Implemented using the pydense libary available at https://github.com/kodalli/pydensecrf. """ from warnings import warn diff --git a/pyproject.toml b/pyproject.toml index 40450f9b..5d5be93b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -120,7 +120,7 @@ line_length = 79 [project.optional-dependencies] crf = [ - "pydensecrf@git+https://github.com/lucasb-eyer/pydensecrf.git#egg=master", + "pydensecrf@git+https://github.com/kodalli/pydensecrf.git#egg=master", ] dev = [ "isort", @@ -142,7 +142,7 @@ test = [ "coverage", "tox", "twine", - "pydensecrf@git+https://github.com/lucasb-eyer/pydensecrf.git#egg=master", + "pydensecrf@git+https://github.com/kodalli/pydensecrf.git#egg=master", ] onnx-cpu = [ "onnx", diff --git a/tox.ini b/tox.ini index b8c76091..195b0dff 100644 --- a/tox.ini +++ b/tox.ini @@ -34,7 +34,7 @@ deps = magicgui pytest-qt qtpy - git+https://github.com/lucasb-eyer/pydensecrf.git@master#egg=pydensecrf + git+https://github.com/kodalli/pydensecrf.git@master#egg=pydensecrf onnx onnxruntime ; pyopencl[pocl] From e4b51d4fe8a0b867673fbb96f5035b66c75ecfa3 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Fri, 28 Jul 2023 11:51:23 +0200 Subject: [PATCH 086/250] Minor fixes --- napari_cellseg3d/_tests/test_supervised_training.py | 1 + napari_cellseg3d/code_models/models/wnet/soft_Ncuts.py | 5 +++-- napari_cellseg3d/code_models/worker_training.py | 8 ++++---- napari_cellseg3d/code_plugins/plugin_model_training.py | 2 +- 4 files changed, 9 insertions(+), 7 deletions(-) diff --git a/napari_cellseg3d/_tests/test_supervised_training.py b/napari_cellseg3d/_tests/test_supervised_training.py index 2ce1ee03..7a5b1d3e 100644 --- a/napari_cellseg3d/_tests/test_supervised_training.py +++ b/napari_cellseg3d/_tests/test_supervised_training.py @@ -15,6 +15,7 @@ def test_create_supervised_worker_from_config(make_napari_viewer_proxy): widget = Trainer(make_napari_viewer_proxy()) + widget.device_choice.setCurrentIndex(0) worker = widget._create_worker() default_config = config.SupervisedTrainingWorkerConfig() excluded = [ diff --git a/napari_cellseg3d/code_models/models/wnet/soft_Ncuts.py b/napari_cellseg3d/code_models/models/wnet/soft_Ncuts.py index db049526..1885ccea 100644 --- a/napari_cellseg3d/code_models/models/wnet/soft_Ncuts.py +++ b/napari_cellseg3d/code_models/models/wnet/soft_Ncuts.py @@ -5,13 +5,14 @@ """ import math - import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from scipy.stats import norm +from napari_cellseg3d.utils import LOGGER as logger + __author__ = "Yves Paychère, Colin Hofmann, Cyril Achard" __credits__ = [ "Yves Paychère", @@ -54,7 +55,7 @@ def __init__( self.W, self.D, ) - print(f"Radius set to {self.radius}") + logger.info(f"Radius set to {self.radius}") def forward(self, labels, inputs): """Forward pass of the Soft N-Cuts loss. diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index d98c6ecf..85796aff 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -205,7 +205,7 @@ def get_patch_dataset(self, train_transforms): return self.config.sample_size, dataset - def get_patch_dataset_eval(self, eval_dataset_dict): + def get_dataset_eval(self, eval_dataset_dict): eval_transforms = Compose( [ LoadImaged(keys=["image", "label"], image_only=True), @@ -373,7 +373,7 @@ def _get_data(self): ) if self.config.eval_volume_dict is not None: - eval_dataset = self.get_dataset(train_transforms) + eval_dataset = self.get_dataset_eval(train_transforms) eval_dataloader = DataLoader( eval_dataset, @@ -620,7 +620,7 @@ def train(self): "data": dec_out, "cmap": "gist_earth", }, - "Input image": {"data": image, "cmap": "inferno"}, + "Input image": {"data": np.squeeze(image), "cmap": "inferno"}, } yield TrainingReport( @@ -667,7 +667,7 @@ def train(self): self.log(f"Reconstruction loss: {rec_losses[-1]:.5f}") self.log(f"Sum of losses: {total_losses[-1]:.5f}") if epoch > 0: - self.lof( + self.log( f"Ncuts loss difference: {ncuts_losses[-1] - ncuts_losses[-2]:.5f}" ) self.log( diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index a956bd3c..91fb7ebd 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -1450,7 +1450,7 @@ def _plot_loss( label="Maximum Dice coeff.", zorder=5, ) - self.plot_2.legend(facecolor=ui.napari_grey, loc="lower right") + self.plot_2.legend(facecolor=ui.napari_grey, loc="lower right") self.canvas.draw_idle() def update_loss_plot(self, loss_1: dict, loss_2: list): From d0a190d0c005c8b4d50c53d9eef6deeb09dee845 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Fri, 28 Jul 2023 13:05:08 +0200 Subject: [PATCH 087/250] Tests & training --- .../_tests/test_supervised_training.py | 5 +++-- .../_tests/test_unsup_training.py | 14 ++++++++------ .../code_models/worker_training.py | 19 ++++++++++++------- 3 files changed, 23 insertions(+), 15 deletions(-) diff --git a/napari_cellseg3d/_tests/test_supervised_training.py b/napari_cellseg3d/_tests/test_supervised_training.py index 7a5b1d3e..676133ff 100644 --- a/napari_cellseg3d/_tests/test_supervised_training.py +++ b/napari_cellseg3d/_tests/test_supervised_training.py @@ -12,9 +12,10 @@ im_path = Path(__file__).resolve().parent / "res/test.tif" im_path_str = str(im_path) - def test_create_supervised_worker_from_config(make_napari_viewer_proxy): - widget = Trainer(make_napari_viewer_proxy()) + + viewer = make_napari_viewer_proxy() + widget = Trainer(viewer=viewer) widget.device_choice.setCurrentIndex(0) worker = widget._create_worker() default_config = config.SupervisedTrainingWorkerConfig() diff --git a/napari_cellseg3d/_tests/test_unsup_training.py b/napari_cellseg3d/_tests/test_unsup_training.py index 163b2a3d..3ebd4768 100644 --- a/napari_cellseg3d/_tests/test_unsup_training.py +++ b/napari_cellseg3d/_tests/test_unsup_training.py @@ -5,13 +5,13 @@ Trainer, ) -im_path = Path(__file__).resolve().parent / "res/test.tif" -im_path_str = str(im_path) - - def test_unsupervised_worker(make_napari_viewer_proxy): + im_path = Path(__file__).resolve().parent / "res/test.tif" + # im_path_str = str(im_path) + unsup_viewer = make_napari_viewer_proxy() widget = Trainer(viewer=unsup_viewer) + widget.device_choice.setCurrentIndex(0) widget.model_choice.setCurrentText("WNet") widget._toggle_unsupervised_mode(enabled=True) @@ -36,9 +36,11 @@ def test_unsupervised_worker(make_napari_viewer_proxy): widget.images_filepaths = [str(im_path.parent)] widget.labels_filepaths = [str(im_path.parent)] - widget.unsupervised_eval_data = widget.create_train_dataset_dict() - assert widget.unsupervised_eval_data is not None + # widget.unsupervised_eval_data = widget.create_train_dataset_dict() worker = widget._create_worker(additional_results_description="TEST_3") dataloader, eval_dataloader, data_shape = worker._get_data() + assert widget.unsupervised_eval_data is not None assert eval_dataloader is not None + assert widget.unsupervised_eval_data[0]["image"] is not None + assert widget.unsupervised_eval_data[0]["label"] is not None assert data_shape == (6, 6, 6) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 85796aff..b28f8285 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -208,7 +208,7 @@ def get_patch_dataset(self, train_transforms): def get_dataset_eval(self, eval_dataset_dict): eval_transforms = Compose( [ - LoadImaged(keys=["image", "label"], image_only=True), + LoadImaged(keys=["image", "label"]), EnsureChannelFirstd( keys=["image", "label"], channel_dim="no_channel" ), @@ -373,7 +373,7 @@ def _get_data(self): ) if self.config.eval_volume_dict is not None: - eval_dataset = self.get_dataset_eval(train_transforms) + eval_dataset = self.get_dataset_eval(self.config.eval_volume_dict) eval_dataloader = DataLoader( eval_dataset, @@ -617,7 +617,7 @@ def train(self): "cmap": "turbo", }, "Decoder output": { - "data": dec_out, + "data": np.squeeze(dec_out), "cmap": "gist_earth", }, "Input image": {"data": np.squeeze(image), "cmap": "inferno"}, @@ -766,22 +766,27 @@ def train(self): if WANDB_INSTALLED: # log validation dice score for each validation round wandb.log({"val/dice_metric": metric}) + + dec_out_val = val_decoder_outputs[0].detach().cpu().numpy() + enc_out_val = val_outputs[0].detach().cpu().numpy() + lab_out_val = val_labels[0].detach().cpu().numpy() + val_in = val_inputs[0].detach.cpu().nummpy() display_dict = { "Decoder output": { - "data": val_decoder_outputs[0], + "data": np.squeeze(dec_out_val), "cmap": "gist_earth", }, "Encoder output": { - "data": val_outputs[0], + "data": enc_out_val, "cmap": "turbo", }, "Labels": { - "data": val_labels[0], + "data": lab_out_val, "cmap": "bop blue", }, "Inputs": { - "data": val_inputs[0], + "data": val_in, "cmap": "inferno", }, } From 06133cb3e8de668db32b40dd411a31016186d1b5 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Fri, 28 Jul 2023 14:14:10 +0200 Subject: [PATCH 088/250] Fix tests + new weights --- napari_cellseg3d/_tests/test_inference.py | 17 +++++++++++++---- .../code_models/models/model_SegResNet.py | 2 +- .../code_models/models/model_SwinUNetR.py | 2 +- .../code_models/models/model_TRAILMAP_MS.py | 2 +- .../code_models/models/model_VNet.py | 2 +- .../pretrained/pretrained_model_urls.json | 8 ++++---- 6 files changed, 21 insertions(+), 12 deletions(-) diff --git a/napari_cellseg3d/_tests/test_inference.py b/napari_cellseg3d/_tests/test_inference.py index 336630f5..2fe996bf 100644 --- a/napari_cellseg3d/_tests/test_inference.py +++ b/napari_cellseg3d/_tests/test_inference.py @@ -66,18 +66,27 @@ def test_inference_on_folder(): config.images_filepaths = [ str(Path(__file__).resolve().parent / "res/test.tif") ] + config.sliding_window_config.window_size = 64 - def mock_work(x): - return x + class mock_work: + def __call__(self, x): + return x + + def eval(self): + return None worker = InferenceWorker(worker_config=config) - worker.aniso_transform = mock_work + worker.aniso_transform = mock_work() image = torch.Tensor(rand_gen.random((1, 1, 64, 64, 64))) res = worker.inference_on_folder( - {"image": image}, 0, model=mock_work, post_process_transforms=mock_work + {"image": image}, + 0, + model=mock_work(), + post_process_transforms=mock_work(), ) assert isinstance(res, InferenceResult) + assert res.result is not None def test_post_processing(): diff --git a/napari_cellseg3d/code_models/models/model_SegResNet.py b/napari_cellseg3d/code_models/models/model_SegResNet.py index 60b74d64..99f8cbfc 100644 --- a/napari_cellseg3d/code_models/models/model_SegResNet.py +++ b/napari_cellseg3d/code_models/models/model_SegResNet.py @@ -3,7 +3,7 @@ class SegResNet_(SegResNetVAE): use_default_training = True - weights_file = "SegResNet.pth" + weights_file = "SegResNet_latest.pth" def __init__( self, input_img_size, out_channels=1, dropout_prob=0.3, **kwargs diff --git a/napari_cellseg3d/code_models/models/model_SwinUNetR.py b/napari_cellseg3d/code_models/models/model_SwinUNetR.py index 0dbf0be5..bce316e8 100644 --- a/napari_cellseg3d/code_models/models/model_SwinUNetR.py +++ b/napari_cellseg3d/code_models/models/model_SwinUNetR.py @@ -7,7 +7,7 @@ class SwinUNETR_(SwinUNETR): use_default_training = True - weights_file = "Swin64_best_metric.pth" + weights_file = "SwinUNetR_latest.pth" def __init__( self, diff --git a/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py b/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py index bc8e43d5..4ee971e2 100644 --- a/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py +++ b/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py @@ -4,7 +4,7 @@ class TRAILMAP_MS_(UNet3D): use_default_training = True - weights_file = "TRAILMAP_MS_best_metric_epoch_26.pth" + weights_file = "TRAILMAP_MS_best_metric.pth" # original model from Liqun Luo lab, transferred to pytorch and trained on mesoSPIM-acquired data (mostly TPH2 as of July 2022) diff --git a/napari_cellseg3d/code_models/models/model_VNet.py b/napari_cellseg3d/code_models/models/model_VNet.py index 4e375a11..8fe18e2b 100644 --- a/napari_cellseg3d/code_models/models/model_VNet.py +++ b/napari_cellseg3d/code_models/models/model_VNet.py @@ -3,7 +3,7 @@ class VNet_(VNet): use_default_training = True - weights_file = "VNet_40e.pth" + weights_file = "VNet_latest.pth" def __init__(self, in_channels=1, out_channels=1, **kwargs): try: diff --git a/napari_cellseg3d/code_models/models/pretrained/pretrained_model_urls.json b/napari_cellseg3d/code_models/models/pretrained/pretrained_model_urls.json index b235a550..3c393d47 100644 --- a/napari_cellseg3d/code_models/models/pretrained/pretrained_model_urls.json +++ b/napari_cellseg3d/code_models/models/pretrained/pretrained_model_urls.json @@ -1,8 +1,8 @@ { - "TRAILMAP_MS": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/TRAILMAP_MS.tar.gz", - "SegResNet": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/SegResNet.tar.gz", - "VNet": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/VNet.tar.gz", - "SwinUNetR": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/Swin64.tar.gz", + "TRAILMAP_MS": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/TRAILMAP_latest.tar.gz", + "SegResNet": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/SegResNet_latest.tar.gz", + "VNet": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/VNet_latest.tar.gz", + "SwinUNetR": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/SwinUNetR_latest.tar.gz", "WNet": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/wnet.tar.gz", "WNet_ONNX": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/wnet_onnx.tar.gz", "test": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/test.tar.gz" From af1188bd3e13503edb9f775122a3d332868763d9 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Fri, 28 Jul 2023 14:56:11 +0200 Subject: [PATCH 089/250] Fixes for UI and tests - Fix SegResNet input dims in test - Fixed all tests - Fixed issues with window inference settings for WNet in UI --- napari_cellseg3d/_tests/test_models.py | 4 +-- .../code_plugins/plugin_model_inference.py | 26 +++++-------------- 2 files changed, 9 insertions(+), 21 deletions(-) diff --git a/napari_cellseg3d/_tests/test_models.py b/napari_cellseg3d/_tests/test_models.py index a9176fa4..e0956c4c 100644 --- a/napari_cellseg3d/_tests/test_models.py +++ b/napari_cellseg3d/_tests/test_models.py @@ -27,7 +27,7 @@ def test_model_list(): for model_name in MODEL_LIST: # if model_name=="test": # continue - dims = 128 + dims = 64 test = MODEL_LIST[model_name]( input_img_size=[dims, dims, dims], in_channels=1, @@ -115,7 +115,7 @@ def test_pretrained_weights_compatibility(): for model_name in MODEL_LIST: file_name = MODEL_LIST[model_name].weights_file WeightsDownloader().download_weights(model_name, file_name) - model = MODEL_LIST[model_name](input_img_size=[128, 128, 128]) + model = MODEL_LIST[model_name](input_img_size=[64, 64, 64]) try: model.load_state_dict( torch.load( diff --git a/napari_cellseg3d/code_plugins/plugin_model_inference.py b/napari_cellseg3d/code_plugins/plugin_model_inference.py index 4bb20231..99230303 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_inference.py +++ b/napari_cellseg3d/code_plugins/plugin_model_inference.py @@ -112,7 +112,7 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): ###################### # TODO : better way to handle SegResNet size reqs ? self.model_input_size = ui.IntIncrementCounter( - lower=1, upper=1024, default=128, text_label="\nModel input size" + lower=1, upper=1024, default=64, text_label="\nModel input size" ) self.model_choice.currentIndexChanged.connect( self._toggle_display_model_input_size @@ -155,16 +155,7 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): sizes_window = ["8", "16", "32", "64", "128", "256", "512"] self._default_window_size = sizes_window.index("64") - # ( - # self.window_size_choice, - # self.window_size_choice.label, - # ) = ui.make_combobox(sizes_window, label="Window size and overlap") - # self.window_overlap = ui.make_n_spinboxes( - # max=1, - # default=0.7, - # step=0.05, - # double=True, - # ) + self.wnet_enabled = False self.window_size_choice = ui.DropdownMenu( sizes_window, text_label="Window size" @@ -310,16 +301,13 @@ def check_ready(self): def _restrict_window_size_for_model(self): """Sets the window size to a value that is compatible with the chosen model""" + self.wnet_enabled = False if self.model_choice.currentText() == "WNet": self.window_size_choice.setCurrentIndex(self._default_window_size) - self.window_size_choice.setDisabled(True) - self.window_infer_box.setChecked(True) - self.window_infer_box.setDisabled(True) - else: - self.window_size_choice.setDisabled(False) - self.window_infer_box.setDisabled(False) - self.window_infer_box.setChecked(True) - self.window_size_choice.setCurrentIndex(self._default_window_size) + self.wnet_enabled = True + self.window_infer_box.setChecked(self.wnet_enabled) + self.window_size_choice.setDisabled(self.wnet_enabled) + self.window_infer_box.setDisabled(self.wnet_enabled) def _toggle_display_model_input_size(self): if ( From 40e480685355574a6fec64ab35fe568600bd6902 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Fri, 28 Jul 2023 14:58:13 +0200 Subject: [PATCH 090/250] Update test_and_deploy.yml --- .github/workflows/test_and_deploy.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/test_and_deploy.yml b/.github/workflows/test_and_deploy.yml index fafb1719..efb218c4 100644 --- a/.github/workflows/test_and_deploy.yml +++ b/.github/workflows/test_and_deploy.yml @@ -7,6 +7,7 @@ on: push: branches: - main + - cy/training tags: - "v*" # Push events to matching v*, i.e. v1.0, v20.15.10 pull_request: From a0d5968dff3a32c3bcddddbfabde87990ee84982 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 12 Jul 2023 17:16:40 +0200 Subject: [PATCH 091/250] Additional tests --- napari_cellseg3d/_tests/test_model_framework.py | 1 + napari_cellseg3d/_tests/test_models.py | 3 +-- napari_cellseg3d/_tests/test_utils.py | 1 - napari_cellseg3d/code_models/model_framework.py | 4 +++- napari_cellseg3d/utils.py | 3 +++ 5 files changed, 8 insertions(+), 4 deletions(-) diff --git a/napari_cellseg3d/_tests/test_model_framework.py b/napari_cellseg3d/_tests/test_model_framework.py index 8ed3d33a..8bcf7c07 100644 --- a/napari_cellseg3d/_tests/test_model_framework.py +++ b/napari_cellseg3d/_tests/test_model_framework.py @@ -1,5 +1,6 @@ from pathlib import Path +from napari_cellseg3d.config import MODEL_LIST from napari_cellseg3d.code_models import model_framework from napari_cellseg3d.config import MODEL_LIST diff --git a/napari_cellseg3d/_tests/test_models.py b/napari_cellseg3d/_tests/test_models.py index 898b4e4c..c31b5e9c 100644 --- a/napari_cellseg3d/_tests/test_models.py +++ b/napari_cellseg3d/_tests/test_models.py @@ -1,6 +1,5 @@ -from pathlib import Path - import numpy as np +from pathlib import Path import pytest import torch from numpy.random import PCG64, Generator diff --git a/napari_cellseg3d/_tests/test_utils.py b/napari_cellseg3d/_tests/test_utils.py index b1342afe..027d8e99 100644 --- a/napari_cellseg3d/_tests/test_utils.py +++ b/napari_cellseg3d/_tests/test_utils.py @@ -1,7 +1,6 @@ import random from functools import partial from pathlib import Path - import numpy as np import pytest import torch diff --git a/napari_cellseg3d/code_models/model_framework.py b/napari_cellseg3d/code_models/model_framework.py index ee9834c2..4860d59e 100644 --- a/napari_cellseg3d/code_models/model_framework.py +++ b/napari_cellseg3d/code_models/model_framework.py @@ -161,7 +161,9 @@ def save_log_to_path(self, path, do_timestamp=True): else: log_name = "Log_report.txt" - path = str(Path(path) / log_name) + path = str( + Path(path) / log_name + ) if len(log) != 0: with Path.open( diff --git a/napari_cellseg3d/utils.py b/napari_cellseg3d/utils.py index 0f9eee69..b6353855 100644 --- a/napari_cellseg3d/utils.py +++ b/napari_cellseg3d/utils.py @@ -94,6 +94,9 @@ def show_result(viewer, layer, image, name): ) +#################### + + class Singleton(type): """ Singleton class that can only be instantiated once at a time, From eab17e8d16c88f7b9dd98d5b3598afa1e90064f9 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 13 Jul 2023 11:34:07 +0200 Subject: [PATCH 092/250] Fix style --- napari_cellseg3d/_tests/test_model_framework.py | 1 - napari_cellseg3d/code_models/model_framework.py | 4 +--- 2 files changed, 1 insertion(+), 4 deletions(-) diff --git a/napari_cellseg3d/_tests/test_model_framework.py b/napari_cellseg3d/_tests/test_model_framework.py index 8bcf7c07..8ed3d33a 100644 --- a/napari_cellseg3d/_tests/test_model_framework.py +++ b/napari_cellseg3d/_tests/test_model_framework.py @@ -1,6 +1,5 @@ from pathlib import Path -from napari_cellseg3d.config import MODEL_LIST from napari_cellseg3d.code_models import model_framework from napari_cellseg3d.config import MODEL_LIST diff --git a/napari_cellseg3d/code_models/model_framework.py b/napari_cellseg3d/code_models/model_framework.py index 4860d59e..ee9834c2 100644 --- a/napari_cellseg3d/code_models/model_framework.py +++ b/napari_cellseg3d/code_models/model_framework.py @@ -161,9 +161,7 @@ def save_log_to_path(self, path, do_timestamp=True): else: log_name = "Log_report.txt" - path = str( - Path(path) / log_name - ) + path = str(Path(path) / log_name) if len(log) != 0: with Path.open( From f516c6257f1a4813479c19bedbff8d4b4322c29e Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 17 Jul 2023 14:10:49 +0200 Subject: [PATCH 093/250] Normalization changes+ fix issues with default path --- napari_cellseg3d/_tests/test_base_plugin.py | 2 +- .../_tests/test_model_framework.py | 8 +-- napari_cellseg3d/_tests/test_training.py | 2 +- napari_cellseg3d/_tests/test_utils.py | 21 ++++++- .../code_models/instance_segmentation.py | 8 +-- napari_cellseg3d/code_models/workers.py | 34 +++++++++- napari_cellseg3d/code_plugins/plugin_base.py | 63 ++++++++++--------- .../code_plugins/plugin_convert.py | 31 +++++---- .../dev_scripts/correct_labels.py | 10 +-- napari_cellseg3d/utils.py | 49 ++++++++++++--- 10 files changed, 161 insertions(+), 67 deletions(-) diff --git a/napari_cellseg3d/_tests/test_base_plugin.py b/napari_cellseg3d/_tests/test_base_plugin.py index 98c35ce6..ff049657 100644 --- a/napari_cellseg3d/_tests/test_base_plugin.py +++ b/napari_cellseg3d/_tests/test_base_plugin.py @@ -15,5 +15,5 @@ def test_base_single_image(make_napari_viewer_proxy): assert plugin._check_results_path(str(test_folder)) plugin.image_path = test_image assert plugin._default_path[0] != test_image - plugin._update_default() + plugin._update_default_paths() assert plugin._default_path[0] == test_image diff --git a/napari_cellseg3d/_tests/test_model_framework.py b/napari_cellseg3d/_tests/test_model_framework.py index 8ed3d33a..497d97e8 100644 --- a/napari_cellseg3d/_tests/test_model_framework.py +++ b/napari_cellseg3d/_tests/test_model_framework.py @@ -15,9 +15,9 @@ def test_update_default(make_napari_viewer_proxy): widget.images_filepaths = [] widget.results_path = None - widget._update_default() + widget._update_default_paths() - assert widget._default_folders == [] + assert widget._default_path == [None, None, None] widget.images_filepaths = [ pth("C:/test/test/images.tif"), @@ -30,9 +30,9 @@ def test_update_default(make_napari_viewer_proxy): widget.results_path = pth("D:/dataset/res") # widget.model_path = None - widget._update_default() + widget._update_default_paths() - assert widget._default_folders == [ + assert widget._default_path == [ pth("C:/test/test"), pth("C:/dataset/labels"), pth("D:/dataset/res"), diff --git a/napari_cellseg3d/_tests/test_training.py b/napari_cellseg3d/_tests/test_training.py index cf327ef9..2378d193 100644 --- a/napari_cellseg3d/_tests/test_training.py +++ b/napari_cellseg3d/_tests/test_training.py @@ -90,7 +90,7 @@ def test_training(make_napari_viewer_proxy, qtbot): res = next(worker.train()) assert isinstance(res, TrainingReport) - assert res.epoch == 0 + assert res.epoch == 1 widget.worker = worker res.show_plot = True diff --git a/napari_cellseg3d/_tests/test_utils.py b/napari_cellseg3d/_tests/test_utils.py index 027d8e99..f02825bb 100644 --- a/napari_cellseg3d/_tests/test_utils.py +++ b/napari_cellseg3d/_tests/test_utils.py @@ -209,16 +209,31 @@ def test_parse_default_path(): user_path = Path().home() assert utils.parse_default_path([None]) == str(user_path) - test_path = "C:/test/test" + test_path = "C:/test/test/test/test" path = [test_path, None, None] - assert utils.parse_default_path(path) == test_path + assert utils.parse_default_path(path, check_existence=False) == test_path + + test_path = "C:/test/does/not/exist" + path = [test_path, None, None] + assert utils.parse_default_path(path, check_existence=True) == str( + Path.home() + ) long_path = "D:/very/long/path/what/a/bore/ifonlytherewas/something/tohelpmenotsearchit/allthetime" path = [test_path, None, None, long_path, ""] - assert utils.parse_default_path(path) == long_path + assert utils.parse_default_path(path, check_existence=False) == long_path def test_thread_test(make_napari_viewer_proxy): viewer = make_napari_viewer_proxy() w = thread_test.create_connected_widget(viewer) viewer.window.add_dock_widget(w) + + +def test_quantile_norm(): + array = rand_gen.random(size=(100, 100, 100)) + low_quantile = np.quantile(array, 0.01) + high_quantile = np.quantile(array, 0.99) + array_norm = utils.quantile_normalization(array) + assert array_norm.min() >= low_quantile + assert array_norm.max() <= high_quantile diff --git a/napari_cellseg3d/code_models/instance_segmentation.py b/napari_cellseg3d/code_models/instance_segmentation.py index 9ea4cbef..0e2c0871 100644 --- a/napari_cellseg3d/code_models/instance_segmentation.py +++ b/napari_cellseg3d/code_models/instance_segmentation.py @@ -251,10 +251,10 @@ def binary_watershed( rem_seed_thres (int): threshold for small seeds removal. Default : 3 """ - logger.debug( - f"Running watershed segmentation with thres_objects={thres_objects}, thres_seeding={thres_seeding}," - f" thres_small={thres_small} and rem_seed_thres={rem_seed_thres}" - ) + # logger.debug( + # f"Running watershed segmentation with thres_objects={thres_objects}, thres_seeding={thres_seeding}," + # f" thres_small={thres_small} and rem_seed_thres={rem_seed_thres}" + # ) semantic = np.squeeze(volume) seed_map = semantic > thres_seeding foreground = semantic > thres_objects diff --git a/napari_cellseg3d/code_models/workers.py b/napari_cellseg3d/code_models/workers.py index 1b1210d7..26044806 100644 --- a/napari_cellseg3d/code_models/workers.py +++ b/napari_cellseg3d/code_models/workers.py @@ -27,6 +27,7 @@ EnsureType, EnsureTyped, LoadImaged, + # NormalizeIntensityd, Orientationd, Rand3DElasticd, RandAffined, @@ -390,6 +391,7 @@ def load_folder(self): EnsureChannelFirstd(keys=["image"]), # Orientationd(keys=["image"], axcodes="PLI"), # anisotropic_transform, + # NormalizeIntensityd(keys=["image"]), # TODO normalize EnsureTyped(keys=["image"]), ] ) @@ -399,6 +401,7 @@ def load_folder(self): LoadImaged(keys=["image"]), # AddChanneld(keys=["image"]), #already done EnsureChannelFirstd(keys=["image"]), + # NormalizeIntensityd(keys=["image"]), # TODO normalize # Orientationd(keys=["image"], axcodes="PLI"), # anisotropic_transform, SpatialPadd(keys=["image"], spatial_size=pad), @@ -1201,8 +1204,9 @@ def train(self): size = self.config.sample_size if do_sampling else check + PADDING = utils.get_padding_dim(size) model = model_class( # FIXME check if correct - input_img_size=utils.get_padding_dim(size), use_checkpoint=True + input_img_size=PADDING, use_checkpoint=True ) model = model.to(self.config.device) @@ -1284,6 +1288,7 @@ def get_loader_func(num_samples): [ LoadImaged(keys=["image", "label"]), EnsureChannelFirstd(keys=["image", "label"]), + # NormalizeIntensityd(keys=["image"]), # TODO normalize RandSpatialCropSamplesd( keys=["image", "label"], roi_size=( @@ -1356,9 +1361,10 @@ def get_loader_func(num_samples): ), EnsureChannelFirstd(keys=["image", "label"]), Orientationd(keys=["image", "label"], axcodes="PLI"), + # NormalizeIntensityd(keys=["image"]), # TODO normalize SpatialPadd( keys=["image", "label"], - spatial_size=(utils.get_padding_dim(check)), + spatial_size=PADDING, ), EnsureTyped(keys=["image", "label"]), ] @@ -1411,6 +1417,13 @@ def get_loader_func(num_samples): best_metric = -1 best_metric_epoch = -1 + def normalize_batch(batch): # TODO(refactor as a transform) + batch_dim = batch.shape[0] + channels_dim = batch.shape[1] + for i in range(batch_dim): + for j in range(channels_dim): + batch[i][j] = utils.quantile_normalization(batch[i][j]) + # time = utils.get_date_time() logger.debug("Weights") @@ -1479,6 +1492,19 @@ def get_loader_func(num_samples): batch_data["image"].to(device), batch_data["label"].to(device), ) + + normalize_batch(inputs) + # images = [im.cpu().numpy() for im in inputs] + # [images.append(l.cpu().numpy()) for l in labels] + # + # yield TrainingReport( + # show_plot=True, + # epoch=1, + # images=images, + # ) + # + # break + optimizer.zero_grad() outputs = model(inputs) # self.log(f"Output dimensions : {outputs.shape}") @@ -1500,6 +1526,8 @@ def get_loader_func(num_samples): show_plot=False, weights=model.state_dict() ) + # return + epoch_loss /= step epoch_loss_values.append(epoch_loss) self.log(f"Epoch: {epoch + 1}, Average loss: {epoch_loss:.4f}") @@ -1527,6 +1555,8 @@ def get_loader_func(num_samples): val_data["image"].to(device), val_data["label"].to(device), ) + + normalize_batch(val_inputs) try: with torch.no_grad(): val_outputs = sliding_window_inference( diff --git a/napari_cellseg3d/code_plugins/plugin_base.py b/napari_cellseg3d/code_plugins/plugin_base.py index b406ee20..7ea5aa11 100644 --- a/napari_cellseg3d/code_plugins/plugin_base.py +++ b/napari_cellseg3d/code_plugins/plugin_base.py @@ -242,6 +242,7 @@ def _show_file_dialog(self): choice = str(f_name[0]) self.filetype = str(Path(choice).suffix) logger.debug(f"Filetype set to {self.filetype}") + self._update_default_paths() return choice def _show_dialog_images(self): @@ -251,7 +252,7 @@ def _show_dialog_images(self): self.image_path = f_name logger.debug(f"Image path set to {self.image_path}") self.image_filewidget.text_field.setText(self.image_path) - self._update_default() + self._update_default_paths() def _show_dialog_labels(self): """Show file dialog and set label path""" @@ -260,13 +261,18 @@ def _show_dialog_labels(self): self.label_path = f_name logger.debug(f"Label path set to {self.label_path}") self.labels_filewidget.text_field.setText(self.label_path) - self._update_default() + self._update_default_paths() def _check_results_path(self, folder: str): + """Check if results folder exists, create it if not""" + logger.debug(f"Checking results folder : {folder}") if folder != "" and isinstance(folder, str): if not Path(folder).is_dir(): Path(folder).mkdir(parents=True, exist_ok=True) if not Path(folder).is_dir(): + logger.info( + f"Could not create missing results folder : {folder}" + ) return False logger.info(f"Created missing results folder : {folder}") return True @@ -276,15 +282,16 @@ def _check_results_path(self, folder: str): def _load_results_path(self): """Show file dialog to set :py:attr:`~results_path`""" + self._update_default_paths() folder = ui.open_folder_dialog(self, self._default_path) if self._check_results_path(folder): - self.results_path = folder - # logger.debug(f"Results path : {self.results_path}") + self.results_path = str(Path(folder).resolve()) + logger.debug(f"Results path : {self.results_path}") self.results_filewidget.text_field.setText(self.results_path) - self._update_default() + self._update_default_paths() - def _update_default(self): + def _update_default_paths(self): """Updates default path for smoother navigation when opening file dialogs""" self._default_path = [ self.image_path, @@ -363,7 +370,7 @@ def __init__( self.results_path = None """str: path to output folder,to save results in""" - self._default_folders = [None] + self._default_path = [None] """Update defaults from PluginBaseFolder with model_path""" self.docked_widgets = [] @@ -404,7 +411,7 @@ def load_dataset_paths(self): array(str): all loaded file paths """ # filetype = self.filetype_choice.currentText() - directory = ui.open_folder_dialog(self, self._default_folders) + directory = ui.open_folder_dialog(self, self._default_path) file_paths = sorted(Path(directory).glob("*" + ".tif")) if len(file_paths) == 0: @@ -424,7 +431,7 @@ def load_image_dataset(self): path = str(Path(filenames[0]).parent) self.image_filewidget.text_field.setText(path) self.image_filewidget.check_ready() - self._update_default() + self._update_default_paths(path) def load_label_dataset(self): """Show file dialog to set :py:attr:`~labels_filepaths`""" @@ -435,26 +442,26 @@ def load_label_dataset(self): path = str(Path(filenames[0]).parent) self.labels_filewidget.text_field.setText(path) self.labels_filewidget.check_ready() - self._update_default() + self._update_default_paths(path) - def _update_default(self): + def _update_default_paths(self, path=None): """Update default path for smoother file dialogs""" - if len(self.images_filepaths) != 0: - from_images = str(Path(self.images_filepaths[0]).parent) - else: - from_images = None - - if len(self.labels_filepaths) != 0: - from_labels = str(Path(self.labels_filepaths[0]).parent) - else: - from_labels = None - - self._default_folders = [ - path - for path in [ - from_images, - from_labels, + logger.debug(f"Updating default paths with {path}") + if path is None: + self._default_path = [ + self.extract_dataset_paths(self.images_filepaths), + self.extract_dataset_paths(self.labels_filepaths), self.results_path, ] - if (path != [] and path is not None) - ] + return + if Path(path).is_dir(): + self._default_path.append(path) + + @staticmethod + def extract_dataset_paths(paths): + """Gets the parent folder name of the first image and label paths""" + if len(paths) == 0: + return None + if paths[0] is None: + return None + return str(Path(paths[0]).parent) diff --git a/napari_cellseg3d/code_plugins/plugin_convert.py b/napari_cellseg3d/code_plugins/plugin_convert.py index 4357e51e..aa70bc73 100644 --- a/napari_cellseg3d/code_plugins/plugin_convert.py +++ b/napari_cellseg3d/code_plugins/plugin_convert.py @@ -179,7 +179,7 @@ def _start(self): utils.mkdir_from_str(self.results_path) remove_size = self.size_for_removal_counter.value() - if self.layer_choice: + if self.layer_choice.isChecked(): if self.image_layer_loader.layer_data() is not None: layer = self.image_layer_loader.layer() @@ -233,8 +233,10 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): self.start_btn = ui.Button("Start", self._start) - self.results_path = Path.home() / Path("cellseg3d/threshold") - self.results_filewidget.text_field.setText(str(self.results_path)) + self.results_path = str( + Path.home() / Path("cellseg3d/instance_labels") + ) + self.results_filewidget.text_field.setText(self.results_path) self.results_filewidget.check_ready() self._build() @@ -260,8 +262,13 @@ def _build(self): def _start(self): Path(self.results_path).mkdir(exist_ok=True, parents=True) + logger.debug(f"Running on layer : {self.layer_choice.isChecked()}") + logger.debug(f"Running on folder : {self.folder_choice.isChecked()}") + logger.debug(f"Images : {self.labels_filepaths}") - if self.layer_choice: + self.results_filewidget.check_ready() + + if self.layer_choice.isChecked(): if self.label_layer_loader.layer_data() is not None: layer = self.label_layer_loader.layer() @@ -277,18 +284,20 @@ def _start(self): self._viewer, layer, semantic, f"semantic_{layer.name}" ) elif ( - self.folder_choice.isChecked() and len(self.images_filepaths) != 0 + self.folder_choice.isChecked() and len(self.labels_filepaths) != 0 ): images = [ to_semantic(file, is_file_path=True) - for file in self.images_filepaths + for file in self.labels_filepaths ] utils.save_folder( self.results_path, f"semantic_results_{utils.get_date_time()}", images, - self.images_filepaths, + self.labels_filepaths, ) + else: + logger.warning("Please specify a layer or a folder") class ToInstanceUtils(BasePluginFolder): @@ -347,7 +356,7 @@ def _build(self): def _start(self): utils.mkdir_from_str(self.results_path) - if self.layer_choice: + if self.layer_choice.isChecked(): if self.label_layer_loader.layer_data() is not None: layer = self.label_layer_loader.layer() @@ -408,8 +417,8 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): text_label="Remove all smaller than (value):", ) - self.results_path = Path.home() / Path("cellseg3d/threshold") - self.results_filewidget.text_field.setText(str(self.results_path)) + self.results_path = str(Path.home() / Path("cellseg3d/threshold")) + self.results_filewidget.text_field.setText(self.results_path) self.results_filewidget.check_ready() self.container = self._build() @@ -443,7 +452,7 @@ def _start(self): utils.mkdir_from_str(self.results_path) remove_size = self.binarize_counter.value() - if self.layer_choice: + if self.layer_choice.isChecked(): if self.image_layer_loader.layer_data() is not None: layer = self.image_layer_loader.layer() diff --git a/napari_cellseg3d/dev_scripts/correct_labels.py b/napari_cellseg3d/dev_scripts/correct_labels.py index f413812d..4f529abe 100644 --- a/napari_cellseg3d/dev_scripts/correct_labels.py +++ b/napari_cellseg3d/dev_scripts/correct_labels.py @@ -364,8 +364,10 @@ def relabel_non_unique_i_folder(folder_path, end_of_new_name="relabeled"): if __name__ == "__main__": - im_path = Path("C:/Users/Cyril/Desktop/Proj_bachelor/data/somatomotor") + im_path = Path.home() / "Desktop/Code/CELLSEG_BENCHMARK/TPH2_DATA/visual" - image_path = str(im_path / "volumes/c1images.tif") - gt_labels_path = str(im_path / "labels/c1labels.tif") - relabel(image_path, gt_labels_path, check_for_unicity=False, go_fast=False) + # i = 4 + # im_id = i+1 + image_path = str(im_path / "visual.tif") + gt_labels_path = str(im_path / "visual_gt.tif") + relabel(image_path, gt_labels_path, check_for_unicity=True, go_fast=False) diff --git a/napari_cellseg3d/utils.py b/napari_cellseg3d/utils.py index b6353855..ca49c29d 100644 --- a/napari_cellseg3d/utils.py +++ b/napari_cellseg3d/utils.py @@ -2,16 +2,14 @@ import math from datetime import datetime from pathlib import Path -from typing import TYPE_CHECKING, Union +from typing import Union import napari import numpy as np +import torch from monai.transforms import Zoom from tifffile import imread, imwrite -if TYPE_CHECKING: - import torch - LOGGER = logging.getLogger(__name__) ############### # Global logging level setting @@ -394,7 +392,7 @@ def fill_list_in_between(lst, n, fill_value): return None -def parse_default_path(possible_paths): +def parse_default_path(possible_paths, check_existence=True): """Returns a default path based on a vector of paths, some of which might be empty. Args: @@ -404,14 +402,18 @@ def parse_default_path(possible_paths): """ default_paths = [] + LOGGER.debug(f"Utils : possible paths are {possible_paths}") if any(path is not None for path in possible_paths): - default_paths = [ - p for p in possible_paths if p is not None and len(p) > 2 - ] + for p in possible_paths: + if p is not None and (Path(p).exists() or not check_existence): + default_paths.append(str(Path(p).resolve().as_posix())) + # default_paths = [ + # str(Path(p).resolve()) for p in possible_paths if (Path(p).exists()) + # ] # default_paths = [ # path for path in default_paths if path is not None and path != [] # ] - print(default_paths) + LOGGER.debug(f"Utils : default paths are {default_paths}") if len(default_paths) == 0: return str(Path().home()) default_path = max(default_paths, key=len) @@ -471,3 +473,32 @@ def load_images(dir_or_path, filetype="", as_folder: bool = False): # "Loading as folder not implemented yet. Use napari to load as folder" # images_original = dask_imread(filename_pattern_original) # ) + + +def quantile_normalization( + image: Union[np.ndarray, torch.Tensor], + quantile_high=0.99, + quantile_low=0.01, +): + """Normalizes an image using the quantiles""" + if quantile_high < quantile_low: + raise ValueError( + f"quantile_high must be greater than quantile_low, got {quantile_high} and {quantile_low}" + ) + + if isinstance(image, torch.Tensor): + qtl = torch.quantile + where = torch.where + elif isinstance(image, np.ndarray): + qtl = np.quantile + where = np.where + else: + raise TypeError("image needs to be torch tensor or numpy array") + + shape = image.shape + image = image.flatten() + high_quantile_value = qtl(image, quantile_high) + low_quantile_value = qtl(image, quantile_low) + image = where(image > high_quantile_value, high_quantile_value, image) + image = where(image < low_quantile_value, low_quantile_value, image) + return image.reshape(shape) From 3492629d3edf7c288a954e393d243fbadcc87f97 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 17 Jul 2023 14:11:04 +0200 Subject: [PATCH 094/250] Create crop_data.py --- napari_cellseg3d/dev_scripts/crop_data.py | 61 +++++++++++++++++++++++ 1 file changed, 61 insertions(+) create mode 100644 napari_cellseg3d/dev_scripts/crop_data.py diff --git a/napari_cellseg3d/dev_scripts/crop_data.py b/napari_cellseg3d/dev_scripts/crop_data.py new file mode 100644 index 00000000..2f24e619 --- /dev/null +++ b/napari_cellseg3d/dev_scripts/crop_data.py @@ -0,0 +1,61 @@ +from pathlib import Path + +import numpy as np +from tifffile import imread, imwrite + + +def crop_3d_image(image, roi_size): + """Crops a 3d image by extracting all regions of size roi_size. + If the edge of the array is reached, the cropped region is overlapped with the previous cropped region. + """ + image_size = image.shape + cropped_images = [] + for i in range(0, image_size[0], roi_size[0]): + for j in range(0, image_size[1], roi_size[1]): + for k in range(0, image_size[2], roi_size[2]): + if i + roi_size[0] >= image_size[0]: + crop_location_i = image_size[0] - roi_size[0] + else: + crop_location_i = i + if j + roi_size[1] >= image_size[1]: + crop_location_j = image_size[1] - roi_size[1] + else: + crop_location_j = j + if k + roi_size[2] >= image_size[2]: + crop_location_k = image_size[2] - roi_size[2] + else: + crop_location_k = k + cropped_images.append( + image[ + crop_location_i : crop_location_i + roi_size[0], + crop_location_j : crop_location_j + roi_size[1], + crop_location_k : crop_location_k + roi_size[2], + ] + ) + return cropped_images + + +if __name__ == "__main__": + image_path = ( + Path().home() + # / "Desktop/Code/CELLSEG_BENCHMARK/TPH2_DATA/somatomotor_iso" + / "Desktop/Code/CELLSEG_BENCHMARK/TPH2_DATA/somatomotor_iso/labels/semantic" + ) + assert image_path.exists() + image_list = image_path.glob("*.tif") + for j in image_list: + print(j) + image = imread(str(j)) + crops = crop_3d_image(image, (64, 64, 64)) + # viewer = napari.Viewer() + if not (image_path / "cropped").exists(): + (image_path / "cropped").mkdir(exist_ok=False) + for i, im in enumerate(crops): + print(im.shape) + # viewer.add_image(im) + imwrite( + str(image_path / f"cropped/{j.stem}_{i}_crop.tif"), + im.astype(np.uint16), + dtype="uint16", + ) + # napari.run() From ca5428edc0dadbb2d2fdddb1de80425d6451c86c Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 17 Jul 2023 15:15:07 +0200 Subject: [PATCH 095/250] Added proper transform for normalization --- napari_cellseg3d/code_models/workers.py | 54 ++++++++++--------- .../code_plugins/plugin_model_training.py | 2 +- 2 files changed, 29 insertions(+), 27 deletions(-) diff --git a/napari_cellseg3d/code_models/workers.py b/napari_cellseg3d/code_models/workers.py index 26044806..5708827f 100644 --- a/napari_cellseg3d/code_models/workers.py +++ b/napari_cellseg3d/code_models/workers.py @@ -27,6 +27,7 @@ EnsureType, EnsureTyped, LoadImaged, + MapTransform, # NormalizeIntensityd, Orientationd, Rand3DElasticd, @@ -236,6 +237,28 @@ def to(self, device): pass +class QuantileNormalizationd(MapTransform): + def __init__(self, keys, allow_missing_keys: bool = False): + super().__init__(keys, allow_missing_keys) + + def __call__(self, data): + d = dict(data) + for key in self.keys: + d[key] = self.normalizer(d[key]) + return d + + def normalizer(self, image: torch.Tensor): + """Normalize each image in a batch individually by quantile normalization.""" + if image.ndim == 4: + for i in range(image.shape[0]): + image[i] = utils.quantile_normalization(image[i]) + else: + raise NotImplementedError( + "QuantileNormalizationd only supports 2D and 3D tensors with NCHWD format" + ) + return image + + @dataclass class InferenceResult: """Class to record results of a segmentation job""" @@ -391,7 +414,7 @@ def load_folder(self): EnsureChannelFirstd(keys=["image"]), # Orientationd(keys=["image"], axcodes="PLI"), # anisotropic_transform, - # NormalizeIntensityd(keys=["image"]), # TODO normalize + QuantileNormalizationd(keys=["image"]), EnsureTyped(keys=["image"]), ] ) @@ -567,9 +590,7 @@ def create_inference_result( if crf_results is not None: crf_results = utils.correct_rotation(crf_results) if instance_labels is not None: - instance_labels = utils.correct_rotation( - instance_labels - ) # TODO(cyril) check if correct + instance_labels = utils.correct_rotation(instance_labels) return InferenceResult( image_id=i + 1, @@ -765,6 +786,7 @@ def inference_on_layer(self, image, model, post_process_transforms): self.log("Inference started on layer...") image = image.type(torch.FloatTensor) + image = utils.quantile_normalization(image) out = self.model_output( image, @@ -1288,7 +1310,7 @@ def get_loader_func(num_samples): [ LoadImaged(keys=["image", "label"]), EnsureChannelFirstd(keys=["image", "label"]), - # NormalizeIntensityd(keys=["image"]), # TODO normalize + QuantileNormalizationd(keys=["image"]), RandSpatialCropSamplesd( keys=["image", "label"], roi_size=( @@ -1361,7 +1383,7 @@ def get_loader_func(num_samples): ), EnsureChannelFirstd(keys=["image", "label"]), Orientationd(keys=["image", "label"], axcodes="PLI"), - # NormalizeIntensityd(keys=["image"]), # TODO normalize + QuantileNormalizationd(keys=["image"]), SpatialPadd( keys=["image", "label"], spatial_size=PADDING, @@ -1417,13 +1439,6 @@ def get_loader_func(num_samples): best_metric = -1 best_metric_epoch = -1 - def normalize_batch(batch): # TODO(refactor as a transform) - batch_dim = batch.shape[0] - channels_dim = batch.shape[1] - for i in range(batch_dim): - for j in range(channels_dim): - batch[i][j] = utils.quantile_normalization(batch[i][j]) - # time = utils.get_date_time() logger.debug("Weights") @@ -1493,18 +1508,6 @@ def normalize_batch(batch): # TODO(refactor as a transform) batch_data["label"].to(device), ) - normalize_batch(inputs) - # images = [im.cpu().numpy() for im in inputs] - # [images.append(l.cpu().numpy()) for l in labels] - # - # yield TrainingReport( - # show_plot=True, - # epoch=1, - # images=images, - # ) - # - # break - optimizer.zero_grad() outputs = model(inputs) # self.log(f"Output dimensions : {outputs.shape}") @@ -1556,7 +1559,6 @@ def normalize_batch(batch): # TODO(refactor as a transform) val_data["label"].to(device), ) - normalize_batch(val_inputs) try: with torch.no_grad(): val_outputs = sliding_window_inference( diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 676ab78c..50b6e54f 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -996,7 +996,7 @@ def on_yield(self, report: TrainingReport): else: for i in rge: if layer_name + str(i) not in [ - layer.name for layer in self.result_layers + layer.name for layer in self._viewer.layers ]: new_layer = self._viewer.add_image( report.images[i], From f2e45d5cc8d5d92371693af62ac3da034a8b7949 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 17 Jul 2023 15:41:46 +0200 Subject: [PATCH 096/250] Training improvments --- napari_cellseg3d/code_models/workers.py | 17 +++++++++++++++++ .../code_plugins/plugin_model_training.py | 2 +- 2 files changed, 18 insertions(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/workers.py b/napari_cellseg3d/code_models/workers.py index 5708827f..d2fa1e41 100644 --- a/napari_cellseg3d/code_models/workers.py +++ b/napari_cellseg3d/code_models/workers.py @@ -1653,7 +1653,24 @@ def get_loader_func(num_samples): f"Train completed, best_metric: {best_metric:.4f} " f"at epoch: {best_metric_epoch}" ) + # Save last checkpoint + weights_filename = f"{model_name}_last_epoch_{epoch + 1}.pth" + self.log("Saving last model") + torch.save( + model.state_dict(), + Path(self.config.results_path_folder) / Path(weights_filename), + ) + self.log("Saving complete, exiting") model.to("cpu") + # clear (V)RAM + val_ds = None + train_ds = None + val_loader = None + train_loader = None + torch.cuda.empty_cache() + import gc + + gc.collect() except Exception as e: self.raise_error(e, "Error in training") diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 50b6e54f..9a8b6195 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -980,7 +980,7 @@ def on_yield(self, report: TrainingReport): if report.show_plot: try: - layer_name = "Training_checkpoint_" + layer_name = "Validation output n°" rge = range(len(report.images)) self.log.print_and_log(len(report.images)) From 9ed88b15cb07ef715f44b73b7883a52f05682692 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 17 Jul 2023 16:34:10 +0200 Subject: [PATCH 097/250] Fix VNet training --- napari_cellseg3d/code_models/models/model_SwinUNetR.py | 5 +++++ napari_cellseg3d/code_models/models/model_VNet.py | 7 +++++-- 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/code_models/models/model_SwinUNetR.py b/napari_cellseg3d/code_models/models/model_SwinUNetR.py index 2d7b5ef6..4a0bc35a 100644 --- a/napari_cellseg3d/code_models/models/model_SwinUNetR.py +++ b/napari_cellseg3d/code_models/models/model_SwinUNetR.py @@ -1,4 +1,5 @@ from monai.networks.nets import SwinUNETR +from torch import softmax from napari_cellseg3d.utils import LOGGER @@ -36,6 +37,10 @@ def __init__( use_checkpoint=use_checkpoint, ) + def forward(self, x_in): + y = super().forward(x_in) + return softmax(y, dim=1) + # def get_output(self, input): # out = self(input) # return torch.sigmoid(out) diff --git a/napari_cellseg3d/code_models/models/model_VNet.py b/napari_cellseg3d/code_models/models/model_VNet.py index b082ccab..4e375a11 100644 --- a/napari_cellseg3d/code_models/models/model_VNet.py +++ b/napari_cellseg3d/code_models/models/model_VNet.py @@ -8,9 +8,12 @@ class VNet_(VNet): def __init__(self, in_channels=1, out_channels=1, **kwargs): try: super().__init__( - in_channels=in_channels, out_channels=out_channels, **kwargs + in_channels=in_channels, + out_channels=out_channels, + bias=True, + **kwargs, ) except TypeError: super().__init__( - in_channels=in_channels, out_channels=out_channels + in_channels=in_channels, out_channels=out_channels, bias=True ) From b95c0b762e21f295a0d8700152335ad71dce268e Mon Sep 17 00:00:00 2001 From: C-Achard Date: Tue, 18 Jul 2023 10:15:35 +0200 Subject: [PATCH 098/250] Split workers file in smaller ones --- .../{test_workers.py => test_inference.py} | 14 +- napari_cellseg3d/_tests/test_models.py | 2 +- .../_tests/test_plugin_inference.py | 2 +- napari_cellseg3d/_tests/test_training.py | 4 +- .../_tests/test_weight_download.py | 2 +- .../code_models/models/model_SwinUNetR.py | 5 +- .../code_models/models/model_TRAILMAP_MS.py | 2 +- .../code_models/models/model_WNet.py | 2 - .../code_models/models/wnet/model.py | 2 - .../code_models/worker_inference.py | 776 ++++++++ .../code_models/worker_training.py | 731 +++++++ napari_cellseg3d/code_models/workers.py | 1679 ----------------- napari_cellseg3d/code_models/workers_utils.py | 221 +++ .../code_plugins/plugin_model_inference.py | 21 +- .../code_plugins/plugin_model_training.py | 9 +- napari_cellseg3d/utils.py | 11 +- 16 files changed, 1768 insertions(+), 1715 deletions(-) rename napari_cellseg3d/_tests/{test_workers.py => test_inference.py} (92%) create mode 100644 napari_cellseg3d/code_models/worker_inference.py create mode 100644 napari_cellseg3d/code_models/worker_training.py delete mode 100644 napari_cellseg3d/code_models/workers.py create mode 100644 napari_cellseg3d/code_models/workers_utils.py diff --git a/napari_cellseg3d/_tests/test_workers.py b/napari_cellseg3d/_tests/test_inference.py similarity index 92% rename from napari_cellseg3d/_tests/test_workers.py rename to napari_cellseg3d/_tests/test_inference.py index 30ebae08..336630f5 100644 --- a/napari_cellseg3d/_tests/test_workers.py +++ b/napari_cellseg3d/_tests/test_inference.py @@ -5,20 +5,16 @@ import pytest import torch from monai.data import DataLoader -from numpy.random import PCG64, Generator -from napari_cellseg3d.code_models.workers import ( +from napari_cellseg3d.code_models.worker_inference import InferenceWorker +from napari_cellseg3d.code_models.workers_utils import ( + PRETRAINED_WEIGHTS_DIR, InferenceResult, - InferenceWorker, ONNXModelWrapper, WeightsDownloader, ) -from napari_cellseg3d.config import ( - PRETRAINED_WEIGHTS_DIR, - InferenceWorkerConfig, -) - -rand_gen = Generator(PCG64(12345)) +from napari_cellseg3d.config import InferenceWorkerConfig +from napari_cellseg3d.utils import rand_gen def test_onnx_inference(make_napari_viewer_proxy): diff --git a/napari_cellseg3d/_tests/test_models.py b/napari_cellseg3d/_tests/test_models.py index c31b5e9c..51ff1365 100644 --- a/napari_cellseg3d/_tests/test_models.py +++ b/napari_cellseg3d/_tests/test_models.py @@ -110,7 +110,7 @@ def on_yield(result): def test_pretrained_weights_compatibility(): - from napari_cellseg3d.code_models.workers import WeightsDownloader + from napari_cellseg3d.code_models.workers_utils import WeightsDownloader from napari_cellseg3d.config import MODEL_LIST, PRETRAINED_WEIGHTS_DIR for model_name in MODEL_LIST: diff --git a/napari_cellseg3d/_tests/test_plugin_inference.py b/napari_cellseg3d/_tests/test_plugin_inference.py index 309c5ab7..de5c78ae 100644 --- a/napari_cellseg3d/_tests/test_plugin_inference.py +++ b/napari_cellseg3d/_tests/test_plugin_inference.py @@ -9,8 +9,8 @@ volume_stats, ) from napari_cellseg3d.code_models.models.model_test import TestModel +from napari_cellseg3d.code_models.workers_utils import InferenceResult from napari_cellseg3d.code_plugins.plugin_model_inference import ( - InferenceResult, Inferer, ) from napari_cellseg3d.config import MODEL_LIST diff --git a/napari_cellseg3d/_tests/test_training.py b/napari_cellseg3d/_tests/test_training.py index 2378d193..f76b6d23 100644 --- a/napari_cellseg3d/_tests/test_training.py +++ b/napari_cellseg3d/_tests/test_training.py @@ -3,9 +3,9 @@ from napari_cellseg3d import config from napari_cellseg3d._tests.fixtures import LogFixture from napari_cellseg3d.code_models.models.model_test import TestModel +from napari_cellseg3d.code_models.workers_utils import TrainingReport from napari_cellseg3d.code_plugins.plugin_model_training import ( Trainer, - TrainingReport, ) from napari_cellseg3d.config import MODEL_LIST @@ -90,7 +90,7 @@ def test_training(make_napari_viewer_proxy, qtbot): res = next(worker.train()) assert isinstance(res, TrainingReport) - assert res.epoch == 1 + assert res.epoch == 0 widget.worker = worker res.show_plot = True diff --git a/napari_cellseg3d/_tests/test_weight_download.py b/napari_cellseg3d/_tests/test_weight_download.py index a00ab1de..e1392436 100644 --- a/napari_cellseg3d/_tests/test_weight_download.py +++ b/napari_cellseg3d/_tests/test_weight_download.py @@ -1,4 +1,4 @@ -from napari_cellseg3d.code_models.workers import ( +from napari_cellseg3d.code_models.workers_utils import ( PRETRAINED_WEIGHTS_DIR, WeightsDownloader, ) diff --git a/napari_cellseg3d/code_models/models/model_SwinUNetR.py b/napari_cellseg3d/code_models/models/model_SwinUNetR.py index 4a0bc35a..621ae0b3 100644 --- a/napari_cellseg3d/code_models/models/model_SwinUNetR.py +++ b/napari_cellseg3d/code_models/models/model_SwinUNetR.py @@ -1,5 +1,5 @@ from monai.networks.nets import SwinUNETR -from torch import softmax +from torch import sigmoid from napari_cellseg3d.utils import LOGGER @@ -39,7 +39,8 @@ def __init__( def forward(self, x_in): y = super().forward(x_in) - return softmax(y, dim=1) + # return softmax(y, dim=1) + return sigmoid(y) # def get_output(self, input): # out = self(input) diff --git a/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py b/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py index baf8635d..8f1a493a 100644 --- a/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py +++ b/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py @@ -8,7 +8,7 @@ class TRAILMAP_MS_(UNet3D): use_default_training = True weights_file = "TRAILMAP_MS_best_metric_epoch_26.pth" - # original model from Liqun Luo lab, transferred to pytorch and trained on mesoSPIM-acquired data (mostly cFOS as of July 2022) + # original model from Liqun Luo lab, transferred to pytorch and trained on mesoSPIM-acquired data (mostly TPH2 as of July 2022) def __init__(self, in_channels=1, out_channels=1, **kwargs): try: diff --git a/napari_cellseg3d/code_models/models/model_WNet.py b/napari_cellseg3d/code_models/models/model_WNet.py index 4058b603..37768616 100644 --- a/napari_cellseg3d/code_models/models/model_WNet.py +++ b/napari_cellseg3d/code_models/models/model_WNet.py @@ -11,11 +11,9 @@ def __init__( in_channels=1, out_channels=2, # num_classes=2, - device="cpu", **kwargs, ): super().__init__( - device=device, in_channels=in_channels, out_channels=out_channels, # num_classes=num_classes, diff --git a/napari_cellseg3d/code_models/models/wnet/model.py b/napari_cellseg3d/code_models/models/wnet/model.py index 5ef726b6..060242a1 100644 --- a/napari_cellseg3d/code_models/models/wnet/model.py +++ b/napari_cellseg3d/code_models/models/wnet/model.py @@ -23,13 +23,11 @@ class WNet_encoder(nn.Module): def __init__( self, - device, in_channels=1, out_channels=2 # num_classes=2 ): super().__init__() - self.device = device self.encoder = UNet( in_channels=in_channels, out_channels=out_channels, diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py new file mode 100644 index 00000000..c4221e7b --- /dev/null +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -0,0 +1,776 @@ +import platform +from pathlib import Path + +import numpy as np +import torch + +# MONAI +from monai.data import DataLoader, Dataset +from monai.inferers import sliding_window_inference +from monai.transforms import ( + AddChannel, + AsDiscrete, + Compose, + EnsureChannelFirstd, + EnsureType, + EnsureTyped, + LoadImaged, + SpatialPad, + SpatialPadd, + ToTensor, + Zoom, +) +from napari._qt.qthreading import GeneratorWorker +from tifffile import imwrite + +# local +from napari_cellseg3d import config, utils +from napari_cellseg3d.code_models.crf import crf_with_config +from napari_cellseg3d.code_models.instance_segmentation import volume_stats +from napari_cellseg3d.code_models.workers_utils import ( + PRETRAINED_WEIGHTS_DIR, + InferenceResult, + LogSignal, + ONNXModelWrapper, + QuantileNormalizationd, + WeightsDownloader, +) +from napari_cellseg3d.utils import LOGGER as logger + +""" +Writing something to log messages from outside the main thread needs specific care, +Following the instructions in the guides below to have a worker with custom signals, +a custom worker function was implemented. +""" + +# https://python-forum.io/thread-31349.html +# https://www.pythoncentral.io/pysidepyqt-tutorial-creating-your-own-signals-and-slots/ +# https://napari-staging-site.github.io/guides/stable/threading.html + + +class InferenceWorker(GeneratorWorker): + """A custom worker to run inference jobs in. + Inherits from :py:class:`napari.qt.threading.GeneratorWorker`""" + + def __init__( + self, + worker_config: config.InferenceWorkerConfig, + ): + """Initializes a worker for inference with the arguments needed by the :py:func:`~inference` function. + + The config contains the following attributes: + * device: cuda or cpu device to use for torch + * model_dict: the :py:attr:`~self.models_dict` dictionary to obtain the model name, class and instance + * weights_dict: dict with "custom" : bool to use custom weights or not; "path" : the path to weights if custom or name of the file if not custom + * results_path: the path to save the results to + * filetype: the file extension to use when saving, + * transforms: a dict containing transforms to perform at various times. + * instance: a dict containing parameters regarding instance segmentation + * use_window: use window inference with specific size or whole image + * window_infer_size: size of window if use_window is True + * keep_on_cpu: keep images on CPU or no + * stats_csv: compute stats on cells and save them to a csv file + * images_filepaths: the paths to the images of the dataset + * layer: the layer to run inference on + + Args: + * worker_config (config.InferenceWorkerConfig): dataclass containing the proper configuration elements + + Note: See :py:func:`~self.inference` + """ + + super().__init__(self.inference) + self._signals = LogSignal() # add custom signals + self.log_signal = self._signals.log_signal + self.warn_signal = self._signals.warn_signal + self.error_signal = self._signals.error_signal + + self.config = worker_config + + """These attributes are all arguments of :py:func:~inference, please see that for reference""" + + self.downloader = WeightsDownloader() + """Download utility""" + + @staticmethod + def create_inference_dict(images_filepaths): + """Create a dict for MONAI with "image" keys with all image paths in :py:attr:`~self.images_filepaths` + + Returns: + dict: list of image paths from loaded folder""" + return [{"image": image_name} for image_name in images_filepaths] + + def set_download_log(self, widget): + self.downloader.log_widget = widget + + def log(self, text): + """Sends a signal that ``text`` should be logged + + Args: + text (str): text to logged + """ + self.log_signal.emit(text) + + def warn(self, warning): + """Sends a warning to main thread""" + self.warn_signal.emit(warning) + + def raise_error(self, exception, msg): + """Raises an error in main thread""" + logger.error(msg, exc_info=True) + logger.error(exception, exc_info=True) + + self.log_signal.emit("!" * 20) + self.log_signal.emit("Error occured") + # self.log_signal.emit(msg) + # self.log_signal.emit(str(exception)) + + self.error_signal.emit(exception, msg) + self.errored.emit(exception) + yield exception + # self.quit() + + def log_parameters(self): + config = self.config + + self.log("-" * 20) + self.log("\nParameters summary :") + + self.log(f"Model is : {config.model_info.name}") + if config.post_process_config.thresholding.enabled: + self.log( + f"Thresholding is enabled at {config.post_process_config.thresholding.threshold_value}" + ) + + status = ( + "enabled" + if config.sliding_window_config.is_enabled() + else "disabled" + ) + + self.log(f"Window inference is {status}\n") + if status == "enabled": + self.log( + f"Window size is {self.config.sliding_window_config.window_size}" + ) + self.log( + f"Window overlap is {self.config.sliding_window_config.window_overlap}" + ) + + if config.keep_on_cpu: + self.log("Dataset loaded to CPU") + else: + self.log(f"Dataset loaded on {config.device} device") + + if config.post_process_config.zoom.enabled: + self.log( + f"Scaling factor : {config.post_process_config.zoom.zoom_values} (x,y,z)" + ) + + instance_config = config.post_process_config.instance + if instance_config.enabled: + self.log( + f"Instance segmentation enabled, method : {instance_config.method.name}\n" + ) + self.log("-" * 20) + + def load_folder(self): + images_dict = self.create_inference_dict(self.config.images_filepaths) + + # TODO : better solution than loading first image always ? + data_check = LoadImaged(keys=["image"])(images_dict[0]) + check = data_check["image"].shape + pad = utils.get_padding_dim(check) + + if self.config.sliding_window_config.is_enabled(): + load_transforms = Compose( + [ + LoadImaged(keys=["image"]), + # AddChanneld(keys=["image"]), #already done + EnsureChannelFirstd(keys=["image"]), + # Orientationd(keys=["image"], axcodes="PLI"), + # anisotropic_transform, + QuantileNormalizationd(keys=["image"]), + EnsureTyped(keys=["image"]), + ] + ) + else: + load_transforms = Compose( + [ + LoadImaged(keys=["image"]), + # AddChanneld(keys=["image"]), #already done + EnsureChannelFirstd(keys=["image"]), + # NormalizeIntensityd(keys=["image"]), # TODO normalize + # Orientationd(keys=["image"], axcodes="PLI"), + # anisotropic_transform, + SpatialPadd(keys=["image"], spatial_size=pad), + EnsureTyped(keys=["image"]), + ] + ) + + self.log("\nLoading dataset...") + inference_ds = Dataset(data=images_dict, transform=load_transforms) + inference_loader = DataLoader( + inference_ds, batch_size=1, num_workers=2 + ) + self.log("Done") + return inference_loader + + def load_layer(self): + self.log("\nLoading layer\n") + data = np.squeeze(self.config.layer.data) + + volume = np.array(data, dtype=np.int16) + + volume_dims = len(volume.shape) + if volume_dims != 3: + raise ValueError( + f"Data array is not 3-dimensional but {volume_dims}-dimensional," + f" please check for extra channel/batch dimensions" + ) + + volume = np.swapaxes( + volume, 0, 2 + ) # for anisotropy to be monai-like, i.e. zyx # FIXME rotation not always correct + + dims_check = volume.shape + + # logger.debug(volume.shape) + # logger.debug(volume.dtype) + if self.config.sliding_window_config.is_enabled(): + load_transforms = Compose( + [ + ToTensor(), + # anisotropic_transform, + AddChannel(), + # SpatialPad(spatial_size=pad), + AddChannel(), + EnsureType(), + ], + map_items=False, + log_stats=True, + ) + else: + self.log("Checking dimensions...") + pad = utils.get_padding_dim(dims_check) + load_transforms = Compose( + [ + ToTensor(), + # anisotropic_transform, + AddChannel(), + SpatialPad(spatial_size=pad), + AddChannel(), + EnsureType(), + ], + map_items=False, + log_stats=True, + ) + + input_image = load_transforms(volume) + self.log("Done") + return input_image + + def model_output( + self, + inputs, + model, + post_process_transforms, + post_process=True, + aniso_transform=None, + ): + inputs = inputs.to("cpu") + + # def model_output(inputs): + # return post_process_transforms( + # self.config.model_info.get_model().get_output(model, inputs) + # ) + + dataset_device = ( + "cpu" if self.config.keep_on_cpu else self.config.device + ) + + if self.config.sliding_window_config.is_enabled(): + window_size = self.config.sliding_window_config.window_size + window_size = [window_size, window_size, window_size] + window_overlap = self.config.sliding_window_config.window_overlap + else: + window_size = None + window_overlap = 0 + try: + # logger.debug(f"model : {model}") + logger.debug(f"inputs shape : {inputs.shape}") + logger.debug(f"inputs type : {inputs.dtype}") + try: + # outputs = model(inputs) + inputs = utils.remap_image(inputs) + + def model_output_wrapper(inputs): + result = model(inputs) + return post_process_transforms(result) + + with torch.no_grad(): + outputs = sliding_window_inference( + inputs, + roi_size=window_size, + sw_batch_size=1, # TODO add param + predictor=model_output_wrapper, + sw_device=self.config.device, + device=dataset_device, + overlap=window_overlap, + mode="gaussian", + sigma_scale=0.01, + progress=True, + ) + except Exception as e: + logger.exception(e) + logger.debug("failed to run sliding window inference") + self.raise_error(e, "Error during sliding window inference") + logger.debug(f"Inference output shape: {outputs.shape}") + self.log("Post-processing...") + out = outputs.detach().cpu().numpy() + if aniso_transform is not None: + out = aniso_transform(out) + if post_process: + out = np.array(out).astype(np.float32) + out = np.squeeze(out) + return out + except Exception as e: + logger.exception(e) + self.raise_error(e, "Error during sliding window inference") + # sys.stdout = old_stdout + # sys.stderr = old_stderr + + def create_inference_result( + self, + semantic_labels, + instance_labels, + crf_results=None, + from_layer: bool = False, + original=None, + stats=None, + i=0, + ) -> InferenceResult: + if not from_layer and original is None: + raise ValueError( + "If the image is not from a layer, an original should always be available" + ) + + if from_layer: + if i != 0: + raise ValueError( + "A layer's ID should always be 0 (default value)" + ) + + if semantic_labels is not None: + semantic_labels = utils.correct_rotation(semantic_labels) + if crf_results is not None: + crf_results = utils.correct_rotation(crf_results) + if instance_labels is not None: + instance_labels = utils.correct_rotation(instance_labels) + + return InferenceResult( + image_id=i + 1, + original=original, + instance_labels=instance_labels, + crf_results=crf_results, + stats=stats, + result=semantic_labels, + model_name=self.config.model_info.name, + ) + + def get_original_filename(self, i): + return Path(self.config.images_filepaths[i]).stem + + def get_instance_result(self, semantic_labels, from_layer=False, i=-1): + if not from_layer and i == -1: + raise ValueError( + "An ID should be provided when running from a file" + ) + + if self.config.post_process_config.instance.enabled: + instance_labels = self.instance_seg( + semantic_labels, + i + 1, + ) + data_dict = self.stats_csv(instance_labels) + else: + instance_labels = None + data_dict = None + return instance_labels, data_dict + + def save_image( + self, + image, + from_layer=False, + i=0, + additional_info="", + ): + if not from_layer: + original_filename = "_" + self.get_original_filename(i) + "_" + filetype = self.config.filetype + else: + original_filename = "_" + filetype = ".tif" + + time = utils.get_date_time() + + file_path = ( + self.config.results_path + + "/" + + f"{additional_info}" + + f"Prediction_{i+1}" + + original_filename + + self.config.model_info.name + + f"_{time}" + + filetype + ) + try: + imwrite(file_path, image) + except ValueError as e: + self.raise_error(e, "Error during image saving") + filename = Path(file_path).stem + + if from_layer: + self.log(f"\nLayer prediction saved as : {filename}") + else: + self.log(f"\nFile n°{i+1} saved as : {filename}") + + def aniso_transform(self, image): + if self.config.post_process_config.zoom.enabled: + zoom = self.config.post_process_config.zoom.zoom_values + anisotropic_transform = Zoom( + zoom=zoom, + keep_size=False, + padding_mode="empty", + ) + return anisotropic_transform(image[0]) + return image + + def instance_seg( + self, semantic_labels, image_id=0, original_filename="layer" + ): + if image_id is not None: + self.log(f"\nRunning instance segmentation for image n°{image_id}") + + method = self.config.post_process_config.instance.method + instance_labels = method.run_method_on_channels(semantic_labels) + self.log(f"DEBUG instance results shape : {instance_labels.shape}") + + filetype = ( + ".tif" + if self.config.filetype == "" + else "_" + self.config.filetype + ) + + instance_filepath = ( + self.config.results_path + + "/" + + f"Instance_seg_labels_{image_id}_" + + original_filename + + "_" + + self.config.model_info.name + + f"_{utils.get_date_time()}" + + filetype + ) + + imwrite(instance_filepath, instance_labels) + self.log( + f"Instance segmentation results for image n°{image_id} have been saved as:" + ) + self.log(Path(instance_filepath).name) + return instance_labels + + def inference_on_folder(self, inf_data, i, model, post_process_transforms): + self.log("-" * 10) + self.log(f"Inference started on image n°{i + 1}...") + + inputs = inf_data["image"] + + out = self.model_output( + inputs, + model, + post_process_transforms, + aniso_transform=self.aniso_transform, + ) + + self.save_image(out, i=i) + instance_labels, stats = self.get_instance_result(out, i=i) + if self.config.use_crf: + crf_in = inputs.detach().cpu().numpy() + try: + crf_results = self.run_crf( + crf_in, + out, + aniso_transform=self.aniso_transform, + image_id=i, + ) + + except ValueError as e: + self.log(f"Error occurred during CRF : {e}") + crf_results = None + else: + crf_results = None + + original = np.array(inf_data["image"]).astype(np.float32) + + self.log(f"Inference completed on image n°{i+1}") + + return self.create_inference_result( + out, + instance_labels, + crf_results, + from_layer=False, + original=original, + stats=stats, + i=i, + ) + + def run_crf(self, image, labels, aniso_transform, image_id=0): + try: + if aniso_transform is not None: + image = aniso_transform(image) + crf_results = crf_with_config( + image, labels, config=self.config.crf_config, log=self.log + ) + self.save_image( + crf_results, + i=image_id, + additional_info="CRF_", + from_layer=True, + ) + return crf_results + except ValueError as e: + self.log(f"Error occurred during CRF : {e}") + return None + + def stats_csv(self, instance_labels): + try: + if self.config.compute_stats: + if len(instance_labels.shape) == 4: + stats = [volume_stats(c) for c in instance_labels] + else: + stats = [volume_stats(instance_labels)] + else: + stats = None + return stats + except ValueError as e: + self.log(f"Error occurred during stats computing : {e}") + return None + + def inference_on_layer(self, image, model, post_process_transforms): + self.log("-" * 10) + self.log("Inference started on layer...") + + image = image.type(torch.FloatTensor) + image = utils.quantile_normalization(image) + + out = self.model_output( + image, + model, + post_process_transforms, + aniso_transform=self.aniso_transform, + ) + + self.save_image(out, from_layer=True) + + instance_labels, stats = self.get_instance_result( + semantic_labels=out, from_layer=True + ) + + crf_results = ( + self.run_crf(image, out, aniso_transform=self.aniso_transform) + if self.config.use_crf + else None + ) + + return self.create_inference_result( + semantic_labels=out, + instance_labels=instance_labels, + crf_results=crf_results, + from_layer=True, + stats=stats, + ) + + # @thread_worker(connect={"errored": self.raise_error}) + def inference(self): + """ + Requires: + * device: cuda or cpu device to use for torch + + * model_dict: the :py:attr:`~self.models_dict` dictionary to obtain the model name, class and instance + + * weights: the loaded weights from the model + + * images_filepaths: the paths to the images of the dataset + + * results_path: the path to save the results to + + * filetype: the file extension to use when saving, + + * transforms: a dict containing transforms to perform at various times. + + * use_window: use window inference with specific size or whole image + + * window_infer_size: size of window if use_window is True + + * keep_on_cpu: keep images on CPU or no + + * stats_csv: compute stats on cells and save them to a csv file + + Yields: + dict: contains : + * "image_id" : index of the returned image + + * "original" : original volume used for inference + + * "result" : inference result + + """ + sys = platform.system() + logger.debug(f"OS is {sys}") + if sys == "Darwin": + torch.set_num_threads(1) # required for threading on macOS ? + self.log("Number of threads has been set to 1 for macOS") + + try: + dims = self.config.model_info.model_input_size + self.log(f"MODEL DIMS : {dims}") + model_name = self.config.model_info.name + model_class = self.config.model_info.get_model() + self.log(f"Model name : {model_name}") + + weights_config = self.config.weights_config + post_process_config = self.config.post_process_config + if Path(weights_config.path).suffix == ".pt": + self.log("Instantiating PyTorch jit model...") + model = torch.jit.load(weights_config.path) + # try: + elif Path(weights_config.path).suffix == ".onnx": + self.log("Instantiating ONNX model...") + model = ONNXModelWrapper(weights_config.path) + else: # assume is .pth + self.log("Instantiating model...") + model = model_class( + input_img_size=[dims, dims, dims], + # device=self.config.device, + num_classes=self.config.model_info.num_classes, + ) + # try: + model = model.to(self.config.device) + # except Exception as e: + # self.raise_error(e, "Issue loading model to device") + # logger.debug(f"model : {model}") + if model is None: + raise ValueError("Model is None") + # try: + self.log("\nLoading weights...") + if weights_config.custom: + weights = weights_config.path + else: + self.downloader.download_weights( + model_name, + model_class.weights_file, + ) + weights = str( + PRETRAINED_WEIGHTS_DIR / Path(model_class.weights_file) + ) + + model.load_state_dict( # note that this is redefined in WNet_ + torch.load( + weights, + map_location=self.config.device, + ) + ) + self.log("Done") + # except Exception as e: + # self.raise_error(e, "Issue loading weights") + # except Exception as e: + # self.raise_error(e, "Issue instantiating model") + + # if model_name == "SegResNet": + # model = model_class( + # input_image_size=[ + # dims, + # dims, + # dims, + # ], + # ) + # elif model_name == "SwinUNetR": + # model = model_class( + # img_size=[dims, dims, dims], + # use_checkpoint=False, + # ) + # else: + # model = model_class.get_net() + + self.log_parameters() + + # load_transforms = Compose( + # [ + # LoadImaged(keys=["image"]), + # # AddChanneld(keys=["image"]), #already done + # EnsureChannelFirstd(keys=["image"]), + # # Orientationd(keys=["image"], axcodes="PLI"), + # # anisotropic_transform, + # SpatialPadd(keys=["image"], spatial_size=pad), + # EnsureTyped(keys=["image"]), + # ] + # ) + + if not post_process_config.thresholding.enabled: + post_process_transforms = EnsureType() + else: + t = post_process_config.thresholding.threshold_value + post_process_transforms = Compose( + AsDiscrete(threshold=t), EnsureType() + ) + + is_folder = self.config.images_filepaths is not None + is_layer = self.config.layer is not None + + if is_layer and is_folder: + raise ValueError( + "Both a layer and a folder have been specified, please specify only one of the two. Aborting." + ) + elif is_folder: + inference_loader = self.load_folder() + ################## + ################## + # DEBUG + # from monai.utils import first + # + # check_data = first(inference_loader) + # image = check_data[0][0] + # logger.debug(image.shape) + ################## + ################## + elif is_layer: + input_image = self.load_layer() + else: + raise ValueError("No data has been provided. Aborting.") + + if model is None: + raise ValueError("Model is None") + + model.eval() + with torch.no_grad(): + ################################ + ################################ + ################################ + if is_folder: + for i, inf_data in enumerate(inference_loader): + yield self.inference_on_folder( + inf_data, i, model, post_process_transforms + ) + elif is_layer: + yield self.inference_on_layer( + input_image, model, post_process_transforms + ) + model.to("cpu") + # self.quit() + except Exception as e: + logger.exception(e) + self.raise_error(e, "Inference failed") + self.quit() + finally: + self.quit() diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py new file mode 100644 index 00000000..03e82ed5 --- /dev/null +++ b/napari_cellseg3d/code_models/worker_training.py @@ -0,0 +1,731 @@ +import platform +import time +from math import ceil +from pathlib import Path + +import torch + +# MONAI +from monai.data import ( + CacheDataset, + DataLoader, + PatchDataset, + decollate_batch, + pad_list_data_collate, +) +from monai.inferers import sliding_window_inference +from monai.metrics import DiceMetric +from monai.transforms import ( + AsDiscrete, + Compose, + EnsureChannelFirstd, + EnsureType, + EnsureTyped, + LoadImaged, + # NormalizeIntensityd, + Orientationd, + Rand3DElasticd, + RandAffined, + RandFlipd, + RandRotate90d, + RandShiftIntensityd, + RandSpatialCropSamplesd, + SpatialPadd, +) +from monai.utils import set_determinism + +# Qt +from napari.qt.threading import GeneratorWorker + +# local +from napari_cellseg3d import config, utils +from napari_cellseg3d.code_models.workers_utils import ( + PRETRAINED_WEIGHTS_DIR, + LogSignal, + QuantileNormalizationd, + TrainingReport, + WeightsDownloader, +) + +logger = utils.LOGGER +VERBOSE_SCHEDULER = True +logger.debug(f"PRETRAINED WEIGHT DIR LOCATION : {PRETRAINED_WEIGHTS_DIR}") + +""" +Writing something to log messages from outside the main thread needs specific care, +Following the instructions in the guides below to have a worker with custom signals, +a custom worker function was implemented. +""" + +# https://python-forum.io/thread-31349.html +# https://www.pythoncentral.io/pysidepyqt-tutorial-creating-your-own-signals-and-slots/ +# https://napari-staging-site.github.io/guides/stable/threading.html + + +class TrainingWorker(GeneratorWorker): + """A custom worker to run training jobs in. + Inherits from :py:class:`napari.qt.threading.GeneratorWorker`""" + + def __init__( + self, + worker_config: config.TrainingWorkerConfig, + ): + """Initializes a worker for inference with the arguments needed by the :py:func:`~train` function. Note: See :py:func:`~train` + + Args: + * device : device to train on, cuda or cpu + + * model_dict : dict containing the model's "name" and "class" + + * weights_path : path to weights files if transfer learning is to be used + + * data_dicts : dict from :py:func:`Trainer.create_train_dataset_dict` + + * validation_percent : percentage of images to use as validation + + * max_epochs : the amout of epochs to train for + + * loss_function : the loss function to use for training + + * learning_rate : the learning rate of the optimizer + + * val_interval : the interval at which to perform validation (e.g. if 2 will validate once every 2 epochs.) Also determines frequency of saving, depending on whether the metric is better or not + + * batch_size : the batch size to use for training + + * results_path : the path to save results in + + * sampling : whether to extract patches from images or not + + * num_samples : the number of samples to extract from an image for training + + * sample_size : the size of the patches to extract when sampling + + * do_augmentation : whether to perform data augmentation or not + + * deterministic : dict with "use deterministic" : bool, whether to use deterministic training, "seed": seed for RNG + + + """ + super().__init__(self.train) + self._signals = LogSignal() + self.log_signal = self._signals.log_signal + self.warn_signal = self._signals.warn_signal + self.error_signal = self._signals.error_signal + + self._weight_error = False + ############################################# + self.config = worker_config + + self.train_files = [] + self.val_files = [] + ####################################### + self.downloader = WeightsDownloader() + + def set_download_log(self, widget): + self.downloader.log_widget = widget + + def log(self, text): + """Sends a signal that ``text`` should be logged + + Args: + text (str): text to logged + """ + self.log_signal.emit(text) + + def warn(self, warning): + """Sends a warning to main thread""" + self.warn_signal.emit(warning) + + def raise_error(self, exception, msg): + """Sends an error to main thread""" + logger.error(msg, exc_info=True) + logger.error(exception, exc_info=True) + self.error_signal.emit(exception, msg) + self.errored.emit(exception) + self.quit() + + def log_parameters(self): + self.log("-" * 20) + self.log("Parameters summary :\n") + + self.log( + f"Percentage of dataset used for validation : {self.config.validation_percent * 100}%" + ) + + self.log("-" * 10) + self.log("Training files :\n") + [ + self.log(f"{Path(train_file['image']).name}\n") + for train_file in self.train_files + ] + self.log("-" * 10) + self.log("Validation files :\n") + [ + self.log(f"{Path(val_file['image']).name}\n") + for val_file in self.val_files + ] + self.log("-" * 10) + + if self.config.deterministic_config.enabled: + self.log("Deterministic training is enabled") + self.log(f"Seed is {self.config.deterministic_config.seed}") + + self.log(f"Training for {self.config.max_epochs} epochs") + self.log(f"Loss function is : {str(self.config.loss_function)}") + self.log( + f"Validation is performed every {self.config.validation_interval} epochs" + ) + self.log(f"Batch size is {self.config.batch_size}") + self.log(f"Learning rate is {self.config.learning_rate}") + + if self.config.sampling: + self.log( + f"Extracting {self.config.num_samples} patches of size {self.config.sample_size}" + ) + else: + self.log("Using whole images as dataset") + + if self.config.do_augmentation: + self.log("Data augmentation is enabled") + + if not self.config.weights_info.use_pretrained: + self.log(f"Using weights from : {self.config.weights_info.path}") + if self._weight_error: + self.log( + ">>>>>>>>>>>>>>>>>\n" + "WARNING:\nChosen weights were incompatible with the model,\n" + "the model will be trained from random weights\n" + "<<<<<<<<<<<<<<<<<\n" + ) + + # self.log("\n") + self.log("-" * 20) + + def train(self): + """Trains the PyTorch model for the given number of epochs, with the selected model and data, + using the chosen batch size, validation interval, loss function, and number of samples. + Will perform validation once every :py:obj:`val_interval` and save results if the mean dice is better + + Requires: + + * device : device to train on, cuda or cpu + + * model_dict : dict containing the model's "name" and "class" + + * weights_path : path to weights files if transfer learning is to be used + + * data_dicts : dict from :py:func:`Trainer.create_train_dataset_dict` + + * validation_percent : percentage of images to use as validation + + * max_epochs : the amount of epochs to train for + + * loss_function : the loss function to use for training + + * learning rate : the learning rate of the optimizer + + * val_interval : the interval at which to perform validation (e.g. if 2 will validate once every 2 epochs.) Also determines frequency of saving, depending on whether the metric is better or not + + * batch_size : the batch size to use for training + + * results_path : the path to save results in + + * sampling : whether to extract patches from images or not + + * num_samples : the number of samples to extract from an image for training + + * sample_size : the size of the patches to extract when sampling + + * do_augmentation : whether to perform data augmentation or not + + * deterministic : dict with "use deterministic" : bool, whether to use deterministic training, "seed": seed for RNG + """ + + ######################### + # error_log = open(results_path +"/error_log.log" % multiprocessing.current_process().name, 'x') + # faulthandler.enable(file=error_log, all_threads=True) + ######################### + model_config = self.config.model_info + weights_config = self.config.weights_info + deterministic_config = self.config.deterministic_config + + start_time = time.time() + + try: + if deterministic_config.enabled: + set_determinism( + seed=deterministic_config.seed + ) # use_deterministic_algorithms = True causes cuda error + + sys = platform.system() + logger.debug(sys) + if sys == "Darwin": # required for macOS ? + torch.set_num_threads(1) + self.log("Number of threads has been set to 1 for macOS") + + self.log(f"config model : {self.config.model_info.name}") + model_name = model_config.name + model_class = model_config.get_model() + + if not self.config.sampling: + data_check = LoadImaged(keys=["image"])( + self.config.train_data_dict[0] + ) + check = data_check["image"].shape + + do_sampling = self.config.sampling + + size = self.config.sample_size if do_sampling else check + + PADDING = utils.get_padding_dim(size) + model = model_class( # FIXME check if correct + input_img_size=PADDING, use_checkpoint=True + ) + model = model.to(self.config.device) + + epoch_loss_values = [] + val_metric_values = [] + + if len(self.config.train_data_dict) > 1: + self.train_files, self.val_files = ( + self.config.train_data_dict[ + 0 : int( + len(self.config.train_data_dict) + * self.config.validation_percent + ) + ], + self.config.train_data_dict[ + int( + len(self.config.train_data_dict) + * self.config.validation_percent + ) : + ], + ) + else: + self.train_files = self.val_files = self.config.train_data_dict + msg = f"Only one image file was provided : {self.config.train_data_dict[0]['image']}.\n" + + logger.debug(f"SAMPLING is {self.config.sampling}") + if not self.config.sampling: + msg += "Sampling is not in use, the only image provided will be used as the validation file." + self.warn(msg) + else: + msg += "Samples for validation will be cropped for the same only volume that is being used for training" + + logger.warning(msg) + + logger.debug( + f"Data dict from config is {self.config.train_data_dict}" + ) + logger.debug(f"Train files : {self.train_files}") + logger.debug(f"Val. files : {self.val_files}") + + if len(self.train_files) == 0: + raise ValueError("Training dataset is empty") + if len(self.val_files) == 0: + raise ValueError("Validation dataset is empty") + + if self.config.do_augmentation: + train_transforms = ( + Compose( # TODO : figure out which ones and values ? + [ + RandShiftIntensityd(keys=["image"], offsets=0.7), + Rand3DElasticd( + keys=["image", "label"], + sigma_range=(0.3, 0.7), + magnitude_range=(0.3, 0.7), + ), + RandFlipd(keys=["image", "label"]), + RandRotate90d(keys=["image", "label"]), + RandAffined( + keys=["image", "label"], + ), + EnsureTyped(keys=["image", "label"]), + ] + ) + ) + else: + train_transforms = EnsureTyped(keys=["image", "label"]) + + val_transforms = Compose( + [ + # LoadImaged(keys=["image", "label"]), + # EnsureChannelFirstd(keys=["image", "label"]), + EnsureTyped(keys=["image", "label"]), + ] + ) + + # self.log("Loading dataset...\n") + def get_loader_func(num_samples): + return Compose( + [ + LoadImaged(keys=["image", "label"]), + EnsureChannelFirstd(keys=["image", "label"]), + QuantileNormalizationd(keys=["image"]), + RandSpatialCropSamplesd( + keys=["image", "label"], + roi_size=( + self.config.sample_size + ), # multiply by axis_stretch_factor if anisotropy + # max_roi_size=(120, 120, 120), + random_size=False, + num_samples=num_samples, + ), + Orientationd(keys=["image", "label"], axcodes="PLI"), + SpatialPadd( + keys=["image", "label"], + spatial_size=( + utils.get_padding_dim(self.config.sample_size) + ), + ), + EnsureTyped(keys=["image", "label"]), + ] + ) + + if do_sampling: + # if there is only one volume, split samples + # TODO(cyril) : maybe implement something in user config to toggle this behavior + if len(self.config.train_data_dict) < 2: + num_train_samples = ceil( + self.config.num_samples + * self.config.validation_percent + ) + num_val_samples = ceil( + self.config.num_samples + * (1 - self.config.validation_percent) + ) + sample_loader_train = get_loader_func(num_train_samples) + sample_loader_eval = get_loader_func(num_val_samples) + else: + num_train_samples = ( + num_val_samples + ) = self.config.num_samples + + sample_loader_train = get_loader_func(num_train_samples) + sample_loader_eval = get_loader_func(num_val_samples) + + logger.debug(f"AMOUNT of train samples : {num_train_samples}") + logger.debug( + f"AMOUNT of validation samples : {num_val_samples}" + ) + + logger.debug("train_ds") + train_ds = PatchDataset( + data=self.train_files, + transform=train_transforms, + patch_func=sample_loader_train, + samples_per_image=num_train_samples, + ) + logger.debug("val_ds") + val_ds = PatchDataset( + data=self.val_files, + transform=val_transforms, + patch_func=sample_loader_eval, + samples_per_image=num_val_samples, + ) + + else: + load_whole_images = Compose( + [ + LoadImaged( + keys=["image", "label"], + # image_only=True, + # reader=WSIReader(backend="tifffile") + ), + EnsureChannelFirstd(keys=["image", "label"]), + Orientationd(keys=["image", "label"], axcodes="PLI"), + QuantileNormalizationd(keys=["image"]), + SpatialPadd( + keys=["image", "label"], + spatial_size=PADDING, + ), + EnsureTyped(keys=["image", "label"]), + ] + ) + logger.debug("Cache dataset : train") + train_ds = CacheDataset( + data=self.train_files, + transform=Compose(load_whole_images, train_transforms), + ) + logger.debug("Cache dataset : val") + val_ds = CacheDataset( + data=self.val_files, transform=load_whole_images + ) + logger.debug("Dataloader") + train_loader = DataLoader( + train_ds, + batch_size=self.config.batch_size, + shuffle=True, + num_workers=2, + collate_fn=pad_list_data_collate, + ) + + val_loader = DataLoader( + val_ds, batch_size=self.config.batch_size, num_workers=2 + ) + logger.info("\nDone") + + logger.debug("Optimizer") + optimizer = torch.optim.Adam( + model.parameters(), self.config.learning_rate + ) + + factor = self.config.scheduler_factor + if factor >= 1.0: + self.log(f"Warning : scheduler factor is {factor} >= 1.0") + self.log("Setting it to 0.5") + factor = 0.5 + + scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + optimizer=optimizer, + mode="min", + factor=factor, + patience=self.config.scheduler_patience, + verbose=VERBOSE_SCHEDULER, + ) + dice_metric = DiceMetric( + include_background=False, reduction="mean" + ) + + best_metric = -1 + best_metric_epoch = -1 + + # time = utils.get_date_time() + logger.debug("Weights") + + if weights_config.custom: + if weights_config.use_pretrained: + weights_file = model_class.weights_file + self.downloader.download_weights(model_name, weights_file) + weights = PRETRAINED_WEIGHTS_DIR / Path(weights_file) + weights_config.path = weights + else: + weights = str(Path(weights_config.path)) + + try: + model.load_state_dict( + torch.load( + weights, + map_location=self.config.device, + ) + ) + except RuntimeError as e: + logger.error(f"Error when loading weights : {e}") + logger.exception(e) + warn = ( + "WARNING:\nIt'd seem that the weights were incompatible with the model,\n" + "the model will be trained from random weights" + ) + self.log(warn) + self.warn(warn) + self._weight_error = True + + if self.config.device.type == "cuda": + self.log("\nUsing GPU :") + self.log(torch.cuda.get_device_name(0)) + else: + self.log("Using CPU") + + self.log_parameters() + + device = self.config.device + + # if model_name == "test": + # self.quit() + # yield TrainingReport(False) + + for epoch in range(self.config.max_epochs): + # self.log("\n") + self.log("-" * 10) + self.log(f"Epoch {epoch + 1}/{self.config.max_epochs}") + if device.type == "cuda": + self.log("Memory Usage:") + alloc_mem = round( + torch.cuda.memory_allocated(0) / 1024**3, 1 + ) + reserved_mem = round( + torch.cuda.memory_reserved(0) / 1024**3, 1 + ) + self.log(f"Allocated: {alloc_mem}GB") + self.log(f"Cached: {reserved_mem}GB") + + model.train() + epoch_loss = 0 + step = 0 + for batch_data in train_loader: + step += 1 + inputs, labels = ( + batch_data["image"].to(device), + batch_data["label"].to(device), + ) + + optimizer.zero_grad() + outputs = model(inputs) + # self.log(f"Output dimensions : {outputs.shape}") + if outputs.shape[1] > 1: + outputs = outputs[ + :, 1:, :, : + ] # FIXME fix channel number + if len(outputs.shape) < 4: + outputs = outputs.unsqueeze(0) + loss = self.config.loss_function(outputs, labels) + loss.backward() + optimizer.step() + epoch_loss += loss.detach().item() + self.log( + f"* {step}/{len(train_ds) // train_loader.batch_size}, " + f"Train loss: {loss.detach().item():.4f}" + ) + yield TrainingReport( + show_plot=False, weights=model.state_dict() + ) + + # return + + epoch_loss /= step + epoch_loss_values.append(epoch_loss) + self.log(f"Epoch: {epoch + 1}, Average loss: {epoch_loss:.4f}") + + self.log("Updating scheduler...") + scheduler.step(epoch_loss) + + checkpoint_output = [] + eta = ( + (time.time() - start_time) + * (self.config.max_epochs / (epoch + 1) - 1) + / 60 + ) + self.log("ETA: " + f"{eta:.2f}" + " minutes") + + if ( + (epoch + 1) % self.config.validation_interval == 0 + or epoch + 1 == self.config.max_epochs + ): + model.eval() + self.log("Performing validation...") + with torch.no_grad(): + for val_data in val_loader: + val_inputs, val_labels = ( + val_data["image"].to(device), + val_data["label"].to(device), + ) + + try: + with torch.no_grad(): + val_outputs = sliding_window_inference( + val_inputs, + roi_size=size, + sw_batch_size=self.config.batch_size, + predictor=model, + overlap=0.25, + sw_device=self.config.device, + device=self.config.device, + progress=False, + ) + except Exception as e: + self.raise_error(e, "Error during validation") + logger.debug( + f"val_outputs shape : {val_outputs.shape}" + ) + # val_outputs = model(val_inputs) + + pred = decollate_batch(val_outputs) + + labs = decollate_batch(val_labels) + + # TODO : more parameters/flexibility + post_pred = Compose( + AsDiscrete(threshold=0.6), EnsureType() + ) # + post_label = EnsureType() + + val_outputs = [ + post_pred(res_tensor) for res_tensor in pred + ] + + val_labels = [ + post_label(res_tensor) for res_tensor in labs + ] + + # logger.debug(len(val_outputs)) + # logger.debug(len(val_labels)) + + dice_metric(y_pred=val_outputs, y=val_labels) + checkpoint_output.append( + [res.detach().cpu() for res in val_outputs] + ) + + checkpoint_output = [ + item.numpy() + for batch in checkpoint_output + for item in batch + ] + + metric = dice_metric.aggregate().detach().item() + dice_metric.reset() + + val_metric_values.append(metric) + + train_report = TrainingReport( + show_plot=True, + epoch=epoch, + loss_values=epoch_loss_values, + validation_metric=val_metric_values, + weights=model.state_dict(), + images=checkpoint_output, + ) + self.log("Validation completed") + yield train_report + + weights_filename = ( + f"{model_name}_best_metric" + + f"_epoch_{epoch + 1}.pth" + ) + + if metric > best_metric: + best_metric = metric + best_metric_epoch = epoch + 1 + self.log("Saving best metric model") + torch.save( + model.state_dict(), + Path(self.config.results_path_folder) + / Path( + weights_filename, + ), + ) + self.log("Saving complete") + self.log( + f"Current epoch: {epoch + 1}, Current mean dice: {metric:.4f}" + f"\nBest mean dice: {best_metric:.4f} " + f"at epoch: {best_metric_epoch}" + ) + self.log("=" * 10) + self.log( + f"Train completed, best_metric: {best_metric:.4f} " + f"at epoch: {best_metric_epoch}" + ) + # Save last checkpoint + weights_filename = f"{model_name}_last_epoch_{epoch + 1}.pth" + self.log("Saving last model") + torch.save( + model.state_dict(), + Path(self.config.results_path_folder) / Path(weights_filename), + ) + self.log("Saving complete, exiting") + model.to("cpu") + # clear (V)RAM + val_ds = None + train_ds = None + val_loader = None + train_loader = None + torch.cuda.empty_cache() + import gc + + gc.collect() + + except Exception as e: + self.raise_error(e, "Error in training") + self.quit() + finally: + self.quit() diff --git a/napari_cellseg3d/code_models/workers.py b/napari_cellseg3d/code_models/workers.py deleted file mode 100644 index d2fa1e41..00000000 --- a/napari_cellseg3d/code_models/workers.py +++ /dev/null @@ -1,1679 +0,0 @@ -import platform -import time -import typing as t -from dataclasses import dataclass -from math import ceil -from pathlib import Path - -import numpy as np -import torch - -# MONAI -from monai.data import ( - CacheDataset, - DataLoader, - Dataset, - PatchDataset, - decollate_batch, - pad_list_data_collate, -) -from monai.inferers import sliding_window_inference -from monai.metrics import DiceMetric -from monai.transforms import ( - AddChannel, - AsDiscrete, - Compose, - EnsureChannelFirstd, - EnsureType, - EnsureTyped, - LoadImaged, - MapTransform, - # NormalizeIntensityd, - Orientationd, - Rand3DElasticd, - RandAffined, - RandFlipd, - RandRotate90d, - RandShiftIntensityd, - RandSpatialCropSamplesd, - SpatialPad, - SpatialPadd, - ToTensor, - Zoom, -) -from monai.utils import set_determinism - -# from napari.qt.threading import thread_worker -# threads -from napari.qt.threading import GeneratorWorker, WorkerBaseSignals - -# Qt -from qtpy.QtCore import Signal -from tifffile import imwrite -from tqdm import tqdm - -# local -from napari_cellseg3d import config, utils -from napari_cellseg3d import interface as ui -from napari_cellseg3d.code_models.crf import crf_with_config -from napari_cellseg3d.code_models.instance_segmentation import ( - ImageStats, - volume_stats, -) - -logger = utils.LOGGER - -PRETRAINED_WEIGHTS_DIR = Path(__file__).parent.resolve() / Path( - "models/pretrained" -) -VERBOSE_SCHEDULER = True -logger.debug(f"PRETRAINED WEIGHT DIR LOCATION : {PRETRAINED_WEIGHTS_DIR}") - - -class WeightsDownloader: - """A utility class the downloads the weights of a model when needed.""" - - def __init__(self, log_widget: t.Optional[ui.Log] = None): - """ - Creates a WeightsDownloader, optionally with a log widget to display the progress. - - Args: - log_widget (log_utility.Log): a Log to display the progress bar in. If None, uses logger.info() - """ - self.log_widget = log_widget - - def download_weights(self, model_name: str, model_weights_filename: str): - """ - Downloads a specific pretrained model. - This code is adapted from DeepLabCut with permission from MWMathis. - - Args: - model_name (str): name of the model to download - model_weights_filename (str): name of the .pth file expected for the model - """ - import json - import tarfile - import urllib.request - - def show_progress(_, block_size, __): # count, block_size, total_size - pbar.update(block_size) - - logger.info("*" * 20) - pretrained_folder_path = PRETRAINED_WEIGHTS_DIR - json_path = pretrained_folder_path / Path("pretrained_model_urls.json") - - check_path = pretrained_folder_path / Path(model_weights_filename) - - if Path(check_path).is_file(): - message = f"Weight file {model_weights_filename} already exists, skipping download" - if self.log_widget is not None: - self.log_widget.print_and_log(message, printing=False) - logger.info(message) - return - - with Path.open(json_path) as f: - neturls = json.load(f) - if model_name in neturls: - url = neturls[model_name] - response = urllib.request.urlopen(url) - - start_message = f"Downloading the model from HuggingFace {url}...." - total_size = int(response.getheader("Content-Length")) - if self.log_widget is None: - logger.info(start_message) - pbar = tqdm(unit="B", total=total_size, position=0) - else: - self.log_widget.print_and_log(start_message) - pbar = tqdm( - unit="B", - total=total_size, - position=0, - file=self.log_widget, - ) - - filename, _ = urllib.request.urlretrieve( - url, reporthook=show_progress - ) - with tarfile.open(filename, mode="r:gz") as tar: - - def is_within_directory(directory, target): - abs_directory = Path(directory).resolve() - abs_target = Path(target).resolve() - # prefix = os.path.commonprefix([abs_directory, abs_target]) - logger.debug(abs_directory) - logger.debug(abs_target) - logger.debug(abs_directory.parents) - - return abs_directory in abs_target.parents - - def safe_extract( - tar, path=".", members=None, *, numeric_owner=False - ): - for member in tar.getmembers(): - member_path = str(Path(path) / member.name) - if not is_within_directory(path, member_path): - raise Exception( - "Attempted Path Traversal in Tar File" - ) - - tar.extractall(path, members, numeric_owner=numeric_owner) - - safe_extract(tar, pretrained_folder_path) - # tar.extractall(pretrained_folder_path) - else: - raise ValueError( - f"Unknown model: {model_name}. Should be one of {', '.join(neturls)}" - ) - - -""" -Writing something to log messages from outside the main thread needs specific care, -Following the instructions in the guides below to have a worker with custom signals, -a custom worker function was implemented. -""" - -# https://python-forum.io/thread-31349.html -# https://www.pythoncentral.io/pysidepyqt-tutorial-creating-your-own-signals-and-slots/ -# https://napari-staging-site.github.io/guides/stable/threading.html - - -class LogSignal(WorkerBaseSignals): - """Signal to send messages to be logged from another thread. - - Separate from Worker instances as indicated `on this post`_ - - .. _on this post: https://stackoverflow.com/questions/2970312/pyqt4-qtcore-pyqtsignal-object-has-no-attribute-connect - """ # TODO link ? - - log_signal = Signal(str) - """qtpy.QtCore.Signal: signal to be sent when some text should be logged""" - warn_signal = Signal(str) - """qtpy.QtCore.Signal: signal to be sent when some warning should be emitted in main thread""" - error_signal = Signal(Exception, str) - """qtpy.QtCore.Signal: signal to be sent when some error should be emitted in main thread""" - - # Should not be an instance variable but a class variable, not defined in __init__, see - # https://stackoverflow.com/questions/2970312/pyqt4-qtcore-pyqtsignal-object-has-no-attribute-connect - - def __init__(self): - super().__init__() - - -# TODO(cyril): move inference and training workers to separate files - - -class ONNXModelWrapper(torch.nn.Module): - """Class to replace torch model by ONNX Runtime session""" - - def __init__(self, file_location): - super().__init__() - try: - import onnxruntime as ort - except ImportError as e: - logger.error("ONNX is not installed but ONNX model was loaded") - logger.error(e) - msg = "PLEASE INSTALL ONNX CPU OR GPU USING pip install napari-cellseg3d[onnx-cpu] OR napari-cellseg3d[onnx-gpu]" - logger.error(msg) - raise ImportError(msg) from e - - self.ort_session = ort.InferenceSession( - file_location, - providers=["CUDAExecutionProvider", "CPUExecutionProvider"], - ) - - def forward(self, modeL_input): - """Wraps ONNX output in a torch tensor""" - outputs = self.ort_session.run( - None, {"input": modeL_input.cpu().numpy()} - ) - return torch.tensor(outputs[0]) - - def eval(self): - """Dummy function to replace model.eval()""" - pass - - def to(self, device): - """Dummy function to replace model.to(device)""" - pass - - -class QuantileNormalizationd(MapTransform): - def __init__(self, keys, allow_missing_keys: bool = False): - super().__init__(keys, allow_missing_keys) - - def __call__(self, data): - d = dict(data) - for key in self.keys: - d[key] = self.normalizer(d[key]) - return d - - def normalizer(self, image: torch.Tensor): - """Normalize each image in a batch individually by quantile normalization.""" - if image.ndim == 4: - for i in range(image.shape[0]): - image[i] = utils.quantile_normalization(image[i]) - else: - raise NotImplementedError( - "QuantileNormalizationd only supports 2D and 3D tensors with NCHWD format" - ) - return image - - -@dataclass -class InferenceResult: - """Class to record results of a segmentation job""" - - image_id: int = 0 - original: np.array = None - instance_labels: np.array = None - crf_results: np.array = None - stats: "np.array[ImageStats]" = None - result: np.array = None - model_name: str = None - - -class InferenceWorker(GeneratorWorker): - """A custom worker to run inference jobs in. - Inherits from :py:class:`napari.qt.threading.GeneratorWorker`""" - - def __init__( - self, - worker_config: config.InferenceWorkerConfig, - ): - """Initializes a worker for inference with the arguments needed by the :py:func:`~inference` function. - - The config contains the following attributes: - * device: cuda or cpu device to use for torch - * model_dict: the :py:attr:`~self.models_dict` dictionary to obtain the model name, class and instance - * weights_dict: dict with "custom" : bool to use custom weights or not; "path" : the path to weights if custom or name of the file if not custom - * results_path: the path to save the results to - * filetype: the file extension to use when saving, - * transforms: a dict containing transforms to perform at various times. - * instance: a dict containing parameters regarding instance segmentation - * use_window: use window inference with specific size or whole image - * window_infer_size: size of window if use_window is True - * keep_on_cpu: keep images on CPU or no - * stats_csv: compute stats on cells and save them to a csv file - * images_filepaths: the paths to the images of the dataset - * layer: the layer to run inference on - - Args: - * worker_config (config.InferenceWorkerConfig): dataclass containing the proper configuration elements - - Note: See :py:func:`~self.inference` - """ - - super().__init__(self.inference) - self._signals = LogSignal() # add custom signals - self.log_signal = self._signals.log_signal - self.warn_signal = self._signals.warn_signal - self.error_signal = self._signals.error_signal - - self.config = worker_config - - """These attributes are all arguments of :py:func:~inference, please see that for reference""" - - self.downloader = WeightsDownloader() - """Download utility""" - - @staticmethod - def create_inference_dict(images_filepaths): - """Create a dict for MONAI with "image" keys with all image paths in :py:attr:`~self.images_filepaths` - - Returns: - dict: list of image paths from loaded folder""" - return [{"image": image_name} for image_name in images_filepaths] - - def set_download_log(self, widget): - self.downloader.log_widget = widget - - def log(self, text): - """Sends a signal that ``text`` should be logged - - Args: - text (str): text to logged - """ - self.log_signal.emit(text) - - def warn(self, warning): - """Sends a warning to main thread""" - self.warn_signal.emit(warning) - - def raise_error(self, exception, msg): - """Raises an error in main thread""" - logger.error(msg, exc_info=True) - logger.error(exception, exc_info=True) - - self.log_signal.emit("!" * 20) - self.log_signal.emit("Error occured") - # self.log_signal.emit(msg) - # self.log_signal.emit(str(exception)) - - self.error_signal.emit(exception, msg) - self.errored.emit(exception) - yield exception - # self.quit() - - def log_parameters(self): - config = self.config - - self.log("-" * 20) - self.log("\nParameters summary :") - - self.log(f"Model is : {config.model_info.name}") - if config.post_process_config.thresholding.enabled: - self.log( - f"Thresholding is enabled at {config.post_process_config.thresholding.threshold_value}" - ) - - status = ( - "enabled" - if config.sliding_window_config.is_enabled() - else "disabled" - ) - - self.log(f"Window inference is {status}\n") - if status == "enabled": - self.log( - f"Window size is {self.config.sliding_window_config.window_size}" - ) - self.log( - f"Window overlap is {self.config.sliding_window_config.window_overlap}" - ) - - if config.keep_on_cpu: - self.log("Dataset loaded to CPU") - else: - self.log(f"Dataset loaded on {config.device} device") - - if config.post_process_config.zoom.enabled: - self.log( - f"Scaling factor : {config.post_process_config.zoom.zoom_values} (x,y,z)" - ) - - instance_config = config.post_process_config.instance - if instance_config.enabled: - self.log( - f"Instance segmentation enabled, method : {instance_config.method.name}\n" - ) - self.log("-" * 20) - - def load_folder(self): - images_dict = self.create_inference_dict(self.config.images_filepaths) - - # TODO : better solution than loading first image always ? - data_check = LoadImaged(keys=["image"])(images_dict[0]) - check = data_check["image"].shape - pad = utils.get_padding_dim(check) - - if self.config.sliding_window_config.is_enabled(): - load_transforms = Compose( - [ - LoadImaged(keys=["image"]), - # AddChanneld(keys=["image"]), #already done - EnsureChannelFirstd(keys=["image"]), - # Orientationd(keys=["image"], axcodes="PLI"), - # anisotropic_transform, - QuantileNormalizationd(keys=["image"]), - EnsureTyped(keys=["image"]), - ] - ) - else: - load_transforms = Compose( - [ - LoadImaged(keys=["image"]), - # AddChanneld(keys=["image"]), #already done - EnsureChannelFirstd(keys=["image"]), - # NormalizeIntensityd(keys=["image"]), # TODO normalize - # Orientationd(keys=["image"], axcodes="PLI"), - # anisotropic_transform, - SpatialPadd(keys=["image"], spatial_size=pad), - EnsureTyped(keys=["image"]), - ] - ) - - self.log("\nLoading dataset...") - inference_ds = Dataset(data=images_dict, transform=load_transforms) - inference_loader = DataLoader( - inference_ds, batch_size=1, num_workers=2 - ) - self.log("Done") - return inference_loader - - def load_layer(self): - self.log("\nLoading layer\n") - data = np.squeeze(self.config.layer.data) - - volume = np.array(data, dtype=np.int16) - - volume_dims = len(volume.shape) - if volume_dims != 3: - raise ValueError( - f"Data array is not 3-dimensional but {volume_dims}-dimensional," - f" please check for extra channel/batch dimensions" - ) - - volume = np.swapaxes( - volume, 0, 2 - ) # for anisotropy to be monai-like, i.e. zyx # FIXME rotation not always correct - - dims_check = volume.shape - self.log("Checking dimensions...") - pad = utils.get_padding_dim(dims_check) - - # logger.debug(volume.shape) - # logger.debug(volume.dtype) - if self.config.sliding_window_config.is_enabled(): - load_transforms = Compose( - [ - ToTensor(), - # anisotropic_transform, - AddChannel(), - # SpatialPad(spatial_size=pad), - AddChannel(), - EnsureType(), - ], - map_items=False, - log_stats=True, - ) - else: - load_transforms = Compose( - [ - ToTensor(), - # anisotropic_transform, - AddChannel(), - SpatialPad(spatial_size=pad), - AddChannel(), - EnsureType(), - ], - map_items=False, - log_stats=True, - ) - - input_image = load_transforms(volume) - self.log("Done") - return input_image - - def model_output( - self, - inputs, - model, - post_process_transforms, - post_process=True, - aniso_transform=None, - ): - inputs = inputs.to("cpu") - - # def model_output(inputs): - # return post_process_transforms( - # self.config.model_info.get_model().get_output(model, inputs) - # ) - - dataset_device = ( - "cpu" if self.config.keep_on_cpu else self.config.device - ) - - if self.config.sliding_window_config.is_enabled(): - window_size = self.config.sliding_window_config.window_size - window_size = [window_size, window_size, window_size] - window_overlap = self.config.sliding_window_config.window_overlap - else: - window_size = None - window_overlap = 0 - try: - # logger.debug(f"model : {model}") - logger.debug(f"inputs shape : {inputs.shape}") - logger.debug(f"inputs type : {inputs.dtype}") - try: - # outputs = model(inputs) - inputs = utils.remap_image(inputs) - - def model_output_wrapper(inputs): - result = model(inputs) - return post_process_transforms(result) - - with torch.no_grad(): - outputs = sliding_window_inference( - inputs, - roi_size=window_size, - sw_batch_size=1, # TODO add param - predictor=model_output_wrapper, - sw_device=self.config.device, - device=dataset_device, - overlap=window_overlap, - mode="gaussian", - sigma_scale=0.01, - progress=True, - ) - except Exception as e: - logger.exception(e) - logger.debug("failed to run sliding window inference") - self.raise_error(e, "Error during sliding window inference") - logger.debug(f"Inference output shape: {outputs.shape}") - self.log("Post-processing...") - out = outputs.detach().cpu().numpy() - if aniso_transform is not None: - out = aniso_transform(out) - if post_process: - out = np.array(out).astype(np.float32) - out = np.squeeze(out) - return out - except Exception as e: - logger.exception(e) - self.raise_error(e, "Error during sliding window inference") - # sys.stdout = old_stdout - # sys.stderr = old_stderr - - def create_inference_result( - self, - semantic_labels, - instance_labels, - crf_results=None, - from_layer: bool = False, - original=None, - stats=None, - i=0, - ) -> InferenceResult: - if not from_layer and original is None: - raise ValueError( - "If the image is not from a layer, an original should always be available" - ) - - if from_layer: - if i != 0: - raise ValueError( - "A layer's ID should always be 0 (default value)" - ) - - if semantic_labels is not None: - semantic_labels = utils.correct_rotation(semantic_labels) - if crf_results is not None: - crf_results = utils.correct_rotation(crf_results) - if instance_labels is not None: - instance_labels = utils.correct_rotation(instance_labels) - - return InferenceResult( - image_id=i + 1, - original=original, - instance_labels=instance_labels, - crf_results=crf_results, - stats=stats, - result=semantic_labels, - model_name=self.config.model_info.name, - ) - - def get_original_filename(self, i): - return Path(self.config.images_filepaths[i]).stem - - def get_instance_result(self, semantic_labels, from_layer=False, i=-1): - if not from_layer and i == -1: - raise ValueError( - "An ID should be provided when running from a file" - ) - - if self.config.post_process_config.instance.enabled: - instance_labels = self.instance_seg( - semantic_labels, - i + 1, - ) - data_dict = self.stats_csv(instance_labels) - else: - instance_labels = None - data_dict = None - return instance_labels, data_dict - - def save_image( - self, - image, - from_layer=False, - i=0, - additional_info="", - ): - if not from_layer: - original_filename = "_" + self.get_original_filename(i) + "_" - filetype = self.config.filetype - else: - original_filename = "_" - filetype = ".tif" - - time = utils.get_date_time() - - file_path = ( - self.config.results_path - + "/" - + f"{additional_info}" - + f"Prediction_{i+1}" - + original_filename - + self.config.model_info.name - + f"_{time}" - + filetype - ) - try: - imwrite(file_path, image) - except ValueError as e: - self.raise_error(e, "Error during image saving") - filename = Path(file_path).stem - - if from_layer: - self.log(f"\nLayer prediction saved as : {filename}") - else: - self.log(f"\nFile n°{i+1} saved as : {filename}") - - def aniso_transform(self, image): - if self.config.post_process_config.zoom.enabled: - zoom = self.config.post_process_config.zoom.zoom_values - anisotropic_transform = Zoom( - zoom=zoom, - keep_size=False, - padding_mode="empty", - ) - return anisotropic_transform(image[0]) - return image - - def instance_seg( - self, semantic_labels, image_id=0, original_filename="layer" - ): - if image_id is not None: - self.log(f"\nRunning instance segmentation for image n°{image_id}") - - method = self.config.post_process_config.instance.method - instance_labels = method.run_method_on_channels(semantic_labels) - self.log(f"DEBUG instance results shape : {instance_labels.shape}") - - filetype = ( - ".tif" - if self.config.filetype == "" - else "_" + self.config.filetype - ) - - instance_filepath = ( - self.config.results_path - + "/" - + f"Instance_seg_labels_{image_id}_" - + original_filename - + "_" - + self.config.model_info.name - + f"_{utils.get_date_time()}" - + filetype - ) - - imwrite(instance_filepath, instance_labels) - self.log( - f"Instance segmentation results for image n°{image_id} have been saved as:" - ) - self.log(Path(instance_filepath).name) - return instance_labels - - def inference_on_folder(self, inf_data, i, model, post_process_transforms): - self.log("-" * 10) - self.log(f"Inference started on image n°{i + 1}...") - - inputs = inf_data["image"] - - out = self.model_output( - inputs, - model, - post_process_transforms, - aniso_transform=self.aniso_transform, - ) - - self.save_image(out, i=i) - instance_labels, stats = self.get_instance_result(out, i=i) - if self.config.use_crf: - crf_in = inputs.detach().cpu().numpy() - try: - crf_results = self.run_crf( - crf_in, - out, - aniso_transform=self.aniso_transform, - image_id=i, - ) - - except ValueError as e: - self.log(f"Error occurred during CRF : {e}") - crf_results = None - else: - crf_results = None - - original = np.array(inf_data["image"]).astype(np.float32) - - self.log(f"Inference completed on image n°{i+1}") - - return self.create_inference_result( - out, - instance_labels, - crf_results, - from_layer=False, - original=original, - stats=stats, - i=i, - ) - - def run_crf(self, image, labels, aniso_transform, image_id=0): - try: - if aniso_transform is not None: - image = aniso_transform(image) - crf_results = crf_with_config( - image, labels, config=self.config.crf_config, log=self.log - ) - self.save_image( - crf_results, - i=image_id, - additional_info="CRF_", - from_layer=True, - ) - return crf_results - except ValueError as e: - self.log(f"Error occurred during CRF : {e}") - return None - - def stats_csv(self, instance_labels): - try: - if self.config.compute_stats: - if len(instance_labels.shape) == 4: - stats = [volume_stats(c) for c in instance_labels] - else: - stats = [volume_stats(instance_labels)] - else: - stats = None - return stats - except ValueError as e: - self.log(f"Error occurred during stats computing : {e}") - return None - - def inference_on_layer(self, image, model, post_process_transforms): - self.log("-" * 10) - self.log("Inference started on layer...") - - image = image.type(torch.FloatTensor) - image = utils.quantile_normalization(image) - - out = self.model_output( - image, - model, - post_process_transforms, - aniso_transform=self.aniso_transform, - ) - - self.save_image(out, from_layer=True) - - instance_labels, stats = self.get_instance_result( - semantic_labels=out, from_layer=True - ) - - crf_results = ( - self.run_crf(image, out, aniso_transform=self.aniso_transform) - if self.config.use_crf - else None - ) - - return self.create_inference_result( - semantic_labels=out, - instance_labels=instance_labels, - crf_results=crf_results, - from_layer=True, - stats=stats, - ) - - # @thread_worker(connect={"errored": self.raise_error}) - def inference(self): - """ - Requires: - * device: cuda or cpu device to use for torch - - * model_dict: the :py:attr:`~self.models_dict` dictionary to obtain the model name, class and instance - - * weights: the loaded weights from the model - - * images_filepaths: the paths to the images of the dataset - - * results_path: the path to save the results to - - * filetype: the file extension to use when saving, - - * transforms: a dict containing transforms to perform at various times. - - * use_window: use window inference with specific size or whole image - - * window_infer_size: size of window if use_window is True - - * keep_on_cpu: keep images on CPU or no - - * stats_csv: compute stats on cells and save them to a csv file - - Yields: - dict: contains : - * "image_id" : index of the returned image - - * "original" : original volume used for inference - - * "result" : inference result - - """ - sys = platform.system() - logger.debug(f"OS is {sys}") - if sys == "Darwin": - torch.set_num_threads(1) # required for threading on macOS ? - self.log("Number of threads has been set to 1 for macOS") - - try: - dims = self.config.model_info.model_input_size - self.log(f"MODEL DIMS : {dims}") - model_name = self.config.model_info.name - model_class = self.config.model_info.get_model() - self.log(f"Model name : {model_name}") - - weights_config = self.config.weights_config - post_process_config = self.config.post_process_config - if Path(weights_config.path).suffix == ".pt": - self.log("Instantiating PyTorch jit model...") - model = torch.jit.load(weights_config.path) - # try: - elif Path(weights_config.path).suffix == ".onnx": - self.log("Instantiating ONNX model...") - model = ONNXModelWrapper(weights_config.path) - else: # assume is .pth - self.log("Instantiating model...") - model = model_class( # FIXME test if works - input_img_size=[dims, dims, dims], - device=self.config.device, - num_classes=self.config.model_info.num_classes, - ) - # try: - model = model.to(self.config.device) - # except Exception as e: - # self.raise_error(e, "Issue loading model to device") - # logger.debug(f"model : {model}") - if model is None: - raise ValueError("Model is None") - # try: - self.log("\nLoading weights...") - if weights_config.custom: - weights = weights_config.path - else: - self.downloader.download_weights( - model_name, - model_class.weights_file, - ) - weights = str( - PRETRAINED_WEIGHTS_DIR / Path(model_class.weights_file) - ) - - model.load_state_dict( # note that this is redefined in WNet_ - torch.load( - weights, - map_location=self.config.device, - ) - ) - self.log("Done") - # except Exception as e: - # self.raise_error(e, "Issue loading weights") - # except Exception as e: - # self.raise_error(e, "Issue instantiating model") - - # if model_name == "SegResNet": - # model = model_class( - # input_image_size=[ - # dims, - # dims, - # dims, - # ], - # ) - # elif model_name == "SwinUNetR": - # model = model_class( - # img_size=[dims, dims, dims], - # use_checkpoint=False, - # ) - # else: - # model = model_class.get_net() - - self.log_parameters() - - # load_transforms = Compose( - # [ - # LoadImaged(keys=["image"]), - # # AddChanneld(keys=["image"]), #already done - # EnsureChannelFirstd(keys=["image"]), - # # Orientationd(keys=["image"], axcodes="PLI"), - # # anisotropic_transform, - # SpatialPadd(keys=["image"], spatial_size=pad), - # EnsureTyped(keys=["image"]), - # ] - # ) - - if not post_process_config.thresholding.enabled: - post_process_transforms = EnsureType() - else: - t = post_process_config.thresholding.threshold_value - post_process_transforms = Compose( - AsDiscrete(threshold=t), EnsureType() - ) - - is_folder = self.config.images_filepaths is not None - is_layer = self.config.layer is not None - - if is_layer and is_folder: - raise ValueError( - "Both a layer and a folder have been specified, please specify only one of the two. Aborting." - ) - elif is_folder: - inference_loader = self.load_folder() - ################## - ################## - # DEBUG - # from monai.utils import first - # - # check_data = first(inference_loader) - # image = check_data[0][0] - # logger.debug(image.shape) - ################## - ################## - elif is_layer: - input_image = self.load_layer() - else: - raise ValueError("No data has been provided. Aborting.") - - if model is None: - raise ValueError("Model is None") - - model.eval() - with torch.no_grad(): - ################################ - ################################ - ################################ - if is_folder: - for i, inf_data in enumerate(inference_loader): - yield self.inference_on_folder( - inf_data, i, model, post_process_transforms - ) - elif is_layer: - yield self.inference_on_layer( - input_image, model, post_process_transforms - ) - model.to("cpu") - # self.quit() - except Exception as e: - logger.exception(e) - self.raise_error(e, "Inference failed") - self.quit() - finally: - self.quit() - - -@dataclass -class TrainingReport: - show_plot: bool = True - epoch: int = 0 - loss_values: t.Dict = None # TODO(cyril) : change to dict and unpack different losses for e.g. WNet with several losses - validation_metric: t.List = None - weights: np.array = None - images: t.List[np.array] = None - - -class TrainingWorker(GeneratorWorker): - """A custom worker to run training jobs in. - Inherits from :py:class:`napari.qt.threading.GeneratorWorker`""" - - def __init__( - self, - worker_config: config.TrainingWorkerConfig, - ): - """Initializes a worker for inference with the arguments needed by the :py:func:`~train` function. Note: See :py:func:`~train` - - Args: - * device : device to train on, cuda or cpu - - * model_dict : dict containing the model's "name" and "class" - - * weights_path : path to weights files if transfer learning is to be used - - * data_dicts : dict from :py:func:`Trainer.create_train_dataset_dict` - - * validation_percent : percentage of images to use as validation - - * max_epochs : the amout of epochs to train for - - * loss_function : the loss function to use for training - - * learning_rate : the learning rate of the optimizer - - * val_interval : the interval at which to perform validation (e.g. if 2 will validate once every 2 epochs.) Also determines frequency of saving, depending on whether the metric is better or not - - * batch_size : the batch size to use for training - - * results_path : the path to save results in - - * sampling : whether to extract patches from images or not - - * num_samples : the number of samples to extract from an image for training - - * sample_size : the size of the patches to extract when sampling - - * do_augmentation : whether to perform data augmentation or not - - * deterministic : dict with "use deterministic" : bool, whether to use deterministic training, "seed": seed for RNG - - - """ - super().__init__(self.train) - self._signals = LogSignal() - self.log_signal = self._signals.log_signal - self.warn_signal = self._signals.warn_signal - self.error_signal = self._signals.error_signal - - self._weight_error = False - ############################################# - self.config = worker_config - - self.train_files = [] - self.val_files = [] - ####################################### - self.downloader = WeightsDownloader() - - def set_download_log(self, widget): - self.downloader.log_widget = widget - - def log(self, text): - """Sends a signal that ``text`` should be logged - - Args: - text (str): text to logged - """ - self.log_signal.emit(text) - - def warn(self, warning): - """Sends a warning to main thread""" - self.warn_signal.emit(warning) - - def raise_error(self, exception, msg): - """Sends an error to main thread""" - logger.error(msg, exc_info=True) - logger.error(exception, exc_info=True) - self.error_signal.emit(exception, msg) - self.errored.emit(exception) - self.quit() - - def log_parameters(self): - self.log("-" * 20) - self.log("Parameters summary :\n") - - self.log( - f"Percentage of dataset used for validation : {self.config.validation_percent * 100}%" - ) - - self.log("-" * 10) - self.log("Training files :\n") - [ - self.log(f"{Path(train_file['image']).name}\n") - for train_file in self.train_files - ] - self.log("-" * 10) - self.log("Validation files :\n") - [ - self.log(f"{Path(val_file['image']).name}\n") - for val_file in self.val_files - ] - self.log("-" * 10) - - if self.config.deterministic_config.enabled: - self.log("Deterministic training is enabled") - self.log(f"Seed is {self.config.deterministic_config.seed}") - - self.log(f"Training for {self.config.max_epochs} epochs") - self.log(f"Loss function is : {str(self.config.loss_function)}") - self.log( - f"Validation is performed every {self.config.validation_interval} epochs" - ) - self.log(f"Batch size is {self.config.batch_size}") - self.log(f"Learning rate is {self.config.learning_rate}") - - if self.config.sampling: - self.log( - f"Extracting {self.config.num_samples} patches of size {self.config.sample_size}" - ) - else: - self.log("Using whole images as dataset") - - if self.config.do_augmentation: - self.log("Data augmentation is enabled") - - if not self.config.weights_info.use_pretrained: - self.log(f"Using weights from : {self.config.weights_info.path}") - if self._weight_error: - self.log( - ">>>>>>>>>>>>>>>>>\n" - "WARNING:\nChosen weights were incompatible with the model,\n" - "the model will be trained from random weights\n" - "<<<<<<<<<<<<<<<<<\n" - ) - - # self.log("\n") - self.log("-" * 20) - - def train(self): - """Trains the PyTorch model for the given number of epochs, with the selected model and data, - using the chosen batch size, validation interval, loss function, and number of samples. - Will perform validation once every :py:obj:`val_interval` and save results if the mean dice is better - - Requires: - - * device : device to train on, cuda or cpu - - * model_dict : dict containing the model's "name" and "class" - - * weights_path : path to weights files if transfer learning is to be used - - * data_dicts : dict from :py:func:`Trainer.create_train_dataset_dict` - - * validation_percent : percentage of images to use as validation - - * max_epochs : the amount of epochs to train for - - * loss_function : the loss function to use for training - - * learning rate : the learning rate of the optimizer - - * val_interval : the interval at which to perform validation (e.g. if 2 will validate once every 2 epochs.) Also determines frequency of saving, depending on whether the metric is better or not - - * batch_size : the batch size to use for training - - * results_path : the path to save results in - - * sampling : whether to extract patches from images or not - - * num_samples : the number of samples to extract from an image for training - - * sample_size : the size of the patches to extract when sampling - - * do_augmentation : whether to perform data augmentation or not - - * deterministic : dict with "use deterministic" : bool, whether to use deterministic training, "seed": seed for RNG - """ - - ######################### - # error_log = open(results_path +"/error_log.log" % multiprocessing.current_process().name, 'x') - # faulthandler.enable(file=error_log, all_threads=True) - ######################### - model_config = self.config.model_info - weights_config = self.config.weights_info - deterministic_config = self.config.deterministic_config - - start_time = time.time() - - try: - if deterministic_config.enabled: - set_determinism( - seed=deterministic_config.seed - ) # use_deterministic_algorithms = True causes cuda error - - sys = platform.system() - logger.debug(sys) - if sys == "Darwin": # required for macOS ? - torch.set_num_threads(1) - self.log("Number of threads has been set to 1 for macOS") - - self.log(f"config model : {self.config.model_info.name}") - model_name = model_config.name - model_class = model_config.get_model() - - if not self.config.sampling: - data_check = LoadImaged(keys=["image"])( - self.config.train_data_dict[0] - ) - check = data_check["image"].shape - - do_sampling = self.config.sampling - - size = self.config.sample_size if do_sampling else check - - PADDING = utils.get_padding_dim(size) - model = model_class( # FIXME check if correct - input_img_size=PADDING, use_checkpoint=True - ) - model = model.to(self.config.device) - - epoch_loss_values = [] - val_metric_values = [] - - if len(self.config.train_data_dict) > 1: - self.train_files, self.val_files = ( - self.config.train_data_dict[ - 0 : int( - len(self.config.train_data_dict) - * self.config.validation_percent - ) - ], - self.config.train_data_dict[ - int( - len(self.config.train_data_dict) - * self.config.validation_percent - ) : - ], - ) - else: - self.train_files = self.val_files = self.config.train_data_dict - msg = f"Only one image file was provided : {self.config.train_data_dict[0]['image']}.\n" - - logger.debug(f"SAMPLING is {self.config.sampling}") - if not self.config.sampling: - msg += "Sampling is not in use, the only image provided will be used as the validation file." - self.warn(msg) - else: - msg += "Samples for validation will be cropped for the same only volume that is being used for training" - - logger.warning(msg) - - logger.debug( - f"Data dict from config is {self.config.train_data_dict}" - ) - logger.debug(f"Train files : {self.train_files}") - logger.debug(f"Val. files : {self.val_files}") - - if len(self.train_files) == 0: - raise ValueError("Training dataset is empty") - if len(self.val_files) == 0: - raise ValueError("Validation dataset is empty") - - if self.config.do_augmentation: - train_transforms = ( - Compose( # TODO : figure out which ones and values ? - [ - RandShiftIntensityd(keys=["image"], offsets=0.7), - Rand3DElasticd( - keys=["image", "label"], - sigma_range=(0.3, 0.7), - magnitude_range=(0.3, 0.7), - ), - RandFlipd(keys=["image", "label"]), - RandRotate90d(keys=["image", "label"]), - RandAffined( - keys=["image", "label"], - ), - EnsureTyped(keys=["image", "label"]), - ] - ) - ) - else: - train_transforms = EnsureTyped(keys=["image", "label"]) - - val_transforms = Compose( - [ - # LoadImaged(keys=["image", "label"]), - # EnsureChannelFirstd(keys=["image", "label"]), - EnsureTyped(keys=["image", "label"]), - ] - ) - - # self.log("Loading dataset...\n") - def get_loader_func(num_samples): - return Compose( - [ - LoadImaged(keys=["image", "label"]), - EnsureChannelFirstd(keys=["image", "label"]), - QuantileNormalizationd(keys=["image"]), - RandSpatialCropSamplesd( - keys=["image", "label"], - roi_size=( - self.config.sample_size - ), # multiply by axis_stretch_factor if anisotropy - # max_roi_size=(120, 120, 120), - random_size=False, - num_samples=num_samples, - ), - Orientationd(keys=["image", "label"], axcodes="PLI"), - SpatialPadd( - keys=["image", "label"], - spatial_size=( - utils.get_padding_dim(self.config.sample_size) - ), - ), - EnsureTyped(keys=["image", "label"]), - ] - ) - - if do_sampling: - # if there is only one volume, split samples - # TODO(cyril) : maybe implement something in user config to toggle this behavior - if len(self.config.train_data_dict) < 2: - num_train_samples = ceil( - self.config.num_samples - * self.config.validation_percent - ) - num_val_samples = ceil( - self.config.num_samples - * (1 - self.config.validation_percent) - ) - sample_loader_train = get_loader_func(num_train_samples) - sample_loader_eval = get_loader_func(num_val_samples) - else: - num_train_samples = ( - num_val_samples - ) = self.config.num_samples - - sample_loader_train = get_loader_func(num_train_samples) - sample_loader_eval = get_loader_func(num_val_samples) - - logger.debug(f"AMOUNT of train samples : {num_train_samples}") - logger.debug( - f"AMOUNT of validation samples : {num_val_samples}" - ) - - logger.debug("train_ds") - train_ds = PatchDataset( - data=self.train_files, - transform=train_transforms, - patch_func=sample_loader_train, - samples_per_image=num_train_samples, - ) - logger.debug("val_ds") - val_ds = PatchDataset( - data=self.val_files, - transform=val_transforms, - patch_func=sample_loader_eval, - samples_per_image=num_val_samples, - ) - - else: - load_whole_images = Compose( - [ - LoadImaged( - keys=["image", "label"], - # image_only=True, - # reader=WSIReader(backend="tifffile") - ), - EnsureChannelFirstd(keys=["image", "label"]), - Orientationd(keys=["image", "label"], axcodes="PLI"), - QuantileNormalizationd(keys=["image"]), - SpatialPadd( - keys=["image", "label"], - spatial_size=PADDING, - ), - EnsureTyped(keys=["image", "label"]), - ] - ) - logger.debug("Cache dataset : train") - train_ds = CacheDataset( - data=self.train_files, - transform=Compose(load_whole_images, train_transforms), - ) - logger.debug("Cache dataset : val") - val_ds = CacheDataset( - data=self.val_files, transform=load_whole_images - ) - logger.debug("Dataloader") - train_loader = DataLoader( - train_ds, - batch_size=self.config.batch_size, - shuffle=True, - num_workers=2, - collate_fn=pad_list_data_collate, - ) - - val_loader = DataLoader( - val_ds, batch_size=self.config.batch_size, num_workers=2 - ) - logger.info("\nDone") - - logger.debug("Optimizer") - optimizer = torch.optim.Adam( - model.parameters(), self.config.learning_rate - ) - - factor = self.config.scheduler_factor - if factor >= 1.0: - self.log(f"Warning : scheduler factor is {factor} >= 1.0") - self.log("Setting it to 0.5") - factor = 0.5 - - scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - optimizer=optimizer, - mode="min", - factor=factor, - patience=self.config.scheduler_patience, - verbose=VERBOSE_SCHEDULER, - ) - dice_metric = DiceMetric( - include_background=False, reduction="mean" - ) - - best_metric = -1 - best_metric_epoch = -1 - - # time = utils.get_date_time() - logger.debug("Weights") - - if weights_config.custom: - if weights_config.use_pretrained: - weights_file = model_class.weights_file - self.downloader.download_weights(model_name, weights_file) - weights = PRETRAINED_WEIGHTS_DIR / Path(weights_file) - weights_config.path = weights - else: - weights = str(Path(weights_config.path)) - - try: - model.load_state_dict( - torch.load( - weights, - map_location=self.config.device, - ) - ) - except RuntimeError as e: - logger.error(f"Error when loading weights : {e}") - logger.exception(e) - warn = ( - "WARNING:\nIt'd seem that the weights were incompatible with the model,\n" - "the model will be trained from random weights" - ) - self.log(warn) - self.warn(warn) - self._weight_error = True - - if self.config.device.type == "cuda": - self.log("\nUsing GPU :") - self.log(torch.cuda.get_device_name(0)) - else: - self.log("Using CPU") - - self.log_parameters() - - device = self.config.device - - # if model_name == "test": - # self.quit() - # yield TrainingReport(False) - - for epoch in range(self.config.max_epochs): - # self.log("\n") - self.log("-" * 10) - self.log(f"Epoch {epoch + 1}/{self.config.max_epochs}") - if device.type == "cuda": - self.log("Memory Usage:") - alloc_mem = round( - torch.cuda.memory_allocated(0) / 1024**3, 1 - ) - reserved_mem = round( - torch.cuda.memory_reserved(0) / 1024**3, 1 - ) - self.log(f"Allocated: {alloc_mem}GB") - self.log(f"Cached: {reserved_mem}GB") - - model.train() - epoch_loss = 0 - step = 0 - for batch_data in train_loader: - step += 1 - inputs, labels = ( - batch_data["image"].to(device), - batch_data["label"].to(device), - ) - - optimizer.zero_grad() - outputs = model(inputs) - # self.log(f"Output dimensions : {outputs.shape}") - if outputs.shape[1] > 1: - outputs = outputs[ - :, 1:, :, : - ] # FIXME fix channel number - if len(outputs.shape) < 4: - outputs = outputs.unsqueeze(0) - loss = self.config.loss_function(outputs, labels) - loss.backward() - optimizer.step() - epoch_loss += loss.detach().item() - self.log( - f"* {step}/{len(train_ds) // train_loader.batch_size}, " - f"Train loss: {loss.detach().item():.4f}" - ) - yield TrainingReport( - show_plot=False, weights=model.state_dict() - ) - - # return - - epoch_loss /= step - epoch_loss_values.append(epoch_loss) - self.log(f"Epoch: {epoch + 1}, Average loss: {epoch_loss:.4f}") - - self.log("Updating scheduler...") - scheduler.step(epoch_loss) - - checkpoint_output = [] - eta = ( - (time.time() - start_time) - * (self.config.max_epochs / (epoch + 1) - 1) - / 60 - ) - self.log("ETA: " + f"{eta:.2f}" + " minutes") - - if ( - (epoch + 1) % self.config.validation_interval == 0 - or epoch + 1 == self.config.max_epochs - ): - model.eval() - self.log("Performing validation...") - with torch.no_grad(): - for val_data in val_loader: - val_inputs, val_labels = ( - val_data["image"].to(device), - val_data["label"].to(device), - ) - - try: - with torch.no_grad(): - val_outputs = sliding_window_inference( - val_inputs, - roi_size=size, - sw_batch_size=self.config.batch_size, - predictor=model, - overlap=0.25, - sw_device=self.config.device, - device=self.config.device, - progress=False, - ) - except Exception as e: - self.raise_error(e, "Error during validation") - logger.debug( - f"val_outputs shape : {val_outputs.shape}" - ) - # val_outputs = model(val_inputs) - - pred = decollate_batch(val_outputs) - - labs = decollate_batch(val_labels) - - # TODO : more parameters/flexibility - post_pred = Compose( - AsDiscrete(threshold=0.6), EnsureType() - ) # - post_label = EnsureType() - - val_outputs = [ - post_pred(res_tensor) for res_tensor in pred - ] - - val_labels = [ - post_label(res_tensor) for res_tensor in labs - ] - - # logger.debug(len(val_outputs)) - # logger.debug(len(val_labels)) - - dice_metric(y_pred=val_outputs, y=val_labels) - checkpoint_output.append( - [res.detach().cpu() for res in val_outputs] - ) - - checkpoint_output = [ - item.numpy() - for batch in checkpoint_output - for item in batch - ] - - metric = dice_metric.aggregate().detach().item() - dice_metric.reset() - - val_metric_values.append(metric) - - train_report = TrainingReport( - show_plot=True, - epoch=epoch, - loss_values=epoch_loss_values, - validation_metric=val_metric_values, - weights=model.state_dict(), - images=checkpoint_output, - ) - self.log("Validation completed") - yield train_report - - weights_filename = ( - f"{model_name}_best_metric" - + f"_epoch_{epoch + 1}.pth" - ) - - if metric > best_metric: - best_metric = metric - best_metric_epoch = epoch + 1 - self.log("Saving best metric model") - torch.save( - model.state_dict(), - Path(self.config.results_path_folder) - / Path( - weights_filename, - ), - ) - self.log("Saving complete") - self.log( - f"Current epoch: {epoch + 1}, Current mean dice: {metric:.4f}" - f"\nBest mean dice: {best_metric:.4f} " - f"at epoch: {best_metric_epoch}" - ) - self.log("=" * 10) - self.log( - f"Train completed, best_metric: {best_metric:.4f} " - f"at epoch: {best_metric_epoch}" - ) - # Save last checkpoint - weights_filename = f"{model_name}_last_epoch_{epoch + 1}.pth" - self.log("Saving last model") - torch.save( - model.state_dict(), - Path(self.config.results_path_folder) / Path(weights_filename), - ) - self.log("Saving complete, exiting") - model.to("cpu") - # clear (V)RAM - val_ds = None - train_ds = None - val_loader = None - train_loader = None - torch.cuda.empty_cache() - import gc - - gc.collect() - - except Exception as e: - self.raise_error(e, "Error in training") - self.quit() - finally: - self.quit() diff --git a/napari_cellseg3d/code_models/workers_utils.py b/napari_cellseg3d/code_models/workers_utils.py new file mode 100644 index 00000000..f377fd22 --- /dev/null +++ b/napari_cellseg3d/code_models/workers_utils.py @@ -0,0 +1,221 @@ +import typing as t +from dataclasses import dataclass +from pathlib import Path +from typing import TYPE_CHECKING + +import numpy as np +import torch +from monai.transforms import MapTransform +from qtpy.QtCore import Signal +from superqt.utils._qthreading import WorkerBaseSignals +from tqdm import tqdm + +# local +from napari_cellseg3d import interface as ui +from napari_cellseg3d import utils +from napari_cellseg3d.utils import LOGGER as logger + +if TYPE_CHECKING: + from napari_cellseg3d.code_models.instance_segmentation import ImageStats + +PRETRAINED_WEIGHTS_DIR = Path(__file__).parent.resolve() / Path( + "models/pretrained" +) + + +class WeightsDownloader: + """A utility class the downloads the weights of a model when needed.""" + + def __init__(self, log_widget: t.Optional[ui.Log] = None): + """ + Creates a WeightsDownloader, optionally with a log widget to display the progress. + + Args: + log_widget (log_utility.Log): a Log to display the progress bar in. If None, uses logger.info() + """ + self.log_widget = log_widget + + def download_weights(self, model_name: str, model_weights_filename: str): + """ + Downloads a specific pretrained model. + This code is adapted from DeepLabCut with permission from MWMathis. + + Args: + model_name (str): name of the model to download + model_weights_filename (str): name of the .pth file expected for the model + """ + import json + import tarfile + import urllib.request + + def show_progress(_, block_size, __): # count, block_size, total_size + pbar.update(block_size) + + logger.info("*" * 20) + pretrained_folder_path = PRETRAINED_WEIGHTS_DIR + json_path = pretrained_folder_path / Path("pretrained_model_urls.json") + + check_path = pretrained_folder_path / Path(model_weights_filename) + + if Path(check_path).is_file(): + message = f"Weight file {model_weights_filename} already exists, skipping download" + if self.log_widget is not None: + self.log_widget.print_and_log(message, printing=False) + logger.info(message) + return + + with Path.open(json_path) as f: + neturls = json.load(f) + if model_name in neturls: + url = neturls[model_name] + response = urllib.request.urlopen(url) + + start_message = f"Downloading the model from HuggingFace {url}...." + total_size = int(response.getheader("Content-Length")) + if self.log_widget is None: + logger.info(start_message) + pbar = tqdm(unit="B", total=total_size, position=0) + else: + self.log_widget.print_and_log(start_message) + pbar = tqdm( + unit="B", + total=total_size, + position=0, + file=self.log_widget, + ) + + filename, _ = urllib.request.urlretrieve( + url, reporthook=show_progress + ) + with tarfile.open(filename, mode="r:gz") as tar: + + def is_within_directory(directory, target): + abs_directory = Path(directory).resolve() + abs_target = Path(target).resolve() + # prefix = os.path.commonprefix([abs_directory, abs_target]) + logger.debug(abs_directory) + logger.debug(abs_target) + logger.debug(abs_directory.parents) + + return abs_directory in abs_target.parents + + def safe_extract( + tar, path=".", members=None, *, numeric_owner=False + ): + for member in tar.getmembers(): + member_path = str(Path(path) / member.name) + if not is_within_directory(path, member_path): + raise Exception( + "Attempted Path Traversal in Tar File" + ) + + tar.extractall(path, members, numeric_owner=numeric_owner) + + safe_extract(tar, pretrained_folder_path) + # tar.extractall(pretrained_folder_path) + else: + raise ValueError( + f"Unknown model: {model_name}. Should be one of {', '.join(neturls)}" + ) + + +class LogSignal(WorkerBaseSignals): + """Signal to send messages to be logged from another thread. + + Separate from Worker instances as indicated `on this post`_ + + .. _on this post: https://stackoverflow.com/questions/2970312/pyqt4-qtcore-pyqtsignal-object-has-no-attribute-connect + """ # TODO link ? + + log_signal = Signal(str) + """qtpy.QtCore.Signal: signal to be sent when some text should be logged""" + warn_signal = Signal(str) + """qtpy.QtCore.Signal: signal to be sent when some warning should be emitted in main thread""" + error_signal = Signal(Exception, str) + """qtpy.QtCore.Signal: signal to be sent when some error should be emitted in main thread""" + + # Should not be an instance variable but a class variable, not defined in __init__, see + # https://stackoverflow.com/questions/2970312/pyqt4-qtcore-pyqtsignal-object-has-no-attribute-connect + + def __init__(self): + super().__init__() + + +class ONNXModelWrapper(torch.nn.Module): + """Class to replace torch model by ONNX Runtime session""" + + def __init__(self, file_location): + super().__init__() + try: + import onnxruntime as ort + except ImportError as e: + logger.error("ONNX is not installed but ONNX model was loaded") + logger.error(e) + msg = "PLEASE INSTALL ONNX CPU OR GPU USING pip install napari-cellseg3d[onnx-cpu] OR napari-cellseg3d[onnx-gpu]" + logger.error(msg) + raise ImportError(msg) from e + + self.ort_session = ort.InferenceSession( + file_location, + providers=["CUDAExecutionProvider", "CPUExecutionProvider"], + ) + + def forward(self, modeL_input): + """Wraps ONNX output in a torch tensor""" + outputs = self.ort_session.run( + None, {"input": modeL_input.cpu().numpy()} + ) + return torch.tensor(outputs[0]) + + def eval(self): + """Dummy function to replace model.eval()""" + pass + + def to(self, device): + """Dummy function to replace model.to(device)""" + pass + + +class QuantileNormalizationd(MapTransform): + def __init__(self, keys, allow_missing_keys: bool = False): + super().__init__(keys, allow_missing_keys) + + def __call__(self, data): + d = dict(data) + for key in self.keys: + d[key] = self.normalizer(d[key]) + return d + + def normalizer(self, image: torch.Tensor): + """Normalize each image in a batch individually by quantile normalization.""" + if image.ndim == 4: + for i in range(image.shape[0]): + image[i] = utils.quantile_normalization(image[i]) + else: + raise NotImplementedError( + "QuantileNormalizationd only supports 2D and 3D tensors with NCHWD format" + ) + return image + + +@dataclass +class InferenceResult: + """Class to record results of a segmentation job""" + + image_id: int = 0 + original: np.array = None + instance_labels: np.array = None + crf_results: np.array = None + stats: "np.array[ImageStats]" = None + result: np.array = None + model_name: str = None + + +@dataclass +class TrainingReport: + show_plot: bool = True + epoch: int = 0 + loss_values: t.Dict = None # TODO(cyril) : change to dict and unpack different losses for e.g. WNet with several losses + validation_metric: t.List = None + weights: np.array = None + images: t.List[np.array] = None diff --git a/napari_cellseg3d/code_plugins/plugin_model_inference.py b/napari_cellseg3d/code_plugins/plugin_model_inference.py index 560cef3d..baa0791d 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_inference.py +++ b/napari_cellseg3d/code_plugins/plugin_model_inference.py @@ -15,10 +15,8 @@ InstanceWidgets, ) from napari_cellseg3d.code_models.model_framework import ModelFramework -from napari_cellseg3d.code_models.workers import ( - InferenceResult, - InferenceWorker, -) +from napari_cellseg3d.code_models.worker_inference import InferenceWorker +from napari_cellseg3d.code_models.workers_utils import InferenceResult from napari_cellseg3d.code_plugins.plugin_crf import CRFParamsWidget logger = utils.LOGGER @@ -242,6 +240,12 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): self._remove_unused() + def _toggle_display_crf(self): + if self.model_choice.currentText() == "WNet": + self.use_crf.setVisible(True) + else: + self.use_crf.setVisible(False) + def _set_tooltips(self): ################## ################## @@ -306,6 +310,7 @@ def check_ready(self): def _restrict_window_size_for_model(self): """Sets the window size to a value that is compatible with the chosen model""" + window_enabled = self.window_infer_box.isChecked() if self.model_choice.currentText() == "WNet": self.window_size_choice.setCurrentIndex(self._default_window_size) self.window_size_choice.setDisabled(True) @@ -314,8 +319,11 @@ def _restrict_window_size_for_model(self): else: self.window_size_choice.setDisabled(False) self.window_infer_box.setDisabled(False) - self.window_infer_box.setChecked(False) - self.window_size_choice.setCurrentIndex(self._default_window_size) + self.window_infer_box.setChecked(window_enabled) + if not window_enabled: + self.window_size_choice.setCurrentIndex( + self._default_window_size + ) def _toggle_display_model_input_size(self): if ( @@ -477,6 +485,7 @@ def _build(self): # self.instance_param_container, # instance segmentation ], ) + self.model_choice.currentIndexChanged.connect(self._toggle_display_crf) ModelFramework._show_io_element( self.save_stats_to_csv_box, self.use_instance_choice ) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 9a8b6195..4ec2aa04 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -32,14 +32,13 @@ from napari_cellseg3d import config, utils from napari_cellseg3d import interface as ui from napari_cellseg3d.code_models.model_framework import ModelFramework -from napari_cellseg3d.code_models.workers import ( - TrainingReport, +from napari_cellseg3d.code_models.worker_training import ( TrainingWorker, ) +from napari_cellseg3d.code_models.workers_utils import TrainingReport -NUMBER_TABS = 3 -DEFAULT_PATCH_SIZE = 64 - +NUMBER_TABS = 3 # how many tabs in the widget +DEFAULT_PATCH_SIZE = 64 # default patch size for training logger = utils.LOGGER diff --git a/napari_cellseg3d/utils.py b/napari_cellseg3d/utils.py index ca49c29d..3122a6c6 100644 --- a/napari_cellseg3d/utils.py +++ b/napari_cellseg3d/utils.py @@ -8,6 +8,7 @@ import numpy as np import torch from monai.transforms import Zoom +from numpy.random import PCG64, Generator from tifffile import imread, imwrite LOGGER = logging.getLogger(__name__) @@ -22,6 +23,8 @@ Definitions of utility functions, classes, and variables """ +rand_gen = Generator(PCG64(12345)) + #################### # viewer utils @@ -317,11 +320,11 @@ def get_padding_dim(image_shape, anisotropy_factor=None): padding = [] dims = len(image_shape) - print(f"Dimension of data for padding : {dims}D") - print(f"Image shape is {image_shape}") + LOGGER.debug(f"Data is {dims}D") + LOGGER.debug(f"Image shape is {image_shape}") if dims != 2 and dims != 3: error = "Please check the dimensions of the input, only 2 or 3-dimensional data is supported currently" - print(error) + LOGGER.error(error) raise ValueError(error) for i in range(dims): @@ -349,7 +352,7 @@ def get_padding_dim(image_shape, anisotropy_factor=None): padding.append(pad) - print(f"Padding sizes are {padding}") + LOGGER.debug(f"Padding sizes are {padding}") return padding From c72bbd2ea4b8b7fb543642c1796cc7c7b7aba3d4 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Tue, 18 Jul 2023 10:18:41 +0200 Subject: [PATCH 099/250] Centralized rand_gen --- napari_cellseg3d/_tests/test_models.py | 4 +--- napari_cellseg3d/_tests/test_plugin_inference.py | 4 +--- napari_cellseg3d/_tests/test_plugin_utils.py | 4 +--- napari_cellseg3d/_tests/test_utils.py | 3 +-- napari_cellseg3d/dev_scripts/thread_test.py | 3 +-- 5 files changed, 5 insertions(+), 13 deletions(-) diff --git a/napari_cellseg3d/_tests/test_models.py b/napari_cellseg3d/_tests/test_models.py index 51ff1365..655fbb3a 100644 --- a/napari_cellseg3d/_tests/test_models.py +++ b/napari_cellseg3d/_tests/test_models.py @@ -2,7 +2,6 @@ from pathlib import Path import pytest import torch -from numpy.random import PCG64, Generator from napari_cellseg3d.code_models.crf import ( CRFWorker, @@ -13,8 +12,7 @@ from napari_cellseg3d.code_models.models.model_TRAILMAP_MS import TRAILMAP_MS_ from napari_cellseg3d.code_models.models.wnet.soft_Ncuts import SoftNCutsLoss from napari_cellseg3d.config import MODEL_LIST, CRFConfig - -rand_gen = Generator(PCG64(12345)) +from napari_cellseg3d.utils import rand_gen def test_correct_shape_for_crf(): diff --git a/napari_cellseg3d/_tests/test_plugin_inference.py b/napari_cellseg3d/_tests/test_plugin_inference.py index de5c78ae..efa9d9fb 100644 --- a/napari_cellseg3d/_tests/test_plugin_inference.py +++ b/napari_cellseg3d/_tests/test_plugin_inference.py @@ -1,6 +1,5 @@ from pathlib import Path -from numpy.random import PCG64, Generator from tifffile import imread from napari_cellseg3d._tests.fixtures import LogFixture @@ -14,8 +13,7 @@ Inferer, ) from napari_cellseg3d.config import MODEL_LIST - -rand_gen = Generator(PCG64(12345)) +from napari_cellseg3d.utils import rand_gen def test_inference(make_napari_viewer_proxy, qtbot): diff --git a/napari_cellseg3d/_tests/test_plugin_utils.py b/napari_cellseg3d/_tests/test_plugin_utils.py index 1427021a..fe6fe2e8 100644 --- a/napari_cellseg3d/_tests/test_plugin_utils.py +++ b/napari_cellseg3d/_tests/test_plugin_utils.py @@ -1,13 +1,11 @@ import numpy as np -from numpy.random import PCG64, Generator from napari_cellseg3d.code_plugins.plugin_crop import Cropping from napari_cellseg3d.code_plugins.plugin_utilities import ( UTILITIES_WIDGETS, Utilities, ) - -rand_gen = Generator(PCG64(12345)) +from napari_cellseg3d.utils import rand_gen def test_utils_plugin(make_napari_viewer_proxy): diff --git a/napari_cellseg3d/_tests/test_utils.py b/napari_cellseg3d/_tests/test_utils.py index f02825bb..11e524b7 100644 --- a/napari_cellseg3d/_tests/test_utils.py +++ b/napari_cellseg3d/_tests/test_utils.py @@ -4,12 +4,11 @@ import numpy as np import pytest import torch -from numpy.random import PCG64, Generator from napari_cellseg3d import utils from napari_cellseg3d.dev_scripts import thread_test -rand_gen = Generator(PCG64(12345)) +rand_gen = utils.rand_gen def test_singleton_class(): diff --git a/napari_cellseg3d/dev_scripts/thread_test.py b/napari_cellseg3d/dev_scripts/thread_test.py index 112ec0ce..82782ef8 100644 --- a/napari_cellseg3d/dev_scripts/thread_test.py +++ b/napari_cellseg3d/dev_scripts/thread_test.py @@ -2,7 +2,6 @@ import napari from napari.qt.threading import thread_worker -from numpy.random import PCG64, Generator from qtpy.QtWidgets import ( QGridLayout, QLabel, @@ -13,7 +12,7 @@ QWidget, ) -rand_gen = Generator(PCG64(12345)) +from napari_cellseg3d.utils import rand_gen #################################### # Tutorial code from napari forums # From 2fa6e645e42fbf309a21957f013f203118bfa7ec Mon Sep 17 00:00:00 2001 From: C-Achard Date: Tue, 18 Jul 2023 11:34:52 +0200 Subject: [PATCH 100/250] Train and inference tweaks --- .../code_models/worker_inference.py | 43 ++++++++++--------- .../code_models/worker_training.py | 6 ++- .../code_plugins/plugin_model_inference.py | 12 ++---- .../code_plugins/plugin_model_training.py | 12 ++++-- 4 files changed, 39 insertions(+), 34 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index c4221e7b..107f0d88 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -35,7 +35,8 @@ QuantileNormalizationd, WeightsDownloader, ) -from napari_cellseg3d.utils import LOGGER as logger + +logger = utils.LOGGER """ Writing something to log messages from outside the main thread needs specific care, @@ -308,6 +309,7 @@ def model_output_wrapper(inputs): result = model(inputs) return post_process_transforms(result) + model.eval() with torch.no_grad(): outputs = sliding_window_inference( inputs, @@ -561,7 +563,7 @@ def inference_on_layer(self, image, model, post_process_transforms): self.log("-" * 10) self.log("Inference started on layer...") - image = image.type(torch.FloatTensor) + # image = image.type(torch.FloatTensor) image = utils.quantile_normalization(image) out = self.model_output( @@ -674,13 +676,17 @@ def inference(self): weights = str( PRETRAINED_WEIGHTS_DIR / Path(model_class.weights_file) ) - - model.load_state_dict( # note that this is redefined in WNet_ - torch.load( - weights, - map_location=self.config.device, + try: + missing = model.load_state_dict( # note that this is redefined in WNet_ + torch.load( + weights, + map_location=self.config.device, + ), + strict=True, ) - ) + self.log(f"Weights status : {missing}") + except RuntimeError as e: + self.raise_error(e, "Error when loading weights") self.log("Done") # except Exception as e: # self.raise_error(e, "Issue loading weights") @@ -752,20 +758,15 @@ def inference(self): if model is None: raise ValueError("Model is None") - model.eval() - with torch.no_grad(): - ################################ - ################################ - ################################ - if is_folder: - for i, inf_data in enumerate(inference_loader): - yield self.inference_on_folder( - inf_data, i, model, post_process_transforms - ) - elif is_layer: - yield self.inference_on_layer( - input_image, model, post_process_transforms + if is_folder: + for i, inf_data in enumerate(inference_loader): + yield self.inference_on_folder( + inf_data, i, model, post_process_transforms ) + elif is_layer: + yield self.inference_on_layer( + input_image, model, post_process_transforms + ) model.to("cpu") # self.quit() except Exception as e: diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 03e82ed5..ed3f08ae 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -653,7 +653,11 @@ def get_loader_func(num_samples): dice_metric(y_pred=val_outputs, y=val_labels) checkpoint_output.append( - [res.detach().cpu() for res in val_outputs] + [ + val_outputs[0].detach().cpu(), + val_inputs[0].detach().cpu(), + val_labels[0].detach().cpu(), + ] ) checkpoint_output = [ diff --git a/napari_cellseg3d/code_plugins/plugin_model_inference.py b/napari_cellseg3d/code_plugins/plugin_model_inference.py index baa0791d..59c24c2c 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_inference.py +++ b/napari_cellseg3d/code_plugins/plugin_model_inference.py @@ -240,7 +240,7 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): self._remove_unused() - def _toggle_display_crf(self): + def _toggle_crf_choice(self): if self.model_choice.currentText() == "WNet": self.use_crf.setVisible(True) else: @@ -310,7 +310,6 @@ def check_ready(self): def _restrict_window_size_for_model(self): """Sets the window size to a value that is compatible with the chosen model""" - window_enabled = self.window_infer_box.isChecked() if self.model_choice.currentText() == "WNet": self.window_size_choice.setCurrentIndex(self._default_window_size) self.window_size_choice.setDisabled(True) @@ -319,11 +318,8 @@ def _restrict_window_size_for_model(self): else: self.window_size_choice.setDisabled(False) self.window_infer_box.setDisabled(False) - self.window_infer_box.setChecked(window_enabled) - if not window_enabled: - self.window_size_choice.setCurrentIndex( - self._default_window_size - ) + self.window_infer_box.setChecked(True) + self.window_size_choice.setCurrentIndex(self._default_window_size) def _toggle_display_model_input_size(self): if ( @@ -485,7 +481,7 @@ def _build(self): # self.instance_param_container, # instance segmentation ], ) - self.model_choice.currentIndexChanged.connect(self._toggle_display_crf) + self.model_choice.currentIndexChanged.connect(self._toggle_crf_choice) ModelFramework._show_io_element( self.save_stats_to_csv_box, self.use_instance_choice ) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 4ec2aa04..d2de8d0f 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -979,7 +979,11 @@ def on_yield(self, report: TrainingReport): if report.show_plot: try: - layer_name = "Validation output n°" + layer_names = [ + "Validation output", + "Validation image", + "Validation labels", + ] rge = range(len(report.images)) self.log.print_and_log(len(report.images)) @@ -988,18 +992,18 @@ def on_yield(self, report: TrainingReport): for i in rge: layer = self._viewer.add_image( report.images[i], - name=layer_name + str(i), + name=layer_names[i] + str(i), colormap="viridis", ) self.result_layers.append(layer) else: for i in rge: - if layer_name + str(i) not in [ + if layer_names[i] + str(i) not in [ layer.name for layer in self._viewer.layers ]: new_layer = self._viewer.add_image( report.images[i], - name=layer_name + str(i), + name=layer_names[i] + str(i), colormap="viridis", ) self.result_layers.append(new_layer) From 5b297df59d2dd672f8c03fc7134ca94a88b2ad60 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Tue, 18 Jul 2023 14:20:31 +0200 Subject: [PATCH 101/250] Working on dtype issues --- .../code_models/models/model_TRAILMAP_MS.py | 4 +--- .../code_models/worker_inference.py | 18 +++++++++--------- 2 files changed, 10 insertions(+), 12 deletions(-) diff --git a/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py b/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py index 8f1a493a..bc8e43d5 100644 --- a/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py +++ b/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py @@ -1,7 +1,5 @@ from napari_cellseg3d.code_models.models.unet.model import UNet3D -from napari_cellseg3d.utils import LOGGER - -logger = LOGGER +from napari_cellseg3d.utils import LOGGER as logger class TRAILMAP_MS_(UNet3D): diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 107f0d88..cba91f50 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -201,7 +201,7 @@ def load_folder(self): LoadImaged(keys=["image"]), # AddChanneld(keys=["image"]), #already done EnsureChannelFirstd(keys=["image"]), - # NormalizeIntensityd(keys=["image"]), # TODO normalize + QuantileNormalizationd(keys=["image"]), # Orientationd(keys=["image"], axcodes="PLI"), # anisotropic_transform, SpatialPadd(keys=["image"], spatial_size=pad), @@ -219,9 +219,9 @@ def load_folder(self): def load_layer(self): self.log("\nLoading layer\n") - data = np.squeeze(self.config.layer.data) - - volume = np.array(data, dtype=np.int16) + image = np.squeeze(self.config.layer.data) + volume = image.astype(np.float32) + self.log(f"Image type : {str(image.dtype)}") volume_dims = len(volume.shape) if volume_dims != 3: @@ -229,15 +229,14 @@ def load_layer(self): f"Data array is not 3-dimensional but {volume_dims}-dimensional," f" please check for extra channel/batch dimensions" ) - volume = np.swapaxes( volume, 0, 2 - ) # for anisotropy to be monai-like, i.e. zyx # FIXME rotation not always correct + ) # for anisotropy to be monai-like, i.e. zyx dims_check = volume.shape - # logger.debug(volume.shape) - # logger.debug(volume.dtype) + logger.debug(volume.shape) + logger.debug(volume.dtype) if self.config.sliding_window_config.is_enabled(): load_transforms = Compose( [ @@ -563,8 +562,9 @@ def inference_on_layer(self, image, model, post_process_transforms): self.log("-" * 10) self.log("Inference started on layer...") - # image = image.type(torch.FloatTensor) image = utils.quantile_normalization(image) + self.log(f"Image type after normalization : {str(image.dtype)}") + self.log(f"Image shape after normalization : {str(image.shape)}") out = self.model_output( image, From 219a6243194f1323ff5961381536b01021f75a08 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Tue, 18 Jul 2023 15:43:28 +0200 Subject: [PATCH 102/250] Fixed issues with remapping Remapping was done in inference always, which was breaking some models outputs. This is now done in the correct place, i.e. the WNet_model file --- .../code_models/models/model_WNet.py | 12 ++-- .../code_models/models/wnet/train_wnet.py | 15 +---- .../code_models/worker_inference.py | 49 ++++++--------- .../code_models/worker_training.py | 38 ++++++------ napari_cellseg3d/code_models/workers_utils.py | 7 ++- .../code_plugins/plugin_model_inference.py | 1 + .../code_plugins/plugin_model_training.py | 62 ++++++++++++------- napari_cellseg3d/dev_scripts/crop_data.py | 13 ++-- 8 files changed, 100 insertions(+), 97 deletions(-) diff --git a/napari_cellseg3d/code_models/models/model_WNet.py b/napari_cellseg3d/code_models/models/model_WNet.py index 37768616..e50a58a1 100644 --- a/napari_cellseg3d/code_models/models/model_WNet.py +++ b/napari_cellseg3d/code_models/models/model_WNet.py @@ -1,5 +1,6 @@ # local from napari_cellseg3d.code_models.models.wnet.model import WNet_encoder +from napari_cellseg3d.utils import remap_image class WNet_(WNet_encoder): @@ -22,13 +23,10 @@ def __init__( # def train(self: T, mode: bool = True) -> T: # raise NotImplementedError("Training not implemented for WNet") - # def forward(self, x): - # """Forward ENCODER pass of the W-Net model. - # Done this way to allow inference on the encoder only when called by sliding_window_inference. - # """ - # return self.forward_encoder(x) - # # enc = self.forward_encoder(x) - # # return self.forward_decoder(enc) + def forward(self, x): + """Forward pass of the W-Net model.""" + norm_x = remap_image(x) + return super().forward(norm_x) def load_state_dict(self, state_dict, strict=True): """Load the model state dict for inference, without the decoder weights.""" diff --git a/napari_cellseg3d/code_models/models/wnet/train_wnet.py b/napari_cellseg3d/code_models/models/wnet/train_wnet.py index 15d0e8ac..3b2ad353 100644 --- a/napari_cellseg3d/code_models/models/wnet/train_wnet.py +++ b/napari_cellseg3d/code_models/models/wnet/train_wnet.py @@ -5,7 +5,6 @@ import glob import time from pathlib import Path -from typing import Union from warnings import warn import numpy as np @@ -44,7 +43,7 @@ from napari_cellseg3d.code_models.models.wnet.model import WNet from napari_cellseg3d.code_models.models.wnet.soft_Ncuts import SoftNCutsLoss from napari_cellseg3d.utils import LOGGER as logger -from napari_cellseg3d.utils import dice_coeff, get_padding_dim +from napari_cellseg3d.utils import dice_coeff, get_padding_dim, remap_image try: import wandb @@ -115,18 +114,6 @@ def create_dataset_dict_no_labs(volume_directory): return [{"image": image_name} for image_name in images_filepaths] -def remap_image( - image: Union[np.ndarray, torch.Tensor], new_max=100, new_min=0 -): - """Normalizes a numpy array or Tensor using the max and min value""" - shape = image.shape - image = image.flatten() - image = (image - image.min()) / (image.max() - image.min()) - image = image * (new_max - new_min) + new_min - # image = set_quantile_to_value(image) - return image.reshape(shape) - - ################################ # Config & WANDB # ################################ diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index cba91f50..f6ad6fbe 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -32,6 +32,7 @@ InferenceResult, LogSignal, ONNXModelWrapper, + QuantileNormalization, QuantileNormalizationd, WeightsDownloader, ) @@ -135,7 +136,7 @@ def log_parameters(self): config = self.config self.log("-" * 20) - self.log("\nParameters summary :") + self.log("Parameters summary :") self.log(f"Model is : {config.model_info.name}") if config.post_process_config.thresholding.enabled: @@ -149,7 +150,7 @@ def log_parameters(self): else "disabled" ) - self.log(f"Window inference is {status}\n") + self.log(f"Window inference is {status}") if status == "enabled": self.log( f"Window size is {self.config.sliding_window_config.window_size}" @@ -171,7 +172,7 @@ def log_parameters(self): instance_config = config.post_process_config.instance if instance_config.enabled: self.log( - f"Instance segmentation enabled, method : {instance_config.method.name}\n" + f"Instance segmentation enabled, method : {instance_config.method.name}" ) self.log("-" * 20) @@ -209,7 +210,7 @@ def load_folder(self): ] ) - self.log("\nLoading dataset...") + self.log("Loading dataset...") inference_ds = Dataset(data=images_dict, transform=load_transforms) inference_loader = DataLoader( inference_ds, batch_size=1, num_workers=2 @@ -218,10 +219,10 @@ def load_folder(self): return inference_loader def load_layer(self): - self.log("\nLoading layer\n") + self.log("Loading layer") image = np.squeeze(self.config.layer.data) volume = image.astype(np.float32) - self.log(f"Image type : {str(image.dtype)}") + # self.log(f"Image type : {str(image.dtype)}") volume_dims = len(volume.shape) if volume_dims != 3: @@ -231,12 +232,13 @@ def load_layer(self): ) volume = np.swapaxes( volume, 0, 2 - ) # for anisotropy to be monai-like, i.e. zyx + ) # for dims to be monai-like, i.e. xyz, from napari zyx dims_check = volume.shape logger.debug(volume.shape) logger.debug(volume.dtype) + if self.config.sliding_window_config.is_enabled(): load_transforms = Compose( [ @@ -244,6 +246,7 @@ def load_layer(self): # anisotropic_transform, AddChannel(), # SpatialPad(spatial_size=pad), + QuantileNormalization(), AddChannel(), EnsureType(), ], @@ -258,6 +261,7 @@ def load_layer(self): ToTensor(), # anisotropic_transform, AddChannel(), + QuantileNormalization(), SpatialPad(spatial_size=pad), AddChannel(), EnsureType(), @@ -267,6 +271,8 @@ def load_layer(self): ) input_image = load_transforms(volume) + logger.debug(f"INPUT IMAGE SHAPE : {input_image.shape}") + logger.debug(f"INPUT IMAGE TYPE : {input_image.dtype}") self.log("Done") return input_image @@ -275,16 +281,9 @@ def model_output( inputs, model, post_process_transforms, - post_process=True, aniso_transform=None, ): inputs = inputs.to("cpu") - - # def model_output(inputs): - # return post_process_transforms( - # self.config.model_info.get_model().get_output(model, inputs) - # ) - dataset_device = ( "cpu" if self.config.keep_on_cpu else self.config.device ) @@ -302,7 +301,6 @@ def model_output( logger.debug(f"inputs type : {inputs.dtype}") try: # outputs = model(inputs) - inputs = utils.remap_image(inputs) def model_output_wrapper(inputs): result = model(inputs) @@ -327,13 +325,13 @@ def model_output_wrapper(inputs): logger.debug("failed to run sliding window inference") self.raise_error(e, "Error during sliding window inference") logger.debug(f"Inference output shape: {outputs.shape}") + self.log("Post-processing...") out = outputs.detach().cpu().numpy() if aniso_transform is not None: out = aniso_transform(out) - if post_process: - out = np.array(out).astype(np.float32) - out = np.squeeze(out) + out = np.array(out).astype(np.float32) + out = np.squeeze(out) return out except Exception as e: logger.exception(e) @@ -432,9 +430,9 @@ def save_image( filename = Path(file_path).stem if from_layer: - self.log(f"\nLayer prediction saved as : {filename}") + self.log(f"Layer prediction saved as : {filename}") else: - self.log(f"\nFile n°{i+1} saved as : {filename}") + self.log(f"File n°{i+1} saved as : {filename}") def aniso_transform(self, image): if self.config.post_process_config.zoom.enabled: @@ -451,7 +449,7 @@ def instance_seg( self, semantic_labels, image_id=0, original_filename="layer" ): if image_id is not None: - self.log(f"\nRunning instance segmentation for image n°{image_id}") + self.log(f"Running instance segmentation for image n°{image_id}") method = self.config.post_process_config.instance.method instance_labels = method.run_method_on_channels(semantic_labels) @@ -513,7 +511,6 @@ def inference_on_folder(self, inf_data, i, model, post_process_transforms): crf_results = None original = np.array(inf_data["image"]).astype(np.float32) - self.log(f"Inference completed on image n°{i+1}") return self.create_inference_result( @@ -562,17 +559,12 @@ def inference_on_layer(self, image, model, post_process_transforms): self.log("-" * 10) self.log("Inference started on layer...") - image = utils.quantile_normalization(image) - self.log(f"Image type after normalization : {str(image.dtype)}") - self.log(f"Image shape after normalization : {str(image.shape)}") - out = self.model_output( image, model, post_process_transforms, aniso_transform=self.aniso_transform, ) - self.save_image(out, from_layer=True) instance_labels, stats = self.get_instance_result( @@ -584,7 +576,6 @@ def inference_on_layer(self, image, model, post_process_transforms): if self.config.use_crf else None ) - return self.create_inference_result( semantic_labels=out, instance_labels=instance_labels, @@ -665,7 +656,7 @@ def inference(self): if model is None: raise ValueError("Model is None") # try: - self.log("\nLoading weights...") + self.log("Loading weights...") if weights_config.custom: weights = weights_config.path else: diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index ed3f08ae..b8d522c8 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -16,7 +16,6 @@ from monai.inferers import sliding_window_inference from monai.metrics import DiceMetric from monai.transforms import ( - AsDiscrete, Compose, EnsureChannelFirstd, EnsureType, @@ -636,7 +635,8 @@ def get_loader_func(num_samples): # TODO : more parameters/flexibility post_pred = Compose( - AsDiscrete(threshold=0.6), EnsureType() + # AsDiscrete(threshold=0.6), # needed ? + EnsureType() ) # post_label = EnsureType() @@ -652,13 +652,14 @@ def get_loader_func(num_samples): # logger.debug(len(val_labels)) dice_metric(y_pred=val_outputs, y=val_labels) - checkpoint_output.append( - [ - val_outputs[0].detach().cpu(), - val_inputs[0].detach().cpu(), - val_labels[0].detach().cpu(), - ] - ) + + checkpoint_output.append( + [ + val_outputs[0].detach().cpu(), + val_inputs[0].detach().cpu(), + val_labels[0].detach().cpu(), + ] + ) checkpoint_output = [ item.numpy() @@ -668,7 +669,6 @@ def get_loader_func(num_samples): metric = dice_metric.aggregate().detach().item() dice_metric.reset() - val_metric_values.append(metric) train_report = TrainingReport( @@ -684,7 +684,8 @@ def get_loader_func(num_samples): weights_filename = ( f"{model_name}_best_metric" - + f"_epoch_{epoch + 1}.pth" + # + f"_epoch_{epoch + 1}" # avoid saving per epoch + + ".pth" ) if metric > best_metric: @@ -710,7 +711,7 @@ def get_loader_func(num_samples): f"at epoch: {best_metric_epoch}" ) # Save last checkpoint - weights_filename = f"{model_name}_last_epoch_{epoch + 1}.pth" + weights_filename = f"{model_name}_latest.pth" self.log("Saving last model") torch.save( model.state_dict(), @@ -719,14 +720,11 @@ def get_loader_func(num_samples): self.log("Saving complete, exiting") model.to("cpu") # clear (V)RAM - val_ds = None - train_ds = None - val_loader = None - train_loader = None - torch.cuda.empty_cache() - import gc - - gc.collect() + # val_ds = None + # train_ds = None + # val_loader = None + # train_loader = None + # torch.cuda.empty_cache() except Exception as e: self.raise_error(e, "Error in training") diff --git a/napari_cellseg3d/code_models/workers_utils.py b/napari_cellseg3d/code_models/workers_utils.py index f377fd22..923bc727 100644 --- a/napari_cellseg3d/code_models/workers_utils.py +++ b/napari_cellseg3d/code_models/workers_utils.py @@ -5,7 +5,7 @@ import numpy as np import torch -from monai.transforms import MapTransform +from monai.transforms import MapTransform, Transform from qtpy.QtCore import Signal from superqt.utils._qthreading import WorkerBaseSignals from tqdm import tqdm @@ -198,6 +198,11 @@ def normalizer(self, image: torch.Tensor): return image +class QuantileNormalization(Transform): + def __call__(self, img): + return utils.quantile_normalization(img) + + @dataclass class InferenceResult: """Class to record results of a segmentation job""" diff --git a/napari_cellseg3d/code_plugins/plugin_model_inference.py b/napari_cellseg3d/code_plugins/plugin_model_inference.py index 59c24c2c..471c02ac 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_inference.py +++ b/napari_cellseg3d/code_plugins/plugin_model_inference.py @@ -481,6 +481,7 @@ def _build(self): # self.instance_param_container, # instance segmentation ], ) + self._toggle_crf_choice() self.model_choice.currentIndexChanged.connect(self._toggle_crf_choice) ModelFramework._show_io_element( self.save_stats_to_csv_box, self.use_instance_choice diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index d2de8d0f..d77d0c28 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -969,6 +969,42 @@ def on_error(self): # self.empty_cuda_cache() # self.clean_cache() + def _display_results(self, images, names, complete_missing=False): + if not complete_missing: + layer_output = self._viewer.add_image( + data=images[0], name=names[0], colormap="viridis" + ) + layer_image = self._viewer.add_image( + data=images[1], name=names[1], colormap="inferno" + ) + layer_labels = self._viewer.add_labels( + data=images[2], name=names[2] + ) + self.result_layers += [layer_output, layer_image, layer_labels] + else: + # add only the missing layers + for i in range(3): + if names[i] not in [ + layer.name for layer in self._viewer.layers + ]: + if i == 0: + layer_output = self._viewer.add_image( + data=images[i], name=names[i], colormap="viridis" + ) + self.result_layers[0] = layer_output + elif i == 1: + layer_image = self._viewer.add_image( + data=images[i], name=names[i], colormap="inferno" + ) + self.result_layers[1] = layer_image + else: + layer_labels = self._viewer.add_labels( + data=images[i], name=names[i] + ) + self.result_layers[2] = layer_labels + self.result_layers[i].data = images[i] + self.result_layers[i].refresh() + def on_yield(self, report: TrainingReport): # logger.info( # f"\nCatching results : for epoch {data['epoch']}, @@ -984,31 +1020,15 @@ def on_yield(self, report: TrainingReport): "Validation image", "Validation labels", ] - rge = range(len(report.images)) - + range(len(report.images)) self.log.print_and_log(len(report.images)) if report.epoch + 1 == self.worker_config.validation_interval: - for i in rge: - layer = self._viewer.add_image( - report.images[i], - name=layer_names[i] + str(i), - colormap="viridis", - ) - self.result_layers.append(layer) + self._display_results(report.images, layer_names) else: - for i in rge: - if layer_names[i] + str(i) not in [ - layer.name for layer in self._viewer.layers - ]: - new_layer = self._viewer.add_image( - report.images[i], - name=layer_names[i] + str(i), - colormap="viridis", - ) - self.result_layers.append(new_layer) - self.result_layers[i].data = report.images[i] - self.result_layers[i].refresh() + self._display_results( + report.images, layer_names, complete_missing=True + ) except Exception as e: logger.exception(e) diff --git a/napari_cellseg3d/dev_scripts/crop_data.py b/napari_cellseg3d/dev_scripts/crop_data.py index 2f24e619..d8be9cad 100644 --- a/napari_cellseg3d/dev_scripts/crop_data.py +++ b/napari_cellseg3d/dev_scripts/crop_data.py @@ -39,14 +39,17 @@ def crop_3d_image(image, roi_size): image_path = ( Path().home() # / "Desktop/Code/CELLSEG_BENCHMARK/TPH2_DATA/somatomotor_iso" - / "Desktop/Code/CELLSEG_BENCHMARK/TPH2_DATA/somatomotor_iso/labels/semantic" + # / "Desktop/Code/CELLSEG_BENCHMARK/TPH2_DATA/somatomotor_iso/labels/semantic" + / "Desktop/Code/CELLSEG_BENCHMARK/TPH2_mesospim/BENCHMARK/visual_small" ) - assert image_path.exists() + if not image_path.exists() or not image_path.is_dir(): + raise ValueError(f"Image path {image_path} does not exist") image_list = image_path.glob("*.tif") for j in image_list: print(j) image = imread(str(j)) - crops = crop_3d_image(image, (64, 64, 64)) + # crops = crop_3d_image(image, (64, 64, 64)) + crops = [image] # viewer = napari.Viewer() if not (image_path / "cropped").exists(): (image_path / "cropped").mkdir(exist_ok=False) @@ -55,7 +58,7 @@ def crop_3d_image(image, roi_size): # viewer.add_image(im) imwrite( str(image_path / f"cropped/{j.stem}_{i}_crop.tif"), - im.astype(np.uint16), - dtype="uint16", + im.astype(np.float32), + dtype="float32", ) # napari.run() From 0a85994fdf66a20680d97a799b16f65be2227963 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 08:59:55 +0200 Subject: [PATCH 103/250] Docs + train WIP --- docs/res/guides/detailed_walkthrough.rst | 14 +--------- docs/res/guides/training_module_guide.rst | 7 +++-- .../code_models/models/model_TRAILMAP_MS.py | 2 +- .../code_plugins/plugin_model_training.py | 27 +++++++++++-------- 4 files changed, 23 insertions(+), 27 deletions(-) diff --git a/docs/res/guides/detailed_walkthrough.rst b/docs/res/guides/detailed_walkthrough.rst index 9415d447..56ef54ed 100644 --- a/docs/res/guides/detailed_walkthrough.rst +++ b/docs/res/guides/detailed_walkthrough.rst @@ -239,26 +239,14 @@ Scoring, review, analysis ---------------------------- -Model performance : Metrics utility -**************************************** - - -Using the metrics utility module, you can compare the model's predictions to any ground truth +.. Using the metrics utility module, you can compare the model's predictions to any ground truth labels you might have. - Simply provide your prediction and ground truth labels, and compute the results. A Dice metric of 1 indicates perfect matching, whereas a score of 0 indicates complete mismatch. - Select which score **you consider as sub-optimal**, and all results below this will be **shown in napari**. - - If at any time the **orientation of your prediction labels changed compared to the ground truth**, check the "Find best orientation" option to compensate for it. -.. note:: - Using inference on the images you used for training might yield to higher scores due to the fitting. - If you wish to know how well the model performs on more general datasets, use different images for inference and scoring. - Labels review ************************ diff --git a/docs/res/guides/training_module_guide.rst b/docs/res/guides/training_module_guide.rst index c398ca94..d910bc50 100644 --- a/docs/res/guides/training_module_guide.rst +++ b/docs/res/guides/training_module_guide.rst @@ -94,20 +94,23 @@ The available loss functions are : Function Reference ======================== ================================================================================================ Dice loss `Dice Loss from MONAI`_ with ``sigmoid=true`` -Focal loss `Focal Loss from MONAI`_ -Dice-Focal loss `Dice-focal Loss from MONAI`_ with ``sigmoid=true`` and ``lambda_dice = 0.5`` Generalized Dice loss `Generalized dice Loss from MONAI`_ with ``sigmoid=true`` Dice-CE loss `Dice-CE Loss from MONAI`_ with ``sigmoid=true`` Tversky loss `Tversky Loss from MONAI`_ with ``sigmoid=true`` ======================== ================================================================================================ +.. Binary cross-entropy `Binary cross entropy (BCE) loss from PyTorch`_ + BCE with logits `BCE loss with logits from PyTorch`_ + .. _Dice Loss from MONAI: https://docs.monai.io/en/stable/losses.html#diceloss .. _Focal Loss from MONAI: https://docs.monai.io/en/stable/losses.html#focalloss .. _Dice-focal Loss from MONAI: https://docs.monai.io/en/stable/losses.html#dicefocalloss .. _Generalized dice Loss from MONAI: https://docs.monai.io/en/stable/losses.html#generalizeddiceloss .. _Dice-CE Loss from MONAI: https://docs.monai.io/en/stable/losses.html#diceceloss .. _Tversky Loss from MONAI: https://docs.monai.io/en/stable/losses.html#tverskyloss +.. _Binary cross entropy (BCE) loss from PyTorch: +.. _BCE loss with logits from PyTorch: Once you are ready, press the Start button to begin training. The module will automatically load your dataset, perform data augmentation if you chose to, select a CUDA device if one is present, and train the model. diff --git a/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py b/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py index bc8e43d5..dbdd171d 100644 --- a/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py +++ b/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py @@ -8,7 +8,7 @@ class TRAILMAP_MS_(UNet3D): # original model from Liqun Luo lab, transferred to pytorch and trained on mesoSPIM-acquired data (mostly TPH2 as of July 2022) - def __init__(self, in_channels=1, out_channels=1, **kwargs): + def __init__(self, in_channels=1, out_channels=2, **kwargs): try: super().__init__( in_channels=in_channels, out_channels=out_channels, **kwargs diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index d77d0c28..9593d986 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -18,9 +18,7 @@ # MONAI from monai.losses import ( DiceCELoss, - DiceFocalLoss, DiceLoss, - FocalLoss, GeneralizedDiceLoss, TverskyLoss, ) @@ -142,13 +140,14 @@ def __init__( self.start_time = None self.loss_dict = { - "Dice loss": DiceLoss(sigmoid=True), - "Focal loss": FocalLoss(), - # "BCELoss":nn.BCELoss(), - "Dice-Focal loss": DiceFocalLoss(sigmoid=True, lambda_dice=0.5), - "Generalized Dice loss": GeneralizedDiceLoss(sigmoid=True), - "DiceCELoss": DiceCELoss(sigmoid=True, lambda_ce=0.5), - "Tversky loss": TverskyLoss(sigmoid=True), + "Dice": DiceLoss(sigmoid=True), + # "BCELoss": torch.nn.BCELoss(), # dev + # "BCELogits": torch.nn.BCEWithLogitsLoss(), + "Generalized Dice": GeneralizedDiceLoss(sigmoid=True), + "DiceCE": DiceCELoss(sigmoid=True, lambda_ce=0.5), + "Tversky": TverskyLoss(sigmoid=True), + # "Focal loss": FocalLoss(), + # "Dice-Focal loss": DiceFocalLoss(sigmoid=True, lambda_dice=0.5), } """Dict of loss functions""" @@ -168,6 +167,7 @@ def __init__( self.validation_values = [] # self.model_choice.setCurrentIndex(0) + ################### # TODO(cyril) : disable if we implement WNet training wnet_index = self.model_choice.findText("WNet") self.model_choice.removeItem(wnet_index) @@ -192,7 +192,11 @@ def __init__( ) self.loss_choice = ui.DropdownMenu( - sorted(self.loss_dict.keys()), text_label="Loss function" + # sorted( + list( + self.loss_dict.keys(), + ), + text_label="Loss function", ) self.lbl_loss_choice = self.loss_choice.label self.loss_choice.setCurrentIndex(0) @@ -310,7 +314,7 @@ def set_tooltips(): self.validation_percent_choice.tooltips = "Choose the proportion of images to retain for training.\nThe remaining images will be used for validation" self.epoch_choice.tooltips = "The number of epochs to train for.\nThe more you train, the better the model will fit the training data" self.loss_choice.setToolTip( - "The loss function to use for training.\nSee the list in the inference guide for more info" + "The loss function to use for training.\nSee the list in the training guide for more info" ) self.sample_choice_slider.tooltips = ( "The number of samples to extract per image" @@ -400,6 +404,7 @@ def _toggle_transfer_param(self): self.custom_weights_choice.setVisible(True) else: self.custom_weights_choice.setVisible(False) + self.weights_filewidget.setVisible(False) def _toggle_deterministic_param(self): if self.use_deterministic_choice.isChecked(): From 6fef0005de4cfe968e0fd183446882bfc2de9a82 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 09:11:10 +0200 Subject: [PATCH 104/250] Update plugin_model_training.py Fix dtype issue for label layer --- napari_cellseg3d/code_plugins/plugin_model_training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 9593d986..ec1c842e 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -983,7 +983,7 @@ def _display_results(self, images, names, complete_missing=False): data=images[1], name=names[1], colormap="inferno" ) layer_labels = self._viewer.add_labels( - data=images[2], name=names[2] + data=images[2].astype(np.uint16), name=names[2] ) self.result_layers += [layer_output, layer_image, layer_labels] else: From 0f78d80551f265ce33f4271967abd69ec0de247a Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 09:17:30 +0200 Subject: [PATCH 105/250] Update plugin_model_training.py Better interrupt --- napari_cellseg3d/code_plugins/plugin_model_training.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index ec1c842e..69d1c4d1 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -974,6 +974,11 @@ def on_error(self): # self.empty_cuda_cache() # self.clean_cache() + def on_stop(self): + self.worker = None + self.btn_start.setText("Start") + [btn.setVisible(True) for btn in self.close_buttons] + def _display_results(self, images, names, complete_missing=False): if not complete_missing: layer_output = self._viewer.add_image( @@ -1057,6 +1062,7 @@ def on_yield(self, report: TrainingReport): ), ) self.log.print_and_log("Saving complete") + self.on_stop() self.stop_requested = False # def clean_cache(self): From ebc91eb7275c505bec860cd29cb7d328d9be1c5a Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 09:18:11 +0200 Subject: [PATCH 106/250] Update model_TRAILMAP_MS.py --- napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py b/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py index dbdd171d..bc8e43d5 100644 --- a/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py +++ b/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py @@ -8,7 +8,7 @@ class TRAILMAP_MS_(UNet3D): # original model from Liqun Luo lab, transferred to pytorch and trained on mesoSPIM-acquired data (mostly TPH2 as of July 2022) - def __init__(self, in_channels=1, out_channels=2, **kwargs): + def __init__(self, in_channels=1, out_channels=1, **kwargs): try: super().__init__( in_channels=in_channels, out_channels=out_channels, **kwargs From 9091937e1a88a6980cfea8861018fc37d4a11220 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 09:48:07 +0200 Subject: [PATCH 107/250] Training and inference QoL Colormap changes --- .../code_plugins/plugin_model_inference.py | 6 +++--- .../code_plugins/plugin_model_training.py | 15 ++++++++++++--- 2 files changed, 15 insertions(+), 6 deletions(-) diff --git a/napari_cellseg3d/code_plugins/plugin_model_inference.py b/napari_cellseg3d/code_plugins/plugin_model_inference.py index 471c02ac..c9dfbc10 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_inference.py +++ b/napari_cellseg3d/code_plugins/plugin_model_inference.py @@ -577,9 +577,9 @@ def _display_results(self, result: InferenceResult): opacity=0.7, ) - out_colormap = "twilight" - if self.worker_config.post_process_config.thresholding.enabled: - out_colormap = "turbo" + out_colormap = "turbo" + # if self.worker_config.post_process_config.thresholding.enabled: + # out_colormap = "twilight" viewer.add_image( result.result, diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 69d1c4d1..ed24b92e 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -975,14 +975,20 @@ def on_error(self): # self.clean_cache() def on_stop(self): + self._remove_result_layers() self.worker = None self.btn_start.setText("Start") [btn.setVisible(True) for btn in self.close_buttons] + def _remove_result_layers(self): + for layer in self.result_layers: + self._viewer.layers.remove(layer) + self.result_layers = [] + def _display_results(self, images, names, complete_missing=False): if not complete_missing: layer_output = self._viewer.add_image( - data=images[0], name=names[0], colormap="viridis" + data=images[0], name=names[0], colormap="turbo" ) layer_image = self._viewer.add_image( data=images[1], name=names[1], colormap="inferno" @@ -991,6 +997,9 @@ def _display_results(self, images, names, complete_missing=False): data=images[2].astype(np.uint16), name=names[2] ) self.result_layers += [layer_output, layer_image, layer_labels] + self._viewer.grid.enabled = True + self._viewer.dims.ndisplay = 3 + self._viewer.reset_view() else: # add only the missing layers for i in range(3): @@ -999,7 +1008,7 @@ def _display_results(self, images, names, complete_missing=False): ]: if i == 0: layer_output = self._viewer.add_image( - data=images[i], name=names[i], colormap="viridis" + data=images[i], name=names[i], colormap="turbo" ) self.result_layers[0] = layer_output elif i == 1: @@ -1009,7 +1018,7 @@ def _display_results(self, images, names, complete_missing=False): self.result_layers[1] = layer_image else: layer_labels = self._viewer.add_labels( - data=images[i], name=names[i] + data=images[i].astype(np.uint16), name=names[i] ) self.result_layers[2] = layer_labels self.result_layers[i].data = images[i] From aa22e56678838254b6ba7ce4123026ea45a125e5 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 10:06:15 +0200 Subject: [PATCH 108/250] Fix dtype for val labels and SegRes output --- .../code_models/models/model_SwinUNetR.py | 9 ++++----- .../code_models/worker_training.py | 18 +++++++++--------- .../code_plugins/plugin_model_training.py | 4 ++-- 3 files changed, 15 insertions(+), 16 deletions(-) diff --git a/napari_cellseg3d/code_models/models/model_SwinUNetR.py b/napari_cellseg3d/code_models/models/model_SwinUNetR.py index 621ae0b3..34502a12 100644 --- a/napari_cellseg3d/code_models/models/model_SwinUNetR.py +++ b/napari_cellseg3d/code_models/models/model_SwinUNetR.py @@ -1,5 +1,4 @@ from monai.networks.nets import SwinUNETR -from torch import sigmoid from napari_cellseg3d.utils import LOGGER @@ -37,10 +36,10 @@ def __init__( use_checkpoint=use_checkpoint, ) - def forward(self, x_in): - y = super().forward(x_in) - # return softmax(y, dim=1) - return sigmoid(y) + # def forward(self, x_in): + # y = super().forward(x_in) + # return softmax(y, dim=1) + # return sigmoid(y) # def get_output(self, input): # out = self(input) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index b8d522c8..e7495f8c 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -3,6 +3,7 @@ from math import ceil from pathlib import Path +import numpy as np import torch # MONAI @@ -655,17 +656,16 @@ def get_loader_func(num_samples): checkpoint_output.append( [ - val_outputs[0].detach().cpu(), - val_inputs[0].detach().cpu(), - val_labels[0].detach().cpu(), + val_outputs[0].detach().cpu().numpy(), + val_inputs[0].detach().cpu().numpy(), + val_labels[0] + .detach() + .cpu() + .numpy() + .astype(np.uint16), ] ) - - checkpoint_output = [ - item.numpy() - for batch in checkpoint_output - for item in batch - ] + [np.squeeze(vol) for vol in checkpoint_output] metric = dice_metric.aggregate().detach().item() dice_metric.reset() diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index ed24b92e..3efbdeca 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -994,7 +994,7 @@ def _display_results(self, images, names, complete_missing=False): data=images[1], name=names[1], colormap="inferno" ) layer_labels = self._viewer.add_labels( - data=images[2].astype(np.uint16), name=names[2] + data=images[2], name=names[2] ) self.result_layers += [layer_output, layer_image, layer_labels] self._viewer.grid.enabled = True @@ -1018,7 +1018,7 @@ def _display_results(self, images, names, complete_missing=False): self.result_layers[1] = layer_image else: layer_labels = self._viewer.add_labels( - data=images[i].astype(np.uint16), name=names[i] + data=images[i], name=names[i] ) self.result_layers[2] = layer_labels self.result_layers[i].data = images[i] From 2e181ec08e4dea67b1de58ef50203c78ace65abb Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 10:27:45 +0200 Subject: [PATCH 109/250] Choose device UI --- .../code_models/model_framework.py | 26 ++++++++++++++++++- .../code_plugins/plugin_model_inference.py | 4 ++- .../code_plugins/plugin_model_training.py | 10 +++++-- 3 files changed, 36 insertions(+), 4 deletions(-) diff --git a/napari_cellseg3d/code_models/model_framework.py b/napari_cellseg3d/code_models/model_framework.py index ee9834c2..9a3b6d39 100644 --- a/napari_cellseg3d/code_models/model_framework.py +++ b/napari_cellseg3d/code_models/model_framework.py @@ -90,6 +90,16 @@ def __init__( "Load custom weights", self._toggle_weights_path, self ) + available_devices = ["CPU"] + [ + f"GPU {i}" for i in range(torch.cuda.device_count()) + ] + self.device_choice = ui.DropdownMenu( + available_devices, + parent=self, + text_label="Device", + ) + self.device_choice.tooltips = "Choose the device to use for training.\nIf you have a GPU, it is recommended to use it" + ################################################### # status report docked widget @@ -298,9 +308,23 @@ def _load_weights_path(self): ) self._update_weights_path(file) + def check_device_choice(self): + choice = self.device_choice.currentText() + if choice == "CPU": + device = "cpu" + elif "GPU" in choice: + i = int(choice.split(" ")[1]) + device = f"cuda:{i}" + else: + device = self.get_device() + logger.debug(f"DEVICE choice : {device}") + return device + @staticmethod def get_device(show=True): - """Automatically discovers any cuda device and uses it for tensor operations. + """ + Tries to use the device specified by user and uses it for tensor operations. + If not available, automatically discovers any cuda device. If none is available (CUDA not installed), uses cpu instead.""" device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if show: diff --git a/napari_cellseg3d/code_plugins/plugin_model_inference.py b/napari_cellseg3d/code_plugins/plugin_model_inference.py index c9dfbc10..7b97f07b 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_inference.py +++ b/napari_cellseg3d/code_plugins/plugin_model_inference.py @@ -447,6 +447,8 @@ def _build(self): self.window_infer_box, self.window_infer_params, self.keep_data_on_cpu_box, + self.device_choice.label, + self.device_choice, ], ) self.window_infer_params.setVisible(False) @@ -786,7 +788,7 @@ def _set_worker_config(self) -> config.InferenceWorkerConfig: window_config = config.SlidingWindowConfig() self.worker_config = config.InferenceWorkerConfig( - device=self.get_device(), + device=self.check_device_choice(), model_info=self.model_info, weights_config=self.weights_config, results_path=self.results_path, diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 3efbdeca..ff1c00a0 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -536,6 +536,11 @@ def _build(self): self.validation_percent_choice.container, data_tab.layout, ) + ui.GroupedWidget.create_single_widget_group( + "Device", + self.device_choice, + data_tab.layout, + ) ################ ui.add_blank(self, data_tab.layout) ################ @@ -692,7 +697,8 @@ def _build(self): ui.LEFT_AL, ) - self.container_seed.setVisible(False) + # self.container_seed.setVisible(False) + self.use_deterministic_choice.setChecked(True) seed_w.setLayout(seed_l) train_tab.layout.addWidget(seed_w) @@ -883,7 +889,7 @@ def _set_worker_config(self) -> config.TrainingWorkerConfig: logger.debug("Loading config...") self.worker_config = config.TrainingWorkerConfig( - device=self.get_device(), + device=self.check_device_choice(), model_info=model_config, weights_info=self.weights_config, train_data_dict=self.data, From ef7e2f9c467deb2b9ab4d9df16c37af61a9af022 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 10:41:11 +0200 Subject: [PATCH 110/250] Update worker_training.py Fixed device --- napari_cellseg3d/code_models/worker_training.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index e7495f8c..b2d1554b 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -521,9 +521,10 @@ def get_loader_func(num_samples): self.warn(warn) self._weight_error = True - if self.config.device.type == "cuda": + if "cuda" in self.config.device: + device_id = self.config.device.split(":")[-1] self.log("\nUsing GPU :") - self.log(torch.cuda.get_device_name(0)) + self.log(torch.cuda.get_device_name(int(device_id))) else: self.log("Using CPU") From 7e9e085c474d6fa44eb27e9aa948ca488e5b7596 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 10:43:36 +0200 Subject: [PATCH 111/250] Update interface.py Update name of utils when called by quick menu --- napari_cellseg3d/interface.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/interface.py b/napari_cellseg3d/interface.py index de856909..30d6f0de 100644 --- a/napari_cellseg3d/interface.py +++ b/napari_cellseg3d/interface.py @@ -203,7 +203,7 @@ def show_utils_menu(self, widget, event): if action == possible_action: text = possible_action.text().split(": ")[1] widget = UTILITIES_WIDGETS[text](widget._viewer) - widget._viewer.window.add_dock_widget(widget) + widget._viewer.window.add_dock_widget(widget, name=text) ############## From affb4ce5b0dd71ade8fc86b144f07a9e33f1f850 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 10:47:54 +0200 Subject: [PATCH 112/250] Update model_framework.py --- napari_cellseg3d/code_models/model_framework.py | 1 + 1 file changed, 1 insertion(+) diff --git a/napari_cellseg3d/code_models/model_framework.py b/napari_cellseg3d/code_models/model_framework.py index 9a3b6d39..4e0cdfe4 100644 --- a/napari_cellseg3d/code_models/model_framework.py +++ b/napari_cellseg3d/code_models/model_framework.py @@ -99,6 +99,7 @@ def __init__( text_label="Device", ) self.device_choice.tooltips = "Choose the device to use for training.\nIf you have a GPU, it is recommended to use it" + self.device_choice.setCurrentIndex(len(available_devices) - 1) ################################################### # status report docked widget From 75d722c4192b08ff9cc5b61ecbf58946a6f4a272 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 10:48:58 +0200 Subject: [PATCH 113/250] Update model_SwinUNetR.py --- napari_cellseg3d/code_models/models/model_SwinUNetR.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/models/model_SwinUNetR.py b/napari_cellseg3d/code_models/models/model_SwinUNetR.py index 34502a12..dbe3075d 100644 --- a/napari_cellseg3d/code_models/models/model_SwinUNetR.py +++ b/napari_cellseg3d/code_models/models/model_SwinUNetR.py @@ -12,7 +12,7 @@ class SwinUNETR_(SwinUNETR): def __init__( self, in_channels=1, - out_channels=1, + out_channels=2, input_img_size=128, use_checkpoint=True, **kwargs, From 4b836813c0f64ba332d203c5a2b65c69a0d77413 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 10:51:15 +0200 Subject: [PATCH 114/250] Update worker_training.py --- napari_cellseg3d/code_models/worker_training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index b2d1554b..fcae2aaa 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -530,7 +530,7 @@ def get_loader_func(num_samples): self.log_parameters() - device = self.config.device + device = torch.device(self.config.device) # if model_name == "test": # self.quit() From b42e480ab4020b1b6604f7f85e580395512faac4 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 11:02:46 +0200 Subject: [PATCH 115/250] Update model_SwinUNetR.py --- napari_cellseg3d/code_models/models/model_SwinUNetR.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/code_models/models/model_SwinUNetR.py b/napari_cellseg3d/code_models/models/model_SwinUNetR.py index dbe3075d..0dbf0be5 100644 --- a/napari_cellseg3d/code_models/models/model_SwinUNetR.py +++ b/napari_cellseg3d/code_models/models/model_SwinUNetR.py @@ -12,8 +12,8 @@ class SwinUNETR_(SwinUNETR): def __init__( self, in_channels=1, - out_channels=2, - input_img_size=128, + out_channels=1, + input_img_size=(64, 64, 64), use_checkpoint=True, **kwargs, ): @@ -24,6 +24,9 @@ def __init__( out_channels=out_channels, feature_size=48, use_checkpoint=use_checkpoint, + drop_rate=0.5, + attn_drop_rate=0.5, + use_v2=True, **kwargs, ) except TypeError as e: @@ -34,6 +37,9 @@ def __init__( out_channels=1, feature_size=48, use_checkpoint=use_checkpoint, + drop_rate=0.5, + attn_drop_rate=0.5, + use_v2=True, ) # def forward(self, x_in): From 71e4c9d0eefcadbf937222a00558ffb0a3f8fca6 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 11:07:38 +0200 Subject: [PATCH 116/250] Update worker_training.py --- napari_cellseg3d/code_models/worker_training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index fcae2aaa..2720a348 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -666,7 +666,7 @@ def get_loader_func(num_samples): .astype(np.uint16), ] ) - [np.squeeze(vol) for vol in checkpoint_output] + # [np.squeeze(vol) for vol in checkpoint_output] metric = dice_metric.aggregate().detach().item() dice_metric.reset() From 716fc1ef6c66ea197e0201f84d540480e62d2e0c Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 11:18:44 +0200 Subject: [PATCH 117/250] Fix training volumes --- .../code_models/worker_training.py | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 2720a348..58550fb5 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -657,16 +657,19 @@ def get_loader_func(num_samples): checkpoint_output.append( [ - val_outputs[0].detach().cpu().numpy(), - val_inputs[0].detach().cpu().numpy(), - val_labels[0] - .detach() - .cpu() - .numpy() - .astype(np.uint16), + val_outputs[0].detach().cpu(), + val_inputs[0].detach().cpu(), + val_labels[0].detach().cpu(), ] ) - # [np.squeeze(vol) for vol in checkpoint_output] + checkpoint_output = [ + item.numpy() + for batch in checkpoint_output + for item in batch + ] + checkpoint_output[2] = checkpoint_output[2].astype( + np.uint16 + ) metric = dice_metric.aggregate().detach().item() dice_metric.reset() From 12999d4a41225e7b9fdf985301552d6337c67877 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 11:46:45 +0200 Subject: [PATCH 118/250] Dice update --- napari_cellseg3d/code_models/worker_training.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 58550fb5..9a438a22 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -484,9 +484,7 @@ def get_loader_func(num_samples): patience=self.config.scheduler_patience, verbose=VERBOSE_SCHEDULER, ) - dice_metric = DiceMetric( - include_background=False, reduction="mean" - ) + dice_metric = DiceMetric(include_background=True, reduction="mean") best_metric = -1 best_metric_epoch = -1 @@ -664,8 +662,8 @@ def get_loader_func(num_samples): ) checkpoint_output = [ item.numpy() - for batch in checkpoint_output - for item in batch + for channel in checkpoint_output + for item in channel ] checkpoint_output[2] = checkpoint_output[2].astype( np.uint16 From 4a3f61159d704ea3350070bb37897f935eb7f030 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 11:49:18 +0200 Subject: [PATCH 119/250] Disable checkpoint plot save --- .../code_plugins/plugin_model_training.py | 26 +++++++++---------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index ff1c00a0..528d06e7 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -1169,19 +1169,19 @@ def plot_loss(self, loss, dice_metric): ) self.canvas.draw_idle() - plot_path = self.worker_config.results_path_folder / Path( - "../Loss_plots" - ) - Path(plot_path).mkdir(parents=True, exist_ok=True) - - if self.canvas is not None: - self.canvas.figure.savefig( - str( - plot_path - / f"checkpoint_metric_plots_{utils.get_date_time()}.png" - ), - format="png", - ) + # plot_path = self.worker_config.results_path_folder / Path( + # "../Loss_plots" + # ) + # Path(plot_path).mkdir(parents=True, exist_ok=True) + # + # if self.canvas is not None: + # self.canvas.figure.savefig( + # str( + # plot_path + # / f"checkpoint_metric_plots_{utils.get_date_time()}.png" + # ), + # format="png", + # ) def update_loss_plot(self, loss, metric): """ From 1cb7c412bd9aaf7c973427ba397cbb3ce1bf5fd7 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 11:54:49 +0200 Subject: [PATCH 120/250] Update worker_training.py --- napari_cellseg3d/code_models/worker_training.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 9a438a22..b7b581af 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -484,7 +484,9 @@ def get_loader_func(num_samples): patience=self.config.scheduler_patience, verbose=VERBOSE_SCHEDULER, ) - dice_metric = DiceMetric(include_background=True, reduction="mean") + dice_metric = DiceMetric( + include_background=True, reduction="mean", ignore_empty=False + ) best_metric = -1 best_metric_epoch = -1 @@ -650,6 +652,13 @@ def get_loader_func(num_samples): # logger.debug(len(val_outputs)) # logger.debug(len(val_labels)) + dice_test = [ + utils.dice_coeff(i, j) + for i, j in zip(val_outputs, val_labels) + ] + logger.debug( + f"TEST VALIDATION Dice score : {dice_test}" + ) dice_metric(y_pred=val_outputs, y=val_labels) From b254710ddabb44d1aeb5d391273a8c34c890f773 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 11:57:06 +0200 Subject: [PATCH 121/250] Restore as_discrete --- napari_cellseg3d/code_models/worker_training.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index b7b581af..6e9bc85b 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -17,6 +17,7 @@ from monai.inferers import sliding_window_inference from monai.metrics import DiceMetric from monai.transforms import ( + AsDiscrete, Compose, EnsureChannelFirstd, EnsureType, @@ -637,8 +638,8 @@ def get_loader_func(num_samples): # TODO : more parameters/flexibility post_pred = Compose( - # AsDiscrete(threshold=0.6), # needed ? - EnsureType() + AsDiscrete(threshold=0.5), # needed ? + EnsureType(), ) # post_label = EnsureType() From 8242cab40099c27b4815332f35e4b26fab28c58a Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 12:00:26 +0200 Subject: [PATCH 122/250] Update worker_training.py --- napari_cellseg3d/code_models/worker_training.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 6e9bc85b..96df980c 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -653,12 +653,14 @@ def get_loader_func(num_samples): # logger.debug(len(val_outputs)) # logger.debug(len(val_labels)) - dice_test = [ - utils.dice_coeff(i, j) - for i, j in zip(val_outputs, val_labels) - ] + dice_test = np.array( + [ + utils.dice_coeff(i, j) + for i, j in zip(val_outputs, val_labels) + ] + ) logger.debug( - f"TEST VALIDATION Dice score : {dice_test}" + f"TEST VALIDATION Dice score : {dice_test.mean()}" ) dice_metric(y_pred=val_outputs, y=val_labels) From 5024bb90f0530de4deac467dd8b813bd4facc0db Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 12:01:49 +0200 Subject: [PATCH 123/250] Update worker_training.py --- napari_cellseg3d/code_models/worker_training.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 96df980c..1a1661ad 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -643,6 +643,7 @@ def get_loader_func(num_samples): ) # post_label = EnsureType() + output_raw = [t for t in pred] val_outputs = [ post_pred(res_tensor) for res_tensor in pred ] @@ -667,7 +668,7 @@ def get_loader_func(num_samples): checkpoint_output.append( [ - val_outputs[0].detach().cpu(), + output_raw[0].detach().cpu(), val_inputs[0].detach().cpu(), val_labels[0].detach().cpu(), ] From c9aa0dbbbdfe931169bb39c150fca47aadf945c7 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 12:12:54 +0200 Subject: [PATCH 124/250] Enforce tensor range [0,1] --- napari_cellseg3d/code_models/worker_inference.py | 14 ++++++++++++-- napari_cellseg3d/code_models/worker_training.py | 10 +++++++--- napari_cellseg3d/code_models/workers_utils.py | 10 ++++++++++ 3 files changed, 29 insertions(+), 5 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index f6ad6fbe..10b72da4 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -34,6 +34,7 @@ ONNXModelWrapper, QuantileNormalization, QuantileNormalizationd, + RemapTensor, WeightsDownloader, ) @@ -715,11 +716,20 @@ def inference(self): # ) if not post_process_config.thresholding.enabled: - post_process_transforms = EnsureType() + post_process_transforms = Compose( + [ + RemapTensor(new_max=1.0, new_min=0.0), + EnsureType(), + ] + ) else: t = post_process_config.thresholding.threshold_value post_process_transforms = Compose( - AsDiscrete(threshold=t), EnsureType() + [ + RemapTensor(new_max=1.0, new_min=0.0), + AsDiscrete(threshold=t), + EnsureType(), + ] ) is_folder = self.config.images_filepaths is not None diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 1a1661ad..139503ef 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -44,6 +44,7 @@ PRETRAINED_WEIGHTS_DIR, LogSignal, QuantileNormalizationd, + RemapTensor, TrainingReport, WeightsDownloader, ) @@ -638,12 +639,15 @@ def get_loader_func(num_samples): # TODO : more parameters/flexibility post_pred = Compose( - AsDiscrete(threshold=0.5), # needed ? - EnsureType(), + [ + RemapTensor(new_max=1, new_min=0), + AsDiscrete(threshold=0.5), # needed ? + EnsureType(), + ] ) # post_label = EnsureType() - output_raw = [t for t in pred] + output_raw = [RemapTensor(0, 1)(t) for t in pred] val_outputs = [ post_pred(res_tensor) for res_tensor in pred ] diff --git a/napari_cellseg3d/code_models/workers_utils.py b/napari_cellseg3d/code_models/workers_utils.py index 923bc727..8427d683 100644 --- a/napari_cellseg3d/code_models/workers_utils.py +++ b/napari_cellseg3d/code_models/workers_utils.py @@ -203,6 +203,16 @@ def __call__(self, img): return utils.quantile_normalization(img) +class RemapTensor(Transform): + def __init__(self, new_max, new_min): + super().__init__() + self.max = new_max + self.min = new_min + + def __call__(self, img): + return utils.remap_image(img, new_max=self.max, new_min=self.min) + + @dataclass class InferenceResult: """Class to record results of a segmentation job""" From 3b6095458c642c8d2a0bd16a2324cc730113707f Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 12:22:32 +0200 Subject: [PATCH 125/250] Add model output and labels to training view --- .../code_models/worker_training.py | 12 +++++---- .../code_plugins/plugin_model_training.py | 26 +++++++++++++++---- 2 files changed, 28 insertions(+), 10 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 139503ef..9b767afe 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -44,7 +44,6 @@ PRETRAINED_WEIGHTS_DIR, LogSignal, QuantileNormalizationd, - RemapTensor, TrainingReport, WeightsDownloader, ) @@ -640,14 +639,16 @@ def get_loader_func(num_samples): # TODO : more parameters/flexibility post_pred = Compose( [ - RemapTensor(new_max=1, new_min=0), + # RemapTensor(new_max=1, new_min=0), AsDiscrete(threshold=0.5), # needed ? EnsureType(), ] ) # post_label = EnsureType() - output_raw = [RemapTensor(0, 1)(t) for t in pred] + # output_raw = [RemapTensor(0, 1)(t) for t in pred] + output_raw = pred + val_outputs = [ post_pred(res_tensor) for res_tensor in pred ] @@ -658,7 +659,7 @@ def get_loader_func(num_samples): # logger.debug(len(val_outputs)) # logger.debug(len(val_labels)) - dice_test = np.array( + dice_test = np.array( # TODO(cyril): remove [ utils.dice_coeff(i, j) for i, j in zip(val_outputs, val_labels) @@ -673,6 +674,7 @@ def get_loader_func(num_samples): checkpoint_output.append( [ output_raw[0].detach().cpu(), + val_outputs[0].detach().cpu(), val_inputs[0].detach().cpu(), val_labels[0].detach().cpu(), ] @@ -682,7 +684,7 @@ def get_loader_func(num_samples): for channel in checkpoint_output for item in channel ] - checkpoint_output[2] = checkpoint_output[2].astype( + checkpoint_output[3] = checkpoint_output[2].astype( np.uint16 ) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 528d06e7..c0a17226 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -996,13 +996,21 @@ def _display_results(self, images, names, complete_missing=False): layer_output = self._viewer.add_image( data=images[0], name=names[0], colormap="turbo" ) + layer_output_discrete = self._viewer.add_image( + data=images[1], name=names[1], colormap="bop orange" + ) layer_image = self._viewer.add_image( - data=images[1], name=names[1], colormap="inferno" + data=images[2], name=names[2], colormap="inferno" ) layer_labels = self._viewer.add_labels( - data=images[2], name=names[2] + data=images[3], name=names[3] ) - self.result_layers += [layer_output, layer_image, layer_labels] + self.result_layers += [ + layer_output, + layer_output_discrete, + layer_image, + layer_labels, + ] self._viewer.grid.enabled = True self._viewer.dims.ndisplay = 3 self._viewer.reset_view() @@ -1018,15 +1026,22 @@ def _display_results(self, images, names, complete_missing=False): ) self.result_layers[0] = layer_output elif i == 1: + layer_output_discrete = self._viewer.add_image( + data=images[i], + name=names[i], + colormap="bop orange", + ) + self.result_layers[1] = layer_output_discrete + elif i == 2: layer_image = self._viewer.add_image( data=images[i], name=names[i], colormap="inferno" ) - self.result_layers[1] = layer_image + self.result_layers[2] = layer_image else: layer_labels = self._viewer.add_labels( data=images[i], name=names[i] ) - self.result_layers[2] = layer_labels + self.result_layers[3] = layer_labels self.result_layers[i].data = images[i] self.result_layers[i].refresh() @@ -1042,6 +1057,7 @@ def on_yield(self, report: TrainingReport): try: layer_names = [ "Validation output", + "Validation output (discrete)", "Validation image", "Validation labels", ] From aa5d16b8c89d110469536e480abc346c0f31cfbc Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 12:23:57 +0200 Subject: [PATCH 126/250] Update worker_training.py --- napari_cellseg3d/code_models/worker_training.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 9b767afe..e11cb1d7 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -44,6 +44,7 @@ PRETRAINED_WEIGHTS_DIR, LogSignal, QuantileNormalizationd, + RemapTensor, TrainingReport, WeightsDownloader, ) @@ -639,7 +640,7 @@ def get_loader_func(num_samples): # TODO : more parameters/flexibility post_pred = Compose( [ - # RemapTensor(new_max=1, new_min=0), + RemapTensor(new_max=1, new_min=0), AsDiscrete(threshold=0.5), # needed ? EnsureType(), ] From a4a71b5130ed37b2f533f6eb0b6cbc981ce1c651 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 12:29:30 +0200 Subject: [PATCH 127/250] Update worker_training.py --- napari_cellseg3d/code_models/worker_training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index e11cb1d7..7f24f906 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -685,7 +685,7 @@ def get_loader_func(num_samples): for channel in checkpoint_output for item in channel ] - checkpoint_output[3] = checkpoint_output[2].astype( + checkpoint_output[3] = checkpoint_output[3].astype( np.uint16 ) From a35e7766dffbf996153c3dab42e2d0490655f498 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 13:49:52 +0200 Subject: [PATCH 128/250] Config + disable remap in inference --- napari_cellseg3d/code_models/worker_inference.py | 5 ++--- napari_cellseg3d/code_plugins/plugin_model_training.py | 2 +- napari_cellseg3d/config.py | 2 +- 3 files changed, 4 insertions(+), 5 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 10b72da4..31399ef9 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -34,7 +34,6 @@ ONNXModelWrapper, QuantileNormalization, QuantileNormalizationd, - RemapTensor, WeightsDownloader, ) @@ -718,7 +717,7 @@ def inference(self): if not post_process_config.thresholding.enabled: post_process_transforms = Compose( [ - RemapTensor(new_max=1.0, new_min=0.0), + # RemapTensor(new_max=1.0, new_min=0.0), EnsureType(), ] ) @@ -726,7 +725,7 @@ def inference(self): t = post_process_config.thresholding.threshold_value post_process_transforms = Compose( [ - RemapTensor(new_max=1.0, new_min=0.0), + # RemapTensor(new_max=1.0, new_min=0.0), AsDiscrete(threshold=t), EnsureType(), ] diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index c0a17226..b22b3f52 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -186,7 +186,7 @@ def __init__( self.epoch_choice = ui.IntIncrementCounter( lower=2, - upper=200, + upper=999, default=self.default_config.max_epochs, text_label="Number of epochs : ", ) diff --git a/napari_cellseg3d/config.py b/napari_cellseg3d/config.py index af42d779..b05f7ac7 100644 --- a/napari_cellseg3d/config.py +++ b/napari_cellseg3d/config.py @@ -247,7 +247,7 @@ class TrainingWorkerConfig: weights_info: WeightsInfo = None train_data_dict: dict = None validation_percent: float = 0.8 - max_epochs: int = 5 + max_epochs: int = 50 loss_function: callable = None learning_rate: np.float64 = 1e-3 scheduler_patience: int = 10 From 976dc6dc51465c46e3369e0c15e5e3be449ad027 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 14:21:21 +0200 Subject: [PATCH 129/250] fix remap --- napari_cellseg3d/code_models/worker_inference.py | 5 +++-- napari_cellseg3d/code_models/worker_training.py | 9 ++++++--- 2 files changed, 9 insertions(+), 5 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 31399ef9..10b72da4 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -34,6 +34,7 @@ ONNXModelWrapper, QuantileNormalization, QuantileNormalizationd, + RemapTensor, WeightsDownloader, ) @@ -717,7 +718,7 @@ def inference(self): if not post_process_config.thresholding.enabled: post_process_transforms = Compose( [ - # RemapTensor(new_max=1.0, new_min=0.0), + RemapTensor(new_max=1.0, new_min=0.0), EnsureType(), ] ) @@ -725,7 +726,7 @@ def inference(self): t = post_process_config.thresholding.threshold_value post_process_transforms = Compose( [ - # RemapTensor(new_max=1.0, new_min=0.0), + RemapTensor(new_max=1.0, new_min=0.0), AsDiscrete(threshold=t), EnsureType(), ] diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 7f24f906..8baddb7c 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -641,14 +641,17 @@ def get_loader_func(num_samples): post_pred = Compose( [ RemapTensor(new_max=1, new_min=0), - AsDiscrete(threshold=0.5), # needed ? + AsDiscrete(threshold=0.25), # needed ? EnsureType(), ] ) # post_label = EnsureType() - # output_raw = [RemapTensor(0, 1)(t) for t in pred] - output_raw = pred + output_raw = [ + RemapTensor(new_max=1, new_min=0)(t) + for t in pred + ] + # output_raw = pred val_outputs = [ post_pred(res_tensor) for res_tensor in pred From de719f6c87bc0866039d268e43c3f42fd48cf133 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 14:32:06 +0200 Subject: [PATCH 130/250] Usage QoL improvements --- napari_cellseg3d/code_plugins/plugin_crop.py | 1 + napari_cellseg3d/code_plugins/plugin_model_training.py | 3 ++- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_plugins/plugin_crop.py b/napari_cellseg3d/code_plugins/plugin_crop.py index df65ac5e..3ff46261 100644 --- a/napari_cellseg3d/code_plugins/plugin_crop.py +++ b/napari_cellseg3d/code_plugins/plugin_crop.py @@ -312,6 +312,7 @@ def _start(self): vw = self._viewer vw.dims.ndisplay = 3 + vw.grid.enabled = False vw.scale_bar.visible = True if self.aniso_widgets.enabled(): diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index b22b3f52..f155ad7a 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -916,6 +916,7 @@ def on_start(self): self.remove_docked_widgets() self.display_status_report() + self._remove_result_layers() self.log.print_and_log(f"Worker started at {utils.get_time()}") self.log.print_and_log("\nWorker is running...") @@ -997,7 +998,7 @@ def _display_results(self, images, names, complete_missing=False): data=images[0], name=names[0], colormap="turbo" ) layer_output_discrete = self._viewer.add_image( - data=images[1], name=names[1], colormap="bop orange" + data=images[1], name=names[1], colormap="bop blue" ) layer_image = self._viewer.add_image( data=images[2], name=names[2], colormap="inferno" From f6c338d5a0989a61f53f9cde48b5ee6c36f0e07c Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 15:08:37 +0200 Subject: [PATCH 131/250] Save w/ loss name and epochs --- napari_cellseg3d/code_plugins/plugin_model_training.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index f155ad7a..ff2c8631 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -879,7 +879,11 @@ def _set_worker_config(self) -> config.TrainingWorkerConfig: validation_percent = self.validation_percent_choice.slider_value / 100 results_path_folder = Path( - self.results_path + f"/{model_config.name}_{utils.get_date_time()}" + self.results_path + + f"/{model_config.name}_" + + f"{self.loss_choice.currentText()}_" + + f"{self.epoch_choice.value()}e_" + + f"{utils.get_date_time()}" ) Path(results_path_folder).mkdir( parents=True, exist_ok=False From b620795026824afa8efc39f2e9b88ccc22d27f30 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 15:26:33 +0200 Subject: [PATCH 132/250] Update worker_inference.py --- napari_cellseg3d/code_models/worker_inference.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 10b72da4..0244e4c5 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -130,7 +130,7 @@ def raise_error(self, exception, msg): self.error_signal.emit(exception, msg) self.errored.emit(exception) - yield exception + raise exception # self.quit() def log_parameters(self): From 03c97d7843b3026711a68dea4b7691d187dd78d5 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 15:28:47 +0200 Subject: [PATCH 133/250] Update worker_inference.py --- napari_cellseg3d/code_models/worker_inference.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 0244e4c5..10b72da4 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -130,7 +130,7 @@ def raise_error(self, exception, msg): self.error_signal.emit(exception, msg) self.errored.emit(exception) - raise exception + yield exception # self.quit() def log_parameters(self): From efe213d426ae0d698271ff4141332e200b2d070d Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 15:40:47 +0200 Subject: [PATCH 134/250] Change threshold for Swin --- napari_cellseg3d/code_models/worker_inference.py | 6 ++++-- napari_cellseg3d/code_models/worker_training.py | 6 ++++-- napari_cellseg3d/code_models/workers_utils.py | 9 +++++++++ 3 files changed, 17 insertions(+), 4 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 10b72da4..6522755a 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -9,7 +9,7 @@ from monai.inferers import sliding_window_inference from monai.transforms import ( AddChannel, - AsDiscrete, + # AsDiscrete, Compose, EnsureChannelFirstd, EnsureType, @@ -35,6 +35,7 @@ QuantileNormalization, QuantileNormalizationd, RemapTensor, + Threshold, WeightsDownloader, ) @@ -727,7 +728,8 @@ def inference(self): post_process_transforms = Compose( [ RemapTensor(new_max=1.0, new_min=0.0), - AsDiscrete(threshold=t), + # AsDiscrete(threshold=t), + Threshold(threshold=t), EnsureType(), ] ) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 8baddb7c..c558a085 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -17,7 +17,7 @@ from monai.inferers import sliding_window_inference from monai.metrics import DiceMetric from monai.transforms import ( - AsDiscrete, + # AsDiscrete, Compose, EnsureChannelFirstd, EnsureType, @@ -45,6 +45,7 @@ LogSignal, QuantileNormalizationd, RemapTensor, + Threshold, TrainingReport, WeightsDownloader, ) @@ -638,10 +639,11 @@ def get_loader_func(num_samples): labs = decollate_batch(val_labels) # TODO : more parameters/flexibility + post_pred = Compose( [ RemapTensor(new_max=1, new_min=0), - AsDiscrete(threshold=0.25), # needed ? + Threshold(threshold=0.5), EnsureType(), ] ) # diff --git a/napari_cellseg3d/code_models/workers_utils.py b/napari_cellseg3d/code_models/workers_utils.py index 8427d683..5efb93a0 100644 --- a/napari_cellseg3d/code_models/workers_utils.py +++ b/napari_cellseg3d/code_models/workers_utils.py @@ -213,6 +213,15 @@ def __call__(self, img): return utils.remap_image(img, new_max=self.max, new_min=self.min) +class Threshold(Transform): + def __init__(self, threshold=0.5): + super().__init__() + self.threshold = threshold + + def __call__(self, img): + return torch.where(img > self.threshold, 1, 0) + + @dataclass class InferenceResult: """Class to record results of a segmentation job""" From 71abc21e4276a9b6b3e707434e028288b343c82b Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 16:03:35 +0200 Subject: [PATCH 135/250] Docs + change norm in inference Normalize each window instead of whole image for memory issues --- docs/res/guides/training_module_guide.rst | 10 ++-------- napari_cellseg3d/code_models/worker_inference.py | 11 ++++++----- 2 files changed, 8 insertions(+), 13 deletions(-) diff --git a/docs/res/guides/training_module_guide.rst b/docs/res/guides/training_module_guide.rst index d910bc50..0a577b86 100644 --- a/docs/res/guides/training_module_guide.rst +++ b/docs/res/guides/training_module_guide.rst @@ -95,22 +95,16 @@ Function Reference ======================== ================================================================================================ Dice loss `Dice Loss from MONAI`_ with ``sigmoid=true`` Generalized Dice loss `Generalized dice Loss from MONAI`_ with ``sigmoid=true`` -Dice-CE loss `Dice-CE Loss from MONAI`_ with ``sigmoid=true`` +Dice-CE loss `Dice-CrossEntropy Loss from MONAI`_ with ``sigmoid=true`` Tversky loss `Tversky Loss from MONAI`_ with ``sigmoid=true`` ======================== ================================================================================================ - -.. Binary cross-entropy `Binary cross entropy (BCE) loss from PyTorch`_ - BCE with logits `BCE loss with logits from PyTorch`_ - .. _Dice Loss from MONAI: https://docs.monai.io/en/stable/losses.html#diceloss .. _Focal Loss from MONAI: https://docs.monai.io/en/stable/losses.html#focalloss .. _Dice-focal Loss from MONAI: https://docs.monai.io/en/stable/losses.html#dicefocalloss .. _Generalized dice Loss from MONAI: https://docs.monai.io/en/stable/losses.html#generalizeddiceloss -.. _Dice-CE Loss from MONAI: https://docs.monai.io/en/stable/losses.html#diceceloss +.. _Dice-CrossEntropy Loss from MONAI: https://docs.monai.io/en/stable/losses.html#diceceloss .. _Tversky Loss from MONAI: https://docs.monai.io/en/stable/losses.html#tverskyloss -.. _Binary cross entropy (BCE) loss from PyTorch: -.. _BCE loss with logits from PyTorch: Once you are ready, press the Start button to begin training. The module will automatically load your dataset, perform data augmentation if you chose to, select a CUDA device if one is present, and train the model. diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 6522755a..9ed0e4d2 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -33,7 +33,6 @@ LogSignal, ONNXModelWrapper, QuantileNormalization, - QuantileNormalizationd, RemapTensor, Threshold, WeightsDownloader, @@ -194,7 +193,7 @@ def load_folder(self): EnsureChannelFirstd(keys=["image"]), # Orientationd(keys=["image"], axcodes="PLI"), # anisotropic_transform, - QuantileNormalizationd(keys=["image"]), + # QuantileNormalizationd(keys=["image"]), EnsureTyped(keys=["image"]), ] ) @@ -204,7 +203,7 @@ def load_folder(self): LoadImaged(keys=["image"]), # AddChanneld(keys=["image"]), #already done EnsureChannelFirstd(keys=["image"]), - QuantileNormalizationd(keys=["image"]), + # QuantileNormalizationd(keys=["image"]), # Orientationd(keys=["image"], axcodes="PLI"), # anisotropic_transform, SpatialPadd(keys=["image"], spatial_size=pad), @@ -248,7 +247,7 @@ def load_layer(self): # anisotropic_transform, AddChannel(), # SpatialPad(spatial_size=pad), - QuantileNormalization(), + # QuantileNormalization(), AddChannel(), EnsureType(), ], @@ -263,7 +262,7 @@ def load_layer(self): ToTensor(), # anisotropic_transform, AddChannel(), - QuantileNormalization(), + # QuantileNormalization(), SpatialPad(spatial_size=pad), AddChannel(), EnsureType(), @@ -301,10 +300,12 @@ def model_output( # logger.debug(f"model : {model}") logger.debug(f"inputs shape : {inputs.shape}") logger.debug(f"inputs type : {inputs.dtype}") + normalizazion = QuantileNormalization() try: # outputs = model(inputs) def model_output_wrapper(inputs): + inputs = normalizazion(inputs) result = model(inputs) return post_process_transforms(result) From 91ff7c3b58b218760e56c75d97c2d6178086b542 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 16:07:50 +0200 Subject: [PATCH 136/250] Update worker_inference.py --- napari_cellseg3d/code_models/worker_inference.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 9ed0e4d2..1790feeb 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -33,6 +33,7 @@ LogSignal, ONNXModelWrapper, QuantileNormalization, + QuantileNormalizationd, RemapTensor, Threshold, WeightsDownloader, @@ -193,7 +194,7 @@ def load_folder(self): EnsureChannelFirstd(keys=["image"]), # Orientationd(keys=["image"], axcodes="PLI"), # anisotropic_transform, - # QuantileNormalizationd(keys=["image"]), + QuantileNormalizationd(keys=["image"]), EnsureTyped(keys=["image"]), ] ) @@ -203,7 +204,7 @@ def load_folder(self): LoadImaged(keys=["image"]), # AddChanneld(keys=["image"]), #already done EnsureChannelFirstd(keys=["image"]), - # QuantileNormalizationd(keys=["image"]), + QuantileNormalizationd(keys=["image"]), # Orientationd(keys=["image"], axcodes="PLI"), # anisotropic_transform, SpatialPadd(keys=["image"], spatial_size=pad), @@ -247,7 +248,7 @@ def load_layer(self): # anisotropic_transform, AddChannel(), # SpatialPad(spatial_size=pad), - # QuantileNormalization(), + QuantileNormalization(), AddChannel(), EnsureType(), ], @@ -262,7 +263,7 @@ def load_layer(self): ToTensor(), # anisotropic_transform, AddChannel(), - # QuantileNormalization(), + QuantileNormalization(), SpatialPad(spatial_size=pad), AddChannel(), EnsureType(), @@ -300,12 +301,12 @@ def model_output( # logger.debug(f"model : {model}") logger.debug(f"inputs shape : {inputs.shape}") logger.debug(f"inputs type : {inputs.dtype}") - normalizazion = QuantileNormalization() + # normalization = QuantileNormalization() try: # outputs = model(inputs) def model_output_wrapper(inputs): - inputs = normalizazion(inputs) + # inputs = normalization(inputs) result = model(inputs) return post_process_transforms(result) From 821f11b5d05ca66b4d296801be587eccc04c6b5e Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 16:13:30 +0200 Subject: [PATCH 137/250] Normalize before ToTensor to avoid OOM from torch --- napari_cellseg3d/code_models/worker_inference.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 1790feeb..0738ecd2 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -244,11 +244,11 @@ def load_layer(self): if self.config.sliding_window_config.is_enabled(): load_transforms = Compose( [ + QuantileNormalization(), ToTensor(), # anisotropic_transform, AddChannel(), # SpatialPad(spatial_size=pad), - QuantileNormalization(), AddChannel(), EnsureType(), ], @@ -260,10 +260,10 @@ def load_layer(self): pad = utils.get_padding_dim(dims_check) load_transforms = Compose( [ + QuantileNormalization(), ToTensor(), # anisotropic_transform, AddChannel(), - QuantileNormalization(), SpatialPad(spatial_size=pad), AddChannel(), EnsureType(), From c4b864bbc48741de6ea738e53f4f500443fb4fe6 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 16:37:53 +0200 Subject: [PATCH 138/250] Improve error handling for csv when aborting --- napari_cellseg3d/code_plugins/plugin_model_training.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index ff2c8631..29698785 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -949,8 +949,10 @@ def on_finish(self): self.log.print_and_log("Done") self.log.print_and_log("*" * 10) - - self._make_csv() + try: + self._make_csv() + except ValueError as e: + logger.warning(f"Error while saving CSV report: {e}") self.btn_start.setText("Start") [btn.setVisible(True) for btn in self.close_buttons] From 5edd0403c2a9e5d657d34e04ce66037670a65f57 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 16:56:59 +0200 Subject: [PATCH 139/250] Show LR from scheduler and fix memory device print --- napari_cellseg3d/code_models/worker_training.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index c558a085..26013795 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -546,10 +546,10 @@ def get_loader_func(num_samples): if device.type == "cuda": self.log("Memory Usage:") alloc_mem = round( - torch.cuda.memory_allocated(0) / 1024**3, 1 + torch.cuda.memory_allocated(device_id) / 1024**3, 1 ) reserved_mem = round( - torch.cuda.memory_reserved(0) / 1024**3, 1 + torch.cuda.memory_reserved(device_id) / 1024**3, 1 ) self.log(f"Allocated: {alloc_mem}GB") self.log(f"Cached: {reserved_mem}GB") @@ -593,6 +593,9 @@ def get_loader_func(num_samples): self.log("Updating scheduler...") scheduler.step(epoch_loss) + self.log( + f"Current learning rate: {optimizer.param_groups[0]['lr']}" + ) checkpoint_output = [] eta = ( From ed865a9c1d8a5d52e6c56a9e1516c439506f04b2 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 16:58:55 +0200 Subject: [PATCH 140/250] Update worker_training.py --- napari_cellseg3d/code_models/worker_training.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 26013795..64381749 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -546,10 +546,10 @@ def get_loader_func(num_samples): if device.type == "cuda": self.log("Memory Usage:") alloc_mem = round( - torch.cuda.memory_allocated(device_id) / 1024**3, 1 + torch.cuda.memory_allocated(device) / 1024**3, 1 ) reserved_mem = round( - torch.cuda.memory_reserved(device_id) / 1024**3, 1 + torch.cuda.memory_reserved(device) / 1024**3, 1 ) self.log(f"Allocated: {alloc_mem}GB") self.log(f"Cached: {reserved_mem}GB") From 5b994f51a50d5162e80442b14ff453ac0e727a8a Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 18:19:51 +0200 Subject: [PATCH 141/250] TODO cleanup --- .../code_models/instance_segmentation.py | 2 +- .../code_models/worker_training.py | 28 ++++++++----------- napari_cellseg3d/code_plugins/plugin_base.py | 7 ----- napari_cellseg3d/code_plugins/plugin_crop.py | 7 ++--- .../code_plugins/plugin_model_inference.py | 6 ++-- .../code_plugins/plugin_model_training.py | 6 ++-- .../code_plugins/plugin_review.py | 4 +-- napari_cellseg3d/interface.py | 2 +- 8 files changed, 23 insertions(+), 39 deletions(-) diff --git a/napari_cellseg3d/code_models/instance_segmentation.py b/napari_cellseg3d/code_models/instance_segmentation.py index 0e2c0871..3ffbdbb9 100644 --- a/napari_cellseg3d/code_models/instance_segmentation.py +++ b/napari_cellseg3d/code_models/instance_segmentation.py @@ -99,7 +99,7 @@ def run_method(self, image): def _make_list_from_channels( self, image - ): # TODO(cyril) : adapt to batch dimension + ): # TODO(cyril) : adapt to batch dimension (needed ?) if len(image.shape) > 4: raise ValueError( f"Image has {len(image.shape)} dimensions, but should have at most 4 dimensions (CHWD)" diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 64381749..047df9c1 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -282,9 +282,7 @@ def train(self): size = self.config.sample_size if do_sampling else check PADDING = utils.get_padding_dim(size) - model = model_class( # FIXME check if correct - input_img_size=PADDING, use_checkpoint=True - ) + model = model_class(input_img_size=PADDING, use_checkpoint=True) model = model.to(self.config.device) epoch_loss_values = [] @@ -570,7 +568,7 @@ def get_loader_func(num_samples): if outputs.shape[1] > 1: outputs = outputs[ :, 1:, :, : - ] # FIXME fix channel number + ] # TODO(cyril): adapt if additional channels if len(outputs.shape) < 4: outputs = outputs.unsqueeze(0) loss = self.config.loss_function(outputs, labels) @@ -632,17 +630,15 @@ def get_loader_func(num_samples): ) except Exception as e: self.raise_error(e, "Error during validation") + logger.debug( f"val_outputs shape : {val_outputs.shape}" ) # val_outputs = model(val_inputs) pred = decollate_batch(val_outputs) - labs = decollate_batch(val_labels) - # TODO : more parameters/flexibility - post_pred = Compose( [ RemapTensor(new_max=1, new_min=0), @@ -668,15 +664,15 @@ def get_loader_func(num_samples): # logger.debug(len(val_outputs)) # logger.debug(len(val_labels)) - dice_test = np.array( # TODO(cyril): remove - [ - utils.dice_coeff(i, j) - for i, j in zip(val_outputs, val_labels) - ] - ) - logger.debug( - f"TEST VALIDATION Dice score : {dice_test.mean()}" - ) + # dice_test = np.array( + # [ + # utils.dice_coeff(i, j) + # for i, j in zip(val_outputs, val_labels) + # ] + # ) + # logger.debug( + # f"TEST VALIDATION Dice score : {dice_test.mean()}" + # ) dice_metric(y_pred=val_outputs, y=val_labels) diff --git a/napari_cellseg3d/code_plugins/plugin_base.py b/napari_cellseg3d/code_plugins/plugin_base.py index 7ea5aa11..f369320b 100644 --- a/napari_cellseg3d/code_plugins/plugin_base.py +++ b/napari_cellseg3d/code_plugins/plugin_base.py @@ -220,13 +220,6 @@ def _build(self): """Method to be defined by children classes""" raise NotImplementedError("To be defined in child classes") - # def _show_filetype_choice(self): # TODO(cyril): remove? - # """Method to show/hide the filetype choice when "loading as folder" is (de)selected""" - # show = self.load_as_stack_choice.isChecked() - # if show is not None: - # self.filetype_choice.setVisible(show) - # # self.lbl_ft.setVisible(show) - def _show_file_dialog(self): """Open file dialog and process path for a single file""" # if self.load_as_stack_choice.isChecked(): diff --git a/napari_cellseg3d/code_plugins/plugin_crop.py b/napari_cellseg3d/code_plugins/plugin_crop.py index 3ff46261..c6e822d4 100644 --- a/napari_cellseg3d/code_plugins/plugin_crop.py +++ b/napari_cellseg3d/code_plugins/plugin_crop.py @@ -37,7 +37,7 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): """ super().__init__(viewer) - self.docked_widgets = [] # TODO add remove on close + self.docked_widgets = [] self.results_path = Path.home() / Path("cellseg3d/cropped") self.btn_start = ui.Button("Start", self._start) @@ -266,9 +266,6 @@ def _start(self): """Launches cropping process by loading the files from the chosen folders, and adds control widgets to the napari Viewer for moving the cropped volume. """ - # TODO maybe implement proper reset function so multiple runs can be done without closing napari - # maybe use singletons or make docked widgets attributes that are hidden upon opening - if not self._check_ready(): logger.warning("Please select at least one valid layer !") return @@ -355,7 +352,7 @@ def add_isotropic_layer( self, layer, colormap="inferno", - contrast_lim=(200, 1000), # TODO generalize ? + contrast_lim=(200, 1000), opacity=0.7, visible=True, ): diff --git a/napari_cellseg3d/code_plugins/plugin_model_inference.py b/napari_cellseg3d/code_plugins/plugin_model_inference.py index 7b97f07b..134e22f4 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_inference.py +++ b/napari_cellseg3d/code_plugins/plugin_model_inference.py @@ -124,9 +124,9 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): self.anisotropy_wdgt = ui.AnisotropyWidgets( self, - default_x=1.5, - default_y=1.5, - default_z=5, # TODO change default + default_x=1, + default_y=1, + default_z=1, ) # self.worker_config.post_process_config.zoom.zoom_values = [ diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 29698785..5c5185b2 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -167,10 +167,10 @@ def __init__( self.validation_values = [] # self.model_choice.setCurrentIndex(0) - ################### # TODO(cyril) : disable if we implement WNet training + ################### + # TODO(cyril) : disable if we implement WNet training wnet_index = self.model_choice.findText("WNet") self.model_choice.removeItem(wnet_index) - ################################ # interface @@ -911,7 +911,7 @@ def _set_worker_config(self) -> config.TrainingWorkerConfig: sample_size=patch_size, do_augmentation=self.augment_choice.isChecked(), deterministic_config=deterministic_config, - ) # TODO(cyril) continue to put params in config + ) return self.worker_config diff --git a/napari_cellseg3d/code_plugins/plugin_review.py b/napari_cellseg3d/code_plugins/plugin_review.py index 4e1a7e66..d3216436 100644 --- a/napari_cellseg3d/code_plugins/plugin_review.py +++ b/napari_cellseg3d/code_plugins/plugin_review.py @@ -144,9 +144,7 @@ def _build(self): # self._show_io_element(self.results_filewidget) self.results_filewidget.text_field.setText( - str( - Path.home() / Path("cellseg3d/review") - ) # TODO(cyril) : check proper behaviour + str(Path.home() / Path("cellseg3d/review")) ) csv_param_w.setLayout(csv_param_l) diff --git a/napari_cellseg3d/interface.py b/napari_cellseg3d/interface.py index 30d6f0de..de605cf6 100644 --- a/napari_cellseg3d/interface.py +++ b/napari_cellseg3d/interface.py @@ -1360,7 +1360,7 @@ def combine_blocks( # TODO FIXME PLEASE this is a horrible design b=11, ): """Combines two QWidget objects and puts them side by side (first on the left/top and second on the right/bottom depending on "horizontal") - Weird argument names due the initial implementation of it. # TODO maybe fix arg names + Weird argument names due the initial implementation of it. # TODO maybe fix arg names or refactor Args: left_or_above (QWidget): First widget, to be added on the left/above of "second" From 95a293e82fdc17a129c9fc13197a9594490a1986 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 19 Jul 2023 22:12:21 +0200 Subject: [PATCH 142/250] Disable normalization for WNet --- napari_cellseg3d/code_models/worker_inference.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 0738ecd2..1ad24899 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -241,10 +241,16 @@ def load_layer(self): logger.debug(volume.shape) logger.debug(volume.dtype) + normalization = ( + QuantileNormalization() + if self.config.model_info.name != "WNet" + else lambda x: x + ) if self.config.sliding_window_config.is_enabled(): load_transforms = Compose( [ - QuantileNormalization(), + # QuantileNormalization(), + normalization, ToTensor(), # anisotropic_transform, AddChannel(), @@ -260,7 +266,8 @@ def load_layer(self): pad = utils.get_padding_dim(dims_check) load_transforms = Compose( [ - QuantileNormalization(), + # QuantileNormalization(), + normalization, ToTensor(), # anisotropic_transform, AddChannel(), From 656d627d7d7070b7f4b3d9724b210506c643c532 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 20 Jul 2023 09:49:11 +0200 Subject: [PATCH 143/250] Fix loss function instantiation --- napari_cellseg3d/_tests/test_training.py | 11 ++++-- .../code_models/worker_training.py | 28 ++++++++++++- .../code_plugins/plugin_model_training.py | 39 ++++++------------- 3 files changed, 46 insertions(+), 32 deletions(-) diff --git a/napari_cellseg3d/_tests/test_training.py b/napari_cellseg3d/_tests/test_training.py index f76b6d23..0c54d36a 100644 --- a/napari_cellseg3d/_tests/test_training.py +++ b/napari_cellseg3d/_tests/test_training.py @@ -53,6 +53,14 @@ def test_update_loss_plot(make_napari_viewer_proxy): assert widget.train_loss_plot is not None +def test_check_matching_losses(): + plugin = Trainer(None) + config = plugin._set_worker_config() + worker = plugin._create_worker_from_config(config) + + assert plugin.loss_list == list(worker.loss_dict.keys()) + + def test_training(make_napari_viewer_proxy, qtbot): im_path = str(Path(__file__).resolve().parent / "res/test.tif") @@ -73,9 +81,6 @@ def test_training(make_napari_viewer_proxy, qtbot): assert widget.check_ready() - ################# - # Training is too long to test properly this way. Do not use on Github - ################# MODEL_LIST["test"] = TestModel widget.model_choice.addItem("test") widget.model_choice.setCurrentText("test") diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 047df9c1..20a98f9b 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -15,6 +15,12 @@ pad_list_data_collate, ) from monai.inferers import sliding_window_inference +from monai.losses import ( + DiceCELoss, + DiceLoss, + GeneralizedDiceLoss, + TverskyLoss, +) from monai.metrics import DiceMetric from monai.transforms import ( # AsDiscrete, @@ -125,6 +131,25 @@ def __init__( ####################################### self.downloader = WeightsDownloader() + self.loss_dict = { + "Dice": DiceLoss(sigmoid=True), + # "BCELoss": torch.nn.BCELoss(), # dev + # "BCELogits": torch.nn.BCEWithLogitsLoss(), + "Generalized Dice": GeneralizedDiceLoss(sigmoid=True), + "DiceCE": DiceCELoss(sigmoid=True, lambda_ce=0.5), + "Tversky": TverskyLoss(sigmoid=True), + # "Focal loss": FocalLoss(), + # "Dice-Focal loss": DiceFocalLoss(sigmoid=True, lambda_dice=0.5), + } + self.loss_function = None + + def set_loss_from_config(self): + try: + self.loss_function = self.loss_dict[self.config.loss_function] + except KeyError as e: + self.raise_error(e, "Loss function not found, aborting job") + return self.loss_function + def set_download_log(self, widget): self.downloader.log_widget = widget @@ -532,6 +557,7 @@ def get_loader_func(num_samples): self.log_parameters() device = torch.device(self.config.device) + self.set_loss_from_config() # if model_name == "test": # self.quit() @@ -571,7 +597,7 @@ def get_loader_func(num_samples): ] # TODO(cyril): adapt if additional channels if len(outputs.shape) < 4: outputs = outputs.unsqueeze(0) - loss = self.config.loss_function(outputs, labels) + loss = self.loss_function(outputs, labels) loss.backward() optimizer.step() epoch_loss += loss.detach().item() diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 5c5185b2..944b0402 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -15,14 +15,6 @@ if TYPE_CHECKING: import napari -# MONAI -from monai.losses import ( - DiceCELoss, - DiceLoss, - GeneralizedDiceLoss, - TverskyLoss, -) - # Qt from qtpy.QtWidgets import QSizePolicy @@ -139,17 +131,15 @@ def __init__( """Whether the worker should stop or not""" self.start_time = None - self.loss_dict = { - "Dice": DiceLoss(sigmoid=True), - # "BCELoss": torch.nn.BCELoss(), # dev - # "BCELogits": torch.nn.BCEWithLogitsLoss(), - "Generalized Dice": GeneralizedDiceLoss(sigmoid=True), - "DiceCE": DiceCELoss(sigmoid=True, lambda_ce=0.5), - "Tversky": TverskyLoss(sigmoid=True), - # "Focal loss": FocalLoss(), - # "Dice-Focal loss": DiceFocalLoss(sigmoid=True, lambda_dice=0.5), - } - """Dict of loss functions""" + self.loss_list = [ # MUST BE MATCHED WITH THE LOSS FUNCTIONS IN THE TRAINING WORKER DICT + "Dice", + "Generalized Dice", + "DiceCE", + "Tversky", + # "Focal loss", + # "Dice-Focal loss", + ] + """List of loss functions""" self.canvas = None """Canvas to plot loss and dice metric in""" @@ -192,10 +182,7 @@ def __init__( ) self.loss_choice = ui.DropdownMenu( - # sorted( - list( - self.loss_dict.keys(), - ), + self.loss_list, text_label="Loss function", ) self.lbl_loss_choice = self.loss_choice.label @@ -383,10 +370,6 @@ def _update_validation_choice(self): elif validation.maximum() < max_epoch: validation.setMaximum(max_epoch) - def get_loss(self, key): - """Getter for loss function selected by user""" - return self.loss_dict[key] - def _toggle_patch_dims(self): if self.patch_choice.isChecked(): [w.setVisible(True) for w in self.patch_size_widgets] @@ -899,7 +882,7 @@ def _set_worker_config(self) -> config.TrainingWorkerConfig: train_data_dict=self.data, validation_percent=validation_percent, max_epochs=self.epoch_choice.value(), - loss_function=self.get_loss(self.loss_choice.currentText()), + loss_function=self.loss_choice.currentText(), learning_rate=float(self.learning_rate_choice.currentText()), scheduler_patience=self.scheduler_patience_choice.value(), scheduler_factor=self.scheduler_factor_choice.slider_value, From 8958aec3a73dc7a4236c8e76847bc983474b8734 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 20 Jul 2023 09:54:29 +0200 Subject: [PATCH 144/250] Update worker_inference.py --- napari_cellseg3d/code_models/worker_inference.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 1ad24899..279e04bf 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -241,11 +241,10 @@ def load_layer(self): logger.debug(volume.shape) logger.debug(volume.dtype) - normalization = ( - QuantileNormalization() - if self.config.model_info.name != "WNet" - else lambda x: x - ) + normalization = QuantileNormalization() + # if self.config.model_info.name != "WNet" + # else lambda x: x + # ) if self.config.sliding_window_config.is_enabled(): load_transforms = Compose( [ From b94c5740e682d7a80c6949a2da3f2344381369e3 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 20 Jul 2023 09:55:35 +0200 Subject: [PATCH 145/250] Revert "Update worker_inference.py" This reverts commit 1ba48dab06a3c50afa0e2bd64acc9076b50817ff. --- napari_cellseg3d/code_models/worker_inference.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 279e04bf..1ad24899 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -241,10 +241,11 @@ def load_layer(self): logger.debug(volume.shape) logger.debug(volume.dtype) - normalization = QuantileNormalization() - # if self.config.model_info.name != "WNet" - # else lambda x: x - # ) + normalization = ( + QuantileNormalization() + if self.config.model_info.name != "WNet" + else lambda x: x + ) if self.config.sliding_window_config.is_enabled(): load_transforms = Compose( [ From 02aa93b6571e12ca6d561a7aad466be44312c6b8 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 20 Jul 2023 11:22:55 +0200 Subject: [PATCH 146/250] Tooltip update --- napari_cellseg3d/code_models/model_framework.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/model_framework.py b/napari_cellseg3d/code_models/model_framework.py index 4e0cdfe4..585c53a0 100644 --- a/napari_cellseg3d/code_models/model_framework.py +++ b/napari_cellseg3d/code_models/model_framework.py @@ -337,7 +337,7 @@ def get_device(show=True): def empty_cuda_cache(self): """Empties the cuda cache if the device is a cuda device""" if self.get_device(show=False).type == "cuda": - logger.info("Attempting to empty cache...") + logger.info("Emptying cache...") torch.cuda.empty_cache() logger.info("Attempt complete : Cache emptied") From b4125def35eb1056a33457bf7d603d8174b7f97b Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 20 Jul 2023 11:23:10 +0200 Subject: [PATCH 147/250] Test disabling smoothing --- napari_cellseg3d/code_models/worker_training.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 20a98f9b..774b4435 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -132,12 +132,12 @@ def __init__( self.downloader = WeightsDownloader() self.loss_dict = { - "Dice": DiceLoss(sigmoid=True), + "Dice": DiceLoss(sigmoid=True, smooth_nr=0.0), # "BCELoss": torch.nn.BCELoss(), # dev # "BCELogits": torch.nn.BCEWithLogitsLoss(), "Generalized Dice": GeneralizedDiceLoss(sigmoid=True), "DiceCE": DiceCELoss(sigmoid=True, lambda_ce=0.5), - "Tversky": TverskyLoss(sigmoid=True), + "Tversky": TverskyLoss(sigmoid=True, smooth_nr=0.0), # "Focal loss": FocalLoss(), # "Dice-Focal loss": DiceFocalLoss(sigmoid=True, lambda_dice=0.5), } From 045e558f8ef77e3056ed7e4c86a79e193e6069c0 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 20 Jul 2023 11:56:55 +0200 Subject: [PATCH 148/250] Remove smooth_nr --- napari_cellseg3d/code_models/worker_training.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 774b4435..20a98f9b 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -132,12 +132,12 @@ def __init__( self.downloader = WeightsDownloader() self.loss_dict = { - "Dice": DiceLoss(sigmoid=True, smooth_nr=0.0), + "Dice": DiceLoss(sigmoid=True), # "BCELoss": torch.nn.BCELoss(), # dev # "BCELogits": torch.nn.BCEWithLogitsLoss(), "Generalized Dice": GeneralizedDiceLoss(sigmoid=True), "DiceCE": DiceCELoss(sigmoid=True, lambda_ce=0.5), - "Tversky": TverskyLoss(sigmoid=True, smooth_nr=0.0), + "Tversky": TverskyLoss(sigmoid=True), # "Focal loss": FocalLoss(), # "Dice-Focal loss": DiceFocalLoss(sigmoid=True, lambda_dice=0.5), } From 95cfbc4f1b164f08c83a9c1a96870ff6dfb423b5 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Fri, 21 Jul 2023 15:57:02 +0200 Subject: [PATCH 149/250] Fix shape label update --- napari_cellseg3d/interface.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/napari_cellseg3d/interface.py b/napari_cellseg3d/interface.py index de605cf6..22f6a4a3 100644 --- a/napari_cellseg3d/interface.py +++ b/napari_cellseg3d/interface.py @@ -757,8 +757,6 @@ def __init__( ): super().__init__(parent=parent, fixed=False) self._viewer = viewer - - self.image = None self.layer_type = layer_type self.layer_list = DropdownMenu( @@ -789,11 +787,14 @@ def _update_tooltip(self): self.layer_list.setToolTip(self.layer_list.currentText()) def _update_description(self): - if self.layer_list.currentText() != "": - self.layer_description.setVisible(True) - shape_desc = f"Shape : {self.layer_data().shape}" - self.layer_description.setText(shape_desc) - else: + try: + if self.layer_list.currentText() != "": + self.layer_description.setVisible(True) + shape_desc = f"Shape : {self.layer_data().shape}" + self.layer_description.setText(shape_desc) + else: + self.layer_description.setVisible(False) + except KeyError: self.layer_description.setVisible(False) def _add_layer(self, event): From 347e26715115bcb08ffcbc993c65345c0e19c352 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Fri, 21 Jul 2023 16:07:07 +0200 Subject: [PATCH 150/250] Update test_plugin_inference.py --- napari_cellseg3d/_tests/test_plugin_inference.py | 14 +++++--------- 1 file changed, 5 insertions(+), 9 deletions(-) diff --git a/napari_cellseg3d/_tests/test_plugin_inference.py b/napari_cellseg3d/_tests/test_plugin_inference.py index efa9d9fb..eb4254e1 100644 --- a/napari_cellseg3d/_tests/test_plugin_inference.py +++ b/napari_cellseg3d/_tests/test_plugin_inference.py @@ -1,7 +1,3 @@ -from pathlib import Path - -from tifffile import imread - from napari_cellseg3d._tests.fixtures import LogFixture from napari_cellseg3d.code_models.instance_segmentation import ( INSTANCE_SEGMENTATION_METHOD_LIST, @@ -17,10 +13,9 @@ def test_inference(make_napari_viewer_proxy, qtbot): - im_path = str(Path(__file__).resolve().parent / "res/test.tif") - image = imread(im_path) - - assert image.shape == (6, 6, 6) + dims = 6 + image = rand_gen.random(size=(dims, dims, dims)) + # assert image.shape == (dims, dims, dims) viewer = make_napari_viewer_proxy() widget = Inferer(viewer) @@ -46,14 +41,15 @@ def test_inference(make_napari_viewer_proxy, qtbot): widget.model_choice.addItem(test_model_name) widget.model_choice.setCurrentText(test_model_name) + widget.window_infer_box.setChecked(False) widget.worker_config = widget._set_worker_config() assert widget.worker_config is not None assert widget.model_info is not None - widget.window_infer_box.setChecked(False) worker = widget._create_worker_from_config(widget.worker_config) assert worker.config is not None assert worker.config.model_info is not None + assert worker.config.sliding_window_config.is_enabled() is False worker.config.layer = viewer.layers[0].data worker.config.post_process_config.instance.enabled = True worker.config.post_process_config.instance.method = ( From 990ac970dff44136a4cc293a9a12f922a8469631 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Fri, 21 Jul 2023 18:46:01 +0200 Subject: [PATCH 151/250] Trying to fix silent error when loading incompatible weights --- .../code_models/worker_inference.py | 35 ++++++++++--------- .../code_plugins/plugin_model_inference.py | 2 ++ .../code_plugins/plugin_model_training.py | 1 + 3 files changed, 22 insertions(+), 16 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 1ad24899..b66647c3 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -119,7 +119,7 @@ def warn(self, warning): """Sends a warning to main thread""" self.warn_signal.emit(warning) - def raise_error(self, exception, msg): + def _raise_error(self, exception, msg): """Raises an error in main thread""" logger.error(msg, exc_info=True) logger.error(exception, exc_info=True) @@ -131,8 +131,8 @@ def raise_error(self, exception, msg): self.error_signal.emit(exception, msg) self.errored.emit(exception) + self.quit() yield exception - # self.quit() def log_parameters(self): config = self.config @@ -334,7 +334,8 @@ def model_output_wrapper(inputs): except Exception as e: logger.exception(e) logger.debug("failed to run sliding window inference") - self.raise_error(e, "Error during sliding window inference") + self._raise_error(e, "Error during sliding window inference") + # raise e logger.debug(f"Inference output shape: {outputs.shape}") self.log("Post-processing...") @@ -346,7 +347,8 @@ def model_output_wrapper(inputs): return out except Exception as e: logger.exception(e) - self.raise_error(e, "Error during sliding window inference") + self._raise_error(e, "Error during sliding window inference") + # raise e # sys.stdout = old_stdout # sys.stderr = old_stderr @@ -437,7 +439,7 @@ def save_image( try: imwrite(file_path, image) except ValueError as e: - self.raise_error(e, "Error during image saving") + raise e filename = Path(file_path).stem if from_layer: @@ -595,7 +597,7 @@ def inference_on_layer(self, image, model, post_process_transforms): stats=stats, ) - # @thread_worker(connect={"errored": self.raise_error}) + # @thread_worker(connect={"errored": self._raise_error}) def inference(self): """ Requires: @@ -659,10 +661,10 @@ def inference(self): # device=self.config.device, num_classes=self.config.model_info.num_classes, ) - # try: - model = model.to(self.config.device) - # except Exception as e: - # self.raise_error(e, "Issue loading model to device") + try: + model = model.to(self.config.device) + except RuntimeError as e: + self._raise_error(e, "Issue loading model to device") # logger.debug(f"model : {model}") if model is None: raise ValueError("Model is None") @@ -687,13 +689,14 @@ def inference(self): strict=True, ) self.log(f"Weights status : {missing}") - except RuntimeError as e: - self.raise_error(e, "Error when loading weights") + except Exception as e: + self._raise_error(e, "Error when loading weights") + return None self.log("Done") # except Exception as e: - # self.raise_error(e, "Issue loading weights") + # self._raise_error(e, "Issue loading weights") # except Exception as e: - # self.raise_error(e, "Issue instantiating model") + # self._raise_error(e, "Issue instantiating model") # if model_name == "SegResNet": # model = model_class( @@ -779,11 +782,11 @@ def inference(self): yield self.inference_on_layer( input_image, model, post_process_transforms ) + model.to("cpu") # self.quit() except Exception as e: logger.exception(e) - self.raise_error(e, "Inference failed") - self.quit() + self._raise_error(e, "Inference failed") finally: self.quit() diff --git a/napari_cellseg3d/code_plugins/plugin_model_inference.py b/napari_cellseg3d/code_plugins/plugin_model_inference.py index 134e22f4..4bb20231 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_inference.py +++ b/napari_cellseg3d/code_plugins/plugin_model_inference.py @@ -843,6 +843,8 @@ def on_yield(self, result: InferenceResult): if isinstance(result, Exception): self.on_error(result) # raise result + if result is None: + self.on_error("Worker yielded None") # viewer, progress, show_res, show_res_number, zoon, show_original # check that viewer checkbox is on and that max number of displays has not been reached. diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 944b0402..80767396 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -903,6 +903,7 @@ def on_start(self): self.remove_docked_widgets() self.display_status_report() + self.log.clear() self._remove_result_layers() self.log.print_and_log(f"Worker started at {utils.get_time()}") From 85549ce19e063bcaa65eaa73177f3fe65e4012d0 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 24 Jul 2023 14:41:45 +0200 Subject: [PATCH 152/250] Set training to load weights w/ strict --- napari_cellseg3d/code_models/worker_training.py | 3 ++- napari_cellseg3d/dev_scripts/crop_data.py | 8 ++++---- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 20a98f9b..d7a49fd9 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -534,7 +534,8 @@ def get_loader_func(num_samples): torch.load( weights, map_location=self.config.device, - ) + ), + strict=True, ) except RuntimeError as e: logger.error(f"Error when loading weights : {e}") diff --git a/napari_cellseg3d/dev_scripts/crop_data.py b/napari_cellseg3d/dev_scripts/crop_data.py index d8be9cad..a8734d29 100644 --- a/napari_cellseg3d/dev_scripts/crop_data.py +++ b/napari_cellseg3d/dev_scripts/crop_data.py @@ -40,7 +40,7 @@ def crop_3d_image(image, roi_size): Path().home() # / "Desktop/Code/CELLSEG_BENCHMARK/TPH2_DATA/somatomotor_iso" # / "Desktop/Code/CELLSEG_BENCHMARK/TPH2_DATA/somatomotor_iso/labels/semantic" - / "Desktop/Code/CELLSEG_BENCHMARK/TPH2_mesospim/BENCHMARK/visual_small" + / "Desktop/Code/CELLSEG_BENCHMARK/TPH2_mesospim/visual_iso/labels/semantic" ) if not image_path.exists() or not image_path.is_dir(): raise ValueError(f"Image path {image_path} does not exist") @@ -48,8 +48,8 @@ def crop_3d_image(image, roi_size): for j in image_list: print(j) image = imread(str(j)) - # crops = crop_3d_image(image, (64, 64, 64)) - crops = [image] + crops = crop_3d_image(image, (64, 64, 64)) + # crops = [image] # viewer = napari.Viewer() if not (image_path / "cropped").exists(): (image_path / "cropped").mkdir(exist_ok=False) @@ -57,7 +57,7 @@ def crop_3d_image(image, roi_size): print(im.shape) # viewer.add_image(im) imwrite( - str(image_path / f"cropped/{j.stem}_{i}_crop.tif"), + str(image_path / f"cropped/a_{j.stem}_{i}_crop.tif"), im.astype(np.float32), dtype="float32", ) From 78f72dd4ab2a04803c76807ca9d0da6b2d2e18b6 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Fri, 28 Jul 2023 14:14:10 +0200 Subject: [PATCH 153/250] Fix tests + new weights --- napari_cellseg3d/_tests/test_inference.py | 17 +++++++++++++---- .../code_models/models/model_SegResNet.py | 2 +- .../code_models/models/model_SwinUNetR.py | 2 +- .../code_models/models/model_TRAILMAP_MS.py | 2 +- .../code_models/models/model_VNet.py | 2 +- .../pretrained/pretrained_model_urls.json | 8 ++++---- 6 files changed, 21 insertions(+), 12 deletions(-) diff --git a/napari_cellseg3d/_tests/test_inference.py b/napari_cellseg3d/_tests/test_inference.py index 336630f5..2fe996bf 100644 --- a/napari_cellseg3d/_tests/test_inference.py +++ b/napari_cellseg3d/_tests/test_inference.py @@ -66,18 +66,27 @@ def test_inference_on_folder(): config.images_filepaths = [ str(Path(__file__).resolve().parent / "res/test.tif") ] + config.sliding_window_config.window_size = 64 - def mock_work(x): - return x + class mock_work: + def __call__(self, x): + return x + + def eval(self): + return None worker = InferenceWorker(worker_config=config) - worker.aniso_transform = mock_work + worker.aniso_transform = mock_work() image = torch.Tensor(rand_gen.random((1, 1, 64, 64, 64))) res = worker.inference_on_folder( - {"image": image}, 0, model=mock_work, post_process_transforms=mock_work + {"image": image}, + 0, + model=mock_work(), + post_process_transforms=mock_work(), ) assert isinstance(res, InferenceResult) + assert res.result is not None def test_post_processing(): diff --git a/napari_cellseg3d/code_models/models/model_SegResNet.py b/napari_cellseg3d/code_models/models/model_SegResNet.py index 60b74d64..99f8cbfc 100644 --- a/napari_cellseg3d/code_models/models/model_SegResNet.py +++ b/napari_cellseg3d/code_models/models/model_SegResNet.py @@ -3,7 +3,7 @@ class SegResNet_(SegResNetVAE): use_default_training = True - weights_file = "SegResNet.pth" + weights_file = "SegResNet_latest.pth" def __init__( self, input_img_size, out_channels=1, dropout_prob=0.3, **kwargs diff --git a/napari_cellseg3d/code_models/models/model_SwinUNetR.py b/napari_cellseg3d/code_models/models/model_SwinUNetR.py index 0dbf0be5..bce316e8 100644 --- a/napari_cellseg3d/code_models/models/model_SwinUNetR.py +++ b/napari_cellseg3d/code_models/models/model_SwinUNetR.py @@ -7,7 +7,7 @@ class SwinUNETR_(SwinUNETR): use_default_training = True - weights_file = "Swin64_best_metric.pth" + weights_file = "SwinUNetR_latest.pth" def __init__( self, diff --git a/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py b/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py index bc8e43d5..4ee971e2 100644 --- a/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py +++ b/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py @@ -4,7 +4,7 @@ class TRAILMAP_MS_(UNet3D): use_default_training = True - weights_file = "TRAILMAP_MS_best_metric_epoch_26.pth" + weights_file = "TRAILMAP_MS_best_metric.pth" # original model from Liqun Luo lab, transferred to pytorch and trained on mesoSPIM-acquired data (mostly TPH2 as of July 2022) diff --git a/napari_cellseg3d/code_models/models/model_VNet.py b/napari_cellseg3d/code_models/models/model_VNet.py index 4e375a11..8fe18e2b 100644 --- a/napari_cellseg3d/code_models/models/model_VNet.py +++ b/napari_cellseg3d/code_models/models/model_VNet.py @@ -3,7 +3,7 @@ class VNet_(VNet): use_default_training = True - weights_file = "VNet_40e.pth" + weights_file = "VNet_latest.pth" def __init__(self, in_channels=1, out_channels=1, **kwargs): try: diff --git a/napari_cellseg3d/code_models/models/pretrained/pretrained_model_urls.json b/napari_cellseg3d/code_models/models/pretrained/pretrained_model_urls.json index b235a550..3c393d47 100644 --- a/napari_cellseg3d/code_models/models/pretrained/pretrained_model_urls.json +++ b/napari_cellseg3d/code_models/models/pretrained/pretrained_model_urls.json @@ -1,8 +1,8 @@ { - "TRAILMAP_MS": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/TRAILMAP_MS.tar.gz", - "SegResNet": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/SegResNet.tar.gz", - "VNet": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/VNet.tar.gz", - "SwinUNetR": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/Swin64.tar.gz", + "TRAILMAP_MS": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/TRAILMAP_latest.tar.gz", + "SegResNet": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/SegResNet_latest.tar.gz", + "VNet": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/VNet_latest.tar.gz", + "SwinUNetR": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/SwinUNetR_latest.tar.gz", "WNet": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/wnet.tar.gz", "WNet_ONNX": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/wnet_onnx.tar.gz", "test": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/test.tar.gz" From 8d0ff5d6756ed1e9165ebf09124ac450fc8bd009 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Fri, 28 Jul 2023 14:56:11 +0200 Subject: [PATCH 154/250] Fixes for UI and tests - Fix SegResNet input dims in test - Fixed all tests - Fixed issues with window inference settings for WNet in UI --- napari_cellseg3d/_tests/test_models.py | 4 +-- .../code_plugins/plugin_model_inference.py | 26 +++++-------------- 2 files changed, 9 insertions(+), 21 deletions(-) diff --git a/napari_cellseg3d/_tests/test_models.py b/napari_cellseg3d/_tests/test_models.py index 655fbb3a..d43754dc 100644 --- a/napari_cellseg3d/_tests/test_models.py +++ b/napari_cellseg3d/_tests/test_models.py @@ -26,7 +26,7 @@ def test_model_list(): for model_name in MODEL_LIST: # if model_name=="test": # continue - dims = 128 + dims = 64 test = MODEL_LIST[model_name]( input_img_size=[dims, dims, dims], in_channels=1, @@ -114,7 +114,7 @@ def test_pretrained_weights_compatibility(): for model_name in MODEL_LIST: file_name = MODEL_LIST[model_name].weights_file WeightsDownloader().download_weights(model_name, file_name) - model = MODEL_LIST[model_name](input_img_size=[128, 128, 128]) + model = MODEL_LIST[model_name](input_img_size=[64, 64, 64]) try: model.load_state_dict( torch.load( diff --git a/napari_cellseg3d/code_plugins/plugin_model_inference.py b/napari_cellseg3d/code_plugins/plugin_model_inference.py index 4bb20231..99230303 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_inference.py +++ b/napari_cellseg3d/code_plugins/plugin_model_inference.py @@ -112,7 +112,7 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): ###################### # TODO : better way to handle SegResNet size reqs ? self.model_input_size = ui.IntIncrementCounter( - lower=1, upper=1024, default=128, text_label="\nModel input size" + lower=1, upper=1024, default=64, text_label="\nModel input size" ) self.model_choice.currentIndexChanged.connect( self._toggle_display_model_input_size @@ -155,16 +155,7 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): sizes_window = ["8", "16", "32", "64", "128", "256", "512"] self._default_window_size = sizes_window.index("64") - # ( - # self.window_size_choice, - # self.window_size_choice.label, - # ) = ui.make_combobox(sizes_window, label="Window size and overlap") - # self.window_overlap = ui.make_n_spinboxes( - # max=1, - # default=0.7, - # step=0.05, - # double=True, - # ) + self.wnet_enabled = False self.window_size_choice = ui.DropdownMenu( sizes_window, text_label="Window size" @@ -310,16 +301,13 @@ def check_ready(self): def _restrict_window_size_for_model(self): """Sets the window size to a value that is compatible with the chosen model""" + self.wnet_enabled = False if self.model_choice.currentText() == "WNet": self.window_size_choice.setCurrentIndex(self._default_window_size) - self.window_size_choice.setDisabled(True) - self.window_infer_box.setChecked(True) - self.window_infer_box.setDisabled(True) - else: - self.window_size_choice.setDisabled(False) - self.window_infer_box.setDisabled(False) - self.window_infer_box.setChecked(True) - self.window_size_choice.setCurrentIndex(self._default_window_size) + self.wnet_enabled = True + self.window_infer_box.setChecked(self.wnet_enabled) + self.window_size_choice.setDisabled(self.wnet_enabled) + self.window_infer_box.setDisabled(self.wnet_enabled) def _toggle_display_model_input_size(self): if ( From ca57aa6dde33ff273cb2b895bc410538077c9816 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Fri, 28 Jul 2023 14:58:13 +0200 Subject: [PATCH 155/250] Update test_and_deploy.yml --- .github/workflows/test_and_deploy.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/test_and_deploy.yml b/.github/workflows/test_and_deploy.yml index fafb1719..efb218c4 100644 --- a/.github/workflows/test_and_deploy.yml +++ b/.github/workflows/test_and_deploy.yml @@ -7,6 +7,7 @@ on: push: branches: - main + - cy/training tags: - "v*" # Push events to matching v*, i.e. v1.0, v20.15.10 pull_request: From 13774622e815459dc05cb0977fcc93c1acf01436 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Fri, 28 Jul 2023 15:30:01 +0200 Subject: [PATCH 156/250] Fix dir for saving in tests --- napari_cellseg3d/code_models/worker_inference.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index b66647c3..70edda6b 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -436,6 +436,8 @@ def save_image( + f"_{time}" + filetype ) + if not Path(self.config.results_path).exists(): + Path(self.config.results_path).mkdir(parents=True, exist_ok=True) try: imwrite(file_path, image) except ValueError as e: From 1f71ea73adb72c825a4bda13087c3acb41dfdf0a Mon Sep 17 00:00:00 2001 From: C-Achard Date: Fri, 28 Jul 2023 15:37:46 +0200 Subject: [PATCH 157/250] Update test_and_deploy.yml --- .github/workflows/test_and_deploy.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/test_and_deploy.yml b/.github/workflows/test_and_deploy.yml index efb218c4..fafb1719 100644 --- a/.github/workflows/test_and_deploy.yml +++ b/.github/workflows/test_and_deploy.yml @@ -7,7 +7,6 @@ on: push: branches: - main - - cy/training tags: - "v*" # Push events to matching v*, i.e. v1.0, v20.15.10 pull_request: From fffed34139bbb8a60cb67470cabf325383f2bca4 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Fri, 28 Jul 2023 14:14:10 +0200 Subject: [PATCH 158/250] Fix tests + new weights --- napari_cellseg3d/_tests/test_inference.py | 1 + napari_cellseg3d/code_models/models/model_SegResNet.py | 2 +- napari_cellseg3d/code_models/models/model_SwinUNetR.py | 2 +- napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py | 2 +- napari_cellseg3d/code_models/models/model_VNet.py | 2 +- .../models/pretrained/pretrained_model_urls.json | 8 ++++---- 6 files changed, 9 insertions(+), 8 deletions(-) diff --git a/napari_cellseg3d/_tests/test_inference.py b/napari_cellseg3d/_tests/test_inference.py index f5a89b14..4fa2c54b 100644 --- a/napari_cellseg3d/_tests/test_inference.py +++ b/napari_cellseg3d/_tests/test_inference.py @@ -89,6 +89,7 @@ def __call__(self, x): post_process_transforms=mock_work(), ) assert isinstance(res, InferenceResult) + assert res.result is not None def test_post_processing(): diff --git a/napari_cellseg3d/code_models/models/model_SegResNet.py b/napari_cellseg3d/code_models/models/model_SegResNet.py index 60b74d64..99f8cbfc 100644 --- a/napari_cellseg3d/code_models/models/model_SegResNet.py +++ b/napari_cellseg3d/code_models/models/model_SegResNet.py @@ -3,7 +3,7 @@ class SegResNet_(SegResNetVAE): use_default_training = True - weights_file = "SegResNet.pth" + weights_file = "SegResNet_latest.pth" def __init__( self, input_img_size, out_channels=1, dropout_prob=0.3, **kwargs diff --git a/napari_cellseg3d/code_models/models/model_SwinUNetR.py b/napari_cellseg3d/code_models/models/model_SwinUNetR.py index 0dbf0be5..bce316e8 100644 --- a/napari_cellseg3d/code_models/models/model_SwinUNetR.py +++ b/napari_cellseg3d/code_models/models/model_SwinUNetR.py @@ -7,7 +7,7 @@ class SwinUNETR_(SwinUNETR): use_default_training = True - weights_file = "Swin64_best_metric.pth" + weights_file = "SwinUNetR_latest.pth" def __init__( self, diff --git a/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py b/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py index bc8e43d5..4ee971e2 100644 --- a/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py +++ b/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py @@ -4,7 +4,7 @@ class TRAILMAP_MS_(UNet3D): use_default_training = True - weights_file = "TRAILMAP_MS_best_metric_epoch_26.pth" + weights_file = "TRAILMAP_MS_best_metric.pth" # original model from Liqun Luo lab, transferred to pytorch and trained on mesoSPIM-acquired data (mostly TPH2 as of July 2022) diff --git a/napari_cellseg3d/code_models/models/model_VNet.py b/napari_cellseg3d/code_models/models/model_VNet.py index 4e375a11..8fe18e2b 100644 --- a/napari_cellseg3d/code_models/models/model_VNet.py +++ b/napari_cellseg3d/code_models/models/model_VNet.py @@ -3,7 +3,7 @@ class VNet_(VNet): use_default_training = True - weights_file = "VNet_40e.pth" + weights_file = "VNet_latest.pth" def __init__(self, in_channels=1, out_channels=1, **kwargs): try: diff --git a/napari_cellseg3d/code_models/models/pretrained/pretrained_model_urls.json b/napari_cellseg3d/code_models/models/pretrained/pretrained_model_urls.json index b235a550..3c393d47 100644 --- a/napari_cellseg3d/code_models/models/pretrained/pretrained_model_urls.json +++ b/napari_cellseg3d/code_models/models/pretrained/pretrained_model_urls.json @@ -1,8 +1,8 @@ { - "TRAILMAP_MS": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/TRAILMAP_MS.tar.gz", - "SegResNet": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/SegResNet.tar.gz", - "VNet": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/VNet.tar.gz", - "SwinUNetR": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/Swin64.tar.gz", + "TRAILMAP_MS": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/TRAILMAP_latest.tar.gz", + "SegResNet": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/SegResNet_latest.tar.gz", + "VNet": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/VNet_latest.tar.gz", + "SwinUNetR": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/SwinUNetR_latest.tar.gz", "WNet": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/wnet.tar.gz", "WNet_ONNX": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/wnet_onnx.tar.gz", "test": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/test.tar.gz" From e235087bb60a575c73e142dc3d437fff25085be5 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Fri, 28 Jul 2023 17:35:57 +0200 Subject: [PATCH 159/250] Fix ETA precision --- napari_cellseg3d/code_models/worker_training.py | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index b28f8285..332e8f2d 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -620,7 +620,10 @@ def train(self): "data": np.squeeze(dec_out), "cmap": "gist_earth", }, - "Input image": {"data": np.squeeze(image), "cmap": "inferno"}, + "Input image": { + "data": np.squeeze(image), + "cmap": "inferno", + }, } yield TrainingReport( @@ -766,8 +769,10 @@ def train(self): if WANDB_INSTALLED: # log validation dice score for each validation round wandb.log({"val/dice_metric": metric}) - - dec_out_val = val_decoder_outputs[0].detach().cpu().numpy() + + dec_out_val = ( + val_decoder_outputs[0].detach().cpu().numpy() + ) enc_out_val = val_outputs[0].detach().cpu().numpy() lab_out_val = val_labels[0].detach().cpu().numpy() val_in = val_inputs[0].detach.cpu().nummpy() @@ -810,9 +815,7 @@ def train(self): * (self.config.max_epochs / (epoch + 1) - 1) / 60 ) - self.log( - f"ETA: {eta} minutes", - ) + self.log(f"ETA: {eta:.2f} minutes") self.log("-" * 20) # Save the model From 0cbd2ec8397c03f0c11f44707fa3d6569660e5f9 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 31 Jul 2023 09:14:37 +0200 Subject: [PATCH 160/250] Docstring update --- napari_cellseg3d/code_models/workers_utils.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/workers_utils.py b/napari_cellseg3d/code_models/workers_utils.py index b07e96c8..5c695fd1 100644 --- a/napari_cellseg3d/code_models/workers_utils.py +++ b/napari_cellseg3d/code_models/workers_utils.py @@ -151,7 +151,7 @@ def __init__(self, file_location): except ImportError as e: logger.error("ONNX is not installed but ONNX model was loaded") logger.error(e) - msg = "PLEASE INSTALL ONNX CPU OR GPU USING pip install napari-cellseg3d[onnx-cpu] OR napari-cellseg3d[onnx-gpu]" + msg = "PLEASE INSTALL ONNX CPU OR GPU USING: pip install napari-cellseg3d[onnx-cpu] OR pip install napari-cellseg3d[onnx-gpu]" logger.error(msg) raise ImportError(msg) from e @@ -177,6 +177,8 @@ def to(self, device): class QuantileNormalizationd(MapTransform): + """MONAI-style dict transform to normalize each image in a batch individually by quantile normalization.""" + def __init__(self, keys, allow_missing_keys: bool = False): super().__init__(keys, allow_missing_keys) @@ -199,6 +201,8 @@ def normalizer(self, image: torch.Tensor): class QuantileNormalization(Transform): + """MONAI-style transform to normalize each image in a batch individually by quantile normalization.""" + def __call__(self, img): return utils.quantile_normalization(img) From ed135a8703c6180639fc38832c64c67b592e3579 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 31 Jul 2023 09:38:18 +0200 Subject: [PATCH 161/250] Update plugin_model_training.py --- napari_cellseg3d/code_plugins/plugin_model_training.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 91fb7ebd..c31001be 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -316,9 +316,9 @@ def __init__( self._set_tooltips() self._build() self.model_choice.currentTextChanged.connect( - self._toggle_unsupervised_mode + partial(self._toggle_unsupervised_mode, enabled=False) ) - self._toggle_unsupervised_mode() + self._toggle_unsupervised_mode(enabled=False) def _set_tooltips(self): # tooltips From f224e7656638f57d202b84bdda612bb830e73d5a Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 31 Jul 2023 09:57:45 +0200 Subject: [PATCH 162/250] Update contrast limit when updating layers --- napari_cellseg3d/code_plugins/plugin_model_training.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index c31001be..4d1e1dfc 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -1291,6 +1291,9 @@ def _display_results(self, images_dict, complete_missing=False): "data" ] self.result_layers[i].refresh() + self.result_layers[ + i + ].contrast_limits.reset_contrast_limits_range() def on_yield(self, report: TrainingReport): # TODO refactor for dict # logger.info( From cbfe4ef5bb1afdcaec96aa32cafda4db52eb59c6 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 31 Jul 2023 09:57:54 +0200 Subject: [PATCH 163/250] Update config.py --- napari_cellseg3d/config.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/config.py b/napari_cellseg3d/config.py index 72f8dfab..f9536d93 100644 --- a/napari_cellseg3d/config.py +++ b/napari_cellseg3d/config.py @@ -287,7 +287,7 @@ class WNetTrainingWorkerConfig(TrainingWorkerConfig): dropout: float = 0.65 use_clipping: bool = False # use gradient clipping clipping: float = 1.0 # clipping value - weight_decay: float = 1e-5 # weight decay (used 0.01 historically) + weight_decay: float = 0.01 # 1e-5 # weight decay (used 0.01 historically) # NCuts loss params intensity_sigma: float = 1.0 spatial_sigma: float = 4.0 From 3cb6a35c3f04f1ab56d42129c3d99ac7bf1c3d25 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 31 Jul 2023 10:05:29 +0200 Subject: [PATCH 164/250] Fixed normalization --- napari_cellseg3d/code_models/worker_training.py | 2 ++ napari_cellseg3d/code_models/workers_utils.py | 17 +++++++++++++++++ 2 files changed, 19 insertions(+) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 332e8f2d..fb74d1de 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -57,6 +57,7 @@ LogSignal, QuantileNormalizationd, RemapTensor, + RemapTensord, Threshold, TrainingReport, WeightsDownloader, @@ -268,6 +269,7 @@ def get_dataset(self, train_transforms): spatial_size=(utils.get_padding_dim(first_volume_shape)), ), EnsureTyped(keys=["image"]), + RemapTensord(keys=["image"], new_min=0.0, new_max=100.0), ] ) diff --git a/napari_cellseg3d/code_models/workers_utils.py b/napari_cellseg3d/code_models/workers_utils.py index 5c695fd1..e5e8b881 100644 --- a/napari_cellseg3d/code_models/workers_utils.py +++ b/napari_cellseg3d/code_models/workers_utils.py @@ -217,6 +217,23 @@ def __call__(self, img): return utils.remap_image(img, new_max=self.max, new_min=self.min) +class RemapTensord(MapTransform): + def __init__( + self, keys, new_max, new_min, allow_missing_keys: bool = False + ): + super().__init__(keys, allow_missing_keys) + self.max = new_max + self.min = new_min + + def __call__(self, data): + d = dict(data) + for key in self.keys: + d[key] = utils.remap_image( + d[key], new_max=self.max, new_min=self.min + ) + return d + + class Threshold(Transform): def __init__(self, threshold=0.5): super().__init__() From 7b14ef38c4fb01de4d8049e74e49cc1f97e1d1d5 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 31 Jul 2023 10:08:31 +0200 Subject: [PATCH 165/250] Update plugin_model_training.py --- napari_cellseg3d/code_plugins/plugin_model_training.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 4d1e1dfc..4a0bb272 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -1291,9 +1291,9 @@ def _display_results(self, images_dict, complete_missing=False): "data" ] self.result_layers[i].refresh() - self.result_layers[ - i - ].contrast_limits.reset_contrast_limits_range() + # self.result_layers[ + # i + # ].contrast_limits.reset_contrast_limits_range() def on_yield(self, report: TrainingReport): # TODO refactor for dict # logger.info( From 267a9c10ca537b19b50db9a153b470532a09d2a5 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 31 Jul 2023 10:10:53 +0200 Subject: [PATCH 166/250] Update workers_utils.py --- napari_cellseg3d/code_models/workers_utils.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/napari_cellseg3d/code_models/workers_utils.py b/napari_cellseg3d/code_models/workers_utils.py index e5e8b881..14d8d023 100644 --- a/napari_cellseg3d/code_models/workers_utils.py +++ b/napari_cellseg3d/code_models/workers_utils.py @@ -228,9 +228,11 @@ def __init__( def __call__(self, data): d = dict(data) for key in self.keys: - d[key] = utils.remap_image( - d[key], new_max=self.max, new_min=self.min - ) + for i in range(d[key].shape[0]): + logger.debug(f"remapping across channel {i}") + d[key][i] = utils.remap_image( + d[key][i], new_max=self.max, new_min=self.min + ) return d From 2b027501a9db7fc5eefe0ace12a70a9f9aefdf65 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 31 Jul 2023 10:17:24 +0200 Subject: [PATCH 167/250] Trying to fix input normalization --- .../code_models/worker_training.py | 11 +++--- napari_cellseg3d/code_models/workers_utils.py | 34 +++++++++---------- 2 files changed, 24 insertions(+), 21 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index fb74d1de..21f0f69b 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -57,7 +57,7 @@ LogSignal, QuantileNormalizationd, RemapTensor, - RemapTensord, + # RemapTensord, Threshold, TrainingReport, WeightsDownloader, @@ -269,7 +269,7 @@ def get_dataset(self, train_transforms): spatial_size=(utils.get_padding_dim(first_volume_shape)), ), EnsureTyped(keys=["image"]), - RemapTensord(keys=["image"], new_min=0.0, new_max=100.0), + # RemapTensord(keys=["image"], new_min=0.0, new_max=100.0), ] ) @@ -541,6 +541,9 @@ def train(self): for _i, batch in enumerate(dataloader): # raise NotImplementedError("testing") image = batch["image"].to(device) + for i in range(image.shape[0]): + for j in range(image.shape[1]): + image[i, j] = normalize_function(image[i, j]) # if self.config.batch_size == 1: # image = image.unsqueeze(0) # else: @@ -580,8 +583,8 @@ def train(self): loss = alpha * Ncuts + beta * reconstruction_loss epoch_loss += loss.item() - # if WANDB_INSTALLED: - # wandb.log({"Sum of losses": loss.item()}) + if WANDB_INSTALLED: + wandb.log({"Sum of losses": loss.item()}) loss.backward(loss) optimizer.step() diff --git a/napari_cellseg3d/code_models/workers_utils.py b/napari_cellseg3d/code_models/workers_utils.py index 14d8d023..600dddd5 100644 --- a/napari_cellseg3d/code_models/workers_utils.py +++ b/napari_cellseg3d/code_models/workers_utils.py @@ -217,23 +217,23 @@ def __call__(self, img): return utils.remap_image(img, new_max=self.max, new_min=self.min) -class RemapTensord(MapTransform): - def __init__( - self, keys, new_max, new_min, allow_missing_keys: bool = False - ): - super().__init__(keys, allow_missing_keys) - self.max = new_max - self.min = new_min - - def __call__(self, data): - d = dict(data) - for key in self.keys: - for i in range(d[key].shape[0]): - logger.debug(f"remapping across channel {i}") - d[key][i] = utils.remap_image( - d[key][i], new_max=self.max, new_min=self.min - ) - return d +# class RemapTensord(MapTransform): +# def __init__( +# self, keys, new_max, new_min, allow_missing_keys: bool = False +# ): +# super().__init__(keys, allow_missing_keys) +# self.max = new_max +# self.min = new_min +# +# def __call__(self, data): +# d = dict(data) +# for key in self.keys: +# for i in range(d[key].shape[0]): +# logger.debug(f"remapping across channel {i}") +# d[key][i] = utils.remap_image( +# d[key][i], new_max=self.max, new_min=self.min +# ) +# return d class Threshold(Transform): From 8e8c8274eced0bb69de5e245429607387f29c8fc Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 31 Jul 2023 10:55:09 +0200 Subject: [PATCH 168/250] Fix name mismatch --- napari_cellseg3d/code_models/worker_training.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 21f0f69b..3f00b894 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -619,7 +619,7 @@ def train(self): "data": AsDiscrete(threshold=0.5)( enc_out ).numpy(), - "cmap": "turbo", + "cmap": "bop blue", }, "Decoder output": { "data": np.squeeze(dec_out), @@ -634,7 +634,7 @@ def train(self): yield TrainingReport( show_plot=True, epoch=epoch, - loss_1_values={"SoftNCuts loss": ncuts_losses}, + loss_1_values={"SoftNCuts": ncuts_losses}, loss_2_values=rec_losses, weights=model.state_dict(), images_dict=images_dict, @@ -804,7 +804,7 @@ def train(self): yield TrainingReport( epoch=epoch, loss_1_values={ - "SoftNCuts loss": ncuts_losses, + "SoftNCuts": ncuts_losses, "Dice metric": metric, }, loss_2_values=rec_losses, From e7af6f5350f8493b43a30a3e297463251bc4c182 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 31 Jul 2023 11:09:15 +0200 Subject: [PATCH 169/250] Fix decoder evaluation --- .../code_models/worker_training.py | 33 +++++-------------- 1 file changed, 9 insertions(+), 24 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 3f00b894..2de05bd6 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -584,7 +584,7 @@ def train(self): loss = alpha * Ncuts + beta * reconstruction_loss epoch_loss += loss.item() if WANDB_INSTALLED: - wandb.log({"Sum of losses": loss.item()}) + wandb.log({"Weighted sum of losses": loss.item()}) loss.backward(loss) optimizer.step() @@ -651,26 +651,6 @@ def train(self): # wandb.log({"learning_rate encoder": optimizerE.param_groups[0]["lr"]}) # wandb.log({"learning_rate model": optimizer.param_groups[0]["lr"]}) - # self.log("Ncuts loss: " + str(ncuts_losses[-1])) - # if epoch > 0: - # self.log( - # "Ncuts loss difference: " - # + str(ncuts_losses[-1] - ncuts_losses[-2]) - # ) - # self.log("Reconstruction loss: " + str(rec_losses[-1])) - # if epoch > 0: - # self.log( - # "Reconstruction loss difference: " - # + str(rec_losses[-1] - rec_losses[-2]) - # ) - # self.log("Sum of losses: " + str(total_losses[-1])) - # if epoch > 0: - # self.log( - # "Sum of losses difference: " - # + str(total_losses[-1] - total_losses[-2]), - # ) - - # show losses and differences with 5 points precision self.log(f"Ncuts loss: {ncuts_losses[-1]:.5f}") self.log(f"Reconstruction loss: {rec_losses[-1]:.5f}") self.log(f"Sum of losses: {total_losses[-1]:.5f}") @@ -718,10 +698,15 @@ def train(self): overlap=0, progress=True, ) - val_outputs = AsDiscrete(threshold=0.5)( - val_outputs + val_decoder_outputs = sliding_window_inference( + val_outputs, + roi_size=[64, 64, 64], + sw_batch_size=1, + predictor=model.forward_decoder, + overlap=0, + progress=True, ) - val_decoder_outputs = model.forward_decoder( + val_outputs = AsDiscrete(threshold=0.5)( val_outputs ) From bde4cbc3f35285afac58ffe2fc62fd82e31badb8 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 31 Jul 2023 11:15:35 +0200 Subject: [PATCH 170/250] Update dice calculation --- .../code_models/worker_training.py | 43 +++++++++++-------- 1 file changed, 26 insertions(+), 17 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 2de05bd6..67db9c8b 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -689,7 +689,9 @@ def train(self): val_inputs[i][j] = normalize_function( val_inputs[i][j] ) - + logger.debug( + f"Val inputs shape: {val_inputs.shape}" + ) val_outputs = sliding_window_inference( val_inputs, roi_size=[64, 64, 64], @@ -709,26 +711,33 @@ def train(self): val_outputs = AsDiscrete(threshold=0.5)( val_outputs ) + logger.debug( + f"Val outputs shape: {val_outputs.shape}" + ) + logger.debug( + f"Val labels shape: {val_labels.shape}" + ) + logger.debug( + f"Val decoder outputs shape: {val_decoder_outputs.shape}" + ) - # compute metric for current iteration + dices = [] for channel in range(val_outputs.shape[1]): - max_dice_channel = torch.argmax( - torch.Tensor( - [ - utils.dice_coeff( - y_pred=val_outputs[ - :, - channel : (channel + 1), - :, - :, - :, - ], - y_true=val_labels, - ) - ] + dices.append( + utils.dice_coeff( + y_pred=val_outputs[ + 0, channel : (channel + 1), :, :, : + ], + y_true=val_labels[0], ) ) - + logger.debug(f"DICE COEFF: {dices}") + max_dice_channel = torch.argmax( + torch.Tensor(dices) + ) + logger.debug( + f"MAX DICE CHANNEL: {max_dice_channel}" + ) dice_metric( y_pred=val_outputs[ :, From 99c2dc181eb55d532f82f5476edbbf8352328cf0 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 31 Jul 2023 11:26:26 +0200 Subject: [PATCH 171/250] Update dice coeff --- napari_cellseg3d/utils.py | 22 ++++++++++++++++------ 1 file changed, 16 insertions(+), 6 deletions(-) diff --git a/napari_cellseg3d/utils.py b/napari_cellseg3d/utils.py index c6a8bbac..db293bbe 100644 --- a/napari_cellseg3d/utils.py +++ b/napari_cellseg3d/utils.py @@ -179,21 +179,31 @@ def sphericity_axis(semi_major, semi_minor): return result -def dice_coeff(y_true, y_pred, smooth=1.0): +def dice_coeff( + y_true: Union[torch.Tensor, np.ndarray], + y_pred: Union[torch.Tensor, np.ndarray], + smooth: float = 1.0, +) -> Union[torch.Tensor, np.float64]: """Compute Dice-Sorensen coefficient between two numpy arrays - Args: y_true: Ground truth label y_pred: Prediction label - Returns: dice coefficient - """ + if isinstance(y_true, np.ndarray) and isinstance(y_pred, np.ndarray): + sum_tensor = np.sum + elif isinstance(y_true, torch.Tensor) and isinstance(y_pred, torch.Tensor): + sum_tensor = torch.sum + else: + raise ValueError( + "y_true and y_pred must both be either numpy arrays or torch tensors" + ) + y_true_f = y_true.flatten() y_pred_f = y_pred.flatten() - intersection = np.sum(y_true_f * y_pred_f) + intersection = sum_tensor(y_true_f * y_pred_f) return (2.0 * intersection + smooth) / ( - np.sum(y_true_f) + np.sum(y_pred_f) + smooth + sum_tensor(y_true_f) + sum_tensor(y_pred_f) + smooth ) From 97706117649d63e71ff8148b084da58bc3e49237 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 31 Jul 2023 11:31:40 +0200 Subject: [PATCH 172/250] Update worker_training.py --- napari_cellseg3d/code_models/worker_training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 67db9c8b..41df7d9c 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -774,7 +774,7 @@ def train(self): ) enc_out_val = val_outputs[0].detach().cpu().numpy() lab_out_val = val_labels[0].detach().cpu().numpy() - val_in = val_inputs[0].detach.cpu().nummpy() + val_in = val_inputs[0].detach.cpu().numpy() display_dict = { "Decoder output": { From bca2262403bf84f288e69550b557c3a234a4feff Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 31 Jul 2023 11:34:18 +0200 Subject: [PATCH 173/250] Fix eval detach --- napari_cellseg3d/code_models/worker_training.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 41df7d9c..e64427c8 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -774,7 +774,7 @@ def train(self): ) enc_out_val = val_outputs[0].detach().cpu().numpy() lab_out_val = val_labels[0].detach().cpu().numpy() - val_in = val_inputs[0].detach.cpu().numpy() + val_in = val_inputs[0].detach().cpu().numpy() display_dict = { "Decoder output": { @@ -782,15 +782,15 @@ def train(self): "cmap": "gist_earth", }, "Encoder output": { - "data": enc_out_val, + "data": np.squeeze(enc_out_val), "cmap": "turbo", }, "Labels": { - "data": lab_out_val, + "data": np.squeeze(lab_out_val), "cmap": "bop blue", }, "Inputs": { - "data": val_in, + "data": np.squeeze(val_in), "cmap": "inferno", }, } From d75dbc58fb002aea620d882381e5e73cbdf23895 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 31 Jul 2023 11:39:13 +0200 Subject: [PATCH 174/250] Fix Dice list for WNet --- napari_cellseg3d/code_models/worker_training.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index e64427c8..2ad1d10a 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -529,6 +529,7 @@ def train(self): rec_losses = [] total_losses = [] best_dice = -1 + dice_values = [] # Train the model for epoch in range(self.config.max_epochs): @@ -751,6 +752,7 @@ def train(self): # aggregate the final mean dice result metric = dice_metric.aggregate().item() + dice_values.append(metric) self.log(f"Validation Dice score: {metric}") if best_dice < metric <= 1: best_dice = metric @@ -799,7 +801,7 @@ def train(self): epoch=epoch, loss_1_values={ "SoftNCuts": ncuts_losses, - "Dice metric": metric, + "Dice metric": dice_values, }, loss_2_values=rec_losses, weights=model.state_dict(), From 1283b08fb211b596835c981641a06e5f4b051f7f Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 31 Jul 2023 12:42:58 +0200 Subject: [PATCH 175/250] Updated validation UI --- .../code_models/worker_training.py | 18 ++++++---- .../code_plugins/plugin_model_training.py | 36 ++++++++++--------- 2 files changed, 32 insertions(+), 22 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 2ad1d10a..5a0121bc 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -698,7 +698,9 @@ def train(self): roi_size=[64, 64, 64], sw_batch_size=1, predictor=model.forward_encoder, - overlap=0, + overlap=0.1, + mode="gaussian", + sigma_scale=0.01, progress=True, ) val_decoder_outputs = sliding_window_inference( @@ -706,7 +708,9 @@ def train(self): roi_size=[64, 64, 64], sw_batch_size=1, predictor=model.forward_decoder, - overlap=0, + overlap=0.1, + mode="gaussian", + sigma_scale=0.01, progress=True, ) val_outputs = AsDiscrete(threshold=0.5)( @@ -787,14 +791,14 @@ def train(self): "data": np.squeeze(enc_out_val), "cmap": "turbo", }, - "Labels": { - "data": np.squeeze(lab_out_val), - "cmap": "bop blue", - }, "Inputs": { "data": np.squeeze(val_in), "cmap": "inferno", }, + "Labels": { + "data": np.squeeze(lab_out_val), + "cmap": "bop blue", + }, } yield TrainingReport( @@ -1414,6 +1418,8 @@ def get_loader_func(num_samples): sw_batch_size=self.config.batch_size, predictor=model, overlap=0.25, + mode="gaussian", + sigma_scale=0.01, sw_device=self.config.device, device=self.config.device, progress=False, diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 4a0bb272..f94ba961 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -1259,6 +1259,7 @@ def _remove_result_layers(self): self.result_layers = [] def _display_results(self, images_dict, complete_missing=False): + """Show various model input/outputs in napari viewer as a list of layers""" layer_list = [] if not complete_missing: for layer_name in list(images_dict.keys()): @@ -1291,9 +1292,8 @@ def _display_results(self, images_dict, complete_missing=False): "data" ] self.result_layers[i].refresh() - # self.result_layers[ - # i - # ].contrast_limits.reset_contrast_limits_range() + clims = self.result_layers[i].contrast_limits + [c.reset_contrast_limits_range() for c in clims] def on_yield(self, report: TrainingReport): # TODO refactor for dict # logger.info( @@ -1395,6 +1395,17 @@ def _make_csv(self): ) self.df.to_csv(path, index=False) + def _show_plot_max(self, plot, y): + x_max = (np.argmax(y) + 1) * self.worker_config.validation_interval + dice_max = np.max(y) + plot.scatter( + x_max, + dice_max, + c="r", + label="Max. Dice.", + zorder=5, + ) + def _plot_loss( self, loss_values_1: dict, @@ -1414,7 +1425,7 @@ def _plot_loss( self.plot_1.set_ylabel(self.plot_2_labels["ylabel"][plot_key]) for metric_name in list(loss_values_1.keys()): - if metric_name == "Dice coefficient": + if metric_name == "Dice metric": x = [ self.worker_config.validation_interval * (i + 1) for i in range(len(loss_values_1[metric_name])) @@ -1423,7 +1434,10 @@ def _plot_loss( x = [i + 1 for i in range(len(loss_values_1[metric_name]))] y = loss_values_1[metric_name] self.plot_1.plot(x, y, label=metric_name) - self.plot_1.legend(loc="lower right") + if metric_name == "Dice metric": + self._show_plot_max(self.plot_1, y) + + self.plot_1.legend(loc="best") # update plot 2 if self._is_current_job_supervised(): @@ -1442,17 +1456,7 @@ def _plot_loss( self.plot_2.set_ylabel(self.plot_2_labels["ylabel"][plot_key]) if show_plot_2_max: - epoch_min = ( - np.argmax(y) + 1 - ) * self.worker_config.validation_interval - dice_min = np.max(y) - self.plot_2.scatter( - epoch_min, - dice_min, - c="r", - label="Maximum Dice coeff.", - zorder=5, - ) + self._show_plot_max(self.plot_2, y) self.plot_2.legend(facecolor=ui.napari_grey, loc="lower right") self.canvas.draw_idle() From 646c5a8930c271d8d3d92e1b698fd5ec77e4c01f Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 31 Jul 2023 12:54:39 +0200 Subject: [PATCH 176/250] Tooltips and show_results update --- .../code_models/worker_training.py | 6 ++--- .../code_plugins/plugin_model_training.py | 23 +++++++++---------- 2 files changed, 14 insertions(+), 15 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 5a0121bc..5b6169e5 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -654,7 +654,7 @@ def train(self): self.log(f"Ncuts loss: {ncuts_losses[-1]:.5f}") self.log(f"Reconstruction loss: {rec_losses[-1]:.5f}") - self.log(f"Sum of losses: {total_losses[-1]:.5f}") + self.log(f"Weighted sum of losses: {total_losses[-1]:.5f}") if epoch > 0: self.log( f"Ncuts loss difference: {ncuts_losses[-1] - ncuts_losses[-2]:.5f}" @@ -663,7 +663,7 @@ def train(self): f"Reconstruction loss difference: {rec_losses[-1] - rec_losses[-2]:.5f}" ) self.log( - f"Sum of losses difference: {total_losses[-1] - total_losses[-2]:.5f}" + f"Weighted sum of losses difference: {total_losses[-1] - total_losses[-2]:.5f}" ) # Update the learning rate @@ -757,7 +757,7 @@ def train(self): # aggregate the final mean dice result metric = dice_metric.aggregate().item() dice_values.append(metric) - self.log(f"Validation Dice score: {metric}") + self.log(f"Validation Dice score: {metric:.3f}") if best_dice < metric <= 1: best_dice = metric # save the best model diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index f94ba961..b7da5a01 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -1,7 +1,7 @@ import shutil from functools import partial from pathlib import Path -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, List import matplotlib.pyplot as plt import numpy as np @@ -149,7 +149,7 @@ def __init__( """Plot for dice metric""" self.plot_dock = None """Docked widget with plots""" - self.result_layers = [] + self.result_layers: List[napari.layers.Layer] = [] """Layers to display checkpoint""" self.plot_1_labels = { @@ -323,9 +323,9 @@ def __init__( def _set_tooltips(self): # tooltips self.zip_choice.setToolTip( - "Checking this will save a copy of the results as a zip folder" + "Save a copy of the results as a zip folder" ) - self.validation_percent_choice.tooltips = "Choose the proportion of images to retain for training.\nThe remaining images will be used for validation" + self.validation_percent_choice.tooltips = "The percentage of images to retain for training.\nThe remaining images will be used for validation" self.epoch_choice.tooltips = "The number of epochs to train for.\nThe more you train, the better the model will fit the training data" self.loss_choice.setToolTip( "The loss function to use for training.\nSee the list in the training guide for more info" @@ -335,10 +335,10 @@ def _set_tooltips(self): ) self.batch_choice.tooltips = ( "The batch size to use for training.\n A larger value will feed more images per iteration to the model,\n" - " which is faster and possibly improves performance, but uses more memory" + " which is faster and can improve performance, but uses more memory on your selected device" ) self.val_interval_choice.tooltips = ( - "The number of epochs to perform before validating data.\n " + "The number of epochs to perform before validating on test data.\n " "The lower the value, the more often the score of the model will be computed and the more often the weights will be saved." ) self.learning_rate_choice.setToolTip( @@ -352,19 +352,19 @@ def _set_tooltips(self): ) self.augment_choice.setToolTip( "Check this to enable data augmentation, which will randomly deform, flip and shift the intensity in images" - " to provide a more general dataset. \nUse this if you're extracting more than 10 samples per image" + " to provide a more diverse dataset" ) [ w.setToolTip("Size of the sample to extract") for w in self.patch_size_widgets ] self.patch_choice.setToolTip( - "Check this to automatically crop your images in smaller, cubic images for training." - "\nShould be used if you have a small dataset (and large images)" + "Check this to automatically crop your images into smaller, cubic images for training." + "\nShould be used if you have a few large images" ) self.use_deterministic_choice.setToolTip( "Enable deterministic training for reproducibility." - "Using the same seed with all other parameters being similar should yield the exact same results between two runs." + "Using the same seed with all other parameters being similar should yield the exact same results across runs." ) self.use_transfer_choice.setToolTip( "Use this you want to initialize the model with pre-trained weights or use your own weights." @@ -1292,8 +1292,7 @@ def _display_results(self, images_dict, complete_missing=False): "data" ] self.result_layers[i].refresh() - clims = self.result_layers[i].contrast_limits - [c.reset_contrast_limits_range() for c in clims] + self.result_layers[i].reset_contrast_limits() def on_yield(self, report: TrainingReport): # TODO refactor for dict # logger.info( From a6964ab11cbc5b3b237cea1c23ac48a20f6bcbd1 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 31 Jul 2023 12:58:23 +0200 Subject: [PATCH 177/250] Plots update --- napari_cellseg3d/code_plugins/plugin_model_training.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index b7da5a01..c6f3c26d 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -1435,8 +1435,8 @@ def _plot_loss( self.plot_1.plot(x, y, label=metric_name) if metric_name == "Dice metric": self._show_plot_max(self.plot_1, y) - - self.plot_1.legend(loc="best") + if len(loss_values_1.keys()) > 1: + self.plot_1.legend(loc="best", fontsize="10", markerscale=0.6) # update plot 2 if self._is_current_job_supervised(): @@ -1520,7 +1520,6 @@ def update_loss_plot(self, loss_1: dict, loss_2: list): with plt.style.context("dark_background"): self.plot_1.cla() self.plot_2.cla() - self._plot_loss(loss_1, loss_2, show_plot_2_max=plot_max) def _reset_loss_plot(self): From 1eed4ead4b9c48986091f53d4111ffdb16512192 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 31 Jul 2023 13:26:48 +0200 Subject: [PATCH 178/250] Plot + log_parameters --- .../code_models/worker_training.py | 53 +++++++++++++++++-- .../code_plugins/plugin_model_training.py | 7 ++- 2 files changed, 53 insertions(+), 7 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 5b6169e5..86e940b2 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -388,6 +388,48 @@ def _get_data(self): eval_dataloader = None return dataloader, eval_dataloader, data_shape + def log_parameters(self): + self.log("*" * 20) + self.log("-- Parameters --") + self.log(f"Device: {self.config.device}") + self.log(f"Batch size: {self.config.batch_size}") + self.log(f"Epochs: {self.config.max_epochs}") + self.log(f"Learning rate: {self.config.learning_rate}") + self.log(f"Validation interval: {self.config.validation_interval}") + if self.config.weights_info.custom: + self.log(f"Custom weights: {self.config.weights_info.path}") + elif self.config.weights_info.use_pretrained: + self.log(f"Pretrained weights: {self.config.weights_info.path}") + if self.config.sampling: + self.log( + f"Using {self.config.num_samples} samples of size {self.config.sample_size}" + ) + if self.config.do_augmentation: + self.log("Using data augmentation") + ############## + self.log("-- Model --") + self.log(f"Using {self.config.num_classes} classes") + self.log(f"Weight decay: {self.config.weight_decay}") + self.log("* NCuts : ") + self.log(f"- Insensity sigma {self.config.intensity_sigma}") + self.log(f"- Spatial sigma {self.config.spatial_sigma}") + self.log(f"- Radius : {self.config.radius}") + self.log(f"* Reconstruction loss : {self.config.reconstruction_loss}") + self.log( + f"Weighted sum : {self.config.n_cuts_weight}*Ncuts + {self.config.rec_loss_weight}*Reconstruction" + ) + ############## + self.log("-- Data --") + self.log("Training data :") + [self.log(f"\n{v}") for k, v in self.config.train_data_dict.items()] + if self.config.eval_volume_dict is not None: + self.log("Validation data :") + [ + self.log(f"\n{k}: {v}") + for d in self.config.eval_volume_dict + for k, v in d.items() + ] + def train(self): try: if self.config is None: @@ -411,8 +453,9 @@ def train(self): self.log(f"Using device: {device}") - self.log("Config:") # FIXME log_parameters func instead - [self.log(str(a)) for a in self.config.__dict__.items()] + # self.log("Config:") # FIXME log_parameters func instead + # [self.log(str(a)) for a in self.config.__dict__.items()] + self.log_parameters() self.log("Initializing training...") self.log("Getting the data") @@ -783,11 +826,11 @@ def train(self): val_in = val_inputs[0].detach().cpu().numpy() display_dict = { - "Decoder output": { + "Reconstruction": { "data": np.squeeze(dec_out_val), "cmap": "gist_earth", }, - "Encoder output": { + "Segmentation": { "data": np.squeeze(enc_out_val), "cmap": "turbo", }, @@ -820,7 +863,7 @@ def train(self): * (self.config.max_epochs / (epoch + 1) - 1) / 60 ) - self.log(f"ETA: {eta:.2f} minutes") + self.log(f"ETA: {eta:.1f} minutes") self.log("-" * 20) # Save the model diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index c6f3c26d..8d570525 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -1036,7 +1036,8 @@ def _set_worker_config( self.weights_config.path = self.weights_config.path self.weights_config.custom = self.custom_weights_choice.isChecked() self.weights_config.use_pretrained = ( - not self.use_transfer_choice.isChecked() + self.use_transfer_choice.isChecked() + and not self.custom_weights_choice.isChecked() ) deterministic_config = config.DeterministicConfig( enabled=self.use_deterministic_choice.isChecked(), @@ -1436,7 +1437,9 @@ def _plot_loss( if metric_name == "Dice metric": self._show_plot_max(self.plot_1, y) if len(loss_values_1.keys()) > 1: - self.plot_1.legend(loc="best", fontsize="10", markerscale=0.6) + self.plot_1.legend( + loc="lower left", fontsize="10", markerscale=0.6 + ) # update plot 2 if self._is_current_job_supervised(): From 79724dd6776288af9f5c60472af8cb452f942131 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 31 Jul 2023 13:28:28 +0200 Subject: [PATCH 179/250] Update worker_training.py --- napari_cellseg3d/code_models/worker_training.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 86e940b2..7e982444 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -421,7 +421,11 @@ def log_parameters(self): ############## self.log("-- Data --") self.log("Training data :") - [self.log(f"\n{v}") for k, v in self.config.train_data_dict.items()] + [ + self.log(f"\n{v}") + for d in self.config.train_data_dict + for k, v in d.items() + ] if self.config.eval_volume_dict is not None: self.log("Validation data :") [ From 7f3a118931de4bf05c30b80769fd381025bb7fd0 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 31 Jul 2023 13:34:52 +0200 Subject: [PATCH 180/250] Disable WANDB for now + log param tweaks --- .../code_models/worker_training.py | 45 ++++++++----------- .../code_plugins/plugin_model_training.py | 2 +- 2 files changed, 20 insertions(+), 27 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 7e982444..18231636 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -68,8 +68,6 @@ logger.debug(f"PRETRAINED WEIGHT DIR LOCATION : {PRETRAINED_WEIGHTS_DIR}") try: - import wandb - WANDB_INSTALLED = True except ImportError: logger.warning( @@ -411,25 +409,25 @@ def log_parameters(self): self.log(f"Using {self.config.num_classes} classes") self.log(f"Weight decay: {self.config.weight_decay}") self.log("* NCuts : ") - self.log(f"- Insensity sigma {self.config.intensity_sigma}") + self.log(f"- Intensity sigma {self.config.intensity_sigma}") self.log(f"- Spatial sigma {self.config.spatial_sigma}") self.log(f"- Radius : {self.config.radius}") self.log(f"* Reconstruction loss : {self.config.reconstruction_loss}") self.log( - f"Weighted sum : {self.config.n_cuts_weight}*Ncuts + {self.config.rec_loss_weight}*Reconstruction" + f"Weighted sum : {self.config.n_cuts_weight}*NCuts + {self.config.rec_loss_weight}*Reconstruction" ) ############## self.log("-- Data --") self.log("Training data :") [ - self.log(f"\n{v}") + self.log(f"{v}") for d in self.config.train_data_dict for k, v in d.items() ] if self.config.eval_volume_dict is not None: self.log("Validation data :") [ - self.log(f"\n{k}: {v}") + self.log(f"{k}: {v}") for d in self.config.eval_volume_dict for k, v in d.items() ] @@ -443,9 +441,9 @@ def train(self): set_track_meta(False) ############## # if WANDB_INSTALLED: - # wandb.init( - # config=WANDB_CONFIG, project="WNet-benchmark", mode=WANDB_MODE - # ) + # wandb.init( + # config=WANDB_CONFIG, project="WNet-benchmark", mode=WANDB_MODE + # ) set_determinism( seed=self.config.deterministic_config.seed @@ -455,12 +453,8 @@ def train(self): normalize_function = utils.remap_image device = self.config.device - self.log(f"Using device: {device}") - - # self.log("Config:") # FIXME log_parameters func instead - # [self.log(str(a)) for a in self.config.__dict__.items()] + # self.log(f"Using device: {device}") self.log_parameters() - self.log("Initializing training...") self.log("Getting the data") @@ -473,7 +467,6 @@ def train(self): # Training the model # ################################################### self.log("Initializing the model:") - self.log("- Getting the model") # Initialize the model model = WNet( @@ -494,8 +487,8 @@ def train(self): ) ) - if WANDB_INSTALLED: - wandb.watch(model, log_freq=100) + # if WANDB_INSTALLED: + # wandb.watch(model, log_freq=100) if self.config.weights_info.custom: if self.config.weights_info.use_pretrained: @@ -619,10 +612,10 @@ def train(self): ) epoch_rec_loss += reconstruction_loss.item() - if WANDB_INSTALLED: - wandb.log( - {"Reconstruction loss": reconstruction_loss.item()} - ) + # if WANDB_INSTALLED: + # wandb.log( + # {"Reconstruction loss": reconstruction_loss.item()} + # ) # Backward pass for the reconstruction loss optimizer.zero_grad() @@ -631,8 +624,8 @@ def train(self): loss = alpha * Ncuts + beta * reconstruction_loss epoch_loss += loss.item() - if WANDB_INSTALLED: - wandb.log({"Weighted sum of losses": loss.item()}) + # if WANDB_INSTALLED: + # wandb.log({"Weighted sum of losses": loss.item()}) loss.backward(loss) optimizer.step() @@ -818,9 +811,9 @@ def train(self): self.log(f"Saving new best model to {save_path}") torch.save(model.state_dict(), save_path) - if WANDB_INSTALLED: - # log validation dice score for each validation round - wandb.log({"val/dice_metric": metric}) + # if WANDB_INSTALLED: + # log validation dice score for each validation round + # wandb.log({"val/dice_metric": metric}) dec_out_val = ( val_decoder_outputs[0].detach().cpu().numpy() diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 8d570525..799ab3e0 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -1402,7 +1402,7 @@ def _show_plot_max(self, plot, y): x_max, dice_max, c="r", - label="Max. Dice.", + label="Max. Dice", zorder=5, ) From 385552b0a23e8d38d4cde198d7e1a978ed6f50e1 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 31 Jul 2023 13:40:07 +0200 Subject: [PATCH 181/250] UI/log tweaks --- napari_cellseg3d/code_models/worker_training.py | 2 -- napari_cellseg3d/interface.py | 2 +- 2 files changed, 1 insertion(+), 3 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 18231636..2a7341d0 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -880,10 +880,8 @@ def train(self): # "best_metric_epoch": best_dice_epoch, # } # ) - self.log("*" * 50) # Save the model - print( "Saving the model to: ", self.config.results_path_folder + "/wnet.pth", diff --git a/napari_cellseg3d/interface.py b/napari_cellseg3d/interface.py index 7d1ec7c5..4efd2269 100644 --- a/napari_cellseg3d/interface.py +++ b/napari_cellseg3d/interface.py @@ -493,7 +493,7 @@ def __init__( elif self._divide_factor == 10: self._value_label.setFixedWidth(30) else: - self._value_label.setFixedWidth(40) + self._value_label.setFixedWidth(60) self._value_label.setAlignment(Qt.AlignCenter) self._value_label.setSizePolicy( QSizePolicy.Policy.Fixed, QSizePolicy.Policy.Fixed From c54ee268b65c38e5b2de854e0b05ff933b92c55b Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 31 Jul 2023 13:55:22 +0200 Subject: [PATCH 182/250] Functional WNet training --- napari_cellseg3d/code_models/worker_training.py | 7 ------- .../code_plugins/plugin_model_training.py | 12 ++++-------- 2 files changed, 4 insertions(+), 15 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 2a7341d0..91aafa69 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -86,13 +86,6 @@ # https://www.pythoncentral.io/pysidepyqt-tutorial-creating-your-own-signals-and-slots/ # https://napari-staging-site.github.io/guides/stable/threading.html -# TODO list for WNet training : -# 1. Create a custom base worker for training to avoid code duplication -# 2. Create a custom worker for WNet training -# 3. Adapt UI for WNet training (Advanced tab + model choice on first tab) -# 4. Adapt plots and TrainingReport for WNet training -# 5. log_parameters function - class TrainingWorkerBase(GeneratorWorker): """A basic worker abstract class, to run training jobs in. diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 799ab3e0..cec77f76 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -1295,13 +1295,9 @@ def _display_results(self, images_dict, complete_missing=False): self.result_layers[i].refresh() self.result_layers[i].reset_contrast_limits() - def on_yield(self, report: TrainingReport): # TODO refactor for dict - # logger.info( - # f"\nCatching results : for epoch {data['epoch']}, - # loss is {data['losses']} and validation is {data['val_metrics']}" - # ) + def on_yield(self, report: TrainingReport): if report == TrainingReport(): - return + return # skip empty reports if report.show_plot: try: @@ -1375,7 +1371,7 @@ def _make_csv(self): dice_metric = self.loss_1_values["Dice metric"] self.df = pd.DataFrame( { - "epoch": size_column, + "Epoch": size_column, "Ncuts loss": ncuts_loss, "Dice metric": dice_metric, "Reconstruction loss": self.loss_2_values, @@ -1384,7 +1380,7 @@ def _make_csv(self): except KeyError: self.df = pd.DataFrame( { - "epoch": size_column, + "Epoch": size_column, "Ncuts loss": ncuts_loss, "Reconstruction loss": self.loss_2_values, } From 1f7c9ede5bce875b4f40f19ccc9bd81b7cec64f5 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 31 Jul 2023 14:26:38 +0200 Subject: [PATCH 183/250] Clean exit / free memory attempt --- .../code_models/worker_training.py | 49 +++++++++++++++++-- .../code_plugins/plugin_model_training.py | 22 ++------- 2 files changed, 50 insertions(+), 21 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 91aafa69..f797b952 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -629,6 +629,20 @@ def train(self): # or self.config.scheduler == "CyclicLR" # ): # scheduler.step() + if self._abort_requested: + dataloader = None + del dataloader + eval_dataloader = None + del eval_dataloader + model = None + del model + optimizer = None + del optimizer + criterionE = None + del criterionE + criterionW = None + del criterionW + torch.cuda.empty_cache() yield TrainingReport( show_plot=False, weights=model.state_dict() @@ -848,6 +862,21 @@ def train(self): # reset the status for next validation round dice_metric.reset() + if self._abort_requested: + dataloader = None + del dataloader + eval_dataloader = None + del eval_dataloader + model = None + del model + optimizer = None + del optimizer + criterionE = None + del criterionE + criterionW = None + del criterionW + torch.cuda.empty_cache() + eta = ( (time.time() - startTime) * (self.config.max_epochs / (epoch + 1) - 1) @@ -875,9 +904,8 @@ def train(self): # ) # Save the model - print( - "Saving the model to: ", - self.config.results_path_folder + "/wnet.pth", + self.log( + f"Saving the model to: {self.config.results_path_folder}/wnet.pth", ) torch.save( model.state_dict(), @@ -894,7 +922,20 @@ def train(self): # model_artifact.add_file(self.config.save_model_path) # wandb.log_artifact(model_artifact) - return ncuts_losses, rec_losses, model + # return ncuts_losses, rec_losses, model + dataloader = None + del dataloader + eval_dataloader = None + del eval_dataloader + model = None + del model + optimizer = None + del optimizer + criterionE = None + del criterionE + criterionW = None + del criterionW + torch.cuda.empty_cache() except Exception as e: msg = f"Training failed with exception: {e}" self.log(msg) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index cec77f76..9a7027ed 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -124,7 +124,7 @@ def __init__( self.worker_config = None self.data = None """Data dictionary containing file paths""" - self.stop_requested = False + self._stop_requested = False """Whether the worker should stop or not""" self.start_time = None """Start time of the latest job""" @@ -926,7 +926,7 @@ def start(self): """ self.start_time = utils.get_time_filepath() - if self.stop_requested: + if self._stop_requested: self.log.print_and_log("Worker is already stopping !") return @@ -987,7 +987,7 @@ def start(self): self.log.print_and_log( f"Stop requested at {utils.get_time()}. \nWaiting for next yielding step..." ) - self.stop_requested = True + self._stop_requested = True self.start_btn.setText("Stopping... Please wait") self.log.print_and_log("*" * 20) self.worker.quit() @@ -1230,23 +1230,11 @@ def on_finish(self): ) self.worker = None - # if zipfile.is_zipfile(self.results_path_folder+".zip"): - - # if not shutil.rmtree.avoids_symlink_attacks: - # raise RuntimeError("shutil.rmtree is not safe on this platform") - - # shutil.rmtree(self.results_path_folder) - - # self.results_path_folder = "" - - # self.clean_cache() # trying to fix memory leak def on_error(self): """Catches errored signal from worker""" self.log.print_and_log(f"WORKER ERRORED at {utils.get_time()}") self.worker = None - # self.empty_cuda_cache() - # self.clean_cache() def on_stop(self): self._remove_result_layers() @@ -1325,7 +1313,7 @@ def on_yield(self, report: TrainingReport): self.loss_1_values = report.loss_1_values self.loss_2_values = report.loss_2_values - if self.stop_requested: + if self._stop_requested: self.log.print_and_log( "Saving weights from aborted training in results folder" ) @@ -1338,7 +1326,7 @@ def on_yield(self, report: TrainingReport): ) self.log.print_and_log("Saving complete") self.on_stop() - self.stop_requested = False + self._stop_requested = False def _make_csv(self): size_column = range(1, self.worker_config.max_epochs + 1) From 7bb5edc04845997f9f72d3e3d88d9c14fb613978 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 31 Jul 2023 15:25:52 +0200 Subject: [PATCH 184/250] Cleanup + tests - Removed previous train script - Fix tests - Enable test workflow on GH --- .github/workflows/test_and_deploy.yml | 1 + napari_cellseg3d/_tests/test_models.py | 2 +- .../_tests/test_unsup_training.py | 5 +- napari_cellseg3d/_tests/test_wnet_training.py | 25 - .../code_models/models/wnet/train_wnet.py | 992 ------------------ 5 files changed, 5 insertions(+), 1020 deletions(-) delete mode 100644 napari_cellseg3d/code_models/models/wnet/train_wnet.py diff --git a/.github/workflows/test_and_deploy.yml b/.github/workflows/test_and_deploy.yml index fafb1719..b6c9d848 100644 --- a/.github/workflows/test_and_deploy.yml +++ b/.github/workflows/test_and_deploy.yml @@ -7,6 +7,7 @@ on: push: branches: - main + - cy/wnet-train tags: - "v*" # Push events to matching v*, i.e. v1.0, v20.15.10 pull_request: diff --git a/napari_cellseg3d/_tests/test_models.py b/napari_cellseg3d/_tests/test_models.py index a9176fa4..89043ba9 100644 --- a/napari_cellseg3d/_tests/test_models.py +++ b/napari_cellseg3d/_tests/test_models.py @@ -115,7 +115,7 @@ def test_pretrained_weights_compatibility(): for model_name in MODEL_LIST: file_name = MODEL_LIST[model_name].weights_file WeightsDownloader().download_weights(model_name, file_name) - model = MODEL_LIST[model_name](input_img_size=[128, 128, 128]) + model = MODEL_LIST[model_name](input_img_size=[64, 64, 64]) try: model.load_state_dict( torch.load( diff --git a/napari_cellseg3d/_tests/test_unsup_training.py b/napari_cellseg3d/_tests/test_unsup_training.py index 3ebd4768..9b26167a 100644 --- a/napari_cellseg3d/_tests/test_unsup_training.py +++ b/napari_cellseg3d/_tests/test_unsup_training.py @@ -5,6 +5,7 @@ Trainer, ) + def test_unsupervised_worker(make_napari_viewer_proxy): im_path = Path(__file__).resolve().parent / "res/test.tif" # im_path_str = str(im_path) @@ -34,8 +35,8 @@ def test_unsupervised_worker(make_napari_viewer_proxy): assert eval_dataloader is None assert data_shape == (6, 6, 6) - widget.images_filepaths = [str(im_path.parent)] - widget.labels_filepaths = [str(im_path.parent)] + widget.images_filepaths = [str(im_path)] + widget.labels_filepaths = [str(im_path)] # widget.unsupervised_eval_data = widget.create_train_dataset_dict() worker = widget._create_worker(additional_results_description="TEST_3") dataloader, eval_dataloader, data_shape = worker._get_data() diff --git a/napari_cellseg3d/_tests/test_wnet_training.py b/napari_cellseg3d/_tests/test_wnet_training.py index afc71479..e69de29b 100644 --- a/napari_cellseg3d/_tests/test_wnet_training.py +++ b/napari_cellseg3d/_tests/test_wnet_training.py @@ -1,25 +0,0 @@ -####################################################### -# Disabled as it takes too much memory for GH actions # -####################################################### - - -# from pathlib import Path -# from napari_cellseg3d.code_models.models.wnet import train_wnet as t -# -# def test_wnet_training(): -# config = t.Config() -# -# config.batch_size = 1 -# config.num_epochs = 1 -# -# config.train_volume_directory = str(Path(__file__).resolve().parent / "res/wnet_test") -# config.eval_volume_directory = config.train_volume_directory -# config.save_every = 1 -# config.val_interval = 2 # skip validation -# config.save_model_path = config.train_volume_directory + "/test.pth" -# -# ncuts_loss, rec_loss, model = t.train(train_config=config) -# -# assert ncuts_loss is not None -# assert rec_loss is not None -# assert model is not None diff --git a/napari_cellseg3d/code_models/models/wnet/train_wnet.py b/napari_cellseg3d/code_models/models/wnet/train_wnet.py deleted file mode 100644 index d999fc17..00000000 --- a/napari_cellseg3d/code_models/models/wnet/train_wnet.py +++ /dev/null @@ -1,992 +0,0 @@ -# """ -# This file contains the code to train the WNet model. -# """ -# # import napari -# import glob -# import time -# from pathlib import Path -# from warnings import warn -# -# import numpy as np -# import tifffile as tiff -# import torch -# import torch.nn as nn -# -# # MONAI -# from monai.data import ( -# CacheDataset, -# DataLoader, -# PatchDataset, -# pad_list_data_collate, -# ) -# from monai.data.meta_obj import set_track_meta -# from monai.metrics import DiceMetric -# from monai.transforms import ( -# AsDiscrete, -# Compose, -# EnsureChannelFirst, -# EnsureChannelFirstd, -# EnsureTyped, -# LoadImaged, -# Orientationd, -# RandFlipd, -# RandRotate90d, -# RandShiftIntensityd, -# RandSpatialCropSamplesd, -# ScaleIntensityRanged, -# SpatialPadd, -# ToTensor, -# ) -# from monai.utils.misc import set_determinism -# -# # local -# from napari_cellseg3d.code_models.models.wnet.model import WNet -# from napari_cellseg3d.code_models.models.wnet.soft_Ncuts import SoftNCutsLoss -# from napari_cellseg3d.utils import LOGGER as logger -# from napari_cellseg3d.utils import dice_coeff, get_padding_dim, remap_image -# -# try: -# import wandb -# -# WANDB_INSTALLED = True -# except ImportError: -# warn( -# "wandb not installed, wandb config will not be taken into account", -# stacklevel=1, -# ) -# WANDB_INSTALLED = False -# -# __author__ = "Yves Paychère, Colin Hofmann, Cyril Achard" -# -# -# ########################## -# # Utils functions # -# ########################## -# -# -# # def create_dataset_dict(volume_directory, label_directory): -# # """Creates data dictionary for MONAI transforms and training.""" -# # images_filepaths = sorted( -# # [str(file) for file in Path(volume_directory).glob("*.tif")] -# # ) -# # -# # labels_filepaths = sorted( -# # [str(file) for file in Path(label_directory).glob("*.tif")] -# # ) -# # if len(images_filepaths) == 0 or len(labels_filepaths) == 0: -# # raise ValueError( -# # f"Data folders are empty \n{volume_directory} \n{label_directory}" -# # ) -# # -# # logger.info("Images :") -# # for file in images_filepaths: -# # logger.info(Path(file).stem) -# # logger.info("*" * 10) -# # logger.info("Labels :") -# # for file in labels_filepaths: -# # logger.info(Path(file).stem) -# # try: -# # data_dicts = [ -# # {"image": image_name, "label": label_name} -# # for image_name, label_name in zip( -# # images_filepaths, labels_filepaths -# # ) -# # ] -# # except ValueError as e: -# # raise ValueError( -# # f"Number of images and labels does not match : \n{volume_directory} \n{label_directory}" -# # ) from e -# # # print(f"Loaded eval image: {data_dicts}") -# # return data_dicts -# -# -# def create_dataset_dict_no_labs(volume_directory): -# """Creates unsupervised data dictionary for MONAI transforms and training.""" -# images_filepaths = sorted(glob.glob(str(Path(volume_directory) / "*.tif"))) -# if len(images_filepaths) == 0: -# raise ValueError(f"Data folder {volume_directory} is empty") -# -# logger.info("Images :") -# for file in images_filepaths: -# logger.info(Path(file).stem) -# logger.info("*" * 10) -# -# return [{"image": image_name} for image_name in images_filepaths] -# -# -# ################################ -# # WNet: Config & WANDB # -# ################################ -# -# -# class WNetTrainingWorkerConfig: -# def __init__(self): -# # WNet -# self.in_channels = 1 -# self.out_channels = 1 -# self.num_classes = 2 -# self.dropout = 0.65 -# self.use_clipping = False -# self.clipping = 1 -# -# self.lr = 1e-6 -# self.scheduler = "None" # "CosineAnnealingLR" # "ReduceLROnPlateau" -# self.weight_decay = 0.01 # None -# -# self.intensity_sigma = 1 -# self.spatial_sigma = 4 -# self.radius = 2 # yields to a radius depending on the data shape -# -# self.n_cuts_weight = 0.5 -# self.reconstruction_loss = "MSE" # "BCE" -# self.rec_loss_weight = 0.5 / 100 -# -# self.num_epochs = 100 -# self.val_interval = 5 -# self.batch_size = 2 -# -# # Data -# # self.train_volume_directory = "./../dataset/VIP_full" -# # self.eval_volume_directory = "./../dataset/VIP_cropped/eval/" -# self.normalize_input = True -# self.normalizing_function = remap_image # normalize_quantile -# # self.use_patch = False -# # self.patch_size = (64, 64, 64) -# # self.num_patches = 30 -# # self.eval_num_patches = 20 -# # self.do_augmentation = True -# # self.parallel = False -# -# # self.save_model = True -# self.save_model_path = ( -# r"./../results/new_model/wnet_new_model_all_data_3class.pth" -# ) -# # self.save_losses_path = ( -# # r"./../results/new_model/wnet_new_model_all_data_3class.pkl" -# # ) -# self.save_every = 5 -# self.weights_path = None -# -# -# c = WNetTrainingWorkerConfig() -# ############### -# # Scheduler config -# ############### -# schedulers = { -# "ReduceLROnPlateau": { -# "factor": 0.5, -# "patience": 50, -# }, -# "CosineAnnealingLR": { -# "T_max": 25000, -# "eta_min": 1e-8, -# }, -# "CosineAnnealingWarmRestarts": { -# "T_0": 50000, -# "eta_min": 1e-8, -# "T_mult": 1, -# }, -# "CyclicLR": { -# "base_lr": 2e-7, -# "max_lr": 2e-4, -# "step_size_up": 250, -# "mode": "triangular", -# }, -# } -# -# ############### -# # WANDB_CONFIG -# ############### -# WANDB_MODE = "disabled" -# # WANDB_MODE = "online" -# -# WANDB_CONFIG = { -# # data setting -# "num_workers": c.num_workers, -# "normalize": c.normalize_input, -# "use_patch": c.use_patch, -# "patch_size": c.patch_size, -# "num_patches": c.num_patches, -# "eval_num_patches": c.eval_num_patches, -# "do_augmentation": c.do_augmentation, -# "model_save_path": c.save_model_path, -# # train setting -# "batch_size": c.batch_size, -# "learning_rate": c.lr, -# "weight_decay": c.weight_decay, -# "scheduler": { -# "name": c.scheduler, -# "ReduceLROnPlateau_config": { -# "factor": schedulers["ReduceLROnPlateau"]["factor"], -# "patience": schedulers["ReduceLROnPlateau"]["patience"], -# }, -# "CosineAnnealingLR_config": { -# "T_max": schedulers["CosineAnnealingLR"]["T_max"], -# "eta_min": schedulers["CosineAnnealingLR"]["eta_min"], -# }, -# "CosineAnnealingWarmRestarts_config": { -# "T_0": schedulers["CosineAnnealingWarmRestarts"]["T_0"], -# "eta_min": schedulers["CosineAnnealingWarmRestarts"]["eta_min"], -# "T_mult": schedulers["CosineAnnealingWarmRestarts"]["T_mult"], -# }, -# "CyclicLR_config": { -# "base_lr": schedulers["CyclicLR"]["base_lr"], -# "max_lr": schedulers["CyclicLR"]["max_lr"], -# "step_size_up": schedulers["CyclicLR"]["step_size_up"], -# "mode": schedulers["CyclicLR"]["mode"], -# }, -# }, -# "max_epochs": c.num_epochs, -# "save_every": c.save_every, -# "val_interval": c.val_interval, -# # loss -# "reconstruction_loss": c.reconstruction_loss, -# "loss weights": { -# "n_cuts_weight": c.n_cuts_weight, -# "rec_loss_weight": c.rec_loss_weight, -# }, -# "loss_params": { -# "intensity_sigma": c.intensity_sigma, -# "spatial_sigma": c.spatial_sigma, -# "radius": c.radius, -# }, -# # model -# "model_type": "wnet", -# "model_params": { -# "in_channels": c.in_channels, -# "out_channels": c.out_channels, -# "num_classes": c.num_classes, -# "dropout": c.dropout, -# "use_clipping": c.use_clipping, -# "clipping_value": c.clipping, -# }, -# # CRF -# "crf_params": { -# "sa": c.sa, -# "sb": c.sb, -# "sg": c.sg, -# "w1": c.w1, -# "w2": c.w2, -# "n_iter": c.n_iter, -# }, -# } -# -# -# def train(weights_path=None, train_config=None): -# if train_config is None: -# config = WNetTrainingWorkerConfig() -# ############## -# # disable metadata tracking in MONAI -# set_track_meta(False) -# ############## -# if WANDB_INSTALLED: -# wandb.init( -# config=WANDB_CONFIG, project="WNet-benchmark", mode=WANDB_MODE -# ) -# -# set_determinism(seed=34936339) # use default seed from NP_MAX -# torch.use_deterministic_algorithms(True, warn_only=True) -# -# config = train_config -# normalize_function = config.normalizing_function -# CUDA = torch.cuda.is_available() -# device = torch.device("cuda" if CUDA else "cpu") -# -# print(f"Using device: {device}") -# -# print("Config:") -# [print(a) for a in config.__dict__.items()] -# -# print("Initializing training...") -# print("Getting the data") -# -# if config.use_patch: -# (data_shape, dataset) = get_patch_dataset(config) -# else: -# (data_shape, dataset) = get_dataset(config) -# transform = Compose( -# [ -# ToTensor(), -# EnsureChannelFirst(channel_dim=0), -# ] -# ) -# dataset = [transform(im) for im in dataset] -# for data in dataset: -# print(f"data shape: {data.shape}") -# break -# -# dataloader = DataLoader( -# dataset, -# batch_size=config.batch_size, -# shuffle=True, -# num_workers=config.num_workers, -# collate_fn=pad_list_data_collate, -# ) -# -# if config.eval_volume_directory is not None: -# # eval_dataset = get_patch_eval_dataset(config) -# eval_dataset = None -# -# eval_dataloader = DataLoader( -# eval_dataset, -# batch_size=config.batch_size, -# shuffle=False, -# num_workers=config.num_workers, -# collate_fn=pad_list_data_collate, -# ) -# -# dice_metric = DiceMetric( -# include_background=False, reduction="mean", get_not_nans=False -# ) -# ################################################### -# # Training the model # -# ################################################### -# print("Initializing the model:") -# -# print("- getting the model") -# # Initialize the model -# model = WNet( -# in_channels=config.in_channels, -# out_channels=config.out_channels, -# num_classes=config.num_classes, -# dropout=config.dropout, -# ) -# model = ( -# nn.DataParallel(model).cuda() if CUDA and config.parallel else model -# ) -# model.to(device) -# -# if config.use_clipping: -# for p in model.parameters(): -# p.register_hook( -# lambda grad: torch.clamp( -# grad, min=-config.clipping, max=config.clipping -# ) -# ) -# -# if WANDB_INSTALLED: -# wandb.watch(model, log_freq=100) -# -# if weights_path is not None: -# model.load_state_dict(torch.load(weights_path, map_location=device)) -# -# print("- getting the optimizers") -# # Initialize the optimizers -# if config.weight_decay is not None: -# decay = config.weight_decay -# optimizer = torch.optim.Adam( -# model.parameters(), lr=config.lr, weight_decay=decay -# ) -# else: -# optimizer = torch.optim.Adam(model.parameters(), lr=config.lr) -# -# print("- getting the loss functions") -# # Initialize the Ncuts loss function -# criterionE = SoftNCutsLoss( -# data_shape=data_shape, -# device=device, -# intensity_sigma=config.intensity_sigma, -# spatial_sigma=config.spatial_sigma, -# radius=config.radius, -# ) -# -# if config.reconstruction_loss == "MSE": -# criterionW = nn.MSELoss() -# elif config.reconstruction_loss == "BCE": -# criterionW = nn.BCELoss() -# else: -# raise ValueError( -# f"Unknown reconstruction loss : {config.reconstruction_loss} not supported" -# ) -# -# print("- getting the learning rate schedulers") -# # Initialize the learning rate schedulers -# scheduler = get_scheduler(config, optimizer) -# # scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( -# # optimizer, mode="min", factor=0.5, patience=10, verbose=True -# # ) -# model.train() -# -# print("Ready") -# print("Training the model") -# print("*" * 50) -# -# startTime = time.time() -# ncuts_losses = [] -# rec_losses = [] -# total_losses = [] -# best_dice = -1 -# best_dice_epoch = -1 -# -# # Train the model -# for epoch in range(config.num_epochs): -# print(f"Epoch {epoch + 1} of {config.num_epochs}") -# -# epoch_ncuts_loss = 0 -# epoch_rec_loss = 0 -# epoch_loss = 0 -# -# for _i, batch in enumerate(dataloader): -# # raise NotImplementedError("testing") -# if config.use_patch: -# image = batch["image"].to(device) -# else: -# image = batch.to(device) -# if config.batch_size == 1: -# image = image.unsqueeze(0) -# else: -# image = image.unsqueeze(0) -# image = torch.swapaxes(image, 0, 1) -# -# # Forward pass -# enc = model.forward_encoder(image) -# # out = model.forward(image) -# -# # Compute the Ncuts loss -# Ncuts = criterionE(enc, image) -# epoch_ncuts_loss += Ncuts.item() -# if WANDB_INSTALLED: -# wandb.log({"Ncuts loss": Ncuts.item()}) -# -# # Forward pass -# enc, dec = model(image) -# -# # Compute the reconstruction loss -# if isinstance(criterionW, nn.MSELoss): -# reconstruction_loss = criterionW(dec, image) -# elif isinstance(criterionW, nn.BCELoss): -# reconstruction_loss = criterionW( -# torch.sigmoid(dec), -# remap_image(image, new_max=1), -# ) -# -# epoch_rec_loss += reconstruction_loss.item() -# if WANDB_INSTALLED: -# wandb.log({"Reconstruction loss": reconstruction_loss.item()}) -# -# # Backward pass for the reconstruction loss -# optimizer.zero_grad() -# alpha = config.n_cuts_weight -# beta = config.rec_loss_weight -# -# loss = alpha * Ncuts + beta * reconstruction_loss -# epoch_loss += loss.item() -# if WANDB_INSTALLED: -# wandb.log({"Sum of losses": loss.item()}) -# loss.backward(loss) -# optimizer.step() -# -# if config.scheduler == "CosineAnnealingWarmRestarts": -# scheduler.step(epoch + _i / len(dataloader)) -# if ( -# config.scheduler == "CosineAnnealingLR" -# or config.scheduler == "CyclicLR" -# ): -# scheduler.step() -# -# ncuts_losses.append(epoch_ncuts_loss / len(dataloader)) -# rec_losses.append(epoch_rec_loss / len(dataloader)) -# total_losses.append(epoch_loss / len(dataloader)) -# -# if WANDB_INSTALLED: -# wandb.log({"Ncuts loss_epoch": ncuts_losses[-1]}) -# wandb.log({"Reconstruction loss_epoch": rec_losses[-1]}) -# wandb.log({"Sum of losses_epoch": total_losses[-1]}) -# # wandb.log({"epoch": epoch}) -# # wandb.log({"learning_rate model": optimizerW.param_groups[0]["lr"]}) -# # wandb.log({"learning_rate encoder": optimizerE.param_groups[0]["lr"]}) -# wandb.log({"learning_rate model": optimizer.param_groups[0]["lr"]}) -# -# print("Ncuts loss: ", ncuts_losses[-1]) -# if epoch > 0: -# print( -# "Ncuts loss difference: ", -# ncuts_losses[-1] - ncuts_losses[-2], -# ) -# print("Reconstruction loss: ", rec_losses[-1]) -# if epoch > 0: -# print( -# "Reconstruction loss difference: ", -# rec_losses[-1] - rec_losses[-2], -# ) -# print("Sum of losses: ", total_losses[-1]) -# if epoch > 0: -# print( -# "Sum of losses difference: ", -# total_losses[-1] - total_losses[-2], -# ) -# -# # Update the learning rate -# if config.scheduler == "ReduceLROnPlateau": -# # schedulerE.step(epoch_ncuts_loss) -# # schedulerW.step(epoch_rec_loss) -# scheduler.step(epoch_rec_loss) -# if ( -# config.eval_volume_directory is not None -# and (epoch + 1) % config.val_interval == 0 -# ): -# model.eval() -# print("Validating...") -# with torch.no_grad(): -# for _k, val_data in enumerate(eval_dataloader): -# val_inputs, val_labels = ( -# val_data["image"].to(device), -# val_data["label"].to(device), -# ) -# -# # normalize val_inputs across channels -# if config.normalize_input: -# for i in range(val_inputs.shape[0]): -# for j in range(val_inputs.shape[1]): -# val_inputs[i][j] = normalize_function( -# val_inputs[i][j] -# ) -# -# val_outputs = model.forward_encoder(val_inputs) -# val_outputs = AsDiscrete(threshold=0.5)(val_outputs) -# -# # compute metric for current iteration -# for channel in range(val_outputs.shape[1]): -# max_dice_channel = torch.argmax( -# torch.Tensor( -# [ -# dice_coeff( -# y_pred=val_outputs[ -# :, -# channel : (channel + 1), -# :, -# :, -# :, -# ], -# y_true=val_labels, -# ) -# ] -# ) -# ) -# -# dice_metric( -# y_pred=val_outputs[ -# :, -# max_dice_channel : (max_dice_channel + 1), -# :, -# :, -# :, -# ], -# y=val_labels, -# ) -# # if plot_val_input: # only once -# # logged_image = val_inputs.detach().cpu().numpy() -# # logged_image = np.swapaxes(logged_image, 2, 4) -# # logged_image = logged_image[0, :, 32, :, :] -# # images = wandb.Image( -# # logged_image, caption="Validation input" -# # ) -# # -# # wandb.log({"val/input": images}) -# # plot_val_input = False -# -# # if k == 2 and (30 <= epoch <= 50 or epoch % 100 == 0): -# # logged_image = val_outputs.detach().cpu().numpy() -# # logged_image = np.swapaxes(logged_image, 2, 4) -# # logged_image = logged_image[ -# # 0, max_dice_channel, 32, :, : -# # ] -# # images = wandb.Image( -# # logged_image, caption="Validation output" -# # ) -# # -# # wandb.log({"val/output": images}) -# # dice_metric(y_pred=val_outputs[:, 2:, :,:,:], y=val_labels) -# # dice_metric(y_pred=val_outputs[:, 1:, :, :, :], y=val_labels) -# -# # import napari -# # view = napari.Viewer() -# # view.add_image(val_inputs.cpu().numpy(), name="input") -# # view.add_image(val_labels.cpu().numpy(), name="label") -# # vis_out = np.array( -# # [i.detach().cpu().numpy() for i in val_outputs], -# # dtype=np.float32, -# # ) -# # crf_out = np.array( -# # [i.detach().cpu().numpy() for i in crf_outputs], -# # dtype=np.float32, -# # ) -# # view.add_image(vis_out, name="output") -# # view.add_image(crf_out, name="crf_output") -# # napari.run() -# -# # aggregate the final mean dice result -# metric = dice_metric.aggregate().item() -# print("Validation Dice score: ", metric) -# if best_dice < metric < 2: -# best_dice = metric -# best_dice_epoch = epoch + 1 -# if config.save_model: -# save_best_path = Path(config.save_model_path).parents[ -# 0 -# ] -# save_best_path.mkdir(parents=True, exist_ok=True) -# save_best_name = Path(config.save_model_path).stem -# save_path = ( -# str(save_best_path / save_best_name) -# + "_best_metric.pth" -# ) -# print(f"Saving new best model to {save_path}") -# torch.save(model.state_dict(), save_path) -# -# if WANDB_INSTALLED: -# # log validation dice score for each validation round -# wandb.log({"val/dice_metric": metric}) -# -# # reset the status for next validation round -# dice_metric.reset() -# -# print( -# "ETA: ", -# (time.time() - startTime) -# * (config.num_epochs / (epoch + 1) - 1) -# / 60, -# "minutes", -# ) -# print("-" * 20) -# -# # Save the model -# if config.save_model and epoch % config.save_every == 0: -# torch.save(model.state_dict(), config.save_model_path) -# # with open(config.save_losses_path, "wb") as f: -# # pickle.dump((ncuts_losses, rec_losses), f) -# -# print("Training finished") -# print(f"Best dice metric : {best_dice}") -# if WANDB_INSTALLED and config.eval_volume_directory is not None: -# wandb.log( -# { -# "best_dice_metric": best_dice, -# "best_metric_epoch": best_dice_epoch, -# } -# ) -# print("*" * 50) -# -# # Save the model -# if config.save_model: -# print("Saving the model to: ", config.save_model_path) -# torch.save(model.state_dict(), config.save_model_path) -# # with open(config.save_losses_path, "wb") as f: -# # pickle.dump((ncuts_losses, rec_losses), f) -# if WANDB_INSTALLED: -# model_artifact = wandb.Artifact( -# "WNet", -# type="model", -# description="WNet benchmark", -# metadata=dict(WANDB_CONFIG), -# ) -# model_artifact.add_file(config.save_model_path) -# wandb.log_artifact(model_artifact) -# -# return ncuts_losses, rec_losses, model -# -# -# def get_dataset(config): -# """Creates a Dataset from the original data using the tifffile library -# -# Args: -# config (WNetTrainingWorkerConfig): The configuration object -# -# Returns: -# (tuple): A tuple containing the shape of the data and the dataset -# """ -# train_files = create_dataset_dict_no_labs( -# volume_directory=config.train_volume_directory -# ) -# train_files = [d.get("image") for d in train_files] -# # logger.debug(f"train_files: {train_files}") -# volumes = tiff.imread(train_files).astype(np.float32) -# volume_shape = volumes.shape -# # logger.debug(f"volume_shape: {volume_shape}") -# -# if len(volume_shape) == 3: -# volumes = np.expand_dims(volumes, axis=0) -# -# if config.normalize_input: -# volumes = np.array( -# [ -# # mad_normalization(volume) -# config.normalizing_function(volume) -# for volume in volumes -# ] -# ) -# # mean = volumes.mean(axis=0) -# # std = volumes.std(axis=0) -# # volumes = (volumes - mean) / std -# # print("NORMALIZED VOLUMES") -# # print(volumes.shape) -# # [print("MIN MAX", volume.flatten().min(), volume.flatten().max()) for volume in volumes] -# # print(volumes.mean(axis=0), volumes.std(axis=0)) -# -# dataset = CacheDataset(data=volumes) -# -# return (volume_shape, dataset) -# -# # train_files = create_dataset_dict_no_labs( -# # volume_directory=config.train_volume_directory -# # ) -# # train_files = [d.get("image") for d in train_files] -# # volumes = [] -# # for file in train_files: -# # image = tiff.imread(file).astype(np.float32) -# # image = np.expand_dims(image, axis=0) # add channel dimension -# # volumes.append(image) -# # # volumes = tiff.imread(train_files).astype(np.float32) -# # volume_shape = volumes[0].shape -# # # print(volume_shape) -# # -# # if config.do_augmentation: -# # augmentation = Compose( -# # [ -# # ScaleIntensityRange( -# # a_min=0, -# # a_max=2000, -# # b_min=0.0, -# # b_max=1.0, -# # clip=True, -# # ), -# # RandShiftIntensity(offsets=0.1, prob=0.5), -# # RandFlip(spatial_axis=[1], prob=0.5), -# # RandFlip(spatial_axis=[2], prob=0.5), -# # RandRotate90(prob=0.1, max_k=3), -# # ] -# # ) -# # else: -# # augmentation = None -# # -# # dataset = CacheDataset(data=np.array(volumes), transform=augmentation) -# # -# # return (volume_shape, dataset) -# -# -# def get_patch_dataset(config): -# """Creates a Dataset from the original data using the tifffile library -# -# Args: -# config (WNetTrainingWorkerConfig): The configuration object -# -# Returns: -# (tuple): A tuple containing the shape of the data and the dataset -# """ -# -# train_files = create_dataset_dict_no_labs( -# volume_directory=config.train_volume_directory -# ) -# -# patch_func = Compose( -# [ -# LoadImaged(keys=["image"], image_only=True), -# EnsureChannelFirstd(keys=["image"], channel_dim="no_channel"), -# RandSpatialCropSamplesd( -# keys=["image"], -# roi_size=( -# config.patch_size -# ), # multiply by axis_stretch_factor if anisotropy -# # max_roi_size=(120, 120, 120), -# random_size=False, -# num_samples=config.num_patches, -# ), -# Orientationd(keys=["image"], axcodes="PLI"), -# SpatialPadd( -# keys=["image"], -# spatial_size=(get_padding_dim(config.patch_size)), -# ), -# EnsureTyped(keys=["image"]), -# ] -# ) -# -# train_transforms = Compose( -# [ -# ScaleIntensityRanged( -# keys=["image"], -# a_min=0, -# a_max=2000, -# b_min=0.0, -# b_max=1.0, -# clip=True, -# ), -# RandShiftIntensityd(keys=["image"], offsets=0.1, prob=0.5), -# RandFlipd(keys=["image"], spatial_axis=[1], prob=0.5), -# RandFlipd(keys=["image"], spatial_axis=[2], prob=0.5), -# RandRotate90d(keys=["image"], prob=0.1, max_k=3), -# EnsureTyped(keys=["image"]), -# ] -# ) -# -# dataset = PatchDataset( -# data=train_files, -# samples_per_image=config.num_patches, -# patch_func=patch_func, -# transform=train_transforms, -# ) -# -# return config.patch_size, dataset -# -# -# # def get_patch_eval_dataset(config): -# # eval_files = create_dataset_dict( -# # volume_directory=config.eval_volume_directory + "/vol", -# # label_directory=config.eval_volume_directory + "/lab", -# # ) -# # -# # patch_func = Compose( -# # [ -# # LoadImaged(keys=["image", "label"], image_only=True), -# # EnsureChannelFirstd( -# # keys=["image", "label"], channel_dim="no_channel" -# # ), -# # # NormalizeIntensityd(keys=["image"]) if config.normalize_input else lambda x: x, -# # RandSpatialCropSamplesd( -# # keys=["image", "label"], -# # roi_size=( -# # config.patch_size -# # ), # multiply by axis_stretch_factor if anisotropy -# # # max_roi_size=(120, 120, 120), -# # random_size=False, -# # num_samples=config.eval_num_patches, -# # ), -# # Orientationd(keys=["image", "label"], axcodes="PLI"), -# # SpatialPadd( -# # keys=["image", "label"], -# # spatial_size=(get_padding_dim(config.patch_size)), -# # ), -# # EnsureTyped(keys=["image", "label"]), -# # ] -# # ) -# # -# # eval_transforms = Compose( -# # [ -# # EnsureTyped(keys=["image", "label"]), -# # ] -# # ) -# # -# # return PatchDataset( -# # data=eval_files, -# # samples_per_image=config.eval_num_patches, -# # patch_func=patch_func, -# # transform=eval_transforms, -# # ) -# -# -# def get_dataset_monai(config): -# """Creates a Dataset applying some transforms/augmentation on the data using the MONAI library -# -# Args: -# config (WNetTrainingWorkerConfig): The configuration object -# -# Returns: -# (tuple): A tuple containing the shape of the data and the dataset -# """ -# train_files = create_dataset_dict_no_labs( -# volume_directory=config.train_volume_directory -# ) -# # print(train_files) -# # print(len(train_files)) -# # print(train_files[0]) -# first_volume = LoadImaged(keys=["image"])(train_files[0]) -# first_volume_shape = first_volume["image"].shape -# -# # Transforms to be applied to each volume -# load_single_images = Compose( -# [ -# LoadImaged(keys=["image"]), -# EnsureChannelFirstd(keys=["image"]), -# Orientationd(keys=["image"], axcodes="PLI"), -# SpatialPadd( -# keys=["image"], -# spatial_size=(get_padding_dim(first_volume_shape)), -# ), -# EnsureTyped(keys=["image"]), -# ] -# ) -# -# if config.do_augmentation: -# train_transforms = Compose( -# [ -# ScaleIntensityRanged( -# keys=["image"], -# a_min=0, -# a_max=2000, -# b_min=0.0, -# b_max=1.0, -# clip=True, -# ), -# RandShiftIntensityd(keys=["image"], offsets=0.1, prob=0.5), -# RandFlipd(keys=["image"], spatial_axis=[1], prob=0.5), -# RandFlipd(keys=["image"], spatial_axis=[2], prob=0.5), -# RandRotate90d(keys=["image"], prob=0.1, max_k=3), -# EnsureTyped(keys=["image"]), -# ] -# ) -# else: -# train_transforms = EnsureTyped(keys=["image"]) -# -# # Create the dataset -# dataset = CacheDataset( -# data=train_files, -# transform=Compose(load_single_images, train_transforms), -# ) -# -# return first_volume_shape, dataset -# -# -# def get_scheduler(config, optimizer, verbose=False): -# scheduler_name = config.scheduler -# if scheduler_name == "None": -# scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( -# optimizer, -# T_max=100, -# eta_min=config.lr - 1e-6, -# verbose=verbose, -# ) -# -# elif scheduler_name == "ReduceLROnPlateau": -# scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( -# optimizer, -# mode="min", -# factor=schedulers["ReduceLROnPlateau"]["factor"], -# patience=schedulers["ReduceLROnPlateau"]["patience"], -# verbose=verbose, -# ) -# elif scheduler_name == "CosineAnnealingLR": -# scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( -# optimizer, -# T_max=schedulers["CosineAnnealingLR"]["T_max"], -# eta_min=schedulers["CosineAnnealingLR"]["eta_min"], -# verbose=verbose, -# ) -# elif scheduler_name == "CosineAnnealingWarmRestarts": -# scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts( -# optimizer, -# T_0=schedulers["CosineAnnealingWarmRestarts"]["T_0"], -# eta_min=schedulers["CosineAnnealingWarmRestarts"]["eta_min"], -# T_mult=schedulers["CosineAnnealingWarmRestarts"]["T_mult"], -# verbose=verbose, -# ) -# elif scheduler_name == "CyclicLR": -# scheduler = torch.optim.lr_scheduler.CyclicLR( -# optimizer, -# base_lr=schedulers["CyclicLR"]["base_lr"], -# max_lr=schedulers["CyclicLR"]["max_lr"], -# step_size_up=schedulers["CyclicLR"]["step_size_up"], -# mode=schedulers["CyclicLR"]["mode"], -# cycle_momentum=False, -# ) -# else: -# raise ValueError(f"Scheduler {scheduler_name} not provided") -# return scheduler -# -# -# if __name__ == "__main__": -# weights_location = str( -# # Path(__file__).resolve().parent / "../weights/wnet.pth" -# # "../wnet_SUM_MSE_DAPI_rad2_best_metric.pth" -# ) -# train( -# # weights_location -# ) From 01938fb8fdd45c8b5d6c5fe6ba8ea13cc87d6116 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 31 Jul 2023 16:11:45 +0200 Subject: [PATCH 185/250] Deploy memory usage fix in inference as well --- napari_cellseg3d/code_models/worker_inference.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index ceedac53..65623b36 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -784,6 +784,12 @@ def inference(self): ) model.to("cpu") + model = None + del model + inference_loader = None + del inference_loader + if torch.cuda.is_available(): + torch.cuda.empty_cache() # self.quit() except Exception as e: logger.exception(e) From 133b8fc9975ade291864e6cc21918854643123dd Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 31 Jul 2023 16:13:03 +0200 Subject: [PATCH 186/250] Memory usage fix --- napari_cellseg3d/code_models/worker_training.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index f797b952..b2c1b264 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -1633,6 +1633,14 @@ def get_loader_func(num_samples): self.log("Saving complete, exiting") model.to("cpu") # clear (V)RAM + model = None + del model + val_loader = None + del val_loader + train_loader = None + del train_loader + if torch.cuda.is_available(): + torch.cuda.empty_cache() # val_ds = None # train_ds = None # val_loader = None From 9b99c11fc272640bdb71f36b1306a83985db807c Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 31 Jul 2023 16:22:16 +0200 Subject: [PATCH 187/250] UI tweak --- napari_cellseg3d/interface.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/interface.py b/napari_cellseg3d/interface.py index 4efd2269..d5778442 100644 --- a/napari_cellseg3d/interface.py +++ b/napari_cellseg3d/interface.py @@ -493,7 +493,7 @@ def __init__( elif self._divide_factor == 10: self._value_label.setFixedWidth(30) else: - self._value_label.setFixedWidth(60) + self._value_label.setFixedWidth(50) self._value_label.setAlignment(Qt.AlignCenter) self._value_label.setSizePolicy( QSizePolicy.Policy.Fixed, QSizePolicy.Policy.Fixed From d3414e83871afe86953d8fa6572c592d89a40451 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 2 Aug 2023 10:58:18 +0200 Subject: [PATCH 188/250] WNet cleanup + supervised training improvements --- .../code_models/worker_training.py | 234 +++++++----------- .../code_plugins/plugin_model_training.py | 8 +- 2 files changed, 90 insertions(+), 152 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index b2c1b264..f9612377 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -32,7 +32,6 @@ EnsureType, EnsureTyped, LoadImaged, - # NormalizeIntensityd, Orientationd, Rand3DElasticd, RandAffined, @@ -57,7 +56,6 @@ LogSignal, QuantileNormalizationd, RemapTensor, - # RemapTensord, Threshold, TrainingReport, WeightsDownloader, @@ -238,12 +236,6 @@ def get_dataset(self, train_transforms): Returns: (tuple): A tuple containing the shape of the data and the dataset """ - # train_files = self.create_dataset_dict_no_labs( - # volume_directory=self.config.train_volume_directory - # ) - # self.log(train_files) - # self.log(len(train_files)) - # self.log(train_files[0]) train_files = self.config.train_data_dict first_volume = LoadImaged(keys=["image"])(train_files[0]) @@ -272,52 +264,6 @@ def get_dataset(self, train_transforms): return first_volume_shape, dataset - # def get_scheduler(self, optimizer, verbose=False): - # scheduler_name = self.config.scheduler - # if scheduler_name == "None": - # scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( - # optimizer, - # T_max=100, - # eta_min=config.lr - 1e-6, - # verbose=verbose, - # ) - # - # elif scheduler_name == "ReduceLROnPlateau": - # scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - # optimizer, - # mode="min", - # factor=schedulers["ReduceLROnPlateau"]["factor"], - # patience=schedulers["ReduceLROnPlateau"]["patience"], - # verbose=verbose, - # ) - # elif scheduler_name == "CosineAnnealingLR": - # scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( - # optimizer, - # T_max=schedulers["CosineAnnealingLR"]["T_max"], - # eta_min=schedulers["CosineAnnealingLR"]["eta_min"], - # verbose=verbose, - # ) - # elif scheduler_name == "CosineAnnealingWarmRestarts": - # scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts( - # optimizer, - # T_0=schedulers["CosineAnnealingWarmRestarts"]["T_0"], - # eta_min=schedulers["CosineAnnealingWarmRestarts"]["eta_min"], - # T_mult=schedulers["CosineAnnealingWarmRestarts"]["T_mult"], - # verbose=verbose, - # ) - # elif scheduler_name == "CyclicLR": - # scheduler = torch.optim.lr_scheduler.CyclicLR( - # optimizer, - # base_lr=schedulers["CyclicLR"]["base_lr"], - # max_lr=schedulers["CyclicLR"]["max_lr"], - # step_size_up=schedulers["CyclicLR"]["step_size_up"], - # mode=schedulers["CyclicLR"]["mode"], - # cycle_momentum=False, - # ) - # else: - # raise ValueError(f"Scheduler {scheduler_name} not provided") - # return scheduler - def _get_data(self): if self.config.do_augmentation: train_transforms = Compose( @@ -346,16 +292,7 @@ def _get_data(self): else: self.log("Loading volume dataset") (data_shape, dataset) = self.get_dataset(train_transforms) - # transform = Compose( - # [ - # ToTensor(), - # EnsureChannelFirst(channel_dim=0), - # ] - # ) - # dataset = [transform(im) for im in dataset] - # for data in dataset: - # self.log(f"Data shape: {data.shape}") - # break + logger.debug(f"Data shape : {data_shape}") dataloader = DataLoader( dataset, @@ -438,18 +375,15 @@ def train(self): # config=WANDB_CONFIG, project="WNet-benchmark", mode=WANDB_MODE # ) - set_determinism( - seed=self.config.deterministic_config.seed - ) # use default seed from NP_MAX + set_determinism(seed=self.config.deterministic_config.seed) torch.use_deterministic_algorithms(True, warn_only=True) normalize_function = utils.remap_image device = self.config.device - # self.log(f"Using device: {device}") self.log_parameters() self.log("Initializing training...") - self.log("Getting the data") + self.log("- Getting the data") dataloader, eval_dataloader, data_shape = self._get_data() @@ -459,7 +393,6 @@ def train(self): ################################################### # Training the model # ################################################### - self.log("Initializing the model:") self.log("- Getting the model") # Initialize the model model = WNet( @@ -545,12 +478,6 @@ def train(self): f"Unknown reconstruction loss : {self.config.reconstruction_loss} not supported" ) - # self.log("- getting the learning rate schedulers") - # Initialize the learning rate schedulers - # scheduler = get_scheduler(self.config, optimizer) - # scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - # optimizer, mode="min", factor=0.5, patience=10, verbose=True - # ) model.train() self.log("Ready") @@ -574,34 +501,31 @@ def train(self): for _i, batch in enumerate(dataloader): # raise NotImplementedError("testing") - image = batch["image"].to(device) - for i in range(image.shape[0]): - for j in range(image.shape[1]): - image[i, j] = normalize_function(image[i, j]) - # if self.config.batch_size == 1: - # image = image.unsqueeze(0) - # else: - # image = image.unsqueeze(0) - # image = torch.swapaxes(image, 0, 1) + image_batch = batch["image"].to(device) + # Normalize the image + for i in range(image_batch.shape[0]): + for j in range(image_batch.shape[1]): + image_batch[i, j] = normalize_function( + image_batch[i, j] + ) # Forward pass - enc = model.forward_encoder(image) + enc = model.forward_encoder(image_batch) # Compute the Ncuts loss - Ncuts = criterionE(enc, image) + Ncuts = criterionE(enc, image_batch) epoch_ncuts_loss += Ncuts.item() # if WANDB_INSTALLED: # wandb.log({"Ncuts loss": Ncuts.item()}) - # Forward pass - enc, dec = model(image) + dec = model.forward_decoder(enc) # Compute the reconstruction loss if isinstance(criterionW, nn.MSELoss): - reconstruction_loss = criterionW(dec, image) + reconstruction_loss = criterionW(dec, image_batch) elif isinstance(criterionW, nn.BCELoss): reconstruction_loss = criterionW( torch.sigmoid(dec), - utils.remap_image(image, new_max=1), + utils.remap_image(image_batch, new_max=1), ) epoch_rec_loss += reconstruction_loss.item() @@ -622,13 +546,6 @@ def train(self): loss.backward(loss) optimizer.step() - # if self.config.scheduler == "CosineAnnealingWarmRestarts": - # scheduler.step(epoch + _i / len(dataloader)) - # if ( - # self.config.scheduler == "CosineAnnealingLR" - # or self.config.scheduler == "CyclicLR" - # ): - # scheduler.step() if self._abort_requested: dataloader = None del dataloader @@ -656,7 +573,7 @@ def train(self): try: enc_out = enc[0].detach().cpu().numpy() dec_out = dec[0].detach().cpu().numpy() - image = image[0].detach().cpu().numpy() + image_batch = image_batch[0].detach().cpu().numpy() images_dict = { "Encoder output": { @@ -674,7 +591,7 @@ def train(self): "cmap": "gist_earth", }, "Input image": { - "data": np.squeeze(image), + "data": np.squeeze(image_batch), "cmap": "inferno", }, } @@ -713,11 +630,6 @@ def train(self): f"Weighted sum of losses difference: {total_losses[-1] - total_losses[-2]:.5f}" ) - # Update the learning rate - # if self.config.scheduler == "ReduceLROnPlateau": - # # schedulerE.step(epoch_ncuts_loss) - # # schedulerW.step(epoch_rec_loss) - # scheduler.step(epoch_rec_loss) if ( eval_dataloader is not None and (epoch + 1) % self.config.validation_interval == 0 @@ -774,6 +686,7 @@ def train(self): ) dices = [] + # Find in which channel the labels are (avoid background) for channel in range(val_outputs.shape[1]): dices.append( utils.dice_coeff( @@ -1020,19 +933,19 @@ def log_parameters(self): f"Percentage of dataset used for validation : {self.config.validation_percent * 100}%" ) - self.log("-" * 10) + # self.log("-" * 10) self.log("Training files :\n") [ - self.log(f"{Path(train_file['image']).name}\n") + self.log(f"- {Path(train_file['image']).name}\n") for train_file in self.train_files ] - self.log("-" * 10) + # self.log("-" * 10) self.log("Validation files :\n") [ - self.log(f"{Path(val_file['image']).name}\n") + self.log(f"- {Path(val_file['image']).name}\n") for val_file in self.val_files ] - self.log("-" * 10) + # self.log("-" * 10) if self.config.deterministic_config.enabled: self.log("Deterministic training is enabled") @@ -1067,7 +980,7 @@ def log_parameters(self): ) # self.log("\n") - self.log("-" * 20) + # self.log("-" * 20) def train(self): """Trains the PyTorch model for the given number of epochs, with the selected model and data, @@ -1147,7 +1060,8 @@ def train(self): PADDING = utils.get_padding_dim(size) model = model_class(input_img_size=PADDING, use_checkpoint=True) - model = model.to(self.config.device) + device = torch.device(self.config.device) + model = model.to(device) epoch_loss_values = [] val_metric_values = [] @@ -1204,9 +1118,9 @@ def train(self): RandFlipd(keys=["image", "label"]), RandRotate90d(keys=["image", "label"]), RandAffined( - keys=["image", "label"], + keys=["image"], ), - EnsureTyped(keys=["image", "label"]), + EnsureTyped(keys=["image"]), ] ) ) @@ -1215,19 +1129,15 @@ def train(self): val_transforms = Compose( [ - # LoadImaged(keys=["image", "label"]), - # EnsureChannelFirstd(keys=["image", "label"]), EnsureTyped(keys=["image", "label"]), ] ) - # self.log("Loading dataset...\n") - def get_loader_func(num_samples): + def get_patch_loader_func(num_samples): return Compose( [ LoadImaged(keys=["image", "label"]), EnsureChannelFirstd(keys=["image", "label"]), - QuantileNormalizationd(keys=["image"]), RandSpatialCropSamplesd( keys=["image", "label"], roi_size=( @@ -1244,7 +1154,8 @@ def get_loader_func(num_samples): utils.get_padding_dim(self.config.sample_size) ), ), - EnsureTyped(keys=["image", "label"]), + QuantileNormalizationd(keys=["image"]), + EnsureTyped(keys=["image"]), ] ) @@ -1260,15 +1171,30 @@ def get_loader_func(num_samples): self.config.num_samples * (1 - self.config.validation_percent) ) - sample_loader_train = get_loader_func(num_train_samples) - sample_loader_eval = get_loader_func(num_val_samples) + if num_train_samples < 2: + self.log( + "WARNING : not enough samples for training. Raising to 2" + ) + num_train_samples = 2 + if num_val_samples < 2: + self.log( + "WARNING : not enough samples for validation. Raising to 2" + ) + num_val_samples = 2 + + sample_loader_train = get_patch_loader_func( + num_train_samples + ) + sample_loader_eval = get_patch_loader_func(num_val_samples) else: num_train_samples = ( num_val_samples ) = self.config.num_samples - sample_loader_train = get_loader_func(num_train_samples) - sample_loader_eval = get_loader_func(num_val_samples) + sample_loader_train = get_patch_loader_func( + num_train_samples + ) + sample_loader_eval = get_patch_loader_func(num_val_samples) logger.debug(f"AMOUNT of train samples : {num_train_samples}") logger.debug( @@ -1276,20 +1202,19 @@ def get_loader_func(num_samples): ) logger.debug("train_ds") - train_ds = PatchDataset( + train_dataset = PatchDataset( data=self.train_files, transform=train_transforms, patch_func=sample_loader_train, samples_per_image=num_train_samples, ) logger.debug("val_ds") - val_ds = PatchDataset( + validation_dataset = PatchDataset( data=self.val_files, transform=val_transforms, patch_func=sample_loader_eval, samples_per_image=num_val_samples, ) - else: load_whole_images = Compose( [ @@ -1309,25 +1234,27 @@ def get_loader_func(num_samples): ] ) logger.debug("Cache dataset : train") - train_ds = CacheDataset( + train_dataset = CacheDataset( data=self.train_files, transform=Compose(load_whole_images, train_transforms), ) logger.debug("Cache dataset : val") - val_ds = CacheDataset( + validation_dataset = CacheDataset( data=self.val_files, transform=load_whole_images ) logger.debug("Dataloader") train_loader = DataLoader( - train_ds, + train_dataset, batch_size=self.config.batch_size, shuffle=True, num_workers=2, collate_fn=pad_list_data_collate, ) - val_loader = DataLoader( - val_ds, batch_size=self.config.batch_size, num_workers=2 + validation_loader = DataLoader( + validation_dataset, + batch_size=self.config.batch_size, + num_workers=2, ) logger.info("\nDone") @@ -1372,7 +1299,7 @@ def get_loader_func(num_samples): model.load_state_dict( torch.load( weights, - map_location=self.config.device, + map_location=device, ), strict=True, ) @@ -1396,7 +1323,7 @@ def get_loader_func(num_samples): self.log_parameters() - device = torch.device(self.config.device) + # device = torch.device(self.config.device) self.set_loss_from_config() # if model_name == "test": @@ -1427,7 +1354,8 @@ def get_loader_func(num_samples): batch_data["image"].to(device), batch_data["label"].to(device), ) - + # logger.debug(f"Inputs shape : {inputs.shape}") + # logger.debug(f"Labels shape : {labels.shape}") optimizer.zero_grad() outputs = model(inputs) # self.log(f"Output dimensions : {outputs.shape}") @@ -1437,14 +1365,31 @@ def get_loader_func(num_samples): ] # TODO(cyril): adapt if additional channels if len(outputs.shape) < 4: outputs = outputs.unsqueeze(0) + # logger.debug(f"Outputs shape : {outputs.shape}") loss = self.loss_function(outputs, labels) loss.backward() optimizer.step() epoch_loss += loss.detach().item() self.log( - f"* {step}/{len(train_ds) // train_loader.batch_size}, " + f"* {step}/{len(train_dataset) // train_loader.batch_size}, " f"Train loss: {loss.detach().item():.4f}" ) + + if self._abort_requested: + self.log("Aborting training...") + model = None + del model + train_loader = None + del train_loader + validation_loader = None + del validation_loader + optimizer = None + del optimizer + scheduler = None + del scheduler + if device.type == "cuda": + torch.cuda.empty_cache() + yield TrainingReport( show_plot=False, weights=model.state_dict() ) @@ -1476,7 +1421,7 @@ def get_loader_func(num_samples): model.eval() self.log("Performing validation...") with torch.no_grad(): - for val_data in val_loader: + for val_data in validation_loader: val_inputs, val_labels = ( val_data["image"].to(device), val_data["label"].to(device), @@ -1635,17 +1580,16 @@ def get_loader_func(num_samples): # clear (V)RAM model = None del model - val_loader = None - del val_loader train_loader = None del train_loader - if torch.cuda.is_available(): + validation_loader = None + del validation_loader + optimizer = None + del optimizer + scheduler = None + del scheduler + if device.type == "cuda": torch.cuda.empty_cache() - # val_ds = None - # train_ds = None - # val_loader = None - # train_loader = None - # torch.cuda.empty_cache() except Exception as e: self.raise_error(e, "Error in training") diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 9a7027ed..568089f6 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -976,10 +976,8 @@ def start(self): self.worker.warn_signal.connect(self.log.warn) self.worker.started.connect(self.on_start) - self.worker.yielded.connect(partial(self.on_yield)) self.worker.finished.connect(self.on_finish) - self.worker.errored.connect(self.on_error) if self.worker.is_running: @@ -1218,17 +1216,12 @@ def on_finish(self): self.start_btn.setText("Start") [btn.setVisible(True) for btn in self.close_buttons] - # del self.worker - - # self.empty_cuda_cache() - if self.config.save_as_zip: shutil.make_archive( self.worker_config.results_path_folder, "zip", self.worker_config.results_path_folder, ) - self.worker = None def on_error(self): @@ -1239,6 +1232,7 @@ def on_error(self): def on_stop(self): self._remove_result_layers() self.worker = None + self._stop_requested = False self.start_btn.setText("Start") [btn.setVisible(True) for btn in self.close_buttons] From 53dabb554ac46570e3e08c6e1e23be2b843f7379 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 2 Aug 2023 11:12:55 +0200 Subject: [PATCH 189/250] Change Dice metric include_background for WNet To avoid Max Dice calculation --- .../code_models/models/wnet/model.py | 2 +- .../code_models/worker_training.py | 83 +++++++++++-------- 2 files changed, 50 insertions(+), 35 deletions(-) diff --git a/napari_cellseg3d/code_models/models/wnet/model.py b/napari_cellseg3d/code_models/models/wnet/model.py index 989ae3b7..28643588 100644 --- a/napari_cellseg3d/code_models/models/wnet/model.py +++ b/napari_cellseg3d/code_models/models/wnet/model.py @@ -62,7 +62,7 @@ def __init__( ) def forward(self, x): - """Forward pass of the W-Net model.""" + """Forward pass of the W-Net model. Returns the segmentation and the reconstructed image.""" enc = self.forward_encoder(x) return enc, self.forward_decoder(enc) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index f9612377..86c0bb78 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -287,10 +287,10 @@ def _get_data(self): train_transforms = EnsureTyped(keys=["image"]) if self.config.sampling: - self.log("Loading patch dataset") + logger.debug("Loading patch dataset") (data_shape, dataset) = self.get_patch_dataset(train_transforms) else: - self.log("Loading volume dataset") + logger.debug("Loading volume dataset") (data_shape, dataset) = self.get_dataset(train_transforms) logger.debug(f"Data shape : {data_shape}") @@ -388,7 +388,7 @@ def train(self): dataloader, eval_dataloader, data_shape = self._get_data() dice_metric = DiceMetric( - include_background=False, reduction="mean", get_not_nans=False + include_background=True, reduction="mean", get_not_nans=False ) ################################################### # Training the model # @@ -510,15 +510,13 @@ def train(self): ) # Forward pass - enc = model.forward_encoder(image_batch) + enc, dec = model(image_batch) # Compute the Ncuts loss Ncuts = criterionE(enc, image_batch) epoch_ncuts_loss += Ncuts.item() # if WANDB_INSTALLED: # wandb.log({"Ncuts loss": Ncuts.item()}) - dec = model.forward_decoder(enc) - # Compute the reconstruction loss if isinstance(criterionW, nn.MSELoss): reconstruction_loss = criterionW(dec, image_batch) @@ -685,32 +683,33 @@ def train(self): f"Val decoder outputs shape: {val_decoder_outputs.shape}" ) - dices = [] + # dices = [] # Find in which channel the labels are (avoid background) - for channel in range(val_outputs.shape[1]): - dices.append( - utils.dice_coeff( - y_pred=val_outputs[ - 0, channel : (channel + 1), :, :, : - ], - y_true=val_labels[0], - ) - ) - logger.debug(f"DICE COEFF: {dices}") - max_dice_channel = torch.argmax( - torch.Tensor(dices) - ) - logger.debug( - f"MAX DICE CHANNEL: {max_dice_channel}" - ) + # for channel in range(val_outputs.shape[1]): + # dices.append( + # utils.dice_coeff( + # y_pred=val_outputs[ + # 0, channel : (channel + 1), :, :, : + # ], + # y_true=val_labels[0], + # ) + # ) + # logger.debug(f"DICE COEFF: {dices}") + # max_dice_channel = torch.argmax( + # torch.Tensor(dices) + # ) + # logger.debug( + # f"MAX DICE CHANNEL: {max_dice_channel}" + # ) dice_metric( - y_pred=val_outputs[ - :, - max_dice_channel : (max_dice_channel + 1), - :, - :, - :, - ], + y_pred=val_outputs, + # [ + # :, + # max_dice_channel : (max_dice_channel + 1), + # :, + # :, + # :, + # ], y=val_labels, ) @@ -736,11 +735,19 @@ def train(self): # wandb.log({"val/dice_metric": metric}) dec_out_val = ( - val_decoder_outputs[0].detach().cpu().numpy() + val_decoder_outputs[0] + .detach() + .cpu() + .numpy() + .copy() + ) + enc_out_val = ( + val_outputs[0].detach().cpu().numpy().copy() + ) + lab_out_val = ( + val_labels[0].detach().cpu().numpy().copy() ) - enc_out_val = val_outputs[0].detach().cpu().numpy() - lab_out_val = val_labels[0].detach().cpu().numpy() - val_in = val_inputs[0].detach().cpu().numpy() + val_in = val_inputs[0].detach().cpu().numpy().copy() display_dict = { "Reconstruction": { @@ -760,6 +767,14 @@ def train(self): "cmap": "bop blue", }, } + val_decoder_outputs = None + del val_decoder_outputs + val_outputs = None + del val_outputs + val_labels = None + del val_labels + val_inputs = None + del val_inputs yield TrainingReport( epoch=epoch, From 1b12c4a671e29515d8e56b657e3ec473e04fb4f6 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 2 Aug 2023 11:18:25 +0200 Subject: [PATCH 190/250] Set better default LR across un/supervised --- napari_cellseg3d/code_plugins/plugin_model_training.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 568089f6..1db818e6 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -443,11 +443,15 @@ def _toggle_unsupervised_mode(self, enabled=False): self.start_btn = self.start_button_unsupervised self.image_filewidget.text_field.setText("Validation images") self.labels_filewidget.text_field.setText("Validation labels") + self.learning_rate_choice.lr_value_choice.setValue(1) + self.learning_rate_choice.lr_exponent_choice.setCurrentIndex(1) else: unsupervised = False self.start_btn = self.start_button_supervised self.image_filewidget.text_field.setText("Images directory") self.labels_filewidget.text_field.setText("Labels directory") + self.learning_rate_choice.lr_value_choice.setValue(2) + self.learning_rate_choice.lr_exponent_choice.setCurrentIndex(3) supervised = not unsupervised self.unsupervised_mode = unsupervised From e5a0be460a6529d6d1e3fe83c0903d8e2ad63a1b Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 2 Aug 2023 11:34:29 +0200 Subject: [PATCH 191/250] Update model.py --- .../code_models/models/wnet/model.py | 38 +++++++++---------- 1 file changed, 19 insertions(+), 19 deletions(-) diff --git a/napari_cellseg3d/code_models/models/wnet/model.py b/napari_cellseg3d/code_models/models/wnet/model.py index 28643588..d8ba3a78 100644 --- a/napari_cellseg3d/code_models/models/wnet/model.py +++ b/napari_cellseg3d/code_models/models/wnet/model.py @@ -99,22 +99,22 @@ def __init__( self.max_pool = nn.MaxPool3d(2) self.in_b = InBlock(in_channels, self.channels[0], dropout=dropout) self.conv1 = Block(channels[0], self.channels[1], dropout=dropout) - # self.conv2 = Block(channels[1], self.channels[2], dropout=dropout) + self.conv2 = Block(channels[1], self.channels[2], dropout=dropout) # self.conv3 = Block(channels[2], self.channels[3], dropout=dropout) # self.bot = Block(channels[3], self.channels[4], dropout=dropout) - # self.bot = Block(channels[2], self.channels[3], dropout=dropout) - self.bot = Block(channels[1], self.channels[2], dropout=dropout) + self.bot = Block(channels[2], self.channels[3], dropout=dropout) + # self.bot = Block(channels[1], self.channels[2], dropout=dropout) # self.bot = Block(channels[0], self.channels[1], dropout=dropout) # self.deconv1 = Block(channels[4], self.channels[3], dropout=dropout) - # self.deconv2 = Block(channels[3], self.channels[2], dropout=dropout) + self.deconv2 = Block(channels[3], self.channels[2], dropout=dropout) self.deconv3 = Block(channels[2], self.channels[1], dropout=dropout) self.out_b = OutBlock(channels[1], out_channels, dropout=dropout) # self.conv_trans1 = nn.ConvTranspose3d( # self.channels[4], self.channels[3], 2, stride=2 # ) - # self.conv_trans2 = nn.ConvTranspose3d( - # self.channels[3], self.channels[2], 2, stride=2 - # ) + self.conv_trans2 = nn.ConvTranspose3d( + self.channels[3], self.channels[2], 2, stride=2 + ) self.conv_trans3 = nn.ConvTranspose3d( self.channels[2], self.channels[1], 2, stride=2 ) @@ -129,11 +129,11 @@ def forward(self, x): """Forward pass of the U-Net model.""" in_b = self.in_b(x) c1 = self.conv1(self.max_pool(in_b)) - # c2 = self.conv2(self.max_pool(c1)) + c2 = self.conv2(self.max_pool(c1)) # c3 = self.conv3(self.max_pool(c2)) # x = self.bot(self.max_pool(c3)) - # x = self.bot(self.max_pool(c2)) - x = self.bot(self.max_pool(c1)) + x = self.bot(self.max_pool(c2)) + # x = self.bot(self.max_pool(c1)) # x = self.bot(self.max_pool(in_b)) # x = self.deconv1( # torch.cat( @@ -144,15 +144,15 @@ def forward(self, x): # dim=1, # ) # ) - # x = self.deconv2( - # torch.cat( - # [ - # c2, - # self.conv_trans2(x), - # ], - # dim=1, - # ) - # ) + x = self.deconv2( + torch.cat( + [ + c2, + self.conv_trans2(x), + ], + dim=1, + ) + ) x = self.deconv3( torch.cat( [ From c6243b8e0d8625a2f95e6c93de9b358db39e70f6 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 2 Aug 2023 11:44:36 +0200 Subject: [PATCH 192/250] Update WNet weights --- napari_cellseg3d/code_models/models/model_WNet.py | 2 +- .../code_models/models/pretrained/pretrained_model_urls.json | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/code_models/models/model_WNet.py b/napari_cellseg3d/code_models/models/model_WNet.py index e50a58a1..bc1b3818 100644 --- a/napari_cellseg3d/code_models/models/model_WNet.py +++ b/napari_cellseg3d/code_models/models/model_WNet.py @@ -5,7 +5,7 @@ class WNet_(WNet_encoder): use_default_training = False - weights_file = "wnet.pth" + weights_file = "wnet_latest.pth" def __init__( self, diff --git a/napari_cellseg3d/code_models/models/pretrained/pretrained_model_urls.json b/napari_cellseg3d/code_models/models/pretrained/pretrained_model_urls.json index 3c393d47..d9e1e4b0 100644 --- a/napari_cellseg3d/code_models/models/pretrained/pretrained_model_urls.json +++ b/napari_cellseg3d/code_models/models/pretrained/pretrained_model_urls.json @@ -3,7 +3,7 @@ "SegResNet": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/SegResNet_latest.tar.gz", "VNet": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/VNet_latest.tar.gz", "SwinUNetR": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/SwinUNetR_latest.tar.gz", - "WNet": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/wnet.tar.gz", + "WNet": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/wnet_latest.tar.gz", "WNet_ONNX": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/wnet_onnx.tar.gz", "test": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/test.tar.gz" } From fe1a2f87ec1ae937430873f105e8ea67b1f5fc86 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 2 Aug 2023 11:53:10 +0200 Subject: [PATCH 193/250] Fix default LR + sup. test --- napari_cellseg3d/_tests/test_supervised_training.py | 7 +++++-- napari_cellseg3d/code_plugins/plugin_model_training.py | 8 ++++---- 2 files changed, 9 insertions(+), 6 deletions(-) diff --git a/napari_cellseg3d/_tests/test_supervised_training.py b/napari_cellseg3d/_tests/test_supervised_training.py index 676133ff..1a7fac06 100644 --- a/napari_cellseg3d/_tests/test_supervised_training.py +++ b/napari_cellseg3d/_tests/test_supervised_training.py @@ -12,12 +12,15 @@ im_path = Path(__file__).resolve().parent / "res/test.tif" im_path_str = str(im_path) -def test_create_supervised_worker_from_config(make_napari_viewer_proxy): +def test_create_supervised_worker_from_config(make_napari_viewer_proxy): viewer = make_napari_viewer_proxy() widget = Trainer(viewer=viewer) widget.device_choice.setCurrentIndex(0) - worker = widget._create_worker() + widget.model_choice.setCurrentIndex(0) + widget._toggle_unsupervised_mode(enabled=False) + assert widget.model_choice.currentText() == list(MODEL_LIST.keys())[0] + worker = widget._create_worker(additional_results_description="test") default_config = config.SupervisedTrainingWorkerConfig() excluded = [ "results_path_folder", diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 1db818e6..811cbf7c 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -443,15 +443,15 @@ def _toggle_unsupervised_mode(self, enabled=False): self.start_btn = self.start_button_unsupervised self.image_filewidget.text_field.setText("Validation images") self.labels_filewidget.text_field.setText("Validation labels") - self.learning_rate_choice.lr_value_choice.setValue(1) - self.learning_rate_choice.lr_exponent_choice.setCurrentIndex(1) + self.learning_rate_choice.lr_value_choice.setValue(2) + self.learning_rate_choice.lr_exponent_choice.setCurrentIndex(3) else: unsupervised = False self.start_btn = self.start_button_supervised self.image_filewidget.text_field.setText("Images directory") self.labels_filewidget.text_field.setText("Labels directory") - self.learning_rate_choice.lr_value_choice.setValue(2) - self.learning_rate_choice.lr_exponent_choice.setCurrentIndex(3) + self.learning_rate_choice.lr_value_choice.setValue(1) + self.learning_rate_choice.lr_exponent_choice.setCurrentIndex(1) supervised = not unsupervised self.unsupervised_mode = unsupervised From 6e9762a761258275da40f2bc1ed444961f4a2150 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 2 Aug 2023 11:59:19 +0200 Subject: [PATCH 194/250] Fix new unsup LR in tests --- napari_cellseg3d/_tests/test_unsup_training.py | 6 ++---- napari_cellseg3d/config.py | 1 + 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/napari_cellseg3d/_tests/test_unsup_training.py b/napari_cellseg3d/_tests/test_unsup_training.py index 9b26167a..acdf2c01 100644 --- a/napari_cellseg3d/_tests/test_unsup_training.py +++ b/napari_cellseg3d/_tests/test_unsup_training.py @@ -5,15 +5,13 @@ Trainer, ) +im_path = Path(__file__).resolve().parent / "res/test.tif" -def test_unsupervised_worker(make_napari_viewer_proxy): - im_path = Path(__file__).resolve().parent / "res/test.tif" - # im_path_str = str(im_path) +def test_unsupervised_worker(make_napari_viewer_proxy): unsup_viewer = make_napari_viewer_proxy() widget = Trainer(viewer=unsup_viewer) widget.device_choice.setCurrentIndex(0) - widget.model_choice.setCurrentText("WNet") widget._toggle_unsupervised_mode(enabled=True) diff --git a/napari_cellseg3d/config.py b/napari_cellseg3d/config.py index f9536d93..6c8db79b 100644 --- a/napari_cellseg3d/config.py +++ b/napari_cellseg3d/config.py @@ -285,6 +285,7 @@ class WNetTrainingWorkerConfig(TrainingWorkerConfig): out_channels: int = 1 # decoder (reconstruction) output channels num_classes: int = 2 # encoder output channels dropout: float = 0.65 + learning_rate: np.float64 = 2e-5 use_clipping: bool = False # use gradient clipping clipping: float = 1.0 # clipping value weight_decay: float = 0.01 # 1e-5 # weight decay (used 0.01 historically) From 420a641090bf22c1b2690f77c7edfb4e23cd0c71 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Fri, 28 Jul 2023 15:30:01 +0200 Subject: [PATCH 195/250] Fix dir for saving in tests --- napari_cellseg3d/code_models/worker_inference.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 65623b36..3fb5bc95 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -436,6 +436,8 @@ def save_image( + f"_{time}" + filetype ) + if not Path(self.config.results_path).exists(): + Path(self.config.results_path).mkdir(parents=True, exist_ok=True) try: imwrite(file_path, image) except ValueError as e: From 912e6bd2790d260bff6da182605bb04ae8430631 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 2 Aug 2023 13:51:40 +0200 Subject: [PATCH 196/250] Testing fixes Due to Singleton Trainer widget --- ...ed_training.py => test_training_plugin.py} | 37 +++++++++++++-- .../_tests/test_unsup_training.py | 45 ------------------- .../code_plugins/plugin_model_training.py | 6 ++- 3 files changed, 39 insertions(+), 49 deletions(-) rename napari_cellseg3d/_tests/{test_supervised_training.py => test_training_plugin.py} (71%) delete mode 100644 napari_cellseg3d/_tests/test_unsup_training.py diff --git a/napari_cellseg3d/_tests/test_supervised_training.py b/napari_cellseg3d/_tests/test_training_plugin.py similarity index 71% rename from napari_cellseg3d/_tests/test_supervised_training.py rename to napari_cellseg3d/_tests/test_training_plugin.py index 1a7fac06..09bf3e9d 100644 --- a/napari_cellseg3d/_tests/test_supervised_training.py +++ b/napari_cellseg3d/_tests/test_training_plugin.py @@ -13,9 +13,10 @@ im_path_str = str(im_path) -def test_create_supervised_worker_from_config(make_napari_viewer_proxy): +def test_worker_configs(make_napari_viewer_proxy): viewer = make_napari_viewer_proxy() widget = Trainer(viewer=viewer) + # test supervised config and worker widget.device_choice.setCurrentIndex(0) widget.model_choice.setCurrentIndex(0) widget._toggle_unsupervised_mode(enabled=False) @@ -34,6 +35,36 @@ def test_create_supervised_worker_from_config(make_napari_viewer_proxy): assert getattr(default_config, attr) == getattr( worker.config, attr ) + # test unsupervised config and worker + widget.model_choice.setCurrentText("WNet") + widget._toggle_unsupervised_mode(enabled=True) + default_config = config.WNetTrainingWorkerConfig() + worker = widget._create_worker(additional_results_description="TEST_1") + excluded = ["results_path_folder", "sample_size", "weights_info"] + for attr in dir(default_config): + if not attr.startswith("__") and attr not in excluded: + assert getattr(default_config, attr) == getattr( + worker.config, attr + ) + widget.unsupervised_images_filewidget.text_field.setText( + str(im_path.parent) + ) + widget.data = widget.create_dataset_dict_no_labs() + worker = widget._create_worker(additional_results_description="TEST_2") + dataloader, eval_dataloader, data_shape = worker._get_data() + assert eval_dataloader is None + assert data_shape == (6, 6, 6) + + widget.images_filepaths = [str(im_path)] + widget.labels_filepaths = [str(im_path)] + # widget.unsupervised_eval_data = widget.create_train_dataset_dict() + worker = widget._create_worker(additional_results_description="TEST_3") + dataloader, eval_dataloader, data_shape = worker._get_data() + assert widget.unsupervised_eval_data is not None + assert eval_dataloader is not None + assert widget.unsupervised_eval_data[0]["image"] is not None + assert widget.unsupervised_eval_data[0]["label"] is not None + assert data_shape == (6, 6, 6) def test_update_loss_plot(make_napari_viewer_proxy): @@ -86,8 +117,8 @@ def test_training(make_napari_viewer_proxy, qtbot): widget.log = LogFixture() viewer.window.add_dock_widget(widget) - widget.images_filepath = None - widget.labels_filepaths = None + widget.images_filepath = [] + widget.labels_filepaths = [] assert not widget.check_ready() diff --git a/napari_cellseg3d/_tests/test_unsup_training.py b/napari_cellseg3d/_tests/test_unsup_training.py deleted file mode 100644 index acdf2c01..00000000 --- a/napari_cellseg3d/_tests/test_unsup_training.py +++ /dev/null @@ -1,45 +0,0 @@ -from pathlib import Path - -from napari_cellseg3d import config -from napari_cellseg3d.code_plugins.plugin_model_training import ( - Trainer, -) - -im_path = Path(__file__).resolve().parent / "res/test.tif" - - -def test_unsupervised_worker(make_napari_viewer_proxy): - unsup_viewer = make_napari_viewer_proxy() - widget = Trainer(viewer=unsup_viewer) - widget.device_choice.setCurrentIndex(0) - widget.model_choice.setCurrentText("WNet") - widget._toggle_unsupervised_mode(enabled=True) - - default_config = config.WNetTrainingWorkerConfig() - worker = widget._create_worker(additional_results_description="TEST_1") - excluded = ["results_path_folder", "sample_size", "weights_info"] - for attr in dir(default_config): - if not attr.startswith("__") and attr not in excluded: - assert getattr(default_config, attr) == getattr( - worker.config, attr - ) - - widget.unsupervised_images_filewidget.text_field.setText( - str(im_path.parent) - ) - widget.data = widget.create_dataset_dict_no_labs() - worker = widget._create_worker(additional_results_description="TEST_2") - dataloader, eval_dataloader, data_shape = worker._get_data() - assert eval_dataloader is None - assert data_shape == (6, 6, 6) - - widget.images_filepaths = [str(im_path)] - widget.labels_filepaths = [str(im_path)] - # widget.unsupervised_eval_data = widget.create_train_dataset_dict() - worker = widget._create_worker(additional_results_description="TEST_3") - dataloader, eval_dataloader, data_shape = worker._get_data() - assert widget.unsupervised_eval_data is not None - assert eval_dataloader is not None - assert widget.unsupervised_eval_data[0]["image"] is not None - assert widget.unsupervised_eval_data[0]["label"] is not None - assert data_shape == (6, 6, 6) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 811cbf7c..c4211ee3 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -431,7 +431,11 @@ def check_ready(self): * False and displays a warning if not """ - if self.images_filepaths == [] and self.labels_filepaths != []: + if ( + self.images_filepaths == [] + or self.labels_filepaths == [] + or len(self.images_filepaths) != len(self.labels_filepaths) + ): logger.warning("Image and label paths are not correctly set") return False return True From c1aecb88efb97740bcfc1598f6dfb75d4861581f Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 2 Aug 2023 14:35:02 +0200 Subject: [PATCH 197/250] Test unsupervised training and raise coverage --- napari_cellseg3d/_tests/fixtures.py | 39 ++++++++ ...ning_plugin.py => test_plugin_training.py} | 51 ---------- napari_cellseg3d/_tests/test_training.py | 94 +++++++++++++++++++ .../code_models/worker_training.py | 23 +++-- 4 files changed, 149 insertions(+), 58 deletions(-) rename napari_cellseg3d/_tests/{test_training_plugin.py => test_plugin_training.py} (70%) create mode 100644 napari_cellseg3d/_tests/test_training.py diff --git a/napari_cellseg3d/_tests/fixtures.py b/napari_cellseg3d/_tests/fixtures.py index b3044799..001b1d64 100644 --- a/napari_cellseg3d/_tests/fixtures.py +++ b/napari_cellseg3d/_tests/fixtures.py @@ -1,3 +1,4 @@ +import torch from qtpy.QtWidgets import QTextEdit from napari_cellseg3d.utils import LOGGER as logger @@ -17,3 +18,41 @@ def warn(self, warning): def error(self, e): raise (e) + + +class WNetFixture(torch.nn.Module): + def __init__(self): + super().__init__() + self.mock_conv = torch.nn.Conv3d(1, 1, 1) + self.mock_conv.requires_grad_(False) + + def forward_encoder(self, x): + return x + + def forward_decoder(self, x): + return x + + def forward(self, x): + return self.forward_encoder(x), self.forward_decoder(x) + + +class OptimizerFixture: + def __call__(self, x): + return x + + def zero_grad(self): + pass + + def step(self): + pass + + +class LossFixture: + def __call__(self, x): + return x + + def backward(self, x): + pass + + def item(self): + return 0 diff --git a/napari_cellseg3d/_tests/test_training_plugin.py b/napari_cellseg3d/_tests/test_plugin_training.py similarity index 70% rename from napari_cellseg3d/_tests/test_training_plugin.py rename to napari_cellseg3d/_tests/test_plugin_training.py index 09bf3e9d..3e6dfe8e 100644 --- a/napari_cellseg3d/_tests/test_training_plugin.py +++ b/napari_cellseg3d/_tests/test_plugin_training.py @@ -1,9 +1,6 @@ from pathlib import Path from napari_cellseg3d import config -from napari_cellseg3d._tests.fixtures import LogFixture -from napari_cellseg3d.code_models.models.model_test import TestModel -from napari_cellseg3d.code_models.workers_utils import TrainingReport from napari_cellseg3d.code_plugins.plugin_model_training import ( Trainer, ) @@ -109,51 +106,3 @@ def test_check_matching_losses(): worker = plugin._create_supervised_worker_from_config(config) assert plugin.loss_list == list(worker.loss_dict.keys()) - - -def test_training(make_napari_viewer_proxy, qtbot): - viewer = make_napari_viewer_proxy() - widget = Trainer(viewer) - widget.log = LogFixture() - viewer.window.add_dock_widget(widget) - - widget.images_filepath = [] - widget.labels_filepaths = [] - - assert not widget.check_ready() - - widget.images_filepaths = [im_path_str] - widget.labels_filepaths = [im_path_str] - widget.epoch_choice.setValue(1) - widget.val_interval_choice.setValue(1) - - assert widget.check_ready() - - MODEL_LIST["test"] = TestModel - widget.model_choice.addItem("test") - widget.model_choice.setCurrentText("test") - widget.unsupervised_mode = False - worker_config = widget._set_worker_config() - assert worker_config.model_info.name == "test" - worker = widget._create_supervised_worker_from_config(worker_config) - worker.config.train_data_dict = [ - {"image": im_path_str, "label": im_path_str} - ] - worker.config.val_data_dict = [ - {"image": im_path_str, "label": im_path_str} - ] - worker.config.max_epochs = 1 - worker.config.validation_interval = 2 - worker.log_parameters() - res = next(worker.train()) - - assert isinstance(res, TrainingReport) - assert res.epoch == 0 - - widget.worker = worker - res.show_plot = True - res.loss_1_values = {"loss": [1, 1, 1, 1]} - res.loss_2_values = [1, 1, 1, 1] - widget.on_yield(res) - assert widget.loss_1_values["loss"] == [1, 1, 1, 1] - assert widget.loss_2_values == [1, 1, 1, 1] diff --git a/napari_cellseg3d/_tests/test_training.py b/napari_cellseg3d/_tests/test_training.py new file mode 100644 index 00000000..14d4b1da --- /dev/null +++ b/napari_cellseg3d/_tests/test_training.py @@ -0,0 +1,94 @@ +from pathlib import Path + +from napari_cellseg3d._tests.fixtures import ( + LogFixture, + LossFixture, + OptimizerFixture, + WNetFixture, +) +from napari_cellseg3d.code_models.models.model_test import TestModel +from napari_cellseg3d.code_models.workers_utils import TrainingReport +from napari_cellseg3d.code_plugins.plugin_model_training import ( + Trainer, +) +from napari_cellseg3d.config import MODEL_LIST + +im_path = Path(__file__).resolve().parent / "res/test.tif" +im_path_str = str(im_path) + + +def test_supervised_training(make_napari_viewer_proxy): + viewer = make_napari_viewer_proxy() + widget = Trainer(viewer) + widget.log = LogFixture() + + widget.images_filepath = [] + widget.labels_filepaths = [] + + assert not widget.check_ready() + + widget.images_filepaths = [im_path_str] + widget.labels_filepaths = [im_path_str] + widget.epoch_choice.setValue(1) + widget.val_interval_choice.setValue(1) + + assert widget.check_ready() + + MODEL_LIST["test"] = TestModel + widget.model_choice.addItem("test") + widget.model_choice.setCurrentText("test") + widget.unsupervised_mode = False + worker_config = widget._set_worker_config() + assert worker_config.model_info.name == "test" + worker = widget._create_supervised_worker_from_config(worker_config) + worker.config.train_data_dict = [ + {"image": im_path_str, "label": im_path_str} + ] + worker.config.val_data_dict = [ + {"image": im_path_str, "label": im_path_str} + ] + worker.config.max_epochs = 1 + worker.config.validation_interval = 2 + worker.log_parameters() + res = next(worker.train()) + + assert isinstance(res, TrainingReport) + assert res.epoch == 0 + + widget.worker = worker + res.show_plot = True + res.loss_1_values = {"loss": [1, 1, 1, 1]} + res.loss_2_values = [1, 1, 1, 1] + widget.on_yield(res) + assert widget.loss_1_values["loss"] == [1, 1, 1, 1] + assert widget.loss_2_values == [1, 1, 1, 1] + + +def test_unsupervised_training(make_napari_viewer_proxy): + viewer = make_napari_viewer_proxy() + widget = Trainer(viewer) + widget.log = LogFixture() + widget.worker = None + widget._toggle_unsupervised_mode(enabled=True) + widget.model_choice.setCurrentText("WNet") + + widget.patch_choice.setChecked(True) + [w.setValue(4) for w in widget.patch_size_widgets] + + widget.unsupervised_images_filewidget.text_field.setText( + str(im_path.parent) + ) + # widget.start() + widget.data = widget.create_dataset_dict_no_labs() + widget.worker = widget._create_worker( + additional_results_description="wnet_test" + ) + assert widget.worker.config.train_data_dict is not None + res = next( + widget.worker.train( + provided_model=WNetFixture(), + provided_optimizer=OptimizerFixture(), + provided_loss=LossFixture(), + ) + ) + assert isinstance(res, TrainingReport) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 86c0bb78..7d5d2c92 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -362,7 +362,9 @@ def log_parameters(self): for k, v in d.items() ] - def train(self): + def train( + self, provided_model=None, provided_optimizer=None, provided_loss=None + ): try: if self.config is None: self.config = config.WNetTrainingWorkerConfig() @@ -395,11 +397,15 @@ def train(self): ################################################### self.log("- Getting the model") # Initialize the model - model = WNet( - in_channels=self.config.in_channels, - out_channels=self.config.out_channels, - num_classes=self.config.num_classes, - dropout=self.config.dropout, + model = ( + WNet( + in_channels=self.config.in_channels, + out_channels=self.config.out_channels, + num_classes=self.config.num_classes, + dropout=self.config.dropout, + ) + if provided_model is None + else provided_model ) model.to(device) @@ -458,7 +464,8 @@ def train(self): optimizer = torch.optim.Adam( model.parameters(), lr=self.config.learning_rate ) - + if provided_optimizer is not None: + optimizer = provided_optimizer self.log("- Getting the loss functions") # Initialize the Ncuts loss function criterionE = SoftNCutsLoss( @@ -538,6 +545,8 @@ def train(self): beta = self.config.rec_loss_weight loss = alpha * Ncuts + beta * reconstruction_loss + if provided_loss is not None: + loss = provided_loss epoch_loss += loss.item() # if WANDB_INSTALLED: # wandb.log({"Weighted sum of losses": loss.item()}) From d35da411c038ed9050db398d7be6d52da6ab1f27 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 2 Aug 2023 15:11:10 +0200 Subject: [PATCH 198/250] WNet eval test --- napari_cellseg3d/_tests/test_training.py | 12 + .../code_models/worker_training.py | 429 +++++++++--------- 2 files changed, 223 insertions(+), 218 deletions(-) diff --git a/napari_cellseg3d/_tests/test_training.py b/napari_cellseg3d/_tests/test_training.py index 14d4b1da..1ae0c2d3 100644 --- a/napari_cellseg3d/_tests/test_training.py +++ b/napari_cellseg3d/_tests/test_training.py @@ -92,3 +92,15 @@ def test_unsupervised_training(make_napari_viewer_proxy): ) ) assert isinstance(res, TrainingReport) + assert not res.show_plot + widget.worker.config.eval_volume_dict = [ + {"image": im_path_str, "label": im_path_str} + ] + widget.worker._get_data() + eval_res = widget.worker._eval( + model=WNetFixture(), + epoch=-10, + ) + assert isinstance(eval_res, TrainingReport) + assert eval_res.show_plot + assert eval_res.epoch == -10 diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 7d5d2c92..8522b183 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -153,6 +153,21 @@ def __init__( super().__init__() self.config = worker_config + self.dice_metric = DiceMetric( + include_background=True, reduction="mean", get_not_nans=False + ) + self.normalize_function = utils.remap_image + self.start_time = time.time() + self.ncuts_losses = [] + self.rec_losses = [] + self.total_losses = [] + self.best_dice = -1 + self.dice_values = [] + + self.dataloader: DataLoader = None + self.eval_dataloader: DataLoader = None + self.data_shape = None + def get_patch_dataset(self, train_transforms): """Creates a Dataset from the original data using the tifffile library @@ -288,13 +303,15 @@ def _get_data(self): if self.config.sampling: logger.debug("Loading patch dataset") - (data_shape, dataset) = self.get_patch_dataset(train_transforms) + (self.data_shape, dataset) = self.get_patch_dataset( + train_transforms + ) else: logger.debug("Loading volume dataset") - (data_shape, dataset) = self.get_dataset(train_transforms) + (self.data_shape, dataset) = self.get_dataset(train_transforms) - logger.debug(f"Data shape : {data_shape}") - dataloader = DataLoader( + logger.debug(f"Data shape : {self.data_shape}") + self.dataloader = DataLoader( dataset, batch_size=self.config.batch_size, shuffle=True, @@ -305,7 +322,7 @@ def _get_data(self): if self.config.eval_volume_dict is not None: eval_dataset = self.get_dataset_eval(self.config.eval_volume_dict) - eval_dataloader = DataLoader( + self.eval_dataloader = DataLoader( eval_dataset, batch_size=self.config.batch_size, shuffle=False, @@ -313,8 +330,8 @@ def _get_data(self): collate_fn=pad_list_data_collate, ) else: - eval_dataloader = None - return dataloader, eval_dataloader, data_shape + self.eval_dataloader = None + return self.dataloader, self.eval_dataloader, self.data_shape def log_parameters(self): self.log("*" * 20) @@ -380,18 +397,14 @@ def train( set_determinism(seed=self.config.deterministic_config.seed) torch.use_deterministic_algorithms(True, warn_only=True) - normalize_function = utils.remap_image device = self.config.device self.log_parameters() self.log("Initializing training...") self.log("- Getting the data") - dataloader, eval_dataloader, data_shape = self._get_data() + self._get_data() - dice_metric = DiceMetric( - include_background=True, reduction="mean", get_not_nans=False - ) ################################################### # Training the model # ################################################### @@ -469,7 +482,7 @@ def train( self.log("- Getting the loss functions") # Initialize the Ncuts loss function criterionE = SoftNCutsLoss( - data_shape=data_shape, + data_shape=self.data_shape, device=device, intensity_sigma=self.config.intensity_sigma, spatial_sigma=self.config.spatial_sigma, @@ -491,13 +504,6 @@ def train( self.log("Training the model") self.log("*" * 20) - startTime = time.time() - ncuts_losses = [] - rec_losses = [] - total_losses = [] - best_dice = -1 - dice_values = [] - # Train the model for epoch in range(self.config.max_epochs): self.log(f"Epoch {epoch + 1} of {self.config.max_epochs}") @@ -506,13 +512,13 @@ def train( epoch_rec_loss = 0 epoch_loss = 0 - for _i, batch in enumerate(dataloader): + for _i, batch in enumerate(self.dataloader): # raise NotImplementedError("testing") image_batch = batch["image"].to(device) # Normalize the image for i in range(image_batch.shape[0]): for j in range(image_batch.shape[1]): - image_batch[i, j] = normalize_function( + image_batch[i, j] = self.normalize_function( image_batch[i, j] ) @@ -554,10 +560,10 @@ def train( optimizer.step() if self._abort_requested: - dataloader = None - del dataloader - eval_dataloader = None - del eval_dataloader + self.dataloader = None + del self.dataloader + self.eval_dataloader = None + del self.eval_dataloader model = None del model optimizer = None @@ -572,11 +578,13 @@ def train( show_plot=False, weights=model.state_dict() ) - ncuts_losses.append(epoch_ncuts_loss / len(dataloader)) - rec_losses.append(epoch_rec_loss / len(dataloader)) - total_losses.append(epoch_loss / len(dataloader)) + self.ncuts_losses.append( + epoch_ncuts_loss / len(self.dataloader) + ) + self.rec_losses.append(epoch_rec_loss / len(self.dataloader)) + self.total_losses.append(epoch_loss / len(self.dataloader)) - if eval_dataloader is None: + if self.eval_dataloader is None: try: enc_out = enc[0].detach().cpu().numpy() dec_out = dec[0].detach().cpu().numpy() @@ -606,8 +614,8 @@ def train( yield TrainingReport( show_plot=True, epoch=epoch, - loss_1_values={"SoftNCuts": ncuts_losses}, - loss_2_values=rec_losses, + loss_1_values={"SoftNCuts": self.ncuts_losses}, + loss_2_values=self.rec_losses, weights=model.state_dict(), images_dict=images_dict, ) @@ -615,207 +623,55 @@ def train( pass # if WANDB_INSTALLED: - # wandb.log({"Ncuts loss_epoch": ncuts_losses[-1]}) - # wandb.log({"Reconstruction loss_epoch": rec_losses[-1]}) - # wandb.log({"Sum of losses_epoch": total_losses[-1]}) + # wandb.log({"Ncuts loss_epoch": self.ncuts_losses[-1]}) + # wandb.log({"Reconstruction loss_epoch": self.rec_losses[-1]}) + # wandb.log({"Sum of losses_epoch": self.total_losses[-1]}) # wandb.log({"epoch": epoch}) # wandb.log({"learning_rate model": optimizerW.param_groups[0]["lr"]}) # wandb.log({"learning_rate encoder": optimizerE.param_groups[0]["lr"]}) # wandb.log({"learning_rate model": optimizer.param_groups[0]["lr"]}) - self.log(f"Ncuts loss: {ncuts_losses[-1]:.5f}") - self.log(f"Reconstruction loss: {rec_losses[-1]:.5f}") - self.log(f"Weighted sum of losses: {total_losses[-1]:.5f}") + self.log(f"Ncuts loss: {self.ncuts_losses[-1]:.5f}") + self.log(f"Reconstruction loss: {self.rec_losses[-1]:.5f}") + self.log( + f"Weighted sum of losses: {self.total_losses[-1]:.5f}" + ) if epoch > 0: self.log( - f"Ncuts loss difference: {ncuts_losses[-1] - ncuts_losses[-2]:.5f}" + f"Ncuts loss difference: {self.ncuts_losses[-1] - self.ncuts_losses[-2]:.5f}" ) self.log( - f"Reconstruction loss difference: {rec_losses[-1] - rec_losses[-2]:.5f}" + f"Reconstruction loss difference: {self.rec_losses[-1] - self.rec_losses[-2]:.5f}" ) self.log( - f"Weighted sum of losses difference: {total_losses[-1] - total_losses[-2]:.5f}" + f"Weighted sum of losses difference: {self.total_losses[-1] - self.total_losses[-2]:.5f}" ) if ( - eval_dataloader is not None + self.eval_dataloader is not None and (epoch + 1) % self.config.validation_interval == 0 ): model.eval() self.log("Validating...") - with torch.no_grad(): - for _k, val_data in enumerate(eval_dataloader): - val_inputs, val_labels = ( - val_data["image"].to(device), - val_data["label"].to(device), - ) - - # normalize val_inputs across channels - for i in range(val_inputs.shape[0]): - for j in range(val_inputs.shape[1]): - val_inputs[i][j] = normalize_function( - val_inputs[i][j] - ) - logger.debug( - f"Val inputs shape: {val_inputs.shape}" - ) - val_outputs = sliding_window_inference( - val_inputs, - roi_size=[64, 64, 64], - sw_batch_size=1, - predictor=model.forward_encoder, - overlap=0.1, - mode="gaussian", - sigma_scale=0.01, - progress=True, - ) - val_decoder_outputs = sliding_window_inference( - val_outputs, - roi_size=[64, 64, 64], - sw_batch_size=1, - predictor=model.forward_decoder, - overlap=0.1, - mode="gaussian", - sigma_scale=0.01, - progress=True, - ) - val_outputs = AsDiscrete(threshold=0.5)( - val_outputs - ) - logger.debug( - f"Val outputs shape: {val_outputs.shape}" - ) - logger.debug( - f"Val labels shape: {val_labels.shape}" - ) - logger.debug( - f"Val decoder outputs shape: {val_decoder_outputs.shape}" - ) - - # dices = [] - # Find in which channel the labels are (avoid background) - # for channel in range(val_outputs.shape[1]): - # dices.append( - # utils.dice_coeff( - # y_pred=val_outputs[ - # 0, channel : (channel + 1), :, :, : - # ], - # y_true=val_labels[0], - # ) - # ) - # logger.debug(f"DICE COEFF: {dices}") - # max_dice_channel = torch.argmax( - # torch.Tensor(dices) - # ) - # logger.debug( - # f"MAX DICE CHANNEL: {max_dice_channel}" - # ) - dice_metric( - y_pred=val_outputs, - # [ - # :, - # max_dice_channel : (max_dice_channel + 1), - # :, - # :, - # :, - # ], - y=val_labels, - ) - - # aggregate the final mean dice result - metric = dice_metric.aggregate().item() - dice_values.append(metric) - self.log(f"Validation Dice score: {metric:.3f}") - if best_dice < metric <= 1: - best_dice = metric - # save the best model - save_best_path = self.config.results_path_folder - # save_best_path.mkdir(parents=True, exist_ok=True) - save_best_name = "wnet" - save_path = ( - str(Path(save_best_path) / save_best_name) - + "_best_metric.pth" - ) - self.log(f"Saving new best model to {save_path}") - torch.save(model.state_dict(), save_path) - - # if WANDB_INSTALLED: - # log validation dice score for each validation round - # wandb.log({"val/dice_metric": metric}) - - dec_out_val = ( - val_decoder_outputs[0] - .detach() - .cpu() - .numpy() - .copy() - ) - enc_out_val = ( - val_outputs[0].detach().cpu().numpy().copy() - ) - lab_out_val = ( - val_labels[0].detach().cpu().numpy().copy() - ) - val_in = val_inputs[0].detach().cpu().numpy().copy() - - display_dict = { - "Reconstruction": { - "data": np.squeeze(dec_out_val), - "cmap": "gist_earth", - }, - "Segmentation": { - "data": np.squeeze(enc_out_val), - "cmap": "turbo", - }, - "Inputs": { - "data": np.squeeze(val_in), - "cmap": "inferno", - }, - "Labels": { - "data": np.squeeze(lab_out_val), - "cmap": "bop blue", - }, - } - val_decoder_outputs = None - del val_decoder_outputs - val_outputs = None - del val_outputs - val_labels = None - del val_labels - val_inputs = None - del val_inputs + yield self._eval(model, epoch) # validation - yield TrainingReport( - epoch=epoch, - loss_1_values={ - "SoftNCuts": ncuts_losses, - "Dice metric": dice_values, - }, - loss_2_values=rec_losses, - weights=model.state_dict(), - images_dict=display_dict, - ) - - # reset the status for next validation round - dice_metric.reset() - - if self._abort_requested: - dataloader = None - del dataloader - eval_dataloader = None - del eval_dataloader - model = None - del model - optimizer = None - del optimizer - criterionE = None - del criterionE - criterionW = None - del criterionW - torch.cuda.empty_cache() + if self._abort_requested: + self.dataloader = None + del self.dataloader + self.eval_dataloader = None + del self.eval_dataloader + model = None + del model + optimizer = None + del optimizer + criterionE = None + del criterionE + criterionW = None + del criterionW + torch.cuda.empty_cache() eta = ( - (time.time() - startTime) + (time.time() - self.start_time) * (self.config.max_epochs / (epoch + 1) - 1) / 60 ) @@ -830,12 +686,12 @@ def train( ) self.log("Training finished") - if best_dice > -1: - self.log(f"Best dice metric : {best_dice}") + if self.best_dice > -1: + self.log(f"Best dice metric : {self.best_dice}") # if WANDB_INSTALLED and self.config.eval_volume_directory is not None: # wandb.log( # { - # "best_dice_metric": best_dice, + # "self.best_dice_metric": self.best_dice, # "best_metric_epoch": best_dice_epoch, # } # ) @@ -859,11 +715,11 @@ def train( # model_artifact.add_file(self.config.save_model_path) # wandb.log_artifact(model_artifact) - # return ncuts_losses, rec_losses, model + # return self.ncuts_losses, self.rec_losses, model dataloader = None del dataloader - eval_dataloader = None - del eval_dataloader + self.eval_dataloader = None + del self.eval_dataloader model = None del model optimizer = None @@ -880,6 +736,143 @@ def train( self.quit() raise e + def _eval(self, model, epoch) -> TrainingReport: + with torch.no_grad(): + device = self.config.device + for _k, val_data in enumerate(self.eval_dataloader): + val_inputs, val_labels = ( + val_data["image"].to(device), + val_data["label"].to(device), + ) + + # normalize val_inputs across channels + for i in range(val_inputs.shape[0]): + for j in range(val_inputs.shape[1]): + val_inputs[i][j] = self.normalize_function( + val_inputs[i][j] + ) + logger.debug(f"Val inputs shape: {val_inputs.shape}") + val_outputs = sliding_window_inference( + val_inputs, + roi_size=[64, 64, 64], + sw_batch_size=1, + predictor=model.forward_encoder, + overlap=0.1, + mode="gaussian", + sigma_scale=0.01, + progress=True, + ) + val_decoder_outputs = sliding_window_inference( + val_outputs, + roi_size=[64, 64, 64], + sw_batch_size=1, + predictor=model.forward_decoder, + overlap=0.1, + mode="gaussian", + sigma_scale=0.01, + progress=True, + ) + val_outputs = AsDiscrete(threshold=0.5)(val_outputs) + logger.debug(f"Val outputs shape: {val_outputs.shape}") + logger.debug(f"Val labels shape: {val_labels.shape}") + logger.debug( + f"Val decoder outputs shape: {val_decoder_outputs.shape}" + ) + + # dices = [] + # Find in which channel the labels are (avoid background) + # for channel in range(val_outputs.shape[1]): + # dices.append( + # utils.dice_coeff( + # y_pred=val_outputs[ + # 0, channel : (channel + 1), :, :, : + # ], + # y_true=val_labels[0], + # ) + # ) + # logger.debug(f"DICE COEFF: {dices}") + # max_dice_channel = torch.argmax( + # torch.Tensor(dices) + # ) + # logger.debug( + # f"MAX DICE CHANNEL: {max_dice_channel}" + # ) + self.dice_metric( + y_pred=val_outputs, + # [ + # :, + # max_dice_channel : (max_dice_channel + 1), + # :, + # :, + # :, + # ], + y=val_labels, + ) + + # aggregate the final mean dice result + metric = self.dice_metric.aggregate().item() + self.dice_values.append(metric) + self.log(f"Validation Dice score: {metric:.3f}") + if self.best_dice < metric <= 1: + self.best_dice = metric + # save the best model + save_best_path = self.config.results_path_folder + # save_best_path.mkdir(parents=True, exist_ok=True) + save_best_name = "wnet" + save_path = ( + str(Path(save_best_path) / save_best_name) + + "_best_metric.pth" + ) + self.log(f"Saving new best model to {save_path}") + torch.save(model.state_dict(), save_path) + + # if WANDB_INSTALLED: + # log validation dice score for each validation round + # wandb.log({"val/dice_metric": metric}) + self.dice_metric.reset() + dec_out_val = val_decoder_outputs[0].detach().cpu().numpy().copy() + enc_out_val = val_outputs[0].detach().cpu().numpy().copy() + lab_out_val = val_labels[0].detach().cpu().numpy().copy() + val_in = val_inputs[0].detach().cpu().numpy().copy() + + display_dict = { + "Reconstruction": { + "data": np.squeeze(dec_out_val), + "cmap": "gist_earth", + }, + "Segmentation": { + "data": np.squeeze(enc_out_val), + "cmap": "turbo", + }, + "Inputs": { + "data": np.squeeze(val_in), + "cmap": "inferno", + }, + "Labels": { + "data": np.squeeze(lab_out_val), + "cmap": "bop blue", + }, + } + val_decoder_outputs = None + del val_decoder_outputs + val_outputs = None + del val_outputs + val_labels = None + del val_labels + val_inputs = None + del val_inputs + + return TrainingReport( + epoch=epoch, + loss_1_values={ + "SoftNCuts": self.ncuts_losses, + "Dice metric": self.dice_values, + }, + loss_2_values=self.rec_losses, + weights=model.state_dict(), + images_dict=display_dict, + ) + class SupervisedTrainingWorker(TrainingWorkerBase): """A custom worker to run supervised training jobs in. From fb1b130629106abb5d829d33d4414f26e75bb9d2 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 2 Aug 2023 15:19:47 +0200 Subject: [PATCH 199/250] Fix order for model deletion --- napari_cellseg3d/_tests/test_training.py | 18 ++++++++++++++++++ .../code_models/worker_training.py | 8 ++++---- 2 files changed, 22 insertions(+), 4 deletions(-) diff --git a/napari_cellseg3d/_tests/test_training.py b/napari_cellseg3d/_tests/test_training.py index 1ae0c2d3..14afd94e 100644 --- a/napari_cellseg3d/_tests/test_training.py +++ b/napari_cellseg3d/_tests/test_training.py @@ -1,5 +1,7 @@ from pathlib import Path +import pytest + from napari_cellseg3d._tests.fixtures import ( LogFixture, LossFixture, @@ -93,6 +95,22 @@ def test_unsupervised_training(make_napari_viewer_proxy): ) assert isinstance(res, TrainingReport) assert not res.show_plot + widget.worker._abort_requested = True + res = next( + widget.worker.train( + provided_model=WNetFixture(), + provided_optimizer=OptimizerFixture(), + provided_loss=LossFixture(), + ) + ) + assert isinstance(res, TrainingReport) + assert not res.show_plot + with pytest.raises( + AttributeError, + match="'WNetTrainingWorker' object has no attribute 'model'", + ): + assert widget.worker.model is None + widget.worker.config.eval_volume_dict = [ {"image": im_path_str, "label": im_path_str} ] diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 8522b183..895b261b 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -559,6 +559,10 @@ def train( loss.backward(loss) optimizer.step() + yield TrainingReport( + show_plot=False, weights=model.state_dict() + ) + if self._abort_requested: self.dataloader = None del self.dataloader @@ -574,10 +578,6 @@ def train( del criterionW torch.cuda.empty_cache() - yield TrainingReport( - show_plot=False, weights=model.state_dict() - ) - self.ncuts_losses.append( epoch_ncuts_loss / len(self.dataloader) ) From e4b10a34daf9b1924d5e7fa2a66e9e603f8938d6 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 2 Aug 2023 16:28:04 +0200 Subject: [PATCH 200/250] Extend supervised train tests --- napari_cellseg3d/_tests/fixtures.py | 31 ++++++++++--- napari_cellseg3d/_tests/test_training.py | 37 ++++++++++------ .../code_models/worker_training.py | 43 +++++++++++++------ 3 files changed, 78 insertions(+), 33 deletions(-) diff --git a/napari_cellseg3d/_tests/fixtures.py b/napari_cellseg3d/_tests/fixtures.py index 001b1d64..4dba351f 100644 --- a/napari_cellseg3d/_tests/fixtures.py +++ b/napari_cellseg3d/_tests/fixtures.py @@ -36,23 +36,42 @@ def forward(self, x): return self.forward_encoder(x), self.forward_decoder(x) -class OptimizerFixture: - def __call__(self, x): +class ModelFixture(torch.nn.Module): + def __init__(self): + super().__init__() + self.mock_conv = torch.nn.Conv3d(1, 1, 1) + self.mock_conv.requires_grad_(False) + + def forward(self, x): return x + +class OptimizerFixture: + def __init__(self): + self.param_groups = [] + self.param_groups.append({"lr": 0}) + def zero_grad(self): pass - def step(self): + def step(self, *args): + pass + + +class SchedulerFixture: + def step(self, *args): pass class LossFixture: - def __call__(self, x): - return x + def __call__(self, *args): + return self - def backward(self, x): + def backward(self, *args): pass def item(self): return 0 + + def detach(self): + return self diff --git a/napari_cellseg3d/_tests/test_training.py b/napari_cellseg3d/_tests/test_training.py index 14afd94e..dc9d17ba 100644 --- a/napari_cellseg3d/_tests/test_training.py +++ b/napari_cellseg3d/_tests/test_training.py @@ -5,7 +5,9 @@ from napari_cellseg3d._tests.fixtures import ( LogFixture, LossFixture, + ModelFixture, OptimizerFixture, + SchedulerFixture, WNetFixture, ) from napari_cellseg3d.code_models.models.model_test import TestModel @@ -33,6 +35,7 @@ def test_supervised_training(make_napari_viewer_proxy): widget.labels_filepaths = [im_path_str] widget.epoch_choice.setValue(1) widget.val_interval_choice.setValue(1) + widget.device_choice.setCurrentIndex(0) assert widget.check_ready() @@ -49,13 +52,19 @@ def test_supervised_training(make_napari_viewer_proxy): worker.config.val_data_dict = [ {"image": im_path_str, "label": im_path_str} ] - worker.config.max_epochs = 1 + worker.config.max_epochs = 2 worker.config.validation_interval = 2 - worker.log_parameters() - res = next(worker.train()) - assert isinstance(res, TrainingReport) - assert res.epoch == 0 + worker.log_parameters() + for res_i in worker.train( + provided_model=ModelFixture(), + provided_optimizer=OptimizerFixture(), + provided_loss=LossFixture(), + provided_scheduler=SchedulerFixture(), + ): + assert isinstance(res_i, TrainingReport) + res = res_i + assert res.epoch == 1 widget.worker = worker res.show_plot = True @@ -86,15 +95,15 @@ def test_unsupervised_training(make_napari_viewer_proxy): additional_results_description="wnet_test" ) assert widget.worker.config.train_data_dict is not None - res = next( - widget.worker.train( - provided_model=WNetFixture(), - provided_optimizer=OptimizerFixture(), - provided_loss=LossFixture(), - ) - ) - assert isinstance(res, TrainingReport) - assert not res.show_plot + widget.worker.config.max_epochs = 1 + for res_i in widget.worker.train( + provided_model=WNetFixture(), + provided_optimizer=OptimizerFixture(), + provided_loss=LossFixture(), + ): + assert isinstance(res_i, TrainingReport) + res = res_i + assert res.epoch == 0 widget.worker._abort_requested = True res = next( widget.worker.train( diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 895b261b..0d0c0659 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -999,7 +999,13 @@ def log_parameters(self): # self.log("\n") # self.log("-" * 20) - def train(self): + def train( + self, + provided_model=None, + provided_optimizer=None, + provided_loss=None, + provided_scheduler=None, + ): """Trains the PyTorch model for the given number of epochs, with the selected model and data, using the chosen batch size, validation interval, loss function, and number of samples. Will perform validation once every :py:obj:`val_interval` and save results if the mean dice is better @@ -1070,13 +1076,16 @@ def train(self): self.config.train_data_dict[0] ) check = data_check["image"].shape - do_sampling = self.config.sampling - size = self.config.sample_size if do_sampling else check - PADDING = utils.get_padding_dim(size) - model = model_class(input_img_size=PADDING, use_checkpoint=True) + + model = ( + model_class(input_img_size=PADDING, use_checkpoint=True) + if provided_model is None + else provided_model + ) + device = torch.device(self.config.device) model = model.to(device) @@ -1276,8 +1285,10 @@ def get_patch_loader_func(num_samples): logger.info("\nDone") logger.debug("Optimizer") - optimizer = torch.optim.Adam( - model.parameters(), self.config.learning_rate + optimizer = ( + torch.optim.Adam(model.parameters(), self.config.learning_rate) + if provided_optimizer is None + else provided_optimizer ) factor = self.config.scheduler_factor @@ -1286,12 +1297,16 @@ def get_patch_loader_func(num_samples): self.log("Setting it to 0.5") factor = 0.5 - scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - optimizer=optimizer, - mode="min", - factor=factor, - patience=self.config.scheduler_patience, - verbose=VERBOSE_SCHEDULER, + scheduler = ( + torch.optim.lr_scheduler.ReduceLROnPlateau( + optimizer=optimizer, + mode="min", + factor=factor, + patience=self.config.scheduler_patience, + verbose=VERBOSE_SCHEDULER, + ) + if provided_scheduler is None + else provided_scheduler ) dice_metric = DiceMetric( include_background=True, reduction="mean", ignore_empty=False @@ -1342,6 +1357,8 @@ def get_patch_loader_func(num_samples): # device = torch.device(self.config.device) self.set_loss_from_config() + if provided_loss is not None: + self.loss_function = provided_loss # if model_name == "test": # self.quit() From 0c3450aa939948f5563f9906140dae8376969f84 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 2 Aug 2023 17:11:06 +0200 Subject: [PATCH 201/250] Started docs update --- docs/res/code/model_framework.rst | 2 +- docs/res/code/plugin_model_training.rst | 4 +-- docs/res/code/workers.rst | 34 +++++++++++++++--- docs/res/guides/detailed_walkthrough.rst | 17 +++++---- docs/res/guides/training_module_guide.rst | 7 ++-- docs/res/guides/training_wnet.rst | 36 +++++++++++++++---- napari_cellseg3d/_tests/test_training.py | 2 +- .../code_models/worker_training.py | 8 ++--- 8 files changed, 80 insertions(+), 30 deletions(-) diff --git a/docs/res/code/model_framework.rst b/docs/res/code/model_framework.rst index a3483f5a..63eef232 100644 --- a/docs/res/code/model_framework.rst +++ b/docs/res/code/model_framework.rst @@ -12,7 +12,7 @@ Class : ModelFramework Methods ********************** .. autoclass:: napari_cellseg3d.code_models.model_framework::ModelFramework - :members: __init__, send_log, save_log, save_log_to_path, display_status_report, create_train_dataset_dict, get_model, get_available_models, get_device, empty_cuda_cache + :members: __init__, send_log, save_log, save_log_to_path, display_status_report, create_train_dataset_dict, get_available_models, get_device, empty_cuda_cache :noindex: diff --git a/docs/res/code/plugin_model_training.rst b/docs/res/code/plugin_model_training.rst index dc1271fc..6a2a39b8 100644 --- a/docs/res/code/plugin_model_training.rst +++ b/docs/res/code/plugin_model_training.rst @@ -11,7 +11,7 @@ Class : Trainer Methods ********************** .. autoclass:: napari_cellseg3d.code_plugins.plugin_model_training::Trainer - :members: __init__, get_loss, check_ready, send_log, start, on_start, on_finish, on_error, on_yield, plot_loss, update_loss_plot + :members: __init__, check_ready, send_log, start, on_start, on_finish, on_error, on_yield, update_loss_plot :noindex: @@ -19,4 +19,4 @@ Methods Attributes ********************* .. autoclass:: napari_cellseg3d.code_plugins.plugin_model_training::Trainer - :members: _viewer, worker, loss_dict, canvas, train_loss_plot, dice_metric_plot + :members: _viewer, worker, canvas diff --git a/docs/res/code/workers.rst b/docs/res/code/workers.rst index 1f5167ad..5964e004 100644 --- a/docs/res/code/workers.rst +++ b/docs/res/code/workers.rst @@ -10,7 +10,7 @@ Class : LogSignal Attributes ************************ -.. autoclass:: napari_cellseg3d.code_models.workers::LogSignal +.. autoclass:: napari_cellseg3d.code_models.workers_utils::LogSignal :members: log_signal :noindex: @@ -24,14 +24,14 @@ Class : InferenceWorker Methods ************************ -.. autoclass:: napari_cellseg3d.code_models.workers::InferenceWorker +.. autoclass:: napari_cellseg3d.code_models.worker_inference::InferenceWorker :members: __init__, log, create_inference_dict, inference :noindex: .. _here: https://napari-staging-site.github.io/guides/stable/threading.html -Class : TrainingWorker +Class : TrainingWorkerBase ------------------------------------------- .. important:: @@ -39,6 +39,32 @@ Class : TrainingWorker Methods ************************ -.. autoclass:: napari_cellseg3d.code_models.workers::TrainingWorker +.. autoclass:: napari_cellseg3d.code_models.worker_training::TrainingWorkerBase :members: __init__, log, train :noindex: + + +Class : WNetTrainingWorker +------------------------------------------- + +.. important:: + Inherits from :py:class:`TrainingWorkerBase` + +Methods +************************ +.. autoclass:: napari_cellseg3d.code_models.worker_training::WNetTrainingWorker + :members: __init__, train, eval, get_patch_dataset, get_dataset_eval, get_dataset + :noindex: + + +Class : SupervisedTrainingWorker +------------------------------------------- + +.. important:: + Inherits from :py:class:`TrainingWorkerBase` + +Methods +************************ +.. autoclass:: napari_cellseg3d.code_models.worker_training::SupervisedTrainingWorker + :members: __init__, train + :noindex: diff --git a/docs/res/guides/detailed_walkthrough.rst b/docs/res/guides/detailed_walkthrough.rst index 56ef54ed..4fd04510 100644 --- a/docs/res/guides/detailed_walkthrough.rst +++ b/docs/res/guides/detailed_walkthrough.rst @@ -120,9 +120,9 @@ Finally, the last tab lets you choose : * SegResNet is a lightweight model (low memory requirements) from MONAI originally designed for 3D fMRI data. * VNet is a larger (than SegResNet) CNN from MONAI designed for medical image segmentation. - * TRAILMAP is our PyTorch implementation of a 3D CNN model trained for axonal detection in cleared tissue. * TRAILMAP_MS is our implementation in PyTorch additionally trained on mouse cortical neural nuclei from mesoSPIM data. - * Note, the code is very modular, so it is relatively straightforward to use (and contribute) your model as well. + * SwinUNetR is a MONAI implementation of the SwinUNetR model. It is costly in compute and memory, but can achieve high performance. + * WNet is our reimplementation of an unsupervised model, which can be used to produce segmentation without labels. * The loss : for object detection in 3D volumes you'll likely want to use the Dice or Dice-focal Loss. @@ -239,13 +239,12 @@ Scoring, review, analysis ---------------------------- -.. Using the metrics utility module, you can compare the model's predictions to any ground truth -labels you might have. -Simply provide your prediction and ground truth labels, and compute the results. -A Dice metric of 1 indicates perfect matching, whereas a score of 0 indicates complete mismatch. -Select which score **you consider as sub-optimal**, and all results below this will be **shown in napari**. -If at any time the **orientation of your prediction labels changed compared to the ground truth**, check the -"Find best orientation" option to compensate for it. +.. Using the metrics utility module, you can compare the model's predictions to any ground truth labels you might have. + Simply provide your prediction and ground truth labels, and compute the results. + A Dice metric of 1 indicates perfect matching, whereas a score of 0 indicates complete mismatch. + Select which score **you consider as sub-optimal**, and all results below this will be **shown in napari**. + If at any time the **orientation of your prediction labels changed compared to the ground truth**, check the + "Find best orientation" option to compensate for it. Labels review diff --git a/docs/res/guides/training_module_guide.rst b/docs/res/guides/training_module_guide.rst index 0a577b86..1a424e98 100644 --- a/docs/res/guides/training_module_guide.rst +++ b/docs/res/guides/training_module_guide.rst @@ -4,7 +4,7 @@ Training module guide - Unsupervised models ============================================== .. important:: - The WNet training is for now only available in the provided jupyter notebook, in the ``notebooks`` folder. + The WNet training is for now available as part of the plugin in the Training module. Please see the :ref:`training_wnet` section for more information. Training module guide - Supervised models @@ -25,14 +25,15 @@ Model Link to original paper ============== ================================================================================================ VNet `Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation`_ SegResNet `3D MRI brain tumor segmentation using autoencoder regularization`_ -TRAILMAP_MS A PyTorch implementation of the `TRAILMAP project on GitHub`_ pretrained with MesoSpim data -TRAILMAP An implementation of the `TRAILMAP project on GitHub`_ using a `3DUNet for PyTorch`_ +TRAILMAP_MS An implementation of the `TRAILMAP project on GitHub`_ using `3DUNet for PyTorch`_ +SwinUNetR `Swin UNETR, Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images`_ ============== ================================================================================================ .. _Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation: https://arxiv.org/pdf/1606.04797.pdf .. _3D MRI brain tumor segmentation using autoencoder regularization: https://arxiv.org/pdf/1810.11654.pdf .. _TRAILMAP project on GitHub: https://github.com/AlbertPun/TRAILMAP .. _3DUnet for Pytorch: https://github.com/wolny/pytorch-3dunet +.. _Swin UNETR, Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images: https://arxiv.org/abs/2201.01266 .. important:: | The machine learning models used by this program require all images of a dataset to be of the same size. diff --git a/docs/res/guides/training_wnet.rst b/docs/res/guides/training_wnet.rst index ecd20542..974a90e9 100644 --- a/docs/res/guides/training_wnet.rst +++ b/docs/res/guides/training_wnet.rst @@ -15,21 +15,45 @@ the model was trained on; you can retrain from our pretrained model to your set The model has two losses, the SoftNCut loss which clusters pixels according to brightness, and a reconstruction loss, either Mean Square Error (MSE) or Binary Cross Entropy (BCE). Unlike the original paper, these losses are added in a weighted sum and the backward pass is performed for the whole model at once. -The SoftNcuts is bounded between 0 and 1; the MSE may take large values. +The SoftNcuts is bounded between 0 and 1; the MSE may take large positive values. -For good performance, one should wait for the SoftNCut to reach a plateau, the reconstruction loss must also diminish but it's generally less critical. +For good performance, one should wait for the SoftNCut to reach a plateau; the reconstruction loss must also diminish but it's generally less critical. +Parameters +------------------------------- + +When using the WNet training module, additional options will be provided in the Advanced tab of the training module: + +- Number of classes : number of classes to segment (default 2). Additional classes will result in a more progressive segmentation according to brightness; can be useful if you have "halos" around your objects or artifacts with a significantly different brightness. +- Reconstruction loss : either MSE or BCE (default MSE). MSE is more sensitive to outliers, but can be more precise; BCE is more robust to outliers but can be less precise. + +- NCuts parameters: + - Intensity sigma : standard deviation of the feature similarity term (brightness here, default 1) + - Spatial sigma : standard deviation of the spatial proximity term (default 4) + - Radius : radius of the loss computation in pixels (default 2) + +.. note:: + Intensity sigma depends on pixel values in the image. The default of 1 is tailored to images being mapped between 0 and 100, which is done automatically by the plugin. +.. note:: + Raising the radius might improve performance in some cases, but will also greatly increase computation time. + +- Weights for the sum of losses : + - NCuts weight : weight of the NCuts loss (default 0.5) + - Reconstruction weight : weight of the reconstruction loss (default 0.5*1e-2) + +.. note:: + The weight of the reconstruction loss should be adjusted according to its empirical value; ideally the reconstruction loss should be of the same order of magnitude as the NCuts loss after being multiplied by its weight. Common issues troubleshooting ------------------------------ -If you do not find a satisfactory answer here, please `open an issue`_ ! +If you do not find a satisfactory answer here, please do not hesitate to `open an issue`_ on GitHub. -- **The NCuts loss explodes after a few epochs** : Lower the learning rate +- **The NCuts loss explodes after a few epochs** : Lower the learning rate, first by a factor of two, then ten. - **The NCuts loss does not converge and is unstable** : - The normalization step might not be adapted to your images. Disable normalization and change intensity_sigma according to the distribution of values in your image; for reference, by default images are remapped to values between 0 and 100, and intensity_sigma=1. + The normalization step might not be adapted to your images. Disable normalization and change intensity_sigma according to the distribution of values in your image. For reference, by default images are remapped to values between 0 and 100, and intensity_sigma=1. -- **Reconstruction (decoder) performance is poor** : switch to BCE and set the scaling factor of the reconstruction loss ot 0.5, OR adjust the weight of the MSE loss to make it closer to 1. +- **Reconstruction (decoder) performance is poor** : switch to BCE and set the scaling factor of the reconstruction loss ot 0.5, OR adjust the weight of the MSE loss to make it closer to 1 in the weighted sum. .. _WNet, A Deep Model for Fully Unsupervised Image Segmentation: https://arxiv.org/abs/1711.08506 diff --git a/napari_cellseg3d/_tests/test_training.py b/napari_cellseg3d/_tests/test_training.py index dc9d17ba..2fe49a76 100644 --- a/napari_cellseg3d/_tests/test_training.py +++ b/napari_cellseg3d/_tests/test_training.py @@ -124,7 +124,7 @@ def test_unsupervised_training(make_napari_viewer_proxy): {"image": im_path_str, "label": im_path_str} ] widget.worker._get_data() - eval_res = widget.worker._eval( + eval_res = widget.worker.eval( model=WNetFixture(), epoch=-10, ) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 0d0c0659..ffdae104 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -108,10 +108,10 @@ def set_download_log(self, widget): self.downloader.log_widget = widget def log(self, text): - """Sends a signal that ``text`` should be logged + """Sends a Qt signal that the provided text should be logged Goes in a Log object, defined in :py:mod:`napari_cellseg3d.interface Sends a signal to the main thread to log the text. - Signal is defined in napari_cellseg3d.workers_utils.LogSignal + Signal is defined in napari_cellseg3d.workers_utils.LogSignal. Args: text (str): text to logged @@ -653,7 +653,7 @@ def train( ): model.eval() self.log("Validating...") - yield self._eval(model, epoch) # validation + yield self.eval(model, epoch) # validation if self._abort_requested: self.dataloader = None @@ -736,7 +736,7 @@ def train( self.quit() raise e - def _eval(self, model, epoch) -> TrainingReport: + def eval(self, model, epoch) -> TrainingReport: with torch.no_grad(): device = self.config.device for _k, val_data in enumerate(self.eval_dataloader): From eaabb1198abce1b52abd1ff3ae61a36d328dd991 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 2 Aug 2023 17:31:08 +0200 Subject: [PATCH 202/250] Update plugin_model_training.py --- napari_cellseg3d/code_plugins/plugin_model_training.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index c4211ee3..4f980b8a 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -1608,14 +1608,14 @@ def __init__(self, parent): text_label="Reconstruction loss", ) self.ncuts_weight_choice = ui.DoubleIncrementCounter( - lower=0.1, + lower=0.01, upper=1.0, default=self.default_config.n_cuts_weight, parent=parent, text_label="NCuts weight", ) self.reconstruction_weight_choice = ui.DoubleIncrementCounter( - lower=0.1, + lower=0.01, upper=1.0, default=0.5, parent=parent, From 885e15bb86c7f3e1f53f08034227c97a35efc68c Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 3 Aug 2023 10:11:33 +0200 Subject: [PATCH 203/250] WANDB integration for WNet --- .gitignore | 2 + .../code_models/worker_training.py | 121 +++++++++++------- .../code_plugins/plugin_model_training.py | 6 +- napari_cellseg3d/config.py | 11 ++ pyproject.toml | 19 +-- 5 files changed, 104 insertions(+), 55 deletions(-) diff --git a/.gitignore b/.gitignore index 7dbc9185..b767cced 100644 --- a/.gitignore +++ b/.gitignore @@ -93,6 +93,8 @@ venv/ # written by setuptools_scm **/_version.py +# WANDB +/wandb/ ######## diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index ffdae104..3ba990f8 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -66,6 +66,8 @@ logger.debug(f"PRETRAINED WEIGHT DIR LOCATION : {PRETRAINED_WEIGHTS_DIR}") try: + import wandb + WANDB_INSTALLED = True except ImportError: logger.warning( @@ -89,6 +91,8 @@ class TrainingWorkerBase(GeneratorWorker): """A basic worker abstract class, to run training jobs in. Contains the minimal common elements required for training models.""" + wandb_config = config.WandBConfig() + def __init__(self): super().__init__(self.train) self._signals = LogSignal() @@ -146,6 +150,8 @@ class WNetTrainingWorker(TrainingWorkerBase): Inherits from :py:class:`napari.qt.threading.GeneratorWorker` via :py:class:`TrainingWorkerBase` """ + # TODO : add wandb parameters + def __init__( self, worker_config: config.WNetTrainingWorkerConfig, @@ -365,19 +371,20 @@ def log_parameters(self): ) ############## self.log("-- Data --") - self.log("Training data :") + self.log("Training data :\n") [ self.log(f"{v}") for d in self.config.train_data_dict for k, v in d.items() ] if self.config.eval_volume_dict is not None: - self.log("Validation data :") + self.log("\nValidation data :\n") [ self.log(f"{k}: {v}") for d in self.config.eval_volume_dict for k, v in d.items() ] + self.log("*" * 20) def train( self, provided_model=None, provided_optimizer=None, provided_loss=None @@ -389,10 +396,14 @@ def train( # disable metadata tracking in MONAI set_track_meta(False) ############## - # if WANDB_INSTALLED: - # wandb.init( - # config=WANDB_CONFIG, project="WNet-benchmark", mode=WANDB_MODE - # ) + if WANDB_INSTALLED: + config_dict = self.config.__dict__ + logger.debug(f"wandb config : {config_dict}") + wandb.init( + config=config_dict, + project="CellSeg3D WNet", + mode=self.wandb_config.mode, + ) set_determinism(seed=self.config.deterministic_config.seed) torch.use_deterministic_algorithms(True, warn_only=True) @@ -432,8 +443,8 @@ def train( ) ) - # if WANDB_INSTALLED: - # wandb.watch(model, log_freq=100) + if WANDB_INSTALLED: + wandb.watch(model, log_freq=100) if self.config.weights_info.custom: if self.config.weights_info.use_pretrained: @@ -526,9 +537,10 @@ def train( enc, dec = model(image_batch) # Compute the Ncuts loss Ncuts = criterionE(enc, image_batch) + epoch_ncuts_loss += Ncuts.item() - # if WANDB_INSTALLED: - # wandb.log({"Ncuts loss": Ncuts.item()}) + if WANDB_INSTALLED: + wandb.log({"Train/Ncuts loss": Ncuts.item()}) # Compute the reconstruction loss if isinstance(criterionW, nn.MSELoss): @@ -540,10 +552,12 @@ def train( ) epoch_rec_loss += reconstruction_loss.item() - # if WANDB_INSTALLED: - # wandb.log( - # {"Reconstruction loss": reconstruction_loss.item()} - # ) + if WANDB_INSTALLED: + wandb.log( + { + "Train/Reconstruction loss": reconstruction_loss.item() + } + ) # Backward pass for the reconstruction loss optimizer.zero_grad() @@ -554,8 +568,12 @@ def train( if provided_loss is not None: loss = provided_loss epoch_loss += loss.item() - # if WANDB_INSTALLED: - # wandb.log({"Weighted sum of losses": loss.item()}) + + if WANDB_INSTALLED: + wandb.log( + {"Train/Weighted sum of losses": loss.item()} + ) + loss.backward(loss) optimizer.step() @@ -622,14 +640,21 @@ def train( except TypeError: pass - # if WANDB_INSTALLED: - # wandb.log({"Ncuts loss_epoch": self.ncuts_losses[-1]}) - # wandb.log({"Reconstruction loss_epoch": self.rec_losses[-1]}) - # wandb.log({"Sum of losses_epoch": self.total_losses[-1]}) - # wandb.log({"epoch": epoch}) - # wandb.log({"learning_rate model": optimizerW.param_groups[0]["lr"]}) - # wandb.log({"learning_rate encoder": optimizerE.param_groups[0]["lr"]}) - # wandb.log({"learning_rate model": optimizer.param_groups[0]["lr"]}) + if WANDB_INSTALLED: + wandb.log({"Ncuts loss for epoch": self.ncuts_losses[-1]}) + wandb.log( + {"Reconstruction loss for epoch": self.rec_losses[-1]} + ) + wandb.log( + {"Sum of losses for epoch": self.total_losses[-1]} + ) + wandb.log( + { + "LR/Model learning rate": optimizer.param_groups[ + 0 + ]["lr"] + } + ) self.log(f"Ncuts loss: {self.ncuts_losses[-1]:.5f}") self.log(f"Reconstruction loss: {self.rec_losses[-1]:.5f}") @@ -687,33 +712,38 @@ def train( self.log("Training finished") if self.best_dice > -1: - self.log(f"Best dice metric : {self.best_dice}") - # if WANDB_INSTALLED and self.config.eval_volume_directory is not None: - # wandb.log( - # { - # "self.best_dice_metric": self.best_dice, - # "best_metric_epoch": best_dice_epoch, - # } - # ) + best_dice_epoch = epoch + self.log( + f"Best dice metric : {self.best_dice} at epoch {best_dice_epoch}" + ) + + if WANDB_INSTALLED: + wandb.log( + { + "Validation/Best Dice": self.best_dice, + "Validation/Best Dice epoch": best_dice_epoch, + } + ) # Save the model self.log( f"Saving the model to: {self.config.results_path_folder}/wnet.pth", ) + save_weights_path = self.config.results_path_folder + "/wnet.pth" torch.save( model.state_dict(), - self.config.results_path_folder + "/wnet.pth", + save_weights_path, ) - # if WANDB_INSTALLED: - # model_artifact = wandb.Artifact( - # "WNet", - # type="model", - # description="WNet benchmark", - # metadata=dict(WANDB_CONFIG), - # ) - # model_artifact.add_file(self.config.save_model_path) - # wandb.log_artifact(model_artifact) + if WANDB_INSTALLED and self.wandb_config.save_model_artifact: + model_artifact = wandb.Artifact( + "WNet", + type="model", + description="CellSeg3D WNet", + metadata=self.config.__dict__, + ) + model_artifact.add_file(save_weights_path) + wandb.log_artifact(model_artifact) # return self.ncuts_losses, self.rec_losses, model dataloader = None @@ -826,9 +856,10 @@ def eval(self, model, epoch) -> TrainingReport: self.log(f"Saving new best model to {save_path}") torch.save(model.state_dict(), save_path) - # if WANDB_INSTALLED: - # log validation dice score for each validation round - # wandb.log({"val/dice_metric": metric}) + if WANDB_INSTALLED: + # log validation dice score for each validation round + wandb.log({"Validation/Dice metric": metric}) + self.dice_metric.reset() dec_out_val = val_decoder_outputs[0].detach().cpu().numpy().copy() enc_out_val = val_outputs[0].detach().cpu().numpy().copy() diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 4f980b8a..2201c9ff 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -28,9 +28,9 @@ ) from napari_cellseg3d.code_models.workers_utils import TrainingReport +logger = utils.LOGGER NUMBER_TABS = 4 # how many tabs in the widget DEFAULT_PATCH_SIZE = 64 # default patch size for training -logger = utils.LOGGER class Trainer(ModelFramework, metaclass=ui.QWidgetSingleton): @@ -315,10 +315,12 @@ def __init__( ############################ self._set_tooltips() self._build() + + self.model_choice.setCurrentIndex(4) self.model_choice.currentTextChanged.connect( partial(self._toggle_unsupervised_mode, enabled=False) ) - self._toggle_unsupervised_mode(enabled=False) + self._toggle_unsupervised_mode(enabled=True) def _set_tooltips(self): # tooltips diff --git a/napari_cellseg3d/config.py b/napari_cellseg3d/config.py index 6c8db79b..82753407 100644 --- a/napari_cellseg3d/config.py +++ b/napari_cellseg3d/config.py @@ -307,6 +307,17 @@ class WNetTrainingWorkerConfig(TrainingWorkerConfig): eval_volume_dict: str = None +################ +# WandB config # +################ +@dataclass +class WandBConfig: + """Class to record parameters for WandB""" + + mode: str = "online" # disabled, online, enabled + save_model_artifact: bool = False + + ################ # CRF config for WNet ################ diff --git a/pyproject.toml b/pyproject.toml index 5d5be93b..094166fb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -122,6 +122,17 @@ line_length = 79 crf = [ "pydensecrf@git+https://github.com/kodalli/pydensecrf.git#egg=master", ] +onnx-cpu = [ + "onnx", + "onnxruntime" +] +onnx-gpu = [ + "onnx", + "onnxruntime-gpu" +] +wandb = [ + "wandb" +] dev = [ "isort", "black", @@ -144,11 +155,3 @@ test = [ "twine", "pydensecrf@git+https://github.com/kodalli/pydensecrf.git#egg=master", ] -onnx-cpu = [ - "onnx", - "onnxruntime" -] -onnx-gpu = [ - "onnx", - "onnxruntime-gpu" -] From e4224a0fbd53f804ee410a22b6ea87b07f93bee7 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 3 Aug 2023 10:38:25 +0200 Subject: [PATCH 204/250] Colab training script --- .../code_models/worker_training.py | 1 - .../dev_scripts/colab_training.py | 738 ++++++++++++++++++ 2 files changed, 738 insertions(+), 1 deletion(-) create mode 100644 napari_cellseg3d/dev_scripts/colab_training.py diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 3ba990f8..129a705c 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -745,7 +745,6 @@ def train( model_artifact.add_file(save_weights_path) wandb.log_artifact(model_artifact) - # return self.ncuts_losses, self.rec_losses, model dataloader = None del dataloader self.eval_dataloader = None diff --git a/napari_cellseg3d/dev_scripts/colab_training.py b/napari_cellseg3d/dev_scripts/colab_training.py new file mode 100644 index 00000000..498a6999 --- /dev/null +++ b/napari_cellseg3d/dev_scripts/colab_training.py @@ -0,0 +1,738 @@ +import time +from pathlib import Path + +import torch +import torch.nn as nn + +# MONAI +from monai.data import ( + CacheDataset, + DataLoader, + PatchDataset, + pad_list_data_collate, +) +from monai.data.meta_obj import set_track_meta +from monai.inferers import sliding_window_inference +from monai.metrics import DiceMetric +from monai.transforms import ( + AsDiscrete, + Compose, + EnsureChannelFirstd, + EnsureTyped, + LoadImaged, + Orientationd, + RandFlipd, + RandRotate90d, + RandShiftIntensityd, + RandSpatialCropSamplesd, + ScaleIntensityRanged, + SpatialPadd, +) +from monai.utils import set_determinism + +# local +from napari_cellseg3d import config, utils +from napari_cellseg3d.code_models.models.wnet.model import WNet +from napari_cellseg3d.code_models.models.wnet.soft_Ncuts import SoftNCutsLoss +from napari_cellseg3d.code_models.worker_training import TrainingWorkerBase +from napari_cellseg3d.code_models.workers_utils import ( + PRETRAINED_WEIGHTS_DIR, +) + +logger = utils.LOGGER +VERBOSE_SCHEDULER = True +logger.debug(f"PRETRAINED WEIGHT DIR LOCATION : {PRETRAINED_WEIGHTS_DIR}") + +try: + import wandb + + WANDB_INSTALLED = True +except ImportError: + logger.warning( + "wandb not installed, wandb config will not be taken into account", + stacklevel=1, + ) + WANDB_INSTALLED = False + + +class WNetTrainingWorkerColab(TrainingWorkerBase): + """A custom worker to run WNet (unsupervised) training jobs in. + Inherits from :py:class:`napari.qt.threading.GeneratorWorker` via :py:class:`TrainingWorkerBase` + """ + + def __init__( + self, + worker_config: config.WNetTrainingWorkerConfig, + wandb_config: config.WandBConfig = None, + ): + super().__init__() + self.config = worker_config + self.wandb_config = ( + wandb_config if wandb_config is not None else config.WandBConfig() + ) + + self.dice_metric = DiceMetric( + include_background=True, reduction="mean", get_not_nans=False + ) + self.normalize_function = utils.remap_image + self.start_time = time.time() + self.ncuts_losses = [] + self.rec_losses = [] + self.total_losses = [] + self.best_dice = -1 + self.dice_values = [] + + self.dataloader: DataLoader = None + self.eval_dataloader: DataLoader = None + self.data_shape = None + + def get_patch_dataset(self, train_transforms): + """Creates a Dataset from the original data using the tifffile library + + Args: + train_data_dict (dict): dict with the Paths to the directory containing the data + + Returns: + (tuple): A tuple containing the shape of the data and the dataset + """ + + patch_func = Compose( + [ + LoadImaged(keys=["image"], image_only=True), + EnsureChannelFirstd(keys=["image"], channel_dim="no_channel"), + RandSpatialCropSamplesd( + keys=["image"], + roi_size=( + self.config.sample_size + ), # multiply by axis_stretch_factor if anisotropy + # max_roi_size=(120, 120, 120), + random_size=False, + num_samples=self.config.num_samples, + ), + Orientationd(keys=["image"], axcodes="PLI"), + SpatialPadd( + keys=["image"], + spatial_size=( + utils.get_padding_dim(self.config.sample_size) + ), + ), + EnsureTyped(keys=["image"]), + ] + ) + dataset = PatchDataset( + data=self.config.train_data_dict, + samples_per_image=self.config.num_samples, + patch_func=patch_func, + transform=train_transforms, + ) + + return self.config.sample_size, dataset + + def get_dataset_eval(self, eval_dataset_dict): + eval_transforms = Compose( + [ + LoadImaged(keys=["image", "label"]), + EnsureChannelFirstd( + keys=["image", "label"], channel_dim="no_channel" + ), + # RandSpatialCropSamplesd( + # keys=["image", "label"], + # roi_size=( + # self.config.sample_size + # ), # multiply by axis_stretch_factor if anisotropy + # # max_roi_size=(120, 120, 120), + # random_size=False, + # num_samples=self.config.num_samples, + # ), + Orientationd(keys=["image", "label"], axcodes="PLI"), + # SpatialPadd( + # keys=["image", "label"], + # spatial_size=( + # utils.get_padding_dim(self.config.sample_size) + # ), + # ), + EnsureTyped(keys=["image", "label"]), + ] + ) + + return CacheDataset( + data=eval_dataset_dict, + transform=eval_transforms, + ) + + def get_dataset(self, train_transforms): + """Creates a Dataset applying some transforms/augmentation on the data using the MONAI library + + Args: + config (WNetTrainingWorkerConfig): The configuration object + + Returns: + (tuple): A tuple containing the shape of the data and the dataset + """ + train_files = self.config.train_data_dict + + first_volume = LoadImaged(keys=["image"])(train_files[0]) + first_volume_shape = first_volume["image"].shape + + # Transforms to be applied to each volume + load_single_images = Compose( + [ + LoadImaged(keys=["image"]), + EnsureChannelFirstd(keys=["image"]), + Orientationd(keys=["image"], axcodes="PLI"), + SpatialPadd( + keys=["image"], + spatial_size=(utils.get_padding_dim(first_volume_shape)), + ), + EnsureTyped(keys=["image"]), + # RemapTensord(keys=["image"], new_min=0.0, new_max=100.0), + ] + ) + + # Create the dataset + dataset = CacheDataset( + data=train_files, + transform=Compose([load_single_images, train_transforms]), + ) + + return first_volume_shape, dataset + + def _get_data(self): + if self.config.do_augmentation: + train_transforms = Compose( + [ + ScaleIntensityRanged( + keys=["image"], + a_min=0, + a_max=2000, + b_min=0.0, + b_max=1.0, + clip=True, + ), + RandShiftIntensityd(keys=["image"], offsets=0.1, prob=0.5), + RandFlipd(keys=["image"], spatial_axis=[1], prob=0.5), + RandFlipd(keys=["image"], spatial_axis=[2], prob=0.5), + RandRotate90d(keys=["image"], prob=0.1, max_k=3), + EnsureTyped(keys=["image"]), + ] + ) + else: + train_transforms = EnsureTyped(keys=["image"]) + + if self.config.sampling: + logger.debug("Loading patch dataset") + (self.data_shape, dataset) = self.get_patch_dataset( + train_transforms + ) + else: + logger.debug("Loading volume dataset") + (self.data_shape, dataset) = self.get_dataset(train_transforms) + + logger.debug(f"Data shape : {self.data_shape}") + self.dataloader = DataLoader( + dataset, + batch_size=self.config.batch_size, + shuffle=True, + num_workers=self.config.num_workers, + collate_fn=pad_list_data_collate, + ) + + if self.config.eval_volume_dict is not None: + eval_dataset = self.get_dataset_eval(self.config.eval_volume_dict) + + self.eval_dataloader = DataLoader( + eval_dataset, + batch_size=self.config.batch_size, + shuffle=False, + num_workers=self.config.num_workers, + collate_fn=pad_list_data_collate, + ) + else: + self.eval_dataloader = None + return self.dataloader, self.eval_dataloader, self.data_shape + + def log_parameters(self): + self.log("*" * 20) + self.log("-- Parameters --") + self.log(f"Device: {self.config.device}") + self.log(f"Batch size: {self.config.batch_size}") + self.log(f"Epochs: {self.config.max_epochs}") + self.log(f"Learning rate: {self.config.learning_rate}") + self.log(f"Validation interval: {self.config.validation_interval}") + if self.config.weights_info.custom: + self.log(f"Custom weights: {self.config.weights_info.path}") + elif self.config.weights_info.use_pretrained: + self.log(f"Pretrained weights: {self.config.weights_info.path}") + if self.config.sampling: + self.log( + f"Using {self.config.num_samples} samples of size {self.config.sample_size}" + ) + if self.config.do_augmentation: + self.log("Using data augmentation") + ############## + self.log("-- Model --") + self.log(f"Using {self.config.num_classes} classes") + self.log(f"Weight decay: {self.config.weight_decay}") + self.log("* NCuts : ") + self.log(f"- Intensity sigma {self.config.intensity_sigma}") + self.log(f"- Spatial sigma {self.config.spatial_sigma}") + self.log(f"- Radius : {self.config.radius}") + self.log(f"* Reconstruction loss : {self.config.reconstruction_loss}") + self.log( + f"Weighted sum : {self.config.n_cuts_weight}*NCuts + {self.config.rec_loss_weight}*Reconstruction" + ) + ############## + self.log("-- Data --") + self.log("Training data :\n") + [ + self.log(f"{v}") + for d in self.config.train_data_dict + for k, v in d.items() + ] + if self.config.eval_volume_dict is not None: + self.log("\nValidation data :\n") + [ + self.log(f"{k}: {v}") + for d in self.config.eval_volume_dict + for k, v in d.items() + ] + self.log("*" * 20) + + def train( + self, provided_model=None, provided_optimizer=None, provided_loss=None + ): + try: + if self.config is None: + self.config = config.WNetTrainingWorkerConfig() + ############## + # disable metadata tracking in MONAI + set_track_meta(False) + ############## + if WANDB_INSTALLED: + config_dict = self.config.__dict__ + logger.debug(f"wandb config : {config_dict}") + wandb.init( + config=config_dict, + project="CellSeg3D WNet", + mode=self.wandb_config.mode, + ) + + set_determinism(seed=self.config.deterministic_config.seed) + torch.use_deterministic_algorithms(True, warn_only=True) + + device = self.config.device + + self.log_parameters() + self.log("Initializing training...") + self.log("- Getting the data") + + self._get_data() + + ################################################### + # Training the model # + ################################################### + self.log("- Getting the model") + # Initialize the model + model = ( + WNet( + in_channels=self.config.in_channels, + out_channels=self.config.out_channels, + num_classes=self.config.num_classes, + dropout=self.config.dropout, + ) + if provided_model is None + else provided_model + ) + model.to(device) + + if self.config.use_clipping: + for p in model.parameters(): + p.register_hook( + lambda grad: torch.clamp( + grad, + min=-self.config.clipping, + max=self.config.clipping, + ) + ) + + if WANDB_INSTALLED: + wandb.watch(model, log_freq=100) + + if self.config.weights_info.custom: + if self.config.weights_info.use_pretrained: + weights_file = "wnet.pth" + self.downloader.download_weights("WNet", weights_file) + weights = PRETRAINED_WEIGHTS_DIR / Path(weights_file) + self.config.weights_info.path = weights + else: + weights = str(Path(self.config.weights_info.path)) + + try: + model.load_state_dict( + torch.load( + weights, + map_location=self.config.device, + ), + strict=True, + ) + except RuntimeError as e: + logger.error(f"Error when loading weights : {e}") + logger.exception(e) + warn = ( + "WARNING:\nIt'd seem that the weights were incompatible with the model,\n" + "the model will be trained from random weights" + ) + self.log(warn) + self.warn(warn) + self._weight_error = True + else: + self.log("Model will be trained from scratch") + self.log("- Getting the optimizer") + # Initialize the optimizers + if self.config.weight_decay is not None: + decay = self.config.weight_decay + optimizer = torch.optim.Adam( + model.parameters(), + lr=self.config.learning_rate, + weight_decay=decay, + ) + else: + optimizer = torch.optim.Adam( + model.parameters(), lr=self.config.learning_rate + ) + if provided_optimizer is not None: + optimizer = provided_optimizer + self.log("- Getting the loss functions") + # Initialize the Ncuts loss function + criterionE = SoftNCutsLoss( + data_shape=self.data_shape, + device=device, + intensity_sigma=self.config.intensity_sigma, + spatial_sigma=self.config.spatial_sigma, + radius=self.config.radius, + ) + + if self.config.reconstruction_loss == "MSE": + criterionW = nn.MSELoss() + elif self.config.reconstruction_loss == "BCE": + criterionW = nn.BCELoss() + else: + raise ValueError( + f"Unknown reconstruction loss : {self.config.reconstruction_loss} not supported" + ) + + model.train() + + self.log("Ready") + self.log("Training the model") + self.log("*" * 20) + + # Train the model + for epoch in range(self.config.max_epochs): + self.log(f"Epoch {epoch + 1} of {self.config.max_epochs}") + + epoch_ncuts_loss = 0 + epoch_rec_loss = 0 + epoch_loss = 0 + + for _i, batch in enumerate(self.dataloader): + # raise NotImplementedError("testing") + image_batch = batch["image"].to(device) + # Normalize the image + for i in range(image_batch.shape[0]): + for j in range(image_batch.shape[1]): + image_batch[i, j] = self.normalize_function( + image_batch[i, j] + ) + + # Forward pass + enc, dec = model(image_batch) + # Compute the Ncuts loss + Ncuts = criterionE(enc, image_batch) + + epoch_ncuts_loss += Ncuts.item() + if WANDB_INSTALLED: + wandb.log({"Train/Ncuts loss": Ncuts.item()}) + + # Compute the reconstruction loss + if isinstance(criterionW, nn.MSELoss): + reconstruction_loss = criterionW(dec, image_batch) + elif isinstance(criterionW, nn.BCELoss): + reconstruction_loss = criterionW( + torch.sigmoid(dec), + utils.remap_image(image_batch, new_max=1), + ) + + epoch_rec_loss += reconstruction_loss.item() + if WANDB_INSTALLED: + wandb.log( + { + "Train/Reconstruction loss": reconstruction_loss.item() + } + ) + + # Backward pass for the reconstruction loss + optimizer.zero_grad() + alpha = self.config.n_cuts_weight + beta = self.config.rec_loss_weight + + loss = alpha * Ncuts + beta * reconstruction_loss + if provided_loss is not None: + loss = provided_loss + epoch_loss += loss.item() + + if WANDB_INSTALLED: + wandb.log( + {"Train/Weighted sum of losses": loss.item()} + ) + + loss.backward(loss) + optimizer.step() + + self.ncuts_losses.append( + epoch_ncuts_loss / len(self.dataloader) + ) + self.rec_losses.append(epoch_rec_loss / len(self.dataloader)) + self.total_losses.append(epoch_loss / len(self.dataloader)) + + if WANDB_INSTALLED: + wandb.log({"Ncuts loss for epoch": self.ncuts_losses[-1]}) + wandb.log( + {"Reconstruction loss for epoch": self.rec_losses[-1]} + ) + wandb.log( + {"Sum of losses for epoch": self.total_losses[-1]} + ) + wandb.log( + { + "LR/Model learning rate": optimizer.param_groups[ + 0 + ]["lr"] + } + ) + + self.log(f"Ncuts loss: {self.ncuts_losses[-1]:.5f}") + self.log(f"Reconstruction loss: {self.rec_losses[-1]:.5f}") + self.log( + f"Weighted sum of losses: {self.total_losses[-1]:.5f}" + ) + if epoch > 0: + self.log( + f"Ncuts loss difference: {self.ncuts_losses[-1] - self.ncuts_losses[-2]:.5f}" + ) + self.log( + f"Reconstruction loss difference: {self.rec_losses[-1] - self.rec_losses[-2]:.5f}" + ) + self.log( + f"Weighted sum of losses difference: {self.total_losses[-1] - self.total_losses[-2]:.5f}" + ) + + if ( + self.eval_dataloader is not None + and (epoch + 1) % self.config.validation_interval == 0 + ): + model.eval() + self.log("Validating...") + self.eval(model, epoch) # validation + + if self._abort_requested: + self.dataloader = None + del self.dataloader + self.eval_dataloader = None + del self.eval_dataloader + model = None + del model + optimizer = None + del optimizer + criterionE = None + del criterionE + criterionW = None + del criterionW + torch.cuda.empty_cache() + + eta = ( + (time.time() - self.start_time) + * (self.config.max_epochs / (epoch + 1) - 1) + / 60 + ) + self.log(f"ETA: {eta:.1f} minutes") + self.log("-" * 20) + + # Save the model + if epoch % 5 == 0: + torch.save( + model.state_dict(), + self.config.results_path_folder + "/wnet_.pth", + ) + + self.log("Training finished") + if self.best_dice > -1: + best_dice_epoch = epoch + self.log( + f"Best dice metric : {self.best_dice} at epoch {best_dice_epoch}" + ) + + if WANDB_INSTALLED: + wandb.log( + { + "Validation/Best Dice": self.best_dice, + "Validation/Best Dice epoch": best_dice_epoch, + } + ) + + # Save the model + self.log( + f"Saving the model to: {self.config.results_path_folder}/wnet.pth", + ) + save_weights_path = self.config.results_path_folder + "/wnet.pth" + torch.save( + model.state_dict(), + save_weights_path, + ) + + if WANDB_INSTALLED and self.wandb_config.save_model_artifact: + model_artifact = wandb.Artifact( + "WNet", + type="model", + description="CellSeg3D WNet", + metadata=self.config.__dict__, + ) + model_artifact.add_file(save_weights_path) + wandb.log_artifact(model_artifact) + + yield model.state_dict() + dataloader = None + del dataloader + self.eval_dataloader = None + del self.eval_dataloader + model = None + del model + optimizer = None + del optimizer + criterionE = None + del criterionE + criterionW = None + del criterionW + torch.cuda.empty_cache() + + except Exception as e: + msg = f"Training failed with exception: {e}" + self.log(msg) + self.raise_error(e, msg) + self.quit() + raise e + + def eval(self, model, _): + with torch.no_grad(): + device = self.config.device + for _k, val_data in enumerate(self.eval_dataloader): + val_inputs, val_labels = ( + val_data["image"].to(device), + val_data["label"].to(device), + ) + + # normalize val_inputs across channels + for i in range(val_inputs.shape[0]): + for j in range(val_inputs.shape[1]): + val_inputs[i][j] = self.normalize_function( + val_inputs[i][j] + ) + logger.debug(f"Val inputs shape: {val_inputs.shape}") + val_outputs = sliding_window_inference( + val_inputs, + roi_size=[64, 64, 64], + sw_batch_size=1, + predictor=model.forward_encoder, + overlap=0.1, + mode="gaussian", + sigma_scale=0.01, + progress=True, + ) + val_decoder_outputs = sliding_window_inference( + val_outputs, + roi_size=[64, 64, 64], + sw_batch_size=1, + predictor=model.forward_decoder, + overlap=0.1, + mode="gaussian", + sigma_scale=0.01, + progress=True, + ) + val_outputs = AsDiscrete(threshold=0.5)(val_outputs) + logger.debug(f"Val outputs shape: {val_outputs.shape}") + logger.debug(f"Val labels shape: {val_labels.shape}") + logger.debug( + f"Val decoder outputs shape: {val_decoder_outputs.shape}" + ) + + # dices = [] + # Find in which channel the labels are (avoid background) + # for channel in range(val_outputs.shape[1]): + # dices.append( + # utils.dice_coeff( + # y_pred=val_outputs[ + # 0, channel : (channel + 1), :, :, : + # ], + # y_true=val_labels[0], + # ) + # ) + # logger.debug(f"DICE COEFF: {dices}") + # max_dice_channel = torch.argmax( + # torch.Tensor(dices) + # ) + # logger.debug( + # f"MAX DICE CHANNEL: {max_dice_channel}" + # ) + self.dice_metric( + y_pred=val_outputs, + # [ + # :, + # max_dice_channel : (max_dice_channel + 1), + # :, + # :, + # :, + # ], + y=val_labels, + ) + + # aggregate the final mean dice result + metric = self.dice_metric.aggregate().item() + self.dice_values.append(metric) + self.log(f"Validation Dice score: {metric:.3f}") + if self.best_dice < metric <= 1: + self.best_dice = metric + # save the best model + save_best_path = self.config.results_path_folder + # save_best_path.mkdir(parents=True, exist_ok=True) + save_best_name = "wnet" + save_path = ( + str(Path(save_best_path) / save_best_name) + + "_best_metric.pth" + ) + self.log(f"Saving new best model to {save_path}") + torch.save(model.state_dict(), save_path) + + if WANDB_INSTALLED: + # log validation dice score for each validation round + wandb.log({"Validation/Dice metric": metric}) + + self.dice_metric.reset() + + val_decoder_outputs = None + del val_decoder_outputs + val_outputs = None + del val_outputs + val_labels = None + del val_labels + val_inputs = None + del val_inputs + + +def train_in_colab(worker_config: config.WNetTrainingWorkerConfig): + """Train a WNet model in Google Colab. + + Args: + worker_config (config.WNetTrainingWorkerConfig): config for the training worker + """ + worker = WNetTrainingWorkerColab(worker_config) + worker.train() From 22ee2ff00ec76df9b751c6f8584258c1f09c7f45 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 3 Aug 2023 11:04:24 +0200 Subject: [PATCH 205/250] Update colab_training.py --- .../dev_scripts/colab_training.py | 54 +++++++++++++++++++ 1 file changed, 54 insertions(+) diff --git a/napari_cellseg3d/dev_scripts/colab_training.py b/napari_cellseg3d/dev_scripts/colab_training.py index 498a6999..eff8cb0f 100644 --- a/napari_cellseg3d/dev_scripts/colab_training.py +++ b/napari_cellseg3d/dev_scripts/colab_training.py @@ -736,3 +736,57 @@ def train_in_colab(worker_config: config.WNetTrainingWorkerConfig): """ worker = WNetTrainingWorkerColab(worker_config) worker.train() + + +def create_dataset_dict_no_labs(volume_directory): + """Creates unsupervised data dictionary for MONAI transforms and training.""" + if not volume_directory.exists(): + raise ValueError(f"Data folder {volume_directory} does not exist") + images_filepaths = sorted(Path.glob(volume_directory, "*.tif")) + if len(images_filepaths) == 0: + raise ValueError(f"Data folder {volume_directory} is empty") + + logger.info("Images :") + for file in images_filepaths: + logger.info(Path(file).stem) + logger.info("*" * 10) + + return [{"image": str(image_name)} for image_name in images_filepaths] + + +def create_train_dataset_dict(image_directory, label_directory): + """Creates data dictionary for MONAI transforms and training. + + Returns: + A dict with the following keys + + * "image": image + * "label" : corresponding label + """ + + images_filepaths = sorted(Path.glob(image_directory, "*.tif")) + labels_filepaths = sorted(Path.glob(label_directory, "*.tif")) + + if len(images_filepaths) == 0 or len(labels_filepaths) == 0: + raise ValueError("Data folders are empty") + + if not Path(images_filepaths[0]).parent.exists(): + raise ValueError("Images folder does not exist") + if not Path(labels_filepaths[0]).parent.exists(): + raise ValueError("Labels folder does not exist") + + logger.info("Images :\n") + for file in images_filepaths: + logger.info(Path(file).name) + logger.info("*" * 10) + logger.info("Labels :\n") + for file in labels_filepaths: + logger.info(Path(file).name) + + data_dicts = [ + {"image": image_name, "label": label_name} + for image_name, label_name in zip(images_filepaths, labels_filepaths) + ] + logger.debug(f"Training data dict : {data_dicts}") + + return data_dicts From 661ff57fae18f948560d3170ab83a52dd7178dd7 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 3 Aug 2023 11:12:08 +0200 Subject: [PATCH 206/250] Update colab_training.py --- napari_cellseg3d/dev_scripts/colab_training.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/napari_cellseg3d/dev_scripts/colab_training.py b/napari_cellseg3d/dev_scripts/colab_training.py index eff8cb0f..2069cfac 100644 --- a/napari_cellseg3d/dev_scripts/colab_training.py +++ b/napari_cellseg3d/dev_scripts/colab_training.py @@ -86,6 +86,9 @@ def __init__( self.eval_dataloader: DataLoader = None self.data_shape = None + def log(self, text): + logger.info(text) + def get_patch_dataset(self, train_transforms): """Creates a Dataset from the original data using the tifffile library @@ -728,13 +731,17 @@ def eval(self, model, _): del val_inputs -def train_in_colab(worker_config: config.WNetTrainingWorkerConfig): +def train_in_colab( + worker_config: config.WNetTrainingWorkerConfig, + wandb_config: config.WandBConfig, +): """Train a WNet model in Google Colab. Args: worker_config (config.WNetTrainingWorkerConfig): config for the training worker """ worker = WNetTrainingWorkerColab(worker_config) + worker.wandb_config = wandb_config worker.train() From 979232edd5c6aaefd854955222b65a730dd2317a Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 3 Aug 2023 11:17:37 +0200 Subject: [PATCH 207/250] Update colab_training.py --- napari_cellseg3d/dev_scripts/colab_training.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/dev_scripts/colab_training.py b/napari_cellseg3d/dev_scripts/colab_training.py index 2069cfac..28d93780 100644 --- a/napari_cellseg3d/dev_scripts/colab_training.py +++ b/napari_cellseg3d/dev_scripts/colab_training.py @@ -731,7 +731,7 @@ def eval(self, model, _): del val_inputs -def train_in_colab( +def get_colab_worker( worker_config: config.WNetTrainingWorkerConfig, wandb_config: config.WandBConfig, ): @@ -742,7 +742,7 @@ def train_in_colab( """ worker = WNetTrainingWorkerColab(worker_config) worker.wandb_config = wandb_config - worker.train() + return worker def create_dataset_dict_no_labs(volume_directory): From f44202fb05884a59220e63679b68d3381e33d37f Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 3 Aug 2023 11:22:44 +0200 Subject: [PATCH 208/250] Update colab_training.py --- napari_cellseg3d/dev_scripts/colab_training.py | 1 + 1 file changed, 1 insertion(+) diff --git a/napari_cellseg3d/dev_scripts/colab_training.py b/napari_cellseg3d/dev_scripts/colab_training.py index 28d93780..ad2e99a8 100644 --- a/napari_cellseg3d/dev_scripts/colab_training.py +++ b/napari_cellseg3d/dev_scripts/colab_training.py @@ -491,6 +491,7 @@ def train( loss.backward(loss) optimizer.step() + yield epoch_loss self.ncuts_losses.append( epoch_ncuts_loss / len(self.dataloader) From b016144211f30a22e1b8c004fe8c113ed7a4a536 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 3 Aug 2023 11:35:57 +0200 Subject: [PATCH 209/250] Update colab_training.py --- napari_cellseg3d/dev_scripts/colab_training.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/napari_cellseg3d/dev_scripts/colab_training.py b/napari_cellseg3d/dev_scripts/colab_training.py index ad2e99a8..ee795899 100644 --- a/napari_cellseg3d/dev_scripts/colab_training.py +++ b/napari_cellseg3d/dev_scripts/colab_training.py @@ -762,7 +762,7 @@ def create_dataset_dict_no_labs(volume_directory): return [{"image": str(image_name)} for image_name in images_filepaths] -def create_train_dataset_dict(image_directory, label_directory): +def create_eval_dataset_dict(image_directory, label_directory): """Creates data dictionary for MONAI transforms and training. Returns: @@ -775,6 +775,9 @@ def create_train_dataset_dict(image_directory, label_directory): images_filepaths = sorted(Path.glob(image_directory, "*.tif")) labels_filepaths = sorted(Path.glob(label_directory, "*.tif")) + logger.debug(f"Images filepaths: {images_filepaths}") + logger.debug(f"Labels filepaths: {labels_filepaths}") + if len(images_filepaths) == 0 or len(labels_filepaths) == 0: raise ValueError("Data folders are empty") From 1d7e4ed951ca99dad5b838ffcbeea751cb007bc5 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 3 Aug 2023 11:52:02 +0200 Subject: [PATCH 210/250] Added minimal Colab notebook --- .../dev_scripts/colab_training.py | 42 +- notebooks/colab_wnet_training.ipynb | 822 ++++++++++++++++++ 2 files changed, 824 insertions(+), 40 deletions(-) create mode 100644 notebooks/colab_wnet_training.ipynb diff --git a/napari_cellseg3d/dev_scripts/colab_training.py b/napari_cellseg3d/dev_scripts/colab_training.py index ee795899..e1850c4a 100644 --- a/napari_cellseg3d/dev_scripts/colab_training.py +++ b/napari_cellseg3d/dev_scripts/colab_training.py @@ -316,7 +316,7 @@ def train( logger.debug(f"wandb config : {config_dict}") wandb.init( config=config_dict, - project="CellSeg3D WNet", + project="CellSeg3D WNet (Colab)", mode=self.wandb_config.mode, ) @@ -539,21 +539,6 @@ def train( self.log("Validating...") self.eval(model, epoch) # validation - if self._abort_requested: - self.dataloader = None - del self.dataloader - self.eval_dataloader = None - del self.eval_dataloader - model = None - del model - optimizer = None - del optimizer - criterionE = None - del criterionE - criterionW = None - del criterionW - torch.cuda.empty_cache() - eta = ( (time.time() - self.start_time) * (self.config.max_epochs / (epoch + 1) - 1) @@ -604,21 +589,6 @@ def train( model_artifact.add_file(save_weights_path) wandb.log_artifact(model_artifact) - yield model.state_dict() - dataloader = None - del dataloader - self.eval_dataloader = None - del self.eval_dataloader - model = None - del model - optimizer = None - del optimizer - criterionE = None - del criterionE - criterionW = None - del criterionW - torch.cuda.empty_cache() - except Exception as e: msg = f"Training failed with exception: {e}" self.log(msg) @@ -758,7 +728,6 @@ def create_dataset_dict_no_labs(volume_directory): for file in images_filepaths: logger.info(Path(file).stem) logger.info("*" * 10) - return [{"image": str(image_name)} for image_name in images_filepaths] @@ -775,9 +744,6 @@ def create_eval_dataset_dict(image_directory, label_directory): images_filepaths = sorted(Path.glob(image_directory, "*.tif")) labels_filepaths = sorted(Path.glob(label_directory, "*.tif")) - logger.debug(f"Images filepaths: {images_filepaths}") - logger.debug(f"Labels filepaths: {labels_filepaths}") - if len(images_filepaths) == 0 or len(labels_filepaths) == 0: raise ValueError("Data folders are empty") @@ -793,11 +759,7 @@ def create_eval_dataset_dict(image_directory, label_directory): logger.info("Labels :\n") for file in labels_filepaths: logger.info(Path(file).name) - - data_dicts = [ + return [ {"image": image_name, "label": label_name} for image_name, label_name in zip(images_filepaths, labels_filepaths) ] - logger.debug(f"Training data dict : {data_dicts}") - - return data_dicts diff --git a/notebooks/colab_wnet_training.ipynb b/notebooks/colab_wnet_training.ipynb new file mode 100644 index 00000000..d56f0225 --- /dev/null +++ b/notebooks/colab_wnet_training.ipynb @@ -0,0 +1,822 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "td_vf_pneSak", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "3d04108f-f471-4440-e48d-debb75bae588" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cloning into './CellSeg3D'...\n", + "remote: Enumerating objects: 5813, done.\u001b[K\n", + "remote: Counting objects: 100% (2306/2306), done.\u001b[K\n", + "remote: Compressing objects: 100% (701/701), done.\u001b[K\n", + "remote: Total 5813 (delta 1893), reused 1796 (delta 1602), pack-reused 3507\u001b[K\n", + "Receiving objects: 100% (5813/5813), 52.37 MiB | 27.04 MiB/s, done.\n", + "Resolving deltas: 100% (4170/4170), done.\n", + "Obtaining file:///content/CellSeg3D\n", + " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Checking if build backend supports build_editable ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build editable ... \u001b[?25l\u001b[?25hdone\n", + " Preparing editable metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + "Requirement already satisfied: numpy in 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"Installing collected packages: PyQt5-Qt5, pathtools, heapdict, transforms3d, tomli-w, smmap, setproctitle, sentry-sdk, QtPy, pytools, PyQt5-sip, pint, napari-plugin-engine, mypy-extensions, jedi, itk-core, in-n-out, imagecodecs, hsluv, freetype-py, einops, docutils, docstring-parser, docker-pycreds, cachey, vispy, superqt, sphinx, PyQt5, pyopencl, psygnal, itk-numerics, itk-io, gitdb, pyclesperanto-prototype, numpydoc, npe2, napari-svg, magicgui, itk-filtering, GitPython, app-model, wandb, qtconsole, napari-plugin-manager, itk-segmentation, itk-registration, napari-console, itk, napari, monai, napari-cellseg3d\n", + " Attempting uninstall: docutils\n", + " Found existing installation: docutils 0.18.1\n", + " Uninstalling docutils-0.18.1:\n", + " Successfully uninstalled docutils-0.18.1\n", + " Attempting uninstall: sphinx\n", + " Found existing installation: Sphinx 5.0.2\n", + " Uninstalling Sphinx-5.0.2:\n", + " Successfully uninstalled Sphinx-5.0.2\n", + "Successfully installed GitPython-3.1.32 PyQt5-5.15.9 PyQt5-Qt5-5.15.2 PyQt5-sip-12.12.2 QtPy-2.3.1 app-model-0.2.0 cachey-0.2.1 docker-pycreds-0.4.0 docstring-parser-0.15 docutils-0.17.1 einops-0.6.1 freetype-py-2.4.0 gitdb-4.0.10 heapdict-1.0.1 hsluv-5.0.3 imagecodecs-2023.7.10 in-n-out-0.1.8 itk-5.3.0 itk-core-5.3.0 itk-filtering-5.3.0 itk-io-5.3.0 itk-numerics-5.3.0 itk-registration-5.3.0 itk-segmentation-5.3.0 jedi-0.19.0 magicgui-0.7.2 monai-1.2.0 mypy-extensions-1.0.0 napari-0.4.18 napari-cellseg3d-0.0.3rc1 napari-console-0.0.8 napari-plugin-engine-0.2.0 napari-plugin-manager-0.1.0a2 napari-svg-0.1.10 npe2-0.7.2 numpydoc-1.5.0 pathtools-0.1.2 pint-0.22 psygnal-0.9.1 pyclesperanto-prototype-0.24.1 pyopencl-2023.1.1 pytools-2023.1.1 qtconsole-5.4.3 sentry-sdk-1.29.2 setproctitle-1.3.2 smmap-5.0.0 sphinx-4.5.0 superqt-0.4.1 tomli-w-1.0.0 transforms3d-0.4.1 vispy-0.12.2 wandb-0.15.8\n" + ] + } + ], + "source": [ + "!git clone https://github.com/AdaptiveMotorControlLab/CellSeg3d.git --branch cy/wnet-extras --single-branch ./CellSeg3D\n", + "!pip install -e CellSeg3D[wandb]" + ] + }, + { + "cell_type": "code", + "source": [ + "#Force session restart\n", + "exit(0)" + ], + "metadata": { + "id": "ctwVtCDXtk_l" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# import wandb\n", + "# wandb.login()" + ], + "metadata": { + "id": "EtsK08ECwlnJ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#@markdown ##Play the cell to connect your Google Drive to Colab\n", + "\n", + "#@markdown * Click on the URL.\n", + "\n", + "#@markdown * Sign in your Google Account.\n", + "\n", + "#@markdown * Copy the authorization code.\n", + "\n", + "#@markdown * Enter the authorization code.\n", + "\n", + "#@markdown * Click on \"Files\" site on the right. Refresh the site. Your Google Drive folder should now be available here as \"drive\".\n", + "\n", + "# mount user's Google Drive to Google Colab.\n", + "from google.colab import drive\n", + "drive.mount('/content/gdrive')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "AsIARCablq1V", + "outputId": "b0a0a85c-ab4b-4d90-abbd-dcf25bfdf31c" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/gdrive\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from pathlib import Path\n", + "from napari_cellseg3d.dev_scripts import colab_training as c\n", + "from napari_cellseg3d.config import WNetTrainingWorkerConfig, WandBConfig, WeightsInfo, PRETRAINED_WEIGHTS_DIR" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wOOhJjkxjXz-", + "outputId": "3665f8b9-6c9f-4f8b-959f-bc876051f00f" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "DEBUG:napari_cellseg3d.utils:PRETRAINED WEIGHT DIR LOCATION : /content/CellSeg3D/napari_cellseg3d/code_models/models/pretrained\n", + "DEBUG:napari_cellseg3d.utils:PRETRAINED WEIGHT DIR LOCATION : /content/CellSeg3D/napari_cellseg3d/code_models/models/pretrained\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "train_data_folder = Path(\"./gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full\")\n", + "results_path = Path(\"./WNET_TRAINING_RESULTS\")\n", + "results_path.mkdir(exist_ok=True)\n", + "eval_image_folder = Path(\"./gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/eval/vol/\")\n", + "eval_label_folder = Path(\"./gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/eval/lab/\")" + ], + "metadata": { + "id": "kowhOfQunuXY" + }, + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "train_config = WNetTrainingWorkerConfig(\n", + " device=\"cuda:0\",\n", + " max_epochs=50,\n", + " learning_rate=2e-5,\n", + " validation_interval=2,\n", + " batch_size=4,\n", + " num_workers=2,\n", + " weights_info=WeightsInfo(),\n", + " results_path_folder=str(results_path),\n", + " train_data_dict=c.create_dataset_dict_no_labs(train_data_folder),\n", + " eval_volume_dict=c.create_eval_dataset_dict(\n", + " eval_image_folder,\n", + " eval_label_folder,\n", + " ),\n", + ")\n", + "wandb_config = WandBConfig(\n", + " mode=\"disabled\",\n", + " save_model_artifact=False,\n", + ")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YOgLyUwPjvUX", + "outputId": "99991c58-c674-4dcd-9d9d-c57f741b72fb" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "INFO:napari_cellseg3d.utils:Images :\n", + "INFO:napari_cellseg3d.utils:c1_images_cropped_10\n", + "INFO:napari_cellseg3d.utils:c1_images_cropped_11\n", + "INFO:napari_cellseg3d.utils:c1_images_cropped_12\n", + "INFO:napari_cellseg3d.utils:c1_images_cropped_13\n", + "INFO:napari_cellseg3d.utils:c1_images_cropped_14\n", + "INFO:napari_cellseg3d.utils:c1_images_cropped_15\n", + "INFO:napari_cellseg3d.utils:c1_images_cropped_16\n", + "INFO:napari_cellseg3d.utils:c1_images_cropped_17\n", + "INFO:napari_cellseg3d.utils:c1_images_cropped_18\n", + "INFO:napari_cellseg3d.utils:c1_images_cropped_19\n", + "INFO:napari_cellseg3d.utils:c1_images_cropped_20\n", + "INFO:napari_cellseg3d.utils:c1_images_cropped_21\n", + "INFO:napari_cellseg3d.utils:c1_images_cropped_22\n", + "INFO:napari_cellseg3d.utils:c1_images_cropped_23\n", + "INFO:napari_cellseg3d.utils:c1_images_cropped_3\n", + "INFO:napari_cellseg3d.utils:c1_images_cropped_4\n", + "INFO:napari_cellseg3d.utils:c1_images_cropped_5\n", + "INFO:napari_cellseg3d.utils:c1_images_cropped_6\n", + "INFO:napari_cellseg3d.utils:c1_images_cropped_7\n", + "INFO:napari_cellseg3d.utils:c1_images_cropped_8\n", + "INFO:napari_cellseg3d.utils:c1_images_cropped_9\n", + "INFO:napari_cellseg3d.utils:c2_images_cropped_0\n", + 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"INFO:napari_cellseg3d.utils:images_cropped_1\n", + "INFO:napari_cellseg3d.utils:images_cropped_10\n", + "INFO:napari_cellseg3d.utils:images_cropped_11\n", + "INFO:napari_cellseg3d.utils:images_cropped_12\n", + "INFO:napari_cellseg3d.utils:images_cropped_13\n", + "INFO:napari_cellseg3d.utils:images_cropped_14\n", + "INFO:napari_cellseg3d.utils:images_cropped_15\n", + "INFO:napari_cellseg3d.utils:images_cropped_16\n", + "INFO:napari_cellseg3d.utils:images_cropped_17\n", + "INFO:napari_cellseg3d.utils:images_cropped_18\n", + "INFO:napari_cellseg3d.utils:images_cropped_19\n", + "INFO:napari_cellseg3d.utils:images_cropped_2\n", + "INFO:napari_cellseg3d.utils:images_cropped_20\n", + "INFO:napari_cellseg3d.utils:images_cropped_21\n", + "INFO:napari_cellseg3d.utils:images_cropped_22\n", + "INFO:napari_cellseg3d.utils:images_cropped_23\n", + "INFO:napari_cellseg3d.utils:images_cropped_24\n", + "INFO:napari_cellseg3d.utils:images_cropped_25\n", + "INFO:napari_cellseg3d.utils:images_cropped_26\n", + "INFO:napari_cellseg3d.utils:images_cropped_27\n", + "INFO:napari_cellseg3d.utils:images_cropped_28\n", + "INFO:napari_cellseg3d.utils:images_cropped_29\n", + "INFO:napari_cellseg3d.utils:images_cropped_3\n", + "INFO:napari_cellseg3d.utils:images_cropped_30\n", + "INFO:napari_cellseg3d.utils:images_cropped_31\n", + "INFO:napari_cellseg3d.utils:images_cropped_4\n", + "INFO:napari_cellseg3d.utils:images_cropped_5\n", + "INFO:napari_cellseg3d.utils:images_cropped_6\n", + "INFO:napari_cellseg3d.utils:images_cropped_7\n", + "INFO:napari_cellseg3d.utils:images_cropped_8\n", + "INFO:napari_cellseg3d.utils:images_cropped_9\n", + "INFO:napari_cellseg3d.utils:**********\n", + "DEBUG:napari_cellseg3d.utils:Images filepaths: [PosixPath('gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/eval/vol/c3_image_cropped_eval.tif')]\n", + "DEBUG:napari_cellseg3d.utils:Labels filepaths: [PosixPath('gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/eval/lab/c3_labels_cropped_eval.tif')]\n", + "INFO:napari_cellseg3d.utils:Images :\n", + "\n", + "INFO:napari_cellseg3d.utils:c3_image_cropped_eval.tif\n", + "INFO:napari_cellseg3d.utils:**********\n", + "INFO:napari_cellseg3d.utils:Labels :\n", + "\n", + "INFO:napari_cellseg3d.utils:c3_labels_cropped_eval.tif\n", + "DEBUG:napari_cellseg3d.utils:Training data dict : [{'image': PosixPath('gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/eval/vol/c3_image_cropped_eval.tif'), 'label': PosixPath('gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/eval/lab/c3_labels_cropped_eval.tif')}]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "worker = c.get_colab_worker(worker_config=train_config, wandb_config=wandb_config)\n", + "for epoch_loss in worker.train():\n", + " continue" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "OXxKZhGMqguz", + "outputId": "7ef12d69-5519-4630-b9f5-bb4f3068c5c1" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "DEBUG:napari_cellseg3d.utils:wandb config : {'device': 'cuda:0', 'max_epochs': 50, 'learning_rate': 2e-05, 'validation_interval': 2, 'batch_size': 4, 'deterministic_config': DeterministicConfig(enabled=True, seed=34936339), 'scheduler_factor': 0.5, 'scheduler_patience': 10, 'weights_info': WeightsInfo(path='/content/CellSeg3D/napari_cellseg3d/code_models/models/pretrained', custom=False, use_pretrained=False), 'results_path_folder': 'WNET_TRAINING_RESULTS', 'sampling': False, 'num_samples': 2, 'sample_size': None, 'do_augmentation': True, 'num_workers': 2, 'train_data_dict': [{'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_10.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_11.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_12.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_13.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_14.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_15.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_16.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_17.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_18.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_19.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_20.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_21.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_22.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_23.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_3.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_4.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_5.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_6.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_7.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_8.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_9.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_0.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_1.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_10.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_11.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_12.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_13.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_14.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_15.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_16.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_17.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_18.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_19.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_2.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_3.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_4.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_5.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_6.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_7.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_8.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_9.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_0.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_1.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_10.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_11.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_12.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_13.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_14.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_15.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_16.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_17.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_18.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_19.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_2.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_20.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_21.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_22.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_23.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_24.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_25.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_26.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_27.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_28.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_29.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_3.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_4.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_5.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_6.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_7.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_8.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_9.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_0.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_10.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_11.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_12.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_13.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_14.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_15.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_16.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_17.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_18.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_19.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_2.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_3.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_4.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_5.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_6.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_7.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_8.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_9.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_0.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_1.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_10.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_11.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_12.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_13.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_14.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_15.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_16.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_17.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_18.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_19.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_2.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_3.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_4.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_5.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_6.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_7.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_8.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_9.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_0.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_1.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_10.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_11.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_12.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_13.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_14.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_15.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_16.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_17.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_18.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_19.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_2.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_20.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_21.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_22.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_23.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_24.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_25.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_26.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_27.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_28.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_29.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_3.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_30.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_31.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_4.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_5.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_6.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_7.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_8.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_9.tif'}], 'in_channels': 1, 'out_channels': 1, 'num_classes': 2, 'dropout': 0.65, 'use_clipping': False, 'clipping': 1.0, 'weight_decay': 0.01, 'intensity_sigma': 1.0, 'spatial_sigma': 4.0, 'radius': 2, 'reconstruction_loss': 'MSE', 'n_cuts_weight': 0.5, 'rec_loss_weight': 0.005, 'eval_volume_dict': [{'image': PosixPath('gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/eval/vol/c3_image_cropped_eval.tif'), 'label': PosixPath('gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/eval/lab/c3_labels_cropped_eval.tif')}]}\n", + "INFO:napari_cellseg3d.utils:********************\n", + "INFO:napari_cellseg3d.utils:-- Parameters --\n", + "INFO:napari_cellseg3d.utils:Device: cuda:0\n", + "INFO:napari_cellseg3d.utils:Batch size: 4\n", + "INFO:napari_cellseg3d.utils:Epochs: 50\n", + "INFO:napari_cellseg3d.utils:Learning rate: 2e-05\n", + "INFO:napari_cellseg3d.utils:Validation interval: 2\n", + "INFO:napari_cellseg3d.utils:Using data augmentation\n", + "INFO:napari_cellseg3d.utils:-- Model --\n", + "INFO:napari_cellseg3d.utils:Using 2 classes\n", + "INFO:napari_cellseg3d.utils:Weight decay: 0.01\n", + "INFO:napari_cellseg3d.utils:* NCuts : \n", + "INFO:napari_cellseg3d.utils:- Intensity sigma 1.0\n", + "INFO:napari_cellseg3d.utils:- Spatial sigma 4.0\n", + "INFO:napari_cellseg3d.utils:- Radius : 2\n", + "INFO:napari_cellseg3d.utils:* Reconstruction loss : MSE\n", + "INFO:napari_cellseg3d.utils:Weighted sum : 0.5*NCuts + 0.005*Reconstruction\n", + "INFO:napari_cellseg3d.utils:-- Data --\n", + "INFO:napari_cellseg3d.utils:Training data :\n", + "\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_10.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_11.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_12.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_13.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_14.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_15.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_16.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_17.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_18.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_19.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_20.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_21.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_22.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_23.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_3.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_4.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_5.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_6.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_7.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_8.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_9.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_0.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_1.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_10.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_11.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_12.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_13.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_14.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_15.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_16.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_17.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_18.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_19.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_2.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_3.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_4.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_5.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_6.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_7.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_8.tif\n", + 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"INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_30.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_31.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_4.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_5.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_6.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_7.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_8.tif\n", + "INFO:napari_cellseg3d.utils:gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_9.tif\n", + "INFO:napari_cellseg3d.utils:\n", + "Validation data :\n", + "\n", + "INFO:napari_cellseg3d.utils:image: gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/eval/vol/c3_image_cropped_eval.tif\n", + "INFO:napari_cellseg3d.utils:label: gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/eval/lab/c3_labels_cropped_eval.tif\n", + "INFO:napari_cellseg3d.utils:********************\n", + "INFO:napari_cellseg3d.utils:Initializing training...\n", + "INFO:napari_cellseg3d.utils:- Getting the data\n", + "DEBUG:napari_cellseg3d.utils:Loading volume dataset\n", + "DEBUG:napari_cellseg3d.utils:Data is 3D\n", + "DEBUG:napari_cellseg3d.utils:Image shape is torch.Size([64, 64, 64])\n", + "DEBUG:napari_cellseg3d.utils:Padding sizes are [64, 64, 64]\n", + "Loading dataset: 100%|██████████| 142/142 [00:00<00:00, 355152.75it/s]\n", + "DEBUG:napari_cellseg3d.utils:Data shape : torch.Size([64, 64, 64])\n", + "Loading dataset: 100%|██████████| 1/1 [00:00<00:00, 4.47it/s]\n", + "INFO:napari_cellseg3d.utils:- Getting the model\n", + "INFO:napari_cellseg3d.utils:Model will be trained from scratch\n", + "INFO:napari_cellseg3d.utils:- Getting the optimizer\n", + "INFO:napari_cellseg3d.utils:- Getting the loss functions\n", + "INFO:napari_cellseg3d.utils:Radius set to 2\n", + "INFO:napari_cellseg3d.utils:Ready\n", + "INFO:napari_cellseg3d.utils:Training the model\n", + "INFO:napari_cellseg3d.utils:********************\n", + "INFO:napari_cellseg3d.utils:Epoch 1 of 50\n", + "max_pool3d_with_indices_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at ../aten/src/ATen/Context.cpp:71.)\n" + ] + } + ] + } + ] +} From de38e8fd544d92ef928729d1f3a56fc093a5175f Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 3 Aug 2023 14:13:19 +0200 Subject: [PATCH 211/250] ZeroCostDL4Mic-style notebook --- notebooks/colab_wnet_training.ipynb | 605 +++++++++++++++++++++++----- 1 file changed, 502 insertions(+), 103 deletions(-) diff --git a/notebooks/colab_wnet_training.ipynb b/notebooks/colab_wnet_training.ipynb index d56f0225..855ff758 100644 --- a/notebooks/colab_wnet_training.ipynb +++ b/notebooks/colab_wnet_training.ipynb @@ -16,6 +16,43 @@ "accelerator": "GPU" }, "cells": [ + { + "cell_type": "markdown", + "source": [ + "# **WNet (3D)**\n", + "\n", + "---\n", + "*Disclaimer:*\n", + "\n", + "This notebook is a work-in-progress allowing to train a WNet model for unsupervised cell segmentation, as part of the [CellSeg3D project](https://github.com/AdaptiveMotorControlLab/CellSeg3d) for the [Mathis Lab of Adaptive Motor Control](https://www.mackenziemathislab.org/).\n", + "\n", + "**It is heavily based on code from the excellent [Zero-Cost Deep-Learning to Enhance Microscopy project](https://github.com/HenriquesLab/ZeroCostDL4Mic)** (ZeroCostDL4Mic), jointly developed by the Jacquemet and Henriques laboratories and created by Daniel Krentzel.\n", + "Credits go to them for all but the provided model." + ], + "metadata": { + "id": "BTUVNXX7R3Go" + } + }, + { + "cell_type": "markdown", + "source": [ + "#**1. Installing dependencies**\n", + "---" + ], + "metadata": { + "id": "zmVCksV0EfVT" + } + }, + { + "cell_type": "markdown", + "source": [ + "##**1.1 Installing CellSeg3D**\n", + "---" + ], + "metadata": { + "id": "kCNRISzSEnIh" + } + }, { "cell_type": "code", "execution_count": 1, @@ -24,7 +61,8 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "3d04108f-f471-4440-e48d-debb75bae588" + "cellView": "form", + "outputId": "aefef0f5-1c38-4a08-df50-2f0fc92a70ed" }, "outputs": [ { @@ -32,12 +70,12 @@ "name": "stdout", "text": [ "Cloning into './CellSeg3D'...\n", - "remote: Enumerating objects: 5813, done.\u001b[K\n", - "remote: Counting objects: 100% (2306/2306), done.\u001b[K\n", - "remote: Compressing objects: 100% (701/701), done.\u001b[K\n", - "remote: Total 5813 (delta 1893), reused 1796 (delta 1602), pack-reused 3507\u001b[K\n", - "Receiving objects: 100% (5813/5813), 52.37 MiB | 27.04 MiB/s, done.\n", - "Resolving deltas: 100% (4170/4170), done.\n", + "remote: Enumerating objects: 5820, done.\u001b[K\n", + "remote: Counting objects: 100% (2103/2103), done.\u001b[K\n", + "remote: Compressing objects: 100% (656/656), done.\u001b[K\n", + "remote: Total 5820 (delta 1712), reused 1642 (delta 1444), pack-reused 3717\u001b[K\n", + "Receiving objects: 100% (5820/5820), 52.37 MiB | 17.56 MiB/s, done.\n", + "Resolving deltas: 100% (4182/4182), done.\n", "Obtaining file:///content/CellSeg3D\n", " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Checking if build backend supports build_editable ... \u001b[?25l\u001b[?25hdone\n", @@ -46,10 +84,10 @@ "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from napari-cellseg3d==0.0.3rc1) (1.22.4)\n", "Collecting napari[all]>=0.4.14 (from napari-cellseg3d==0.0.3rc1)\n", " Downloading napari-0.4.18-py3-none-any.whl (2.6 MB)\n", - 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" Created wheel for transforms3d: filename=transforms3d-0.4.1-py3-none-any.whl size=1376757 sha256=bcde1f1174b9cab0f3b6fa1db15dfa8a2521e3f26e7c3ddc2ac78b3d28775e11\n", + " Created wheel for transforms3d: filename=transforms3d-0.4.1-py3-none-any.whl size=1376757 sha256=2efb50be6283ac3c4647803ec34da295bc9d59d03ec5754cbe807436fef11f38\n", " Stored in directory: /root/.cache/pip/wheels/06/37/d0/6e0fe02010be074e8138f2b5ffff5254b74751aafb60bb5666\n", "Successfully built napari-cellseg3d pathtools transforms3d\n", "Installing collected packages: PyQt5-Qt5, pathtools, heapdict, transforms3d, tomli-w, smmap, setproctitle, sentry-sdk, QtPy, pytools, PyQt5-sip, pint, napari-plugin-engine, mypy-extensions, jedi, itk-core, in-n-out, imagecodecs, hsluv, freetype-py, einops, docutils, docstring-parser, docker-pycreds, cachey, vispy, superqt, sphinx, PyQt5, pyopencl, psygnal, itk-numerics, itk-io, gitdb, pyclesperanto-prototype, numpydoc, npe2, napari-svg, magicgui, itk-filtering, GitPython, app-model, wandb, qtconsole, napari-plugin-manager, itk-segmentation, itk-registration, napari-console, itk, napari, monai, napari-cellseg3d\n", @@ -305,33 +343,181 @@ } ], "source": [ + "#@markdown ##Play to install WNet dependencies\n", "!git clone https://github.com/AdaptiveMotorControlLab/CellSeg3d.git --branch cy/wnet-extras --single-branch ./CellSeg3D\n", "!pip install -e CellSeg3D[wandb]" ] }, + { + "cell_type": "markdown", + "source": [ + "## **1.2. Restart your runtime**\n", + "---\n", + "\n", + "\n", + "\n", + "** Ignore the following message error message. Your Runtime will be automatically restarted. This is normal.**\n", + "\n", + "\"\"
" + ], + "metadata": { + "id": "JilkN37yEUrs" + } + }, { "cell_type": "code", "source": [ + "# @title\n", "#Force session restart\n", "exit(0)" ], "metadata": { - "id": "ctwVtCDXtk_l" + "cellView": "form", + "id": "2i3XU68IEG-K" }, "execution_count": 2, "outputs": [] }, + { + "cell_type": "markdown", + "source": [ + "##**1.3 Load key dependencies**\n", + "---" + ], + "metadata": { + "id": "nqctRognFGDT" + } + }, { "cell_type": "code", "source": [ - "# import wandb\n", - "# wandb.login()" + "# @title\n", + "from pathlib import Path\n", + "from napari_cellseg3d.dev_scripts import colab_training as c\n", + "from napari_cellseg3d.config import WNetTrainingWorkerConfig, WandBConfig, WeightsInfo, PRETRAINED_WEIGHTS_DIR" ], "metadata": { - "id": "EtsK08ECwlnJ" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "cellView": "form", + "id": "wOOhJjkxjXz-", + "outputId": "57e75f0f-febf-40f0-d1fd-d1da380478bc" }, - "execution_count": null, - "outputs": [] + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "DEBUG:napari_cellseg3d.utils:PRETRAINED WEIGHT DIR LOCATION : /content/CellSeg3D/napari_cellseg3d/code_models/models/pretrained\n", + "DEBUG:napari_cellseg3d.utils:PRETRAINED WEIGHT DIR LOCATION : /content/CellSeg3D/napari_cellseg3d/code_models/models/pretrained\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **2. Complete the Colab session**\n", + "---" + ], + "metadata": { + "id": "Zi9gRBHFFyX-" + } + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "## **2.1. Check for GPU access**\n", + "---\n", + "\n", + "By default, the session should be using Python 3 and GPU acceleration, but it is possible to ensure that these are set properly by doing the following:\n", + "\n", + "Go to **Runtime -> Change the Runtime type**\n", + "\n", + "**Runtime type: Python 3** *(Python 3 is programming language in which this program is written)*\n", + "\n", + "**Accelerator: GPU** *(Graphics processing unit)*\n" + ], + "metadata": { + "id": "zSU-LYTfFnvF" + } + }, + { + "cell_type": "code", + "source": [ + "#@markdown ##Run this cell to check if you have GPU access\n", + "\n", + "import torch\n", + "if not torch.cuda.is_available():\n", + " print('You do not have GPU access.')\n", + " print('Did you change your runtime?')\n", + " print('If the runtime setting is correct then Google did not allocate a GPU for your session')\n", + " print('Expect slow performance. To access GPU try reconnecting later')\n", + "\n", + "else:\n", + " print('You have GPU access')\n", + " !nvidia-smi\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "cellView": "form", + "id": "Ie7bXiMgFtPH", + "outputId": "8e5c79c3-f35f-47aa-b878-a204e4188fa5" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "You have GPU access\n", + "Thu Aug 3 11:54:18 2023 \n", + "+-----------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 525.105.17 Driver Version: 525.105.17 CUDA Version: 12.0 |\n", + "|-------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|===============================+======================+======================|\n", + "| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n", + "| N/A 53C P8 9W / 70W | 3MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + " \n", + "+-----------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=============================================================================|\n", + "| No running processes found |\n", + "+-----------------------------------------------------------------------------+\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## **2.2. Mount Google Drive**\n", + "---\n", + " To use this notebook with your **own data**, place it in a folder on **Google Drive** following one of the directory structures outlined in **Section 0**.\n", + "\n", + "1. **Run** the **cell** below to mount your Google Drive and follow the link.\n", + "\n", + "2. **Sign in** to your Google account and press 'Allow'.\n", + "\n", + "3. Next, copy the **authorization code**, paste it into the cell and press enter. This will allow Colab to read and write data from and to your Google Drive.\n", + "\n", + "4. Once this is done, your data can be viewed in the **Files tab** on the top left of the notebook after hitting 'Refresh'." + ], + "metadata": { + "id": "X_bbk7RAF2yw" + } }, { "cell_type": "code", @@ -357,9 +543,9 @@ "base_uri": "https://localhost:8080/" }, "id": "AsIARCablq1V", - "outputId": "b0a0a85c-ab4b-4d90-abbd-dcf25bfdf31c" + "outputId": "ebbd132e-b112-4c4c-bb59-864a03bfb885" }, - "execution_count": 3, + "execution_count": 4, "outputs": [ { "output_type": "stream", @@ -370,53 +556,178 @@ } ] }, + { + "cell_type": "markdown", + "source": [ + "** If you cannot see your files, reactivate your session by connecting to your hosted runtime.**\n", + "\n", + "\n", + "\"Example
Connect to a hosted runtime.
" + ], + "metadata": { + "id": "r6FI22lkQLTv" + } + }, { "cell_type": "code", "source": [ - "from pathlib import Path\n", - "from napari_cellseg3d.dev_scripts import colab_training as c\n", - "from napari_cellseg3d.config import WNetTrainingWorkerConfig, WandBConfig, WeightsInfo, PRETRAINED_WEIGHTS_DIR" + "# @title\n", + "# import wandb\n", + "# wandb.login()" ], "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "wOOhJjkxjXz-", - "outputId": "3665f8b9-6c9f-4f8b-959f-bc876051f00f" + "cellView": "form", + "id": "EtsK08ECwlnJ" }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "DEBUG:napari_cellseg3d.utils:PRETRAINED WEIGHT DIR LOCATION : /content/CellSeg3D/napari_cellseg3d/code_models/models/pretrained\n", - "DEBUG:napari_cellseg3d.utils:PRETRAINED WEIGHT DIR LOCATION : /content/CellSeg3D/napari_cellseg3d/code_models/models/pretrained\n" - ] - } - ] + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# **3. Select your parameters and paths**\n", + "---" + ], + "metadata": { + "id": "IkOpxYjaGM0m" + } + }, + { + "cell_type": "markdown", + "source": [ + "## **3.1. Choosing parameters**\n", + "\n", + "---\n", + "\n", + "### **Paths to training data and model**\n", + "\n", + "* **`training_source`** specifies the paths to the training data. They must be a single multipage TIF file each\n", + "\n", + "* **`model_path`** specifies the directory where the model checkpoints will be saved.\n", + "\n", + "**Note:** You can copy paths from the 'Files' tab by right-clicking any folder or file and selecting 'Copy path'.\n", + "\n", + "### **Training parameters**\n", + "\n", + "* **`number_of_epochs`** is the number of times the entire training data will be seen by the model. Default: 50\n", + "\n", + "* **`batchs_size`** is the number of image that will be bundled together at each training step. Default: 4\n", + "\n", + "* **`learning_rate`** is the step size of the update of the model's weight. Try decreasing it if the NCuts loss is unstable. Default: 2e-5\n", + "\n", + "* **`num_classes`** is the number of brightness clusters to segment the image in. Try raising it to 3 if you have artifacts or \"halos\" around your cells that have significantly different brightness. Default: 2\n", + "\n", + "* **`weight_decay`** is a regularization parameter used to prevent overfitting. Default: 0.01\n", + "\n", + "* **`validation_frequency`** is the frequency at which the provided evaluation data is used to estimate the model's performance.\n", + "\n", + "* **`intensity_sigma`** is the standard deviation of the feature similarity term. Default: 1\n", + "\n", + "* **`spatial_sigma`** is the standard deviation of the spatial proximity term. Default: 4\n", + "\n", + "* **`ncuts_radius`** is the radius for the NCuts loss computation, in pixels. Default: 2\n", + "\n", + "* **`rec_loss`** is the loss to use for the decoder. Can be Mean Square Error (MSE) or Binary Cross Entropy (BCE). Default : MSE\n", + "\n", + "* **`n_cuts_weight`** is the weight of the NCuts loss in the weighted sum for the backward pass. Default: 0.5\n", + "* **`rec_loss_weight`** is the weight of the reconstruction loss. Default: 0.005\n" + ], + "metadata": { + "id": "65FhTkYlGKRt" + } }, { "cell_type": "code", "source": [ - "train_data_folder = Path(\"./gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full\")\n", - "results_path = Path(\"./WNET_TRAINING_RESULTS\")\n", - "results_path.mkdir(exist_ok=True)\n", - "eval_image_folder = Path(\"./gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/eval/vol/\")\n", - "eval_label_folder = Path(\"./gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/eval/lab/\")" + "#@markdown ###Path to training data:\n", + "training_source = \"./gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full\" #@param {type:\"string\"}\n", + "#@markdown ###Model name and path to model folder:\n", + "model_path = \"./gdrive/MyDrive/CELLSEG_BENCHMARK/WNET_TRAINING_RESULTS\" #@param {type:\"string\"}\n", + "#@markdown ---\n", + "#@markdown ###Perform validation on a test dataset\n", + "do_validation = True #@param {type:\"boolean\"}\n", + "#@markdown ###Path to evaluation data (optional, use if checked above):\n", + "eval_source = \"./gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/eval/vol/\" #@param {type:\"string\"}\n", + "eval_target = \"./gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/eval/lab/\" #@param {type:\"string\"}\n", + "#@markdown ---\n", + "#@markdown ###Training parameters\n", + "number_of_epochs = 50 #@param {type:\"number\"}\n", + "#@markdown ###Default advanced parameters\n", + "use_default_advanced_parameters = False #@param {type:\"boolean\"}\n", + "#@markdown If not, please change:\n", + "\n", + "#@markdown Training parameters:\n", + "batch_size = 4 #@param {type:\"number\"}\n", + "learning_rate = 2e-5 #@param {type:\"number\"}\n", + "num_classes = 2 #@param {type:\"number\"}\n", + "weight_decay = 0.01 #@param {type:\"number\"}\n", + "#@markdown Validation parameters:\n", + "validation_frequency = 2 #@param {type:\"number\"}\n", + "#@markdown SoftNCuts parameters:\n", + "intensity_sigma = 1.0 #@param {type:\"number\"}\n", + "spatial_sigma = 4.0 #@param {type:\"number\"}\n", + "ncuts_radius = 2 #@param {type:\"number\"}\n", + "#@markdown Reconstruction loss:\n", + "rec_loss = \"MSE\" #@param[\"MSE\", \"BCE\"]\n", + "#@markdown Weighted sum of losses:\n", + "n_cuts_weight = 0.5 #@param {type:\"number\"}\n", + "rec_loss_weight = 0.005 #@param {type:\"number\"}" ], "metadata": { - "id": "kowhOfQunuXY" + "cellView": "form", + "id": "tTSCC6ChGuuA" }, "execution_count": 5, "outputs": [] }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "id": "HtoIo5GcKIXX" + } + }, + { + "cell_type": "markdown", + "source": [ + "# **4. Train the network**\n", + "---\n", + "\n", + "**CRITICAL NOTE:** Google Colab has a time limit for processing (to prevent using GPU power for datamining). Training times must be less than 12 hours! If training takes longer than 12 hours, please decrease `number_of_epochs`." + ], + "metadata": { + "id": "arWhMU6aKsri" + } + }, + { + "cell_type": "markdown", + "source": [ + "## **4.1. Initialize the config**\n", + "---" + ], + "metadata": { + "id": "L59J90S_Kva3" + } + }, { "cell_type": "code", "source": [ + "# @title\n", + "train_data_folder = Path(training_source)\n", + "results_path = Path(model_path)\n", + "results_path.mkdir(exist_ok=True)\n", + "eval_image_folder = Path(eval_source)\n", + "eval_label_folder = Path(eval_target)\n", + "\n", + "eval_dict = c.create_eval_dataset_dict(\n", + " eval_image_folder,\n", + " eval_label_folder,\n", + " ) if do_validation else None\n", + "\n", + "\n", "train_config = WNetTrainingWorkerConfig(\n", " device=\"cuda:0\",\n", - " max_epochs=50,\n", + " max_epochs=number_of_epochs,\n", " learning_rate=2e-5,\n", " validation_interval=2,\n", " batch_size=4,\n", @@ -424,10 +735,27 @@ " weights_info=WeightsInfo(),\n", " results_path_folder=str(results_path),\n", " train_data_dict=c.create_dataset_dict_no_labs(train_data_folder),\n", - " eval_volume_dict=c.create_eval_dataset_dict(\n", - " eval_image_folder,\n", - " eval_label_folder,\n", - " ),\n", + " eval_volume_dict=eval_dict,\n", + ") if use_default_advanced_parameters else WNetTrainingWorkerConfig(\n", + " device=\"cuda:0\",\n", + " max_epochs=number_of_epochs,\n", + " learning_rate=learning_rate,\n", + " validation_interval=validation_frequency,\n", + " batch_size=batch_size,\n", + " num_workers=2,\n", + " weights_info=WeightsInfo(),\n", + " results_path_folder=str(results_path),\n", + " train_data_dict=c.create_dataset_dict_no_labs(train_data_folder),\n", + " eval_volume_dict=eval_dict,\n", + " # advanced\n", + " num_classes=num_classes,\n", + " weight_decay=weight_decay,\n", + " intensity_sigma=intensity_sigma,\n", + " spatial_sigma=spatial_sigma,\n", + " radius=ncuts_radius,\n", + " reconstruction_loss=rec_loss,\n", + " n_cuts_weight=n_cuts_weight,\n", + " rec_loss_weight=rec_loss_weight,\n", ")\n", "wandb_config = WandBConfig(\n", " mode=\"disabled\",\n", @@ -439,14 +767,21 @@ "base_uri": "https://localhost:8080/" }, "id": "YOgLyUwPjvUX", - "outputId": "99991c58-c674-4dcd-9d9d-c57f741b72fb" + "outputId": "9f76fb2a-d811-432b-aa69-31c47459f977" }, - "execution_count": 6, + "execution_count": 8, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ + "INFO:napari_cellseg3d.utils:Images :\n", + "\n", + "INFO:napari_cellseg3d.utils:c3_image_cropped_eval.tif\n", + "INFO:napari_cellseg3d.utils:**********\n", + "INFO:napari_cellseg3d.utils:Labels :\n", + "\n", + "INFO:napari_cellseg3d.utils:c3_labels_cropped_eval.tif\n", "INFO:napari_cellseg3d.utils:Images :\n", "INFO:napari_cellseg3d.utils:c1_images_cropped_10\n", "INFO:napari_cellseg3d.utils:c1_images_cropped_11\n", @@ -590,24 +925,25 @@ "INFO:napari_cellseg3d.utils:images_cropped_7\n", "INFO:napari_cellseg3d.utils:images_cropped_8\n", "INFO:napari_cellseg3d.utils:images_cropped_9\n", - "INFO:napari_cellseg3d.utils:**********\n", - "DEBUG:napari_cellseg3d.utils:Images filepaths: [PosixPath('gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/eval/vol/c3_image_cropped_eval.tif')]\n", - "DEBUG:napari_cellseg3d.utils:Labels filepaths: [PosixPath('gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/eval/lab/c3_labels_cropped_eval.tif')]\n", - "INFO:napari_cellseg3d.utils:Images :\n", - "\n", - "INFO:napari_cellseg3d.utils:c3_image_cropped_eval.tif\n", - "INFO:napari_cellseg3d.utils:**********\n", - "INFO:napari_cellseg3d.utils:Labels :\n", - "\n", - "INFO:napari_cellseg3d.utils:c3_labels_cropped_eval.tif\n", - "DEBUG:napari_cellseg3d.utils:Training data dict : [{'image': PosixPath('gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/eval/vol/c3_image_cropped_eval.tif'), 'label': PosixPath('gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/eval/lab/c3_labels_cropped_eval.tif')}]\n" + "INFO:napari_cellseg3d.utils:**********\n" ] } ] }, + { + "cell_type": "markdown", + "source": [ + "## **4.2. Start training**\n", + "---" + ], + "metadata": { + "id": "idowGpeQPIm2" + } + }, { "cell_type": "code", "source": [ + "# @title\n", "worker = c.get_colab_worker(worker_config=train_config, wandb_config=wandb_config)\n", "for epoch_loss in worker.train():\n", " continue" @@ -617,8 +953,9 @@ "base_uri": "https://localhost:8080/", "height": 1000 }, + "cellView": "form", "id": "OXxKZhGMqguz", - "outputId": "7ef12d69-5519-4630-b9f5-bb4f3068c5c1" + "outputId": "60f2ef25-00fe-4e0f-916a-4adda9f118d9" }, "execution_count": null, "outputs": [ @@ -626,7 +963,7 @@ "output_type": "stream", "name": "stderr", "text": [ - "DEBUG:napari_cellseg3d.utils:wandb config : {'device': 'cuda:0', 'max_epochs': 50, 'learning_rate': 2e-05, 'validation_interval': 2, 'batch_size': 4, 'deterministic_config': DeterministicConfig(enabled=True, seed=34936339), 'scheduler_factor': 0.5, 'scheduler_patience': 10, 'weights_info': WeightsInfo(path='/content/CellSeg3D/napari_cellseg3d/code_models/models/pretrained', custom=False, use_pretrained=False), 'results_path_folder': 'WNET_TRAINING_RESULTS', 'sampling': False, 'num_samples': 2, 'sample_size': None, 'do_augmentation': True, 'num_workers': 2, 'train_data_dict': [{'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_10.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_11.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_12.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_13.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_14.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_15.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_16.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_17.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_18.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_19.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_20.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_21.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_22.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_23.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_3.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_4.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_5.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_6.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_7.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_8.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_9.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_0.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_1.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_10.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_11.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_12.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_13.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_14.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_15.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_16.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_17.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_18.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_19.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_2.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_3.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_4.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_5.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_6.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_7.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_8.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c2_images_cropped_9.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_0.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_1.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_10.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_11.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_12.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_13.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_14.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_15.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_16.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_17.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_18.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_19.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_2.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_20.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_21.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_22.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_23.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_24.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_25.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_26.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_27.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_28.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_29.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_3.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_4.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_5.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_6.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_7.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_8.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c3_images_cropped_9.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_0.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_10.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_11.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_12.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_13.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_14.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_15.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_16.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_17.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_18.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_19.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_2.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_3.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_4.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_5.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_6.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_7.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_8.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c4_images_cropped_9.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_0.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_1.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_10.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_11.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_12.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_13.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_14.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_15.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_16.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_17.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_18.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_19.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_2.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_3.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_4.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_5.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_6.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_7.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_8.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c5_images_cropped_9.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_0.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_1.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_10.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_11.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_12.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_13.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_14.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_15.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_16.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_17.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_18.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_19.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_2.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_20.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_21.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_22.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_23.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_24.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_25.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_26.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_27.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_28.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_29.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_3.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_30.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_31.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_4.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_5.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_6.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_7.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_8.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_9.tif'}], 'in_channels': 1, 'out_channels': 1, 'num_classes': 2, 'dropout': 0.65, 'use_clipping': False, 'clipping': 1.0, 'weight_decay': 0.01, 'intensity_sigma': 1.0, 'spatial_sigma': 4.0, 'radius': 2, 'reconstruction_loss': 'MSE', 'n_cuts_weight': 0.5, 'rec_loss_weight': 0.005, 'eval_volume_dict': [{'image': PosixPath('gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/eval/vol/c3_image_cropped_eval.tif'), 'label': PosixPath('gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/eval/lab/c3_labels_cropped_eval.tif')}]}\n", + "DEBUG:napari_cellseg3d.utils:wandb config : {'device': 'cuda:0', 'max_epochs': 50, 'learning_rate': 2e-05, 'validation_interval': 2, 'batch_size': 4, 'deterministic_config': DeterministicConfig(enabled=True, seed=34936339), 'scheduler_factor': 0.5, 'scheduler_patience': 10, 'weights_info': WeightsInfo(path='/content/CellSeg3D/napari_cellseg3d/code_models/models/pretrained', custom=False, use_pretrained=False), 'results_path_folder': 'gdrive/MyDrive/CELLSEG_BENCHMARK/WNET_TRAINING_RESULTS', 'sampling': False, 'num_samples': 2, 'sample_size': None, 'do_augmentation': True, 'num_workers': 2, 'train_data_dict': [{'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_10.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_11.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_12.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_13.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_14.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_15.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_16.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_17.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_18.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_19.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_20.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_21.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_22.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_23.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_3.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_4.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_5.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/c1_images_cropped_6.tif'}, {'image': 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'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_25.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_26.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_27.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_28.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_29.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_3.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_30.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_31.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_4.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_5.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_6.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_7.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_8.tif'}, {'image': 'gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full/images_cropped_9.tif'}], 'in_channels': 1, 'out_channels': 1, 'num_classes': 2, 'dropout': 0.65, 'use_clipping': False, 'clipping': 1.0, 'weight_decay': 0.01, 'intensity_sigma': 1.0, 'spatial_sigma': 4.0, 'radius': 2, 'reconstruction_loss': 'MSE', 'n_cuts_weight': 0.5, 'rec_loss_weight': 0.005, 'eval_volume_dict': [{'image': PosixPath('gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/eval/vol/c3_image_cropped_eval.tif'), 'label': PosixPath('gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/eval/lab/c3_labels_cropped_eval.tif')}]}\n", "INFO:napari_cellseg3d.utils:********************\n", "INFO:napari_cellseg3d.utils:-- Parameters --\n", "INFO:napari_cellseg3d.utils:Device: cuda:0\n", @@ -798,12 +1135,14 @@ "INFO:napari_cellseg3d.utils:Initializing training...\n", "INFO:napari_cellseg3d.utils:- Getting the data\n", "DEBUG:napari_cellseg3d.utils:Loading volume dataset\n", + "monai.transforms.io.dictionary LoadImaged.__init__:image_only: Current default value of argument `image_only=False` has been deprecated since version 1.1. It will be changed to `image_only=True` in version 1.3.\n", "DEBUG:napari_cellseg3d.utils:Data is 3D\n", "DEBUG:napari_cellseg3d.utils:Image shape is torch.Size([64, 64, 64])\n", "DEBUG:napari_cellseg3d.utils:Padding sizes are [64, 64, 64]\n", - "Loading dataset: 100%|██████████| 142/142 [00:00<00:00, 355152.75it/s]\n", + "Loading dataset: 100%|██████████| 142/142 [00:00<00:00, 414468.45it/s]\n", "DEBUG:napari_cellseg3d.utils:Data shape : torch.Size([64, 64, 64])\n", - "Loading dataset: 100%|██████████| 1/1 [00:00<00:00, 4.47it/s]\n", + "Loading dataset: 0%| | 0/1 [00:00 Date: Thu, 3 Aug 2023 16:57:13 +0200 Subject: [PATCH 212/250] Added WANDB to Colab notebook --- notebooks/colab_wnet_training.ipynb | 522 ++++++++++++++++------------ 1 file changed, 301 insertions(+), 221 deletions(-) diff --git a/notebooks/colab_wnet_training.ipynb b/notebooks/colab_wnet_training.ipynb index 855ff758..710eb5cd 100644 --- a/notebooks/colab_wnet_training.ipynb +++ b/notebooks/colab_wnet_training.ipynb @@ -61,125 +61,74 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "cellView": "form", - "outputId": "aefef0f5-1c38-4a08-df50-2f0fc92a70ed" + "outputId": "df656d73-015c-473b-f63a-dd71ed17085e" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Cloning into './CellSeg3D'...\n", - "remote: Enumerating objects: 5820, done.\u001b[K\n", - "remote: Counting objects: 100% (2103/2103), done.\u001b[K\n", - "remote: Compressing objects: 100% (656/656), done.\u001b[K\n", - "remote: Total 5820 (delta 1712), reused 1642 (delta 1444), pack-reused 3717\u001b[K\n", - "Receiving objects: 100% (5820/5820), 52.37 MiB | 17.56 MiB/s, done.\n", - "Resolving deltas: 100% (4182/4182), done.\n", + "fatal: destination path './CellSeg3D' already exists and is not an empty directory.\n", "Obtaining file:///content/CellSeg3D\n", " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Checking if build backend supports build_editable ... \u001b[?25l\u001b[?25hdone\n", " Getting requirements to build editable ... \u001b[?25l\u001b[?25hdone\n", " Preparing editable metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from napari-cellseg3d==0.0.3rc1) (1.22.4)\n", - 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" Created wheel for transforms3d: filename=transforms3d-0.4.1-py3-none-any.whl size=1376757 sha256=2efb50be6283ac3c4647803ec34da295bc9d59d03ec5754cbe807436fef11f38\n", - " Stored in directory: /root/.cache/pip/wheels/06/37/d0/6e0fe02010be074e8138f2b5ffff5254b74751aafb60bb5666\n", - "Successfully built napari-cellseg3d pathtools transforms3d\n", - "Installing collected packages: PyQt5-Qt5, pathtools, heapdict, transforms3d, tomli-w, smmap, setproctitle, sentry-sdk, QtPy, pytools, PyQt5-sip, pint, napari-plugin-engine, mypy-extensions, jedi, itk-core, in-n-out, imagecodecs, hsluv, freetype-py, einops, docutils, docstring-parser, docker-pycreds, cachey, vispy, superqt, sphinx, PyQt5, pyopencl, psygnal, itk-numerics, itk-io, gitdb, pyclesperanto-prototype, numpydoc, npe2, napari-svg, magicgui, itk-filtering, GitPython, app-model, wandb, qtconsole, napari-plugin-manager, itk-segmentation, itk-registration, napari-console, itk, napari, monai, napari-cellseg3d\n", - " Attempting uninstall: docutils\n", - " Found existing installation: docutils 0.18.1\n", - " Uninstalling docutils-0.18.1:\n", - " Successfully uninstalled docutils-0.18.1\n", - " Attempting uninstall: sphinx\n", - " Found existing installation: Sphinx 5.0.2\n", - " Uninstalling Sphinx-5.0.2:\n", - " Successfully uninstalled Sphinx-5.0.2\n", - "Successfully installed GitPython-3.1.32 PyQt5-5.15.9 PyQt5-Qt5-5.15.2 PyQt5-sip-12.12.2 QtPy-2.3.1 app-model-0.2.0 cachey-0.2.1 docker-pycreds-0.4.0 docstring-parser-0.15 docutils-0.17.1 einops-0.6.1 freetype-py-2.4.0 gitdb-4.0.10 heapdict-1.0.1 hsluv-5.0.3 imagecodecs-2023.7.10 in-n-out-0.1.8 itk-5.3.0 itk-core-5.3.0 itk-filtering-5.3.0 itk-io-5.3.0 itk-numerics-5.3.0 itk-registration-5.3.0 itk-segmentation-5.3.0 jedi-0.19.0 magicgui-0.7.2 monai-1.2.0 mypy-extensions-1.0.0 napari-0.4.18 napari-cellseg3d-0.0.3rc1 napari-console-0.0.8 napari-plugin-engine-0.2.0 napari-plugin-manager-0.1.0a2 napari-svg-0.1.10 npe2-0.7.2 numpydoc-1.5.0 pathtools-0.1.2 pint-0.22 psygnal-0.9.1 pyclesperanto-prototype-0.24.1 pyopencl-2023.1.1 pytools-2023.1.1 qtconsole-5.4.3 sentry-sdk-1.29.2 setproctitle-1.3.2 smmap-5.0.0 sphinx-4.5.0 superqt-0.4.1 tomli-w-1.0.0 transforms3d-0.4.1 vispy-0.12.2 wandb-0.15.8\n" + " Created wheel for napari-cellseg3d: filename=napari_cellseg3d-0.0.3rc1-0.editable-py3-none-any.whl size=6209 sha256=307ee3cf2e41bdc6b51500507122f9cdac4161970a01fe63986c0743dbc738f7\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-l3d04gq9/wheels/a1/e1/dc/cc9f89fc6f907d6bd38a2cbf3335706054a5435e97f664034d\n", + "Successfully built napari-cellseg3d\n", + "Installing collected packages: napari-cellseg3d\n", + " Attempting uninstall: napari-cellseg3d\n", + " Found existing installation: napari-cellseg3d 0.0.3rc1\n", + " Uninstalling napari-cellseg3d-0.0.3rc1:\n", + " Successfully uninstalled napari-cellseg3d-0.0.3rc1\n", + "Successfully installed napari-cellseg3d-0.0.3rc1\n" ] } ], "source": [ "#@markdown ##Play to install WNet dependencies\n", "!git clone https://github.com/AdaptiveMotorControlLab/CellSeg3d.git --branch cy/wnet-extras --single-branch ./CellSeg3D\n", - "!pip install -e CellSeg3D[wandb]" + "!pip install -e CellSeg3D" ] }, { @@ -400,11 +283,10 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "cellView": "form", "id": "wOOhJjkxjXz-", - "outputId": "57e75f0f-febf-40f0-d1fd-d1da380478bc" + "outputId": "d0cf25c9-4255-4a35-e35c-31578231a92d" }, - "execution_count": 1, + "execution_count": 4, "outputs": [ { "output_type": "stream", @@ -419,9 +301,34 @@ { "cell_type": "markdown", "source": [ - "# **2. Complete the Colab session**\n", + "##**1.3 Initialize Weights & Biases integration (optional)**\n", "---" ], + "metadata": { + "id": "Ax-vJAWRwIKi" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -q wandb\n", + "import wandb\n", + "wandb.login()" + ], + "metadata": { + "id": "QNgC3awjwb7G" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# **2. Complete the Colab session**\n", + "---\n", + "Use the following cell if you'd like to enable WandB logging for your training run.\n", + "Simply enter your API key below." + ], "metadata": { "id": "Zi9gRBHFFyX-" } @@ -467,16 +374,16 @@ }, "cellView": "form", "id": "Ie7bXiMgFtPH", - "outputId": "8e5c79c3-f35f-47aa-b878-a204e4188fa5" + "outputId": "95bc0d9f-7be1-49ac-b1e1-93e1b1d0bed1" }, - "execution_count": 3, + "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "You have GPU access\n", - "Thu Aug 3 11:54:18 2023 \n", + "Thu Aug 3 14:22:50 2023 \n", "+-----------------------------------------------------------------------------+\n", "| NVIDIA-SMI 525.105.17 Driver Version: 525.105.17 CUDA Version: 12.0 |\n", "|-------------------------------+----------------------+----------------------+\n", @@ -485,7 +392,7 @@ "| | | MIG M. |\n", "|===============================+======================+======================|\n", "| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n", - "| N/A 53C P8 9W / 70W | 3MiB / 15360MiB | 0% Default |\n", + "| N/A 57C P8 10W / 70W | 3MiB / 15360MiB | 0% Default |\n", "| | | N/A |\n", "+-------------------------------+----------------------+----------------------+\n", " \n", @@ -543,15 +450,15 @@ "base_uri": "https://localhost:8080/" }, "id": "AsIARCablq1V", - "outputId": "ebbd132e-b112-4c4c-bb59-864a03bfb885" + "outputId": "18638e0f-fd7b-41cf-c761-81e1c43ca8ab" }, - "execution_count": 4, + "execution_count": 6, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Mounted at /content/gdrive\n" + "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n" ] } ] @@ -579,7 +486,7 @@ "cellView": "form", "id": "EtsK08ECwlnJ" }, - "execution_count": null, + "execution_count": 7, "outputs": [] }, { @@ -724,6 +631,12 @@ " eval_label_folder,\n", " ) if do_validation else None\n", "\n", + "try:\n", + " import wandb\n", + " WANDB_INSTALLED = True\n", + "except ImportError:\n", + " WANDB_INSTALLED = False\n", + "\n", "\n", "train_config = WNetTrainingWorkerConfig(\n", " device=\"cuda:0\",\n", @@ -758,7 +671,7 @@ " rec_loss_weight=rec_loss_weight,\n", ")\n", "wandb_config = WandBConfig(\n", - " mode=\"disabled\",\n", + " mode=\"disabled\" if not WANDB_INSTALLED else \"online\",\n", " save_model_artifact=False,\n", ")" ], @@ -767,9 +680,9 @@ "base_uri": "https://localhost:8080/" }, "id": "YOgLyUwPjvUX", - "outputId": "9f76fb2a-d811-432b-aa69-31c47459f977" + "outputId": "fe7bbf61-4f27-483e-ef3d-e93553c5d8ad" }, - "execution_count": 8, + "execution_count": 7, "outputs": [ { "output_type": "stream", @@ -955,7 +868,7 @@ }, "cellView": "form", "id": "OXxKZhGMqguz", - "outputId": "60f2ef25-00fe-4e0f-916a-4adda9f118d9" + "outputId": "aa79f4a6-dced-495d-95f9-42d31702506d" }, "execution_count": null, "outputs": [ @@ -964,6 +877,73 @@ "name": "stderr", "text": [ "DEBUG:napari_cellseg3d.utils:wandb config 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Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "Tracking run with wandb version 0.15.8" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "Run data is saved locally in /content/wandb/run-20230803_142423-rbekeggu" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "Syncing run astral-rain-1 to Weights & Biases (docs)
" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + " View project at https://wandb.ai/c-achard/CellSeg3D%20WNet%20%28Colab%29" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + " View run at https://wandb.ai/c-achard/CellSeg3D%20WNet%20%28Colab%29/runs/rbekeggu" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ "INFO:napari_cellseg3d.utils:********************\n", "INFO:napari_cellseg3d.utils:-- Parameters --\n", "INFO:napari_cellseg3d.utils:Device: cuda:0\n", @@ -1139,10 +1119,10 @@ "DEBUG:napari_cellseg3d.utils:Data is 3D\n", "DEBUG:napari_cellseg3d.utils:Image shape is torch.Size([64, 64, 64])\n", "DEBUG:napari_cellseg3d.utils:Padding sizes are [64, 64, 64]\n", - "Loading dataset: 100%|██████████| 142/142 [00:00<00:00, 414468.45it/s]\n", + "Loading dataset: 100%|██████████| 142/142 [00:00<00:00, 44275.29it/s]\n", "DEBUG:napari_cellseg3d.utils:Data shape : torch.Size([64, 64, 64])\n", "Loading dataset: 0%| | 0/1 [00:00 Date: Fri, 4 Aug 2023 14:52:15 +0200 Subject: [PATCH 213/250] Added softmax param --- napari_cellseg3d/code_models/models/model_WNet.py | 1 + napari_cellseg3d/code_models/models/wnet/model.py | 7 ++++--- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/napari_cellseg3d/code_models/models/model_WNet.py b/napari_cellseg3d/code_models/models/model_WNet.py index bc1b3818..164ede21 100644 --- a/napari_cellseg3d/code_models/models/model_WNet.py +++ b/napari_cellseg3d/code_models/models/model_WNet.py @@ -18,6 +18,7 @@ def __init__( in_channels=in_channels, out_channels=out_channels, # num_classes=num_classes, + softmax=True, ) # def train(self: T, mode: bool = True) -> T: diff --git a/napari_cellseg3d/code_models/models/wnet/model.py b/napari_cellseg3d/code_models/models/wnet/model.py index d8ba3a78..6c1fcee7 100644 --- a/napari_cellseg3d/code_models/models/wnet/model.py +++ b/napari_cellseg3d/code_models/models/wnet/model.py @@ -25,14 +25,15 @@ class WNet_encoder(nn.Module): def __init__( self, in_channels=1, - out_channels=2 - # num_classes=2 + out_channels=2, + # num_classes=2, + softmax=True, ): super().__init__() self.encoder = UNet( in_channels=in_channels, out_channels=out_channels, - softmax=False, + softmax=softmax, ) def forward(self, x): From 22196e126f6c8855fe7aab5b0086038ae3cef85b Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 7 Aug 2023 10:14:46 +0200 Subject: [PATCH 214/250] Fix reset of paths in training UI --- .../code_plugins/plugin_model_training.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 2201c9ff..026cf3ed 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -447,15 +447,19 @@ def _toggle_unsupervised_mode(self, enabled=False): if self.model_choice.currentText() == "WNet" or enabled: unsupervised = True self.start_btn = self.start_button_unsupervised - self.image_filewidget.text_field.setText("Validation images") - self.labels_filewidget.text_field.setText("Validation labels") + if self.image_filewidget.text_field.text() == "Images directory": + self.image_filewidget.text_field.setText("Validation images") + if self.labels_filewidget.text_field.text() == "Labels directory": + self.labels_filewidget.text_field.setText("Validation labels") self.learning_rate_choice.lr_value_choice.setValue(2) self.learning_rate_choice.lr_exponent_choice.setCurrentIndex(3) else: unsupervised = False self.start_btn = self.start_button_supervised - self.image_filewidget.text_field.setText("Images directory") - self.labels_filewidget.text_field.setText("Labels directory") + if self.image_filewidget.text_field.text() == "Validation images": + self.image_filewidget.text_field.setText("Images directory") + if self.labels_filewidget.text_field.text() == "Validation labels": + self.labels_filewidget.text_field.setText("Labels directory") self.learning_rate_choice.lr_value_choice.setValue(1) self.learning_rate_choice.lr_exponent_choice.setCurrentIndex(1) From d2bb6dcfa82f105303d00fa40999b5cc02292bc9 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 7 Aug 2023 16:41:28 +0200 Subject: [PATCH 215/250] WNet visualization --- docs/res/images/WNet_architecture.svg | 817 ++++++++++++++++ .../code_models/models/wnet/soft_Ncuts.py | 90 +- .../dev_scripts/colab_training.py | 2 + napari_cellseg3d/dev_scripts/view_wnet.ipynb | 897 ++++++++++++++++++ 4 files changed, 1717 insertions(+), 89 deletions(-) create mode 100644 docs/res/images/WNet_architecture.svg create mode 100644 napari_cellseg3d/dev_scripts/view_wnet.ipynb diff --git a/docs/res/images/WNet_architecture.svg 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- # """Precompute the spatial distance of the pixels for the weights calculation, to avoid recomputing it at each iteration. - # - # Returns: - # distances (dict): for each pixel index, we get the distances to the pixels in a radius around it. - # """ - # - # distances = dict() - # indexes = np.array( - # [ - # (i, j, k) - # for i in range(self.H) - # for j in range(self.W) - # for k in range(self.D) - # ] - # ) - # - # for i in indexes: - # iTuple = (i[0], i[1], i[2]) - # distances[iTuple] = dict() - # - # sliceD = indexes[ - # i[0] * self.H - # + i[1] * self.W - # + max(0, i[2] - self.radius) : i[0] * self.H - # + i[1] * self.W - # + min(self.D, i[2] + self.radius) - # ] - # sliceW = indexes[ - # i[0] * self.H - # + max(0, i[1] - self.radius) * self.W - # + i[2] : i[0] * self.H - # + min(self.W, i[1] + self.radius) * self.W - # + i[2] : self.D - # ] - # sliceH = indexes[ - # max(0, i[0] - self.radius) * self.H - # + i[1] * self.W - # + i[2] : min(self.H, i[0] + self.radius) * self.H - # + i[1] * self.W - # + i[2] : self.D * self.W - # ] - # - # for j in np.concatenate((sliceD, sliceW, sliceH)): - # jTuple = (j[0], j[1], j[2]) - # distance = np.linalg.norm(i - j) - # if distance > self.radius: - # continue - # distance = math.exp( - # -(distance**2) / (self.spatial_sigma**2) - # ) - # - # if jTuple not in distances: - # distances[iTuple][jTuple] = distance - # - # return distances, indexes - - # def get_weights(self, inputs): - # """Computes the weights matrix for the Soft N-Cuts loss. - # - # Args: - # inputs (torch.Tensor): Tensor of shape (N, C, H, W, D) containing the input images. - # - # Returns: - # list: List of the weights dict for each image in the batch. - # """ - # - # # Compute the brightness distance of the pixels - # flatted_inputs = inputs.view( - # inputs.shape[0], inputs.shape[1], -1 - # ) # (N, C, H*W*D) - # I_diff = torch.subtract( - # flatted_inputs.unsqueeze(3), flatted_inputs.unsqueeze(2) - # ) # (N, C, H*W*D, H*W*D) - # masked_I_diff = torch.mul(I_diff, self.mask) # (N, C, H*W*D, H*W*D) - # squared_I_diff = torch.pow(masked_I_diff, 2) # (N, C, H*W*D, H*W*D) - # - # W_I = torch.exp( - # torch.neg(torch.div(squared_I_diff, self.intensity_sigma)) - # ) # (N, C, H*W*D, H*W*D) - # W_I = torch.mul(W_I, self.mask) # (N, C, H*W*D, H*W*D) - # - # # Get the spatial distance of the pixels - # unsqueezed_W_X = self.W_X.view( - # 1, 1, self.W_X.shape[0], self.W_X.shape[1] - # ) # (1, 1, H*W*D, H*W*D) - # - # return torch.mul(W_I, unsqueezed_W_X) # (N, C, H*W*D, H*W*D) diff --git a/napari_cellseg3d/dev_scripts/colab_training.py b/napari_cellseg3d/dev_scripts/colab_training.py index e1850c4a..413cb25b 100644 --- a/napari_cellseg3d/dev_scripts/colab_training.py +++ b/napari_cellseg3d/dev_scripts/colab_training.py @@ -54,6 +54,8 @@ ) WANDB_INSTALLED = False +# TODO subclass to reduce code duplication + class WNetTrainingWorkerColab(TrainingWorkerBase): """A custom worker to run WNet (unsupervised) training jobs in. diff --git 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128, 64, 64, 64) \n", + "\n", + "output: \n", + "\n", + "(1, 2, 64, 64, 64) \n", + "\n", + "\n", + "\n", + "32->33\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "34\n", + "\n", + "\n", + "output-tensor\n", + "depth:0\n", + "\n", + "(1, 2, 64, 64, 64)\n", + "\n", + "\n", + "\n", + "33->34\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = WNet()\n", + "batch_size = 1\n", + "model_graph = draw_graph(model, input_size=(batch_size, 1, 64,64,64), device='cpu')\n", + "model_graph.visual_graph" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "610dad0c-7bee-4076-8fb1-aef2a934c32c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From cf201431aa170a8a2a0685149aa10bd89e7f9ed6 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 7 Aug 2023 16:42:43 +0200 Subject: [PATCH 216/250] Disable intensity transforms in WNet --- .../code_models/worker_training.py | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 129a705c..b96abaed 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -39,7 +39,6 @@ RandRotate90d, RandShiftIntensityd, RandSpatialCropSamplesd, - ScaleIntensityRanged, SpatialPadd, ) from monai.utils import set_determinism @@ -289,15 +288,15 @@ def _get_data(self): if self.config.do_augmentation: train_transforms = Compose( [ - ScaleIntensityRanged( - keys=["image"], - a_min=0, - a_max=2000, - b_min=0.0, - b_max=1.0, - clip=True, - ), - RandShiftIntensityd(keys=["image"], offsets=0.1, prob=0.5), + # ScaleIntensityRanged( + # keys=["image"], + # a_min=0, + # a_max=2000, + # b_min=0.0, + # b_max=1.0, + # clip=True, + # ), + # RandShiftIntensityd(keys=["image"], offsets=0.1, prob=0.5), RandFlipd(keys=["image"], spatial_axis=[1], prob=0.5), RandFlipd(keys=["image"], spatial_axis=[2], prob=0.5), RandRotate90d(keys=["image"], prob=0.1, max_k=3), From ab443dc3b59c39abe30359ed63c0ab8d757316eb Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 7 Aug 2023 16:55:54 +0200 Subject: [PATCH 217/250] Fixed supervised loss saving --- napari_cellseg3d/code_plugins/plugin_model_training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 026cf3ed..83b365a2 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -1353,7 +1353,7 @@ def _make_csv(self): self.df = pd.DataFrame( { "epoch": size_column, - "loss": self.loss_1_values, + "loss": self.loss_1_values["Loss"], "validation": val, } ) From c9fc4470839a9d6ace3866d479a7dc6acd877035 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Mon, 7 Aug 2023 17:35:11 +0200 Subject: [PATCH 218/250] Update view_wnet.ipynb --- napari_cellseg3d/dev_scripts/view_wnet.ipynb | 853 +------------------ 1 file changed, 22 insertions(+), 831 deletions(-) diff --git a/napari_cellseg3d/dev_scripts/view_wnet.ipynb b/napari_cellseg3d/dev_scripts/view_wnet.ipynb index 8f1f448b..0c6c91fd 100644 --- a/napari_cellseg3d/dev_scripts/view_wnet.ipynb +++ b/napari_cellseg3d/dev_scripts/view_wnet.ipynb @@ -4,7 +4,12 @@ "cell_type": "code", "execution_count": 1, "id": "637a4617-39d0-4a60-b661-f5824128cf27", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-07T15:31:49.980341500Z", + "start_time": "2023-08-07T15:31:46.415285900Z" + } + }, "outputs": [], "source": [ "from torchview import draw_graph\n", @@ -15,853 +20,36 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "55bfaff6-edca-4d72-8d59-4ccdf53e18dd", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-07T15:32:33.321405900Z", + "start_time": "2023-08-07T15:32:23.897950600Z" + } + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\n", - "(dot.exe:11196): Pango-WARNING **: couldn't load font \"Linux libertine Not-Rotated 10\", falling back to \"Sans Not-Rotated 10\", expect ugly output.\n" + "(dot.exe:15460): Pango-WARNING **: couldn't load font \"Linux libertine Not-Rotated 10\", falling back to \"Sans Not-Rotated 10\", expect ugly output.\n" ] }, { "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "model\n", - "\n", - 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\n\n\n\n30->43\n\n\n\n\n\n32\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 256, 8, 8, 8) \n\noutput: \n\n(1, 256, 8, 8, 8) \n\n\n\n31->32\n\n\n\n\n\n33\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 256, 8, 8, 8) \n\noutput: \n\n(1, 512, 8, 8, 8) \n\n\n\n32->33\n\n\n\n\n\n34\n\n\nReLU\ndepth:4\n\ninput:\n\n(1, 512, 8, 8, 8) \n\noutput: \n\n(1, 512, 8, 8, 8) \n\n\n\n33->34\n\n\n\n\n\n35\n\n\nDropout\ndepth:4\n\ninput:\n\n(1, 512, 8, 8, 8) \n\noutput: \n\n(1, 512, 8, 8, 8) \n\n\n\n34->35\n\n\n\n\n\n36\n\n\nGroupNorm\ndepth:4\n\ninput:\n\n(1, 512, 8, 8, 8) \n\noutput: \n\n(1, 512, 8, 8, 8) \n\n\n\n35->36\n\n\n\n\n\n37\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 512, 8, 8, 8) \n\noutput: \n\n(1, 512, 8, 8, 8) \n\n\n\n36->37\n\n\n\n\n\n38\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 512, 8, 8, 8) \n\noutput: \n\n(1, 512, 8, 8, 8) \n\n\n\n37->38\n\n\n\n\n\n39\n\n\nReLU\ndepth:4\n\ninput:\n\n(1, 512, 8, 8, 8) \n\noutput: \n\n(1, 512, 8, 8, 8) \n\n\n\n38->39\n\n\n\n\n\n40\n\n\nDropout\ndepth:4\n\ninput:\n\n(1, 512, 8, 8, 8) \n\noutput: \n\n(1, 512, 8, 8, 8) \n\n\n\n39->40\n\n\n\n\n\n41\n\n\nGroupNorm\ndepth:4\n\ninput:\n\n(1, 512, 8, 8, 8) \n\noutput: \n\n(1, 512, 8, 8, 8) \n\n\n\n40->41\n\n\n\n\n\n42\n\n\nConvTranspose3d\ndepth:2\n\ninput:\n\n(1, 512, 8, 8, 8) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n41->42\n\n\n\n\n\n42->43\n\n\n\n\n\n44\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 512, 16, 16, 16) \n\noutput: \n\n(1, 512, 16, 16, 16) \n\n\n\n43->44\n\n\n\n\n\n45\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 512, 16, 16, 16) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n44->45\n\n\n\n\n\n46\n\n\nReLU\ndepth:4\n\ninput:\n\n(1, 256, 16, 16, 16) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n45->46\n\n\n\n\n\n47\n\n\nDropout\ndepth:4\n\ninput:\n\n(1, 256, 16, 16, 16) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n46->47\n\n\n\n\n\n48\n\n\nGroupNorm\ndepth:4\n\ninput:\n\n(1, 256, 16, 16, 16) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n47->48\n\n\n\n\n\n49\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 256, 16, 16, 16) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n48->49\n\n\n\n\n\n50\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 256, 16, 16, 16) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n49->50\n\n\n\n\n\n51\n\n\nReLU\ndepth:4\n\ninput:\n\n(1, 256, 16, 16, 16) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n50->51\n\n\n\n\n\n52\n\n\nDropout\ndepth:4\n\ninput:\n\n(1, 256, 16, 16, 16) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n51->52\n\n\n\n\n\n53\n\n\nGroupNorm\ndepth:4\n\ninput:\n\n(1, 256, 16, 16, 16) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n52->53\n\n\n\n\n\n54\n\n\nConvTranspose3d\ndepth:2\n\ninput:\n\n(1, 256, 16, 16, 16) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n53->54\n\n\n\n\n\n54->55\n\n\n\n\n\n56\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 256, 32, 32, 32) \n\noutput: \n\n(1, 256, 32, 32, 32) \n\n\n\n55->56\n\n\n\n\n\n57\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 256, 32, 32, 32) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n56->57\n\n\n\n\n\n58\n\n\nReLU\ndepth:4\n\ninput:\n\n(1, 128, 32, 32, 32) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n57->58\n\n\n\n\n\n59\n\n\nDropout\ndepth:4\n\ninput:\n\n(1, 128, 32, 32, 32) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n58->59\n\n\n\n\n\n60\n\n\nGroupNorm\ndepth:4\n\ninput:\n\n(1, 128, 32, 32, 32) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n59->60\n\n\n\n\n\n61\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 128, 32, 32, 32) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n60->61\n\n\n\n\n\n62\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 128, 32, 32, 32) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n61->62\n\n\n\n\n\n63\n\n\nReLU\ndepth:4\n\ninput:\n\n(1, 128, 32, 32, 32) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n62->63\n\n\n\n\n\n64\n\n\nDropout\ndepth:4\n\ninput:\n\n(1, 128, 32, 32, 32) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n63->64\n\n\n\n\n\n65\n\n\nGroupNorm\ndepth:4\n\ninput:\n\n(1, 128, 32, 32, 32) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n64->65\n\n\n\n\n\n66\n\n\nConvTranspose3d\ndepth:2\n\ninput:\n\n(1, 128, 32, 32, 32) \n\noutput: \n\n(1, 64, 64, 64, 64) \n\n\n\n65->66\n\n\n\n\n\n66->67\n\n\n\n\n\n68\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 128, 64, 64, 64) \n\noutput: \n\n(1, 64, 64, 64, 64) \n\n\n\n67->68\n\n\n\n\n\n69\n\n\nReLU\ndepth:4\n\ninput:\n\n(1, 64, 64, 64, 64) \n\noutput: \n\n(1, 64, 64, 64, 64) \n\n\n\n68->69\n\n\n\n\n\n70\n\n\nDropout\ndepth:4\n\ninput:\n\n(1, 64, 64, 64, 64) \n\noutput: \n\n(1, 64, 64, 64, 64) \n\n\n\n69->70\n\n\n\n\n\n71\n\n\nGroupNorm\ndepth:4\n\ninput:\n\n(1, 64, 64, 64, 64) \n\noutput: \n\n(1, 64, 64, 64, 64) \n\n\n\n70->71\n\n\n\n\n\n72\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 64, 64, 64, 64) \n\noutput: \n\n(1, 64, 64, 64, 64) \n\n\n\n71->72\n\n\n\n\n\n73\n\n\nReLU\ndepth:4\n\ninput:\n\n(1, 64, 64, 64, 64) \n\noutput: \n\n(1, 64, 64, 64, 64) \n\n\n\n72->73\n\n\n\n\n\n74\n\n\nDropout\ndepth:4\n\ninput:\n\n(1, 64, 64, 64, 64) \n\noutput: \n\n(1, 64, 64, 64, 64) \n\n\n\n73->74\n\n\n\n\n\n75\n\n\nGroupNorm\ndepth:4\n\ninput:\n\n(1, 64, 64, 64, 64) \n\noutput: \n\n(1, 64, 64, 64, 64) \n\n\n\n74->75\n\n\n\n\n\n76\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 64, 64, 64, 64) \n\noutput: \n\n(1, 2, 64, 64, 64) \n\n\n\n75->76\n\n\n\n\n\n77\n\n\nSoftmax\ndepth:2\n\ninput:\n\n(1, 2, 64, 64, 64) \n\noutput: \n\n(1, 2, 64, 64, 64) \n\n\n\n76->77\n\n\n\n\n\n78\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 2, 64, 64, 64) \n\noutput: \n\n(1, 64, 64, 64, 64) \n\n\n\n77->78\n\n\n\n\n\n155\n\n\noutput-tensor\ndepth:0\n\n(1, 2, 64, 64, 64)\n\n\n\n77->155\n\n\n\n\n\n79\n\n\nReLU\ndepth:4\n\ninput:\n\n(1, 64, 64, 64, 64) \n\noutput: \n\n(1, 64, 64, 64, 64) \n\n\n\n78->79\n\n\n\n\n\n80\n\n\nDropout\ndepth:4\n\ninput:\n\n(1, 64, 64, 64, 64) \n\noutput: \n\n(1, 64, 64, 64, 64) \n\n\n\n79->80\n\n\n\n\n\n81\n\n\nGroupNorm\ndepth:4\n\ninput:\n\n(1, 64, 64, 64, 64) \n\noutput: \n\n(1, 64, 64, 64, 64) \n\n\n\n80->81\n\n\n\n\n\n82\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 64, 64, 64, 64) \n\noutput: \n\n(1, 64, 64, 64, 64) \n\n\n\n81->82\n\n\n\n\n\n83\n\n\nReLU\ndepth:4\n\ninput:\n\n(1, 64, 64, 64, 64) \n\noutput: \n\n(1, 64, 64, 64, 64) \n\n\n\n82->83\n\n\n\n\n\n84\n\n\nDropout\ndepth:4\n\ninput:\n\n(1, 64, 64, 64, 64) \n\noutput: \n\n(1, 64, 64, 64, 64) \n\n\n\n83->84\n\n\n\n\n\n85\n\n\nGroupNorm\ndepth:4\n\ninput:\n\n(1, 64, 64, 64, 64) \n\noutput: \n\n(1, 64, 64, 64, 64) \n\n\n\n84->85\n\n\n\n\n\n86\n\n\nMaxPool3d\ndepth:2\n\ninput:\n\n(1, 64, 64, 64, 64) \n\noutput: \n\n(1, 64, 32, 32, 32) \n\n\n\n85->86\n\n\n\n\n\n144\n\n\ncat\ndepth:2\n\ninput:\n\n2 x (1, 64, 64, 64, 64) \n\noutput: \n\n(1, 128, 64, 64, 64) \n\n\n\n85->144\n\n\n\n\n\n87\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 64, 32, 32, 32) \n\noutput: \n\n(1, 64, 32, 32, 32) \n\n\n\n86->87\n\n\n\n\n\n88\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 64, 32, 32, 32) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n87->88\n\n\n\n\n\n89\n\n\nReLU\ndepth:4\n\ninput:\n\n(1, 128, 32, 32, 32) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n88->89\n\n\n\n\n\n90\n\n\nDropout\ndepth:4\n\ninput:\n\n(1, 128, 32, 32, 32) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n89->90\n\n\n\n\n\n91\n\n\nGroupNorm\ndepth:4\n\ninput:\n\n(1, 128, 32, 32, 32) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n90->91\n\n\n\n\n\n92\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 128, 32, 32, 32) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n91->92\n\n\n\n\n\n93\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 128, 32, 32, 32) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n92->93\n\n\n\n\n\n94\n\n\nReLU\ndepth:4\n\ninput:\n\n(1, 128, 32, 32, 32) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n93->94\n\n\n\n\n\n95\n\n\nDropout\ndepth:4\n\ninput:\n\n(1, 128, 32, 32, 32) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n94->95\n\n\n\n\n\n96\n\n\nGroupNorm\ndepth:4\n\ninput:\n\n(1, 128, 32, 32, 32) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n95->96\n\n\n\n\n\n97\n\n\nMaxPool3d\ndepth:2\n\ninput:\n\n(1, 128, 32, 32, 32) \n\noutput: \n\n(1, 128, 16, 16, 16) \n\n\n\n96->97\n\n\n\n\n\n132\n\n\ncat\ndepth:2\n\ninput:\n\n2 x (1, 128, 32, 32, 32) \n\noutput: \n\n(1, 256, 32, 32, 32) \n\n\n\n96->132\n\n\n\n\n\n98\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 128, 16, 16, 16) \n\noutput: \n\n(1, 128, 16, 16, 16) \n\n\n\n97->98\n\n\n\n\n\n99\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 128, 16, 16, 16) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n98->99\n\n\n\n\n\n100\n\n\nReLU\ndepth:4\n\ninput:\n\n(1, 256, 16, 16, 16) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n99->100\n\n\n\n\n\n101\n\n\nDropout\ndepth:4\n\ninput:\n\n(1, 256, 16, 16, 16) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n100->101\n\n\n\n\n\n102\n\n\nGroupNorm\ndepth:4\n\ninput:\n\n(1, 256, 16, 16, 16) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n101->102\n\n\n\n\n\n103\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 256, 16, 16, 16) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n102->103\n\n\n\n\n\n104\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 256, 16, 16, 16) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n103->104\n\n\n\n\n\n105\n\n\nReLU\ndepth:4\n\ninput:\n\n(1, 256, 16, 16, 16) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n104->105\n\n\n\n\n\n106\n\n\nDropout\ndepth:4\n\ninput:\n\n(1, 256, 16, 16, 16) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n105->106\n\n\n\n\n\n107\n\n\nGroupNorm\ndepth:4\n\ninput:\n\n(1, 256, 16, 16, 16) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n106->107\n\n\n\n\n\n108\n\n\nMaxPool3d\ndepth:2\n\ninput:\n\n(1, 256, 16, 16, 16) \n\noutput: \n\n(1, 256, 8, 8, 8) \n\n\n\n107->108\n\n\n\n\n\n120\n\n\ncat\ndepth:2\n\ninput:\n\n2 x (1, 256, 16, 16, 16) \n\noutput: \n\n(1, 512, 16, 16, 16) \n\n\n\n107->120\n\n\n\n\n\n109\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 256, 8, 8, 8) \n\noutput: \n\n(1, 256, 8, 8, 8) \n\n\n\n108->109\n\n\n\n\n\n110\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 256, 8, 8, 8) \n\noutput: \n\n(1, 512, 8, 8, 8) \n\n\n\n109->110\n\n\n\n\n\n111\n\n\nReLU\ndepth:4\n\ninput:\n\n(1, 512, 8, 8, 8) \n\noutput: \n\n(1, 512, 8, 8, 8) \n\n\n\n110->111\n\n\n\n\n\n112\n\n\nDropout\ndepth:4\n\ninput:\n\n(1, 512, 8, 8, 8) \n\noutput: \n\n(1, 512, 8, 8, 8) \n\n\n\n111->112\n\n\n\n\n\n113\n\n\nGroupNorm\ndepth:4\n\ninput:\n\n(1, 512, 8, 8, 8) \n\noutput: \n\n(1, 512, 8, 8, 8) \n\n\n\n112->113\n\n\n\n\n\n114\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 512, 8, 8, 8) \n\noutput: \n\n(1, 512, 8, 8, 8) \n\n\n\n113->114\n\n\n\n\n\n115\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 512, 8, 8, 8) \n\noutput: \n\n(1, 512, 8, 8, 8) \n\n\n\n114->115\n\n\n\n\n\n116\n\n\nReLU\ndepth:4\n\ninput:\n\n(1, 512, 8, 8, 8) \n\noutput: \n\n(1, 512, 8, 8, 8) \n\n\n\n115->116\n\n\n\n\n\n117\n\n\nDropout\ndepth:4\n\ninput:\n\n(1, 512, 8, 8, 8) \n\noutput: \n\n(1, 512, 8, 8, 8) \n\n\n\n116->117\n\n\n\n\n\n118\n\n\nGroupNorm\ndepth:4\n\ninput:\n\n(1, 512, 8, 8, 8) \n\noutput: \n\n(1, 512, 8, 8, 8) \n\n\n\n117->118\n\n\n\n\n\n119\n\n\nConvTranspose3d\ndepth:2\n\ninput:\n\n(1, 512, 8, 8, 8) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n118->119\n\n\n\n\n\n119->120\n\n\n\n\n\n121\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 512, 16, 16, 16) \n\noutput: \n\n(1, 512, 16, 16, 16) \n\n\n\n120->121\n\n\n\n\n\n122\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 512, 16, 16, 16) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n121->122\n\n\n\n\n\n123\n\n\nReLU\ndepth:4\n\ninput:\n\n(1, 256, 16, 16, 16) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n122->123\n\n\n\n\n\n124\n\n\nDropout\ndepth:4\n\ninput:\n\n(1, 256, 16, 16, 16) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n123->124\n\n\n\n\n\n125\n\n\nGroupNorm\ndepth:4\n\ninput:\n\n(1, 256, 16, 16, 16) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n124->125\n\n\n\n\n\n126\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 256, 16, 16, 16) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n125->126\n\n\n\n\n\n127\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 256, 16, 16, 16) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n126->127\n\n\n\n\n\n128\n\n\nReLU\ndepth:4\n\ninput:\n\n(1, 256, 16, 16, 16) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n127->128\n\n\n\n\n\n129\n\n\nDropout\ndepth:4\n\ninput:\n\n(1, 256, 16, 16, 16) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n128->129\n\n\n\n\n\n130\n\n\nGroupNorm\ndepth:4\n\ninput:\n\n(1, 256, 16, 16, 16) \n\noutput: \n\n(1, 256, 16, 16, 16) \n\n\n\n129->130\n\n\n\n\n\n131\n\n\nConvTranspose3d\ndepth:2\n\ninput:\n\n(1, 256, 16, 16, 16) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n130->131\n\n\n\n\n\n131->132\n\n\n\n\n\n133\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 256, 32, 32, 32) \n\noutput: \n\n(1, 256, 32, 32, 32) \n\n\n\n132->133\n\n\n\n\n\n134\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 256, 32, 32, 32) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n133->134\n\n\n\n\n\n135\n\n\nReLU\ndepth:4\n\ninput:\n\n(1, 128, 32, 32, 32) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n134->135\n\n\n\n\n\n136\n\n\nDropout\ndepth:4\n\ninput:\n\n(1, 128, 32, 32, 32) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n135->136\n\n\n\n\n\n137\n\n\nGroupNorm\ndepth:4\n\ninput:\n\n(1, 128, 32, 32, 32) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n136->137\n\n\n\n\n\n138\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 128, 32, 32, 32) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n137->138\n\n\n\n\n\n139\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 128, 32, 32, 32) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n138->139\n\n\n\n\n\n140\n\n\nReLU\ndepth:4\n\ninput:\n\n(1, 128, 32, 32, 32) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n139->140\n\n\n\n\n\n141\n\n\nDropout\ndepth:4\n\ninput:\n\n(1, 128, 32, 32, 32) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n140->141\n\n\n\n\n\n142\n\n\nGroupNorm\ndepth:4\n\ninput:\n\n(1, 128, 32, 32, 32) \n\noutput: \n\n(1, 128, 32, 32, 32) \n\n\n\n141->142\n\n\n\n\n\n143\n\n\nConvTranspose3d\ndepth:2\n\ninput:\n\n(1, 128, 32, 32, 32) \n\noutput: \n\n(1, 64, 64, 64, 64) \n\n\n\n142->143\n\n\n\n\n\n143->144\n\n\n\n\n\n145\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 128, 64, 64, 64) \n\noutput: \n\n(1, 64, 64, 64, 64) \n\n\n\n144->145\n\n\n\n\n\n146\n\n\nReLU\ndepth:4\n\ninput:\n\n(1, 64, 64, 64, 64) \n\noutput: \n\n(1, 64, 64, 64, 64) \n\n\n\n145->146\n\n\n\n\n\n147\n\n\nDropout\ndepth:4\n\ninput:\n\n(1, 64, 64, 64, 64) \n\noutput: \n\n(1, 64, 64, 64, 64) \n\n\n\n146->147\n\n\n\n\n\n148\n\n\nGroupNorm\ndepth:4\n\ninput:\n\n(1, 64, 64, 64, 64) \n\noutput: \n\n(1, 64, 64, 64, 64) \n\n\n\n147->148\n\n\n\n\n\n149\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 64, 64, 64, 64) \n\noutput: \n\n(1, 64, 64, 64, 64) \n\n\n\n148->149\n\n\n\n\n\n150\n\n\nReLU\ndepth:4\n\ninput:\n\n(1, 64, 64, 64, 64) \n\noutput: \n\n(1, 64, 64, 64, 64) \n\n\n\n149->150\n\n\n\n\n\n151\n\n\nDropout\ndepth:4\n\ninput:\n\n(1, 64, 64, 64, 64) \n\noutput: \n\n(1, 64, 64, 64, 64) \n\n\n\n150->151\n\n\n\n\n\n152\n\n\nGroupNorm\ndepth:4\n\ninput:\n\n(1, 64, 64, 64, 64) \n\noutput: \n\n(1, 64, 64, 64, 64) \n\n\n\n151->152\n\n\n\n\n\n153\n\n\nConv3d\ndepth:4\n\ninput:\n\n(1, 64, 64, 64, 64) \n\noutput: \n\n(1, 2, 64, 64, 64) \n\n\n\n152->153\n\n\n\n\n\n154\n\n\noutput-tensor\ndepth:0\n\n(1, 2, 64, 64, 64)\n\n\n\n153->154\n\n\n\n\n\n", + "text/plain": "" }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = WNet()\n", - "batch_size = 1\n", - "model_graph = draw_graph(model, input_size=(batch_size, 1, 64,64,64), device='cpu')\n", - "model_graph.visual_graph" + "batch_size = 1" ] }, { @@ -870,7 +58,10 @@ "id": "610dad0c-7bee-4076-8fb1-aef2a934c32c", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "model_graph = draw_graph(model, input_size=(batch_size, 1, 64,64,64), device='cpu', expand_nested=True, depth=5)\n", + "model_graph.visual_graph" + ] } ], "metadata": { From ab46464a23a41a80b60882def82bba55d0778b56 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 9 Aug 2023 10:46:59 +0200 Subject: [PATCH 219/250] Update view_wnet.ipynb --- {napari_cellseg3d/dev_scripts => notebooks}/view_wnet.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename {napari_cellseg3d/dev_scripts => notebooks}/view_wnet.ipynb (100%) diff --git a/napari_cellseg3d/dev_scripts/view_wnet.ipynb b/notebooks/view_wnet.ipynb similarity index 100% rename from napari_cellseg3d/dev_scripts/view_wnet.ipynb rename to notebooks/view_wnet.ipynb From 7e397f932e99988a52467e03d0864f3cc89ffd55 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 9 Aug 2023 16:54:27 +0200 Subject: [PATCH 220/250] Fixed filepaths --- napari_cellseg3d/code_plugins/plugin_convert.py | 12 +++++------- napari_cellseg3d/code_plugins/plugin_crf.py | 2 +- napari_cellseg3d/code_plugins/plugin_crop.py | 2 +- napari_cellseg3d/code_plugins/plugin_review.py | 2 +- napari_cellseg3d/config.py | 6 +++--- 5 files changed, 11 insertions(+), 13 deletions(-) diff --git a/napari_cellseg3d/code_plugins/plugin_convert.py b/napari_cellseg3d/code_plugins/plugin_convert.py index aa70bc73..18af29c5 100644 --- a/napari_cellseg3d/code_plugins/plugin_convert.py +++ b/napari_cellseg3d/code_plugins/plugin_convert.py @@ -46,7 +46,7 @@ def __init__(self, viewer: "napari.Viewer.viewer", parent=None): self.aniso_widgets = ui.AnisotropyWidgets(self, always_visible=True) self.start_btn = ui.Button("Start", self._start) - self.results_path = str(Path.home() / Path("cellseg3d/anisotropy")) + self.results_path = str(Path.home() / "cellseg3d" / "anisotropy") self.results_filewidget.text_field.setText(str(self.results_path)) self.results_filewidget.check_ready() @@ -145,7 +145,7 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): text_label="Remove all smaller than (pxs):", ) - self.results_path = Path.home() / Path("cellseg3d/small_removed") + self.results_path = Path.home() / "cellseg3d" / "small_removed" self.results_filewidget.text_field.setText(str(self.results_path)) self.results_filewidget.check_ready() @@ -233,9 +233,7 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): self.start_btn = ui.Button("Start", self._start) - self.results_path = str( - Path.home() / Path("cellseg3d/instance_labels") - ) + self.results_path = str(Path.home() / "cellseg3d" / "instance_labels") self.results_filewidget.text_field.setText(self.results_path) self.results_filewidget.check_ready() @@ -326,7 +324,7 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): self.start_btn = ui.Button("Start", self._start) - self.results_path = Path.home() / Path("cellseg3d/instance") + self.results_path = Path.home() / "cellseg3d" / "instance" self.results_filewidget.text_field.setText(str(self.results_path)) self.results_filewidget.check_ready() @@ -417,7 +415,7 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): text_label="Remove all smaller than (value):", ) - self.results_path = str(Path.home() / Path("cellseg3d/threshold")) + self.results_path = str(Path.home() / "cellseg3d" / "threshold") self.results_filewidget.text_field.setText(self.results_path) self.results_filewidget.check_ready() diff --git a/napari_cellseg3d/code_plugins/plugin_crf.py b/napari_cellseg3d/code_plugins/plugin_crf.py index 76194e87..dcc0af57 100644 --- a/napari_cellseg3d/code_plugins/plugin_crf.py +++ b/napari_cellseg3d/code_plugins/plugin_crf.py @@ -138,7 +138,7 @@ def __init__(self, viewer, parent=None): self.result_name = None self.crf_results = [] - self.results_path = Path.home() / Path("cellseg3d/crf") + self.results_path = Path.home() / "cellseg3d" / "crf" self.results_filewidget.text_field.setText(str(self.results_path)) self.results_filewidget.check_ready() diff --git a/napari_cellseg3d/code_plugins/plugin_crop.py b/napari_cellseg3d/code_plugins/plugin_crop.py index c6e822d4..37b26b13 100644 --- a/napari_cellseg3d/code_plugins/plugin_crop.py +++ b/napari_cellseg3d/code_plugins/plugin_crop.py @@ -38,7 +38,7 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): super().__init__(viewer) self.docked_widgets = [] - self.results_path = Path.home() / Path("cellseg3d/cropped") + self.results_path = Path.home() / "cellseg3d" / "cropped" self.btn_start = ui.Button("Start", self._start) diff --git a/napari_cellseg3d/code_plugins/plugin_review.py b/napari_cellseg3d/code_plugins/plugin_review.py index d3216436..712b3193 100644 --- a/napari_cellseg3d/code_plugins/plugin_review.py +++ b/napari_cellseg3d/code_plugins/plugin_review.py @@ -144,7 +144,7 @@ def _build(self): # self._show_io_element(self.results_filewidget) self.results_filewidget.text_field.setText( - str(Path.home() / Path("cellseg3d/review")) + str(Path.home() / "cellseg3d" / "review") ) csv_param_w.setLayout(csv_param_l) diff --git a/napari_cellseg3d/config.py b/napari_cellseg3d/config.py index 6c8db79b..449b58b5 100644 --- a/napari_cellseg3d/config.py +++ b/napari_cellseg3d/config.py @@ -45,7 +45,7 @@ class ReviewConfig: image: np.array = None labels: np.array = None - csv_path: str = Path.home() / Path("cellseg3d/review") + csv_path: str = Path.home() / "cellseg3d" / "review" model_name: str = "" new_csv: bool = True filetype: str = ".tif" @@ -210,7 +210,7 @@ class InferenceWorkerConfig: device: str = "cpu" model_info: ModelInfo = ModelInfo() weights_config: WeightsInfo = WeightsInfo() - results_path: str = str(Path.home() / Path("cellseg3d/inference")) + results_path: str = str(Path.home() / "cellseg3d/inference") filetype: str = ".tif" keep_on_cpu: bool = False compute_stats: bool = False @@ -258,7 +258,7 @@ class TrainingWorkerConfig: scheduler_patience: int = 10 weights_info: WeightsInfo = WeightsInfo() # data params - results_path_folder: str = str(Path.home() / Path("cellseg3d/training")) + results_path_folder: str = str(Path.home() / "cellseg3d" / "training") sampling: bool = False num_samples: int = 2 sample_size: List[int] = None From 342b568aaa25c09c1c6f8d9e6935fc7122f172b5 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 9 Aug 2023 16:54:27 +0200 Subject: [PATCH 221/250] Fixed filepaths --- napari_cellseg3d/code_plugins/plugin_convert.py | 12 +++++------- napari_cellseg3d/code_plugins/plugin_crf.py | 2 +- napari_cellseg3d/code_plugins/plugin_crop.py | 2 +- napari_cellseg3d/code_plugins/plugin_review.py | 2 +- napari_cellseg3d/config.py | 6 +++--- 5 files changed, 11 insertions(+), 13 deletions(-) diff --git a/napari_cellseg3d/code_plugins/plugin_convert.py b/napari_cellseg3d/code_plugins/plugin_convert.py index aa70bc73..18af29c5 100644 --- a/napari_cellseg3d/code_plugins/plugin_convert.py +++ b/napari_cellseg3d/code_plugins/plugin_convert.py @@ -46,7 +46,7 @@ def __init__(self, viewer: "napari.Viewer.viewer", parent=None): self.aniso_widgets = ui.AnisotropyWidgets(self, always_visible=True) self.start_btn = ui.Button("Start", self._start) - self.results_path = str(Path.home() / Path("cellseg3d/anisotropy")) + self.results_path = str(Path.home() / "cellseg3d" / "anisotropy") self.results_filewidget.text_field.setText(str(self.results_path)) self.results_filewidget.check_ready() @@ -145,7 +145,7 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): text_label="Remove all smaller than (pxs):", ) - self.results_path = Path.home() / Path("cellseg3d/small_removed") + self.results_path = Path.home() / "cellseg3d" / "small_removed" self.results_filewidget.text_field.setText(str(self.results_path)) self.results_filewidget.check_ready() @@ -233,9 +233,7 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): self.start_btn = ui.Button("Start", self._start) - self.results_path = str( - Path.home() / Path("cellseg3d/instance_labels") - ) + self.results_path = str(Path.home() / "cellseg3d" / "instance_labels") self.results_filewidget.text_field.setText(self.results_path) self.results_filewidget.check_ready() @@ -326,7 +324,7 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): self.start_btn = ui.Button("Start", self._start) - self.results_path = Path.home() / Path("cellseg3d/instance") + self.results_path = Path.home() / "cellseg3d" / "instance" self.results_filewidget.text_field.setText(str(self.results_path)) self.results_filewidget.check_ready() @@ -417,7 +415,7 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): text_label="Remove all smaller than (value):", ) - self.results_path = str(Path.home() / Path("cellseg3d/threshold")) + self.results_path = str(Path.home() / "cellseg3d" / "threshold") self.results_filewidget.text_field.setText(self.results_path) self.results_filewidget.check_ready() diff --git a/napari_cellseg3d/code_plugins/plugin_crf.py b/napari_cellseg3d/code_plugins/plugin_crf.py index 76194e87..dcc0af57 100644 --- a/napari_cellseg3d/code_plugins/plugin_crf.py +++ b/napari_cellseg3d/code_plugins/plugin_crf.py @@ -138,7 +138,7 @@ def __init__(self, viewer, parent=None): self.result_name = None self.crf_results = [] - self.results_path = Path.home() / Path("cellseg3d/crf") + self.results_path = Path.home() / "cellseg3d" / "crf" self.results_filewidget.text_field.setText(str(self.results_path)) self.results_filewidget.check_ready() diff --git a/napari_cellseg3d/code_plugins/plugin_crop.py b/napari_cellseg3d/code_plugins/plugin_crop.py index c6e822d4..37b26b13 100644 --- a/napari_cellseg3d/code_plugins/plugin_crop.py +++ b/napari_cellseg3d/code_plugins/plugin_crop.py @@ -38,7 +38,7 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): super().__init__(viewer) self.docked_widgets = [] - self.results_path = Path.home() / Path("cellseg3d/cropped") + self.results_path = Path.home() / "cellseg3d" / "cropped" self.btn_start = ui.Button("Start", self._start) diff --git a/napari_cellseg3d/code_plugins/plugin_review.py b/napari_cellseg3d/code_plugins/plugin_review.py index d3216436..712b3193 100644 --- a/napari_cellseg3d/code_plugins/plugin_review.py +++ b/napari_cellseg3d/code_plugins/plugin_review.py @@ -144,7 +144,7 @@ def _build(self): # self._show_io_element(self.results_filewidget) self.results_filewidget.text_field.setText( - str(Path.home() / Path("cellseg3d/review")) + str(Path.home() / "cellseg3d" / "review") ) csv_param_w.setLayout(csv_param_l) diff --git a/napari_cellseg3d/config.py b/napari_cellseg3d/config.py index 82753407..e3d17354 100644 --- a/napari_cellseg3d/config.py +++ b/napari_cellseg3d/config.py @@ -45,7 +45,7 @@ class ReviewConfig: image: np.array = None labels: np.array = None - csv_path: str = Path.home() / Path("cellseg3d/review") + csv_path: str = Path.home() / "cellseg3d" / "review" model_name: str = "" new_csv: bool = True filetype: str = ".tif" @@ -210,7 +210,7 @@ class InferenceWorkerConfig: device: str = "cpu" model_info: ModelInfo = ModelInfo() weights_config: WeightsInfo = WeightsInfo() - results_path: str = str(Path.home() / Path("cellseg3d/inference")) + results_path: str = str(Path.home() / "cellseg3d/inference") filetype: str = ".tif" keep_on_cpu: bool = False compute_stats: bool = False @@ -258,7 +258,7 @@ class TrainingWorkerConfig: scheduler_patience: int = 10 weights_info: WeightsInfo = WeightsInfo() # data params - results_path_folder: str = str(Path.home() / Path("cellseg3d/training")) + results_path_folder: str = str(Path.home() / "cellseg3d" / "training") sampling: bool = False num_samples: int = 2 sample_size: List[int] = None From 3cf7052ceeb7b761b802ea3f01fc18d11a0c4284 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 10 Aug 2023 09:46:29 +0200 Subject: [PATCH 222/250] Added more checks for csv + fix eval batch size --- napari_cellseg3d/code_models/worker_training.py | 15 +++++++++++---- napari_cellseg3d/code_models/workers_utils.py | 1 + .../code_plugins/plugin_model_training.py | 10 +++++++++- napari_cellseg3d/config.py | 3 ++- 4 files changed, 23 insertions(+), 6 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index b96abaed..fb930de9 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -326,10 +326,10 @@ def _get_data(self): if self.config.eval_volume_dict is not None: eval_dataset = self.get_dataset_eval(self.config.eval_volume_dict) - + logger.debug(f"Eval batch size : {self.config.eval_batch_size}") self.eval_dataloader = DataLoader( eval_dataset, - batch_size=self.config.batch_size, + batch_size=self.config.eval_batch_size, shuffle=False, num_workers=self.config.num_workers, collate_fn=pad_list_data_collate, @@ -577,7 +577,9 @@ def train( optimizer.step() yield TrainingReport( - show_plot=False, weights=model.state_dict() + show_plot=False, + weights=model.state_dict(), + supervised=False, ) if self._abort_requested: @@ -635,6 +637,7 @@ def train( loss_2_values=self.rec_losses, weights=model.state_dict(), images_dict=images_dict, + supervised=False, ) except TypeError: pass @@ -900,6 +903,7 @@ def eval(self, model, epoch) -> TrainingReport: loss_2_values=self.rec_losses, weights=model.state_dict(), images_dict=display_dict, + supervised=False, ) @@ -1454,7 +1458,9 @@ def get_patch_loader_func(num_samples): torch.cuda.empty_cache() yield TrainingReport( - show_plot=False, weights=model.state_dict() + show_plot=False, + weights=model.state_dict(), + supervised=True, ) # return @@ -1599,6 +1605,7 @@ def get_patch_loader_func(num_samples): loss_2_values=val_metric_values, weights=model.state_dict(), images_dict=images_dict, + supervised=True, ) self.log("Validation completed") yield train_report diff --git a/napari_cellseg3d/code_models/workers_utils.py b/napari_cellseg3d/code_models/workers_utils.py index 600dddd5..4c197c28 100644 --- a/napari_cellseg3d/code_models/workers_utils.py +++ b/napari_cellseg3d/code_models/workers_utils.py @@ -268,5 +268,6 @@ class TrainingReport: images_dict: t.Dict = ( None # output, discrete output, target, target labels ) + supervised: bool = True # OR decoder output, encoder output, target, target labels # format : {"Layer name" : {"data" : np.array, "cmap" : "turbo"}} diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 83b365a2..207202c7 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -176,6 +176,7 @@ def __init__( self.df = None self.loss_1_values = {} self.loss_2_values = [] + self.supervised_job = False ########### # interface @@ -1178,6 +1179,9 @@ def _set_unsupervised_worker_config( n_cuts_weight=self.wnet_widgets.ncuts_weight_choice.value(), rec_loss_weight=self.wnet_widgets.get_reconstruction_weight(), eval_volume_dict=eval_volume_dict, + eval_batch_size=len(eval_volume_dict) + if eval_volume_dict is not None + else 1, ) return self.worker_config @@ -1295,6 +1299,8 @@ def on_yield(self, report: TrainingReport): if report == TrainingReport(): return # skip empty reports + self.supervised_job = report.supervised + if report.show_plot: try: self.log.print_and_log(len(report.images_dict)) @@ -1343,7 +1349,9 @@ def _make_csv(self): logger.warning("No loss values to add to csv !") return - if self._is_current_job_supervised(): + if ( + self._is_current_job_supervised() or self.supervised_job + ): # extra check for when worker is None after aborting val = utils.fill_list_in_between( self.loss_2_values, self.worker_config.validation_interval - 1, diff --git a/napari_cellseg3d/config.py b/napari_cellseg3d/config.py index e3d17354..13cbdcc5 100644 --- a/napari_cellseg3d/config.py +++ b/napari_cellseg3d/config.py @@ -305,6 +305,7 @@ class WNetTrainingWorkerConfig(TrainingWorkerConfig): # data params train_data_dict: dict = None eval_volume_dict: str = None + eval_batch_size: int = 1 ################ @@ -314,7 +315,7 @@ class WNetTrainingWorkerConfig(TrainingWorkerConfig): class WandBConfig: """Class to record parameters for WandB""" - mode: str = "online" # disabled, online, enabled + mode: str = "disabled" # disabled, online, enabled save_model_artifact: bool = False From 23b44a6906fe5728df596018d4ab3a116ae0278a Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 10 Aug 2023 15:50:06 +0200 Subject: [PATCH 223/250] Utils+training fixes --- .../code_models/models/model_WNet.py | 2 +- .../code_plugins/plugin_convert.py | 16 ++++++---- .../code_plugins/plugin_model_training.py | 32 ++++++++++++------- napari_cellseg3d/dev_scripts/crop_data.py | 10 +++--- 4 files changed, 36 insertions(+), 24 deletions(-) diff --git a/napari_cellseg3d/code_models/models/model_WNet.py b/napari_cellseg3d/code_models/models/model_WNet.py index 164ede21..ae16e9bb 100644 --- a/napari_cellseg3d/code_models/models/model_WNet.py +++ b/napari_cellseg3d/code_models/models/model_WNet.py @@ -18,7 +18,7 @@ def __init__( in_channels=in_channels, out_channels=out_channels, # num_classes=num_classes, - softmax=True, + softmax=False, ) # def train(self: T, mode: bool = True) -> T: diff --git a/napari_cellseg3d/code_plugins/plugin_convert.py b/napari_cellseg3d/code_plugins/plugin_convert.py index 18af29c5..5e168ef0 100644 --- a/napari_cellseg3d/code_plugins/plugin_convert.py +++ b/napari_cellseg3d/code_plugins/plugin_convert.py @@ -314,14 +314,16 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): super().__init__( viewer, parent, - loads_images=False, + loads_labels=False, ) self.data_panel = self._build_io_panel() - self.label_layer_loader.set_layer_type(napari.layers.Layer) + self._set_io_visibility() - self.instance_widgets = InstanceWidgets(parent=self) + self.image_layer_loader.layer_list.label.setText("Layer :") + self.image_layer_loader.set_layer_type(napari.layers.Layer) + self.instance_widgets = InstanceWidgets(parent=self) self.start_btn = ui.Button("Start", self._start) self.results_path = Path.home() / "cellseg3d" / "instance" @@ -352,11 +354,11 @@ def _build(self): ) def _start(self): - utils.mkdir_from_str(self.results_path) + utils.mkdir_from_str(str(self.results_path)) if self.layer_choice.isChecked(): - if self.label_layer_loader.layer_data() is not None: - layer = self.label_layer_loader.layer() + if self.image_layer_loader.layer_data() is not None: + layer = self.image_layer_loader.layer() data = np.array(layer.data) instance = self.instance_widgets.run_method(data) @@ -374,7 +376,7 @@ def _start(self): self.folder_choice.isChecked() and len(self.images_filepaths) != 0 ): images = [ - self.instance_widgets.run_method_on_channels(imread(file)) + self.instance_widgets.run_method(imread(file)) for file in self.images_filepaths ] utils.save_folder( diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 207202c7..f92f5dea 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -176,7 +176,6 @@ def __init__( self.df = None self.loss_1_values = {} self.loss_2_values = [] - self.supervised_job = False ########### # interface @@ -1158,6 +1157,7 @@ def _set_unsupervised_worker_config( Returns: A worker config """ + batch_size = self.batch_choice.slider_value self.worker_config = config.WNetTrainingWorkerConfig( device=self.check_device_choice(), weights_info=self.weights_config, @@ -1165,7 +1165,7 @@ def _set_unsupervised_worker_config( max_epochs=self.epoch_choice.value(), learning_rate=self.learning_rate_choice.get_learning_rate(), validation_interval=self.val_interval_choice.value(), - batch_size=self.batch_choice.slider_value, + batch_size=batch_size, results_path_folder=str(results_path_folder), sampling=self.patch_choice.isChecked(), num_samples=self.sample_choice_slider.slider_value, @@ -1179,14 +1179,16 @@ def _set_unsupervised_worker_config( n_cuts_weight=self.wnet_widgets.ncuts_weight_choice.value(), rec_loss_weight=self.wnet_widgets.get_reconstruction_weight(), eval_volume_dict=eval_volume_dict, - eval_batch_size=len(eval_volume_dict) - if eval_volume_dict is not None - else 1, + eval_batch_size=1 + if len(eval_volume_dict) < batch_size + else batch_size, ) return self.worker_config - def _is_current_job_supervised(self): + def _is_current_job_supervised( + self, + ): # TODO(cyril) rework for better check and _make_csv if isinstance(self.worker, WNetTrainingWorker): return False return True @@ -1299,8 +1301,6 @@ def on_yield(self, report: TrainingReport): if report == TrainingReport(): return # skip empty reports - self.supervised_job = report.supervised - if report.show_plot: try: self.log.print_and_log(len(report.images_dict)) @@ -1349,9 +1349,19 @@ def _make_csv(self): logger.warning("No loss values to add to csv !") return - if ( - self._is_current_job_supervised() or self.supervised_job - ): # extra check for when worker is None after aborting + try: + self.loss_1_values["Loss"] + supervised = True + except KeyError("Loss"): + try: + self.loss_1_values["SoftNCuts"] + supervised = False + except KeyError("SoftNCuts") as e: + raise KeyError( + "Error when making csv. Check loss dict keys ?" + ) from e + + if supervised: val = utils.fill_list_in_between( self.loss_2_values, self.worker_config.validation_interval - 1, diff --git a/napari_cellseg3d/dev_scripts/crop_data.py b/napari_cellseg3d/dev_scripts/crop_data.py index a8734d29..ff337e34 100644 --- a/napari_cellseg3d/dev_scripts/crop_data.py +++ b/napari_cellseg3d/dev_scripts/crop_data.py @@ -48,8 +48,8 @@ def crop_3d_image(image, roi_size): for j in image_list: print(j) image = imread(str(j)) - crops = crop_3d_image(image, (64, 64, 64)) - # crops = [image] + # crops = crop_3d_image(image, (64, 64, 64)) + crops = [image] # viewer = napari.Viewer() if not (image_path / "cropped").exists(): (image_path / "cropped").mkdir(exist_ok=False) @@ -57,8 +57,8 @@ def crop_3d_image(image, roi_size): print(im.shape) # viewer.add_image(im) imwrite( - str(image_path / f"cropped/a_{j.stem}_{i}_crop.tif"), - im.astype(np.float32), - dtype="float32", + str(image_path / f"cropped/{j.stem}_{i}_crop.tif"), + im.astype(np.uint16), + dtype="uint16", ) # napari.run() From 6e70b1b20b7f35ad37aad3bc4731732bd52eb408 Mon Sep 17 00:00:00 2001 From: Timokleia <86098649+Timokleia@users.noreply.github.com> Date: Thu, 10 Aug 2023 15:53:56 +0200 Subject: [PATCH 224/250] Colab training improvements (#49) * Update colab_wnet_training.ipynb * Update colab_wnet_training.ipynb --------- Co-authored-by: Cyril Achard <94955160+C-Achard@users.noreply.github.com> --- notebooks/colab_wnet_training.ipynb | 43 ++++++++++++++--------------- 1 file changed, 21 insertions(+), 22 deletions(-) diff --git a/notebooks/colab_wnet_training.ipynb b/notebooks/colab_wnet_training.ipynb index 710eb5cd..5b984673 100644 --- a/notebooks/colab_wnet_training.ipynb +++ b/notebooks/colab_wnet_training.ipynb @@ -19,15 +19,14 @@ { "cell_type": "markdown", "source": [ - "# **WNet (3D)**\n", + "# **WNet: 3D Unsupervised Cell Segmentation**\n", "\n", "---\n", "*Disclaimer:*\n", "\n", - "This notebook is a work-in-progress allowing to train a WNet model for unsupervised cell segmentation, as part of the [CellSeg3D project](https://github.com/AdaptiveMotorControlLab/CellSeg3d) for the [Mathis Lab of Adaptive Motor Control](https://www.mackenziemathislab.org/).\n", + "This notebook, part of the [CellSeg3D project](https://github.com/AdaptiveMotorControlLab/CellSeg3d) under the [Mathis Lab of Adaptive Motor Control](https://www.mackenziemathislab.org/), is a work-in-progress resource for training the WNet model for unsupervised cell segmentation.\n", "\n", - "**It is heavily based on code from the excellent [Zero-Cost Deep-Learning to Enhance Microscopy project](https://github.com/HenriquesLab/ZeroCostDL4Mic)** (ZeroCostDL4Mic), jointly developed by the Jacquemet and Henriques laboratories and created by Daniel Krentzel.\n", - "Credits go to them for all but the provided model." + "The foundation of this notebook owes much to the **[ZeroCostDL4Mic](https://github.com/HenriquesLab/ZeroCostDL4Mic)** project —a collaborative effort between the Jacquemet and Henriques laboratories, and created by Daniel Krentzel. Except for the model provided herein, all credits are duly given to their team." ], "metadata": { "id": "BTUVNXX7R3Go" @@ -239,7 +238,7 @@ "\n", "\n", "\n", - "** Ignore the following message error message. Your Runtime will be automatically restarted. This is normal.**\n", + "** Please ignore the subsequent error message. An automatic restart of your Runtime is expected and is part of the process.**\n", "\n", "\"\"
" ], @@ -301,7 +300,7 @@ { "cell_type": "markdown", "source": [ - "##**1.3 Initialize Weights & Biases integration (optional)**\n", + "##**1.4 Initialize Weights & Biases integration (optional)**\n", "---" ], "metadata": { @@ -326,8 +325,8 @@ "source": [ "# **2. Complete the Colab session**\n", "---\n", - "Use the following cell if you'd like to enable WandB logging for your training run.\n", - "Simply enter your API key below." + "If you wish to utilize Weights & Biases (WandB) for monitoring and logging your training session, execute the cell below.\n", + "To enable it, just input your API key in the space provided." ], "metadata": { "id": "Zi9gRBHFFyX-" @@ -340,13 +339,13 @@ "## **2.1. Check for GPU access**\n", "---\n", "\n", - "By default, the session should be using Python 3 and GPU acceleration, but it is possible to ensure that these are set properly by doing the following:\n", + "By default, this session is configured to use Python 3 and GPU acceleration. To verify or adjust these settings:\n", "\n", - "Go to **Runtime -> Change the Runtime type**\n", + "Navigate to Runtime and select Change the Runtime type.\n", "\n", - "**Runtime type: Python 3** *(Python 3 is programming language in which this program is written)*\n", + "For Runtime type, ensure it's set to Python 3 (the programming language this program is written in).\n", "\n", - "**Accelerator: GPU** *(Graphics processing unit)*\n" + "Under Accelerator, choose GPU (Graphics Processing Unit).\n" ], "metadata": { "id": "zSU-LYTfFnvF" @@ -355,7 +354,7 @@ { "cell_type": "code", "source": [ - "#@markdown ##Run this cell to check if you have GPU access\n", + "#@markdown ##Execute the cell below to verify if GPU access is available.\n", "\n", "import torch\n", "if not torch.cuda.is_available():\n", @@ -412,15 +411,15 @@ "source": [ "## **2.2. Mount Google Drive**\n", "---\n", - " To use this notebook with your **own data**, place it in a folder on **Google Drive** following one of the directory structures outlined in **Section 0**.\n", + "To integrate this notebook with your personal data, save your data on Google Drive in accordance with the directory structures detailed in Section 0.\n", "\n", - "1. **Run** the **cell** below to mount your Google Drive and follow the link.\n", + "1. **Run** the **cell** below and click on the provided link.\n", "\n", - "2. **Sign in** to your Google account and press 'Allow'.\n", + "2. Log in to your Google account and grant the necessary permissions by clicking 'Allow'.\n", "\n", - "3. Next, copy the **authorization code**, paste it into the cell and press enter. This will allow Colab to read and write data from and to your Google Drive.\n", + "3. Copy the generated authorization code and paste it into the cell, then press 'Enter'. This grants Colab access to read and write data to your Google Drive.\n", "\n", - "4. Once this is done, your data can be viewed in the **Files tab** on the top left of the notebook after hitting 'Refresh'." + "4. After completion, you can view your data in the notebook. Simply click the Files tab on the top left and select 'Refresh'." ], "metadata": { "id": "X_bbk7RAF2yw" @@ -506,13 +505,13 @@ "\n", "---\n", "\n", - "### **Paths to training data and model**\n", + "### **Paths to the training data and model**\n", "\n", "* **`training_source`** specifies the paths to the training data. They must be a single multipage TIF file each\n", "\n", "* **`model_path`** specifies the directory where the model checkpoints will be saved.\n", "\n", - "**Note:** You can copy paths from the 'Files' tab by right-clicking any folder or file and selecting 'Copy path'.\n", + "**Tip:** To easily copy paths, navigate to the 'Files' tab, right-click on a folder or file, and choose 'Copy path'.\n", "\n", "### **Training parameters**\n", "\n", @@ -546,7 +545,7 @@ { "cell_type": "code", "source": [ - "#@markdown ###Path to training data:\n", + "#@markdown ###Path to the training data:\n", "training_source = \"./gdrive/MyDrive/CELLSEG_BENCHMARK/DATA/WNET/VIP_full\" #@param {type:\"string\"}\n", "#@markdown ###Model name and path to model folder:\n", "model_path = \"./gdrive/MyDrive/CELLSEG_BENCHMARK/WNET_TRAINING_RESULTS\" #@param {type:\"string\"}\n", @@ -600,7 +599,7 @@ "# **4. Train the network**\n", "---\n", "\n", - "**CRITICAL NOTE:** Google Colab has a time limit for processing (to prevent using GPU power for datamining). Training times must be less than 12 hours! If training takes longer than 12 hours, please decrease `number_of_epochs`." + "Important Reminder: Google Colab imposes a maximum session time to prevent extended GPU usage, such as for data mining. Ensure your training duration stays under 12 hours. If your training is projected to exceed this limit, consider reducing the `number_of_epochs`." ], "metadata": { "id": "arWhMU6aKsri" From 4e454c0406c10842660b9e05300004cc6d97c4b8 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Tue, 15 Aug 2023 16:17:31 +0200 Subject: [PATCH 225/250] Fix paths in test (use pathlib) --- napari_cellseg3d/_tests/test_utils.py | 18 +++++++++++++++--- 1 file changed, 15 insertions(+), 3 deletions(-) diff --git a/napari_cellseg3d/_tests/test_utils.py b/napari_cellseg3d/_tests/test_utils.py index a5ac7fdb..71362e57 100644 --- a/napari_cellseg3d/_tests/test_utils.py +++ b/napari_cellseg3d/_tests/test_utils.py @@ -209,17 +209,29 @@ def test_parse_default_path(): user_path = Path().home() assert utils.parse_default_path([None]) == str(user_path) - test_path = "C:/test/test/test/test" + test_path = Path("C:") / "test" / "test" / "test" / "test" path = [test_path, None, None] assert utils.parse_default_path(path, check_existence=False) == test_path - test_path = "C:/test/does/not/exist" + test_path = Path("C:") / "test" / "does" / "not" / "exist" path = [test_path, None, None] assert utils.parse_default_path(path, check_existence=True) == str( Path.home() ) - long_path = "D:/very/long/path/what/a/bore/ifonlytherewas/something/tohelpmenotsearchit/allthetime" + long_path = Path("D:") + long_path = ( + long_path + / "very" + / "long" + / "path" + / "what" + / "a" + / "bore" + / "ifonlytherewassomething" + / "tohelpmenotsearchit" + / "allthetime" + ) path = [test_path, None, None, long_path, ""] assert utils.parse_default_path(path, check_existence=False) == long_path From c72c5cc5ca3de9a3a769614ae451d7e625502040 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Tue, 15 Aug 2023 16:17:45 +0200 Subject: [PATCH 226/250] Updated workers config --- napari_cellseg3d/code_models/worker_inference.py | 1 - napari_cellseg3d/code_models/worker_training.py | 4 ++-- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 3fb5bc95..8dcd084b 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -181,7 +181,6 @@ def log_parameters(self): def load_folder(self): images_dict = self.create_inference_dict(self.config.images_filepaths) - # TODO : better solution than loading first image always ? data_check = LoadImaged(keys=["image"])(images_dict[0]) check = data_check["image"].shape pad = utils.get_padding_dim(check) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index ffdae104..39b730c5 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -1273,14 +1273,14 @@ def get_patch_loader_func(num_samples): train_dataset, batch_size=self.config.batch_size, shuffle=True, - num_workers=2, + num_workers=self.config.num_workers, collate_fn=pad_list_data_collate, ) validation_loader = DataLoader( validation_dataset, batch_size=self.config.batch_size, - num_workers=2, + num_workers=self.config.num_workers, ) logger.info("\nDone") From 7a4e31fae4115cc9c36b11dca54587fb04a43471 Mon Sep 17 00:00:00 2001 From: Cyril Achard <94955160+C-Achard@users.noreply.github.com> Date: Tue, 19 Sep 2023 11:23:29 +0200 Subject: [PATCH 227/250] Improved Utilities (#44) * Utils improvements - Set new results path now remains across utils - Utils generate one layer and update it on subsequent runs - Layer choice tries to be conserved across utils * Cleanup + CRF fixes * Fragmenting utility * Temp fix for CRF (#46) * Update plugin_convert.py * Update test_and_deploy.yml * Update test_inference.py * Fix tests + new weights * Update test_models.py * Fix dir for saving in tests * Docstring update * CRF and utils colormap adjustment * Update plugin_crf.py * Fix typo in crf.py Co-authored-by: Jessy Lauer <30733203+jeylau@users.noreply.github.com> * Fix Pathlib typo in plugin_convert.py Co-authored-by: Jessy Lauer <30733203+jeylau@users.noreply.github.com> * Fixed filepaths + comments cleanup * Fixed merge comment deletion --------- Co-authored-by: Jessy Lauer <30733203+jeylau@users.noreply.github.com> --- .github/workflows/test_and_deploy.yml | 1 + napari_cellseg3d/code_models/crf.py | 3 +- napari_cellseg3d/code_models/workers_utils.py | 6 +- napari_cellseg3d/code_plugins/plugin_base.py | 37 ++- .../code_plugins/plugin_convert.py | 212 +++++++++++++----- napari_cellseg3d/code_plugins/plugin_crf.py | 25 ++- napari_cellseg3d/code_plugins/plugin_crop.py | 27 ++- .../code_plugins/plugin_review.py | 2 +- .../code_plugins/plugin_utilities.py | 103 ++++++++- napari_cellseg3d/config.py | 6 +- napari_cellseg3d/interface.py | 60 ++--- napari_cellseg3d/utils.py | 56 +++-- 12 files changed, 398 insertions(+), 140 deletions(-) diff --git a/.github/workflows/test_and_deploy.yml b/.github/workflows/test_and_deploy.yml index fafb1719..488d3190 100644 --- a/.github/workflows/test_and_deploy.yml +++ b/.github/workflows/test_and_deploy.yml @@ -7,6 +7,7 @@ on: push: branches: - main + - cy/utils tags: - "v*" # Push events to matching v*, i.e. v1.0, v20.15.10 pull_request: diff --git a/napari_cellseg3d/code_models/crf.py b/napari_cellseg3d/code_models/crf.py index 79951fc5..e90529ce 100644 --- a/napari_cellseg3d/code_models/crf.py +++ b/napari_cellseg3d/code_models/crf.py @@ -7,8 +7,9 @@ Philipp Krähenbühl and Vladlen Koltun NIPS 2011 -Implemented using the pydense libary available at https://github.com/kodalli/pydensecrf. +Implemented using the pydense library available at https://github.com/kodalli/pydensecrf. """ + from warnings import warn try: diff --git a/napari_cellseg3d/code_models/workers_utils.py b/napari_cellseg3d/code_models/workers_utils.py index 5efb93a0..22f4e30d 100644 --- a/napari_cellseg3d/code_models/workers_utils.py +++ b/napari_cellseg3d/code_models/workers_utils.py @@ -151,7 +151,7 @@ def __init__(self, file_location): except ImportError as e: logger.error("ONNX is not installed but ONNX model was loaded") logger.error(e) - msg = "PLEASE INSTALL ONNX CPU OR GPU USING pip install napari-cellseg3d[onnx-cpu] OR napari-cellseg3d[onnx-gpu]" + msg = "PLEASE INSTALL ONNX CPU OR GPU USING: pip install napari-cellseg3d[onnx-cpu] OR pip install napari-cellseg3d[onnx-gpu]" logger.error(msg) raise ImportError(msg) from e @@ -177,6 +177,8 @@ def to(self, device): class QuantileNormalizationd(MapTransform): + """MONAI-style dict transform to normalize each image in a batch individually by quantile normalization.""" + def __init__(self, keys, allow_missing_keys: bool = False): super().__init__(keys, allow_missing_keys) @@ -199,6 +201,8 @@ def normalizer(self, image: torch.Tensor): class QuantileNormalization(Transform): + """MONAI-style transform to normalize each image in a batch individually by quantile normalization.""" + def __call__(self, img): return utils.quantile_normalization(img) diff --git a/napari_cellseg3d/code_plugins/plugin_base.py b/napari_cellseg3d/code_plugins/plugin_base.py index f369320b..3b3a9926 100644 --- a/napari_cellseg3d/code_plugins/plugin_base.py +++ b/napari_cellseg3d/code_plugins/plugin_base.py @@ -446,9 +446,10 @@ def _update_default_paths(self, path=None): self.extract_dataset_paths(self.labels_filepaths), self.results_path, ] - return + return utils.parse_default_path(self._default_path) if Path(path).is_dir(): self._default_path.append(path) + return utils.parse_default_path(self._default_path) @staticmethod def extract_dataset_paths(paths): @@ -458,3 +459,37 @@ def extract_dataset_paths(paths): if paths[0] is None: return None return str(Path(paths[0]).parent) + + +class BasePluginUtils(BasePluginFolder): + """Small subclass used to have centralized widgets layer and result path selection in utilities""" + + save_path = None + utils_default_paths = [Path.home() / "cellseg3d"] + + def __init__( + self, + viewer: napari.viewer.Viewer, + parent=None, + loads_images=True, + loads_labels=True, + ): + super().__init__( + viewer=viewer, + loads_images=loads_images, + loads_labels=loads_labels, + parent=parent, + ) + if parent is not None: + self.setParent(parent) + self.parent = parent + + self.layer = None + """Should contain the layer associated with the results of the utility widget""" + + def _update_default_paths(self, path=None): + """Override to also update utilities' pool of default paths""" + default_path = super()._update_default_paths(path) + logger.debug(f"Trying to update default with {default_path}") + if default_path is not None: + self.utils_default_paths.append(default_path) diff --git a/napari_cellseg3d/code_plugins/plugin_convert.py b/napari_cellseg3d/code_plugins/plugin_convert.py index aa70bc73..d1878cdf 100644 --- a/napari_cellseg3d/code_plugins/plugin_convert.py +++ b/napari_cellseg3d/code_plugins/plugin_convert.py @@ -13,7 +13,8 @@ threshold, to_semantic, ) -from napari_cellseg3d.code_plugins.plugin_base import BasePluginFolder +from napari_cellseg3d.code_plugins.plugin_base import BasePluginUtils +from napari_cellseg3d.dev_scripts.crop_data import crop_3d_image MAX_W = ui.UTILS_MAX_WIDTH MAX_H = ui.UTILS_MAX_HEIGHT @@ -21,9 +22,103 @@ logger = utils.LOGGER -class AnisoUtils(BasePluginFolder): +class FragmentUtils(BasePluginUtils): + """Class to crop large 3D volumes into smaller fragments""" + + save_path = Path.home() / "cellseg3d" / "fragmented" + + def __init__(self, viewer: "napari.Viewer.viewer", parent=None): + """Creates a FragmentUtils widget + + Args: + viewer: viewer in which to process data + parent: parent widget + """ + super().__init__( + viewer, + parent, + loads_labels=False, + ) + self.data_panel = self._build_io_panel() + self.start_btn = ui.Button("Start", self._start) + self.size_selection = ui.AnisotropyWidgets( + parent=self, + default_x=64, + default_y=64, + default_z=64, + always_visible=True, + use_integer_counter=True, + ) + [ + lbl.setText(f"Size in {ax} (pixels):") + for lbl, ax in zip(self.size_selection.box_widgets_lbl, "xyz") + ] + [ + w.setToolTip(f"Size of crop for {dim} axis") + for w, dim in zip(self.size_selection.box_widgets, "xyz") + ] + + self.image_layer_loader.layer_list.label.setText("Layer :") + self.image_layer_loader.set_layer_type(napari.layers.Layer) + + self.results_path = str(self.save_path) + self.results_filewidget.text_field.setText(str(self.results_path)) + self.results_filewidget.check_ready() + + self._build() + + def _build(self): + container = ui.ContainerWidget() + + ui.add_widgets( + container.layout, + [ + self.data_panel, + self.size_selection, + self.start_btn, + ], + ) + + ui.ScrollArea.make_scrollable( + container.layout, + self, + max_wh=[MAX_W, MAX_H], + ) + + self._set_io_visibility() + self.setSizePolicy( + QSizePolicy.MinimumExpanding, QSizePolicy.MinimumExpanding + ) + + def _fragment(self, crops, name): + dir_name = f"/{name}_fragmented_{utils.get_time_filepath()}" + Path(self.results_path + dir_name).mkdir(parents=True, exist_ok=False) + for i, crop in enumerate(crops): + utils.save_layer( + self.results_path + dir_name, + f"{name}_fragmented_{i}.tif", + crop, + ) + + def _start(self): + sizes = self.size_selection.resolution_zyx() + if self.layer_choice.isChecked(): + layer = self.image_layer_loader.layer() + crops = crop_3d_image(layer.data, sizes) + self._fragment(crops, layer.name) + elif self.folder_choice.isChecked(): + paths = self.images_filepaths + for path in paths: + data = imread(path) + crops = crop_3d_image(data, sizes) + self._fragment(crops, Path(path).stem) + + +class AnisoUtils(BasePluginUtils): """Class to correct anisotropy in images""" + save_path = Path.home() / "cellseg3d" / "anisotropy" + def __init__(self, viewer: "napari.Viewer.viewer", parent=None): """ Creates a AnisoUtils widget @@ -46,7 +141,7 @@ def __init__(self, viewer: "napari.Viewer.viewer", parent=None): self.aniso_widgets = ui.AnisotropyWidgets(self, always_visible=True) self.start_btn = ui.Button("Start", self._start) - self.results_path = str(Path.home() / Path("cellseg3d/anisotropy")) + self.results_path = str(self.save_path) self.results_filewidget.text_field.setText(str(self.results_path)) self.results_filewidget.check_ready() @@ -91,11 +186,16 @@ def _start(self): f"isotropic_{layer.name}_{utils.get_date_time()}.tif", isotropic_image, ) - utils.show_result( + if isinstance(layer, napari.layers.Image): + isotropic_image = isotropic_image.astype(layer.data.dtype) + else: + isotropic_image = isotropic_image.astype(np.uint16) + self.layer = utils.show_result( self._viewer, layer, isotropic_image, f"isotropic_{layer.name}", + existing_layer=self.layer, ) elif ( @@ -113,7 +213,8 @@ def _start(self): ) -class RemoveSmallUtils(BasePluginFolder): +class RemoveSmallUtils(BasePluginUtils): + save_path = Path.home() / "cellseg3d" / "small_removed" """ Widget to remove small objects """ @@ -128,14 +229,13 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): """ super().__init__( viewer, - parent, - loads_labels=False, + parent=parent, + loads_images=False, ) self.data_panel = self._build_io_panel() - self.image_layer_loader.layer_list.label.setText("Layer :") - self.image_layer_loader.set_layer_type(napari.layers.Layer) + self.label_layer_loader.layer_list.label.setText("Layer :") self.start_btn = ui.Button("Start", self._start) self.size_for_removal_counter = ui.IntIncrementCounter( @@ -145,8 +245,9 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): text_label="Remove all smaller than (pxs):", ) - self.results_path = Path.home() / Path("cellseg3d/small_removed") - self.results_filewidget.text_field.setText(str(self.results_path)) + self.results_path = str(self.save_path) + self.results_filewidget.text_field.setText(self.results_path) + self.results_filewidget.check_ready() self.container = self._build() @@ -180,8 +281,8 @@ def _start(self): remove_size = self.size_for_removal_counter.value() if self.layer_choice.isChecked(): - if self.image_layer_loader.layer_data() is not None: - layer = self.image_layer_loader.layer() + if self.label_layer_loader.layer_data() is not None: + layer = self.label_layer_loader.layer() data = np.array(layer.data) removed = self.function(data, remove_size) @@ -191,8 +292,12 @@ def _start(self): f"cleared_{layer.name}_{utils.get_date_time()}.tif", removed, ) - utils.show_result( - self._viewer, layer, removed, f"cleared_{layer.name}" + self.layer = utils.show_result( + self._viewer, + layer, + removed, + f"cleared_{layer.name}", + existing_layer=self.layer, ) elif ( self.folder_choice.isChecked() and len(self.images_filepaths) != 0 @@ -210,7 +315,8 @@ def _start(self): return -class ToSemanticUtils(BasePluginFolder): +class ToSemanticUtils(BasePluginUtils): + save_path = Path.home() / "cellseg3d" / "semantic_labels" """ Widget to create semantic labels from instance labels """ @@ -225,20 +331,15 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): """ super().__init__( viewer, - parent, + parent=parent, loads_images=False, ) self.data_panel = self._build_io_panel() - self.start_btn = ui.Button("Start", self._start) - - self.results_path = str( - Path.home() / Path("cellseg3d/instance_labels") - ) + self.results_path = str(self.save_path) self.results_filewidget.text_field.setText(self.results_path) self.results_filewidget.check_ready() - self._build() def _build(self): @@ -280,8 +381,12 @@ def _start(self): f"semantic_{layer.name}_{utils.get_date_time()}.tif", semantic, ) - utils.show_result( - self._viewer, layer, semantic, f"semantic_{layer.name}" + self.layer = utils.show_result( + self._viewer, + layer, + semantic, + f"semantic_{layer.name}", + existing_layer=self.layer, ) elif ( self.folder_choice.isChecked() and len(self.labels_filepaths) != 0 @@ -300,7 +405,8 @@ def _start(self): logger.warning("Please specify a layer or a folder") -class ToInstanceUtils(BasePluginFolder): +class ToInstanceUtils(BasePluginUtils): + save_path = Path.home() / "cellseg3d" / "instance_labels" """ Widget to convert semantic labels to instance labels """ @@ -315,18 +421,16 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): """ super().__init__( viewer, - parent, + parent=parent, loads_images=False, ) self.data_panel = self._build_io_panel() - self.label_layer_loader.set_layer_type(napari.layers.Layer) - + self.image_layer_loader.set_layer_type(napari.layers.Layer) self.instance_widgets = InstanceWidgets(parent=self) - self.start_btn = ui.Button("Start", self._start) - self.results_path = Path.home() / Path("cellseg3d/instance") + self.results_path = str(self.save_path) self.results_filewidget.text_field.setText(str(self.results_path)) self.results_filewidget.check_ready() @@ -368,8 +472,12 @@ def _start(self): f"instance_{layer.name}_{utils.get_date_time()}.tif", instance, ) - self._viewer.add_labels( - instance, name=f"instance_{layer.name}" + self.layer = utils.show_result( + self._viewer, + layer, + instance, + f"instance_{layer.name}", + existing_layer=self.layer, ) elif ( @@ -387,7 +495,8 @@ def _start(self): ) -class ThresholdUtils(BasePluginFolder): +class ThresholdUtils(BasePluginUtils): + save_path = Path.home() / "cellseg3d" / "threshold" """ Creates a ThresholdUtils widget Args: @@ -398,16 +507,14 @@ class ThresholdUtils(BasePluginFolder): def __init__(self, viewer: "napari.viewer.Viewer", parent=None): super().__init__( viewer, - parent, + parent=parent, loads_labels=False, ) self.data_panel = self._build_io_panel() self._set_io_visibility() - self.image_layer_loader.layer_list.label.setText("Layer :") self.image_layer_loader.set_layer_type(napari.layers.Layer) - self.start_btn = ui.Button("Start", self._start) self.binarize_counter = ui.DoubleIncrementCounter( lower=0.0, @@ -417,12 +524,11 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): text_label="Remove all smaller than (value):", ) - self.results_path = str(Path.home() / Path("cellseg3d/threshold")) + self.results_path = str(self.save_path) self.results_filewidget.text_field.setText(self.results_path) self.results_filewidget.check_ready() self.container = self._build() - self.function = threshold def _build(self): @@ -464,8 +570,12 @@ def _start(self): f"threshold_{layer.name}_{utils.get_date_time()}.tif", removed, ) - utils.show_result( - self._viewer, layer, removed, f"threshold{layer.name}" + self.layer = utils.show_result( + self._viewer, + layer, + removed, + f"threshold_{layer.name}", + existing_layer=self.layer, ) elif ( self.folder_choice.isChecked() and len(self.images_filepaths) != 0 @@ -480,23 +590,3 @@ def _start(self): images, self.images_filepaths, ) - - -# class ConvertUtils(BasePluginFolder): -# """Utility widget that allows to convert labels from instance to semantic and the reverse.""" -# -# def __init__(self, viewer: "napari.viewer.Viewer", parent): -# """Builds a ConvertUtils widget with the following buttons: -# -# * A button to convert a folder of labels to semantic labels -# -# * A button to convert a folder of labels to instance labels -# -# * A button to convert a currently selected layer to semantic labels -# -# * A button to convert a currently selected layer to instance labels -# """ -# -# super().__init__(viewer, parent) -# self._viewer = viewer -# pass diff --git a/napari_cellseg3d/code_plugins/plugin_crf.py b/napari_cellseg3d/code_plugins/plugin_crf.py index 76194e87..64503e10 100644 --- a/napari_cellseg3d/code_plugins/plugin_crf.py +++ b/napari_cellseg3d/code_plugins/plugin_crf.py @@ -13,7 +13,7 @@ CRFWorker, crf_with_config, ) -from napari_cellseg3d.code_plugins.plugin_base import BasePluginSingleImage +from napari_cellseg3d.code_plugins.plugin_base import BasePluginUtils from napari_cellseg3d.utils import LOGGER as logger @@ -107,7 +107,11 @@ def make_config(self): ) -class CRFWidget(BasePluginSingleImage): +class CRFWidget(BasePluginUtils): + """Widget to run CRF post-processing""" + + save_path = Path.home() / "cellseg3d" / "crf" + def __init__(self, viewer, parent=None): """ Create a widget for CRF post-processing. @@ -115,7 +119,7 @@ def __init__(self, viewer, parent=None): viewer: napari viewer to display the widget parent: parent widget. Defaults to None. """ - super().__init__(viewer, parent) + super().__init__(viewer, parent=parent) self._viewer = viewer self.start_button = ui.Button("Start", self._start, parent=self) @@ -129,6 +133,8 @@ def __init__(self, viewer, parent=None): napari.layers.Image ) # to load all crf-compatible inputs, not int only self.image_layer_loader.setVisible(True) + self.label_layer_loader.layer_list.label.setText("Model output :") + if CRF_INSTALLED: self.start_button.setVisible(True) else: @@ -138,8 +144,9 @@ def __init__(self, viewer, parent=None): self.result_name = None self.crf_results = [] - self.results_path = Path.home() / Path("cellseg3d/crf") - self.results_filewidget.text_field.setText(str(self.results_path)) + self.results_path = str(self.save_path) + self.results_filewidget.text_field.setText(self.results_path) + self.results_filewidget.check_ready() self._container = ui.ContainerWidget(parent=self, l=11, t=11, r=11) @@ -149,6 +156,7 @@ def __init__(self, viewer, parent=None): self.worker = None self.log = None + self.layer = None def _build(self): self.setMinimumWidth(100) @@ -262,15 +270,18 @@ def _start(self): def _on_yield(self, result): self.crf_results.append(result) - utils.save_layer( self.results_filewidget.text_field.text(), str(self.result_name + "_crf.tif"), result, ) - self._viewer.add_image( + self.layer = utils.show_result( + self._viewer, + self.result_layer, result, name="crf_" + self.result_name, + existing_layer=self.layer, + colormap="bop orange", ) def _on_start(self): diff --git a/napari_cellseg3d/code_plugins/plugin_crop.py b/napari_cellseg3d/code_plugins/plugin_crop.py index c6e822d4..c4889249 100644 --- a/napari_cellseg3d/code_plugins/plugin_crop.py +++ b/napari_cellseg3d/code_plugins/plugin_crop.py @@ -17,9 +17,14 @@ logger = utils.LOGGER -class Cropping(BasePluginSingleImage): +class Cropping( + BasePluginSingleImage +): # not a BasePLuginUtils since it's not runnning on folders """A utility plugin for cropping 3D volumes.""" + save_path = Path.home() / "cellseg3d" / "cropped" + utils_default_paths = [] + def __init__(self, viewer: "napari.viewer.Viewer", parent=None): """Creates a Cropping plugin with several buttons : @@ -37,13 +42,21 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): """ super().__init__(viewer) + + if parent is not None: + self.setParent(parent) + self.docked_widgets = [] - self.results_path = Path.home() / Path("cellseg3d/cropped") + self.results_path = str(self.save_path) self.btn_start = ui.Button("Start", self._start) self.image_layer_loader.set_layer_type(napari.layers.Layer) - self.image_layer_loader.layer_list.label.setText("Image 1") + self.image_layer_loader.layer_list.label.setText("Image") + # self.image_layer_loader.layer_list.currentIndexChanged.connect( + # self.auto_set_dims + # ) + self.image_layer_loader.layer_list.currentIndexChanged.connect( self.auto_set_dims ) @@ -73,14 +86,8 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): self.layer_choice.clicked.connect( self._toggle_second_image_io_visibility ) + self.results_filewidget.text_field.setText(str(self.save_path)) - # self.results_filewidget = ui.FilePathWidget( - # "Results path", - # self._load_results_path, - # default=str(self.results_path), - # ) - # self.results_filewidget.tooltips = str(self.results_path) - self.results_filewidget.text_field.setText(str(self.results_path)) self.results_filewidget.check_ready() self.crop_size_widgets = ui.IntIncrementCounter.make_n( diff --git a/napari_cellseg3d/code_plugins/plugin_review.py b/napari_cellseg3d/code_plugins/plugin_review.py index d3216436..712b3193 100644 --- a/napari_cellseg3d/code_plugins/plugin_review.py +++ b/napari_cellseg3d/code_plugins/plugin_review.py @@ -144,7 +144,7 @@ def _build(self): # self._show_io_element(self.results_filewidget) self.results_filewidget.text_field.setText( - str(Path.home() / Path("cellseg3d/review")) + str(Path.home() / "cellseg3d" / "review") ) csv_param_w.setLayout(csv_param_l) diff --git a/napari_cellseg3d/code_plugins/plugin_utilities.py b/napari_cellseg3d/code_plugins/plugin_utilities.py index 6e1a606a..abe56699 100644 --- a/napari_cellseg3d/code_plugins/plugin_utilities.py +++ b/napari_cellseg3d/code_plugins/plugin_utilities.py @@ -9,8 +9,11 @@ # local import napari_cellseg3d.interface as ui +from napari_cellseg3d import utils +from napari_cellseg3d.code_plugins.plugin_base import BasePluginUtils from napari_cellseg3d.code_plugins.plugin_convert import ( AnisoUtils, + FragmentUtils, RemoveSmallUtils, ThresholdUtils, ToInstanceUtils, @@ -18,9 +21,11 @@ ) from napari_cellseg3d.code_plugins.plugin_crf import CRFWidget from napari_cellseg3d.code_plugins.plugin_crop import Cropping +from napari_cellseg3d.utils import LOGGER as logger UTILITIES_WIDGETS = { "Crop": Cropping, + "Fragment 3D volume": FragmentUtils, "Correct anisotropy": AnisoUtils, "Remove small objects": RemoveSmallUtils, "Convert to instance labels": ToInstanceUtils, @@ -34,17 +39,19 @@ class Utilities(QWidget, metaclass=ui.QWidgetSingleton): def __init__(self, viewer: "napari.viewer.Viewer"): super().__init__() self._viewer = viewer - - attr_names = ["crop", "aniso", "small", "inst", "sem", "thresh", "crf"] + self.current_widget = None + + attr_names = [ + "crop", + "frag", + "aniso", + "small", + "inst", + "sem", + "thresh", + "crf", + ] self._create_utils_widgets(attr_names) - - # self.crop = Cropping(self._viewer) - # self.sem = ToSemanticUtils(self._viewer) - # self.aniso = AnisoUtils(self._viewer) - # self.inst = ToInstanceUtils(self._viewer) - # self.thresh = ThresholdUtils(self._viewer) - # self.small = RemoveSmallUtils(self._viewer) - self.utils_choice = ui.DropdownMenu( UTILITIES_WIDGETS.keys(), text_label="Utilities" ) @@ -52,10 +59,21 @@ def __init__(self, viewer: "napari.viewer.Viewer"): self._build() self.utils_choice.currentIndexChanged.connect(self._update_visibility) + self.utils_choice.currentIndexChanged.connect( + self._update_current_widget + ) # self._dock_util() self._update_visibility() qInstallMessageHandler(ui.handle_adjust_errors_wrapper(self)) + def _update_current_widget(self): + self.current_widget = self.utils_widgets[ + self.utils_choice.currentIndex() + ] + + def _update_results_path(self, widget): + self.results_filewidget.text_field.setText(str(widget.save_path)) + def _build(self): layout = QVBoxLayout() ui.add_widgets(layout, self.utils_widgets) @@ -72,11 +90,14 @@ def _build(self): # layout.setSizeConstraint(QLayout.SetFixedSize) self.setLayout(layout) # self.setMinimumHeight(2000) - self.setSizePolicy(QSizePolicy.Fixed, QSizePolicy.MinimumExpanding) + self.setSizePolicy( + QSizePolicy.MinimumExpanding, QSizePolicy.MinimumExpanding + ) self._update_visibility() def _create_utils_widgets(self, names): for key, name in zip(UTILITIES_WIDGETS, names): + logger.debug(f"Creating {name} widget") setattr(self, name, UTILITIES_WIDGETS[key](self._viewer)) self.utils_widgets = [] @@ -84,22 +105,82 @@ def _create_utils_widgets(self, names): wid = getattr(self, n) self.utils_widgets.append(wid) + self.current_widget = self.utils_widgets[0] if len(self.utils_widgets) != len(UTILITIES_WIDGETS.keys()): raise RuntimeError( "One or several utility widgets are missing/erroneous" ) + def _update_layers(self, current_loader, new_loader): + current_layer = current_loader.layer() + if not isinstance(current_layer, new_loader.layer_type): + return + if ( + current_layer is not None + and current_layer.name in new_loader.layer_list.get_items() + ): + index = new_loader.layer_list.get_items().index(current_layer.name) + logger.debug( + f"Index of layer {current_layer.name} in new loader : {index}" + ) + new_loader.layer_list.setCurrentIndex(index) + + def _update_fields(self, widget: BasePluginUtils): + try: + # checks all combinations to find if a layer could be recovered across widgets + # correctness is ensured by the types of the layer loaders + self._update_layers( + self.current_widget.image_layer_loader, + widget.image_layer_loader, + ) + self._update_layers( + self.current_widget.image_layer_loader, + widget.label_layer_loader, + ) + self._update_layers( + self.current_widget.label_layer_loader, + widget.image_layer_loader, + ) + self._update_layers( + self.current_widget.label_layer_loader, + widget.label_layer_loader, + ) + except KeyError: + pass + + logger.debug( + f"Current widget save path : {self.current_widget.save_path}" + ) + logger.debug( + f"Current widget text field : {self.current_widget.results_filewidget.text_field.text()}" + ) + logger.debug( + f"Matching : {self.current_widget.results_filewidget.text_field.text() == self.current_widget.results_path}" + ) + if len(self.current_widget.utils_default_paths) > 1: + try: + path = self.current_widget.utils_default_paths + default = utils.parse_default_path(path) + widget.results_filewidget.text_field.setText(default) + widget.utils_default_paths.append(default) + except AttributeError: + pass + def _update_visibility(self): widget_class = UTILITIES_WIDGETS[self.utils_choice.currentText()] # print("vis. updated") # print(self.utils_widgets) self._hide_all() + widget = None for _i, w in enumerate(self.utils_widgets): if isinstance(w, widget_class): w.setVisible(True) w.adjustSize() + widget = w # else: # self.utils_widgets[i].setVisible(False) + self._update_fields(widget) + self.current_widget = widget def _hide_all(self): for w in self.utils_widgets: diff --git a/napari_cellseg3d/config.py b/napari_cellseg3d/config.py index b05f7ac7..027aef2b 100644 --- a/napari_cellseg3d/config.py +++ b/napari_cellseg3d/config.py @@ -44,7 +44,7 @@ class ReviewConfig: image: np.array = None labels: np.array = None - csv_path: str = Path.home() / Path("cellseg3d/review") + csv_path: str = Path.home() / "cellseg3d" / "review" model_name: str = "" new_csv: bool = True filetype: str = ".tif" @@ -206,7 +206,7 @@ class InferenceWorkerConfig: device: str = "cpu" model_info: ModelInfo = ModelInfo() weights_config: WeightsInfo = WeightsInfo() - results_path: str = str(Path.home() / Path("cellseg3d/inference")) + results_path: str = str(Path.home() / "cellseg3d" / "inference") filetype: str = ".tif" keep_on_cpu: bool = False compute_stats: bool = False @@ -254,7 +254,7 @@ class TrainingWorkerConfig: scheduler_factor: float = 0.5 validation_interval: int = 2 batch_size: int = 1 - results_path_folder: str = str(Path.home() / Path("cellseg3d/training")) + results_path_folder: str = str(Path.home() / "cellseg3d" / "training") sampling: bool = False num_samples: int = 2 sample_size: List[int] = None diff --git a/napari_cellseg3d/interface.py b/napari_cellseg3d/interface.py index 22f6a4a3..e6cc3f27 100644 --- a/napari_cellseg3d/interface.py +++ b/napari_cellseg3d/interface.py @@ -4,13 +4,10 @@ from typing import List, Optional import napari - -# Qt -# from qtpy.QtCore import QtWarningMsg from qtpy import QtCore -# from qtpy.QtCore import QtWarningMsg -from qtpy.QtCore import QObject, Qt, QUrl +# Qt +from qtpy.QtCore import QObject, Qt, QtWarningMsg, QUrl from qtpy.QtGui import QCursor, QDesktopServices, QTextCursor from qtpy.QtWidgets import ( QAbstractSpinBox, @@ -110,25 +107,25 @@ def __call__(cls, *args, **kwargs): def handle_adjust_errors(widget, warning_type, context, msg: str): """Qt message handler that attempts to react to errors when setting the window size and resizes the main window""" - pass - # head = msg.split(": ")[0] - # if warning_type == QtWarningMsg and head == "QWindowsWindow::setGeometry": - # logger.warning( - # f"Qt resize error : {msg}\nhas been handled by attempting to resize the window" - # ) - # try: - # if widget.parent() is not None: - # state = int(widget.parent().parent().windowState()) - # if state == 0: # normal state - # widget.parent().parent().adjustSize() - # logger.debug("Non-max. size adjust attempt") - # logger.debug(f"{widget.parent().parent()}") - # elif state == 2: # maximized state - # widget.parent().parent().showNormal() - # widget.parent().parent().showMaximized() - # logger.debug("Maximized size adjust attempt") - # except RuntimeError: - # pass + # pass + head = msg.split(": ")[0] + if warning_type == QtWarningMsg and head == "QWindowsWindow::setGeometry": + logger.warning( + f"Qt resize error : {msg}\nhas been handled by attempting to resize the window" + ) + try: + if widget.parent() is not None: + state = int(widget.parent().parent().windowState()) + if state == 0: # normal state + widget.parent().parent().adjustSize() + logger.debug("Non-max. size adjust attempt") + logger.debug(f"{widget.parent().parent()}") + elif state == 2: # maximized state + widget.parent().parent().showNormal() + widget.parent().parent().showMaximized() + logger.debug("Maximized size adjust attempt") + except RuntimeError: + pass def handle_adjust_errors_wrapper(widget): @@ -430,6 +427,9 @@ def __init__( if fixed: self.setSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) + def get_items(self): + return [self.itemText(i) for i in range(self.count())] + class CheckBox(QCheckBox): """Shortcut class for creating QCheckBox with a title and a function""" @@ -640,6 +640,7 @@ def __init__( default_y: Optional[float] = 1.0, default_z: Optional[float] = 1.0, always_visible: Optional[bool] = False, + use_integer_counter: Optional[bool] = False, ): """Creates an instance of AnisotropyWidgets Args: @@ -659,9 +660,14 @@ def __init__( "Anisotropic data", self._toggle_display_aniso, parent ) - self.box_widgets = DoubleIncrementCounter.make_n( - n=3, lower=1.0, upper=1000.0, default=1.0, step=0.5 - ) + if use_integer_counter: + self.box_widgets = IntIncrementCounter.make_n( + n=3, lower=1, upper=9999, default=64, step=1 + ) + else: + self.box_widgets = DoubleIncrementCounter.make_n( + n=3, lower=1.0, upper=1000.0, default=1.0, step=0.5 + ) self.box_widgets[0].setValue(default_x) self.box_widgets[1].setValue(default_y) self.box_widgets[2].setValue(default_z) diff --git a/napari_cellseg3d/utils.py b/napari_cellseg3d/utils.py index c6a8bbac..45bed204 100644 --- a/napari_cellseg3d/utils.py +++ b/napari_cellseg3d/utils.py @@ -61,16 +61,21 @@ def save_layer(results_path, image_name, image): results_path: path to folder containing result image_name: image name for saving image: data array containing image - - Returns: - """ path = str(results_path / Path(image_name)) # TODO flexible filetype LOGGER.info(f"Saved as : {path}") - imwrite(path, image) - - -def show_result(viewer, layer, image, name): + image = image.astype(np.float32) + imwrite(path, image, dtype="float32") + + +def show_result( + viewer, + layer, + image, + name, + existing_layer: napari.layers.Layer = None, + colormap="bop orange", +) -> napari.layers.Layer: """ Adds layers to a viewer to show result to user @@ -81,18 +86,35 @@ def show_result(viewer, layer, image, name): name: name of the added layer Returns: - + napari.layers.Layer: the layer added to the viewer """ - if isinstance(layer, napari.layers.Image): - LOGGER.debug("Added resulting image layer") - viewer.add_image(image, name=name) - elif isinstance(layer, napari.layers.Labels): - LOGGER.debug("Added resulting label layer") - viewer.add_labels(image, name=name) + colormap = colormap if colormap is not None else "gray" + if existing_layer is None: + if isinstance(layer, napari.layers.Image): + LOGGER.info("Added resulting image layer") + results_layer = viewer.add_image( + image, name=name, colormap=colormap + ) + elif isinstance(layer, napari.layers.Labels): + LOGGER.info("Added resulting label layer") + results_layer = viewer.add_labels(image, name=name) + else: + LOGGER.warning( + f"Results not shown, unsupported layer type {type(layer)}" + ) else: - LOGGER.warning( - f"Results not shown, unsupported layer type {type(layer)}" - ) + try: + viewer.layers[existing_layer.name].data = image + results_layer = viewer.layers[existing_layer.name] + except KeyError: + LOGGER.warning( + f"Results not shown, layer {existing_layer.name} not found" + "Showing new layer instead" + ) + results_layer = show_result( + viewer, layer, image, name, existing_layer=None + ) + return results_layer class Singleton(type): From d533a3b951fc003447e5df9e5b7a732458927e2a Mon Sep 17 00:00:00 2001 From: C-Achard Date: Tue, 19 Sep 2023 13:02:43 +0200 Subject: [PATCH 228/250] Fixed parse_default_path test --- napari_cellseg3d/_tests/test_utils.py | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/napari_cellseg3d/_tests/test_utils.py b/napari_cellseg3d/_tests/test_utils.py index 71362e57..5d4677ac 100644 --- a/napari_cellseg3d/_tests/test_utils.py +++ b/napari_cellseg3d/_tests/test_utils.py @@ -206,20 +206,22 @@ def test_load_images(): def test_parse_default_path(): - user_path = Path().home() + user_path = Path.home() assert utils.parse_default_path([None]) == str(user_path) - test_path = Path("C:") / "test" / "test" / "test" / "test" + test_path = (Path.home() / "test" / "test" / "test" / "test").as_posix() path = [test_path, None, None] - assert utils.parse_default_path(path, check_existence=False) == test_path + assert utils.parse_default_path(path, check_existence=False) == str( + test_path + ) - test_path = Path("C:") / "test" / "does" / "not" / "exist" + test_path = (Path.home() / "test" / "does" / "not" / "exist").as_posix() path = [test_path, None, None] assert utils.parse_default_path(path, check_existence=True) == str( Path.home() ) - long_path = Path("D:") + long_path = Path("D:/") long_path = ( long_path / "very" @@ -233,7 +235,9 @@ def test_parse_default_path(): / "allthetime" ) path = [test_path, None, None, long_path, ""] - assert utils.parse_default_path(path, check_existence=False) == long_path + assert utils.parse_default_path(path, check_existence=False) == str( + long_path.as_posix() + ) def test_thread_test(make_napari_viewer_proxy): From ef9c18c5daa3aa4735c9ab38e40ab05cd33b5bbe Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 21 Sep 2023 15:03:55 +0200 Subject: [PATCH 229/250] Ignore wandb results in gitignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 7dbc9185..6ee49040 100644 --- a/.gitignore +++ b/.gitignore @@ -103,6 +103,7 @@ venv/ /docs/res/logo/old_logo/ /reqs/ /loss_plots/ +/wandb/ notebooks/csv_cell_plot.html notebooks/full_plot.html *.csv From c43c9955b3865d57ac12d7a6d11fd59d0396f814 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 21 Sep 2023 15:05:07 +0200 Subject: [PATCH 230/250] Enable GH Actions tests on branch temporarily --- .github/workflows/test_and_deploy.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/test_and_deploy.yml b/.github/workflows/test_and_deploy.yml index fafb1719..b6c9d848 100644 --- a/.github/workflows/test_and_deploy.yml +++ b/.github/workflows/test_and_deploy.yml @@ -7,6 +7,7 @@ on: push: branches: - main + - cy/wnet-train tags: - "v*" # Push events to matching v*, i.e. v1.0, v20.15.10 pull_request: From f12577a3655c74b375eecbea26f36780b160a66a Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 21 Sep 2023 15:41:36 +0200 Subject: [PATCH 231/250] Fixed deletion of Qt imports in interface --- napari_cellseg3d/interface.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/interface.py b/napari_cellseg3d/interface.py index c3ecd50f..3c6699c4 100644 --- a/napari_cellseg3d/interface.py +++ b/napari_cellseg3d/interface.py @@ -7,7 +7,7 @@ # Qt # from qtpy.QtCore import QtWarningMsg from qtpy import QtCore -from qtpy.QtCore import QObject, Q +from qtpy.QtCore import QObject, Qt, QtWarningMsg, QUrl from qtpy.QtGui import QCursor, QDesktopServices, QTextCursor from qtpy.QtWidgets import ( QAbstractSpinBox, From b4b86f8574a6ee5a8e4df98a0db6851a52348a74 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 21 Sep 2023 15:46:19 +0200 Subject: [PATCH 232/250] Reverted include_background=True in Dice --- napari_cellseg3d/code_models/worker_training.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 39b730c5..21df039d 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -154,7 +154,7 @@ def __init__( self.config = worker_config self.dice_metric = DiceMetric( - include_background=True, reduction="mean", get_not_nans=False + include_background=False, reduction="mean", get_not_nans=False ) self.normalize_function = utils.remap_image self.start_time = time.time() @@ -1309,7 +1309,7 @@ def get_patch_loader_func(num_samples): else provided_scheduler ) dice_metric = DiceMetric( - include_background=True, reduction="mean", ignore_empty=False + include_background=False, reduction="mean", ignore_empty=False ) best_metric = -1 From 6605081c20743e7e8efcca62a5d2377b6b2a2dc1 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 21 Sep 2023 16:37:22 +0200 Subject: [PATCH 233/250] Reintroduced best Dice channel seeking + refacto --- .../code_models/worker_training.py | 38 ++++++------------- napari_cellseg3d/utils.py | 23 +++++++++++ 2 files changed, 34 insertions(+), 27 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 21df039d..1c26c5c7 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -779,33 +779,17 @@ def eval(self, model, epoch) -> TrainingReport: f"Val decoder outputs shape: {val_decoder_outputs.shape}" ) - # dices = [] - # Find in which channel the labels are (avoid background) - # for channel in range(val_outputs.shape[1]): - # dices.append( - # utils.dice_coeff( - # y_pred=val_outputs[ - # 0, channel : (channel + 1), :, :, : - # ], - # y_true=val_labels[0], - # ) - # ) - # logger.debug(f"DICE COEFF: {dices}") - # max_dice_channel = torch.argmax( - # torch.Tensor(dices) - # ) - # logger.debug( - # f"MAX DICE CHANNEL: {max_dice_channel}" - # ) + max_dice_channel = utils.seek_best_dice_coeff_channel( + y_pred=val_outputs, y_true=val_labels + ) self.dice_metric( - y_pred=val_outputs, - # [ - # :, - # max_dice_channel : (max_dice_channel + 1), - # :, - # :, - # :, - # ], + y_pred=val_outputs[ + :, + max_dice_channel : (max_dice_channel + 1), + :, + :, + :, + ], y=val_labels, ) @@ -1282,7 +1266,7 @@ def get_patch_loader_func(num_samples): batch_size=self.config.batch_size, num_workers=self.config.num_workers, ) - logger.info("\nDone") + logger.debug("\nDone") logger.debug("Optimizer") optimizer = ( diff --git a/napari_cellseg3d/utils.py b/napari_cellseg3d/utils.py index 15eae20c..dabcda1f 100644 --- a/napari_cellseg3d/utils.py +++ b/napari_cellseg3d/utils.py @@ -229,6 +229,29 @@ def dice_coeff( ) +def seek_best_dice_coeff_channel(y_pred, y_true) -> torch.Tensor: + """Compute Dice-Sorensen coefficient between unsupervised model output and ground truth labels; + returns the channel with the highest dice coefficient. + Args: + y_true: Ground truth label + y_pred: Prediction label + Returns: best Dice coefficient channel + """ + dices = [] + # Find in which channel the labels are (to avoid background) + for channel in range(y_pred.shape[1]): + dices.append( + dice_coeff( + y_pred=y_pred[0, channel : (channel + 1), :, :, :], + y_true=y_true[0], + ) + ) + LOGGER.debug(f"DICE COEFF: {dices}") + max_dice_channel = torch.argmax(torch.Tensor(dices)) + LOGGER.debug(f"MAX DICE CHANNEL: {max_dice_channel}") + return max_dice_channel + + def correct_rotation(image): """Rotates the exes 0 and 2 in [DHW] section of image array""" extra_dims = len(image.shape) - 3 From f6711c39c3ec4b3913893d2bde37c4aecd1692b3 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 21 Sep 2023 16:38:47 +0200 Subject: [PATCH 234/250] Improve filepath messages --- .../code_models/model_framework.py | 14 ++++++++++---- napari_cellseg3d/code_plugins/plugin_base.py | 19 ++++++++++++------- napari_cellseg3d/interface.py | 2 +- 3 files changed, 23 insertions(+), 12 deletions(-) diff --git a/napari_cellseg3d/code_models/model_framework.py b/napari_cellseg3d/code_models/model_framework.py index 9bcd67a6..d4e7af06 100644 --- a/napari_cellseg3d/code_models/model_framework.py +++ b/napari_cellseg3d/code_models/model_framework.py @@ -245,16 +245,22 @@ def _toggle_weights_path(self): self.custom_weights_choice, self.weights_filewidget ) - def create_dataset_dict_no_labs(self): - """Creates unsupervised data dictionary for MONAI transforms and training.""" + def get_unsupervised_image_filepaths(self): volume_directory = Path( self.unsupervised_images_filewidget.text_field.text() ) + if not volume_directory.exists(): raise ValueError(f"Data folder {volume_directory} does not exist") - images_filepaths = sorted(Path.glob(volume_directory, "*.tif")) + return sorted(Path.glob(volume_directory, "*.tif")) + + def create_dataset_dict_no_labs(self): + """Creates unsupervised data dictionary for MONAI transforms and training.""" + images_filepaths = self.get_unsupervised_image_filepaths() if len(images_filepaths) == 0: - raise ValueError(f"Data folder {volume_directory} is empty") + raise ValueError( + f"Data folder {self.unsupervised_images_filewidget.text_field.text()} is empty" + ) logger.info("Images :") for file in images_filepaths: diff --git a/napari_cellseg3d/code_plugins/plugin_base.py b/napari_cellseg3d/code_plugins/plugin_base.py index 2fbbe8d3..90c61adf 100644 --- a/napari_cellseg3d/code_plugins/plugin_base.py +++ b/napari_cellseg3d/code_plugins/plugin_base.py @@ -389,7 +389,7 @@ def __init__( # Validation images widget self.unsupervised_images_filewidget = ui.FilePathWidget( description="Training directory", - file_function=self.load_validation_images_dataset, + file_function=self.load_unsup_images_dataset, parent=self, ) self.unsupervised_images_filewidget.setVisible(False) @@ -421,19 +421,23 @@ def load_dataset_paths(self): def load_image_dataset(self): """Show file dialog to set :py:attr:`~images_filepaths`""" filenames = self.load_dataset_paths() - logger.debug(f"image filenames : {filenames}") if filenames: + logger.info("Images loaded :") + for f in filenames: + logger.info(f"{str(Path(f).name)}") self.images_filepaths = [str(path) for path in sorted(filenames)] path = str(Path(filenames[0]).parent) self.image_filewidget.text_field.setText(path) self.image_filewidget.check_ready() self._update_default_paths(path) - def load_validation_images_dataset(self): + def load_unsup_images_dataset(self): """Show file dialog to set :py:attr:`~val_images_filepaths`""" filenames = self.load_dataset_paths() - logger.debug(f"val filenames : {filenames}") if filenames: + logger.info("Images loaded (unsupervised training) :") + for f in filenames: + logger.info(f"{str(Path(f).name)}") self.validation_filepaths = [ str(path) for path in sorted(filenames) ] @@ -445,8 +449,10 @@ def load_validation_images_dataset(self): def load_label_dataset(self): """Show file dialog to set :py:attr:`~labels_filepaths`""" filenames = self.load_dataset_paths() - logger.debug(f"labels filenames : {filenames}") if filenames: + logger.info("Labels loaded :") + for f in filenames: + logger.info(f"{str(Path(f).name)}") self.labels_filepaths = [str(path) for path in sorted(filenames)] path = str(Path(filenames[0]).parent) self.labels_filewidget.text_field.setText(path) @@ -477,13 +483,13 @@ def extract_dataset_paths(paths): return None return str(Path(paths[0]).parent) - def _check_all_filepaths(self): self.image_filewidget.check_ready() self.labels_filewidget.check_ready() self.results_filewidget.check_ready() self.unsupervised_images_filewidget.check_ready() + class BasePluginUtils(BasePluginFolder): """Small subclass used to have centralized widgets layer and result path selection in utilities""" @@ -516,4 +522,3 @@ def _update_default_paths(self, path=None): logger.debug(f"Trying to update default with {default_path}") if default_path is not None: self.utils_default_paths.append(default_path) - diff --git a/napari_cellseg3d/interface.py b/napari_cellseg3d/interface.py index 3c6699c4..fa7dedbf 100644 --- a/napari_cellseg3d/interface.py +++ b/napari_cellseg3d/interface.py @@ -1258,7 +1258,7 @@ def open_folder_dialog( ): default_path = utils.parse_default_path(possible_paths) - logger.info(f"Default : {default_path}") + logger.debug(f"Default : {default_path}") return QFileDialog.getExistingDirectory( widget, "Open directory", default_path # + "/.." ) From cdc7dde35f995d62a478f7452a3221bb9e46184f Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 21 Sep 2023 16:41:13 +0200 Subject: [PATCH 235/250] Fix unsup image loading when not validating --- .../code_plugins/plugin_model_training.py | 23 ++++++++++++------- 1 file changed, 15 insertions(+), 8 deletions(-) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 4f980b8a..53ae5ce4 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -1,4 +1,5 @@ import shutil +import warnings from functools import partial from pathlib import Path from typing import TYPE_CHECKING, List @@ -431,13 +432,18 @@ def check_ready(self): * False and displays a warning if not """ - if ( - self.images_filepaths == [] - or self.labels_filepaths == [] - or len(self.images_filepaths) != len(self.labels_filepaths) - ): - logger.warning("Image and label paths are not correctly set") - return False + if not self.unsupervised_mode: + if ( + self.images_filepaths == [] + or self.labels_filepaths == [] + or len(self.images_filepaths) != len(self.labels_filepaths) + ): + logger.warning("Image and label paths are not correctly set") + return False + else: + if self.get_unsupervised_image_filepaths() == []: + logger.warning("Image paths are not correctly set") + return False return True def _toggle_unsupervised_mode(self, enabled=False): @@ -940,8 +946,9 @@ def start(self): if not self.check_ready(): # issues a warning if not ready err = "Aborting, please set all required paths" - self.log.print_and_log(err) + # self.log.print_and_log(err) logger.warning(err) + warnings.warn(err, stacklevel=1) return if self.worker is not None: From 4bb359fb655a96c910512984230de508306fdf09 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 21 Sep 2023 15:46:19 +0200 Subject: [PATCH 236/250] Reverted include_background=True in Dice --- napari_cellseg3d/code_models/worker_training.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index d90b1304..247f2f98 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -159,7 +159,7 @@ def __init__( self.config = worker_config self.dice_metric = DiceMetric( - include_background=True, reduction="mean", get_not_nans=False + include_background=False, reduction="mean", get_not_nans=False ) self.normalize_function = utils.remap_image self.start_time = time.time() @@ -1342,7 +1342,7 @@ def get_patch_loader_func(num_samples): else provided_scheduler ) dice_metric = DiceMetric( - include_background=True, reduction="mean", ignore_empty=False + include_background=False, reduction="mean", ignore_empty=False ) best_metric = -1 From 3bf0585cf1e172329d286260a23f2f2674cd8aac Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 21 Sep 2023 16:37:22 +0200 Subject: [PATCH 237/250] Reintroduced best Dice channel seeking + refacto --- .../code_models/worker_training.py | 38 ++++++------------- napari_cellseg3d/utils.py | 23 +++++++++++ 2 files changed, 34 insertions(+), 27 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 247f2f98..95ea6aec 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -810,33 +810,17 @@ def eval(self, model, epoch) -> TrainingReport: f"Val decoder outputs shape: {val_decoder_outputs.shape}" ) - # dices = [] - # Find in which channel the labels are (avoid background) - # for channel in range(val_outputs.shape[1]): - # dices.append( - # utils.dice_coeff( - # y_pred=val_outputs[ - # 0, channel : (channel + 1), :, :, : - # ], - # y_true=val_labels[0], - # ) - # ) - # logger.debug(f"DICE COEFF: {dices}") - # max_dice_channel = torch.argmax( - # torch.Tensor(dices) - # ) - # logger.debug( - # f"MAX DICE CHANNEL: {max_dice_channel}" - # ) + max_dice_channel = utils.seek_best_dice_coeff_channel( + y_pred=val_outputs, y_true=val_labels + ) self.dice_metric( - y_pred=val_outputs, - # [ - # :, - # max_dice_channel : (max_dice_channel + 1), - # :, - # :, - # :, - # ], + y_pred=val_outputs[ + :, + max_dice_channel : (max_dice_channel + 1), + :, + :, + :, + ], y=val_labels, ) @@ -1315,7 +1299,7 @@ def get_patch_loader_func(num_samples): batch_size=self.config.batch_size, num_workers=self.config.num_workers, ) - logger.info("\nDone") + logger.debug("\nDone") logger.debug("Optimizer") optimizer = ( diff --git a/napari_cellseg3d/utils.py b/napari_cellseg3d/utils.py index 15eae20c..dabcda1f 100644 --- a/napari_cellseg3d/utils.py +++ b/napari_cellseg3d/utils.py @@ -229,6 +229,29 @@ def dice_coeff( ) +def seek_best_dice_coeff_channel(y_pred, y_true) -> torch.Tensor: + """Compute Dice-Sorensen coefficient between unsupervised model output and ground truth labels; + returns the channel with the highest dice coefficient. + Args: + y_true: Ground truth label + y_pred: Prediction label + Returns: best Dice coefficient channel + """ + dices = [] + # Find in which channel the labels are (to avoid background) + for channel in range(y_pred.shape[1]): + dices.append( + dice_coeff( + y_pred=y_pred[0, channel : (channel + 1), :, :, :], + y_true=y_true[0], + ) + ) + LOGGER.debug(f"DICE COEFF: {dices}") + max_dice_channel = torch.argmax(torch.Tensor(dices)) + LOGGER.debug(f"MAX DICE CHANNEL: {max_dice_channel}") + return max_dice_channel + + def correct_rotation(image): """Rotates the exes 0 and 2 in [DHW] section of image array""" extra_dims = len(image.shape) - 3 From 4cdb179cd477113be560bb51f7244206db4743a3 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 21 Sep 2023 16:38:47 +0200 Subject: [PATCH 238/250] Improve filepath messages --- .../code_models/model_framework.py | 14 ++++++++++---- napari_cellseg3d/code_plugins/plugin_base.py | 19 ++++++++++++------- napari_cellseg3d/interface.py | 2 +- 3 files changed, 23 insertions(+), 12 deletions(-) diff --git a/napari_cellseg3d/code_models/model_framework.py b/napari_cellseg3d/code_models/model_framework.py index 9bcd67a6..d4e7af06 100644 --- a/napari_cellseg3d/code_models/model_framework.py +++ b/napari_cellseg3d/code_models/model_framework.py @@ -245,16 +245,22 @@ def _toggle_weights_path(self): self.custom_weights_choice, self.weights_filewidget ) - def create_dataset_dict_no_labs(self): - """Creates unsupervised data dictionary for MONAI transforms and training.""" + def get_unsupervised_image_filepaths(self): volume_directory = Path( self.unsupervised_images_filewidget.text_field.text() ) + if not volume_directory.exists(): raise ValueError(f"Data folder {volume_directory} does not exist") - images_filepaths = sorted(Path.glob(volume_directory, "*.tif")) + return sorted(Path.glob(volume_directory, "*.tif")) + + def create_dataset_dict_no_labs(self): + """Creates unsupervised data dictionary for MONAI transforms and training.""" + images_filepaths = self.get_unsupervised_image_filepaths() if len(images_filepaths) == 0: - raise ValueError(f"Data folder {volume_directory} is empty") + raise ValueError( + f"Data folder {self.unsupervised_images_filewidget.text_field.text()} is empty" + ) logger.info("Images :") for file in images_filepaths: diff --git a/napari_cellseg3d/code_plugins/plugin_base.py b/napari_cellseg3d/code_plugins/plugin_base.py index 2fbbe8d3..90c61adf 100644 --- a/napari_cellseg3d/code_plugins/plugin_base.py +++ b/napari_cellseg3d/code_plugins/plugin_base.py @@ -389,7 +389,7 @@ def __init__( # Validation images widget self.unsupervised_images_filewidget = ui.FilePathWidget( description="Training directory", - file_function=self.load_validation_images_dataset, + file_function=self.load_unsup_images_dataset, parent=self, ) self.unsupervised_images_filewidget.setVisible(False) @@ -421,19 +421,23 @@ def load_dataset_paths(self): def load_image_dataset(self): """Show file dialog to set :py:attr:`~images_filepaths`""" filenames = self.load_dataset_paths() - logger.debug(f"image filenames : {filenames}") if filenames: + logger.info("Images loaded :") + for f in filenames: + logger.info(f"{str(Path(f).name)}") self.images_filepaths = [str(path) for path in sorted(filenames)] path = str(Path(filenames[0]).parent) self.image_filewidget.text_field.setText(path) self.image_filewidget.check_ready() self._update_default_paths(path) - def load_validation_images_dataset(self): + def load_unsup_images_dataset(self): """Show file dialog to set :py:attr:`~val_images_filepaths`""" filenames = self.load_dataset_paths() - logger.debug(f"val filenames : {filenames}") if filenames: + logger.info("Images loaded (unsupervised training) :") + for f in filenames: + logger.info(f"{str(Path(f).name)}") self.validation_filepaths = [ str(path) for path in sorted(filenames) ] @@ -445,8 +449,10 @@ def load_validation_images_dataset(self): def load_label_dataset(self): """Show file dialog to set :py:attr:`~labels_filepaths`""" filenames = self.load_dataset_paths() - logger.debug(f"labels filenames : {filenames}") if filenames: + logger.info("Labels loaded :") + for f in filenames: + logger.info(f"{str(Path(f).name)}") self.labels_filepaths = [str(path) for path in sorted(filenames)] path = str(Path(filenames[0]).parent) self.labels_filewidget.text_field.setText(path) @@ -477,13 +483,13 @@ def extract_dataset_paths(paths): return None return str(Path(paths[0]).parent) - def _check_all_filepaths(self): self.image_filewidget.check_ready() self.labels_filewidget.check_ready() self.results_filewidget.check_ready() self.unsupervised_images_filewidget.check_ready() + class BasePluginUtils(BasePluginFolder): """Small subclass used to have centralized widgets layer and result path selection in utilities""" @@ -516,4 +522,3 @@ def _update_default_paths(self, path=None): logger.debug(f"Trying to update default with {default_path}") if default_path is not None: self.utils_default_paths.append(default_path) - diff --git a/napari_cellseg3d/interface.py b/napari_cellseg3d/interface.py index c3ecd50f..91e8da44 100644 --- a/napari_cellseg3d/interface.py +++ b/napari_cellseg3d/interface.py @@ -1258,7 +1258,7 @@ def open_folder_dialog( ): default_path = utils.parse_default_path(possible_paths) - logger.info(f"Default : {default_path}") + logger.debug(f"Default : {default_path}") return QFileDialog.getExistingDirectory( widget, "Open directory", default_path # + "/.." ) From 202b49ee5e14afdc32538d3b6f59e4de6b2af3c4 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 21 Sep 2023 16:41:13 +0200 Subject: [PATCH 239/250] Fix unsup image loading when not validating --- .../code_plugins/plugin_model_training.py | 23 ++++++++++++------- 1 file changed, 15 insertions(+), 8 deletions(-) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index f92f5dea..91192e10 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -1,4 +1,5 @@ import shutil +import warnings from functools import partial from pathlib import Path from typing import TYPE_CHECKING, List @@ -433,13 +434,18 @@ def check_ready(self): * False and displays a warning if not """ - if ( - self.images_filepaths == [] - or self.labels_filepaths == [] - or len(self.images_filepaths) != len(self.labels_filepaths) - ): - logger.warning("Image and label paths are not correctly set") - return False + if not self.unsupervised_mode: + if ( + self.images_filepaths == [] + or self.labels_filepaths == [] + or len(self.images_filepaths) != len(self.labels_filepaths) + ): + logger.warning("Image and label paths are not correctly set") + return False + else: + if self.get_unsupervised_image_filepaths() == []: + logger.warning("Image paths are not correctly set") + return False return True def _toggle_unsupervised_mode(self, enabled=False): @@ -946,8 +952,9 @@ def start(self): if not self.check_ready(): # issues a warning if not ready err = "Aborting, please set all required paths" - self.log.print_and_log(err) + # self.log.print_and_log(err) logger.warning(err) + warnings.warn(err, stacklevel=1) return if self.worker is not None: From d79646ae26415f9887d9cad090c95bf8ceef24c5 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 21 Sep 2023 15:41:36 +0200 Subject: [PATCH 240/250] Fixed deletion of Qt imports in interface --- napari_cellseg3d/interface.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/napari_cellseg3d/interface.py b/napari_cellseg3d/interface.py index 91e8da44..fa7dedbf 100644 --- a/napari_cellseg3d/interface.py +++ b/napari_cellseg3d/interface.py @@ -7,7 +7,7 @@ # Qt # from qtpy.QtCore import QtWarningMsg from qtpy import QtCore -from qtpy.QtCore import QObject, Q +from qtpy.QtCore import QObject, Qt, QtWarningMsg, QUrl from qtpy.QtGui import QCursor, QDesktopServices, QTextCursor from qtpy.QtWidgets import ( QAbstractSpinBox, From 328ef81ae9da5a615afd9b7dc39c35bf00d4f134 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 21 Sep 2023 17:00:19 +0200 Subject: [PATCH 241/250] Fix training tests --- napari_cellseg3d/_tests/test_training.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/napari_cellseg3d/_tests/test_training.py b/napari_cellseg3d/_tests/test_training.py index 2fe49a76..e764ff37 100644 --- a/napari_cellseg3d/_tests/test_training.py +++ b/napari_cellseg3d/_tests/test_training.py @@ -25,10 +25,12 @@ def test_supervised_training(make_napari_viewer_proxy): viewer = make_napari_viewer_proxy() widget = Trainer(viewer) widget.log = LogFixture() + widget.model_choice.setCurrentIndex(0) widget.images_filepath = [] widget.labels_filepaths = [] + assert not widget.unsupervised_mode assert not widget.check_ready() widget.images_filepaths = [im_path_str] From 3eaf653303d8e1f142c62befea34471262b07192 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 21 Sep 2023 17:10:48 +0200 Subject: [PATCH 242/250] Added auto-check for which channel has foreground Preventing background to be shown first when using WNet inference, for better UX --- .../code_plugins/plugin_model_inference.py | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/napari_cellseg3d/code_plugins/plugin_model_inference.py b/napari_cellseg3d/code_plugins/plugin_model_inference.py index 99230303..683bd1d6 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_inference.py +++ b/napari_cellseg3d/code_plugins/plugin_model_inference.py @@ -577,6 +577,24 @@ def _display_results(self, result: InferenceResult): name=f"pred_{image_id}_{model_name}", opacity=0.8, ) + + if ( + len(result.result.shape) == 4 + ): # seek channel that is most likely to be foreground + for i, channel in enumerate(result.result): + channel = channel.flatten() + above_thresh = np.where(channel > 0.5, 1, 0) + fraction = np.count_nonzero(above_thresh) / np.count_nonzero( + channel + ) + logger.debug(f"channel {i} fraction : {fraction}") + if fraction < 0.5: + logger.debug("channel might be foreground") + viewer.dims.set_point( + 0, i + ) # TODO(cyril: check if this is always the right axis + break + if result.crf_results is not None: logger.debug(f"CRF results shape : {result.crf_results.shape}") viewer.add_image( From 3f989b51d0e869368949507cea63fab5f8f63bef Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 21 Sep 2023 17:21:30 +0200 Subject: [PATCH 243/250] Fixed eval_batch_size assignment --- napari_cellseg3d/code_plugins/plugin_model_training.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 91192e10..766c2c89 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -1165,6 +1165,12 @@ def _set_unsupervised_worker_config( A worker config """ batch_size = self.batch_choice.slider_value + if eval_volume_dict is None: + eval_batch_size = 1 + else: + eval_batch_size = ( + 1 if len(eval_volume_dict) < batch_size else batch_size + ) self.worker_config = config.WNetTrainingWorkerConfig( device=self.check_device_choice(), weights_info=self.weights_config, @@ -1186,9 +1192,7 @@ def _set_unsupervised_worker_config( n_cuts_weight=self.wnet_widgets.ncuts_weight_choice.value(), rec_loss_weight=self.wnet_widgets.get_reconstruction_weight(), eval_volume_dict=eval_volume_dict, - eval_batch_size=1 - if len(eval_volume_dict) < batch_size - else batch_size, + eval_batch_size=eval_batch_size, ) return self.worker_config From fc7735d6cc5e9fdb2e7b140ad42180b08e67459d Mon Sep 17 00:00:00 2001 From: C-Achard Date: Thu, 21 Sep 2023 17:00:19 +0200 Subject: [PATCH 244/250] Fix training tests --- napari_cellseg3d/_tests/test_training.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/napari_cellseg3d/_tests/test_training.py b/napari_cellseg3d/_tests/test_training.py index 2fe49a76..e764ff37 100644 --- a/napari_cellseg3d/_tests/test_training.py +++ b/napari_cellseg3d/_tests/test_training.py @@ -25,10 +25,12 @@ def test_supervised_training(make_napari_viewer_proxy): viewer = make_napari_viewer_proxy() widget = Trainer(viewer) widget.log = LogFixture() + widget.model_choice.setCurrentIndex(0) widget.images_filepath = [] widget.labels_filepaths = [] + assert not widget.unsupervised_mode assert not widget.check_ready() widget.images_filepaths = [im_path_str] From ec337f70b0b0de3f31870b9d385bcc610925525e Mon Sep 17 00:00:00 2001 From: C-Achard Date: Fri, 22 Sep 2023 14:49:16 +0200 Subject: [PATCH 245/250] Experimental auto-discard --- .../code_models/worker_inference.py | 32 +++++++++++++++ .../code_plugins/plugin_model_inference.py | 20 ++++------ napari_cellseg3d/utils.py | 40 +++++++++++++++++++ 3 files changed, 79 insertions(+), 13 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 8dcd084b..7eebed8e 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -40,6 +40,10 @@ ) logger = utils.LOGGER +# experimental code to auto-remove erroneously over-labeled empty regions from instance segmentation +EXPERIMENTAL_AUTO_DISCARD_EMPTY_REGIONS = True +EXPERIMENTAL_AUTO_DISCARD_THRESHOLD = 0.01 +EXPERIMENTAL_AUTO_DISCARD_VALUE = 0.01 """ Writing something to log messages from outside the main thread needs specific care, @@ -314,6 +318,34 @@ def model_output( def model_output_wrapper(inputs): # inputs = normalization(inputs) result = model(inputs) + + ####################### EXPERIMENTAL CODE + if EXPERIMENTAL_AUTO_DISCARD_EMPTY_REGIONS: + logger.debug("Checking for empty regions") + check_result = result.detach().cpu().numpy() + for i in range(check_result.shape[0]): + for j in range(check_result.shape[1]): + fraction_labeled = ( + utils.fraction_above_threshold( + check_result[i, j], + EXPERIMENTAL_AUTO_DISCARD_VALUE, + ) + ) + logger.debug( + f"Fraction labeled: {fraction_labeled}" + ) + if ( + fraction_labeled + > EXPERIMENTAL_AUTO_DISCARD_THRESHOLD + ): + logger.debug( + f"Discarding empty region with fraction {fraction_labeled}" + ) + result[i, j] = torch.zeros_like( + result[i, j] + ) + ########################################## + return post_process_transforms(result) model.eval() diff --git a/napari_cellseg3d/code_plugins/plugin_model_inference.py b/napari_cellseg3d/code_plugins/plugin_model_inference.py index 683bd1d6..cc3e6ede 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_inference.py +++ b/napari_cellseg3d/code_plugins/plugin_model_inference.py @@ -581,19 +581,13 @@ def _display_results(self, result: InferenceResult): if ( len(result.result.shape) == 4 ): # seek channel that is most likely to be foreground - for i, channel in enumerate(result.result): - channel = channel.flatten() - above_thresh = np.where(channel > 0.5, 1, 0) - fraction = np.count_nonzero(above_thresh) / np.count_nonzero( - channel - ) - logger.debug(f"channel {i} fraction : {fraction}") - if fraction < 0.5: - logger.debug("channel might be foreground") - viewer.dims.set_point( - 0, i - ) # TODO(cyril: check if this is always the right axis - break + fractions_per_channel = utils.channels_fraction_above_threshold( + result.result, 0.5 + ) + index_channel_least_labelled = np.argmin(fractions_per_channel) + viewer.dims.set_point( + 0, index_channel_least_labelled + ) # TODO(cyril: check if this is always the right axis if result.crf_results is not None: logger.debug(f"CRF results shape : {result.crf_results.shape}") diff --git a/napari_cellseg3d/utils.py b/napari_cellseg3d/utils.py index dabcda1f..dfd6866e 100644 --- a/napari_cellseg3d/utils.py +++ b/napari_cellseg3d/utils.py @@ -557,3 +557,43 @@ def quantile_normalization( image = where(image > high_quantile_value, high_quantile_value, image) image = where(image < low_quantile_value, low_quantile_value, image) return image.reshape(shape) + + +def channels_fraction_above_threshold(volume: np.array, threshold=0.5) -> list: + """Computes the fraction of pixels above a certain value in a 4D volume for each channel + + Args: + volume (np.ndarray): Array of shape (C, H, W, D) containing the input volume + threshold (float): Threshold value to use for the computation + + Returns: + list: List of length C containing the fraction of pixels above the threshold for each channel + """ + if volume.shape != 4: + raise ValueError( + f"Volume shape {volume.shape} is not 4D. Expecting CxHxWxD." + ) + fractions = [] + for _i, channel in enumerate(volume): + fractions.append( + fraction_above_threshold(channel, threshold=threshold) + ) + return fractions + + +def fraction_above_threshold(volume: np.array, threshold=0.5) -> float: + """ + Computes the fraction of pixels above a certain value in a volume + Args: + volume (np.ndarray): Array containing the input volume + threshold (float): Threshold value to use for the computation + + Returns: + float: Fraction of pixels above the threshold + """ + flattened = volume.flatten() + above_thresh = np.where(flattened > threshold, 1, 0) + LOGGER.debug( + f"non zero in above_thresh : {np.count_nonzero(above_thresh)}" + ) + return np.count_nonzero(above_thresh) / np.size(flattened) From c6c347a0de5dca989584414e6de390e97f7d2a11 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Fri, 22 Sep 2023 16:30:57 +0200 Subject: [PATCH 246/250] Improved auto-discard utility --- napari_cellseg3d/code_models/worker_inference.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index 7eebed8e..d903a798 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -42,8 +42,8 @@ logger = utils.LOGGER # experimental code to auto-remove erroneously over-labeled empty regions from instance segmentation EXPERIMENTAL_AUTO_DISCARD_EMPTY_REGIONS = True -EXPERIMENTAL_AUTO_DISCARD_THRESHOLD = 0.01 -EXPERIMENTAL_AUTO_DISCARD_VALUE = 0.01 +EXPERIMENTAL_AUTO_DISCARD_FRACTION_THRESHOLD = 0.9 +EXPERIMENTAL_AUTO_DISCARD_VALUE = 0.2 """ Writing something to log messages from outside the main thread needs specific care, @@ -321,6 +321,7 @@ def model_output_wrapper(inputs): ####################### EXPERIMENTAL CODE if EXPERIMENTAL_AUTO_DISCARD_EMPTY_REGIONS: + result = post_process_transforms(result) logger.debug("Checking for empty regions") check_result = result.detach().cpu().numpy() for i in range(check_result.shape[0]): @@ -336,7 +337,7 @@ def model_output_wrapper(inputs): ) if ( fraction_labeled - > EXPERIMENTAL_AUTO_DISCARD_THRESHOLD + > EXPERIMENTAL_AUTO_DISCARD_FRACTION_THRESHOLD ): logger.debug( f"Discarding empty region with fraction {fraction_labeled}" @@ -344,6 +345,7 @@ def model_output_wrapper(inputs): result[i, j] = torch.zeros_like( result[i, j] ) + return result ########################################## return post_process_transforms(result) From 7c3a696f5e9385c1ae71ad25d85a4148cbecfbef Mon Sep 17 00:00:00 2001 From: C-Achard Date: Fri, 22 Sep 2023 18:23:20 +0200 Subject: [PATCH 247/250] Create experimental sliding window Voronoi-Otsu for large volumes --- .../code_models/instance_segmentation.py | 20 ++++++- .../code_models/worker_inference.py | 2 +- .../dev_scripts/sliding_window_voronoi.py | 56 +++++++++++++++++++ 3 files changed, 74 insertions(+), 4 deletions(-) create mode 100644 napari_cellseg3d/dev_scripts/sliding_window_voronoi.py diff --git a/napari_cellseg3d/code_models/instance_segmentation.py b/napari_cellseg3d/code_models/instance_segmentation.py index 3ffbdbb9..018d385e 100644 --- a/napari_cellseg3d/code_models/instance_segmentation.py +++ b/napari_cellseg3d/code_models/instance_segmentation.py @@ -18,14 +18,16 @@ # from skimage.measure import marching_cubes # from skimage.measure import mesh_surface_area - - # from napari_cellseg3d.utils import sphericity_volume_area -# list of methods : +################ +# LIST OF INSTANCE SEGMENTATION METHODS WATERSHED = "Watershed" CONNECTED_COMP = "Connected Components" VORONOI_OTSU = "Voronoi-Otsu" +################ + +USE_EXPERIMENTAL_VORONOI_OTSU_WITH_SLIDING_WINDOW = False class InstanceMethod: @@ -178,6 +180,18 @@ def voronoi_otsu( Instance segmentation labels from Voronoi-Otsu method """ + if USE_EXPERIMENTAL_VORONOI_OTSU_WITH_SLIDING_WINDOW: + from napari_cellseg3d.dev_scripts.sliding_window_voronoi import ( + sliding_window_voronoi_otsu, + ) + + instance = sliding_window_voronoi_otsu( + volume, + spot_sigma=spot_sigma, + outline_sigma=outline_sigma, + patch_size=1024, + ) + return np.array(instance) # remove_small_size (float): remove all objects smaller than the specified size in pixels # semantic = np.squeeze(volume) logger.debug( diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index d903a798..f5d3e5a1 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -41,7 +41,7 @@ logger = utils.LOGGER # experimental code to auto-remove erroneously over-labeled empty regions from instance segmentation -EXPERIMENTAL_AUTO_DISCARD_EMPTY_REGIONS = True +EXPERIMENTAL_AUTO_DISCARD_EMPTY_REGIONS = False EXPERIMENTAL_AUTO_DISCARD_FRACTION_THRESHOLD = 0.9 EXPERIMENTAL_AUTO_DISCARD_VALUE = 0.2 diff --git a/napari_cellseg3d/dev_scripts/sliding_window_voronoi.py b/napari_cellseg3d/dev_scripts/sliding_window_voronoi.py new file mode 100644 index 00000000..f8ed4303 --- /dev/null +++ b/napari_cellseg3d/dev_scripts/sliding_window_voronoi.py @@ -0,0 +1,56 @@ +import numpy as np +import pyclesperanto_prototype as cle +from tqdm import tqdm + + +def sliding_window_voronoi_otsu(volume, spot_sigma, outline_sigma, patch_size): + """Given a volume of dimensions HxWxD, a spot_sigma and an outline_sigma, + perform Voronoi-Otsu segmentation on the volume using a sliding window of + size patch_size. If the edge has been reached, the patch size is reduced + to fit the remaining space. The result is a segmentation of the same size + as the input volume. + + Args: + volume (np.array): The volume to segment. + spot_sigma (float): The sigma for the spot detection. + outline_sigma (float): The sigma for the outline detection. + patch_size (int): The size of the sliding window. + """ + result = np.zeros(volume.shape, dtype=np.uint32) + max_label_id = 0 + x, y, z = volume.shape + for i in tqdm(range(0, x, patch_size)): + for j in range(0, y, patch_size): + for k in range(0, z, patch_size): + patch = volume[ + i : min(i + patch_size, x), + j : min(j + patch_size, y), + k : min(k + patch_size, z), + ] + patch_result = cle.voronoi_otsu_labeling( + patch, spot_sigma=spot_sigma, outline_sigma=outline_sigma + ) + patch_result = np.array(patch_result) + # make sure labels are unique, only where result is not 0 + patch_result[patch_result > 0] += max_label_id + result[ + i : min(i + patch_size, x), + j : min(j + patch_size, y), + k : min(k + patch_size, z), + ] = patch_result + max_label_id = np.max(patch_result) + return result + + +# if __name__ == "__main__": +# import napari +# +# rand_array = np.random.random((525, 621, 400)) +# rand_array = rand_array > 0.999 +# +# result = sliding_window_voronoi_otsu(rand_array, 0.1, 0.1, 128) +# +# viewer = napari.Viewer() +# viewer.add_image(rand_array) +# viewer.add_labels(result) +# napari.run() From be383df4a40d1387135ec4fe07a26201a442c6a1 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Tue, 26 Sep 2023 17:12:01 +0200 Subject: [PATCH 248/250] Disable resize error handler --- napari_cellseg3d/interface.py | 40 +++++++++++++++++------------------ 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/napari_cellseg3d/interface.py b/napari_cellseg3d/interface.py index fa7dedbf..6f832e23 100644 --- a/napari_cellseg3d/interface.py +++ b/napari_cellseg3d/interface.py @@ -7,7 +7,7 @@ # Qt # from qtpy.QtCore import QtWarningMsg from qtpy import QtCore -from qtpy.QtCore import QObject, Qt, QtWarningMsg, QUrl +from qtpy.QtCore import QObject, Qt, QUrl from qtpy.QtGui import QCursor, QDesktopServices, QTextCursor from qtpy.QtWidgets import ( QAbstractSpinBox, @@ -107,25 +107,25 @@ def __call__(cls, *args, **kwargs): def handle_adjust_errors(widget, warning_type, context, msg: str): """Qt message handler that attempts to react to errors when setting the window size and resizes the main window""" - # pass - head = msg.split(": ")[0] - if warning_type == QtWarningMsg and head == "QWindowsWindow::setGeometry": - logger.warning( - f"Qt resize error : {msg}\nhas been handled by attempting to resize the window" - ) - try: - if widget.parent() is not None: - state = int(widget.parent().parent().windowState()) - if state == 0: # normal state - widget.parent().parent().adjustSize() - logger.debug("Non-max. size adjust attempt") - logger.debug(f"{widget.parent().parent()}") - elif state == 2: # maximized state - widget.parent().parent().showNormal() - widget.parent().parent().showMaximized() - logger.debug("Maximized size adjust attempt") - except RuntimeError: - pass + pass + # head = msg.split(": ")[0] + # if warning_type == QtWarningMsg and head == "QWindowsWindow::setGeometry": + # logger.warning( + # f"Qt resize error : {msg}\nhas been handled by attempting to resize the window" + # ) + # try: + # if widget.parent() is not None: + # state = int(widget.parent().parent().windowState()) + # if state == 0: # normal state + # widget.parent().parent().adjustSize() + # logger.debug("Non-max. size adjust attempt") + # logger.debug(f"{widget.parent().parent()}") + # elif state == 2: # maximized state + # widget.parent().parent().showNormal() + # widget.parent().parent().showMaximized() + # logger.debug("Maximized size adjust attempt") + # except (RuntimeError, TypeError) as e: + # pass def handle_adjust_errors_wrapper(widget): From 8ae9a147d56c2f777b821a4a3d05d3ae05262a4c Mon Sep 17 00:00:00 2001 From: Cyril Achard <94955160+C-Achard@users.noreply.github.com> Date: Wed, 15 Nov 2023 12:44:03 +0100 Subject: [PATCH 249/250] Docs update & jupyter-books port (#50) * Moved docs to source folder * Updated to jupyter-books * Update GH Action to jupyter-book * Updated guides * Update reqs for jupyter-books - Fixed typo * Fix typo and file duplicate * Added auto API docs * Update welcome.rst * Update interface docstrings * Update ruff to enforce docstring style * Updated docstrings in interface/config * Add additional docstring rule exceptions * Update utils docstring * Update plugins.py * Update dev_scripts * Updated code_plugin folder docstrings * Update code_models docstrings * Update tests docstrings * Update docstring rules to enforce docs * Fix warnings when building docs * Restore docs images * Remove old API docs * Added module docstring for all public modules * Update pyproject.toml * Update folder desc * Update inference and training guides * Update inference_module_guide.rst * Guides updates * Change indexing, guides updates * Fix unindent warning * Update _toc.yml * Fixed model list inconsistencies * Update model list * Trying to fully use autosummary (see #51) * Docs update * Fix missing refactor in CRF * Update cropping_module_guide.rst * Updated docs ref for code * Figs, refs, utils guide update * Update images to figures where relevant * Updated refs to source code * Improved utils guide layout and contents * Review update + crop/utils * Review update * Small inference tweaks for pipeline * Config changes * Update worker_inference.py * Small instance seg refactor * Added properties for instance seg params * Utils cleanup * Small refactor of unclear train/val split terminology * Fix log parameters incorrectly printing def dir * Enable sliding window for all instance seg methods * Update instance_segmentation.py Refactor sliding window to not shadow builtin "function" * Fixed mistake in auto-check for WNet results * Fix for restarting train worker after force stop * Docs & images update * mv welcome image * Improved inference docs * Update inference_module_guide.rst * Attempt to fix M1 yaml * Config docstring update * Welcome page update * Docs update * Add WandB for supervised models * Added auto contrast reset in cropping * Added checks for instance labels in training * Check for custom weights before locking window inference * Improve review plots makers * Documentation update (Timokleia) (#53) * Update cropping_module_guide.rst changes in the cropping module guide * Update cropping_module_guide.rst add `` * Update inference_module_guide.rst additions to inference module * Update metrics_module_guide.rst adjusting the metrics module * Update review_module_guide.rst review module updates * Update training_module_guide.rst changes in the training module * Update training_wnet.rst additions in the wnet module * Update utils_module_guide.rst utilities updates * review parameters image * Update cropping_module_guide.rst emoji * Update metrics_module_guide.rst emoji * Update training_module_guide.rst emoji * Update utils_module_guide.rst emoji * Update cropping_module_guide.rst mild style changes * Update review_module_guide.rst adjusting image code * Update welcome.rst changes to welcome * Add files via upload * Review and training tweaks - Fixed a few links, indents, typos - Replaced some emojis and added new ones for other pages * Welcome and inference tweaks - Fixed minor formatting, tables, broken links issues, etc * RM unused model wrappers attributes * Inference edits * Update WNet and inference * Images update * Update plots_train.png * Restore walkthrough * Update detailed_walkthrough.rst * Update detailed_walkthrough.rst editions * Minor edits --------- Co-authored-by: C-Achard * Added floats in small remove * Try to fix issue with float/long * Add except handling for layer removal in train * Add first version of dedicated install page - Add install page - Refacto/clean up welcome page - Add more useful plugins - Update TODO * Improve training guide * Move M1 install * Added label stats utils * Added label stats doc * Added check for dtype in stats labels * Update utils_module_guide.rst * Experimental classifier to reject bad preds on empty * Experimental classifier to reject bad preds on empty * Update classifier_test.ipynb * WIP Trying to fix some QoL issues - Better exception handling in inference - Swapaxes at mode output - Remove small in Voronoi-Otsu * Fix exception handling * Locating csv issue * Enable ruff again * Handle empty labels in csv stats * Fix call to get_dict when stats is None * Ensure consistent rotation * Fix rotation checks + auto-channel select * Fix test for review * Update installation_guide.rst (#54) refining * Minor edits to install guide * [WIP] GUI-less training example script (#52) * Add remote train scripts * mv dockerfile * Paths for remote train * Change device * Update remote_training.py * Change results path to include more info * Update colab_training.py * Delete Dockerfile.cellseg3d * Make all wandb inits point to same project * Remove deprecated call to AddChannel * Fix tests issue with MONAI 1.3.0 * Create whole_brain_utils.py Add segmentation correction scripts * Update whole_brain_utils.py * Update whole_brain_utils.py * Update whole_brain_utils.py * Update whole_brain_utils.py * Update _config.yml --------- Co-authored-by: Timokleia <86098649+Timokleia@users.noreply.github.com> Co-authored-by: Mackenzie Mathis --- .github/workflows/build_docs.yml | 35 +- .gitignore | 8 +- .pre-commit-config.yaml | 1 + conda/napari_cellseg3d_m1.yml | 2 +- docs/Makefile | 196 - docs/TODO.md | 8 + docs/_config.yml | 51 + docs/_templates/custom-class-template.rst | 32 + docs/_templates/custom-module-template.rst | 66 + docs/_toc.yml | 23 + docs/conf.py | 306 -- docs/index.rst | 57 - docs/make.bat | 263 -- docs/references.bib | 56 + docs/res/code/instance_segmentation.rst | 49 - docs/res/code/interface.rst | 107 - docs/res/code/model_framework.rst | 23 - docs/res/code/plugin_base.rst | 38 - docs/res/code/plugin_convert.rst | 30 - docs/res/code/plugin_crop.rst | 25 - docs/res/code/plugin_metrics.rst | 22 - docs/res/code/plugin_model_inference.rst | 24 - docs/res/code/plugin_model_training.rst | 22 - docs/res/code/plugin_review.rst | 26 - docs/res/code/plugin_review_dock.rst | 21 - docs/res/code/utils.rst | 60 - docs/res/code/workers.rst | 70 - docs/res/guides/cropping_module_guide.rst | 66 - docs/res/guides/detailed_walkthrough.rst | 293 -- docs/res/guides/inference_module_guide.rst | 146 - docs/res/guides/metrics_module_guide.rst | 49 - docs/res/guides/review_module_guide.rst | 63 - docs/res/guides/training_module_guide.rst | 136 - docs/res/guides/training_wnet.rst | 60 - docs/res/guides/utils_module_guide.rst | 46 - docs/res/images/WNet_architecture.svg | 817 ---- docs/res/images/cropping_process_example.png | Bin 2870505 -> 0 bytes docs/res/images/inference_plugin_layout.png | Bin 54942 -> 0 bytes docs/res/images/plots_train.png | Bin 407334 -> 0 bytes docs/res/images/review_process_example.png | Bin 1436026 -> 0 bytes docs/res/welcome.rst | 123 - docs/source/code/api.rst | 11 + docs/source/guides/cropping_module_guide.rst | 83 + .../guides/custom_model_template.rst | 5 +- docs/source/guides/detailed_walkthrough.rst | 271 ++ docs/source/guides/inference_module_guide.rst | 170 + docs/source/guides/installation_guide.rst | 111 + docs/source/guides/metrics_module_guide.rst | 40 + docs/source/guides/review_module_guide.rst | 76 + docs/source/guides/training_module_guide.rst | 243 ++ docs/source/guides/training_wnet.rst | 65 + docs/source/guides/utils_module_guide.rst | 97 + docs/source/images/Review_Parameters.png | Bin 0 -> 110046 bytes docs/source/images/WNet_architecture.svg | 3607 +++++++++++++++++ .../images/converted_labels.png | Bin .../images/cropping_process_example.png | Bin 0 -> 716456 bytes .../source/images/inference_plugin_layout.png | Bin 0 -> 21733 bytes .../images/inference_results_example.png | Bin .../images/init_image_labels.png | Bin .../images/plot_example_metrics.png | Bin docs/source/images/plots_train.png | Bin 0 -> 186916 bytes docs/source/images/plugin_crop.png | Bin 0 -> 1163775 bytes docs/source/images/plugin_inference.png | Bin 0 -> 1111335 bytes docs/source/images/plugin_menu.png | Bin 0 -> 206179 bytes docs/source/images/plugin_review.png | Bin 0 -> 294587 bytes docs/source/images/plugin_train.png | Bin 0 -> 490814 bytes docs/source/images/plugin_welcome.png | Bin 0 -> 1399657 bytes docs/source/images/review_process_example.png | Bin 0 -> 764245 bytes docs/{res => source}/images/stat_plots.png | Bin docs/source/images/training_tab_1.png | Bin 0 -> 4665 bytes docs/source/images/training_tab_2.png | Bin 0 -> 11256 bytes docs/source/images/training_tab_3.png | Bin 0 -> 12982 bytes docs/source/images/training_tab_4.png | Bin 0 -> 11952 bytes docs/{res => source}/logo/logo_alpha.png | Bin docs/welcome.rst | 179 + napari_cellseg3d/_tests/fixtures.py | 24 +- napari_cellseg3d/_tests/test_utils.py | 1 + napari_cellseg3d/code_models/__init__.py | 11 + napari_cellseg3d/code_models/crf.py | 38 +- .../code_models/instance_segmentation.py | 401 +- .../code_models/model_framework.py | 38 +- .../code_models/models/TEMPLATE_model.py | 7 +- .../code_models/models/__init__.py | 1 + .../code_models/models/model_SegResNet.py | 13 +- .../code_models/models/model_SwinUNetR.py | 13 +- .../code_models/models/model_TRAILMAP.py | 2 +- .../code_models/models/model_TRAILMAP_MS.py | 11 +- .../code_models/models/model_VNet.py | 11 +- .../code_models/models/model_WNet.py | 18 +- .../code_models/models/model_test.py | 6 +- .../code_models/models/pretrained/__init__.py | 5 + .../code_models/models/unet/__init__.py | 4 + .../code_models/models/wnet/__init__.py | 1 + .../code_models/models/wnet/model.py | 30 +- .../code_models/models/wnet/soft_Ncuts.py | 13 +- .../code_models/worker_inference.py | 212 +- .../code_models/worker_training.py | 156 +- napari_cellseg3d/code_models/workers_utils.py | 55 +- napari_cellseg3d/code_plugins/__init__.py | 1 + napari_cellseg3d/code_plugins/plugin_base.py | 65 +- .../code_plugins/plugin_convert.py | 154 +- napari_cellseg3d/code_plugins/plugin_crf.py | 19 +- napari_cellseg3d/code_plugins/plugin_crop.py | 28 +- .../code_plugins/plugin_helper.py | 4 + .../code_plugins/plugin_metrics.py | 15 +- .../code_plugins/plugin_model_inference.py | 144 +- .../code_plugins/plugin_model_training.py | 101 +- .../code_plugins/plugin_review.py | 54 +- .../code_plugins/plugin_review_dock.py | 103 +- .../code_plugins/plugin_utilities.py | 7 + napari_cellseg3d/config.py | 182 +- .../dev_scripts/artefact_labeling.py | 25 +- .../dev_scripts/classifier_test.ipynb | 587 +++ .../dev_scripts/colab_training.py | 34 +- .../dev_scripts/correct_labels.py | 36 +- .../dev_scripts/evaluate_labels.py | 234 +- .../dev_scripts/remote_training.py | 138 + napari_cellseg3d/dev_scripts/thread_test.py | 7 +- .../dev_scripts/whole_brain_utils.py | 50 + napari_cellseg3d/interface.py | 333 +- napari_cellseg3d/plugins.py | 4 + napari_cellseg3d/utils.py | 125 +- pyproject.toml | 11 +- requirements.txt | 4 +- 124 files changed, 7739 insertions(+), 4175 deletions(-) delete mode 100644 docs/Makefile create mode 100644 docs/TODO.md create mode 100644 docs/_config.yml create mode 100644 docs/_templates/custom-class-template.rst create mode 100644 docs/_templates/custom-module-template.rst create mode 100644 docs/_toc.yml delete mode 100644 docs/conf.py delete mode 100644 docs/index.rst delete mode 100644 docs/make.bat create mode 100644 docs/references.bib delete mode 100644 docs/res/code/instance_segmentation.rst delete mode 100644 docs/res/code/interface.rst delete mode 100644 docs/res/code/model_framework.rst delete mode 100644 docs/res/code/plugin_base.rst delete mode 100644 docs/res/code/plugin_convert.rst delete mode 100644 docs/res/code/plugin_crop.rst delete mode 100644 docs/res/code/plugin_metrics.rst delete mode 100644 docs/res/code/plugin_model_inference.rst delete mode 100644 docs/res/code/plugin_model_training.rst delete mode 100644 docs/res/code/plugin_review.rst delete mode 100644 docs/res/code/plugin_review_dock.rst delete mode 100644 docs/res/code/utils.rst delete mode 100644 docs/res/code/workers.rst delete mode 100644 docs/res/guides/cropping_module_guide.rst delete mode 100644 docs/res/guides/detailed_walkthrough.rst delete mode 100644 docs/res/guides/inference_module_guide.rst delete mode 100644 docs/res/guides/metrics_module_guide.rst delete mode 100644 docs/res/guides/review_module_guide.rst delete mode 100644 docs/res/guides/training_module_guide.rst delete mode 100644 docs/res/guides/training_wnet.rst delete mode 100644 docs/res/guides/utils_module_guide.rst delete mode 100644 docs/res/images/WNet_architecture.svg delete mode 100644 docs/res/images/cropping_process_example.png delete mode 100644 docs/res/images/inference_plugin_layout.png delete mode 100644 docs/res/images/plots_train.png delete mode 100644 docs/res/images/review_process_example.png delete mode 100644 docs/res/welcome.rst create mode 100644 docs/source/code/api.rst create mode 100644 docs/source/guides/cropping_module_guide.rst rename docs/{res => source}/guides/custom_model_template.rst (81%) create mode 100644 docs/source/guides/detailed_walkthrough.rst create mode 100644 docs/source/guides/inference_module_guide.rst create mode 100644 docs/source/guides/installation_guide.rst create mode 100644 docs/source/guides/metrics_module_guide.rst create mode 100644 docs/source/guides/review_module_guide.rst create mode 100644 docs/source/guides/training_module_guide.rst create mode 100644 docs/source/guides/training_wnet.rst create mode 100644 docs/source/guides/utils_module_guide.rst create mode 100644 docs/source/images/Review_Parameters.png create mode 100644 docs/source/images/WNet_architecture.svg rename docs/{res => source}/images/converted_labels.png (100%) create mode 100644 docs/source/images/cropping_process_example.png create mode 100644 docs/source/images/inference_plugin_layout.png rename docs/{res => source}/images/inference_results_example.png (100%) rename docs/{res => source}/images/init_image_labels.png (100%) rename docs/{res => source}/images/plot_example_metrics.png (100%) create mode 100644 docs/source/images/plots_train.png create mode 100644 docs/source/images/plugin_crop.png create mode 100644 docs/source/images/plugin_inference.png create mode 100644 docs/source/images/plugin_menu.png create mode 100644 docs/source/images/plugin_review.png create mode 100644 docs/source/images/plugin_train.png create mode 100644 docs/source/images/plugin_welcome.png create mode 100644 docs/source/images/review_process_example.png rename docs/{res => source}/images/stat_plots.png (100%) create mode 100644 docs/source/images/training_tab_1.png create mode 100644 docs/source/images/training_tab_2.png create mode 100644 docs/source/images/training_tab_3.png create mode 100644 docs/source/images/training_tab_4.png rename docs/{res => source}/logo/logo_alpha.png (100%) create mode 100644 docs/welcome.rst create mode 100644 napari_cellseg3d/dev_scripts/classifier_test.ipynb create mode 100644 napari_cellseg3d/dev_scripts/remote_training.py create mode 100644 napari_cellseg3d/dev_scripts/whole_brain_utils.py diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml index d650bc66..c764bf41 100644 --- a/.github/workflows/build_docs.yml +++ b/.github/workflows/build_docs.yml @@ -1,19 +1,38 @@ -name: Deploy Sphinx documentation to Pages +name: deploy on: push: branches: # branch to trigger deployment - main +# This job installs dependencies, build the book, and pushes it to `gh-pages` jobs: - pages: - runs-on: ubuntu-20.04 + build-and-deploy-book: + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [ubuntu-latest] + python-version: [3.8] steps: - - id: deployment - uses: sphinx-notes/pages@v3 + - uses: actions/checkout@v2 + + # Install dependencies + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v1 with: - publish: false - - uses: peaceiris/actions-gh-pages@v3 + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + pip install jupyter-book + + # Build the book + - name: Build the book + run: | + jupyter-book build docs/ + + # Deploy the book's HTML to gh-pages branch + - name: GitHub Pages action + uses: peaceiris/actions-gh-pages@v3.6.1 with: github_token: ${{ secrets.GITHUB_TOKEN }} - publish_dir: ${{ steps.deployment.outputs.artifact }} + publish_dir: docs/_build/html diff --git a/.gitignore b/.gitignore index b767cced..9e08da2e 100644 --- a/.gitignore +++ b/.gitignore @@ -102,7 +102,8 @@ venv/ #dataset, weights, old logos, requirements /napari_cellseg3d/code_models/models/dataset/ /napari_cellseg3d/code_models/models/saved_weights/ -/docs/res/logo/old_logo/ +/docs/source/logo/old_logo/ +/docs/source/code/_autosummary/ /reqs/ /loss_plots/ notebooks/csv_cell_plot.html @@ -120,3 +121,8 @@ notebooks/instance_test.ipynb !napari_cellseg3d/_tests/res/wnet_test/lab/*.tif !napari_cellseg3d/_tests/res/wnet_test/vol/*.tif cov.syspath.txt + +#include docs images +!docs/source/logo/* +!docs/source/images/* +napari_cellseg3d/dev_scripts/wandb diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index f9fe2853..88f44ebe 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -7,6 +7,7 @@ repos: - id: trailing-whitespace - id: check-yaml - id: check-added-large-files + args: [--maxkb=5000] - id: check-toml # - repo: https://github.com/pycqa/isort # rev: 5.12.0 diff --git a/conda/napari_cellseg3d_m1.yml b/conda/napari_cellseg3d_m1.yml index 8209be9e..d901c429 100644 --- a/conda/napari_cellseg3d_m1.yml +++ b/conda/napari_cellseg3d_m1.yml @@ -16,7 +16,7 @@ dependencies: - tifffile>=2022.2.9 - imageio-ffmpeg>=0.4.5 - torch>=1.11 - - monai[nibabel,einops]>=0.9.0 + - monai>=0.9.0 - tqdm - nibabel - scikit-image diff --git a/docs/Makefile b/docs/Makefile deleted file mode 100644 index 5769afc3..00000000 --- a/docs/Makefile +++ /dev/null @@ -1,196 +0,0 @@ -# Makefile for Sphinx documentation -# - -# You can set these variables from the command line. -SPHINXOPTS = -SPHINXBUILD = sphinx-build -PAPER = -BUILDDIR = _build - -# User-friendly check for sphinx-build -ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1) -$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/) -endif - -# Internal variables. -PAPEROPT_a4 = -D latex_paper_size=a4 -PAPEROPT_letter = -D latex_paper_size=letter -ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . -# the i18n builder cannot share the environment and doctrees with the others -I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . - -.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest coverage gettext - -help: - @echo "Please use \`make ' where is one of" - @echo " html to make standalone HTML files" - @echo " dirhtml to make HTML files named index.html in directories" - @echo " singlehtml to make a single large HTML file" - @echo " pickle to make pickle files" - @echo " json to make JSON files" - @echo " htmlhelp to make HTML files and a HTML help project" - @echo " qthelp to make HTML files and a qthelp project" - @echo " applehelp to make an Apple Help Book" - @echo " devhelp to make HTML files and a Devhelp project" - @echo " epub to make an epub" - @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" - @echo " latexpdf to make LaTeX files and run them through pdflatex" - @echo " latexpdfja to make LaTeX files and run them through platex/dvipdfmx" - @echo " text to make text files" - @echo " man to make manual pages" - @echo " texinfo to make Texinfo files" - @echo " info to make Texinfo files and run them through makeinfo" - @echo " gettext to make PO message catalogs" - @echo " changes to make an overview of all changed/added/deprecated items" - @echo " xml to make Docutils-native XML files" - @echo " pseudoxml to make pseudoxml-XML files for display purposes" - @echo " linkcheck to check all external links for integrity" - @echo " doctest to run all doctests embedded in the documentation (if enabled)" - @echo " coverage to run coverage check of the documentation (if enabled)" - -clean: - rm -rf $(BUILDDIR)/* - -html: - $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html - @echo - @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." - -dirhtml: - $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml - @echo - @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml." - -singlehtml: - $(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml - @echo - @echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml." - -pickle: - $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle - @echo - @echo "Build finished; now you can process the pickle files." - -json: - $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json - @echo - @echo "Build finished; now you can process the JSON files." - -github: - @make html - @cp -a build/html/. ./docs - -htmlhelp: - $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp - @echo - @echo "Build finished; now you can run HTML Help Workshop with the" \ - ".hhp project file in $(BUILDDIR)/htmlhelp." - -qthelp: - $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp - @echo - @echo "Build finished; now you can run "qcollectiongenerator" with the" \ - ".qhcp project file in $(BUILDDIR)/qthelp, like this:" - @echo "# qcollectiongenerator $(BUILDDIR)/qthelp/napari-cookiecutterplugin_name.qhcp" - @echo "To view the help file:" - @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/napari-cookiecutterplugin_name.qhc" - -applehelp: - $(SPHINXBUILD) -b applehelp $(ALLSPHINXOPTS) $(BUILDDIR)/applehelp - @echo - @echo "Build finished. The help book is in $(BUILDDIR)/applehelp." - @echo "N.B. You won't be able to view it unless you put it in" \ - "~/Library/Documentation/Help or install it in your application" \ - "bundle." - -devhelp: - $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp - @echo - @echo "Build finished." - @echo "To view the help file:" - @echo "# mkdir -p $$HOME/.local/share/devhelp/napari-cookiecutterplugin_name" - @echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/napari-cookiecutterplugin_name" - @echo "# devhelp" - -epub: - $(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub - @echo - @echo "Build finished. The epub file is in $(BUILDDIR)/epub." - -latex: - $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex - @echo - @echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex." - @echo "Run \`make' in that directory to run these through (pdf)latex" \ - "(use \`make latexpdf' here to do that automatically)." - -latexpdf: - $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex - @echo "Running LaTeX files through pdflatex..." - $(MAKE) -C $(BUILDDIR)/latex all-pdf - @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." - -latexpdfja: - $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex - @echo "Running LaTeX files through platex and dvipdfmx..." - $(MAKE) -C $(BUILDDIR)/latex all-pdf-ja - @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." - -text: - $(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text - @echo - @echo "Build finished. The text files are in $(BUILDDIR)/text." - -man: - $(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man - @echo - @echo "Build finished. The manual pages are in $(BUILDDIR)/man." - -texinfo: - $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo - @echo - @echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo." - @echo "Run \`make' in that directory to run these through makeinfo" \ - "(use \`make info' here to do that automatically)." - -info: - $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo - @echo "Running Texinfo files through makeinfo..." - make -C $(BUILDDIR)/texinfo info - @echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo." - -gettext: - $(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale - @echo - @echo "Build finished. The message catalogs are in $(BUILDDIR)/locale." - -changes: - $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes - @echo - @echo "The overview file is in $(BUILDDIR)/changes." - -linkcheck: - $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck - @echo - @echo "Link check complete; look for any errors in the above output " \ - "or in $(BUILDDIR)/linkcheck/output.txt." - -doctest: - $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest - @echo "Testing of doctests in the sources finished, look at the " \ - "results in $(BUILDDIR)/doctest/output.txt." - -coverage: - $(SPHINXBUILD) -b coverage $(ALLSPHINXOPTS) $(BUILDDIR)/coverage - @echo "Testing of coverage in the sources finished, look at the " \ - "results in $(BUILDDIR)/coverage/python.txt." - -xml: - $(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml - @echo - @echo "Build finished. The XML files are in $(BUILDDIR)/xml." - -pseudoxml: - $(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml - @echo - @echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml." diff --git a/docs/TODO.md b/docs/TODO.md new file mode 100644 index 00000000..31473b5b --- /dev/null +++ b/docs/TODO.md @@ -0,0 +1,8 @@ +[//]: # ( +TODO: +- [ ] Add a way to get the current version of the library +- [x] Update all modules +- [x] Better WNet tutorial +- [x] Setup GH Actions +- [ ] Add a bibliography +) diff --git a/docs/_config.yml b/docs/_config.yml new file mode 100644 index 00000000..1dc49385 --- /dev/null +++ b/docs/_config.yml @@ -0,0 +1,51 @@ +# Book settings +# Learn more at https://jupyterbook.org/customize/config.html + +title: napari-cellseg3d Documentation +author: Cyril Achard, Maxime Vidal, Timokleia Kousi, Mackenzie Mathis | Mathis Laboratory +logo: source/logo/logo_alpha.png + +# Force re-execution of notebooks on each build. +# See https://jupyterbook.org/content/execute.html +execute: + execute_notebooks: force + +# Define the name of the latex output file for PDF builds +latex: + latex_documents: + targetname: book.tex + +# Add a bibtex file so that we can create citations +bibtex_bibfiles: + - references.bib + +# Information about where the book exists on the web +repository: + url: https://github.com/AdaptiveMotorControlLab/CellSeg3d # Online location of your book + path_to_book: docs # Optional path to your book, relative to the repository root + branch: main # Which branch of the repository should be used when creating links (optional) + +# Add GitHub buttons to your book +# See https://jupyterbook.org/customize/config.html#add-a-link-to-your-repository +html: + use_issues_button: true + use_repository_button: true + +# Add auto-generated API docs +sphinx: + extra_extensions: + - 'sphinx.ext.napoleon' + - 'sphinx.ext.autodoc' + - 'sphinx.ext.autosummary' + - 'sphinx.ext.viewcode' + - 'sphinx.ext.autosectionlabel' + config: + add_module_names: False + autosectionlabel_prefix_document: True + autosummary_generate: True + autoclass_content: "both" + # templates_path: ['_templates'] + exclude_patterns: + - '_build' + - '_templates' + # - 'napari_cellseg3d/__pycache__' diff --git a/docs/_templates/custom-class-template.rst b/docs/_templates/custom-class-template.rst new file mode 100644 index 00000000..cbe78b90 --- /dev/null +++ b/docs/_templates/custom-class-template.rst @@ -0,0 +1,32 @@ +{{ fullname | escape | underline}} + +.. currentmodule:: {{ module }} + +.. autoclass:: {{ objname }} + :members: + :show-inheritance: + + {% block methods %} + {% if methods %} + .. rubric:: {{ _('Methods') }} + + .. autosummary:: + :nosignatures: + {% for item in methods %} + {%- if not item.startswith('_') %} + ~{{ name }}.{{ item }} + {%- endif -%} + {%- endfor %} + {% endif %} + {% endblock %} + + {% block attributes %} + {% if attributes %} + .. rubric:: {{ _('Attributes') }} + + .. autosummary:: + {% for item in attributes %} + ~{{ name }}.{{ item }} + {%- endfor %} + {% endif %} + {% endblock %} diff --git a/docs/_templates/custom-module-template.rst b/docs/_templates/custom-module-template.rst new file mode 100644 index 00000000..d066d0e4 --- /dev/null +++ b/docs/_templates/custom-module-template.rst @@ -0,0 +1,66 @@ +{{ fullname | escape | underline}} + +.. automodule:: {{ fullname }} + + {% block attributes %} + {% if attributes %} + .. rubric:: Module attributes + + .. autosummary:: + :toctree: + {% for item in attributes %} + {{ item }} + {%- endfor %} + {% endif %} + {% endblock %} + + {% block functions %} + {% if functions %} + .. rubric:: {{ _('Functions') }} + + .. autosummary:: + :toctree: + :nosignatures: + {% for item in functions %} + {{ item }} + {%- endfor %} + {% endif %} + {% endblock %} + + {% block classes %} + {% if classes %} + .. rubric:: {{ _('Classes') }} + + .. autosummary:: + :toctree: + :template: custom-class-template.rst + :nosignatures: + {% for item in classes %} + {{ item }} + {%- endfor %} + {% endif %} + {% endblock %} + + {% block exceptions %} + {% if exceptions %} + .. rubric:: {{ _('Exceptions') }} + + .. autosummary:: + :toctree: + {% for item in exceptions %} + {{ item }} + {%- endfor %} + {% endif %} + {% endblock %} + +{% block modules %} +{% if modules %} +.. autosummary:: + :toctree: + :template: custom-module-template.rst + :recursive: +{% for item in modules %} + {{ item }} +{%- endfor %} +{% endif %} +{% endblock %} diff --git a/docs/_toc.yml b/docs/_toc.yml new file mode 100644 index 00000000..7b4e39fb --- /dev/null +++ b/docs/_toc.yml @@ -0,0 +1,23 @@ +# Table of contents + +format: jb-book +root: welcome.rst +parts: + - caption : User guides + chapters: + - file: source/guides/installation_guide.rst + - file: source/guides/review_module_guide.rst + - file: source/guides/training_module_guide.rst + - file: source/guides/inference_module_guide.rst + - file: source/guides/cropping_module_guide.rst + - file: source/guides/utils_module_guide.rst + - caption : Walkthroughs + chapters: + - file: source/guides/detailed_walkthrough.rst + - caption : Advanced guides + chapters: + - file: source/guides/training_wnet.rst + - file: source/guides/custom_model_template.rst + - caption : Code + chapters: + - file: source/code/api.rst diff --git a/docs/conf.py b/docs/conf.py deleted file mode 100644 index bc766d97..00000000 --- a/docs/conf.py +++ /dev/null @@ -1,306 +0,0 @@ -# -*- coding: utf-8 -*- -# -# napari-cellseg3d documentation build configuration file, created by -# sphinx-quickstart on Thu Oct 1 00:43:18 2015. -# -# This file is execfile()d with the current directory set to its -# containing dir. -# -# Note that not all possible configuration values are present in this -# autogenerated file. -# -# All configuration values have a default; values that are commented out -# serve to show the default. - -import os -import sys - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -sys.path.insert(0, os.path.abspath("../src")) - -# -- General configuration ------------------------------------------------ - -# If your documentation needs a minimal Sphinx version, state it here. -# needs_sphinx = '1.0' - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = [ - "sphinx.ext.ifconfig", - "sphinx.ext.autodoc", - "sphinx.ext.napoleon", - "sphinx_autodoc_typehints", - "sphinx.ext.duration", -] - -# Add any paths that contain templates here, relative to this directory. -templates_path = ["_templates"] - -# The suffix(es) of source filenames. -# You can specify multiple suffix as a list of string: -# source_suffix = ['.rst', '.md'] -source_suffix = ".rst" - -# The encoding of source files. -# source_encoding = 'utf-8-sig' - -# The main toctree document. -main_doc = "index" - -# General information about the project. -project = "napari-cellseg3d" -copyright = "2022-2023, Cyril Achard, Maxime Vidal" -author = "Cyril Achard, Maxime Vidal" - -# The version info for the project you're documenting, acts as replacement for -# |version| and |release|, also used in various other places throughout the -# built documents. -# -# The short X.Y version. -# import __version__ from your package... -# version = '' -# # The full version, including alpha/beta/rc tags. -# release = '' - -# The language for content autogenerated by Sphinx. Refer to documentation -# for a list of supported languages. -# -# This is also used if you do content translation via gettext catalogs. -# Usually you set "language" from the command line for these cases. -language = "en" - -# There are two options for replacing |today|: either, you set today to some -# non-false value, then it is used: -# today = '' -# Else, today_fmt is used as the format for a strftime call. -# today_fmt = '%B %d, %Y' - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -exclude_patterns = ["_build"] - -# The reST default role (used for this markup: `text`) to use for all -# documents. -# default_role = None - -# If true, '()' will be appended to :func: etc. cross-reference text. -# add_function_parentheses = True - -# If true, the current module name will be prepended to all description -# unit titles (such as .. function::). -# add_module_names = True - -# If true, sectionauthor and moduleauthor directives will be shown in the -# output. They are ignored by default. -# show_authors = False - -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = "sphinx" - -# A list of ignored prefixes for module index sorting. -# modindex_common_prefix = [] - -# If true, keep warnings as "system message" paragraphs in the built documents. -# keep_warnings = False - -# If true, `todo` and `todoList` produce output, else they produce nothing. -todo_include_todos = False - - -# -- Options for HTML output ---------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -html_theme = "sphinx_rtd_theme" - -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. -html_theme_options = { - "logo_only": True, - "display_version": False, -} - -# Add any paths that contain custom themes here, relative to this directory. -# html_theme_path = [] - -# The name for this set of Sphinx documents. If None, it defaults to -# " v documentation". -# html_title = None - -# A shorter title for the navigation bar. Default is the same as html_title. -# html_short_title = None - -# The name of an image file (relative to this directory) to place at the top -# of the sidebar. -html_logo = "res/logo/logo_alpha.png" - -# The name of an image file (within the static path) to use as favicon of the -# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 -# pixels large. -# html_favicon = None - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = [] - -# Add any extra paths that contain custom files (such as robots.txt or -# .htaccess) here, relative to this directory. These files are copied -# directly to the root of the documentation. -# html_extra_path = [] - -# If not '', a 'Last updated on:' timestamp is inserted at every page bottom, -# using the given strftime format. -# html_last_updated_fmt = '%b %d, %Y' - -# If true, SmartyPants will be used to convert quotes and dashes to -# typographically correct entities. -# html_use_smartypants = True - -# Custom sidebar templates, maps document names to template names. -# html_sidebars = {} - -# Additional templates that should be rendered to pages, maps page names to -# template names. -# html_additional_pages = {} - -# If false, no module index is generated. -# html_domain_indices = True - -# If false, no index is generated. -# html_use_index = True - -# If true, the index is split into individual pages for each letter. -# html_split_index = False - -# If true, links to the reST sources are added to the pages. -# html_show_sourcelink = True - -# If true, "Created using Sphinx" is shown in the HTML footer. Default is True. -# html_show_sphinx = True - -# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. -# html_show_copyright = True - -# If true, an OpenSearch description file will be output, and all pages will -# contain a tag referring to it. The value of this option must be the -# base URL from which the finished HTML is served. -# html_use_opensearch = '' - -# This is the file name suffix for HTML files (e.g. ".xhtml"). -# html_file_suffix = None - -# Language to be used for generating the HTML full-text search index. -# Sphinx supports the following languages: -# 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja' -# 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr' -# html_search_language = 'en' - -# A dictionary with options for the search language support, empty by default. -# Now only 'ja' uses this config value -# html_search_options = {'type': 'default'} - -# The name of a javascript file (relative to the configuration directory) that -# implements a search results scorer. If empty, the default will be used. -# html_search_scorer = 'scorer.js' - -# Output file base name for HTML help builder. -htmlhelp_basename = "napari-cookiecutterplugin_namedoc" - -# -- Options for LaTeX output --------------------------------------------- - -latex_elements = { - # The paper size ('letterpaper' or 'a4paper'). - #'papersize': 'letterpaper', - # The font size ('10pt', '11pt' or '12pt'). - #'pointsize': '10pt', - # Additional stuff for the LaTeX preamble. - #'preamble': '', - # Latex figure (float) alignment - #'figure_align': 'htbp', -} - -# Grouping the document tree into LaTeX files. List of tuples -# (source start file, target name, title, -# author, documentclass [howto, manual, or own class]). -latex_documents = [ - ( - main_doc, - "napari-cookiecutterplugin_name.tex", - "napari-\\{\\{cookiecutter.plugin\\_name\\}\\} Documentation", - "\\{\\{cookiecutter.full\\_name\\}\\}", - "manual", - ), -] - -# The name of an image file (relative to this directory) to place at the top of -# the title page. -# latex_logo = None - -# For "manual" documents, if this is true, then toplevel headings are parts, -# not chapters. -# latex_use_parts = False - -# If true, show page references after internal links. -# latex_show_pagerefs = False - -# If true, show URL addresses after external links. -# latex_show_urls = False - -# Documents to append as an appendix to all manuals. -# latex_appendices = [] - -# If false, no module index is generated. -# latex_domain_indices = True - - -# -- Options for manual page output --------------------------------------- - -# One entry per manual page. List of tuples -# (source start file, name, description, authors, manual section). -man_pages = [ - ( - main_doc, - "napari-cookiecutterplugin_name", - "napari-cellseg3d Documentation", - [author], - 1, - ) -] - -# If true, show URL addresses after external links. -# man_show_urls = False - - -# -- Options for Texinfo output ------------------------------------------- - -# Grouping the document tree into Texinfo files. List of tuples -# (source start file, target name, title, author, -# dir menu entry, description, category) -texinfo_documents = [ - ( - main_doc, - "napari-cookiecutterplugin_name", - "napari-cellseg3d Documentation", - author, - "napari-cookiecutterplugin_name", - "One line description of project.", - "Miscellaneous", - ), -] - -# Documents to append as an appendix to all manuals. -# texinfo_appendices = [] - -# If false, no module index is generated. -# texinfo_domain_indices = True - -# How to display URL addresses: 'footnote', 'no', or 'inline'. -# texinfo_show_urls = 'footnote' - -# If true, do not generate a @detailmenu in the "Top" node's menu. -# texinfo_no_detailmenu = False diff --git a/docs/index.rst b/docs/index.rst deleted file mode 100644 index 58f68606..00000000 --- a/docs/index.rst +++ /dev/null @@ -1,57 +0,0 @@ -Welcome to napari-cellseg3d's documentation! -=============================================================== - -.. toctree:: - :maxdepth: 1 - :caption: Main modules guides: - - res/welcome - res/guides/review_module_guide - res/guides/inference_module_guide - res/guides/training_module_guide - - -.. toctree:: - :maxdepth: 1 - :caption: Utilities : - - res/guides/metrics_module_guide - res/guides/utils_module_guide - res/guides/cropping_module_guide - - -.. toctree:: - :maxdepth: 2 - :caption: Advanced guides and walk-through: - - res/guides/training_wnet - res/guides/detailed_walkthrough - res/guides/custom_model_template - -.. toctree:: - :maxdepth: 1 - :caption: Source files: - - res/code/interface - res/code/plugin_base - res/code/plugin_review - res/code/plugin_review_dock - res/code/plugin_crop - res/code/plugin_convert - res/code/plugin_metrics - res/code/model_framework - res/code/workers - res/code/instance_segmentation - res/code/plugin_model_inference - res/code/plugin_model_training - res/code/utils - - - - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/docs/make.bat b/docs/make.bat deleted file mode 100644 index 3ab8ac64..00000000 --- a/docs/make.bat +++ /dev/null @@ -1,263 +0,0 @@ -@ECHO OFF - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set BUILDDIR=_build -set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% . -set I18NSPHINXOPTS=%SPHINXOPTS% . -if NOT "%PAPER%" == "" ( - set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS% - set I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS% -) - -if "%1" == "" goto help - -if "%1" == "help" ( - :help - echo.Please use `make ^` where ^ is one of - echo. html to make standalone HTML files - echo. dirhtml to make HTML files named index.html in directories - echo. singlehtml to make a single large HTML file - echo. pickle to make pickle files - echo. json to make JSON files - echo. htmlhelp to make HTML files and a HTML help project - echo. qthelp to make HTML files and a qthelp project - echo. devhelp to make HTML files and a Devhelp project - echo. epub to make an epub - echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter - echo. text to make text files - echo. man to make manual pages - echo. texinfo to make Texinfo files - echo. gettext to make PO message catalogs - echo. changes to make an overview over all changed/added/deprecated items - echo. xml to make Docutils-native XML files - echo. pseudoxml to make pseudoxml-XML files for display purposes - echo. linkcheck to check all external links for integrity - echo. doctest to run all doctests embedded in the documentation if enabled - echo. coverage to run coverage check of the documentation if enabled - goto end -) - -if "%1" == "clean" ( - for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i - del /q /s %BUILDDIR%\* - goto end -) - - -REM Check if sphinx-build is available and fallback to Python version if any -%SPHINXBUILD% 2> nul -if errorlevel 9009 goto sphinx_python -goto sphinx_ok - -:sphinx_python - -set SPHINXBUILD=python -m sphinx.__init__ -%SPHINXBUILD% 2> nul -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.http://sphinx-doc.org/ - exit /b 1 -) - -:sphinx_ok - - -if "%1" == "html" ( - %SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The HTML pages are in %BUILDDIR%/html. - goto end -) - -if "%1" == "dirhtml" ( - %SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml. - goto end -) - -if "%1" == "singlehtml" ( - %SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml. - goto end -) - -if "%1" == "pickle" ( - %SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle - if errorlevel 1 exit /b 1 - echo. - echo.Build finished; now you can process the pickle files. - goto end -) - -if "%1" == "json" ( - %SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json - if errorlevel 1 exit /b 1 - echo. - echo.Build finished; now you can process the JSON files. - goto end -) - -if "%1" == "htmlhelp" ( - %SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp - if errorlevel 1 exit /b 1 - echo. - echo.Build finished; now you can run HTML Help Workshop with the ^ -.hhp project file in %BUILDDIR%/htmlhelp. - goto end -) - -if "%1" == "qthelp" ( - %SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp - if errorlevel 1 exit /b 1 - echo. - echo.Build finished; now you can run "qcollectiongenerator" with the ^ -.qhcp project file in %BUILDDIR%/qthelp, like this: - echo.^> qcollectiongenerator %BUILDDIR%\qthelp\napari-cookiecutterplugin_name.qhcp - echo.To view the help file: - echo.^> assistant -collectionFile %BUILDDIR%\qthelp\napari-cookiecutterplugin_name.ghc - goto end -) - -if "%1" == "devhelp" ( - %SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. - goto end -) - -if "%1" == "epub" ( - %SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The epub file is in %BUILDDIR%/epub. - goto end -) - -if "%1" == "latex" ( - %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex - if errorlevel 1 exit /b 1 - echo. - echo.Build finished; the LaTeX files are in %BUILDDIR%/latex. - goto end -) - -if "%1" == "latexpdf" ( - %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex - cd %BUILDDIR%/latex - make all-pdf - cd %~dp0 - echo. - echo.Build finished; the PDF files are in %BUILDDIR%/latex. - goto end -) - -if "%1" == "latexpdfja" ( - %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex - cd %BUILDDIR%/latex - make all-pdf-ja - cd %~dp0 - echo. - echo.Build finished; the PDF files are in %BUILDDIR%/latex. - goto end -) - -if "%1" == "text" ( - %SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The text files are in %BUILDDIR%/text. - goto end -) - -if "%1" == "man" ( - %SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The manual pages are in %BUILDDIR%/man. - goto end -) - -if "%1" == "texinfo" ( - %SPHINXBUILD% -b texinfo %ALLSPHINXOPTS% %BUILDDIR%/texinfo - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The Texinfo files are in %BUILDDIR%/texinfo. - goto end -) - -if "%1" == "gettext" ( - %SPHINXBUILD% -b gettext %I18NSPHINXOPTS% %BUILDDIR%/locale - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The message catalogs are in %BUILDDIR%/locale. - goto end -) - -if "%1" == "changes" ( - %SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes - if errorlevel 1 exit /b 1 - echo. - echo.The overview file is in %BUILDDIR%/changes. - goto end -) - -if "%1" == "linkcheck" ( - %SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck - if errorlevel 1 exit /b 1 - echo. - echo.Link check complete; look for any errors in the above output ^ -or in %BUILDDIR%/linkcheck/output.txt. - goto end -) - -if "%1" == "doctest" ( - %SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest - if errorlevel 1 exit /b 1 - echo. - echo.Testing of doctests in the sources finished, look at the ^ -results in %BUILDDIR%/doctest/output.txt. - goto end -) - -if "%1" == "coverage" ( - %SPHINXBUILD% -b coverage %ALLSPHINXOPTS% %BUILDDIR%/coverage - if errorlevel 1 exit /b 1 - echo. - echo.Testing of coverage in the sources finished, look at the ^ -results in %BUILDDIR%/coverage/python.txt. - goto end -) - -if "%1" == "xml" ( - %SPHINXBUILD% -b xml %ALLSPHINXOPTS% %BUILDDIR%/xml - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The XML files are in %BUILDDIR%/xml. - goto end -) - -if "%1" == "pseudoxml" ( - %SPHINXBUILD% -b pseudoxml %ALLSPHINXOPTS% %BUILDDIR%/pseudoxml - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The pseudo-XML files are in %BUILDDIR%/pseudoxml. - goto end -) - -:end diff --git a/docs/references.bib b/docs/references.bib new file mode 100644 index 00000000..783ec6aa --- /dev/null +++ b/docs/references.bib @@ -0,0 +1,56 @@ +--- +--- + +@inproceedings{holdgraf_evidence_2014, + address = {Brisbane, Australia, Australia}, + title = {Evidence for {Predictive} {Coding} in {Human} {Auditory} {Cortex}}, + booktitle = {International {Conference} on {Cognitive} {Neuroscience}}, + publisher = {Frontiers in Neuroscience}, + author = {Holdgraf, Christopher Ramsay and de Heer, Wendy and Pasley, Brian N. and Knight, Robert T.}, + year = {2014} +} + +@article{holdgraf_rapid_2016, + title = {Rapid tuning shifts in human auditory cortex enhance speech intelligibility}, + volume = {7}, + issn = {2041-1723}, + url = {http://www.nature.com/doifinder/10.1038/ncomms13654}, + doi = {10.1038/ncomms13654}, + number = {May}, + journal = {Nature Communications}, + author = {Holdgraf, Christopher Ramsay and de Heer, Wendy and Pasley, Brian N. and Rieger, Jochem W. and Crone, Nathan and Lin, Jack J. and Knight, Robert T. and Theunissen, Frédéric E.}, + year = {2016}, + pages = {13654}, + file = {Holdgraf et al. - 2016 - Rapid tuning shifts in human auditory cortex enhance speech intelligibility.pdf:C\:\\Users\\chold\\Zotero\\storage\\MDQP3JWE\\Holdgraf et al. - 2016 - Rapid tuning shifts in human auditory cortex enhance speech intelligibility.pdf:application/pdf} +} + +@inproceedings{holdgraf_portable_2017, + title = {Portable learning environments for hands-on computational instruction using container-and cloud-based technology to teach data science}, + volume = {Part F1287}, + isbn = {978-1-4503-5272-7}, + doi = {10.1145/3093338.3093370}, + abstract = {© 2017 ACM. There is an increasing interest in learning outside of the traditional classroom setting. This is especially true for topics covering computational tools and data science, as both are challenging to incorporate in the standard curriculum. These atypical learning environments offer new opportunities for teaching, particularly when it comes to combining conceptual knowledge with hands-on experience/expertise with methods and skills. Advances in cloud computing and containerized environments provide an attractive opportunity to improve the effciency and ease with which students can learn. This manuscript details recent advances towards using commonly-Available cloud computing services and advanced cyberinfrastructure support for improving the learning experience in bootcamp-style events. We cover the benets (and challenges) of using a server hosted remotely instead of relying on student laptops, discuss the technology that was used in order to make this possible, and give suggestions for how others could implement and improve upon this model for pedagogy and reproducibility.}, + booktitle = {{ACM} {International} {Conference} {Proceeding} {Series}}, + author = {Holdgraf, Christopher Ramsay and Culich, A. and Rokem, A. and Deniz, F. and Alegro, M. and Ushizima, D.}, + year = {2017}, + keywords = {Teaching, Bootcamps, Cloud computing, Data science, Docker, Pedagogy} +} + +@article{holdgraf_encoding_2017, + title = {Encoding and decoding models in cognitive electrophysiology}, + volume = {11}, + issn = {16625137}, + doi = {10.3389/fnsys.2017.00061}, + abstract = {© 2017 Holdgraf, Rieger, Micheli, Martin, Knight and Theunissen. Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded from the human brain as well as in the computational tools available to analyze this data. This data explosion has resulted in an increased use of multivariate, model-based methods for asking neuroscience questions, allowing scientists to investigate multiple hypotheses with a single dataset, to use complex, time-varying stimuli, and to study the human brain under more naturalistic conditions. These tools come in the form of “Encoding” models, in which stimulus features are used to model brain activity, and “Decoding” models, in which neural features are used to generated a stimulus output. Here we review the current state of encoding and decoding models in cognitive electrophysiology and provide a practical guide toward conducting experiments and analyses in this emerging field. Our examples focus on using linear models in the study of human language and audition. We show how to calculate auditory receptive fields from natural sounds as well as how to decode neural recordings to predict speech. The paper aims to be a useful tutorial to these approaches, and a practical introduction to using machine learning and applied statistics to build models of neural activity. The data analytic approaches we discuss may also be applied to other sensory modalities, motor systems, and cognitive systems, and we cover some examples in these areas. In addition, a collection of Jupyter notebooks is publicly available as a complement to the material covered in this paper, providing code examples and tutorials for predictive modeling in python. The aimis to provide a practical understanding of predictivemodeling of human brain data and to propose best-practices in conducting these analyses.}, + journal = {Frontiers in Systems Neuroscience}, + author = {Holdgraf, Christopher Ramsay and Rieger, J.W. and Micheli, C. and Martin, S. and Knight, R.T. and Theunissen, F.E.}, + year = {2017}, + keywords = {Decoding models, Encoding models, Electrocorticography (ECoG), Electrophysiology/evoked potentials, Machine learning applied to neuroscience, Natural stimuli, Predictive modeling, Tutorials} +} + +@book{ruby, + title = {The Ruby Programming Language}, + author = {Flanagan, David and Matsumoto, Yukihiro}, + year = {2008}, + publisher = {O'Reilly Media} +} diff --git a/docs/res/code/instance_segmentation.rst b/docs/res/code/instance_segmentation.rst deleted file mode 100644 index 348648f8..00000000 --- a/docs/res/code/instance_segmentation.rst +++ /dev/null @@ -1,49 +0,0 @@ -instance_segmentation.py -=========================================== - -Classes -------------- - -InstanceMethod -************************************** -.. autoclass:: napari_cellseg3d.code_models.instance_segmentation::InstanceMethod - :members: __init__ - -ConnectedComponents -************************************** -.. autoclass:: napari_cellseg3d.code_models.instance_segmentation::ConnectedComponents - :members: __init__ - -Watershed -************************************** -.. autoclass:: napari_cellseg3d.code_models.instance_segmentation::Watershed - :members: __init__ - -VoronoiOtsu -************************************** -.. autoclass:: napari_cellseg3d.code_models.instance_segmentation::VoronoiOtsu - :members: __init__ - - -Functions -------------- - -binary_connected -************************************** -.. autofunction:: napari_cellseg3d.code_models.instance_segmentation::binary_connected - -binary_watershed -************************************** -.. autofunction:: napari_cellseg3d.code_models.instance_segmentation::binary_watershed - -volume_stats -************************************** -.. autofunction:: napari_cellseg3d.code_models.instance_segmentation::volume_stats - -clear_small_objects -************************************** -.. autofunction:: napari_cellseg3d.code_models.instance_segmentation::clear_small_objects - -to_semantic -************************************** -.. autofunction:: napari_cellseg3d.code_models.instance_segmentation::to_semantic diff --git a/docs/res/code/interface.rst b/docs/res/code/interface.rst deleted file mode 100644 index 8bf43e04..00000000 --- a/docs/res/code/interface.rst +++ /dev/null @@ -1,107 +0,0 @@ -interface.py -============= - -Classes -------------- - -QWidgetSingleton -************************************** -.. autoclass:: napari_cellseg3d.interface::QWidgetSingleton - :members: __call__ - -UtilsDropdown -************************************** -.. autoclass:: napari_cellseg3d.interface::UtilsDropdown - :members: __init__, dropdown_menu_call, show_utils_menu - -Log -************************************** -.. autoclass:: napari_cellseg3d.interface::Log - :members: __init__, write, replace_last_line, print_and_log, warn - - -ContainerWidget -************************************** -.. autoclass:: napari_cellseg3d.interface::ContainerWidget - :members: __init__ - -Button -************************************** -.. autoclass:: napari_cellseg3d.interface::Button - :members: __init__, visibility_condition - -DropdownMenu -************************************** -.. autoclass:: napari_cellseg3d.interface::DropdownMenu - :members: __init__ - -CheckBox -************************************** -.. autoclass:: napari_cellseg3d.interface::CheckBox - :members: __init__ - -AnisotropyWidgets -************************************** -.. autoclass:: napari_cellseg3d.interface::AnisotropyWidgets - :members: __init__, build, scaling_zyx, resolution_zyx, scaling_xyz, resolution_xyz,enabled - - -FilePathWidget -************************************** -.. autoclass:: napari_cellseg3d.interface::FilePathWidget - :members: __init__, build, text_field, button, check_ready, required, update_field_color, tooltips - -ScrollArea -************************************** -.. autoclass:: napari_cellseg3d.interface::ScrollArea - :members: __init__, make_scrollable - -DoubleIncrementCounter -************************************** -.. autoclass:: napari_cellseg3d.interface::DoubleIncrementCounter - :members: __init__, precision, make_n - -IntIncrementCounter -************************************** -.. autoclass:: napari_cellseg3d.interface::IntIncrementCounter - :members: __init__, make_n - - -Functions -------------- - -handle_adjust_errors -************************************** -.. autofunction:: napari_cellseg3d.interface::handle_adjust_errors - -handle_adjust_errors_wrapper -************************************** -.. autofunction:: napari_cellseg3d.interface::handle_adjust_errors_wrapper - -open_url -************************************** -.. autofunction:: napari_cellseg3d.interface::open_url - -make_group -************************************** -.. autofunction:: napari_cellseg3d.interface::make_group - -combine_blocks -************************************** -.. autofunction:: napari_cellseg3d.interface::combine_blocks - -add_blank -************************************** -.. autofunction:: napari_cellseg3d.interface::add_blank - -add_label -************************************** -.. autofunction:: napari_cellseg3d.interface::add_label - -toggle_visibility -************************************** -.. autofunction:: napari_cellseg3d.interface::toggle_visibility - -open_file_dialog -************************************** -.. autofunction:: napari_cellseg3d.interface::open_file_dialog diff --git a/docs/res/code/model_framework.rst b/docs/res/code/model_framework.rst deleted file mode 100644 index 63eef232..00000000 --- a/docs/res/code/model_framework.rst +++ /dev/null @@ -1,23 +0,0 @@ -.. _model_framework.py: - - -model_framework.py -==================================================== - - -Class : ModelFramework ----------------------------------------------------- - - -Methods -********************** -.. autoclass:: napari_cellseg3d.code_models.model_framework::ModelFramework - :members: __init__, send_log, save_log, save_log_to_path, display_status_report, create_train_dataset_dict, get_available_models, get_device, empty_cuda_cache - :noindex: - - -Attributes -********************* - -.. autoclass:: napari_cellseg3d.code_models.model_framework::ModelFramework - :members: _viewer, worker, docked_widgets, images_filepaths, labels_filepaths, results_path diff --git a/docs/res/code/plugin_base.rst b/docs/res/code/plugin_base.rst deleted file mode 100644 index f1015c7e..00000000 --- a/docs/res/code/plugin_base.rst +++ /dev/null @@ -1,38 +0,0 @@ -plugin_base.py -==================================================== - - -Class : BasePluginSingleImage ----------------------------------------------------- - - -Methods -********************** -.. autoclass:: napari_cellseg3d.code_plugins.plugin_base::BasePluginSingleImage - :members: __init__, enable_utils_menu, remove_from_viewer, remove_docked_widgets - :noindex: - -Attributes -********************* - -.. autoclass:: napari_cellseg3d.code_plugins.plugin_base::BasePluginSingleImage - :members: _viewer, image_path, label_path, image_layer_loader, label_layer_loader - - - - -Class : BasePluginFolder -------------------------------------------------------- - - -Methods -*********************** -.. autoclass:: napari_cellseg3d.code_plugins.plugin_base::BasePluginFolder - :members: __init__, load_dataset_paths,load_image_dataset,load_label_dataset - :noindex: - -Attributes -********************* - -.. autoclass:: napari_cellseg3d.code_plugins.plugin_base::BasePluginFolder - :members: _viewer, images_filepaths, labels_filepaths, results_path diff --git a/docs/res/code/plugin_convert.rst b/docs/res/code/plugin_convert.rst deleted file mode 100644 index 25006d0f..00000000 --- a/docs/res/code/plugin_convert.rst +++ /dev/null @@ -1,30 +0,0 @@ -plugin_convert.py -================================== - -Classes ----------------------------------- - -AnisoUtils -********************************** -.. autoclass:: napari_cellseg3d.code_plugins.plugin_convert::AnisoUtils - :members: __init__ - -RemoveSmallUtils -********************************** -.. autoclass:: napari_cellseg3d.code_plugins.plugin_convert::RemoveSmallUtils - :members: __init__ - -ToSemanticUtils -********************************** -.. autoclass:: napari_cellseg3d.code_plugins.plugin_convert::ToSemanticUtils - :members: __init__ - -ToInstanceUtils -********************************** -.. autoclass:: napari_cellseg3d.code_plugins.plugin_convert::ToInstanceUtils - :members: __init__ - -ThresholdUtils -********************************** -.. autoclass:: napari_cellseg3d.code_plugins.plugin_convert::ThresholdUtils - :members: __init__ diff --git a/docs/res/code/plugin_crop.rst b/docs/res/code/plugin_crop.rst deleted file mode 100644 index f52fd025..00000000 --- a/docs/res/code/plugin_crop.rst +++ /dev/null @@ -1,25 +0,0 @@ -plugin_crop.py -==================================================== - - -Class : Cropping ----------------------------------------------------- - -.. important:: - Inherits from : :doc:`plugin_base` - - -Methods -********************** -.. autoclass:: napari_cellseg3d.code_plugins.plugin_crop::Cropping - :members: __init__, _start, quicksave, remove_from_viewer - :noindex: - - - - -Attributes -********************* - -.. autoclass:: napari_cellseg3d.code_plugins.plugin_crop::Cropping - :members: _viewer, image_path, label_path diff --git a/docs/res/code/plugin_metrics.rst b/docs/res/code/plugin_metrics.rst deleted file mode 100644 index 29c2fe25..00000000 --- a/docs/res/code/plugin_metrics.rst +++ /dev/null @@ -1,22 +0,0 @@ -plugin_metrics.py -================================== - - -Class : MetricsUtils ------------------------------------------- - -.. important:: - Inherits from : :doc:`plugin_base` - -Methods -********************** - -.. autoclass:: napari_cellseg3d.code_plugins.plugin_metrics::MetricsUtils - :members: __init__, plot_dice, remove_plots, compute_dice - :noindex: - -Attributes -********************* - -.. autoclass:: napari_cellseg3d.code_plugins.plugin_metrics::MetricsUtils - :members: _viewer, layout, canvas, plots diff --git a/docs/res/code/plugin_model_inference.rst b/docs/res/code/plugin_model_inference.rst deleted file mode 100644 index cdd4d6eb..00000000 --- a/docs/res/code/plugin_model_inference.rst +++ /dev/null @@ -1,24 +0,0 @@ -plugin_model_inference.py -==================================================== - - -Class : Inferer ----------------------------------------------------- - -.. important:: - Inherits from : :doc:`model_framework` - -Methods -********************** -.. autoclass:: napari_cellseg3d.code_plugins.plugin_model_inference::Inferer - :members: __init__, start,on_start,on_error,on_finish,on_yield, check_ready, remove_from_viewer - :noindex: - - - - -Attributes -********************* - -.. autoclass:: napari_cellseg3d.code_plugins.plugin_model_inference::Inferer - :members: _viewer, worker, config, instance_config, post_process_config, worker_config, model_info diff --git a/docs/res/code/plugin_model_training.rst b/docs/res/code/plugin_model_training.rst deleted file mode 100644 index 6a2a39b8..00000000 --- a/docs/res/code/plugin_model_training.rst +++ /dev/null @@ -1,22 +0,0 @@ -plugin_model_training.py -==================================================== - - -Class : Trainer ----------------------------------------------------- - -.. important:: - Inherits from : :doc:`model_framework` - -Methods -********************** -.. autoclass:: napari_cellseg3d.code_plugins.plugin_model_training::Trainer - :members: __init__, check_ready, send_log, start, on_start, on_finish, on_error, on_yield, update_loss_plot - :noindex: - - - -Attributes -********************* -.. autoclass:: napari_cellseg3d.code_plugins.plugin_model_training::Trainer - :members: _viewer, worker, canvas diff --git a/docs/res/code/plugin_review.rst b/docs/res/code/plugin_review.rst deleted file mode 100644 index 69397400..00000000 --- a/docs/res/code/plugin_review.rst +++ /dev/null @@ -1,26 +0,0 @@ -plugin_review.py -==================================================== - - -Class : Loader ----------------------------------------------------- - -.. important:: - Inherits from : :doc:`plugin_base` - -Methods -********************** -.. autoclass:: napari_cellseg3d.code_plugins.plugin_review::Reviewer - :members: __init__, run_review, launch_review, check_image_data, remove_from_viewer - :noindex: - - - - - - -Attributes -********************* - -.. autoclass:: napari_cellseg3d.code_plugins.plugin_review::Reviewer - :members: _viewer, image_path, label_path diff --git a/docs/res/code/plugin_review_dock.rst b/docs/res/code/plugin_review_dock.rst deleted file mode 100644 index 3aa0f6ae..00000000 --- a/docs/res/code/plugin_review_dock.rst +++ /dev/null @@ -1,21 +0,0 @@ -plugin_dock.py -==================================================== - -Datamanager ----------------------------------------------------- - -Methods -********************** -.. autoclass:: napari_cellseg3d.code_plugins.plugin_review_dock::Datamanager - :members: __init__, prepare, update, load_csv, create - :noindex: - - - - - -Attributes -********************* - -.. autoclass:: napari_cellseg3d.code_plugins.plugin_review_dock::Datamanager - :members: viewer diff --git a/docs/res/code/utils.rst b/docs/res/code/utils.rst deleted file mode 100644 index 88046b6f..00000000 --- a/docs/res/code/utils.rst +++ /dev/null @@ -1,60 +0,0 @@ -utils.py -============= - -Classes -------------- - -Singleton -************************************** -.. autoclass:: napari_cellseg3d.utils::Singleton - -Functions -------------- - -get_date_time -************************************** -.. autofunction:: napari_cellseg3d.utils::get_date_time - -get_time -************************************** -.. autofunction:: napari_cellseg3d.utils::get_time - -time_difference -************************************** -.. autofunction:: napari_cellseg3d.utils::time_difference - -get_time_filepath -************************************** -.. autofunction:: napari_cellseg3d.utils::get_time_filepath - -get_padding_dim -************************************** -.. autofunction:: napari_cellseg3d.utils::get_padding_dim - -dice_coeff -************************************** -.. autofunction:: napari_cellseg3d.utils::dice_coeff - -sphericity_volume_area -************************************** -.. autofunction:: napari_cellseg3d.utils::sphericity_volume_area - -sphericity_axis -************************************** -.. autofunction:: napari_cellseg3d.utils::sphericity_axis - -normalize_x -************************************** -.. autofunction:: napari_cellseg3d.utils::normalize_x - -normalize_y -************************************** -.. autofunction:: napari_cellseg3d.utils::normalize_y - -denormalize_y -************************************** -.. autofunction:: napari_cellseg3d.utils::denormalize_y - -load_images -************************************** -.. autofunction:: napari_cellseg3d.utils::load_images diff --git a/docs/res/code/workers.rst b/docs/res/code/workers.rst deleted file mode 100644 index 5964e004..00000000 --- a/docs/res/code/workers.rst +++ /dev/null @@ -1,70 +0,0 @@ -workers.py -=========================================== - - -Class : LogSignal -------------------------------------------- - -.. important:: - Inherits from :py:class:`napari.qt.threading.WorkerBaseSignals` - -Attributes -************************ -.. autoclass:: napari_cellseg3d.code_models.workers_utils::LogSignal - :members: log_signal - :noindex: - - - -Class : InferenceWorker -------------------------------------------- - -.. important:: - Inherits from :py:class:`napari.qt.threading.GeneratorWorker` - -Methods -************************ -.. autoclass:: napari_cellseg3d.code_models.worker_inference::InferenceWorker - :members: __init__, log, create_inference_dict, inference - :noindex: - -.. _here: https://napari-staging-site.github.io/guides/stable/threading.html - - -Class : TrainingWorkerBase -------------------------------------------- - -.. important:: - Inherits from :py:class:`napari.qt.threading.GeneratorWorker` - -Methods -************************ -.. autoclass:: napari_cellseg3d.code_models.worker_training::TrainingWorkerBase - :members: __init__, log, train - :noindex: - - -Class : WNetTrainingWorker -------------------------------------------- - -.. important:: - Inherits from :py:class:`TrainingWorkerBase` - -Methods -************************ -.. autoclass:: napari_cellseg3d.code_models.worker_training::WNetTrainingWorker - :members: __init__, train, eval, get_patch_dataset, get_dataset_eval, get_dataset - :noindex: - - -Class : SupervisedTrainingWorker -------------------------------------------- - -.. important:: - Inherits from :py:class:`TrainingWorkerBase` - -Methods -************************ -.. autoclass:: napari_cellseg3d.code_models.worker_training::SupervisedTrainingWorker - :members: __init__, train - :noindex: diff --git a/docs/res/guides/cropping_module_guide.rst b/docs/res/guides/cropping_module_guide.rst deleted file mode 100644 index 89cbb39a..00000000 --- a/docs/res/guides/cropping_module_guide.rst +++ /dev/null @@ -1,66 +0,0 @@ -.. _cropping_module_guide: - -Cropping utility guide -================================= - -This module allows you to crop your volumes and labels dynamically, -by selecting a fixed size volume and moving it around the image. - -You can then save the cropped volume and labels directly using napari, -by using the **Quicksave** button, -or by selecting the layer and then using **File -> Save selected layer**, -or simply **CTRL+S** once you have selected the correct layer. - - -Launching the cropping process ---------------------------------- - -First, simply pick your images using the layer selection dropdown menu. -If you'd like to crop a second image, e.g. labels, at the same time, -simply check the *"Crop another image simultaneously"* checkbox and -pick the corresponding layer. - -You can then choose the size of the cropped volume, which will be constant throughout the process; make sure it is correct beforehand. - -You can also opt to correct the anisotropy if your image is anisotropic : -simply set the resolution to the one of your microscope. - -.. important:: - This will simply scale the image in the viewer, but saved images will **still be anisotropic.** To resize your image, see :doc:`utils_module_guide` - -Once you are ready, you can press **Start** to start the review process. -If you'd like to change the size of the volume, change the parameters as previously to your desired size and hit start again. - -Creating new layers ---------------------------------- -To "zoom in" your volume, you can use the "Create new layers" checkbox to make a new cropping layer controlled by the sliders -next time you hit Start. This way, you can first select your region of interest by using the tool as described above, -then enable the option, select the cropped region produced before as the input layer, and define a smaller crop size in order to crop within your region of interest. - -Interface & functionalities ---------------------------------------------------------------- - -.. image:: ../images/cropping_process_example.png - -Once you have launched the review process, you will have access to three sliders that will let -you **change the position** of the cropped volumes and labels in the x,y and z positions. - -.. hint:: - If you **cannot see your cropped volume well**, feel free to change the **colormap** of the image and the cropped - volume to better see them. - You may want to change the **opacity** and **contrast thresholds** depending on your image, too. - - -.. note:: - When you are done you can save the cropped volume and labels directly with the - **Quicksave** button on the lower left, which will save in the folder you picked the image from, or as - a separate folder if you loaded a folder as a stack. - If you want more options (name, format) you can save by selecting the layer and then - using **File -> Save selected layer**, or simply **CTRL+S** once you have selected the correct layer. - -.. - Source code - ------------------------------------------------- - - * :doc:`../code/plugin_crop` - * :doc:`../code/plugin_base` diff --git a/docs/res/guides/detailed_walkthrough.rst b/docs/res/guides/detailed_walkthrough.rst deleted file mode 100644 index 4fd04510..00000000 --- a/docs/res/guides/detailed_walkthrough.rst +++ /dev/null @@ -1,293 +0,0 @@ -.. _detailed_walkthrough: - -Detailed walkthrough - Supervised learning -=================================================== - -The following guide will show you how to use the plugin's workflow, starting from human-labeled annotation volume, to running inference on novel volumes. - -Preparing images and labels -------------------------------- - -CellSeg3D was designed for cleared-brain tissue data (collected on mesoSPIM ligthsheet systems). Specifically, we provide a series -of deep learning models that we have found to work well on cortical whole-neuron data. We also provide support for MONAI models, and -we have ported TRAILMAP to PyTorch and trained the model on mesoSPIM collected data. We provide all the tooling for you to use these -weights and also perform transfer learning by fine-tuning the model(s) on your data for even better performance! - -To get started with the entire workflow (i.e., fine-tuning on your data), you'll need at least one pair of image and corresponding labels; -let's assume you have part of a cleared brain from mesoSPIM imaging as a large .tif file. - -If you want to test the models "as is", please see "Inference" sections in our docs. - - -.. figure:: ../images/init_image_labels.png - :scale: 40 % - :align: center - - Example of an anisotropic volume (i.e., often times the z resolution is not the same as x and y) and its associated labels. - - -.. note:: - The approach here will be human-in-the-loop review of labels. - If you need to label your volumes from scratch, - or initially correct labels, please read the Review section right after Cropping. - - -Cropping -***************** - -To reduce memory requirements and build a dataset from a single, large volume, -you can use the **cropping** tool to extract multiple smaller images from a large volume for training. - -Simply load your image and labels (by checking the "Crop labels simultaneously" option), -and select the volume size you desire to use. - -.. note:: - The best way to choose the size when cropping images you intend to use for training models is to use - cubic images sized with a power of two : the default :math:`64^3` should be a good start if you're unsure. - Stick to the same size for a given dataset. - If you simply want to isolate specific regions with variable sizes, you can still compensate for it in training though. - You may also use different image sizes for inference, simply ensure that images in a folder are of a similar size if you - wish to run inference on all of them. - -You can now use the sliders to choose the regions you want to extract, -then either quicksave the results or select the layer you'd like to save and use **CTRL+S** to save it -(useful if you want to name the results or change the file extension). - -.. figure:: ../images/cropping_process_example.png - :align: center - - Cropping module layout - -Label conversion : convert utility -************************************* - -Assuming you have instance labels, you'll need to convert them to semantic labels before using them for training. -To this end, you can use the folder conversion functions in the *Convert* tab of Utilities : -choose your output directory for the results, select the folder containing your cropped volumes in the *Convert folder* -section, and click on **"Convert to semantic labels"**. - -.. figure:: ../images/converted_labels.png - :scale: 40 % - :align: center - - Example of instance labels from above converted to semantic labels - -If you wish to remove small objects, or convert a single image, you can open a single image with **CTRL+O** -and, after selecting the corresponding layer on the left, use the layer-related buttons to perform -your operations. - -Models for object detection ------------------------------------------------------------ - -Training -***************** - -If you have a dataset of reasonably sized images (see cropping above) with semantic labels, you're all set to proceed! -First, load your data by inputting the paths to images and labels, as well as where you want the results to be saved. - -There are a few more options on this tab: - -* Transfer weights : you can start the model with our pre-trained weights if your dataset comes from cleared brain tissue - imaged by a mesoSPIM (or other lightsheet). If you have your own weights for the provided models, you can also choose to load them by - checking the related option; simply make sure they are compatible with the model you selected. - - To import your own model, see : :ref:`custom_model_guide`, please note this is still a WIP. - -* Validation proportion : the percentage is listed is how many images will be used for training versus validation. - Validation can work with as little as one image, however performance will greatly improve the more images there are. - Use 90% only if you have a very small dataset (less than 5 images). - -* Save as zip : simply copies the results in a zip archive for easier transfer. - -Now, we can switch to the next tab : data augmentation. - -If you have cropped cubic images with a power of two as the edge length, you do not need to extract patches, -your images are usable as is. -However, if you are using larger images or with dissimilar sizes, -you can use this option to auto-extract smaller patches that will be automatically padded back to a power -of two no matter the size you choose. For optimal performance, make sure to use a value close or equal to -a power of two still, such as 64 or 120. - -.. important:: - Using a too large value for the size will cause memory issues. If this happens, restart the worker with smaller volumes. - -You also have the option to use data augmentation, which can improve performance and generalization. -In most cases this should left enabled. - -Finally, the last tab lets you choose : - -* The models - - * SegResNet is a lightweight model (low memory requirements) from MONAI originally designed for 3D fMRI data. - * VNet is a larger (than SegResNet) CNN from MONAI designed for medical image segmentation. - * TRAILMAP_MS is our implementation in PyTorch additionally trained on mouse cortical neural nuclei from mesoSPIM data. - * SwinUNetR is a MONAI implementation of the SwinUNetR model. It is costly in compute and memory, but can achieve high performance. - * WNet is our reimplementation of an unsupervised model, which can be used to produce segmentation without labels. - - -* The loss : for object detection in 3D volumes you'll likely want to use the Dice or Dice-focal Loss. - -* Batch size : chose a value that fits your available memory. If you want to avoid memory issues due to the batch size, - leave it on one. - -* Learning rate : if you are not using pre-trained weights or loading your own custom ones, try with 1e-3. Use smaller values - if you are using custom/pre-trained weights. - -* Number of epochs : The larger the value, the longer the training will take, but performance might improve with longer - training. You could start with 40, and see if the loss decreases while the validation metric rises. - -.. note:: - During training, you can monitor the process using the plots : the one on the right (validation) should increase - whereas the loss should decrease. If the validation starts lowering after reaching a maximum, but the loss still decreases, - it could indicate over-fitting, which will negatively impact generalization for the given weights. - You might want use weights generated from the epoch with the maximum validation score if that is the case. - -.. figure:: ../images/plots_train.png - :align: center - - Plots displayed by the training module after 40 epochs - -* Validation interval : if the value is e.g. 2, the training will stop every 2 epochs to perform validation (check performance) - and save the results if the score is better than previously. A larger value will accelerate training, but might cause the saving to miss - better scores. Reasonably, start with 1 for short training sessions (less than 10 epochs) and increase it to two or three if you are training - for 20-60 epochs. - -* Deterministic training : if you wish for the training to have reproducibility, enable this and remember the seed you use. - Using the same seed with the same model, images, and parameters should consistently yield similar results. See `MONAI deterministic training`_. - -.. _MONAI deterministic training: https://docs.monai.io/en/stable/utils.html#module-monai.utils.misc - -Once all these parameters are set, you can start the training. You can monitor the progress with the plots; should you want to stop -the training you can do so anytime by pressing the Start button again, whose text should change to **Click to stop**. - -In the results folder, you will have access to the weights from training (**.pth** files), -which you can then use in inference. - -Inference -************* - -To start, simply choose which folder of images you'd like to run inference on, then the folder in which you'd like the results to be. - -Then, select the model you trained (see note below for SegResNet), and load your weights from training. - -.. note:: - If you already trained a SegResNet, set the counter below the model choice to the size of the images you trained the model on. - (Either use the size of the image itself if you did not extract patches, or the size of the nearest superior power of two of the patches you extracted) - - Example : - - * If you used :math:`64^3` whole volumes to train the model, enter :math:`64` in the counter. - * If you extracted :math:`120^3` patches from larger images, enter :math:`128` - - -Next, you can choose to use window inference, use this if you have very large images. -Please note that using too small of a window might degrade performance, set the size appropriately. - -You can also keep the dataset on the CPU to reduce memory usage, but this might slow down the inference process. - -If you have anisotropic volumes, you can compensate for it by entering the resolution of your microscope. - -By default, inference will calculate and display probability maps (values between 0 and 1). - -If you'd like to have semantic labels (only 0 and 1) rather than a probability map, set the thresholding to the desired probability. - -If instead you'd prefer to have instance labels, you can enable instance segmentation and select : - -* The method - - * Connected components : all separated items with a value above the threshold will be labeled as an instance - * Watershed : objects will be assigned an ID by using the gradient probability at the center of each (set the threshold to a decently high probability for best results). - -* The threshold : Objects above this threshold will be retained as single instances. - -* Small object removal : Use this to remove small artifacts; all objects below this volume in pixels will be removed. - -Using instance segmentation, you can also analyze the results by checking the *Save stats to CSV* option. - -This will compute : - -* The volume of each cell in pixels -* The centroid coordinates in :math:`X,Y,Z` -* The sphericity of each cell -* The original size of the image -* The total volume in pixels -* The total volume occupied by objects -* The ratio of :math:`\frac {Volume_{label}} {Volume_{total}}` -* The total number of unique object instance - -If you wish to see some of the results, you can leave the *View results in napari* option checked. - -.. note:: - If you'd like some of these results to be plotted for you, check out the `provided notebooks`_ - -.. _provided notebooks: https://github.com/AdaptiveMotorControlLab/CellSeg3d/tree/main/notebooks - - -You can then launch inference and the results will be saved to your specified folder. - -.. figure:::: ../image/inference_results_example.png - - Example of results from inference with original volumes, as well as semantic and instance predictions. - -.. figure:: ../images/plot_example_metrics.png - :scale: 30 % - :align: right - - Dice metric score plot - -Scoring, review, analysis ----------------------------- - - -.. Using the metrics utility module, you can compare the model's predictions to any ground truth labels you might have. - Simply provide your prediction and ground truth labels, and compute the results. - A Dice metric of 1 indicates perfect matching, whereas a score of 0 indicates complete mismatch. - Select which score **you consider as sub-optimal**, and all results below this will be **shown in napari**. - If at any time the **orientation of your prediction labels changed compared to the ground truth**, check the - "Find best orientation" option to compensate for it. - - -Labels review -************************ - -Using the review module, you can correct the predictions from the model. -Simply load your images and labels, enter the name of the CSV (to keep track of the review process, it will -record which slices have been checked or not and the time taken). - -See the `napari tutorial on annotation`_ for instruction on correcting and adding labels. - -.. _napari tutorial on annotation: https://napari.org/howtos/layers/labels.html#selecting-a-label - -If you wish to see the surroundings of an object to ensure it should be labeled, -you can use **Shift+Click** on the location you wish to see; this will plot -the surroundings of the selected location for easier viewing. - -.. figure:: ../images/review_process_example.png - :align: center - - Layout of the review module - -Once you are done with the review of a slice, press the "Not checked" button to switch the status to -"Checked" and save the time taken in the csv file. - -Finally, when you are done, press the *Save* button to record your work. - -Analysis : Jupyter notebooks -********************************* - -In the `notebooks folder of the repository`_, you can find notebooks you can use directly to plot -labels (full_plot.ipynb) or notebooks for plotting the results from your inference csv with object stats (csv_cell_plot.ipynb). - -Simply enter your folder or csv file path and the notebooks will plot your results. -Make sure you have all required libraries installed and jupyter extensions set up as explained -for the plots to work. - -.. figure:: ../images/stat_plots.png - :align: center - - Example of the plot present in the notebooks. - Coordinates are based on centroids, the size represents the volume, the color the sphericity. - -.. _notebooks folder of the repository: https://github.com/AdaptiveMotorControlLab/CellSeg3d/tree/main/notebooks - -With this complete, you can repeat the workflow as needed. diff --git a/docs/res/guides/inference_module_guide.rst b/docs/res/guides/inference_module_guide.rst deleted file mode 100644 index 77f3a859..00000000 --- a/docs/res/guides/inference_module_guide.rst +++ /dev/null @@ -1,146 +0,0 @@ -.. _inference_module_guide: - -Inference module guide -================================= - -This module allows you to use pre-trained segmentation algorithms (written in Pytorch) on 3D volumes -to automatically label cells. - -.. important:: - Currently, only inference on **3D volumes is supported**. If using folders, your images and labels folders - should both contain a set of **3D image files**, either **.tif** or **.tiff**. - Otherwise you may run inference on layers in napari. - -Currently, the following pre-trained models are available : - -============== ================================================================================================ -Model Link to original paper -============== ================================================================================================ -VNet `Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation`_ -SegResNet `3D MRI brain tumor segmentation using autoencoder regularization`_ -TRAILMAP_MS A PyTorch implementation of the `TRAILMAP project on GitHub`_ pretrained with mesoSPIM data -TRAILMAP An implementation of the `TRAILMAP project on GitHub`_ using a `3DUNet for PyTorch`_ -SwinUNetR `Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images`_ -WNet `WNet, A Deep Model for Fully Unsupervised Image Segmentation`_ -============== ================================================================================================ - -.. _Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation: https://arxiv.org/pdf/1606.04797.pdf -.. _3D MRI brain tumor segmentation using autoencoder regularization: https://arxiv.org/pdf/1810.11654.pdf -.. _TRAILMAP project on GitHub: https://github.com/AlbertPun/TRAILMAP -.. _3DUnet for Pytorch: https://github.com/wolny/pytorch-3dunet -.. _Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images: https://arxiv.org/abs/2201.01266 -.. _WNet, A Deep Model for Fully Unsupervised Image Segmentation: https://arxiv.org/abs/1711.08506 - -.. note:: - For WNet-specific instruction please refer to the appropriate section below. - -Interface and functionalities --------------------------------- - -.. image:: ../images/inference_plugin_layout.png - :align: right - :scale: 40% - -* **Loading data** : - - | When launching the module, you will be asked to provide an **image layer** or an **image folder** with the 3D volumes you'd like to be labeled. - | If loading from folder : All images with the chosen extension (**.tif** or **.tiff** currently supported) in this folder will be labeled. - | You can then choose an **output folder**, where all the results will be saved. - -* **Model choice** : - - | You can then choose one of the provided **models** above, which will be used for inference. - | You may also choose to **load custom weights** rather than the pre-trained ones, simply ensure they are **compatible** (e.g. produced from the training module for the same model) - | If you choose to use SegResNet or SwinUNetR with custom weights, you will have to provide the size of images it was trained on to ensure compatibility. (See note below) - -.. note:: - Currently the SegResNet and SwinUNetR models requires you to provide the size of the images the model was trained with. - Provided weights use a size of 128, please leave it on the default value if you're not using custom weights. - -* **Inference parameters** : - - | You can choose to use inference on the whole image at once, which generally yields better performance at the cost of more memory, or you can use a specific window size to run inference on smaller chunks one by one, for lower memory usage. - | You can also choose to keep the dataset in the RAM rather than the VRAM (cpu vs cuda device) to avoid running out of VRAM if you have several images. - -* **Anisotropy** : - - | If you want to see your results without **anisotropy** when you have anisotropic images, you can specify that you have anisotropic data and set the **resolution of your imaging method in micron**, this wil save and show the results without anisotropy. - -* **Thresholding** : - - | You can perform thresholding to **binarize your labels**, - | all values beneath the **confidence threshold** will be set to 0 using this. - -* **Instance segmentation** : - - | You can convert the semantic segmentation into instance labels by using either the Voronoi-Otsu, `Watershed`_ or `Connected Components`_ method, as detailed in :ref:`utils_module_guide`. - | Instance labels will be saved (and shown if applicable) separately from other results. - - -.. _watershed: https://scikit-image.org/docs/dev/auto_examples/segmentation/plot_watershed.html -.. _connected components: https://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.label - - -* **Computing objects statistics** : - - You can choose to compute various stats from the labels and save them to a .csv for later use. - - This includes, for each object : - - * Object volume (pixels) - * :math:`X,Y,Z` coordinates of the centroid - * Sphericity - - - And more general statistics : - - * Image size - * Total image volume (pixels) - * Total object (labeled) volume (pixels) - * Filling ratio (fraction of the volume that is labeled) - * The number of labeled objects - - - In the ``notebooks`` folder you can find an example of plotting cell statistics using the result csv. - -When you are done choosing your parameters, you can press the **Start** button to begin the inference process. -Once it has finished, results will be saved then displayed in napari; each output will be paired with its original. -On the left side, a progress bar and a log will keep you informed on the process. - - -.. note:: - | The files will be saved using the following format : - | ``{original_name}_{model}_{date & time}_pred{id}.file_ext`` - | For example, using a VNet on the third image of a folder, called "somatomotor.tif" will yield the following name : - | *somatomotor_VNet_2022_04_06_15_49_42_pred3.tif* - | Instance labels will have the "Instance_seg" prefix appended to the name. - - -.. hint:: - | **Results** will be displayed using the **twilight shifted** colormap if raw or **turbo** if thresholding has been applied, whereas the **original** image will be shown in the **inferno** colormap. - | Feel free to change the **colormap** or **contrast** when viewing results to ensure you can properly see the labels. - | You'll most likely want to use **3D view** and **grid mode** in napari when checking results more broadly. - -.. image:: ../images/inference_results_example.png - -.. note:: - You can save the log after the worker is finished to easily remember which parameters you ran inference with. - -WNet --------------------------------- - -The WNet model, from the paper `WNet, A Deep Model for Fully Unsupervised Image Segmentation`_, is a fully unsupervised model that can be used to segment images without any labels. -It clusters pixels based on brightness, and can be used to segment cells in a variety of modalities. -Its use and available options are similar to the above models, with a few differences : - -.. note:: - | Our provided, pre-trained model should use an input size of 64x64x64. As such, window inference is always enabled - | and set to 64. If you want to use a different size, you will have to train your own model using the provided notebook. - -All it requires are 3D .tif images (you can also load a 2D stack as 3D via napari). - -Source code --------------------------------- -* :doc:`../code/plugin_model_inference` -* :doc:`../code/model_framework` -* :doc:`../code/workers` diff --git a/docs/res/guides/metrics_module_guide.rst b/docs/res/guides/metrics_module_guide.rst deleted file mode 100644 index 98899ad9..00000000 --- a/docs/res/guides/metrics_module_guide.rst +++ /dev/null @@ -1,49 +0,0 @@ -.. _metrics_module_guide: - -Metrics utility guide -========================== - -.. figure:: ../images/plot_example_metrics.png - :scale: 35 % - :align: right - - Dice metric plot result - -This utility allows to compute the Dice coefficient between two folders of labels. - -The Dice coefficient is defined as : - -.. math:: \frac {2|X \cap Y|} {|X|+|Y|} - -It is a measure of similarity between two sets- :math:`0` indicating no similarity and :math:`1` complete similarity. - -You will need to provide the following parameters: - -* Two folders : one for ground truth labels and one for prediction labels. - -* The threshold below which the score is considered insufficient. - Any pair below that score will be shown on the viewer; and be displayed in red in the plot. - -* Whether to automatically determine the best orientation for the computation by rotating and flipping; - use this if your images do not have the same orientation. - -.. note:: - Due to changes in orientation of images after running inference, the utility can rotate and flip images randomly to find the best Dice coefficient - to compensate. If you have small images with a very large number of labels, this can lead to an inexact metric being computed. - Images with a low score might be in the wrong orientation as well when displayed for comparison. - -.. important:: - This utility assumes that **predictions are padded to a power of two already.** Ground truth labels can be smaller, - they will be padded to match the prediction size. - Your files should have names that allow them to be sorted numerically as well; please ensure that each ground truth label has a matching prediction label. - -Once you are ready, press the "Compute Dice" button. This will plot the Dice score for each ground truth-prediction labels pair on the side. -Pairs with a low score will be displayed on the viewer for checking, ground truth in **blue**, low score prediction in **red**. - - - -Source code -------------------------------------------------- - -* :doc:`../code/plugin_base` -* :doc:`../code/plugin_metrics` diff --git a/docs/res/guides/review_module_guide.rst b/docs/res/guides/review_module_guide.rst deleted file mode 100644 index ffecf9a0..00000000 --- a/docs/res/guides/review_module_guide.rst +++ /dev/null @@ -1,63 +0,0 @@ -.. _loader_module_guide: - -Review module guide -================================= - -This module allows you to review your labels, from predictions or manual labeling, -and correct them if needed. It then saves the status of each file in a csv, as well as the time taken per slice review, for easier monitoring. - - - -Launching the review process ---------------------------------- - -* Data paths : - First, you will be asked to load your images and labels; you can use the checkbox above the Open buttons to - choose whether you want to load a single 3D **.tif** image or a folder of 2D images as a 3D stack. - Folders can be stacks of either **.png** or **.tif** files, ideally numbered with the index of the slice at the end. - -.. note:: - Only single 3D **.tif** files or 2D stacks of several **.png** or **.tif** in a folder are currently supported. - -* Anisotropic data : - This will scale the images to visually remove the anisotropy, so as to make review easier. - -.. important:: - Results will still be saved as anisotropic images. If you wish to resize your images, see the :doc:`utils_module_guide` - -* CSV file name : - You can then provide a model name, which will be used to name the csv file recording the status of each slice. - - If a corresponding csv file exists already, it will be used. If not, a new one will be created. - If you choose to create a new dataset, a new csv will always be created, - with a trailing number if several copies of it already exists. - -* Start : - Once you are ready, you can press **Start reviewing** to start the review process. - -.. note:: - You can find the csv file containing the annotation status **in the same folder as the labels** - - -Interface & functionalities ---------------------------------------------------------------- - -.. image:: ../images/review_process_example.png - -Once you have launched the review process, you will have access to the following functionalities: - -* A dialog to choose the folder in which you want to save the verified and/or corrected annotations, and a button to save the labels. They will be saved based on the file format you provided. - -* A button to update the status of the slice in the csv file (in this case : checked/not checked) - -* A plot with three projections in the x-y, y-z and x-z planes, to allow the reviewer to better see the surroundings of the label and properly establish whether the image should be labeled or not. You can **shift-click** anywhere on the image or label layer to update the plot to the location being reviewed. - -Using these, you can check your labels, correct them, save them and keep track of which slices have been checked or not. - - -Source code -------------------------------------------------- - -* :doc:`../code/plugin_review` -* :doc:`../code/plugin_review_dock` -* :doc:`../code/plugin_base` diff --git a/docs/res/guides/training_module_guide.rst b/docs/res/guides/training_module_guide.rst deleted file mode 100644 index 1a424e98..00000000 --- a/docs/res/guides/training_module_guide.rst +++ /dev/null @@ -1,136 +0,0 @@ -.. _training_module_guide: - -Training module guide - Unsupervised models -============================================== - -.. important:: - The WNet training is for now available as part of the plugin in the Training module. - Please see the :ref:`training_wnet` section for more information. - -Training module guide - Supervised models -============================================== - -This module allows you to train pre-defined Pytorch models for cell segmentation. -Pre-defined models are stored in napari-cellseg-3d/models. - -.. important:: - Currently, only inference on **3D volumes is supported**. Your image and label folders should both contain a set of - **3D image files**, currently either **.tif** or **.tiff**. Loading a folder of 2D images as a stack is not supported as of yet. - - -Currently, the following pre-defined models are available : - -============== ================================================================================================ -Model Link to original paper -============== ================================================================================================ -VNet `Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation`_ -SegResNet `3D MRI brain tumor segmentation using autoencoder regularization`_ -TRAILMAP_MS An implementation of the `TRAILMAP project on GitHub`_ using `3DUNet for PyTorch`_ -SwinUNetR `Swin UNETR, Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images`_ -============== ================================================================================================ - -.. _Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation: https://arxiv.org/pdf/1606.04797.pdf -.. _3D MRI brain tumor segmentation using autoencoder regularization: https://arxiv.org/pdf/1810.11654.pdf -.. _TRAILMAP project on GitHub: https://github.com/AlbertPun/TRAILMAP -.. _3DUnet for Pytorch: https://github.com/wolny/pytorch-3dunet -.. _Swin UNETR, Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images: https://arxiv.org/abs/2201.01266 - -.. important:: - | The machine learning models used by this program require all images of a dataset to be of the same size. - | Please ensure that all the images you are loading are of the **same size**, or to use the **"extract patches" (in augmentation tab)** with an appropriately small size to ensure all images being used by the model are of a workable size. - -The training module is comprised of several tabs. - - -1) The first one, **Data**, will let you set : - -* The path to the images folder (3D image files) -* The path to the labels folder (3D image files) -* The path to the results folder - -* Whether to copy results to a zip file (for easier transferability) - -* Whether to use pre-trained weights that are provided; if you choose to do so, the model will be initialized with the specified weights, possibly improving performance (transfer learning). - You can also load custom weights; simply ensure they are compatible with the model. - -* The proportion of the dataset to keep for training versus validation; if you have a large dataset, you can set it to a lower value to have more accurate validation steps. - -2) The second tab, **Augmentation**, lets you define dataset and augmentation parameters such as : - -* Whether to use images "as is" (**requires all images to be of the same size and cubic**) or extract patches. - -.. important:: - | **All image sizes used should be as close to a power of two as possible, or equal to a power of two.** - | Images are automatically padded; a 64 pixels cube will be used as is, but a 65 pixel cube will be padded up to 128 pixels, resulting in much higher memory use. - -* If you're extracting patches : - - * The size of patches to be extracted (ideally, please use a value **close or equal to a power of two**, such as 120 or 60 to ensure correct size. See above note.) - * The number of samples to extract from each of your images. A larger number will likely mean better performances, but longer training and larger memory usage. - -.. note:: If you're using a single image (preferably large) it is recommended to enable patch extraction. - -* Whether to perform data augmentation or not (elastic deforms, intensity shifts. random flipping,etc). - Ideally it should always be enabled, but you can disable it if it causes issues. - - -3) The third contains training related parameters : - -* The **model** to use for training (see table above) -* The **loss function** used for training (see table below) -* The **learning rate** of the optimizer. Setting it to a lower value if you're using pre-trained weights can improve performance. -* The **batch size** (larger means quicker training and possibly better performance but increased memory usage) -* The **number of epochs** (a possibility is to start with 60 epochs, and decrease or increase depending on performance.) -* The **epoch interval** for validation (for example, if set to two, the module will use the validation dataset to evaluate the model with the dice metric every two epochs.) -* The **schedular patience**, which is the amount of epoch at a plateau that is waited for until the learning rate is reduced -* The **scheduler factor**, which is the factor by which to reduce the learning rate once a plateau is reached -* Whether to use deterministic training, and the seed to use. - -.. note:: - If the dice metric is better on a given validation interval, the model weights will be saved in the results folder. - -The available loss functions are : - -======================== ================================================================================================ -Function Reference -======================== ================================================================================================ -Dice loss `Dice Loss from MONAI`_ with ``sigmoid=true`` -Generalized Dice loss `Generalized dice Loss from MONAI`_ with ``sigmoid=true`` -Dice-CE loss `Dice-CrossEntropy Loss from MONAI`_ with ``sigmoid=true`` -Tversky loss `Tversky Loss from MONAI`_ with ``sigmoid=true`` -======================== ================================================================================================ - -.. _Dice Loss from MONAI: https://docs.monai.io/en/stable/losses.html#diceloss -.. _Focal Loss from MONAI: https://docs.monai.io/en/stable/losses.html#focalloss -.. _Dice-focal Loss from MONAI: https://docs.monai.io/en/stable/losses.html#dicefocalloss -.. _Generalized dice Loss from MONAI: https://docs.monai.io/en/stable/losses.html#generalizeddiceloss -.. _Dice-CrossEntropy Loss from MONAI: https://docs.monai.io/en/stable/losses.html#diceceloss -.. _Tversky Loss from MONAI: https://docs.monai.io/en/stable/losses.html#tverskyloss - -Once you are ready, press the Start button to begin training. The module will automatically load your dataset, -perform data augmentation if you chose to, select a CUDA device if one is present, and train the model. - -.. note:: - You can stop the training at any time by clicking on the start button again. - - **The training will stop after the next batch has been processed, and will try to save the model. Please note that results might be broken if you stop the training.** - -.. note:: - You can save the log with the button underneath it to record the losses and validation metrics numerical values at each step. This log is autosaved as well when training completes. - -After two validations steps have been performed (depending on the interval you set), -the training loss values and validation metrics will be automatically plotted -and shown on napari every time a validation step completes. -This plot automatically saved each time validation is performed for now. -The final version is stored separately in the results folder. - -.. figure:: ../images/plots_train.png - :align: center - - Example of plots displayed by the training module after 40 epochs - -Source code --------------------------------- -* :doc:`../code/plugin_model_training` -* :doc:`../code/model_framework` -* :doc:`../code/workers` diff --git a/docs/res/guides/training_wnet.rst b/docs/res/guides/training_wnet.rst deleted file mode 100644 index 974a90e9..00000000 --- a/docs/res/guides/training_wnet.rst +++ /dev/null @@ -1,60 +0,0 @@ -.. _training_wnet: - -WNet model training -=================== - -This plugin provides a reimplemented, custom version of the WNet model from `WNet, A Deep Model for Fully Unsupervised Image Segmentation`_. -In order to train your own model, you may use the provided Jupyter notebook; support for in-plugin training might be added in the future. - -The WNet uses brightness to cluster objects vs background; to get the most out of the model please use image regions with minimal -artifacts. You may then train one of the supervised models in order to achieve more resilient segmentation if you have many artifacts. - -The WNet should not require a very large amount of data to train, but during inference images should be similar to those -the model was trained on; you can retrain from our pretrained model to your set of images to quickly reach good performance. - -The model has two losses, the SoftNCut loss which clusters pixels according to brightness, and a reconstruction loss, either -Mean Square Error (MSE) or Binary Cross Entropy (BCE). -Unlike the original paper, these losses are added in a weighted sum and the backward pass is performed for the whole model at once. -The SoftNcuts is bounded between 0 and 1; the MSE may take large positive values. - -For good performance, one should wait for the SoftNCut to reach a plateau; the reconstruction loss must also diminish but it's generally less critical. - -Parameters -------------------------------- - -When using the WNet training module, additional options will be provided in the Advanced tab of the training module: - -- Number of classes : number of classes to segment (default 2). Additional classes will result in a more progressive segmentation according to brightness; can be useful if you have "halos" around your objects or artifacts with a significantly different brightness. -- Reconstruction loss : either MSE or BCE (default MSE). MSE is more sensitive to outliers, but can be more precise; BCE is more robust to outliers but can be less precise. - -- NCuts parameters: - - Intensity sigma : standard deviation of the feature similarity term (brightness here, default 1) - - Spatial sigma : standard deviation of the spatial proximity term (default 4) - - Radius : radius of the loss computation in pixels (default 2) - -.. note:: - Intensity sigma depends on pixel values in the image. The default of 1 is tailored to images being mapped between 0 and 100, which is done automatically by the plugin. -.. note:: - Raising the radius might improve performance in some cases, but will also greatly increase computation time. - -- Weights for the sum of losses : - - NCuts weight : weight of the NCuts loss (default 0.5) - - Reconstruction weight : weight of the reconstruction loss (default 0.5*1e-2) - -.. note:: - The weight of the reconstruction loss should be adjusted according to its empirical value; ideally the reconstruction loss should be of the same order of magnitude as the NCuts loss after being multiplied by its weight. - -Common issues troubleshooting ------------------------------- -If you do not find a satisfactory answer here, please do not hesitate to `open an issue`_ on GitHub. - -- **The NCuts loss explodes after a few epochs** : Lower the learning rate, first by a factor of two, then ten. - -- **The NCuts loss does not converge and is unstable** : - The normalization step might not be adapted to your images. Disable normalization and change intensity_sigma according to the distribution of values in your image. For reference, by default images are remapped to values between 0 and 100, and intensity_sigma=1. - -- **Reconstruction (decoder) performance is poor** : switch to BCE and set the scaling factor of the reconstruction loss ot 0.5, OR adjust the weight of the MSE loss to make it closer to 1 in the weighted sum. - - -.. _WNet, A Deep Model for Fully Unsupervised Image Segmentation: https://arxiv.org/abs/1711.08506 -.. _open an issue: https://github.com/AdaptiveMotorControlLab/CellSeg3d/issues diff --git a/docs/res/guides/utils_module_guide.rst b/docs/res/guides/utils_module_guide.rst deleted file mode 100644 index 64e8a3ce..00000000 --- a/docs/res/guides/utils_module_guide.rst +++ /dev/null @@ -1,46 +0,0 @@ -.. _utils_module_guide: - -Label conversion utility guide -================================== - -This utility will let you convert labels to various different formats. - -You will have to specify the results directory for saving; afterwards you can run each action on a folder or on the currently selected layer. - -You can : - -* Crop 3D volumes : - Please refer to :ref:`cropping_module_guide` for a guide on using the cropping utility. - -* Convert to instance labels : - This will convert 0/1 semantic labels to instance label, with a unique ID for each object. - The available methods for this are : - - * Connected components : simple method that will assign a unique ID to each connected component. Does not work well for touching objects (objects will often be fused), works for anisotropic volumes. - * Watershed : method based on topographic maps. Works well for touching objects and anisotropic volumes; touching objects may be fused. - * Voronoi-Otsu : method based on Voronoi diagrams. Works well for touching objects but only for isotropic volumes. -* Convert to semantic labels : - This will convert instance labels with unique IDs per object into 0/1 semantic labels, for example for training. - -* Remove small objects : - You can specify a size threshold in pixels; all objects smaller than this size will be removed in the image. - -* Resize anisotropic images : - Specify the resolution of your microscope to remove anisotropy from images. - -.. important:: Does not work for instance labels currently. - -* Threshold images : - Remove all values below a threshold in an image. - -.. figure:: ../images/converted_labels.png - :scale: 30 % - :align: center - - Example of instance labels (left) converted to semantic labels (right) - -Source code -------------------------------------------------- - -* :doc:`../code/plugin_base` -* :doc:`../code/plugin_convert` diff --git a/docs/res/images/WNet_architecture.svg b/docs/res/images/WNet_architecture.svg deleted file mode 100644 index e513996a..00000000 --- a/docs/res/images/WNet_architecture.svg +++ /dev/null @@ -1,817 +0,0 @@ - - - - - - -model - - - -0 - - -input-tensor -depth:0 - -(1, 1, 64, 64, 64) - - - -1 - - -Sequential -depth:3 - -input: - -(1, 1, 64, 64, 64) - -output: - -(1, 64, 64, 64, 64) - - - -0->1 - - - - - -2 - - -MaxPool3d -depth:2 - -input: - -(1, 64, 64, 64, 64) - -output: - -(1, 64, 32, 32, 32) - - - -1->2 - - - - - -15 - - -cat -depth:2 - -input: - -2 x (1, 64, 64, 64, 64) - -output: - -(1, 128, 64, 64, 64) - - - -1->15 - - - - - -3 - - -Sequential -depth:3 - -input: - -(1, 64, 32, 32, 32) - -output: - -(1, 128, 32, 32, 32) - - - -2->3 - - - - - -4 - - -MaxPool3d -depth:2 - -input: - -(1, 128, 32, 32, 32) - -output: - -(1, 128, 16, 16, 16) - - - -3->4 - - - - - -12 - - -cat -depth:2 - -input: - -2 x (1, 128, 32, 32, 32) - -output: - -(1, 256, 32, 32, 32) - - - -3->12 - - - - - -5 - - -Sequential -depth:3 - -input: - -(1, 128, 16, 16, 16) - -output: - -(1, 256, 16, 16, 16) - - - -4->5 - - - - - -6 - - -MaxPool3d -depth:2 - -input: - -(1, 256, 16, 16, 16) - -output: - -(1, 256, 8, 8, 8) - - - -5->6 - - - - - -9 - - -cat -depth:2 - -input: - -2 x (1, 256, 16, 16, 16) - -output: - -(1, 512, 16, 16, 16) - - - -5->9 - - - - - -7 - - -Sequential -depth:3 - -input: - -(1, 256, 8, 8, 8) - -output: - -(1, 512, 8, 8, 8) - - - -6->7 - - - - - -8 - - -ConvTranspose3d -depth:2 - -input: - -(1, 512, 8, 8, 8) - -output: - -(1, 256, 16, 16, 16) - - - -7->8 - - - - - -8->9 - - - - - -10 - - -Sequential -depth:3 - -input: - -(1, 512, 16, 16, 16) - -output: - -(1, 256, 16, 16, 16) - - - -9->10 - - - - - -11 - - -ConvTranspose3d -depth:2 - -input: - -(1, 256, 16, 16, 16) - -output: - -(1, 128, 32, 32, 32) - - - -10->11 - - - - - -11->12 - - - - - -13 - - -Sequential -depth:3 - -input: - -(1, 256, 32, 32, 32) - -output: - -(1, 128, 32, 32, 32) - - - -12->13 - - - - - -14 - - -ConvTranspose3d -depth:2 - -input: - -(1, 128, 32, 32, 32) - -output: - -(1, 64, 64, 64, 64) - - - -13->14 - - - - - -14->15 - - - - - -16 - - -Sequential -depth:3 - -input: - -(1, 128, 64, 64, 64) - -output: - -(1, 2, 64, 64, 64) - - - -15->16 - - - - - -17 - - -Softmax -depth:2 - -input: - -(1, 2, 64, 64, 64) - -output: - -(1, 2, 64, 64, 64) - - - -16->17 - - - - - -18 - - -Sequential -depth:3 - -input: - -(1, 2, 64, 64, 64) - -output: - -(1, 64, 64, 64, 64) - - - -17->18 - - - - - -35 - - -output-tensor -depth:0 - -(1, 2, 64, 64, 64) - - - -17->35 - - - - - -19 - - -MaxPool3d -depth:2 - -input: - -(1, 64, 64, 64, 64) - -output: - -(1, 64, 32, 32, 32) - - - -18->19 - - - - - -32 - - -cat -depth:2 - -input: - -2 x (1, 64, 64, 64, 64) - -output: - -(1, 128, 64, 64, 64) - - - -18->32 - - - - - -20 - - -Sequential -depth:3 - -input: - -(1, 64, 32, 32, 32) - -output: - -(1, 128, 32, 32, 32) - - - -19->20 - - - - - -21 - - -MaxPool3d -depth:2 - -input: - -(1, 128, 32, 32, 32) - -output: - -(1, 128, 16, 16, 16) - - - -20->21 - - - - - -29 - - -cat -depth:2 - -input: - -2 x (1, 128, 32, 32, 32) - -output: - -(1, 256, 32, 32, 32) - - - -20->29 - - - - - -22 - - -Sequential -depth:3 - -input: - -(1, 128, 16, 16, 16) - -output: - -(1, 256, 16, 16, 16) - - - -21->22 - - - - - -23 - - -MaxPool3d -depth:2 - -input: - -(1, 256, 16, 16, 16) - -output: - -(1, 256, 8, 8, 8) - - - -22->23 - - - - - -26 - - -cat -depth:2 - -input: - -2 x (1, 256, 16, 16, 16) - -output: - -(1, 512, 16, 16, 16) - - - -22->26 - - - - - -24 - - -Sequential -depth:3 - -input: - -(1, 256, 8, 8, 8) - -output: - -(1, 512, 8, 8, 8) - - - -23->24 - - - - - -25 - - -ConvTranspose3d -depth:2 - -input: - -(1, 512, 8, 8, 8) - -output: - -(1, 256, 16, 16, 16) - - - -24->25 - - - - - -25->26 - - - - - -27 - - -Sequential -depth:3 - -input: - -(1, 512, 16, 16, 16) - -output: - -(1, 256, 16, 16, 16) - - - -26->27 - - - - - -28 - - -ConvTranspose3d -depth:2 - -input: - -(1, 256, 16, 16, 16) - -output: - -(1, 128, 32, 32, 32) - - - -27->28 - - - - - -28->29 - - - - - -30 - - -Sequential -depth:3 - -input: - -(1, 256, 32, 32, 32) - 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zEE!1vxc6Dm3}%F-kA!E@jQax+edD4~Hf~?gx(Ll0%{kJ(HJu*fB{u#JeXx-&NlV)` zGz;zUN)_MpDLq?0)th_^&BgHflQG~U1ZXg>nAxxAp9k&(H)e%FLb4#4sxWZ%!L1m# zeu}FJhRqw7Mq5pH^hZXKws%H8pqmsQ#(NSbleb*=$TTXn723!C2UO#br`XUJcgF{f zfYlWoyp9ykdPawUGSpY(CgtRW%Bw-Wkyb+PV;l>&zG3^JYZ<0V}vT>Lu7LeYoczFqNkNrf?TG|J-hll^#E-)V{QrX_;m;ElpHc zPvr05g6E?JI~2v#$7;Z(WrqEKSe^ZE>zuSh6X`?$X*Ku%o}UlaFsBZtzyHrG!2fRJ zS#Q2}SW?lpySYP=&)UWhBBPc6_sos|ZahunKjnG+e>?l^;a$ZLsR#b!9oBxo?7gmB Ma=ch|!Rz7w0R!?*LjV8( diff --git a/docs/res/welcome.rst b/docs/res/welcome.rst deleted file mode 100644 index dbe2b849..00000000 --- a/docs/res/welcome.rst +++ /dev/null @@ -1,123 +0,0 @@ -Introduction -=================== - - -Here you will find instructions on how to use the plugin for direct segmentation in 3D. -If the installation was successful, you'll see the napari-cellseg3D plugin -in the Plugins section of napari. - -This plugin was initially developed for the review of labeled cell volumes [#]_ from mice whole-brain samples -imaged by mesoSPIM microscopy [#]_ , and for training and using segmentation models from the MONAI project [#]_, -or any custom model written in PyTorch. -It should be adaptable to other tasks related to detection of 3D objects, as long as labels are available. - - -From this page you can access the guides on the several modules available for your tasks, such as : - -* Main modules : - * Training : :ref:`training_module_guide` - * Inference: :ref:`inference_module_guide` - * Review : :ref:`loader_module_guide` -* Utilities : - * Cropping (3D) : :ref:`cropping_module_guide` - * Other utilities : :ref:`utils_module_guide` - -.. - * Convert labels : :ref:`utils_module_guide` -.. - * Compute scores : :ref:`metrics_module_guide` - -* Advanced : - * Training WNet (in jupyter, support might be added for plugin later) : :ref:`training_wnet` - * Defining custom models directly in the plugin (WIP) : :ref:`custom_model_guide` - - -Installation --------------------------------------------- - -You can install `napari-cellseg3d` via [pip]: - - ``pip install napari-cellseg3d`` - -For local installation after cloning, please run in the CellSeg3D folder: - - ``pip install -e .`` - -Requirements --------------------------------------------- - -.. note:: - A **CUDA-capable GPU** is not needed but **very strongly recommended**, especially for training and possibly inference. - -.. important:: - This package requires you have napari installed with PyQt5 or PySide2 first. - If you do not have a Qt backend you can use : - ``pip install napari-cellseg3d[all]`` - to install PyQt5 by default. - -It also depends on PyTorch and some optional dependencies of MONAI. These come in the pip package as requirements, but if -you need further assistance see below. - -* For help with PyTorch, please see `PyTorch's website`_ for installation instructions, with or without CUDA depending on your hardware. - Depending on your setup, you might wish to install torch first. - -* If you get errors from MONAI regarding missing readers, please see `MONAI's optional dependencies`_ page for instructions on getting the readers required by your images. - -.. _MONAI's optional dependencies: https://docs.monai.io/en/stable/installation.html#installing-the-recommended-dependencies -.. _PyTorch's website: https://pytorch.org/get-started/locally/ - - -Usage --------------------------------------------- - -To use the plugin, please run: - - ``napari`` - -Then go into Plugins > napari-cellseg3d, and choose which tool to use: - -- **Review**: This module allows you to review your labels, from predictions or manual labeling, and correct them if needed. It then saves the status of each file in a csv, for easier monitoring -- **Inference**: This module allows you to use pre-trained segmentation algorithms on volumes to automatically label cells -- **Training**: This module allows you to train segmentation algorithms from labeled volumes -- **Utilities**: This module allows you to use several utilities, e.g. to crop your volumes and labels, compute prediction scores or convert labels -- **Help/About...** : Quick access to version info, Github page and docs - -See above for links to detailed guides regarding the usage of the modules. - -Acknowledgments & References ---------------------------------------------- -This plugin has been developed by Cyril Achard and Maxime Vidal, supervised by Mackenzie Mathis for the `Mathis Laboratory of Adaptive Motor Control`_. - -We also greatly thank Timokleia Kousi for her contributions to this project and the `Wyss Center`_ for project funding. - -The TRAILMAP models and original weights used here were ported to PyTorch but originate from the `TRAILMAP project on GitHub`_ [1]_. -We also provide a model that was trained in-house on mesoSPIM nuclei data in collaboration with Dr. Stephane Pages and Timokleia Kousi. - -This plugin mainly uses the following libraries and software: - -* `napari`_ - -* `PyTorch`_ - -* `MONAI project`_ (various models used here are credited `on their website`_) - -* `pyclEsperanto`_ (for the Voronoi Otsu labeling) by Robert Haase - -* A custom re-implementation of the `WNet model`_ by Xia and Kulis [#]_ - -.. _Mathis Laboratory of Adaptive Motor Control: http://www.mackenziemathislab.org/ -.. _Wyss Center: https://wysscenter.ch/ -.. _TRAILMAP project on GitHub: https://github.com/AlbertPun/TRAILMAP -.. _napari: https://napari.org/ -.. _PyTorch: https://pytorch.org/ -.. _MONAI project: https://monai.io/ -.. _on their website: https://docs.monai.io/en/stable/networks.html#nets -.. _pyclEsperanto: https://github.com/clEsperanto/pyclesperanto_prototype -.. _WNet model: https://arxiv.org/abs/1711.08506 - -.. rubric:: References - -.. [#] Mapping mesoscale axonal projections in the mouse brain using a 3D convolutional network, Friedmann et al., 2020 ( https://pnas.org/cgi/doi/10.1073/pnas.1918465117 ) -.. [#] The mesoSPIM initiative: open-source light-sheet microscopes for imaging cleared tissue, Voigt et al., 2019 ( https://doi.org/10.1038/s41592-019-0554-0 ) -.. [#] MONAI Project website ( https://monai.io/ ) -.. [#] W-Net: A Deep Model for Fully Unsupervised Image Segmentation, Xia and Kulis, 2018 ( https://arxiv.org/abs/1711.08506 ) diff --git a/docs/source/code/api.rst b/docs/source/code/api.rst new file mode 100644 index 00000000..77a2036f --- /dev/null +++ b/docs/source/code/api.rst @@ -0,0 +1,11 @@ +API reference +================= + +.. autosummary:: + :toctree: _autosummary + :recursive: + + napari_cellseg3d.interface + napari_cellseg3d.code_models + napari_cellseg3d.code_plugins + napari_cellseg3d.utils diff --git a/docs/source/guides/cropping_module_guide.rst b/docs/source/guides/cropping_module_guide.rst new file mode 100644 index 00000000..701a0b4e --- /dev/null +++ b/docs/source/guides/cropping_module_guide.rst @@ -0,0 +1,83 @@ +.. _cropping_module_guide: + +Cropping✂️ +========== + +.. figure:: ../images/plugin_crop.png + :align: center + + Layout of the cropping module + +**Cropping** allows you to crop your volumes and labels dynamically, +by selecting a fixed size volume and moving it around the image. + +To access it: + - Navigate to **`Plugins -> Utilities`**. + - Choose **`Crop`** from the bottom menu. + +Once cropped, you have multiple options to save the volumes and labels: + - Use the **`Quicksave`** button in Napari. + - Select the layer and then go to **`File` -> `Save selected layers`**. + - With the correct layer highlighted, simply press **`CTRL + S`**. + +.. Note:: + For more on utility tools, see :doc:`utils_module_guide`. + +Launching the cropping process +------------------------------ +1. From the layer selection dropdown menu, select your image. If you want to crop a second image with the same dimensions simultaneously, +check the **`Crop another image simultaneously`** option and then select the relevant layer. + +2. Define your desired cropped volume size. This size will remain fixed for the duration of the session. +To update the size, you will need to restart the process. + +3. You can also correct the anisotropy, if you work with anisotropic data: simply set your microscope's resolution in microns. + +.. important:: + This will scale the image in the viewer, but saved images will **still be anisotropic.** To resize your image, see :doc:`utils_module_guide`. + +4. Press **`Start`** to start the cropping process. +If you'd like to modify the volume size, change the parameters as described and hit **`Start`** again. + +Creating new layers +------------------- +To "zoom in" on a specific portion of your volume: + +- Use the `Create new layers` checkbox next time you hit `Start`. This option lets you make an additional cropping layer instead of replacing the current one. + +- This way, you can first select your region of interest by using the tool as described above, then enable the option, select the cropped region produced before as the input layer, and define a smaller crop size in order to further crop within your region of interest. + +Interface & functionalities +--------------------------- + +.. figure:: ../images/cropping_process_example.png + :align: center + + Example of the cropping process interface. + +Once you have launched the review process, you will gain control over three sliders, which will let +you to **adjust the position** of the cropped volumes and labels in the x,y and z positions. + +.. note:: + * If your **cropped volume isnt visible**, consider changing the **colormap** of the image and the cropped + volume to improve their visibility. + * You may want to adjust the **opacity** and **contrast thresholds** depending on your image. + * If the image appears empty: + - Right-click on the contrast limits sliders. + - Select **`Full Range`** and then **`Reset`**. + +Saving your cropped volume +-------------------------- +- When you are done, you can save the cropped volume and labels directly with the **`Quicksave`** button located at the bottom left. Your work will be saved in the same folder as the image you choose. + +- If you want more options (name, format) when saving: + - Select the desired layer. + - Navigate in the napari menu to **`File -> Save selected layer`**. + - Press **`CTRL+S`** once you have selected the correct layer. + + +Source code +------------------------------------------------- + +* :doc:`../code/_autosummary/napari_cellseg3d.code_plugins.plugin_crop` +* :doc:`../code/_autosummary/napari_cellseg3d.code_plugins.plugin_base` diff --git a/docs/res/guides/custom_model_template.rst b/docs/source/guides/custom_model_template.rst similarity index 81% rename from docs/res/guides/custom_model_template.rst rename to docs/source/guides/custom_model_template.rst index b7eb65e3..7d7f2264 100644 --- a/docs/res/guides/custom_model_template.rst +++ b/docs/source/guides/custom_model_template.rst @@ -1,6 +1,6 @@ .. _custom_model_guide: -Advanced : Declaring a custom model +Advanced : Custom models ============================================= .. warning:: @@ -11,7 +11,6 @@ Advanced : Declaring a custom model To add a custom model, you will need a **.py** file with the following structure to be placed in the *napari_cellseg3d/models* folder:: class ModelTemplate_(ABC): # replace ABC with your PyTorch model class name - use_default_training = True # not needed for now, will serve for WNet training if added to the plugin weights_file = ( "model_template.pth" # specify the file name of the weights file only ) # download URL goes in pretrained_models.json @@ -30,6 +29,6 @@ To add a custom model, you will need a **.py** file with the following structure .. note:: - **WIP** : Currently you must modify :ref:`model_framework.py` as well : import your model class and add it to the ``model_dict`` attribute + **WIP** : Currently you must modify :doc:`model_framework.py <../code/_autosummary/napari_cellseg3d.code_models.model_framework>` as well : import your model class and add it to the ``model_dict`` attribute .. _file an issue: https://github.com/AdaptiveMotorControlLab/CellSeg3d/issues diff --git a/docs/source/guides/detailed_walkthrough.rst b/docs/source/guides/detailed_walkthrough.rst new file mode 100644 index 00000000..2858c3f7 --- /dev/null +++ b/docs/source/guides/detailed_walkthrough.rst @@ -0,0 +1,271 @@ +.. _detailed_walkthrough: + +Detailed walkthrough - Supervised learning +========================================== + +This guide will show you step-by-step how to use the plugin's workflow, beginning with human annotated datasets, to generating predictions on new volumes. + +Setting up images and labels +---------------------------- + +CellSeg3D is designed for cleared-brain tissue data (collected using mesoSPIM ligthsheet systems). +Here's what we offer: + +- **Ready-to-use deep learning models**: Optimised for whole-neuron imaging in the cortex. +- **MONAI models support** +- **Trailmap in Pytorch**: We've integrated TRAILMAP into PyTorch, harnessing mesoSPIM data. + +Ready to get started? Ensure you have part of a cleared brain from mesoSPIM imaging as a big **.tif** file and its label at hand. + +For quick model checks, check the "Inference" sections in our docs. + + +.. figure:: ../images/init_image_labels.png + :scale: 40 % + :align: center + + Example of an anisotropic volume (i.e., often times the z resolution is not the same as x and y) and its associated labels. + + +.. note:: + This guide emphasizes a human-in-the-loop review of labels. + If you need to start labeling volumes from scratch or correct initial labels, we recommend consulting the sections on :ref:`Review` section right after :ref:`Cropping `. + + +Cropping +********* +.. _walkthrough_cropping: + +To reduce memory requirements and build a dataset from a single, large volume, +you can use the **cropping** tool to extract multiple smaller images from a large volume for training. + +1. Load your primary image and any corresponding labels. If cropping labels, ensure "Crop labels simultaneously" is selected. +2. Choose your desired volume size for extraction. + +.. note:: + For optimal training, opt for cubic images set to powers of two : a default of :math:`64^3` should be a good start if you're unsure. + Stick to this size across your dataset. + However, if specific regions need varied sizes, you can compensate for it during training. + When running inference on multiple images in a folder, try to maintain size uniformity. + + +1. Use the slider to choose the exact areas you want to extract. +2. Options for saving: + - Use the **quicksave** feature + - Or, select the intended layer and press **`CTRL+S`** + +.. figure:: ../images/cropping_process_example.png + :align: center + + Cropping module layout + +Label conversion utility +************************ + +Assuming you have instance labels, you'll need to convert them to semantic labels before using them for training. + +.. note:: + Instance labels used in training will be converted to semantic labels automatically, but this might not always result in the desired behavior. + +Step-by-Step Instructions: + 1. Launch the *Convert* tab within Utilities. + 2. Specify your preferred output directory. + 3. Load the folder with your cropped volumes under **`Convert Folder`**. + 4. Click on **`Convert to semantic labels`**. + +.. figure:: ../images/converted_labels.png + :scale: 40 % + :align: center + + Example of instance labels from above converted to semantic labels + +To remove small objects, or to convert a single image, use the **`CTRL+O`** shortcut to open the image you wish to manipulate. +Then, select its corresponding layer, and start the process using layer-specific controls. + +Models for object detection +--------------------------- + +Training Guide +************** + +1. Preparation: + + - **Size**: Ensure that your images are appropriately sized. Please see the cropping section for guidelines. + - **Data paths**: Input the paths for your images and labels. Additionally, specify an output location where the results will be saved. + +2. Training Options and Features: + + - **Transfer weights**: While we offer pre-trained weights designed specifically for cleared brain tissue imagery, the flexibility to incorporate your own weights exists. If you're choosing the latter, ensure they are compatible with the model you selected (see : :ref:`custom_model_guide`). + - **Validation proportion**: Decide on a specific percentage to determine the number of images which will be used for training versus validation. While validation can in theory work with even one image, the richness of data in validation will greatly improve model's performance. Use 90% only if you have a very small dataset (less than 5 images). + - **Save as zip** : Copies the results in a zip archive for easier transfer. + +3. Data augmentation: + + * If you have cropped cubic images with a power of two as the edge length, you do not need to extract patches, your images are usable as is. + * However, if you are using larger images or with variable sizes, you can use this option to auto-extract smaller patches that will be automatically padded back to a power of two no matter the size you choose. For optimal performance, make sure to use a value close or equal to a power of two still, such as 64 or 120. + +.. important:: + Using a large value for the size will cause memory issues. If this happens, restart the work with smaller volumes. + +You also have the option to use data augmentation, which can improve performance and generalization. +In most cases this should left enabled. + +1. Model selection: You can choose from a variety of models, based on the needs of your project: + + * **SegResNet** is a lightweight model (low memory requirements) from MONAI originally designed for 3D fMRI data. + * **VNet** is a larger (than SegResNet) CNN from MONAI designed for medical image segmentation. + * **TRAILMAP** is our implementation in PyTorch additionally trained on mouse cortical neural nuclei from mesoSPIM data. + * **SwinUNetR** is a MONAI implementation of the SwinUNetR model. It is costly in compute and memory, but can achieve high performance. + * **WNet** is our reimplementation of an unsupervised model, which can be used to produce segmentation without labels. + + +* **The loss** : For 3D volume object detection, the Dice or Dice-focal Loss is the most efficient. + +* **Batch size** : Chose a value suited to your memory. To avoid memory issues, leave it to one. + +* **Learning rate** : Default to 1e-3 unless using specific weights, then adjust. + +* **Number of epochs** : More epochs mean longer training but potentially better results. Begin with 40 epochs. + +.. note:: + During training, you can monitor the process using plots : ideally the validation curve should ascend + whereas the loss curve should descend. If the validation starts lowering after reaching a maximum, but the loss still decreases, + it could indicate over-fitting, which will negatively impact generalization for the given weights. + You might want use weights generated from the epoch with the maximum validation score if that is the case. + +.. figure:: ../images/plots_train.png + :align: center + + Plots displayed by the training module after 40 epochs + +* **Validation interval** : Dictates how frequently the model halts training to validate its current performance. If the value is e.g. 2, the training will stop every 2 epochs to perform validation and save the results if the score is better than the previous one.Pausing frequently (smaller value) ensures you capture the best model state more often. Yet, it extends the overall training time. + +* **Deterministic training** : To guarantee reproducibility in results across training sessions. When deterministic training is enabled, remember the seed you've inputted. Using the same seed with the same model, images, and parameters should consistently yield similar results. See `MONAI deterministic training`_. + +.. _MONAI deterministic training: https://docs.monai.io/en/stable/utils.html#module-monai.utils.misc + +Once you set all these parameters, you can start the training. You can monitor progress with the plots; should you want to stop +the training you can do so anytime by pressing the **Start** button again, whose text should change to **Click to stop**. + +In the results folder, you will have access to the weights from training (**.pth** files), +which you can then use in inference. + +Inference +********* + +To start, choose the folder with images ready for inference, and the location you want to store your results. + +Then, select the model you trained (see note below for SegResNet), and load your weights from training. + +.. note:: + If you already trained a SegResNet, set the counter below the model choice to the size of the images you trained the model on. + (Either use the size of the image itself if you did not extract patches, or the size of the nearest superior power of two of the patches you extracted) + + Example : + + * If you used :math:`64^3` whole volumes to train the model, enter :math:`64` in the counter. + * If you extracted :math:`120^3` patches from larger images, enter :math:`128` + + +Use **window inference** when the size of your images is substantial. Ensure the size aligns with your images, as under-sizing might impact the quality of your results. You can keep the dataset on the CPU to reduce memory usage, but this might decelerate the process. + +If you have **anisotropic volumes**, you can compensate by entering the resolution of your microscope. + +By default, inference will calculate and display probability maps (values between 0 and 1). For a segmentation output with distinct labels, modify the threshold to the desired probability. + +If instead you'd prefer instance labels, you can enable instance segmentation and select : + +* The method: + + * **Voronoi-Otsu** : objects will be assigned an ID by using the Voronoi diagram of the centroids of each object, then using Otsu's thresholding to separate them. The sigmas should roughly match cell diameter. + * **Connected components** : Every seperated object above the threshold will be labeled as an instance. + * **Watershed** : Assigns identifiers to objects based on the gradient probability at the their center (set the threshold to a decently high value). + +* **The threshold** : Objects above this threshold will be retained as single instances. +* **Small object removal** : To filter small artifacts; all objects below this volume in pixels will be removed. + +Using instance segmentation, you can also analyze the results by checking the *Save stats to CSV* option. + +This will compute : + +* The volume of each cell in pixels. +* The centroid coordinates in :math:`X,Y,Z`. +* The sphericity of each cell. +* The original size of the image. +* The total volume in pixels. +* The total volume occupied by objects. +* The ratio of :math:`\frac {Volume_{label}} {Volume_{total}}`. +* The total number of unique object instance. + +To visualise some of the results when running on a folder, you can leave the **View results in napari** option checked. + +.. note:: + Too plot your results, check out the `provided notebooks`_ + +.. _provided notebooks: https://github.com/AdaptiveMotorControlLab/CellSeg3d/tree/main/notebooks + + +You can then launch inference and the results will be saved in your specified folder. + +.. figure:::: ../image/inference_results_example.png + + Example of results from inference with original volumes, as well as semantic and instance predictions. + + + +Scoring, review, analysis +---------------------------- + +.. Using the metrics utility module, you can compare the model's predictions to any ground truth labels you might have. + Simply provide your prediction and ground truth labels, and compute the results. + A Dice metric of 1 indicates perfect matching, whereas a score of 0 indicates complete mismatch. + Select which score **you consider as sub-optimal**, and all results below this will be **shown in napari**. + If at any time the **orientation of your prediction labels changed compared to the ground truth**, check the + "Find best orientation" option to compensate for it. + + +Labels review +************************ +.. _walkthrough_reviewing: + +Using the review module, you can correct the model's predictions. +Load your images and labels, and enter the name of the csv file, keeps tracking of the review process( it +records which slices have been checked or not and the time taken). + +See the `napari tutorial on annotation`_ for instruction on correcting and adding labels. + +.. _napari tutorial on annotation: https://napari.org/howtos/layers/labels.html#selecting-a-label + +If you wish to see the surroundings of an object to ensure it should be labeled, +you can use **`Shift+Click`** on the location you wish to see; this will plot +the surroundings of this location for easy viewing. + +.. figure:: ../images/review_process_example.png + :align: center + + Layout of the review module + +Once you finish reviewing, press the **Not checked** button to switch the status to +**checked** and save the time spent in the csv file. + +once satisfied with your review, press the **Save** button to record your work. + +Analysis : Jupyter notebooks +********************************* + +In the `notebooks folder of the repository`_, you can find notebooks for plotting +labels (full_plot.ipynb), and notebooks for plotting the inference results (csv_cell_plot.ipynb). + +Simply enter your folder or csv file path and the notebooks will plot your results. +Make sure you have all required libraries installed and jupyter extensions set up as explained +for the plots to work. + +.. figure:: ../images/stat_plots.png + :align: center + + Example of the plot present in the notebooks. + Coordinates are based on centroids, the size represents the volume, the color, and the sphericity. + +.. _notebooks folder of the repository: https://github.com/AdaptiveMotorControlLab/CellSeg3d/tree/main/notebooks + +With this complete, you can repeat the workflow as needed. diff --git a/docs/source/guides/inference_module_guide.rst b/docs/source/guides/inference_module_guide.rst new file mode 100644 index 00000000..4fd995e0 --- /dev/null +++ b/docs/source/guides/inference_module_guide.rst @@ -0,0 +1,170 @@ +.. _inference_module_guide: + +Inference📊 +============== + +.. figure:: ../images/plugin_inference.png + :align: center + + Layout of the inference module + +**Inference** allows you to use pre-trained segmentation algorithms, written in Pytorch, +to automatically label cells in 3D volumes. + +.. important:: + Currently, the module supports inference on **3D volumes**. When running on folders, make sure that your image folder + only contains a set of **3D image files** saved with the **`.tif`** extension. + Otherwise you can run inference directly on layers within napari. Stacks of 2D files can be loaded as 3D volumes in napari. + +At present, the following pre-trained models are available : + +============== ================================================================================================ +Model Link to original paper +============== ================================================================================================ +SwinUNetR `Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images`_ +SegResNet `3D MRI brain tumor segmentation using autoencoder regularization`_ +WNet `WNet, A Deep Model for Fully Unsupervised Image Segmentation`_ +TRAILMAP_MS An implementation of the `TRAILMAP project on GitHub`_ using `3DUNet for PyTorch`_ +VNet `Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation`_ +============== ================================================================================================ + +.. _Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation: https://arxiv.org/pdf/1606.04797.pdf +.. _3D MRI brain tumor segmentation using autoencoder regularization: https://arxiv.org/pdf/1810.11654.pdf +.. _TRAILMAP project on GitHub: https://github.com/AlbertPun/TRAILMAP +.. _3DUnet for Pytorch: https://github.com/wolny/pytorch-3dunet +.. _Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images: https://arxiv.org/abs/2201.01266 +.. _WNet, A Deep Model for Fully Unsupervised Image Segmentation: https://arxiv.org/abs/1711.08506 + +.. note:: + For WNet-specific instruction please refer to the appropriate section below. + + +Interface and functionalities +----------------------------- + +.. figure:: ../images/inference_plugin_layout.png + :align: right + + Inference parameters + +* **Loading data** : + + | When launching the module, select either an **image layer** or an **image folder** containing the 3D volumes you wish to label. + | When loading from folder : All images with the chosen extension ( currently **.tif**) will be labeled. + | Specify an **output folder**, where the labelled results will be saved. + +* **Model selection** : + + | You can then choose from the listed **models** for inference. + | You may also **load custom weights** rather than the pre-trained ones. Make sure these weights are **compatible** (e.g. produced from the training module for the same model). + | For SegResNet or SwinUNetR with custom weights, you will have to provide the size of images it was trained on to ensure compatibility. (See note below) + +.. note:: + Currently the SegResNet and SwinUNetR models require you to provide the size of the images the model was trained with. + Provided weights use a size of 64, please leave it on the default value if you're not using custom weights. + +* **Inference parameters** : + + * **Window inference**: You can choose to use inference on the entire image at once (disabled) or divide the image (enabled) on smaller chunks, based on your memory constraints. + * **Window overlap**: Define the overlap between windows to reduce border effects; + recommended values are 0.1-0.3 for 3D inference. + * **Keep on CPU**: You can choose to keep the dataset in RAM rather than VRAM to avoid running out of VRAM if you have several images. + * **Device Selection**: You can choose to run inference on either CPU or GPU. A GPU is recommended for faster inference. + +* **Anisotropy** : + + For **anisotropic images** you may set the **resolution of your volume in micron**, to view and save the results without anisotropy. + +* **Thresholding** : + + You can perform thresholding to **binarize your labels**. + All values below the **confidence threshold** will be set to 0. + +.. hint:: + It is recommended to first run without thresholding. You can then use the napari contrast limits to find a good threshold value, + and run inference later with your chosen threshold. + +* **Instance segmentation** : + + | You can convert the semantic segmentation into instance labels by using either the `Voronoi-Otsu`_, `Watershed`_ or `Connected Components`_ method, as detailed in :ref:`utils_module_guide`. + | Instance labels will be saved (and shown if applicable) separately from other results. + + +.. _Watershed: https://scikit-image.org/docs/dev/auto_examples/segmentation/plot_watershed.html +.. _Connected Components: https://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.label +.. _Voronoi-Otsu: https://haesleinhuepf.github.io/BioImageAnalysisNotebooks/20_image_segmentation/11_voronoi_otsu_labeling.html + + +* **Computing objects statistics** : + + You can choose to compute various stats from the labels and save them to a **`.csv`** file for later use. + Statistics include individual object details and general metrics. + For each object : + + * Object volume (pixels) + * :math:`X,Y,Z` coordinates of the centroid + * Sphericity + + + Global metrics : + + * Image size + * Total image volume (pixels) + * Total object (labeled) volume (pixels) + * Filling ratio (fraction of the volume that is labeled) + * The number of labeled objects + + +* **Display options** : + + When running inference on a folder, you can choose to display the results in napari. + If selected, you may choose the display quantity, and whether to display the original image alongside the results. + +Once you are ready, hit the **`Start`** button to begin inference. +The log will keep you updated on the progress. + +.. note:: + You can save the log to keep track of the parameters you ran inference with. + +Once the job has finished, the semantic segmentation will be saved in the output folder. + +| The files will be saved using the following format : +| ``{original_name}_{model}_{date & time}_pred{id}.file_ext`` + +.. hint:: + | Adjust **colormap** or **contrast** to enhance the visibility of labels. + | Experiment with **3D view** and **grid mode** in napari when checking your results. + +Plotting results +---------------- + +In the ``notebooks`` folder you will find a plotting guide for cell statistics derived from the inference module. +Simply load the csv file in the notebook and use the provided functions to plot the desired statistics. + + +Unsupervised model - WNet +------------------------- + +| The `WNet model` is a fully unsupervised model used to segment images without any labels. +| It functions similarly to the above models, with a few notable differences. + +.. _WNet model: https://arxiv.org/abs/1711.08506 + +.. note:: + Our provided, pre-trained model uses an input size of 64x64x64. As such, window inference is always enabled + and set to 64. If you want to use a different size, you will have to train your own model using the options listed in :ref:`training_wnet`. + +For the best inference performance, the model should be retrained on images of the same modality as the ones you want to segment. +Please see :ref:`training_wnet` for more details on how to train your own model. + +.. hint:: + | WNet, as an unsupervised model, may not always output the background class in the same dimension. + | This might cause the result from inference to appear densely populated. + | The plugin will automatically attempt to show the foreground class, but this might not always succeed. + | If the displayed output seems dominated by the background, you can manually adjust the visible class. To do this, **use the slider positioned at the bottom of the napari window**. + +Source code +-------------------------------- +* :doc:`../code/_autosummary/napari_cellseg3d.code_plugins.plugin_model_inference` +* :doc:`../code/_autosummary/napari_cellseg3d.code_models.worker_inference` +* :doc:`../code/_autosummary/napari_cellseg3d.code_models.models` diff --git a/docs/source/guides/installation_guide.rst b/docs/source/guides/installation_guide.rst new file mode 100644 index 00000000..ccedcaca --- /dev/null +++ b/docs/source/guides/installation_guide.rst @@ -0,0 +1,111 @@ +Installation guide ⚙ +====================== +This guide outlines the steps for installing CellSeg3D and its dependencies. The plugin is compatible with Windows, Linux, and MacOS. + +**Note for M1/M2 (ARM64) Mac Users:** +Please refer to the :ref:`section below ` for specific instructions. + +.. warning:: + If you encounter any issues during installation, feel free to open an issue on our `GitHub repository`_. + +.. _GitHub repository: https://github.com/AdaptiveMotorControlLab/CellSeg3d/issues + + +Installing pre-requisites +--------------------------- + +PyQt5 or PySide2 +_____________________ + +CellSeg3D requires either **PyQt5** or **PySide2** as a Qt backend for napari. If you don't have a Qt backend installed: + +.. code-block:: + + pip install napari[all] + +This command installs PyQt5 by default. + +PyTorch +_____________________ + +For PyTorch installation, refer to `PyTorch's website`_ , with or without CUDA according to your hardware. +Select the installation criteria that match your OS and hardware (GPU or CPU). + +.. note:: + While a **CUDA-capable GPU** is not mandatory, it is highly recommended for both training and inference. + + +* Running into MONAI-related errors? Consult MONAI’s optional dependencies for solutions. Please see `MONAI's optional dependencies`_ page for instructions on getting the readers required by your images. + +.. _MONAI's optional dependencies: https://docs.monai.io/en/stable/installation.html#installing-the-recommended-dependencies +.. _PyTorch's website: https://pytorch.org/get-started/locally/ + + + +Installing CellSeg3D +-------------------------------------------- + +.. warning:: + For M1 Mac users, please see the :ref:`section below ` + +**Via pip**: + +.. code-block:: + + pip install napari-cellseg3d + +**Directly in napari**: + +- Navigate to **Plugins > Install/Uninstall Packages** +- Search for ``napari-cellseg3d`` + +**For local installation** (after cloning from GitHub) +Navigate to the cloned CellSeg3D folder and run: + +.. code-block:: + + pip install -e . + +Successful installation will add the napari-cellseg3D plugin to napari’s Plugins section. + + +M1/M2 (ARM64) Mac installation +------------------------------- +.. _ARM64_Mac_installation: + +For ARM64 Macs, we recommend using our custom CONDA environment. This is particularly important for M1 or M2 MacBooks. + +Start by installing `miniconda3`_. + +.. _miniconda3: https://docs.conda.io/projects/conda/en/latest/user-guide/install/macos.html + +1. **Clone the repository** (`link `_): + +.. code-block:: + + git clone https://github.com/AdaptiveMotorControlLab/CellSeg3d.git + +2. **Create the Conda Environment** : +In the terminal, navigate to the CellSeg3D folder: + +.. code-block:: + + conda env create -f conda/napari_cellseg3d_m1.yml + +3. **Activate the environment** : + +.. code-block:: + + conda activate napari_cellseg3d_m1 + +4. **Install PyQt5 via conda** : + +.. code-block:: + + conda install -c anaconda pyqt + +5. **Install the plugin** : + +.. code-block:: + + pip install napari-cellseg3d diff --git a/docs/source/guides/metrics_module_guide.rst b/docs/source/guides/metrics_module_guide.rst new file mode 100644 index 00000000..b31f0e14 --- /dev/null +++ b/docs/source/guides/metrics_module_guide.rst @@ -0,0 +1,40 @@ +.. _metrics_module_guide: + +Metrics utility guide 📈 +======================== + +.. figure:: ../images/plot_example_metrics.png + :scale: 35 % + :align: right + + Dice metric plot result + +This tool computes the Dice coefficient, a similarity measure, between two sets of label folders. +Ranges from 0 (no similarity) to 1 (perfect similarity). + +The Dice coefficient is defined as : + +.. math:: \frac {2|X \cap Y|} {|X|+|Y|} + +Required parameters: +-------------------- + +* Ground Truth Labels folder +* Prediction Labels folder +* Threshold for sufficient score. Pairs below this score are highlighted in the viewer and marked in red on the plot. +* Whether to automatically determine the best orientation for the computation by rotating and flipping; + useful if your images have varied orientation. + +.. note:: + - The tool might rotate and flip images randomly to find the best Dice coefficient. If you have small images with a large number of labels, this might lead to metric inaccuracies. Low score images might be in the wrong orientation when displayed for comparison. + - This tool assumes that **predictions are padded to a power of two.** Ground truth labels can be smaller, as they will be padded to match the prediction size. + - Your files should have names that can be sorted numerically; please ensure that each ground truth label has a matching prediction label. + +To begin, press the **`Compute Dice`** button. This will plot the Dice score for each ground truth-prediction labels pair. +Pairs below the threshold will be displayed on the viewer for verification, ground truth appears in **blue**, and low score predictions in **red**. + +Source code +------------------------------------------------- + +* :doc:`../code/plugin_base` +* :doc:`../code/plugin_metrics` diff --git a/docs/source/guides/review_module_guide.rst b/docs/source/guides/review_module_guide.rst new file mode 100644 index 00000000..907aa1f4 --- /dev/null +++ b/docs/source/guides/review_module_guide.rst @@ -0,0 +1,76 @@ +.. _review_module_guide: + +Review🔍 +================================= + +.. figure:: ../images/plugin_review.png + :align: center + + Layout of the review module + +**Review** allows you to inspect your labels, which may be manually created or predicted by a model, and make necessary corrections. +The system will save the updated status of each file in a csv file. +Additionally, the time taken per slice review is logged, enabling efficient monitoring. + +Launching the review process +--------------------------------- +.. figure:: ../images/Review_Parameters.png + :align: right + :width: 300px + + +1. **Data paths:** + - *Starting a new review:* Choose the **`New review`** option, and select the corresponding layers within Napari. + - *Continuing an existing review:* Select the **`Existing review`** option, and choose the folder that contains the image, labels, and CSV file. + +.. note:: + Cellseg3D supports 3D **`.tif`** files at the moment. + If you have a stack, open it as a folder in Napari, then save it as a single **`.tif`** file. + +2. **Managing anisotropic data:** + Check this option to scale your images to visually remove the anisotropy, so as to make review easier. + +.. note:: + The results will be saved as anisotropic images. If you want to resize them, check the :doc:`utils_module_guide` + +3. **CSV file naming:** + - Select a name for your review, which will be used for the CSV file that logs the status of each slice. + - If an identical CSV file already exists, it will be used. If not, a new one will be generated. + - If you choose to create a new dataset, a new CSV will always be created. If multiple copies already exist, a sequential number will be appended to the new file's name. + +4. **Beginning the Review:** + Press **`Start reviewing`** once you are ready to start the review process. + +.. warning:: + Starting a review session opens a new window and closes the current one. + Make sure you have saved your work before starting the review session. + +Interface & functionalities +--------------------------- + +.. figure:: ../images/review_process_example.png + :align: center + + Interface of the review process. + +Once you have launched the review process, you will have access to the following functionalities: + +.. hlist:: + :columns: 1 + + * A dialog to choose where to save the verified and/or corrected annotations, and a button to save the labels. They will be using the provided file format. + * A button to update the status of the slice in the csv file (in this case : checked/not checked) + * A graph with projections in the x-y, y-z and x-z planes, to allow the reviewer to better understand the context of the volume and decide whether the image should be labeled or not. Use **shift-click** anywhere on the image or label layer to update the plot to the location being reviewed. + +To recap, you can check your labels, correct them, save them and keep track of which slices have been checked or not. + +.. note:: + You can find the csv file containing the annotation status **in the same folder as the labels**. + It will also keep track of the time taken to review each slice, which can be useful to monitor the progress of the review. + +Source code +------------------------------------------------- + +* :doc:`../code/_autosummary/napari_cellseg3d.code_plugins.plugin_review` +* :doc:`../code/_autosummary/napari_cellseg3d.code_plugins.plugin_review_dock` +* :doc:`../code/_autosummary/napari_cellseg3d.code_plugins.plugin_base` diff --git a/docs/source/guides/training_module_guide.rst b/docs/source/guides/training_module_guide.rst new file mode 100644 index 00000000..7668c0b9 --- /dev/null +++ b/docs/source/guides/training_module_guide.rst @@ -0,0 +1,243 @@ +.. _training_module_guide: + +Training📉 +---------------- + +.. figure:: ../images/plugin_train.png + :align: center + + Layout of the training module + + +**Training** allows you to train models for cell segmentation. +Whenever necessary, pre-trained weights will be automatically downloaded and integrated. + +.. important:: + At present, only inference on **3D volumes is supported**. Ensure that both your image and label folders contain a set of + **3D image files**, in either **`.tif`** or **`.tiff`** format. Loading a folder of 2D images as a stack is supported only if + you use napari to load the stack as a 3D image, and save it as a 3D image file. + +Models +=================== +Currently, we provide the following pre-defined models: + +============== ================================================================================================ +Model Link to original paper +============== ================================================================================================ +SegResNet `3D MRI brain tumor segmentation using autoencoder regularization`_ +SwinUNetR `Swin UNETR, Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images`_ +WNet `WNet, A Deep Model for Fully Unsupervised Image Segmentation`_ +TRAILMAP_MS An implementation of the `TRAILMAP project on GitHub`_ using `3DUNet for PyTorch`_ +VNet `Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation`_ +============== ================================================================================================ + +.. _Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation: https://arxiv.org/pdf/1606.04797.pdf +.. _3D MRI brain tumor segmentation using autoencoder regularization: https://arxiv.org/pdf/1810.11654.pdf +.. _TRAILMAP project on GitHub: https://github.com/AlbertPun/TRAILMAP +.. _3DUnet for Pytorch: https://github.com/wolny/pytorch-3dunet +.. _Swin UNETR, Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images: https://arxiv.org/abs/2201.01266 +.. _WNet, A Deep Model for Fully Unsupervised Image Segmentation: https://arxiv.org/abs/1711.08506 + +Training +=================== + +.. important:: + | For the optimal performance of the machine learning models within this program, it is crucial that all images in a dataset have the same dimensions. + | Before starting loading, please ensure that all images are of the **same size**. + | If there is a variance of size, you can use the ``Extract patches`` option located under the augmentation tab, please see below. + | This will let you define a reduced, consistent size for all the images. + | If you need to fragment a large file into cubes, please refer to the Fragment utility in :ref:`utils_module_guide`. + +The training module is comprised of several tabs : + +1) **Model** tab +___________________ + +.. figure:: ../images/training_tab_1.png + :align: right + + Model tab + +* Select which model to use (see table above). +* Decide on using pre-trained weights. + +.. note:: + The model will be initialized with our pre-trained weights, + possibly improving performance via transfer learning. + Custom weights may also be loaded; + simply ensure they are compatible with the chosen model. + +* Select between CPU and GPU (if CUDA is available). + +2) **Data** tab +___________________ + +.. figure:: ../images/training_tab_2.png + :align: right + + Data tab + +For Supervised Models: +********************** +1. **Paths**: + - Image Folder (3D image files) + - Labels Folder (3D image files) + - Results Folder + +2. **Options**: + - Save a copy of results as a **`zip`** file + - Either use images "as is" (requires uniform size and cubic volume) or extract patches. + +.. note:: + Preferably, the image dimensions should be equal to a power of two. Images are automatically padded; a 64 pixels cube will be used as is, while a 65 pixel cube will be padded up to 128 pixels, resulting in much higher memory consumption. + +3. If you're extracting patches: + - Define patches (ideally, please use a value close or equal to a power of two. See above note.) + - Decide on the number of samples to extract from each image. A larger number will likely improve performances, but will also extend training time and increase memory usage. + +If you're using a single image (preferably large) it is recommended to enable patch extraction. + +4. Decide on executing data augmentation (elastic deforms, intensity shifts. random flipping,etc). +5. Define the training versus validation proportion according to your dataset. + +For Unsupervised models +*********************** +1. **Paths**: + - Training Images Folder (3D image files) + - Validation Images Folder (3D image files - **OPTIONAL**) + - Validation Labels Folder (3D image files - **OPTIONAL**) + - Results Folder + +2. **Options**: + - Save a copy of results as a **`zip`** file + - Either use images "as is" (requires uniform size and cubic volume) or extract patches. + +3. Decide on executing data augmentation (elastic deforms, intensity shifts. random flipping,etc). + +3) **Training** tab +____________________ + +.. figure:: ../images/training_tab_3.png + :align: right + + Training tab + + +* **Loss function** : + - `Dice Loss from MONAI`_ + - `Generalized dice Loss from MONAI`_ + - `Dice-CrossEntropy Loss from MONAI`_ + - `Tversky Loss from MONAI`_ + +.. _Dice Loss from MONAI: https://docs.monai.io/en/stable/losses.html#diceloss +.. _Focal Loss from MONAI: https://docs.monai.io/en/stable/losses.html#focalloss +.. _Dice-focal Loss from MONAI: https://docs.monai.io/en/stable/losses.html#dicefocalloss +.. _Generalized dice Loss from MONAI: https://docs.monai.io/en/stable/losses.html#generalizeddiceloss +.. _Dice-CrossEntropy Loss from MONAI: https://docs.monai.io/en/stable/losses.html#diceceloss +.. _Tversky Loss from MONAI: https://docs.monai.io/en/stable/losses.html#tverskyloss + +* **Batch size** : + The batch size determines the number of samples that will be propagated through the network simultaneously. + Larger values can lead to quicker training and potentially better performance, but they will also require more memory. Adjust based on your system's capabilities. + +* **Learning rate of the optimizer** : + This parameter controls the step size during the optimization process. + When using pre-trained weights, setting a lower learning rate can enhance performance. + +* **Number of epochs** : + Refers to the number of times the algorithm will work through the entire training dataset. + A starting suggestion could be 100 epochs, but this might need to be adjusted based on the speed of convergence. + +* **Validation epoch interval** : + Determines how frequently the model is evaluated on the validation dataset. + For instance, if set to two, the module will assess the model's performance using the dice metric every two epochs. + +* **Scheduler patience** : + It defines how many epochs at a plateau the algorithm should wait before reducing the learning rate. + +* **Scheduler factor** : + Once a plateau in model performance is detected, the learning rate is reduced by this factor. + +* **Deterministic training** : + If enabled, the training process becomes reproducible. You can also specify a seed value. + +.. note:: + If the dice metric is better on a given validation interval, the model weights will be saved in the results folder. + +1) **Advanced** tab +___________________ + +This tab is only available with WNet training. For more information please see the :ref:`WNet parameters list ` section. + +Running the training +____________________ + +Once you are ready, press the **`Start`** button to begin training. The module will automatically train the model. + +.. note:: + You can stop the training process at any moment by clicking on the **`Start`** button again. + **The training will stop after processing the upcoming batch, and will try to save the model. However, be aware that interrupting will result in partial results.** + +After conducting at least two validation steps (which depends on the interval you set), +the training loss values and validation metrics will be plotted +and shown on napari every time a validation step completes. +This plot is automatically saved each time validation is performed and the final version is stored separately in the results folder. +The model's inputs (image, label) and outputs (raw & binarized) will also be displayed in the napari viewer. + +.. figure:: ../images/plots_train.png + :align: center + + Example of plots displayed by the training module after 40 epochs + +.. note:: + You can save the log with the button underneath it to record the losses and validation metrics numerical values at each step. This log is autosaved as well when training completes. + +Unsupervised model +============================================== + +The training of our custom WNet implementation is now available as part of the Training module. + +Please see the :ref:`training_wnet` section for more information. + +WandB integration (optional) +============================================== + +.. _wandb_integration: + +You can use the `Weights and Biases `_ platform to track your training metrics and results. + +.. note:: + WandB integration is available for all provided models. + +To use wandb, you will need to create an account [HERE](https://wandb.ai/site) and install the wandb python package. + +* Install : + +.. code-block:: + + pip install wandb + +* Alternatively, you can install it as an optional requirement with the following command : + +.. code-block:: + + pip install napari-cellseg3d[wandb] + +* Connect your account : + +.. code-block:: + + wandb login + +Your API key will be requested. You can find it on your account page on the website. +Once this is done, your training runs will be automatically logged to WandB. +You can find them under **CellSeg3D {MODEL NAME}** on your project page. + +.. note:: + User parameters for WandB as well as improved model comparisons might be added in the future. + +Source code +============================================== +* :doc:`../code/_autosummary/napari_cellseg3d.code_plugins.plugin_model_training` +* :doc:`../code/_autosummary/napari_cellseg3d.code_models.worker_training` +* :doc:`../code/_autosummary/napari_cellseg3d.code_models.models` diff --git a/docs/source/guides/training_wnet.rst b/docs/source/guides/training_wnet.rst new file mode 100644 index 00000000..35aedd2d --- /dev/null +++ b/docs/source/guides/training_wnet.rst @@ -0,0 +1,65 @@ +.. _training_wnet: + +Advanced : WNet training +======================== + +This plugin provides a reimplemented, custom version of the WNet model from `WNet, A Deep Model for Fully Unsupervised Image Segmentation`_. +For training your model, you can choose among: + +* Directly within the plugin +* The provided Jupyter notebook (locally) +* Our Colab notebook (inspired by ZeroCostDL4Mic) + +The WNet does not require a large amount of data to train, but during inference images should be similar to those +the model was trained on; you can retrain from our pretrained model to your image dataset to quickly reach good performance. + +.. note:: + - The WNet relies on brightness to distinguish objects from the background. For better results, use image regions with minimal artifacts. If you notice many artifacts, consider training on one of the supervised models. + - The model has two losses, the **`SoftNCut loss`**, which clusters pixels according to brightness, and a reconstruction loss, either **`Mean Square Error (MSE)`** or **`Binary Cross Entropy (BCE)`**. Unlike the method described in the original paper, these losses are added in a weighted sum and the backward pass is performed for the whole model at once. The SoftNcuts and BCE are bounded between 0 and 1; the MSE may take large positive values. It is recommended to watch for the weighted sum of losses to be **close to one on the first epoch**, for training stability. + - For good performance, you should wait for the SoftNCut to reach a plateau; the reconstruction loss must also decrease but is generally less critical. + +Parameters +---------- + +.. figure:: ../images/training_tab_4.png + :scale: 100 % + :align: right + + Advanced tab + +_`When using the WNet training module`, the **Advanced** tab contains a set of additional options: + +- **Number of classes** : Dictates the segmentation classes (default is 2). Increasing the number of classes will result in a more progressive segmentation according to brightness; can be useful if you have "halos" around your objects or artifacts with a significantly different brightness. +- **Reconstruction loss** : Choose between MSE or BCE (default is MSE). MSE is more precise but also sensitive to outliers; BCE is more robust against outliers at the cost of precision. + +- NCuts parameters: + - **Intensity sigma** : Standard deviation of the feature similarity term, focusing on brightness (default is 1). + - **Spatial sigma** : Standard deviation for the spatial proximity term (default is 4). + - **Radius** : Pixel radius for the loss computation (default is 2). + +.. note:: + - The **Intensity Sigma** depends on image pixel values. The default of 1 is optimised for images being mapped between 0 and 100, which is done automatically by the plugin. + - Raising the **Radius** might improve performance in certain cases, but will also greatly increase computation time. + +- Weights for the sum of losses : + - **NCuts weight** : Sets the weight of the NCuts loss (default is 0.5). + - **Reconstruction weight** : Sets the weight for the reconstruction loss (default is 0.5*1e-2). + +.. note:: + The weight of the reconstruction loss should be adjusted to ensure the weighted sum is around one during the first epoch; + ideally the reconstruction loss should be of the same order of magnitude as the NCuts loss after being multiplied by its weight. + +Common issues troubleshooting +------------------------------ +If you do not find a satisfactory answer here, please do not hesitate to `open an issue`_ on GitHub. + +- **The NCuts loss explodes after a few epochs** : Lower the learning rate, first by a factor of two, then ten. + +- **The NCuts loss does not converge and is unstable** : + The normalization step might not be adapted to your images. Disable normalization and change intensity_sigma according to the distribution of values in your image. For reference, by default images are remapped to values between 0 and 100, and intensity_sigma=1. + +- **Reconstruction (decoder) performance is poor** : switch to BCE and set the scaling factor of the reconstruction loss to 0.5, OR adjust the weight of the MSE loss to make it closer to 1 in the weighted sum. + + +.. _WNet, A Deep Model for Fully Unsupervised Image Segmentation: https://arxiv.org/abs/1711.08506 +.. _open an issue: https://github.com/AdaptiveMotorControlLab/CellSeg3d/issues diff --git a/docs/source/guides/utils_module_guide.rst b/docs/source/guides/utils_module_guide.rst new file mode 100644 index 00000000..37bbd9d5 --- /dev/null +++ b/docs/source/guides/utils_module_guide.rst @@ -0,0 +1,97 @@ +.. _utils_module_guide: + +Utilities 🛠 +============ + +Here you will find a range of tools for image processing and analysis. + +.. note:: + The utility selection menu is found at the bottom of the plugin window. + +You may specify the results directory for saving; afterwards you can run each action on a folder or on the currently selected layer. + +Available actions +__________________ + +1. Crop 3D volumes +------------------ +Please refer to :ref:`cropping_module_guide` for a guide on using the cropping utility. + +2. Convert to instance labels +----------------------------- +This will convert semantic (binary) labels to instance labels (with a unique ID for each object). +The available methods for this are: + +* `Connected Components`_ : simple method that will assign a unique ID to each connected component. Does not work well for touching objects (objects will often be fused). +* `Watershed`_ : method based on topographic maps. Works well for clumped objects and anisotropic volumes depending on the quality of topography; clumed objects may be fused if this is not true. +* `Voronoi-Otsu`_ : method based on Voronoi diagrams and Otsu thresholding. Works well for clumped objects but only for "round" objects. + +3. Convert to semantic labels +----------------------------- +Transforms instance labels into 0/1 semantic labels, useful for training purposes. + +4. Remove small objects +----------------------- +Input a size threshold (in pixels) to eliminate objects below this size. + +5. Resize anisotropic images +---------------------------- +Input your microscope's resolution to remove anisotropy in images. + +6. Threshold images +------------------- +Removes values beneath a certain threshold. + +7. Fragment image +----------------- +Break down large images into smaller cubes, optimal for training. + +8. Conditional Random Field (CRF) +--------------------------------- + +.. note:: + This utility is only available if you have installed the `pydensecrf` package. + You may install it by using the command ``pip install cellseg3d[crf]``. + +| Refines semantic predictions by pairing them with the original image. +| For a list of parameters, see the :doc:`CRF API page<../code/_autosummary/napari_cellseg3d.code_models.crf>`. + +9. Labels statistics +------------------------------------------------ +| Computes statistics for each object in the image. +| Enter the name of the csv file to save the results, then select your layer or folder of labels to compute the statistics. + +.. note:: + Images that are not only integer labels will be ignored. + +The available statistics are: + +For each object : + +* Object volume (pixels) +* :math:`X,Y,Z` coordinates of the centroid +* Sphericity + +Global metrics : + +* Image size +* Total image volume (pixels) +* Total object (labeled) volume (pixels) +* Filling ratio (fraction of the volume that is labeled) +* The number of labeled objects + +.. hint:: + Check the ``notebooks`` folder for examples of plots using the statistics CSV file. + +Source code +___________ + +* :doc:`../code/_autosummary/napari_cellseg3d.code_plugins.plugin_convert` +* :doc:`../code/_autosummary/napari_cellseg3d.code_plugins.plugin_crf` + + +.. links + +.. _Watershed: https://scikit-image.org/docs/dev/auto_examples/segmentation/plot_watershed.html +.. _Connected Components: https://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.label +.. _Voronoi-Otsu: https://haesleinhuepf.github.io/BioImageAnalysisNotebooks/20_image_segmentation/11_voronoi_otsu_labeling.html diff --git a/docs/source/images/Review_Parameters.png b/docs/source/images/Review_Parameters.png new file mode 100644 index 0000000000000000000000000000000000000000..384354604fd4040f2ce430d77c6c63127a673458 GIT binary patch literal 110046 zcmeFY1y>x)7BGry2o52*I|K;s?(XjH?yf;ksu%-P^6{AR3IRrKoAgcs|awwot3)gVhD&g0@k9U zO46dDBuXxh7S?v=5D-$IQZ?bVR7Y@fbycJh(1e6!e#@hkLw*hW0VTy8689MffpSPl zsjesnsal`GNK{iCiaI)~{qlvJ%HZHQqo;=d;_8LFOZpV|S+dQ^cZdVHjJcxJ0 z_bS#zAXuU9uSz(?ID_vqGKa|cVa@VU-vz-n-i1KXQgd)1_0DX(bai&TMHBb^CS7nn z_42CI7+wek2_eNWh}RD#EHL;8*;@glqD=}R2J6UOrv&P)h0@1+gSPFBlwI7fj+9-{ z-;v7k>0B>L7lLH=&QBW$A~T(2iVnj}7-fPeN}occ4h8}a+2r-<>0Bggmb5vPLnR9P 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napari. +Use CellSeg3D to: + +* Review labeled cell volumes from whole-brain samples of mice imaged by mesoSPIM microscopy [1]_ +* Train and use segmentation models from the MONAI project [2]_ or implement your own custom 3D segmentation models using PyTorch. + +No labeled data? Try our unsupervised model to automate your data labelling. + +The models provided should be adaptable to other tasks related to detection of 3D objects, +outside of whole-brain light-sheet microscopy. + +.. figure:: https://images.squarespace-cdn.com/content/v1/57f6d51c9f74566f55ecf271/0d16a71b-3ff2-477a-9d83-18d96cb1ce28/full_demo.gif?format=500w + :alt: CellSeg3D demo + :width: 500 + :align: center + + Demo of the plugin + + +Requirements +-------------------------------------------- + +.. important:: + This package requires **PyQt5** or **PySide2** to be installed first for napari to run. + If you do not have a Qt backend installed you can use : + ``pip install napari[all]`` + to install PyQt5 by default. + +This package depends on PyTorch and certain optional dependencies of MONAI. These come as requirements, but if +you need further assistance, please see below. + +.. note:: + A **CUDA-capable GPU** is not needed but **very strongly recommended**, especially for training and to a lesser degree inference. + +* For help with PyTorch, please see `PyTorch's website`_ for installation instructions, with or without CUDA according to your hardware. + **Depending on your setup, you might wish to install torch first.** + +* If you get errors from MONAI regarding missing readers, please see `MONAI's optional dependencies`_ page for instructions on getting the readers required by your images. + +.. _MONAI's optional dependencies: https://docs.monai.io/en/stable/installation.html#installing-the-recommended-dependencies +.. _PyTorch's website: https://pytorch.org/get-started/locally/ + + + +Installation +-------------------------------------------- +CellSeg3D can be run on Windows, Linux, or MacOS. +For detailed installation instructions, including installing pre-requisites, +please see :ref:`source/guides/installation_guide:Installation guide ⚙` + +.. warning:: + **M1/M2 MacOS users**, please refer to the :ref:`dedicated section ` + +You can install ``napari-cellseg3d`` via pip: + +.. code-block:: + + pip install napari-cellseg3d + +For local installation after cloning from GitHub, please run the following in the CellSeg3D folder: + +.. code-block:: + + pip install -e . + +If the installation was successful, you will find the napari-cellseg3D plugin in the Plugins section of napari. + + + +Usage +-------------------------------------------- + +To use the plugin, please run: + +.. code-block:: + + napari + +Then go into **Plugins > CellSeg3D** + +.. figure:: ./source/images/plugin_menu.png + :align: center + + +and choose the correct tool to use: + +- :ref:`review_module_guide`: Examine and refine your labels, whether manually annotated or predicted by a pre-trained model. +- :ref:`training_module_guide`: Train segmentation algorithms on your own data. +- :ref:`inference_module_guide`: Use pre-trained segmentation algorithms on volumes to automate cell labelling. +- :ref:`utils_module_guide`: Leverage various utilities, including cropping your volumes and labels, converting semantic to instance labels, and more. +- **Help/About...** : Quick access to version info, Github pages and documentation. + +.. hint:: + Many buttons have tooltips to help you understand what they do. + Simply hover over them to see the tooltip. + + +Documentation contents +-------------------------------------------- +_`From this page you can access the guides on the several modules available for your tasks`, such as : + + +* Main modules : + * :ref:`review_module_guide` + * :ref:`training_module_guide` + * :ref:`inference_module_guide` +* Utilities : + * :ref:`cropping_module_guide` + * :ref:`utils_module_guide` + +.. + * Convert labels : :ref:`utils_module_guide` +.. + * Compute scores : :ref:`metrics_module_guide` + +* Advanced : + * :ref:`training_wnet` + * :ref:`custom_model_guide` **(WIP)** + +Other useful napari plugins +--------------------------------------------- + +.. important:: + | Please note that these plugins are not developed by us, and we cannot guarantee their compatibility, functionality or support. + | Installing napari plugins in separated environments is recommended. + +* `brainreg-napari`_ : Whole-brain registration in napari +* `napari-brightness-contrast`_ : Adjust brightness and contrast of your images, visualize histograms and more +* `napari-pyclesperanto-assistant`_ : Image processing workflows using pyclEsperanto +* `napari-skimage-regionprops`_ : Compute region properties on your labels + +.. _napari-pyclesperanto-assistant: https://www.napari-hub.org/plugins/napari-pyclesperanto-assistant +.. _napari-brightness-contrast: https://www.napari-hub.org/plugins/napari-brightness-contrast +.. _brainreg-napari: https://www.napari-hub.org/plugins/brainreg-napari +.. _napari-skimage-regionprops: https://www.napari-hub.org/plugins/napari-skimage-regionprops + +Acknowledgments & References +--------------------------------------------- +This plugin has been developed by Cyril Achard and Maxime Vidal, supervised by Mackenzie Mathis for the `Mathis Laboratory of Adaptive Motor Control`_. + +We also greatly thank Timokleia Kousi for her contributions to this project and the `Wyss Center`_ for project funding. + +The TRAILMAP models and original weights used here were ported to PyTorch but originate from the `TRAILMAP project on GitHub`_. +We also provide a model that was trained in-house on mesoSPIM nuclei data in collaboration with Dr. Stephane Pages and Timokleia Kousi. + +This plugin mainly uses the following libraries and software: + +* `napari`_ + +* `PyTorch`_ + +* `MONAI project`_ (various models used here are credited `on their website`_) + +* `pyclEsperanto`_ (for the Voronoi Otsu labeling) by Robert Haase + +* A custom re-implementation of the `WNet model`_ by Xia and Kulis [3]_ + +.. _Mathis Laboratory of Adaptive Motor Control: http://www.mackenziemathislab.org/ +.. _Wyss Center: https://wysscenter.ch/ +.. _TRAILMAP project on GitHub: https://github.com/AlbertPun/TRAILMAP +.. _napari: https://napari.org/ +.. _PyTorch: https://pytorch.org/ +.. _MONAI project: https://monai.io/ +.. _on their website: https://docs.monai.io/en/stable/networks.html#nets +.. _pyclEsperanto: https://github.com/clEsperanto/pyclesperanto_prototype +.. _WNet model: https://arxiv.org/abs/1711.08506 + +.. rubric:: References + +.. [1] The mesoSPIM initiative: open-source light-sheet microscopes for imaging cleared tissue, Voigt et al., 2019 ( https://doi.org/10.1038/s41592-019-0554-0 ) +.. [2] MONAI Project website ( https://monai.io/ ) +.. [3] W-Net: A Deep Model for Fully Unsupervised Image Segmentation, Xia and Kulis, 2018 ( https://arxiv.org/abs/1711.08506 ) diff --git a/napari_cellseg3d/_tests/fixtures.py b/napari_cellseg3d/_tests/fixtures.py index 4dba351f..acd2f977 100644 --- a/napari_cellseg3d/_tests/fixtures.py +++ b/napari_cellseg3d/_tests/fixtures.py @@ -5,7 +5,7 @@ class LogFixture(QTextEdit): - """Fixture for testing, replaces napari_cellseg3d.interface.Log in model_workers during testing""" + """Fixture for testing, replaces napari_cellseg3d.interface.Log in model_workers during testing.""" def __init__(self): super(LogFixture, self).__init__() @@ -21,57 +21,79 @@ def error(self, e): class WNetFixture(torch.nn.Module): + """Fixture for testing, replaces napari_cellseg3d.models.WNet during testing.""" + def __init__(self): super().__init__() self.mock_conv = torch.nn.Conv3d(1, 1, 1) self.mock_conv.requires_grad_(False) def forward_encoder(self, x): + """Forward pass through encoder.""" return x def forward_decoder(self, x): + """Forward pass through decoder.""" return x def forward(self, x): + """Forward pass through WNet.""" return self.forward_encoder(x), self.forward_decoder(x) class ModelFixture(torch.nn.Module): + """Fixture for testing, replaces napari_cellseg3d models during testing.""" + def __init__(self): + """Fixture for testing, replaces models during testing.""" super().__init__() self.mock_conv = torch.nn.Conv3d(1, 1, 1) self.mock_conv.requires_grad_(False) def forward(self, x): + """Forward pass through model.""" return x class OptimizerFixture: + """Fixture for testing, replaces optimizers during testing.""" + def __init__(self): self.param_groups = [] self.param_groups.append({"lr": 0}) def zero_grad(self): + """Dummy function for zero_grad.""" pass def step(self, *args): + """Dummy function for step.""" pass class SchedulerFixture: + """Fixture for testing, replaces schedulers during testing.""" + def step(self, *args): + """Dummy function for step.""" pass class LossFixture: + """Fixture for testing, replaces losses during testing.""" + def __call__(self, *args): + """Dummy function for __call__.""" return self def backward(self, *args): + """Dummy function for backward.""" pass def item(self): + """Dummy function for item.""" return 0 def detach(self): + """Dummy function for detach.""" return self diff --git a/napari_cellseg3d/_tests/test_utils.py b/napari_cellseg3d/_tests/test_utils.py index 65388172..5d4677ac 100644 --- a/napari_cellseg3d/_tests/test_utils.py +++ b/napari_cellseg3d/_tests/test_utils.py @@ -1,6 +1,7 @@ import random from functools import partial from pathlib import Path + import numpy as np import pytest import torch diff --git a/napari_cellseg3d/code_models/__init__.py b/napari_cellseg3d/code_models/__init__.py index e69de29b..6ee556c2 100644 --- a/napari_cellseg3d/code_models/__init__.py +++ b/napari_cellseg3d/code_models/__init__.py @@ -0,0 +1,11 @@ +"""This folder contains the code used by models in the plugin. + +* ``models`` folder: contains the model classes, which are wrappers for the actual models. The wrappers are used to ensure that the models are compatible with the plugin. +* model_framework.py: contains the code for the model framework, used by training and inference plugins +* worker_inference.py: contains the code for the inference worker +* worker_training.py: contains the code for the training worker +* instance_segmentation.py: contains the code for instance segmentation +* crf.py: contains the code for the CRF postprocessing +* worker_utils.py: contains functions used by the workers + +""" diff --git a/napari_cellseg3d/code_models/crf.py b/napari_cellseg3d/code_models/crf.py index e90529ce..d59698e6 100644 --- a/napari_cellseg3d/code_models/crf.py +++ b/napari_cellseg3d/code_models/crf.py @@ -1,7 +1,16 @@ -""" -Implements the CRF post-processing step for the W-Net. -Inspired by https://arxiv.org/abs/1606.00915 and https://arxiv.org/abs/1711.08506. +"""Implements the CRF post-processing step for the W-Net. + +The CRF requires the following parameters: + +* images : Array of shape (N, C, H, W, D) containing the input images. +* predictions: Array of shape (N, K, H, W, D) containing the predicted class probabilities for each pixel. +* sa: alpha standard deviation, the scale of the spatial part of the appearance/bilateral kernel. +* sb: beta standard deviation, the scale of the color part of the appearance/bilateral kernel. +* sg: gamma standard deviation, the scale of the smoothness/gaussian kernel. +* w1: weight of the appearance/bilateral kernel. +* w2: weight of the smoothness/gaussian kernel. +Inspired by https://arxiv.org/abs/1606.00915 and https://arxiv.org/abs/1711.08506. Also uses research from: Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials Philipp Krähenbühl and Vladlen Koltun @@ -55,6 +64,7 @@ def correct_shape_for_crf(image, desired_dims=4): + """Corrects the shape of the image to be compatible with the CRF post-processing step.""" logger.debug(f"Correcting shape for CRF, desired_dims={desired_dims}") logger.debug(f"Image shape: {image.shape}") if len(image.shape) > desired_dims: @@ -78,6 +88,9 @@ def crf_batch(images, probs, sa, sb, sg, w1, w2, n_iter=5): sa (float): alpha standard deviation, the scale of the spatial part of the appearance/bilateral kernel. sb (float): beta standard deviation, the scale of the color part of the appearance/bilateral kernel. sg (float): gamma standard deviation, the scale of the smoothness/gaussian kernel. + w1 (float): weight of the appearance/bilateral kernel. + w2 (float): weight of the smoothness/gaussian kernel. + n_iter (int, optional): Number of iterations for the CRF post-processing step. Defaults to 5. Returns: np.ndarray: Array of shape (N, K, H, W, D) containing the refined class probabilities for each pixel. @@ -96,6 +109,7 @@ def crf_batch(images, probs, sa, sb, sg, w1, w2, n_iter=5): def crf(image, prob, sa, sb, sg, w1, w2, n_iter=5): """Implements the CRF post-processing step for the W-Net. + Inspired by https://arxiv.org/abs/1210.5644, https://arxiv.org/abs/1606.00915 and https://arxiv.org/abs/1711.08506. Implemented using the pydensecrf library. @@ -107,11 +121,11 @@ def crf(image, prob, sa, sb, sg, w1, w2, n_iter=5): sg (float): gamma standard deviation, the scale of the smoothness/gaussian kernel. w1 (float): weight of the appearance/bilateral kernel. w2 (float): weight of the smoothness/gaussian kernel. + n_iter (int, optional): Number of iterations for the CRF post-processing step. Defaults to 5. Returns: np.ndarray: Array of shape (K, H, W, D) containing the refined class probabilities for each pixel. """ - if not CRF_INSTALLED: return None @@ -154,6 +168,14 @@ def crf(image, prob, sa, sb, sg, w1, w2, n_iter=5): def crf_with_config(image, prob, config: CRFConfig = None, log=logger.info): + """Implements the CRF post-processing step for the W-Net. + + Args: + image (np.ndarray): Array of shape (C, H, W, D) containing the input image. + prob (np.ndarray): Array of shape (K, H, W, D) containing the predicted class probabilities for each pixel. + config (CRFConfig, optional): Configuration for the CRF post-processing step. Defaults to None. + log (function, optional): Logging function. Defaults to logger.info. + """ if config is None: config = CRFConfig() if image.shape[-3:] != prob.shape[-3:]: @@ -192,6 +214,14 @@ def __init__( config: CRFConfig = None, log=None, ): + """Initializes the CRFWorker. + + Args: + images_list (list): List of images to process. + labels_list (list): List of labels to process. + config (CRFConfig, optional): Configuration for the CRF post-processing step. Defaults to None. + log (function, optional): Logging function. Defaults to None. + """ super().__init__(self._run_crf_job) self.images = images_list diff --git a/napari_cellseg3d/code_models/instance_segmentation.py b/napari_cellseg3d/code_models/instance_segmentation.py index 018d385e..1736f5a4 100644 --- a/napari_cellseg3d/code_models/instance_segmentation.py +++ b/napari_cellseg3d/code_models/instance_segmentation.py @@ -1,3 +1,4 @@ +"""Instance segmentation methods for 3D images.""" import abc from dataclasses import dataclass from functools import partial @@ -10,6 +11,7 @@ from skimage.morphology import remove_small_objects from skimage.segmentation import watershed from tifffile import imread +from tqdm import tqdm # local from napari_cellseg3d import interface as ui @@ -27,10 +29,13 @@ VORONOI_OTSU = "Voronoi-Otsu" ################ -USE_EXPERIMENTAL_VORONOI_OTSU_WITH_SLIDING_WINDOW = False +USE_SLIDING_WINDOW = True +"""If True, uses a sliding window to perform instance segmentation to avoid memory issues.""" class InstanceMethod: + """Base class for instance segmentation methods. Contains the method name, the function to use, and the corresponding UI elements.""" + def __init__( self, name: str, @@ -39,8 +44,7 @@ def __init__( num_counters: int, widget_parent: QWidget = None, ): - """ - Methods for instance segmentation + """Methods for instance segmentation. Args: name: Name of the instance segmentation method (for UI) @@ -58,13 +62,16 @@ def __init__( num_counters, num_sliders, widget_parent=widget_parent ) + self.recorded_parameters = {} + """Stores the parameters when calling self.record_parameters()""" + def _setup_widgets(self, num_counters, num_sliders, widget_parent=None): - """Initializes the needed widgets for the instance segmentation method, adding sliders and counters to the - instance segmentation widget. + """Initializes the needed widgets for the instance segmentation method, adding sliders and counters to the instance segmentation widget. + Args: num_counters: Number of DoubleIncrementCounter UI elements needed to set the parameters of the function num_sliders: Number of Slider UI elements needed to set the parameters of the function - widget_parent: parent for the declared widgets + widget_parent: parent for the declared widgets. """ if num_sliders > 0: for i in range(num_sliders): @@ -97,6 +104,7 @@ def _setup_widgets(self, num_counters, num_sliders, widget_parent=None): @abc.abstractmethod def run_method(self, image): + """Runs the method on the image with the parameters set in the widget.""" raise NotImplementedError() def _make_list_from_channels( @@ -116,14 +124,116 @@ def _make_list_from_channels( return [im for im in image] return [image] + def record_parameters(self): + """Records all the parameters of the instance segmentation method from the current values of the widgets.""" + if len(self.sliders) > 0: + for slider in self.sliders: + self.recorded_parameters[ + slider.label.text() + ] = slider.slider_value + if len(self.counters) > 0: + for counter in self.counters: + self.recorded_parameters[ + counter.label.text() + ] = counter.value() + + def run_method_from_params(self, image): + """Runs the method on the image with the RECORDED parameters set in the widget. + + See self.record_parameters() and self.run_method() + + Args: + image: image data to run method on + + Returns: processed image from self._method + """ + if len(self.recorded_parameters) == 0: + logger.warning( + "No parameters recorded, running with values from widgets" + ) + self.record_parameters() + + parameters = [ + self.recorded_parameters[key] for key in self.recorded_parameters + ] + + assert len(parameters) == len(self.sliders) + len(self.counters), ( + f"Number of parameters recorded ({len(parameters)}) " + f"does not match number of sliders ({len(self.sliders)}) " + f"and counters ({len(self.counters)})" + ) + + return self.function(image, *parameters) + def run_method_on_channels(self, image): + """Runs the method on each channel of the image with the parameters set in the widget. + + Args: + image: image data to run method on + + Returns: processed image from self._method + """ image_list = self._make_list_from_channels(image) result = np.array([self.run_method(im) for im in image_list]) return result.squeeze() + def run_method_on_channels_from_params(self, image): + """Runs the method on each channel of the image with the RECORDED parameters set in the widget. + + Args: + image: image data to run method on + + Returns: processed image from self._method + """ + image_list = self._make_list_from_channels(image) + result = np.array( + [self.run_method_from_params(im) for im in image_list] + ) + return result.squeeze() + + @staticmethod + def sliding_window(volume, func, patch_size=512): + """Given a volume of dimensions HxWxD, runs the provided function segmentation on the volume using a sliding window of size patch_size. + + If the edge has been reached, the patch size is reduced to fit the remaining space. + The result is a segmentation of the same size as the input volume. + + Args: + volume (np.array): The volume to segment + func (callable): Function to use for instance segmentation. Should be a partial function with the parameters already set. + patch_size (int): The size of the sliding window. + + Returns: + np.array: Instance segmentation labels from + """ + result = np.zeros(volume.shape, dtype=np.uint32) + max_label_id = 0 + x, y, z = volume.shape + for i in tqdm(range(0, x, patch_size)): + for j in range(0, y, patch_size): + for k in range(0, z, patch_size): + patch = volume[ + i : min(i + patch_size, x), + j : min(j + patch_size, y), + k : min(k + patch_size, z), + ] + patch_result = func(patch) + patch_result = np.array(patch_result) + # make sure labels are unique, only where result is not 0 + patch_result[patch_result > 0] += max_label_id + result[ + i : min(i + patch_size, x), + j : min(j + patch_size, y), + k : min(k + patch_size, z), + ] = patch_result + max_label_id = np.max(patch_result) + return result + @dataclass class ImageStats: + """Dataclass containing various statistics from instance labels.""" + volume: List[float] centroid_x: List[float] centroid_y: List[float] @@ -136,6 +246,7 @@ class ImageStats: number_objects: int def get_dict(self): + """Returns a dict containing the statistics.""" return { "Volume": self.volume, "Centroid x": self.centroid_x, @@ -152,7 +263,7 @@ def get_dict(self): def threshold(volume, thresh): - """Remove all values smaller than the specified threshold in the volume""" + """Remove all values smaller than the specified threshold in the volume.""" im = np.squeeze(volume) binary = im > thresh return np.where(binary, im, np.zeros_like(im)) @@ -162,46 +273,35 @@ def voronoi_otsu( volume: np.ndarray, spot_sigma: float, outline_sigma: float, - # remove_small_size: float, + remove_small_size: float = None, ): - """ - Voronoi-Otsu labeling from pyclesperanto. + """Voronoi-Otsu labeling from pyclesperanto. + BASED ON CODE FROM : napari_pyclesperanto_assistant by Robert Haase https://github.com/clEsperanto/napari_pyclesperanto_assistant Original code at : - https://github.com/clEsperanto/pyclesperanto_prototype/blob/master/pyclesperanto_prototype/_tier9/_voronoi_otsu_labeling.py + https://github.com/clEsperanto/pyclesperanto_prototype/blob/master/pyclesperanto_prototype/_tier9/_voronoi_otsu_labeling.py. Args: volume (np.ndarray): volume to segment spot_sigma (float): parameter determining how close detected objects can be outline_sigma (float): determines the smoothness of the segmentation + remove_small_size (float): remove all objects smaller than the specified size in pixel Returns: Instance segmentation labels from Voronoi-Otsu method """ - if USE_EXPERIMENTAL_VORONOI_OTSU_WITH_SLIDING_WINDOW: - from napari_cellseg3d.dev_scripts.sliding_window_voronoi import ( - sliding_window_voronoi_otsu, - ) - - instance = sliding_window_voronoi_otsu( - volume, - spot_sigma=spot_sigma, - outline_sigma=outline_sigma, - patch_size=1024, - ) - return np.array(instance) - # remove_small_size (float): remove all objects smaller than the specified size in pixels - # semantic = np.squeeze(volume) logger.debug( f"Running voronoi otsu segmentation with spot_sigma={spot_sigma} and outline_sigma={outline_sigma}" ) instance = cle.voronoi_otsu_labeling( volume, spot_sigma=spot_sigma, outline_sigma=outline_sigma ) - # instance = remove_small_objects(instance, remove_small_size) - return np.array(instance) + instance = np.array(instance) + if remove_small_size is not None: + instance = remove_small_objects(instance, remove_small_size) + return instance def binary_connected( @@ -209,8 +309,7 @@ def binary_connected( thres=0.5, thres_small=3, ): - r"""Convert binary foreground probability maps to instance masks via - connected-component labeling. + r"""Convert binary foreground probability maps to instance masks via connected-component labeling. Args: volume (numpy.ndarray): foreground probability of shape :math:`(C, Z, Y, X)`. @@ -220,26 +319,11 @@ def binary_connected( logger.debug( f"Running connected components segmentation with thres={thres} and thres_small={thres_small}" ) - # if len(volume.shape) > 3: semantic = np.squeeze(volume) foreground = np.where(semantic > thres, volume, 0) # int(255 * thres) segm = label(foreground) segm = remove_small_objects(segm, thres_small) - # if not all(x == 1.0 for x in scale_factors): - # target_size = ( - # int(semantic.shape[0] * scale_factors[0]), - # int(semantic.shape[1] * scale_factors[1]), - # int(semantic.shape[2] * scale_factors[2]), - # ) - # segm = resize( - # segm, - # target_size, - # order=0, - # anti_aliasing=False, - # preserve_range=True, - # ) - return segm @@ -250,8 +334,7 @@ def binary_watershed( thres_small=10, rem_seed_thres=3, ): - r"""Convert binary foreground probability maps to instance masks via - watershed segmentation algorithm. + r"""Convert binary foreground probability maps to instance masks via watershed segmentation algorithm. Note: This function uses the `skimage.segmentation.watershed `_ @@ -265,10 +348,10 @@ def binary_watershed( rem_seed_thres (int): threshold for small seeds removal. Default : 3 """ - # logger.debug( - # f"Running watershed segmentation with thres_objects={thres_objects}, thres_seeding={thres_seeding}," - # f" thres_small={thres_small} and rem_seed_thres={rem_seed_thres}" - # ) + logger.debug( + f"Running watershed segmentation with thres_objects={thres_objects}, thres_seeding={thres_seeding}," + f" thres_small={thres_small} and rem_seed_thres={rem_seed_thres}" + ) semantic = np.squeeze(volume) seed_map = semantic > thres_seeding foreground = semantic > thres_objects @@ -277,20 +360,6 @@ def binary_watershed( segm = watershed(-semantic.astype(np.float64), seed, mask=foreground) segm = remove_small_objects(segm, thres_small) - # if not all(x == 1.0 for x in scale_factors): - # target_size = ( - # int(semantic.shape[0] * scale_factors[0]), - # int(semantic.shape[1] * scale_factors[1]), - # int(semantic.shape[2] * scale_factors[2]), - # ) - # segm = resize( - # segm, - # target_size, - # order=0, - # anti_aliasing=False, - # preserve_range=True, - # ) - return np.array(segm) @@ -305,7 +374,6 @@ def clear_small_objects(image, threshold, is_file_path=False): Returns: array: The image with small objects removed """ - if is_file_path: image = imread(image) @@ -327,25 +395,6 @@ def clear_small_objects(image, threshold, is_file_path=False): return result -# def to_instance(image, is_file_path=False): -# """Converts a **ground-truth** label to instance (unique id per object) labels. Does not remove small objects. -# -# Args: -# image: image or path to image -# is_file_path: if True, will consider ``image`` to be a string containing a path to a file, if not treats it as an image data array. -# -# Returns: resulting converted labels -# -# """ -# if is_file_path: -# image = [imread(image)] -# image = image.compute() -# -# return binary_watershed( -# image, thres_small=0, thres_seeding=0.3, rem_seed_thres=0 -# ) - - def to_semantic(image, is_file_path=False): """Converts a **ground-truth** label to semantic (binary 0/1) labels. @@ -366,6 +415,7 @@ def to_semantic(image, is_file_path=False): def volume_stats(volume_image): """Computes various statistics from instance labels and returns them in a dict. + Currently provided : * "Volume": volume of each object @@ -383,10 +433,13 @@ def volume_stats(volume_image): Returns: dict: Statistics described above """ + # check if empty or all 0 + if np.sum(volume_image) == 0: + logger.debug("Skipped empty label image") + return None properties = regionprops(volume_image) - # sphericity_va = [] def sphericity(region): try: return sphericity_axis( @@ -410,15 +463,13 @@ def sphericity(region): volume = [region.area for region in properties] - # def fill(lst, n=len(properties) - 1): - # return fill_list_in_between(lst, n, "") - fill = partial(fill_list_in_between, n=len(properties) - 1, fill_value="") if len(volume_image.flatten()) != 0: ratio = fill([np.sum(volume) / len(volume_image.flatten())]) else: - ratio = 0 + ratio = [0] + ratio = fill(ratio) return ImageStats( volume, @@ -435,9 +486,10 @@ def sphericity(region): class Watershed(InstanceMethod): - """Widget class for Watershed segmentation. Requires 4 parameters, see binary_watershed""" + """Widget class for Watershed segmentation. Requires 4 parameters, see binary_watershed.""" def __init__(self, widget_parent=None): + """Creates a Watershed widget.""" super().__init__( name=WATERSHED, function=binary_watershed, @@ -470,7 +522,61 @@ def __init__(self, widget_parent=None): ) self.counters[1].setValue(3) + @property + def foreground_threshold(self): + """Returns the value of the foreground threshold slider.""" + return self.sliders[0].slider_value + + @foreground_threshold.setter + def foreground_threshold(self, value): + """Sets the value of the foreground threshold slider.""" + self.sliders[0].setValue(value) + + @property + def seed_threshold(self): + """Returns the value of the seed threshold slider.""" + return self.sliders[1].slider_value + + @seed_threshold.setter + def seed_threshold(self, value): + """Sets the value of the seed threshold slider.""" + self.sliders[1].setValue(value) + + @property + def small_object_removal(self): + """Returns the value of the small object removal counter.""" + return self.counters[0].value() + + @small_object_removal.setter + def small_object_removal(self, value): + """Sets the value of the small object removal counter.""" + self.counters[0].setValue(value) + + @property + def small_seed_removal(self): + """Returns the value of the small seed removal counter.""" + return self.counters[1].value() + + @small_seed_removal.setter + def small_seed_removal(self, value): + """Sets the value of the small seed removal counter.""" + self.counters[1].setValue(value) + def run_method(self, image): + """Runs the method on the image with the parameters set in the widget. + + If USE_SLIDING_WINDOW is True, uses a sliding window to perform instance segmentation to avoid memory issues. + """ + if USE_SLIDING_WINDOW: + func = partial( + self.function, + thres_objects=self.sliders[0].slider_value, + thres_seeding=self.sliders[1].slider_value, + thres_small=self.counters[0].value(), + rem_seed_thres=self.counters[1].value(), + ) + return self.sliding_window(image, func) + return self.function( image, self.sliders[0].slider_value, @@ -484,6 +590,7 @@ class ConnectedComponents(InstanceMethod): """Widget class for Connected Components instance segmentation. Requires 2 parameters, see binary_connected.""" def __init__(self, widget_parent=None): + """Creates a ConnectedComponents widget.""" super().__init__( name=CONNECTED_COMP, function=binary_connected, @@ -505,21 +612,54 @@ def __init__(self, widget_parent=None): ) self.counters[0].setValue(3) + @property + def foreground_threshold(self): + """Returns the value of the foreground threshold slider.""" + return self.sliders[0].slider_value + + @foreground_threshold.setter + def foreground_threshold(self, value): + """Sets the value of the foreground threshold slider.""" + self.sliders[0].setValue(value) + + @property + def small_object_removal(self): + """Returns the value of the small object removal counter.""" + return self.counters[0].value() + + @small_object_removal.setter + def small_object_removal(self, value): + """Sets the value of the small object removal counter.""" + self.counters[0].setValue(value) + def run_method(self, image): + """Runs the method on the image with the parameters set in the widget. + + If USE_SLIDING_WINDOW is True, uses a sliding window to perform instance segmentation to avoid memory issues. + """ + if USE_SLIDING_WINDOW: + func = partial( + self.function, + thres=self.sliders[0].slider_value, + thres_small=self.counters[0].value(), + ) + return self.sliding_window(image, func) + return self.function( image, self.sliders[0].slider_value, self.counters[0].value() ) class VoronoiOtsu(InstanceMethod): - """Widget class for Voronoi-Otsu labeling from pyclesperanto. Requires 2 parameter, see voronoi_otsu""" + """Widget class for Voronoi-Otsu labeling from pyclesperanto. Requires 2 parameter, see voronoi_otsu.""" def __init__(self, widget_parent=None): + """Creates a VoronoiOtsu widget.""" super().__init__( name=VORONOI_OTSU, function=voronoi_otsu, num_sliders=0, - num_counters=2, + num_counters=3, widget_parent=widget_parent, ) self.counters[0].label.setText("Spot sigma") # closeness @@ -536,38 +676,70 @@ def __init__(self, widget_parent=None): self.counters[1].setMaximum(100) self.counters[1].setValue(2) - # self.counters[2].label.setText("Small object removal") - # self.counters[2].tooltips = ( - # "Volume/size threshold for small object removal." - # "\nAll objects with a volume/size below this value will be removed." - # ) - # self.counters[2].setValue(30) + self.counters[2].label.setText("Small object removal") + self.counters[2].tooltips = ( + "Volume/size threshold for small object removal." + "\nAll objects with a volume/size below this value will be removed." + ) + self.counters[2].setValue(1) + + @property + def spot_sigma(self): + """Returns the value of the spot sigma counter.""" + return self.counters[0].value() + + @spot_sigma.setter + def spot_sigma(self, value): + """Sets the value of the spot sigma counter.""" + self.counters[0].setValue(value) + + @property + def outline_sigma(self): + """Returns the value of the outline sigma counter.""" + return self.counters[1].value() + + @outline_sigma.setter + def outline_sigma(self, value): + """Sets the value of the outline sigma counter.""" + self.counters[1].setValue(value) + + @property + def small_object_removal(self): + """Returns the value of the small object removal counter.""" + return self.counters[2].value() + + @small_object_removal.setter + def small_object_removal(self, value): + """Sets the value of the small object removal counter.""" + self.counters[2].setValue(value) def run_method(self, image): - ################ - # For debugging - # import napari - # view = napari.Viewer() - # view.add_image(image) - # napari.run() - ################ + """Runs the method on the image with the parameters set in the widget. + + If USE_SLIDING_WINDOW is True, uses a sliding window to perform instance segmentation to avoid memory issues. + """ + if USE_SLIDING_WINDOW: + func = partial( + self.function, + spot_sigma=self.counters[0].value(), + outline_sigma=self.counters[1].value(), + remove_small_size=self.counters[2].value(), + ) + return self.sliding_window(image, func) return self.function( image, - self.counters[0].value(), - self.counters[1].value(), - # self.counters[2].value(), + spot_sigma=self.counters[0].value(), + outline_sigma=self.counters[1].value(), + remove_small_size=self.counters[2].value(), ) class InstanceWidgets(QWidget): - """ - Base widget with several sliders, for use in instance segmentation parameters - """ + """Base widget with several sliders, for use in instance segmentation parameters.""" def __init__(self, parent=None): - """ - Creates an InstanceWidgets widget + """Creates an InstanceWidgets widget. Args: parent: parent widget @@ -622,8 +794,7 @@ def _set_visibility(self): widget.set_visibility(True) def run_method(self, volume): - """ - Calls instance function with chosen parameters + """Calls instance function with chosen parameters. Args: volume: image data to run method on diff --git a/napari_cellseg3d/code_models/model_framework.py b/napari_cellseg3d/code_models/model_framework.py index d4e7af06..2faf5020 100644 --- a/napari_cellseg3d/code_models/model_framework.py +++ b/napari_cellseg3d/code_models/model_framework.py @@ -1,3 +1,4 @@ +"""Basic napari plugin framework for inference and training.""" from pathlib import Path from typing import TYPE_CHECKING @@ -18,7 +19,7 @@ class ModelFramework(BasePluginFolder): - """A framework with buttons to use for loading images, labels, models, etc. for both inference and training""" + """A framework with buttons to use for loading images, labels, models, etc. for both inference and training.""" def __init__( self, @@ -28,7 +29,7 @@ def __init__( loads_labels=True, has_results=True, ): - """Creates a plugin framework with the following elements : + """Creates a plugin framework with the following elements. * A button to choose an image folder containing the images of a dataset (e.g. dataset/images) @@ -128,12 +129,12 @@ def __init__( self.btn_save_log.setVisible(False) def send_log(self, text): - """Emit a signal to print in a Log""" + """Emit a signal to print in a Log.""" if self.log is not None: self.log.print_and_log(text) def save_log(self, do_timestamp=True): - """Saves the worker's log to disk at self.results_path when called""" + """Saves the worker's log to disk at self.results_path when called.""" if self.log is not None: log = self.log.toPlainText() @@ -163,8 +164,8 @@ def save_log_to_path(self, path, do_timestamp=True): Args: path (str): path to save folder + do_timestamp (bool, optional): whether to add a timestamp to the log name. Defaults to True. """ - log = self.log.toPlainText() if do_timestamp: @@ -187,9 +188,7 @@ def save_log_to_path(self, path, do_timestamp=True): ) def display_status_report(self): - """Adds a text log, a progress bar and a "save log" button on the left side of the viewer - (usually when starting a worker)""" - + """Adds a text log, a progress bar and a "save log" button on the left side of the viewer (usually when starting a worker).""" # if self.container_report is None or self.log is None: # logger.warning( # "Status report widget has been closed. Trying to re-instantiate..." @@ -240,7 +239,7 @@ def display_status_report(self): self.progress.setValue(0) def _toggle_weights_path(self): - """Toggle visibility of weight path""" + """Toggle visibility of weight path.""" ui.toggle_visibility( self.custom_weights_choice, self.weights_filewidget ) @@ -273,12 +272,8 @@ def create_train_dataset_dict(self): """Creates data dictionary for MONAI transforms and training. Returns: - A dict with the following keys - - * "image": image - * "label" : corresponding label + A dict with the following keys: "image", "label" """ - logger.debug(f"Images : {self.images_filepaths}") logger.debug(f"Labels : {self.labels_filepaths}") @@ -310,7 +305,7 @@ def create_train_dataset_dict(self): @staticmethod def get_available_models(): - """Getter for module (class and functions) associated to currently selected model""" + """Getter for module (class and functions) associated to currently selected model.""" return config.MODEL_LIST # def load_model_path(self): # TODO add custom models @@ -330,8 +325,7 @@ def _update_weights_path(self, file): self._default_weights_folder = str(Path(file[0]).parent) def _load_weights_path(self): - """Show file dialog to set :py:attr:`model_path`""" - + """Show file dialog to set :py:attr:`model_path`.""" # logger.debug(self._default_weights_folder) file = ui.open_file_dialog( @@ -342,6 +336,7 @@ def _load_weights_path(self): self._update_weights_path(file) def check_device_choice(self): + """Checks the device choice in the UI and returns the corresponding torch device.""" choice = self.device_choice.currentText() if choice == "CPU": device = "cpu" @@ -355,10 +350,11 @@ def check_device_choice(self): @staticmethod def get_device(show=True): - """ - Tries to use the device specified by user and uses it for tensor operations. + """Tries to use the device specified by user and uses it for tensor operations. + If not available, automatically discovers any cuda device. - If none is available (CUDA not installed), uses cpu instead.""" + If none is available (CUDA not installed), uses cpu instead. + """ device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if show: logger.info(f"Using {device} device") @@ -367,7 +363,7 @@ def get_device(show=True): return device def empty_cuda_cache(self): - """Empties the cuda cache if the device is a cuda device""" + """Empties the cuda cache if the device is a cuda device.""" if self.get_device(show=False).type == "cuda": logger.info("Emptying cache...") torch.cuda.empty_cache() diff --git a/napari_cellseg3d/code_models/models/TEMPLATE_model.py b/napari_cellseg3d/code_models/models/TEMPLATE_model.py index f68e5f4f..0586c0b4 100644 --- a/napari_cellseg3d/code_models/models/TEMPLATE_model.py +++ b/napari_cellseg3d/code_models/models/TEMPLATE_model.py @@ -1,8 +1,13 @@ +"""This is a template for a model class. It is not used in the plugin, but is here to show how to implement a model class. + +Please note that custom model implementations are not fully supported out of the box yet, but might be in the future. +""" from abc import ABC, abstractmethod class ModelTemplate_(ABC): - use_default_training = True # not needed for now, will serve for WNet training if added to the plugin + """Template for a model class. This is not used in the plugin, but is here to show how to implement a model class.""" + weights_file = ( "model_template.pth" # specify the file name of the weights file only ) diff --git a/napari_cellseg3d/code_models/models/__init__.py b/napari_cellseg3d/code_models/models/__init__.py index e69de29b..0be67101 100644 --- a/napari_cellseg3d/code_models/models/__init__.py +++ b/napari_cellseg3d/code_models/models/__init__.py @@ -0,0 +1 @@ +"""Contains model code and wrappers for the models, as classes.""" diff --git a/napari_cellseg3d/code_models/models/model_SegResNet.py b/napari_cellseg3d/code_models/models/model_SegResNet.py index 99f8cbfc..58b932e8 100644 --- a/napari_cellseg3d/code_models/models/model_SegResNet.py +++ b/napari_cellseg3d/code_models/models/model_SegResNet.py @@ -1,13 +1,23 @@ +"""SegResNet wrapper for napari_cellseg3d.""" from monai.networks.nets import SegResNetVAE class SegResNet_(SegResNetVAE): - use_default_training = True + """SegResNet_ wrapper for napari_cellseg3d.""" + weights_file = "SegResNet_latest.pth" def __init__( self, input_img_size, out_channels=1, dropout_prob=0.3, **kwargs ): + """Create a SegResNet model. + + Args: + input_img_size (tuple): input image size + out_channels (int): number of output channels + dropout_prob (float): dropout probability. + **kwargs: additional arguments to SegResNetVAE. + """ super().__init__( input_img_size, out_channels=out_channels, @@ -15,6 +25,7 @@ def __init__( ) def forward(self, x): + """Forward pass of the SegResNet model.""" res = SegResNetVAE.forward(self, x) # logger.debug(f"SegResNetVAE.forward: {res[0].shape}") return res[0] diff --git a/napari_cellseg3d/code_models/models/model_SwinUNetR.py b/napari_cellseg3d/code_models/models/model_SwinUNetR.py index bce316e8..286defb9 100644 --- a/napari_cellseg3d/code_models/models/model_SwinUNetR.py +++ b/napari_cellseg3d/code_models/models/model_SwinUNetR.py @@ -1,3 +1,4 @@ +"""SwinUNetR wrapper for napari_cellseg3d.""" from monai.networks.nets import SwinUNETR from napari_cellseg3d.utils import LOGGER @@ -6,7 +7,8 @@ class SwinUNETR_(SwinUNETR): - use_default_training = True + """SwinUNETR wrapper for napari_cellseg3d.""" + weights_file = "SwinUNetR_latest.pth" def __init__( @@ -17,6 +19,15 @@ def __init__( use_checkpoint=True, **kwargs, ): + """Create a SwinUNetR model. + + Args: + in_channels (int): number of input channels + out_channels (int): number of output channels + input_img_size (tuple): input image size + use_checkpoint (bool): whether to use checkpointing during training. + **kwargs: additional arguments to SwinUNETR. + """ try: super().__init__( input_img_size, diff --git a/napari_cellseg3d/code_models/models/model_TRAILMAP.py b/napari_cellseg3d/code_models/models/model_TRAILMAP.py index 118e1e85..6673d1d1 100644 --- a/napari_cellseg3d/code_models/models/model_TRAILMAP.py +++ b/napari_cellseg3d/code_models/models/model_TRAILMAP.py @@ -1,3 +1,4 @@ +"""Legacy version of adapted TRAILMAP model, not used in the current version of the plugin.""" # import torch # from torch import nn # @@ -90,7 +91,6 @@ # # # class TRAILMAP_(TRAILMAP): -# use_default_training = True # weights_file = "TRAILMAP_PyTorch.pth" # model additionally trained on Mathis/Wyss mesoSPIM data # # FIXME currently incorrect, find good weights from TRAILMAP_test and upload them # diff --git a/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py b/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py index 4ee971e2..2aacc333 100644 --- a/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py +++ b/napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py @@ -1,14 +1,23 @@ +"""TRAILMAP model, reimplemented in PyTorch.""" from napari_cellseg3d.code_models.models.unet.model import UNet3D from napari_cellseg3d.utils import LOGGER as logger class TRAILMAP_MS_(UNet3D): - use_default_training = True + """TRAILMAP_MS wrapper for napari_cellseg3d.""" + weights_file = "TRAILMAP_MS_best_metric.pth" # original model from Liqun Luo lab, transferred to pytorch and trained on mesoSPIM-acquired data (mostly TPH2 as of July 2022) def __init__(self, in_channels=1, out_channels=1, **kwargs): + """Create a TRAILMAP_MS model. + + Args: + in_channels (int): number of input channels + out_channels (int): number of output channels. + **kwargs: additional arguments to UNet3D. + """ try: super().__init__( in_channels=in_channels, out_channels=out_channels, **kwargs diff --git a/napari_cellseg3d/code_models/models/model_VNet.py b/napari_cellseg3d/code_models/models/model_VNet.py index 8fe18e2b..2e2e618f 100644 --- a/napari_cellseg3d/code_models/models/model_VNet.py +++ b/napari_cellseg3d/code_models/models/model_VNet.py @@ -1,11 +1,20 @@ +"""VNet wrapper for napari_cellseg3d.""" from monai.networks.nets import VNet class VNet_(VNet): - use_default_training = True + """VNet wrapper for napari_cellseg3d.""" + weights_file = "VNet_latest.pth" def __init__(self, in_channels=1, out_channels=1, **kwargs): + """Create a VNet model. + + Args: + in_channels (int): number of input channels + out_channels (int): number of output channels. + **kwargs: additional arguments to VNet. + """ try: super().__init__( in_channels=in_channels, diff --git a/napari_cellseg3d/code_models/models/model_WNet.py b/napari_cellseg3d/code_models/models/model_WNet.py index ae16e9bb..e80884e8 100644 --- a/napari_cellseg3d/code_models/models/model_WNet.py +++ b/napari_cellseg3d/code_models/models/model_WNet.py @@ -1,10 +1,19 @@ +"""Wrapper for the W-Net model, with the decoder weights removed. + +.. important:: Used for inference only. For training the base class is used. +""" + # local from napari_cellseg3d.code_models.models.wnet.model import WNet_encoder from napari_cellseg3d.utils import remap_image class WNet_(WNet_encoder): - use_default_training = False + """W-Net wrapper for napari_cellseg3d. + + ..important:: Used for inference only, therefore only the encoder is used. For training the base class is used. + """ + weights_file = "wnet_latest.pth" def __init__( @@ -14,6 +23,13 @@ def __init__( # num_classes=2, **kwargs, ): + """Create a W-Net model. + + Args: + in_channels (int): number of input channels + out_channels (int): number of output channels. + **kwargs: additional arguments to WNet_encoder. + """ super().__init__( in_channels=in_channels, out_channels=out_channels, diff --git a/napari_cellseg3d/code_models/models/model_test.py b/napari_cellseg3d/code_models/models/model_test.py index 28f3a05b..39f81392 100644 --- a/napari_cellseg3d/code_models/models/model_test.py +++ b/napari_cellseg3d/code_models/models/model_test.py @@ -1,16 +1,20 @@ +"""Model for testing purposes.""" import torch from torch import nn class TestModel(nn.Module): - use_default_training = True + """For tests only.""" + weights_file = "test.pth" def __init__(self, **kwargs): + """Create a TestModel model.""" super().__init__() self.linear = nn.Linear(8, 8) def forward(self, x): + """Forward pass of the TestModel model.""" return self.linear(torch.tensor(x, requires_grad=True)) # def get_output(self, _, input): diff --git a/napari_cellseg3d/code_models/models/pretrained/__init__.py b/napari_cellseg3d/code_models/models/pretrained/__init__.py index e69de29b..06fc9e2d 100644 --- a/napari_cellseg3d/code_models/models/pretrained/__init__.py +++ b/napari_cellseg3d/code_models/models/pretrained/__init__.py @@ -0,0 +1,5 @@ +"""Hosts the downloaded pretrained model weights. + +Please feel free to delete weights if you do not need them. +They will be downloaded again automatically if needed. +""" diff --git a/napari_cellseg3d/code_models/models/unet/__init__.py b/napari_cellseg3d/code_models/models/unet/__init__.py index e69de29b..ab9eb0b0 100644 --- a/napari_cellseg3d/code_models/models/unet/__init__.py +++ b/napari_cellseg3d/code_models/models/unet/__init__.py @@ -0,0 +1,4 @@ +"""Building block of a UNet model. + +Used mostly by the TRAILMAP model. +""" diff --git a/napari_cellseg3d/code_models/models/wnet/__init__.py b/napari_cellseg3d/code_models/models/wnet/__init__.py index e69de29b..0dc6d0e4 100644 --- a/napari_cellseg3d/code_models/models/wnet/__init__.py +++ b/napari_cellseg3d/code_models/models/wnet/__init__.py @@ -0,0 +1 @@ +"""Building blocks for WNet model.""" diff --git a/napari_cellseg3d/code_models/models/wnet/model.py b/napari_cellseg3d/code_models/models/wnet/model.py index 6c1fcee7..4817e307 100644 --- a/napari_cellseg3d/code_models/models/wnet/model.py +++ b/napari_cellseg3d/code_models/models/wnet/model.py @@ -1,7 +1,4 @@ -""" -Implementation of a 3D W-Net model, based on the 2D version from https://arxiv.org/abs/1711.08506. -The model performs unsupervised segmentation of 3D images. -""" +"""Implementation of a 3D W-Net model, based on the 2D version from https://arxiv.org/abs/1711.08506. The model performs unsupervised segmentation of 3D images.""" from typing import List @@ -29,6 +26,7 @@ def __init__( # num_classes=2, softmax=True, ): + """Initialize the W-Net encoder.""" super().__init__() self.encoder = UNet( in_channels=in_channels, @@ -43,6 +41,7 @@ def forward(self, x): class WNet(nn.Module): """Implementation of a 3D W-Net model, based on the 2D version from https://arxiv.org/abs/1711.08506. + The model performs unsupervised segmentation of 3D images. It first encodes the input image into a latent space using the U-Net UEncoder, then decodes it back to the original image using the U-Net UDecoder. """ @@ -54,6 +53,7 @@ def __init__( num_classes=2, dropout=0.65, ): + """Initialize the W-Net model.""" super(WNet, self).__init__() self.encoder = UNet( in_channels, num_classes, softmax=True, dropout=dropout @@ -88,6 +88,7 @@ def __init__( softmax: bool = True, dropout: float = 0.65, ): + """Creates a U-Net model, which is half of the W-Net model.""" if channels is None: channels = [64, 128, 256, 512, 1024] if len(channels) != 5: @@ -181,6 +182,13 @@ class InBlock(nn.Module): """Input block of the U-Net architecture.""" def __init__(self, in_channels, out_channels, dropout=0.65): + """Create the input block. + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + dropout (float, optional): Dropout probability. Defaults to 0.65. + """ super(InBlock, self).__init__() # self.device = device self.module = nn.Sequential( @@ -205,6 +213,13 @@ class Block(nn.Module): """Basic block of the U-Net architecture.""" def __init__(self, in_channels, out_channels, dropout=0.65): + """Initialize the basic block. + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + dropout (float, optional): Dropout probability. Defaults to 0.65. + """ super(Block, self).__init__() # self.device = device self.module = nn.Sequential( @@ -231,6 +246,13 @@ class OutBlock(nn.Module): """Output block of the U-Net architecture.""" def __init__(self, in_channels, out_channels, dropout=0.65): + """Initialize the output block. + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + dropout (float, optional): Dropout probability. Defaults to 0.65. + """ super(OutBlock, self).__init__() # self.device = device self.module = nn.Sequential( diff --git a/napari_cellseg3d/code_models/models/wnet/soft_Ncuts.py b/napari_cellseg3d/code_models/models/wnet/soft_Ncuts.py index 866c844d..48cf905e 100644 --- a/napari_cellseg3d/code_models/models/wnet/soft_Ncuts.py +++ b/napari_cellseg3d/code_models/models/wnet/soft_Ncuts.py @@ -1,5 +1,5 @@ -""" -Implementation of a 3D Soft N-Cuts loss based on https://arxiv.org/abs/1711.08506 and https://ieeexplore.ieee.org/document/868688. +"""Implementation of a 3D Soft N-Cuts loss based on https://arxiv.org/abs/1711.08506 and https://ieeexplore.ieee.org/document/868688. + The implementation was adapted and approximated to reduce computational and memory cost. This faster version was proposed on https://github.com/fkodom/wnet-unsupervised-image-segmentation. """ @@ -40,6 +40,15 @@ class SoftNCutsLoss(nn.Module): def __init__( self, data_shape, device, intensity_sigma, spatial_sigma, radius=None ): + """Initialize the Soft N-Cuts loss. + + Args: + data_shape (H, W, D): shape of the images as a tuple. + device (torch.device): device on which the loss is computed. + intensity_sigma (scalar): scale of the gaussian kernel of pixels brightness. + spatial_sigma (scalar): scale of the gaussian kernel of pixels spacial distance. + radius (scalar): radius of pixels for which we compute the weights + """ super(SoftNCutsLoss, self).__init__() self.intensity_sigma = intensity_sigma self.spatial_sigma = spatial_sigma diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py index f5d3e5a1..f849b437 100644 --- a/napari_cellseg3d/code_models/worker_inference.py +++ b/napari_cellseg3d/code_models/worker_inference.py @@ -1,3 +1,4 @@ +"""Contains the :py:class:`~InferenceWorker` class, which is a custom worker to run inference jobs in.""" import platform from pathlib import Path @@ -8,7 +9,7 @@ from monai.data import DataLoader, Dataset from monai.inferers import sliding_window_inference from monai.transforms import ( - AddChannel, + # AddChannel, # AsDiscrete, Compose, EnsureChannelFirstd, @@ -42,15 +43,18 @@ logger = utils.LOGGER # experimental code to auto-remove erroneously over-labeled empty regions from instance segmentation EXPERIMENTAL_AUTO_DISCARD_EMPTY_REGIONS = False +"""Whether to automatically discard erroneously over-labeled empty regions from semantic segmentation or not.""" EXPERIMENTAL_AUTO_DISCARD_FRACTION_THRESHOLD = 0.9 -EXPERIMENTAL_AUTO_DISCARD_VALUE = 0.2 +"""The fraction of pixels above which a region is considered wrongly labeled.""" +EXPERIMENTAL_AUTO_DISCARD_VALUE = 0.35 +"""The value above which a pixel is considered to contribute to over-labeling.""" -""" -Writing something to log messages from outside the main thread needs specific care, -Following the instructions in the guides below to have a worker with custom signals, -a custom worker function was implemented. -""" +# Writing something to log messages from outside the main thread needs specific care, +# Following the instructions in the guides below to have a worker with custom signals, +# a custom worker function was implemented. + +# References: # https://python-forum.io/thread-31349.html # https://www.pythoncentral.io/pysidepyqt-tutorial-creating-your-own-signals-and-slots/ # https://napari-staging-site.github.io/guides/stable/threading.html @@ -58,7 +62,9 @@ class InferenceWorker(GeneratorWorker): """A custom worker to run inference jobs in. - Inherits from :py:class:`napari.qt.threading.GeneratorWorker`""" + + Inherits from :py:class:`napari.qt.threading.GeneratorWorker`. + """ def __init__( self, @@ -66,6 +72,8 @@ def __init__( ): """Initializes a worker for inference with the arguments needed by the :py:func:`~inference` function. + Note: See :py:func:`~self.inference` for more details on the arguments. + The config contains the following attributes: * device: cuda or cpu device to use for torch * model_dict: the :py:attr:`~self.models_dict` dictionary to obtain the model name, class and instance @@ -82,11 +90,10 @@ def __init__( * layer: the layer to run inference on Args: - * worker_config (config.InferenceWorkerConfig): dataclass containing the proper configuration elements + worker_config (config.InferenceWorkerConfig): dataclass containing the proper configuration elements - Note: See :py:func:`~self.inference` - """ + """ super().__init__(self.inference) self._signals = LogSignal() # add custom signals self.log_signal = self._signals.log_signal @@ -102,17 +109,19 @@ def __init__( @staticmethod def create_inference_dict(images_filepaths): - """Create a dict for MONAI with "image" keys with all image paths in :py:attr:`~self.images_filepaths` + """Create a dict for MONAI with "image" keys with all image paths in :py:attr:`~self.images_filepaths`. Returns: - dict: list of image paths from loaded folder""" + dict: list of image paths from loaded folder + """ return [{"image": image_name} for image_name in images_filepaths] def set_download_log(self, widget): + """Sets the log widget for the downloader.""" self.downloader.log_widget = widget def log(self, text): - """Sends a signal that ``text`` should be logged + """Sends a signal that ``text`` should be logged. Args: text (str): text to logged @@ -120,11 +129,11 @@ def log(self, text): self.log_signal.emit(text) def warn(self, warning): - """Sends a warning to main thread""" + """Sends a warning to main thread.""" self.warn_signal.emit(warning) def _raise_error(self, exception, msg): - """Raises an error in main thread""" + """Raises an error in main thread.""" logger.error(msg, exc_info=True) logger.error(exception, exc_info=True) @@ -139,6 +148,7 @@ def _raise_error(self, exception, msg): yield exception def log_parameters(self): + """Logs the parameters of the inference.""" config = self.config self.log("-" * 20) @@ -183,16 +193,19 @@ def log_parameters(self): self.log("-" * 20) def load_folder(self): + """Loads the folder specified in :py:attr:`~self.images_filepaths` and returns a MONAI DataLoader.""" images_dict = self.create_inference_dict(self.config.images_filepaths) - data_check = LoadImaged(keys=["image"])(images_dict[0]) + data_check = LoadImaged(keys=["image"], image_only=True)( + images_dict[0] + ) check = data_check["image"].shape pad = utils.get_padding_dim(check) if self.config.sliding_window_config.is_enabled(): load_transforms = Compose( [ - LoadImaged(keys=["image"]), + LoadImaged(keys=["image"], image_only=True), # AddChanneld(keys=["image"]), #already done EnsureChannelFirstd(keys=["image"]), # Orientationd(keys=["image"], axcodes="PLI"), @@ -204,7 +217,7 @@ def load_folder(self): else: load_transforms = Compose( [ - LoadImaged(keys=["image"]), + LoadImaged(keys=["image"], image_only=True), # AddChanneld(keys=["image"]), #already done EnsureChannelFirstd(keys=["image"]), QuantileNormalizationd(keys=["image"]), @@ -224,6 +237,7 @@ def load_folder(self): return inference_loader def load_layer(self): + """Loads the layer specified in :py:attr:`~self.layer` and returns a MONAI DataLoader.""" self.log("Loading layer") image = np.squeeze(self.config.layer.data) volume = image.astype(np.float32) @@ -235,9 +249,8 @@ def load_layer(self): f"Data array is not 3-dimensional but {volume_dims}-dimensional," f" please check for extra channel/batch dimensions" ) - volume = np.swapaxes( - volume, 0, 2 - ) # for dims to be monai-like, i.e. xyz, from napari zyx + volume = utils.correct_rotation(volume) + # volume = np.reshape(volume, newshape=(1, 1, *volume.shape)) dims_check = volume.shape @@ -249,6 +262,7 @@ def load_layer(self): if self.config.model_info.name != "WNet" else lambda x: x ) + volume = np.reshape(volume, newshape=(1, *volume.shape)) if self.config.sliding_window_config.is_enabled(): load_transforms = Compose( [ @@ -256,9 +270,9 @@ def load_layer(self): normalization, ToTensor(), # anisotropic_transform, - AddChannel(), + # AddChannel(), # SpatialPad(spatial_size=pad), - AddChannel(), + # AddChannel(), EnsureType(), ], map_items=False, @@ -273,9 +287,9 @@ def load_layer(self): normalization, ToTensor(), # anisotropic_transform, - AddChannel(), + # AddChannel(), SpatialPad(spatial_size=pad), - AddChannel(), + # AddChannel(), EnsureType(), ], map_items=False, @@ -283,6 +297,7 @@ def load_layer(self): ) input_image = load_transforms(volume) + input_image = input_image.unsqueeze(0) logger.debug(f"INPUT IMAGE SHAPE : {input_image.shape}") logger.debug(f"INPUT IMAGE TYPE : {input_image.dtype}") self.log("Done") @@ -295,6 +310,14 @@ def model_output( post_process_transforms, aniso_transform=None, ): + """Runs the model on the inputs and returns the output. + + Args: + inputs (torch.Tensor): the input tensor to run the model on + model (torch.nn.Module): the model to run + post_process_transforms (monai.transforms.Compose): the transforms to apply to the output + aniso_transform (monai.transforms.Zoom): the anisotropic transform to apply to the output + """ inputs = inputs.to("cpu") dataset_device = ( "cpu" if self.config.keep_on_cpu else self.config.device @@ -385,6 +408,23 @@ def model_output_wrapper(inputs): # sys.stdout = old_stdout # sys.stderr = old_stderr + def _correct_results_rotation(self, array, shape): + """Corrects the shape of the array if needed.""" + if array is None: + return None + if array.shape[-3:] != shape[-3:]: + logger.debug( + f"Correcting rotation due to results shape mismatch: target {shape}, got {array.shape}" + ) + array = utils.correct_rotation(array) + if ( + array.shape[-3:] != shape[-3:] + ): # check only non-channel dimensions + logger.warning( + f"Results shape mismatch: target {shape}, got {array.shape}" + ) + return array + def create_inference_result( self, semantic_labels, @@ -395,23 +435,34 @@ def create_inference_result( stats=None, i=0, ) -> InferenceResult: + """Creates an :py:class:`~InferenceResult` object from the inference results. + + Args: + semantic_labels (np.ndarray): the semantic labels + instance_labels (np.ndarray): the instance labels + crf_results (np.ndarray): the CRF results + from_layer (bool, optional): whether the inference was run on a layer or not. Defaults to False. + original (np.ndarray, optional): the original image. Defaults to None. + stats (list, optional): the stats of the instance labels. Defaults to None. + i (int, optional): the index of the image. Defaults to 0. + + Raises: + ValueError: if the image is not from a layer and no original is provided + + Returns: + InferenceResult: the inference result. See :py:class:`~InferenceResult` for more details. + """ if not from_layer and original is None: raise ValueError( "If the image is not from a layer, an original should always be available" ) - if from_layer: - if i != 0: - raise ValueError( - "A layer's ID should always be 0 (default value)" - ) + if from_layer and i != 0: + raise ValueError("A layer's ID should always be 0 (default value)") - if semantic_labels is not None: - semantic_labels = utils.correct_rotation(semantic_labels) - if crf_results is not None: - crf_results = utils.correct_rotation(crf_results) - if instance_labels is not None: - instance_labels = utils.correct_rotation(instance_labels) + # semantic_labels = self._correct_results_rotation(semantic_labels, shape) # done at the level of model_output already + # instance_labels = self._correct_results_rotation(instance_labels, shape) + # crf_results = self._correct_results_rotation(crf_results, shape) return InferenceResult( image_id=i + 1, @@ -424,9 +475,25 @@ def create_inference_result( ) def get_original_filename(self, i): + """Gets the original filename from the :py:attr:`~self.images_filepaths` attribute.""" return Path(self.config.images_filepaths[i]).stem def get_instance_result(self, semantic_labels, from_layer=False, i=-1): + """Gets the instance segmentation result. + + Args: + semantic_labels (np.ndarray): the semantic labels + from_layer (bool, optional): whether the inference was run on a layer or not. Defaults to False. + i (int, optional): the index of the image. Defaults to -1. + + Raises: + ValueError: if the image is not from a layer and no ID is provided + + Returns: + tuple: a tuple containing: + * the instance labels + * the stats of the instance labels + """ if not from_layer and i == -1: raise ValueError( "An ID should be provided when running from a file" @@ -437,11 +504,11 @@ def get_instance_result(self, semantic_labels, from_layer=False, i=-1): semantic_labels, i + 1, ) - data_dict = self.stats_csv(instance_labels) + stats = self.stats_csv(instance_labels) else: instance_labels = None - data_dict = None - return instance_labels, data_dict + stats = None + return instance_labels, stats def save_image( self, @@ -450,6 +517,14 @@ def save_image( i=0, additional_info="", ): + """Save the image to the :py:attr:`~self.results_path` folder. + + Args: + image (np.ndarray): the image to save + from_layer (bool, optional): whether the inference was run on a layer or not. Defaults to False. + i (int, optional): the index of the image. Defaults to 0. + additional_info (str, optional): additional info to add to the filename. Defaults to "". + """ if not from_layer: original_filename = "_" + self.get_original_filename(i) + "_" filetype = self.config.filetype @@ -483,6 +558,7 @@ def save_image( self.log(f"File n°{i+1} saved as : {filename}") def aniso_transform(self, image): + """Applies an anisotropic transform to the image.""" if self.config.post_process_config.zoom.enabled: zoom = self.config.post_process_config.zoom.zoom_values anisotropic_transform = Zoom( @@ -496,12 +572,21 @@ def aniso_transform(self, image): def instance_seg( self, semantic_labels, image_id=0, original_filename="layer" ): + """Runs the instance segmentation on the semantic labels. + + Args: + semantic_labels (np.ndarray): the semantic labels + image_id (int, optional): the index of the image. Defaults to 0. + original_filename (str, optional): the original filename. Defaults to "layer". + """ if image_id is not None: self.log(f"Running instance segmentation for image n°{image_id}") method = self.config.post_process_config.instance.method - instance_labels = method.run_method_on_channels(semantic_labels) - self.log(f"DEBUG instance results shape : {instance_labels.shape}") + instance_labels = method.run_method_on_channels_from_params( + semantic_labels + ) + logger.debug(f"DEBUG instance results shape : {instance_labels.shape}") filetype = ( ".tif" @@ -528,6 +613,7 @@ def instance_seg( return instance_labels def inference_on_folder(self, inf_data, i, model, post_process_transforms): + """Runs inference on a folder.""" self.log("-" * 10) self.log(f"Inference started on image n°{i + 1}...") @@ -540,6 +626,15 @@ def inference_on_folder(self, inf_data, i, model, post_process_transforms): aniso_transform=self.aniso_transform, ) + out = utils.correct_rotation(out) + extra_dims = len(inputs.shape) - 3 + inputs_shape_corrected = np.swapaxes( + inputs, extra_dims, 2 + extra_dims + ).shape + if out.shape[-3:] != inputs_shape_corrected[-3:]: + logger.debug( + f"Output shape {out.shape[-3:]} does not match input shape {inputs_shape_corrected[-3:]} on HWD dims even after rotation" + ) self.save_image(out, i=i) instance_labels, stats = self.get_instance_result(out, i=i) if self.config.use_crf: @@ -572,9 +667,18 @@ def inference_on_folder(self, inf_data, i, model, post_process_transforms): ) def run_crf(self, image, labels, aniso_transform, image_id=0): + """Runs CRF on the image and labels.""" try: if aniso_transform is not None: image = aniso_transform(image) + + if image.shape[-3:] != labels.shape[-3:]: + image = utils.correct_rotation(image) + if image.shape[-3:] != labels.shape[-3:]: + logger.warning( + f"Labels shape mismatch: target {image.shape}, got {labels.shape}. CRF will likely fail." + ) + crf_results = crf_with_config( image, labels, config=self.config.crf_config, log=self.log ) @@ -590,8 +694,12 @@ def run_crf(self, image, labels, aniso_transform, image_id=0): return None def stats_csv(self, instance_labels): + """Computes the stats of the instance labels.""" try: if self.config.compute_stats: + logger.debug( + f"Stats csv instance labels shape : {instance_labels.shape}" + ) if len(instance_labels.shape) == 4: stats = [volume_stats(c) for c in instance_labels] else: @@ -604,15 +712,26 @@ def stats_csv(self, instance_labels): return None def inference_on_layer(self, image, model, post_process_transforms): + """Runs inference on a layer.""" self.log("-" * 10) self.log("Inference started on layer...") - + logger.debug(f"Layer shape @ inference input: {image.shape}") out = self.model_output( image, model, post_process_transforms, aniso_transform=self.aniso_transform, ) + logger.debug(f"Inference on layer result shape : {out.shape}") + out = utils.correct_rotation(out) + extra_dims = len(image.shape) - 3 + layer_shape_corrected = np.swapaxes( + image, extra_dims, 2 + extra_dims + ).shape + if out.shape[-3:] != layer_shape_corrected[-3:]: + logger.debug( + f"Output shape {out.shape[-3:]} does not match input shape {layer_shape_corrected[-3:]} on HWD dims even after rotation" + ) self.save_image(out, from_layer=True) instance_labels, stats = self.get_instance_result( @@ -634,9 +753,10 @@ def inference_on_layer(self, image, model, post_process_transforms): # @thread_worker(connect={"errored": self._raise_error}) def inference(self): - """ + """Main inference function. + Requires: - * device: cuda or cpu device to use for torch + * device: cuda or cpu device to use for torch. * model_dict: the :py:attr:`~self.models_dict` dictionary to obtain the model name, class and instance @@ -705,7 +825,7 @@ def inference(self): raise ValueError("Model is None") # try: self.log("Loading weights...") - if weights_config.custom: + if weights_config.use_custom: weights = weights_config.path else: self.downloader.download_weights( diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py index 95ea6aec..9ba8be40 100644 --- a/napari_cellseg3d/code_models/worker_training.py +++ b/napari_cellseg3d/code_models/worker_training.py @@ -1,3 +1,4 @@ +"""Contains the workers used to train the models.""" import platform import time from abc import abstractmethod @@ -88,11 +89,14 @@ class TrainingWorkerBase(GeneratorWorker): """A basic worker abstract class, to run training jobs in. - Contains the minimal common elements required for training models.""" + + Contains the minimal common elements required for training models. + """ wandb_config = config.WandBConfig() def __init__(self): + """Initializes the worker.""" super().__init__(self.train) self._signals = LogSignal() self.log_signal = self._signals.log_signal @@ -107,12 +111,13 @@ def __init__(self): ################################ def set_download_log(self, widget): - """Sets the log widget for the downloader to output to""" + """Sets the log widget for the downloader to output to.""" self.downloader.log_widget = widget def log(self, text): - """Sends a Qt signal that the provided text should be logged - Goes in a Log object, defined in :py:mod:`napari_cellseg3d.interface + """Sends a Qt signal that the provided text should be logged. + + Goes in a Log object, defined in :py:mod:`napari_cellseg3d.interface`. Sends a signal to the main thread to log the text. Signal is defined in napari_cellseg3d.workers_utils.LogSignal. @@ -122,11 +127,11 @@ def log(self, text): self.log_signal.emit(text) def warn(self, warning): - """Sends a warning to main thread""" + """Sends a warning to main thread.""" self.warn_signal.emit(warning) def raise_error(self, exception, msg): - """Sends an error to main thread""" + """Sends an error to main thread.""" logger.error(msg, exc_info=True) logger.error(exception, exc_info=True) self.error_signal.emit(exception, msg) @@ -135,18 +140,19 @@ def raise_error(self, exception, msg): @abstractmethod def log_parameters(self): - """Logs the parameters of the training""" + """Logs the parameters of the training.""" raise NotImplementedError @abstractmethod def train(self): - """Starts a training job""" + """Starts a training job.""" raise NotImplementedError class WNetTrainingWorker(TrainingWorkerBase): """A custom worker to run WNet (unsupervised) training jobs in. - Inherits from :py:class:`napari.qt.threading.GeneratorWorker` via :py:class:`TrainingWorkerBase` + + Inherits from :py:class:`napari.qt.threading.GeneratorWorker` via :py:class:`TrainingWorkerBase`. """ # TODO : add wandb parameters @@ -155,6 +161,11 @@ def __init__( self, worker_config: config.WNetTrainingWorkerConfig, ): + """Initializes the worker. + + Args: + worker_config (config.WNetTrainingWorkerConfig): The configuration object + """ super().__init__() self.config = worker_config @@ -174,15 +185,14 @@ def __init__( self.data_shape = None def get_patch_dataset(self, train_transforms): - """Creates a Dataset from the original data using the tifffile library + """Creates a Dataset from the original data using the tifffile library. Args: - train_data_dict (dict): dict with the Paths to the directory containing the data + train_transforms (monai.transforms.Compose): The transforms to apply to the data Returns: (tuple): A tuple containing the shape of the data and the dataset """ - patch_func = Compose( [ LoadImaged(keys=["image"], image_only=True), @@ -216,6 +226,7 @@ def get_patch_dataset(self, train_transforms): return self.config.sample_size, dataset def get_dataset_eval(self, eval_dataset_dict): + """Creates a Dataset applying some transforms/augmentation on the data using the MONAI library.""" eval_transforms = Compose( [ LoadImaged(keys=["image", "label"]), @@ -248,10 +259,10 @@ def get_dataset_eval(self, eval_dataset_dict): ) def get_dataset(self, train_transforms): - """Creates a Dataset applying some transforms/augmentation on the data using the MONAI library + """Creates a Dataset applying some transforms/augmentation on the data using the MONAI library. Args: - config (WNetTrainingWorkerConfig): The configuration object + train_transforms (monai.transforms.Compose): The transforms to apply to the data Returns: (tuple): A tuple containing the shape of the data and the dataset @@ -339,6 +350,7 @@ def _get_data(self): return self.dataloader, self.eval_dataloader, self.data_shape def log_parameters(self): + """Logs the parameters of the training.""" self.log("*" * 20) self.log("-- Parameters --") self.log(f"Device: {self.config.device}") @@ -346,7 +358,7 @@ def log_parameters(self): self.log(f"Epochs: {self.config.max_epochs}") self.log(f"Learning rate: {self.config.learning_rate}") self.log(f"Validation interval: {self.config.validation_interval}") - if self.config.weights_info.custom: + if self.config.weights_info.use_custom: self.log(f"Custom weights: {self.config.weights_info.path}") elif self.config.weights_info.use_pretrained: self.log(f"Pretrained weights: {self.config.weights_info.path}") @@ -388,6 +400,15 @@ def log_parameters(self): def train( self, provided_model=None, provided_optimizer=None, provided_loss=None ): + """Main training function. + + Note : args are mainly used for testing purposes. Model is otherwise initialized in the function. + + Args: + provided_model (WNet, optional): A model to use for training. Defaults to None. + provided_optimizer (torch.optim.Optimizer, optional): An optimizer to use for training. Defaults to None. + provided_loss (torch.nn.Module, optional): A loss function to use for training. Defaults to None. + """ try: if self.config is None: self.config = config.WNetTrainingWorkerConfig() @@ -400,7 +421,7 @@ def train( logger.debug(f"wandb config : {config_dict}") wandb.init( config=config_dict, - project="CellSeg3D WNet", + project="CellSeg3D", mode=self.wandb_config.mode, ) @@ -445,7 +466,7 @@ def train( if WANDB_INSTALLED: wandb.watch(model, log_freq=100) - if self.config.weights_info.custom: + if self.config.weights_info.use_custom: if self.config.weights_info.use_pretrained: weights_file = "wnet.pth" self.downloader.download_weights("WNet", weights_file) @@ -768,6 +789,15 @@ def train( raise e def eval(self, model, epoch) -> TrainingReport: + """Evaluates the model on the validation set. + + Args: + model (WNet): The model to evaluate + epoch (int): The current epoch + + Returns: + TrainingReport: A training report containing the results of the evaluation. See :py:class:`napari_cellseg3d.workers_utils.TrainingReport` + """ with torch.no_grad(): device = self.config.device for _k, val_data in enumerate(self.eval_dataloader): @@ -893,16 +923,19 @@ def eval(self, model, epoch) -> TrainingReport: class SupervisedTrainingWorker(TrainingWorkerBase): """A custom worker to run supervised training jobs in. - Inherits from :py:class:`napari.qt.threading.GeneratorWorker` via :py:class:`TrainingWorkerBase` + + Inherits from :py:class:`napari.qt.threading.GeneratorWorker` via :py:class:`TrainingWorkerBase`. """ + labels_not_semantic = False + def __init__( self, worker_config: config.SupervisedTrainingWorkerConfig, ): - """Initializes a worker for inference with the arguments needed by the :py:func:`~train` function. Note: See :py:func:`~train` + """Initializes a worker for inference with the arguments needed by the :py:func:`~train` function. Note: See :py:func:`~train`. - Args: + Config provides the following attributes: * device : device to train on, cuda or cpu * model_dict : dict containing the model's "name" and "class" @@ -935,7 +968,6 @@ def __init__( * deterministic : dict with "use deterministic" : bool, whether to use deterministic training, "seed": seed for RNG - """ super().__init__() # worker function is self.train in parent class self.config = worker_config @@ -952,7 +984,7 @@ def __init__( } self.loss_function = None - def set_loss_from_config(self): + def _set_loss_from_config(self): try: self.loss_function = self.loss_dict[self.config.loss_function] except KeyError as e: @@ -960,11 +992,12 @@ def set_loss_from_config(self): return self.loss_function def log_parameters(self): + """Logs the parameters of the training.""" self.log("-" * 20) self.log("Parameters summary :\n") self.log( - f"Percentage of dataset used for validation : {self.config.validation_percent * 100}%" + f"Percentage of dataset used for training : {self.config.training_percent * 100}%" ) # self.log("-" * 10) @@ -1003,7 +1036,7 @@ def log_parameters(self): if self.config.do_augmentation: self.log("Data augmentation is enabled") - if not self.config.weights_info.use_pretrained: + if self.config.weights_info.use_custom: self.log(f"Using weights from : {self.config.weights_info.path}") if self._weight_error: self.log( @@ -1023,9 +1056,10 @@ def train( provided_loss=None, provided_scheduler=None, ): - """Trains the PyTorch model for the given number of epochs, with the selected model and data, - using the chosen batch size, validation interval, loss function, and number of samples. - Will perform validation once every :py:obj:`val_interval` and save results if the mean dice is better + """Trains the PyTorch model for the given number of epochs. + + Uses the selected model and data, using the chosen batch size, validation interval, loss function, and number of samples. + Will perform validation once every :py:obj:`val_interval` and save results if the mean dice is better. Requires: @@ -1061,7 +1095,6 @@ def train( * deterministic : dict with "use deterministic" : bool, whether to use deterministic training, "seed": seed for RNG """ - ######################### # error_log = open(results_path +"/error_log.log" % multiprocessing.current_process().name, 'x') # faulthandler.enable(file=error_log, all_threads=True) @@ -1073,6 +1106,15 @@ def train( start_time = time.time() try: + if WANDB_INSTALLED: + config_dict = self.config.__dict__ + logger.debug(f"wandb config : {config_dict}") + wandb.init( + config=config_dict, + project="CellSeg3D", + mode=self.wandb_config.mode, + ) + if deterministic_config.enabled: set_determinism( seed=deterministic_config.seed @@ -1088,6 +1130,17 @@ def train( model_name = model_config.name model_class = model_config.get_model() + ######## Check that labels are semantic, not instance + check_labels = LoadImaged(keys=["label"])( + self.config.train_data_dict[0] + ) + if check_labels["label"].max() > 1: + self.warn( + "Labels are not semantic, but instance. Converting to semantic, this might cause errors." + ) + self.labels_not_semantic = True + ######## + if not self.config.sampling: data_check = LoadImaged(keys=["image"])( self.config.train_data_dict[0] @@ -1106,6 +1159,9 @@ def train( device = torch.device(self.config.device) model = model.to(device) + if WANDB_INSTALLED: + wandb.watch(model, log_freq=100) + epoch_loss_values = [] val_metric_values = [] @@ -1114,13 +1170,13 @@ def train( self.config.train_data_dict[ 0 : int( len(self.config.train_data_dict) - * self.config.validation_percent + * self.config.training_percent ) ], self.config.train_data_dict[ int( len(self.config.train_data_dict) - * self.config.validation_percent + * self.config.training_percent ) : ], ) @@ -1177,6 +1233,7 @@ def train( ) def get_patch_loader_func(num_samples): + """Returns a function that will be used to extract patches from the images.""" return Compose( [ LoadImaged(keys=["image", "label"]), @@ -1207,12 +1264,11 @@ def get_patch_loader_func(num_samples): # TODO(cyril) : maybe implement something in user config to toggle this behavior if len(self.config.train_data_dict) < 2: num_train_samples = ceil( - self.config.num_samples - * self.config.validation_percent + self.config.num_samples * self.config.training_percent ) num_val_samples = ceil( self.config.num_samples - * (1 - self.config.validation_percent) + * (1 - self.config.training_percent) ) if num_train_samples < 2: self.log( @@ -1279,7 +1335,7 @@ def get_patch_loader_func(num_samples): logger.debug("Cache dataset : train") train_dataset = CacheDataset( data=self.train_files, - transform=Compose(load_whole_images, train_transforms), + transform=Compose([load_whole_images, train_transforms]), ) logger.debug("Cache dataset : val") validation_dataset = CacheDataset( @@ -1335,7 +1391,7 @@ def get_patch_loader_func(num_samples): # time = utils.get_date_time() logger.debug("Weights") - if weights_config.custom: + if weights_config.use_custom: if weights_config.use_pretrained: weights_file = model_class.weights_file self.downloader.download_weights(model_name, weights_file) @@ -1373,7 +1429,7 @@ def get_patch_loader_func(num_samples): self.log_parameters() # device = torch.device(self.config.device) - self.set_loss_from_config() + self._set_loss_from_config() if provided_loss is not None: self.loss_function = provided_loss @@ -1407,9 +1463,12 @@ def get_patch_loader_func(num_samples): ) # logger.debug(f"Inputs shape : {inputs.shape}") # logger.debug(f"Labels shape : {labels.shape}") + if self.labels_not_semantic: + labels = labels.clamp(0, 1) + optimizer.zero_grad() outputs = model(inputs) - # self.log(f"Output dimensions : {outputs.shape}") + # logger.debug(f"Output dimensions : {outputs.shape}") if outputs.shape[1] > 1: outputs = outputs[ :, 1:, :, : @@ -1418,6 +1477,10 @@ def get_patch_loader_func(num_samples): outputs = outputs.unsqueeze(0) # logger.debug(f"Outputs shape : {outputs.shape}") loss = self.loss_function(outputs, labels) + + if WANDB_INSTALLED: + wandb.log({"Training/Loss": loss.item()}) + loss.backward() optimizer.step() epoch_loss += loss.detach().item() @@ -1447,7 +1510,15 @@ def get_patch_loader_func(num_samples): supervised=True, ) - # return + if WANDB_INSTALLED: + wandb.log({"Training/Epoch loss": epoch_loss / step}) + wandb.log( + { + "LR/Model learning rate": optimizer.param_groups[ + 0 + ]["lr"] + } + ) epoch_loss /= step epoch_loss_values.append(epoch_loss) @@ -1560,6 +1631,10 @@ def get_patch_loader_func(num_samples): ) metric = dice_metric.aggregate().detach().item() + + if WANDB_INSTALLED: + wandb.log({"Validation/Dice metric": metric}) + dice_metric.reset() val_metric_values.append(metric) @@ -1622,6 +1697,11 @@ def get_patch_loader_func(num_samples): f"Train completed, best_metric: {best_metric:.4f} " f"at epoch: {best_metric_epoch}" ) + + if WANDB_INSTALLED: + wandb.log({"Validation/Best metric": best_metric}) + wandb.log({"Validation/Best metric epoch": best_metric_epoch}) + # Save last checkpoint weights_filename = f"{model_name}_latest.pth" self.log("Saving last model") diff --git a/napari_cellseg3d/code_models/workers_utils.py b/napari_cellseg3d/code_models/workers_utils.py index 4c197c28..c8ba6655 100644 --- a/napari_cellseg3d/code_models/workers_utils.py +++ b/napari_cellseg3d/code_models/workers_utils.py @@ -1,3 +1,4 @@ +"""Several worker-related utilities for inference and training.""" import typing as t from dataclasses import dataclass from pathlib import Path @@ -27,8 +28,7 @@ class WeightsDownloader: """A utility class the downloads the weights of a model when needed.""" def __init__(self, log_widget: t.Optional[ui.Log] = None): - """ - Creates a WeightsDownloader, optionally with a log widget to display the progress. + """Creates a WeightsDownloader, optionally with a log widget to display the progress. Args: log_widget (log_utility.Log): a Log to display the progress bar in. If None, uses logger.info() @@ -36,8 +36,8 @@ def __init__(self, log_widget: t.Optional[ui.Log] = None): self.log_widget = log_widget def download_weights(self, model_name: str, model_weights_filename: str): - """ - Downloads a specific pretrained model. + """Downloads a specific pretrained model. + This code is adapted from DeepLabCut with permission from MWMathis. Args: @@ -137,14 +137,16 @@ class LogSignal(WorkerBaseSignals): # Should not be an instance variable but a class variable, not defined in __init__, see # https://stackoverflow.com/questions/2970312/pyqt4-qtcore-pyqtsignal-object-has-no-attribute-connect - def __init__(self): - super().__init__() + def __init__(self, parent=None): + """Creates a LogSignal.""" + super().__init__(parent=parent) class ONNXModelWrapper(torch.nn.Module): - """Class to replace torch model by ONNX Runtime session""" + """Class to replace torch model by ONNX Runtime session.""" def __init__(self, file_location): + """Creates an ONNXModelWrapper.""" super().__init__() try: import onnxruntime as ort @@ -161,18 +163,24 @@ def __init__(self, file_location): ) def forward(self, modeL_input): - """Wraps ONNX output in a torch tensor""" + """Wraps ONNX output in a torch tensor.""" outputs = self.ort_session.run( None, {"input": modeL_input.cpu().numpy()} ) return torch.tensor(outputs[0]) def eval(self): - """Dummy function to replace model.eval()""" + """Dummy function. + + Replaces model.eval(). + """ pass def to(self, device): - """Dummy function to replace model.to(device)""" + """Dummy function. + + Replaces model.to(device). + """ pass @@ -180,9 +188,11 @@ class QuantileNormalizationd(MapTransform): """MONAI-style dict transform to normalize each image in a batch individually by quantile normalization.""" def __init__(self, keys, allow_missing_keys: bool = False): + """Creates a QuantileNormalizationd transform.""" super().__init__(keys, allow_missing_keys) def __call__(self, data): + """Normalize each image in a batch individually by quantile normalization.""" d = dict(data) for key in self.keys: d[key] = self.normalizer(d[key]) @@ -204,16 +214,26 @@ class QuantileNormalization(Transform): """MONAI-style transform to normalize each image in a batch individually by quantile normalization.""" def __call__(self, img): + """Normalize each image in a batch individually by quantile normalization.""" return utils.quantile_normalization(img) class RemapTensor(Transform): + """Remap the values of a tensor to a new range.""" + def __init__(self, new_max, new_min): + """Creates a RemapTensor transform. + + Args: + new_max (float): new maximum value + new_min (float): new minimum value + """ super().__init__() self.max = new_max self.min = new_min def __call__(self, img): + """Remap the values of a tensor to a new range.""" return utils.remap_image(img, new_max=self.max, new_min=self.min) @@ -237,17 +257,26 @@ def __call__(self, img): class Threshold(Transform): + """Threshold a tensor to 0 or 1.""" + def __init__(self, threshold=0.5): + """Creates a Threshold transform. + + Args: + threshold (float): threshold value + """ super().__init__() self.threshold = threshold def __call__(self, img): - return torch.where(img > self.threshold, 1, 0) + """Threshold a tensor to 0 or 1.""" + res = torch.where(img > self.threshold, 1, 0) + return torch.Tensor(res).float() @dataclass class InferenceResult: - """Class to record results of a segmentation job""" + """Class to record results of a segmentation job.""" image_id: int = 0 original: np.array = None @@ -260,6 +289,8 @@ class InferenceResult: @dataclass class TrainingReport: + """Class to record results of a training job.""" + show_plot: bool = True epoch: int = 0 loss_1_values: t.Dict = None # example : {"Loss" : [0.1, 0.2, 0.3]} diff --git a/napari_cellseg3d/code_plugins/__init__.py b/napari_cellseg3d/code_plugins/__init__.py index e69de29b..24b044f6 100644 --- a/napari_cellseg3d/code_plugins/__init__.py +++ b/napari_cellseg3d/code_plugins/__init__.py @@ -0,0 +1 @@ +"""This folder contains all plugin-related code.""" diff --git a/napari_cellseg3d/code_plugins/plugin_base.py b/napari_cellseg3d/code_plugins/plugin_base.py index 90c61adf..2e2807d9 100644 --- a/napari_cellseg3d/code_plugins/plugin_base.py +++ b/napari_cellseg3d/code_plugins/plugin_base.py @@ -1,3 +1,4 @@ +"""Base classes for napari_cellseg3d plugins.""" from functools import partial from pathlib import Path @@ -15,7 +16,7 @@ class BasePluginSingleImage(QTabWidget): - """A basic plugin template for working with **single images**""" + """A basic plugin template for working with **single images**.""" def __init__( self, @@ -25,8 +26,7 @@ def __init__( loads_labels=True, has_results=True, ): - """ - Creates a Base plugin with several buttons pre-defined + """Creates a Base plugin with several buttons pre-defined. Args: viewer: napari viewer to display in @@ -107,9 +107,9 @@ def __init__( qInstallMessageHandler(ui.handle_adjust_errors_wrapper(self)) def enable_utils_menu(self): - """ - Enables the usage of the CTRL+right-click shortcut to the utilities. - Should only be used in "high-level" widgets (provided in napari Plugins menu) to avoid multiple activation + """Enables the usage of the CTRL+right-click shortcut to the utilities. + + Should only be used in "high-level" widgets (provided in napari Plugins menu) to avoid multiple activation. """ viewer = self._viewer @@ -185,10 +185,11 @@ def _set_io_visibility(self): @staticmethod def _show_io_element(widget: QWidget, toggle: QWidget = None): - """ + """Show widget and connect it to toggle if any. + Args: widget: Widget to be shown or hidden - toggle: Toggle to be used to determine whether widget should be shown (Checkbox or RadioButton) + toggle: Toggle to be used to determine whether widget should be shown (Checkbox or RadioButton). """ widget.setVisible(True) @@ -199,13 +200,12 @@ def _show_io_element(widget: QWidget, toggle: QWidget = None): @staticmethod def _hide_io_element(widget: QWidget, toggle: QWidget = None): - """ - Attempts to disconnect widget from toggle and hide it. + """Attempts to disconnect widget from toggle and hide it. + Args: widget: Widget to be hidden - toggle: Toggle to be disconnected from widget, if any + toggle: Toggle to be disconnected from widget, if any. """ - if toggle is not None: try: toggle.toggled.disconnect() @@ -217,11 +217,11 @@ def _hide_io_element(widget: QWidget, toggle: QWidget = None): widget.setVisible(False) def _build(self): - """Method to be defined by children classes""" + """Method to be defined by children classes.""" raise NotImplementedError("To be defined in child classes") def _show_file_dialog(self): - """Open file dialog and process path for a single file""" + """Open file dialog and process path for a single file.""" # if self.load_as_stack_choice.isChecked(): # return ui.open_folder_dialog( # self, @@ -239,7 +239,7 @@ def _show_file_dialog(self): return choice def _show_dialog_images(self): - """Show file dialog and set image path""" + """Show file dialog and set image path.""" f_name = self._show_file_dialog() if type(f_name) is str and Path(f_name).is_file(): self.image_path = f_name @@ -248,7 +248,7 @@ def _show_dialog_images(self): self._update_default_paths() def _show_dialog_labels(self): - """Show file dialog and set label path""" + """Show file dialog and set label path.""" f_name = self._show_file_dialog() if isinstance(f_name, str) and Path(f_name).is_file(): self.label_path = f_name @@ -257,7 +257,7 @@ def _show_dialog_labels(self): self._update_default_paths() def _check_results_path(self, folder: str): - """Check if results folder exists, create it if not""" + """Check if results folder exists, create it if not.""" logger.debug(f"Checking results folder : {folder}") if folder != "" and isinstance(folder, str): if not Path(folder).is_dir(): @@ -274,7 +274,7 @@ def _check_results_path(self, folder: str): return False def _load_results_path(self): - """Show file dialog to set :py:attr:`~results_path`""" + """Show file dialog to set :py:attr:`~results_path`.""" self._update_default_paths() folder = ui.open_folder_dialog(self, self._default_path) @@ -285,7 +285,7 @@ def _load_results_path(self): self._update_default_paths() def _update_default_paths(self): - """Updates default path for smoother navigation when opening file dialogs""" + """Updates default path for smoother navigation when opening file dialogs.""" self._default_path = [ self.image_path, self.label_path, @@ -311,12 +311,14 @@ def _make_next_button(self): def remove_from_viewer(self): """Removes the widget from the napari window. - Can be re-implemented in children classes if needed""" + + Must be re-implemented in children classes where needed. + """ self.remove_docked_widgets() self._viewer.window.remove_dock_widget(self) def remove_docked_widgets(self): - """Removes all docked widgets from napari window""" + """Removes all docked widgets from napari window.""" try: if len(self.docked_widgets) != 0: [ @@ -332,7 +334,7 @@ def remove_docked_widgets(self): class BasePluginFolder(BasePluginSingleImage): - """A basic plugin template for working with **folders of images**""" + """A basic plugin template for working with **folders of images**.""" def __init__( self, @@ -342,7 +344,7 @@ def __init__( loads_labels=True, has_results=True, ): - """Creates a plugin template with the following widgets defined but not added in a layout : + """Creates a plugin template with the following widgets defined but not added in a layout. * A button to load a folder of images @@ -401,7 +403,7 @@ def __init__( # self._set_io_visibility() def load_dataset_paths(self): - """Loads all image paths (as str) in a given folder for which the extension matches the set filetype + """Loads all image paths (as str) in a given folder for which the extension matches the set filetype. Returns: array(str): all loaded file paths @@ -419,7 +421,7 @@ def load_dataset_paths(self): return file_paths def load_image_dataset(self): - """Show file dialog to set :py:attr:`~images_filepaths`""" + """Show file dialog to set :py:attr:`~images_filepaths`.""" filenames = self.load_dataset_paths() if filenames: logger.info("Images loaded :") @@ -432,7 +434,7 @@ def load_image_dataset(self): self._update_default_paths(path) def load_unsup_images_dataset(self): - """Show file dialog to set :py:attr:`~val_images_filepaths`""" + """Show file dialog to set :py:attr:`~val_images_filepaths`.""" filenames = self.load_dataset_paths() if filenames: logger.info("Images loaded (unsupervised training) :") @@ -447,7 +449,7 @@ def load_unsup_images_dataset(self): self._update_default_paths(path) def load_label_dataset(self): - """Show file dialog to set :py:attr:`~labels_filepaths`""" + """Show file dialog to set :py:attr:`~labels_filepaths`.""" filenames = self.load_dataset_paths() if filenames: logger.info("Labels loaded :") @@ -460,7 +462,7 @@ def load_label_dataset(self): self._update_default_paths(path) def _update_default_paths(self, path=None): - """Update default path for smoother file dialogs""" + """Update default path for smoother file dialogs.""" logger.debug(f"Updating default paths with {path}") if path is None: self._default_path = [ @@ -476,7 +478,7 @@ def _update_default_paths(self, path=None): @staticmethod def extract_dataset_paths(paths): - """Gets the parent folder name of the first image and label paths""" + """Gets the parent folder name of the first image and label paths.""" if len(paths) == 0: return None if paths[0] is None: @@ -491,7 +493,7 @@ def _check_all_filepaths(self): class BasePluginUtils(BasePluginFolder): - """Small subclass used to have centralized widgets layer and result path selection in utilities""" + """Small subclass used to have centralized widgets layer and result path selection in utilities.""" save_path = None utils_default_paths = [Path.home() / "cellseg3d"] @@ -503,6 +505,7 @@ def __init__( loads_images=True, loads_labels=True, ): + """Creates a plugin template with the following widgets defined but not added in a layout.""" super().__init__( viewer=viewer, loads_images=loads_images, @@ -517,7 +520,7 @@ def __init__( """Should contain the layer associated with the results of the utility widget""" def _update_default_paths(self, path=None): - """Override to also update utilities' pool of default paths""" + """Override to also update utilities' pool of default paths.""" default_path = super()._update_default_paths(path) logger.debug(f"Trying to update default with {default_path}") if default_path is not None: diff --git a/napari_cellseg3d/code_plugins/plugin_convert.py b/napari_cellseg3d/code_plugins/plugin_convert.py index edfe3c89..6567e918 100644 --- a/napari_cellseg3d/code_plugins/plugin_convert.py +++ b/napari_cellseg3d/code_plugins/plugin_convert.py @@ -1,8 +1,11 @@ +"""Several image processing utilities.""" from pathlib import Path +from warnings import warn import napari import numpy as np -from qtpy.QtWidgets import QSizePolicy +import pandas as pd +from qtpy.QtWidgets import QLineEdit, QSizePolicy from tifffile import imread import napari_cellseg3d.interface as ui @@ -12,6 +15,7 @@ clear_small_objects, threshold, to_semantic, + volume_stats, ) from napari_cellseg3d.code_plugins.plugin_base import BasePluginUtils from napari_cellseg3d.dev_scripts.crop_data import crop_3d_image @@ -23,12 +27,12 @@ class FragmentUtils(BasePluginUtils): - """Class to crop large 3D volumes into smaller fragments""" + """Class to crop large 3D volumes into smaller fragments.""" save_path = Path.home() / "cellseg3d" / "fragmented" def __init__(self, viewer: "napari.Viewer.viewer", parent=None): - """Creates a FragmentUtils widget + """Creates a FragmentUtils widget. Args: viewer: viewer in which to process data @@ -115,13 +119,12 @@ def _start(self): class AnisoUtils(BasePluginUtils): - """Class to correct anisotropy in images""" + """Class to correct anisotropy in images.""" save_path = Path.home() / "cellseg3d" / "anisotropy" def __init__(self, viewer: "napari.Viewer.viewer", parent=None): - """ - Creates a AnisoUtils widget + """Creates a AnisoUtils widget. Args: viewer: viewer in which to process data @@ -214,14 +217,12 @@ def _start(self): class RemoveSmallUtils(BasePluginUtils): + """Widget to remove small objects.""" + save_path = Path.home() / "cellseg3d" / "small_removed" - """ - Widget to remove small objects - """ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): - """ - Creates a RemoveSmallUtils widget + """Creates a RemoveSmallUtils widget. Args: viewer: viewer in which to process data @@ -238,10 +239,10 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): self.label_layer_loader.layer_list.label.setText("Layer :") self.start_btn = ui.Button("Start", self._start) - self.size_for_removal_counter = ui.IntIncrementCounter( - lower=1, - upper=100000, - default=10, + self.size_for_removal_counter = ui.DoubleIncrementCounter( + lower=0.0, + upper=100000.0, + default=10.0, text_label="Remove all smaller than (pxs):", ) @@ -316,14 +317,12 @@ def _start(self): class ToSemanticUtils(BasePluginUtils): + """Widget to create semantic labels from instance labels.""" + save_path = Path.home() / "cellseg3d" / "semantic_labels" - """ - Widget to create semantic labels from instance labels - """ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): - """ - Creates a ToSemanticUtils widget + """Creates a ToSemanticUtils widget. Args: viewer: viewer in which to process data @@ -407,14 +406,12 @@ def _start(self): class ToInstanceUtils(BasePluginUtils): + """Widget to convert semantic labels to instance labels.""" + save_path = Path.home() / "cellseg3d" / "instance_labels" - """ - Widget to convert semantic labels to instance labels - """ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): - """ - Creates a ToInstanceUtils widget + """Creates a ToInstanceUtils widget. Args: viewer: viewer in which to process data @@ -501,15 +498,17 @@ def _start(self): class ThresholdUtils(BasePluginUtils): - save_path = Path.home() / "cellseg3d" / "threshold" - """ - Creates a ThresholdUtils widget + """Creates a ThresholdUtils widget. + Args: viewer: viewer in which to process data - parent: parent widget + parent: parent widget. """ + save_path = Path.home() / "cellseg3d" / "threshold" + def __init__(self, viewer: "napari.viewer.Viewer", parent=None): + """Creates a ThresholdUtils widget.""" super().__init__( viewer, parent=parent, @@ -596,3 +595,100 @@ def _start(self): images, self.images_filepaths, ) + + +class StatsUtils(BasePluginUtils): + """Widget to save statistics of a labels layer.""" + + save_path = Path.home() / "cellseg3d" / "stats" + + def __init__(self, viewer: "napari.viewer.Viewer", parent=None): + """Creates a StatsUtils widget. + + Args: + viewer: viewer in which to process data + parent: parent widget + """ + super().__init__( + viewer, + parent=parent, + loads_images=False, + ) + + self.data_panel = self._build_io_panel() + + self.csv_name = QLineEdit("volume_stats", parent=self) + self.csv_name.setToolTip( + "Name of the csv file.\nThe extension is added automatically;\nif running on a folder, the id of the image will be added to the name." + ) + + self.start_btn = ui.Button("Start", self._start) + + self.results_path = str(self.save_path) + self.results_filewidget.text_field.setText(self.results_path) + self.results_filewidget.check_ready() + + self.container = self._build() + + def _build(self): + container = ui.ContainerWidget() + + ui.add_widgets( + self.data_panel.layout, + [ + self.csv_name, + self.start_btn, + ], + ) + container.layout.addWidget(self.data_panel) + + ui.ScrollArea.make_scrollable( + container.layout, self, max_wh=[MAX_W, MAX_H] + ) + self._set_io_visibility() + container.setSizePolicy( + QSizePolicy.MinimumExpanding, QSizePolicy.MinimumExpanding + ) + return container + + def _start(self): + utils.mkdir_from_str(self.results_path) + if self.layer_choice.isChecked(): + if self.label_layer_loader.layer_data() is not None: + layer = self.label_layer_loader.layer() + + data = np.array(layer.data) + stats = volume_stats(data) + if stats is None: + logger.warning( + "No stats to save, please ensure your label array is not empty" + ) + return + + stats_df = pd.DataFrame(stats.get_dict()) + csv_name = self.csv_name.text() + ".csv" + stats_df.to_csv( + self.results_path + "/" + csv_name, index=False + ) + elif ( + self.folder_choice.isChecked() and len(self.labels_filepaths) != 0 + ): + images = [imread(file) for file in self.labels_filepaths] + for i, image in enumerate(images): + if image.sum() == 0: + m = f"Image {i} is empty, skipping." + logger.warning(m) + warn(m, stacklevel=0) + continue + if not np.issubdtype(image.dtype, np.integer): + m = f"Image {i} is not integer, skipping. Make sure your labels are saved as integer values" + logger.warning(m) + warn(m, stacklevel=0) + continue + stats_df = pd.DataFrame(stats.get_dict()) + csv_name = self.csv_name.text() + f"_{i}.csv" + stats_df.to_csv( + self.results_path + "/" + csv_name, index=False + ) + else: + logger.warning("Please specify a layer or a folder") diff --git a/napari_cellseg3d/code_plugins/plugin_crf.py b/napari_cellseg3d/code_plugins/plugin_crf.py index 64503e10..424a6fd5 100644 --- a/napari_cellseg3d/code_plugins/plugin_crf.py +++ b/napari_cellseg3d/code_plugins/plugin_crf.py @@ -1,3 +1,4 @@ +"""CRF plugin for napari_cellseg3d.""" import contextlib from functools import partial from pathlib import Path @@ -19,9 +20,10 @@ # TODO add CRF on folder class CRFParamsWidget(ui.GroupedWidget): - """Use this widget when adding the crf as part of another widget (rather than a standalone widget)""" + """Use this widget when adding the crf as part of another widget (rather than a standalone widget).""" def __init__(self, parent=None): + """Create a widget to set CRF parameters.""" super().__init__(title="CRF parameters", parent=parent) ####### # CRF params # @@ -57,7 +59,7 @@ def _build(self): ), ], ) - self.set_layout() + self._set_layout() return ui.add_widgets( self.layout, @@ -76,7 +78,7 @@ def _build(self): self.n_iter_choice, ], ) - self.set_layout() + self._set_layout() def _set_tooltips(self): self.sa_choice.setToolTip( @@ -97,6 +99,7 @@ def _set_tooltips(self): self.n_iter_choice.setToolTip("Number of iterations of the CRF.") def make_config(self): + """Make a CRF config from the widget values.""" return config.CRFConfig( sa=self.sa_choice.value(), sb=self.sb_choice.value(), @@ -108,13 +111,13 @@ def make_config(self): class CRFWidget(BasePluginUtils): - """Widget to run CRF post-processing""" + """Widget to run CRF post-processing.""" save_path = Path.home() / "cellseg3d" / "crf" def __init__(self, viewer, parent=None): - """ - Create a widget for CRF post-processing. + """Create a widget for CRF post-processing. + Args: viewer: napari viewer to display the widget parent: parent widget. Defaults to None. @@ -185,9 +188,11 @@ def _build(self): return self._container def make_config(self): + """Make a CRF config from the widget values.""" return self.crf_params_widget.make_config() def print_config(self): + """Print the CRF config to the logger.""" logger.info("CRF config:") for item in self.make_config().__dict__.items(): logger.info(f"{item[0]}: {item[1]}") @@ -216,6 +221,7 @@ def _check_ready(self): return True def run_crf_on_batch(self, images_list: list, labels_list: list, log=None): + """Run CRF on a batch of images and labels.""" self.crf_results = [] for image, label in zip(images_list, labels_list): tqdm( @@ -229,6 +235,7 @@ def run_crf_on_batch(self, images_list: list, labels_list: list, log=None): return self.crf_results def _prepare_worker(self, images_list: list, labels_list: list): + """Prepare the CRF worker.""" self.worker = CRFWorker( images_list=images_list, labels_list=labels_list, diff --git a/napari_cellseg3d/code_plugins/plugin_crop.py b/napari_cellseg3d/code_plugins/plugin_crop.py index c4889249..e8bad56d 100644 --- a/napari_cellseg3d/code_plugins/plugin_crop.py +++ b/napari_cellseg3d/code_plugins/plugin_crop.py @@ -1,3 +1,4 @@ +"""Crop utility plugin for napari_cellseg3d.""" from math import floor from pathlib import Path @@ -26,7 +27,7 @@ class Cropping( utils_default_paths = [] def __init__(self, viewer: "napari.viewer.Viewer", parent=None): - """Creates a Cropping plugin with several buttons : + """Creates a Cropping plugin with several buttons. * Open file prompt to select volumes directory @@ -40,7 +41,6 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): * A button to close the widget """ - super().__init__(viewer) if parent is not None: @@ -58,7 +58,7 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): # ) self.image_layer_loader.layer_list.currentIndexChanged.connect( - self.auto_set_dims + self._auto_set_dims ) # ui.LayerSelecter(self._viewer, "Image 1") # self.layer_selection2 = ui.LayerSelecter(self._viewer, "Image 2") @@ -125,7 +125,7 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): self._build() self._toggle_second_image_io_visibility() self._check_image_list() - self.auto_set_dims() + self._auto_set_dims() def _toggle_second_image_io_visibility(self): crop_2nd = self.crop_second_image_choice.isChecked() @@ -146,7 +146,7 @@ def _check_image_list(self): except IndexError: return - def auto_set_dims(self): + def _auto_set_dims(self): logger.debug(self.image_layer_loader.layer_name()) data = self.image_layer_loader.layer_data() if data is not None: @@ -159,8 +159,7 @@ def auto_set_dims(self): box.setValue(floor(data.shape[i] / 2)) def _build(self): - """Build buttons in a layout and add them to the napari Viewer""" - + """Build buttons in a layout and add them to the napari Viewer.""" container = ui.ContainerWidget(0, 0, 1, 11) layout = container.layout @@ -237,7 +236,6 @@ def quicksave(self): * If labels are present, saves the cropped version as a single file or 2D stacks folder depending on what was loaded. """ - viewer = self._viewer self._check_results_path(str(self.results_path)) @@ -270,9 +268,7 @@ def _check_ready(self): return False def _start(self): - """Launches cropping process by loading the files from the chosen folders, - and adds control widgets to the napari Viewer for moving the cropped volume. - """ + """Launches cropping process by loading the files from the chosen folders, and adds control widgets to the napari Viewer for moving the cropped volume.""" if not self._check_ready(): logger.warning("Please select at least one valid layer !") return @@ -324,10 +320,10 @@ def _start(self): layer.visible = False # hide other layers, because of anisotropy - self.image_layer1 = self.add_isotropic_layer(self.image_layer1) + self.image_layer1 = self._add_isotropic_layer(self.image_layer1) if self.crop_second_image: - self.image_layer2 = self.add_isotropic_layer( + self.image_layer2 = self._add_isotropic_layer( self.image_layer2, visible=False ) else: @@ -355,7 +351,7 @@ def save_widget(): self._add_crop_sliders() - def add_isotropic_layer( + def _add_isotropic_layer( self, layer, colormap="inferno", @@ -503,7 +499,7 @@ def set_slice( labels_crop_layer=None, crop_lbls=False, ): - """ "Update cropped volume position""" + """Update cropped volume position.""" # self._check_for_empty_layer(highres_crop_layer, highres_crop_layer.data) # logger.debug(f"axis : {axis}") @@ -539,6 +535,7 @@ def set_slice( i : i + cropx, j : j + cropy, k : k + cropz ] highres_crop_layer.translate = scale * izyx + highres_crop_layer.reset_contrast_limits() highres_crop_layer.refresh() # self._check_for_empty_layer( @@ -550,6 +547,7 @@ def set_slice( i : i + cropx, j : j + cropy, k : k + cropz ] labels_crop_layer.translate = scale * izyx + highres_crop_layer.reset_contrast_limits() labels_crop_layer.refresh() self._x = i diff --git a/napari_cellseg3d/code_plugins/plugin_helper.py b/napari_cellseg3d/code_plugins/plugin_helper.py index 552f70ea..f787f3c4 100644 --- a/napari_cellseg3d/code_plugins/plugin_helper.py +++ b/napari_cellseg3d/code_plugins/plugin_helper.py @@ -1,3 +1,4 @@ +"""Tiny plugin showing link to documentation and about page.""" import pathlib from typing import TYPE_CHECKING @@ -14,7 +15,10 @@ class Helper(QWidget, metaclass=ui.QWidgetSingleton): + """Tiny plugin showing link to documentation and about page.""" + def __init__(self, viewer: "napari.viewer.Viewer"): + """Creates a widget with links to documentation and about page.""" super().__init__() self.help_url = "https://adaptivemotorcontrollab.github.io/CellSeg3d/" diff --git a/napari_cellseg3d/code_plugins/plugin_metrics.py b/napari_cellseg3d/code_plugins/plugin_metrics.py index b0aa3df8..ae58314e 100644 --- a/napari_cellseg3d/code_plugins/plugin_metrics.py +++ b/napari_cellseg3d/code_plugins/plugin_metrics.py @@ -1,3 +1,4 @@ +"""CURRENTLY UNUSED.""" from typing import TYPE_CHECKING import matplotlib.pyplot as plt @@ -21,13 +22,14 @@ class MetricsUtils(BasePluginFolder): - """Plugin to evaluate metrics between two sets of labels, ground truth and prediction""" + """Plugin to evaluate metrics between two sets of labels, ground truth and prediction.""" def __init__(self, viewer: "napari.viewer.Viewer", parent=None): """Creates a MetricsUtils widget for computing and plotting dice metrics between labels. + Args: viewer: viewer to display the widget in - parent : parent widget + parent : parent widget. """ super().__init__(viewer, parent, has_results=False) @@ -76,7 +78,6 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): def _build(self): """Builds the layout of the widget.""" - # self.filetype_choice.label.setVisible(False) w = ui.ContainerWidget() @@ -132,7 +133,7 @@ def _build(self): ui.ScrollArea.make_scrollable(self.layout, self) def plot_dice(self, dice_coeffs, threshold=DEFAULT_THRESHOLD): - """Plots the dice loss for each pair of labels on viewer""" + """Plots the dice loss for each pair of labels on viewer.""" self.btn_reset_plot.setVisible(True) colors = [] @@ -171,7 +172,7 @@ def plot_dice(self, dice_coeffs, threshold=DEFAULT_THRESHOLD): self.canvas.draw_idle() def remove_plots(self): - """Clears plots from window view""" + """Clears plots from window view.""" if len(self.plots) != 0: for p in self.plots: p.setVisible(False) @@ -180,7 +181,9 @@ def remove_plots(self): def compute_dice(self): """Computes the dice metric between pairs of labels. - Rotates the prediction label to find matching orientation as well.""" + + Rotates the prediction label to find matching orientation as well. + """ # u = 0 # t = 0 diff --git a/napari_cellseg3d/code_plugins/plugin_model_inference.py b/napari_cellseg3d/code_plugins/plugin_model_inference.py index cc3e6ede..49a365e6 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_inference.py +++ b/napari_cellseg3d/code_plugins/plugin_model_inference.py @@ -1,3 +1,4 @@ +"""Inference plugin for napari_cellseg3d.""" from functools import partial from typing import TYPE_CHECKING @@ -23,12 +24,10 @@ class Inferer(ModelFramework, metaclass=ui.QWidgetSingleton): - """A plugin to run already trained models in evaluation mode to preform inference and output a label on all - given volumes.""" + """A plugin to run already trained models in evaluation mode to preform inference and output a label on all given volumes.""" def __init__(self, viewer: "napari.viewer.Viewer", parent=None): - """ - Creates an Inference loader plugin with the following widgets : + """Creates an Inference loader plugin with the following widgets. * Data : * A file extension choice for the images to load from selected folders @@ -61,6 +60,7 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): Args: viewer (napari.viewer.Viewer): napari viewer to display the widget in + parent (QWidget, optional): Defaults to None. """ super().__init__( viewer, @@ -287,8 +287,7 @@ def _set_tooltips(self): ################## def check_ready(self): - """Checks if the paths to the files are properly set""" - + """Checks if the paths to the files are properly set.""" if self.layer_choice.isChecked(): if self.image_layer_loader.layer_data() is not None: return True @@ -300,14 +299,18 @@ def check_ready(self): return False def _restrict_window_size_for_model(self): - """Sets the window size to a value that is compatible with the chosen model""" + """Sets the window size to a value that is compatible with the chosen model.""" self.wnet_enabled = False if self.model_choice.currentText() == "WNet": - self.window_size_choice.setCurrentIndex(self._default_window_size) self.wnet_enabled = True + self.window_size_choice.setCurrentIndex(self._default_window_size) self.window_infer_box.setChecked(self.wnet_enabled) - self.window_size_choice.setDisabled(self.wnet_enabled) - self.window_infer_box.setDisabled(self.wnet_enabled) + self.window_size_choice.setDisabled( + self.wnet_enabled and not self.custom_weights_choice.isChecked() + ) + self.window_infer_box.setDisabled( + self.wnet_enabled and not self.custom_weights_choice.isChecked() + ) def _toggle_display_model_input_size(self): if ( @@ -321,30 +324,29 @@ def _toggle_display_model_input_size(self): self.model_input_size.label.setVisible(False) def _toggle_display_number(self): - """Shows the choices for viewing results depending on whether :py:attr:`self.view_checkbox` is checked""" + """Shows the choices for viewing results depending on whether :py:attr:`self.view_checkbox` is checked.""" ui.toggle_visibility(self.view_checkbox, self.view_results_container) def _toggle_display_thresh(self): - """Shows the choices for thresholding results depending on whether :py:attr:`self.thresholding_checkbox` is checked""" + """Shows the choices for thresholding results depending on whether :py:attr:`self.thresholding_checkbox` is checked.""" ui.toggle_visibility( self.thresholding_checkbox, self.thresholding_slider.container ) def _toggle_display_crf(self): - """Shows the choices for CRF post-processing depending on whether :py:attr:`self.use_crf` is checked""" + """Shows the choices for CRF post-processing depending on whether :py:attr:`self.use_crf` is checked.""" ui.toggle_visibility(self.use_crf, self.crf_widgets) def _toggle_display_instance(self): - """Shows or hides the options for instance segmentation based on current user selection""" + """Shows or hides the options for instance segmentation based on current user selection.""" ui.toggle_visibility(self.use_instance_choice, self.instance_widgets) def _toggle_display_window_size(self): - """Show or hide window size choice depending on status of self.window_infer_box""" + """Show or hide window size choice depending on status of self.window_infer_box.""" ui.toggle_visibility(self.window_infer_box, self.window_infer_params) def _load_weights_path(self): - """Show file dialog to set :py:attr:`model_path`""" - + """Show file dialog to set :py:attr:`model_path`.""" # logger.debug(self._default_weights_folder) file = ui.open_file_dialog( @@ -355,8 +357,7 @@ def _load_weights_path(self): self._update_weights_path(file) def _build(self): - """Puts all widgets in a layout and adds them to the napari Viewer""" - + """Puts all widgets in a layout and adds them to the napari Viewer.""" # ui.add_blank(self.view_results_container, view_results_layout) ui.add_widgets( self.view_results_container.layout, @@ -555,7 +556,7 @@ def _display_results(self, result: InferenceResult): image_id = result.image_id model_name = self.model_choice.currentText() - viewer.dims.ndisplay = 3 + # viewer.dims.ndisplay = 3 # let user choose viewer.scale_bar.visible = True if self.config.show_original and result.original is not None: @@ -584,12 +585,18 @@ def _display_results(self, result: InferenceResult): fractions_per_channel = utils.channels_fraction_above_threshold( result.result, 0.5 ) - index_channel_least_labelled = np.argmin(fractions_per_channel) + index_channel_sorted = np.argsort(fractions_per_channel) + for channel in index_channel_sorted: + if result.result[channel].sum() > 0: + index_channel_least_labelled = channel + break viewer.dims.set_point( 0, index_channel_least_labelled ) # TODO(cyril: check if this is always the right axis - if result.crf_results is not None: + if result.crf_results is not None and not isinstance( + result.crf_results, Exception + ): logger.debug(f"CRF results shape : {result.crf_results.shape}") viewer.add_image( result.crf_results, @@ -598,15 +605,41 @@ def _display_results(self, result: InferenceResult): ) if ( result.instance_labels is not None + and not isinstance(result.instance_labels, Exception) and self.worker_config.post_process_config.instance.enabled ): method_name = ( self.worker_config.post_process_config.instance.method.name ) - number_cells = ( - np.unique(result.instance_labels.flatten()).size - 1 - ) # remove background + if len(result.instance_labels.shape) >= 4: + channels_by_labels = np.argsort( + result.instance_labels.sum(axis=(1, 2, 3)) + ) + min_objs_channel = channels_by_labels[0] + # if least labeled is empty, use next least labeled channel + for i in range(1, len(channels_by_labels)): + if ( + np.unique( + result.instance_labels[ + channels_by_labels[i] + ].flatten() + ).size + > 1 + ): + min_objs_channel = channels_by_labels[i] + break + + number_cells = ( + np.unique( + result.instance_labels[min_objs_channel].flatten() + ).size + - 1 + ) + else: + number_cells = ( + np.unique(result.instance_labels.flatten()).size - 1 + ) # remove background with -1 name = f"({number_cells} objects)_{method_name}_instance_labels_{image_id}" @@ -615,28 +648,32 @@ def _display_results(self, result: InferenceResult): if result.stats is not None and isinstance( result.stats, list ): # list for several channels - # logger.debug(f"len stats : {len(result.stats)}") + logger.debug(f"len stats : {len(result.stats)}") for i, stats in enumerate(result.stats): # stats = result.stats if self.worker_config.compute_stats and stats is not None: - stats_dict = stats.get_dict() - stats_df = pd.DataFrame(stats_dict) - - self.log.print_and_log( - f"Number of instances in channel {i} : {stats.number_objects[0]}" - ) - - csv_name = f"/{method_name}_seg_results_{image_id}_channel_{i}_{utils.get_date_time()}.csv" - stats_df.to_csv( - self.worker_config.results_path + csv_name, - index=False, - ) - - # self.log.print_and_log( - # f"OBJECTS DETECTED : {number_cells}\n" - # ) + try: + stats_dict = stats.get_dict() + stats_df = pd.DataFrame(stats_dict) + + self.log.print_and_log( + f"Number of instances in channel {i} : {stats.number_objects[0]}" + ) + + csv_name = f"/{model_name}_{method_name}_seg_results_{image_id}_channel_{i}_{utils.get_date_time()}.csv" + + stats_df.to_csv( + self.worker_config.results_path + csv_name, + index=False, + ) + except ValueError as e: + logger.warning(f"Error saving stats to csv : {e}") + logger.debug( + f"Length of stats array : {[len(s) for s in stats.get_dict().values()]}" + ) + # logger.debug(f"Stats dict : {stats.get_dict()}") def _setup_worker(self): if self.folder_choice.isChecked(): @@ -658,7 +695,7 @@ def _setup_worker(self): self.worker.warn_signal.connect(self.log.warn) self.worker.error_signal.connect(self.log.error) - self.worker.yielded.connect(partial(self.on_yield)) # + self.worker.yielded.connect(partial(self.on_yield)) self.worker.errored.connect(partial(self.on_error)) self.worker.finished.connect(self.on_finish) @@ -667,7 +704,7 @@ def _setup_worker(self): return self.worker def start(self): - """Start the inference process, enables :py:attr:`~self.worker` and does the following: + """Start the inference process, enables :py:attr:`~self.worker` and does the following. * Checks if the output and input folders are correctly set @@ -684,11 +721,7 @@ def start(self): * If the option has been selected, display the results in napari, up to the maximum number selected * Runs instance segmentation, thresholding, and stats computing if requested - - Args: - on_layer: if True, will start inference on a selected layer """ - if not self.check_ready(): err = "Aborting, please choose valid inputs" self.log.print_and_log(err) @@ -745,7 +778,7 @@ def _set_worker_config(self) -> config.InferenceWorkerConfig: model_input_size=self.model_input_size.value(), ) - self.weights_config.custom = self.custom_weights_choice.isChecked() + self.weights_config.use_custom = self.custom_weights_choice.isChecked() save_path = self.results_filewidget.text_field.text() if not self._check_results_path(save_path): @@ -771,6 +804,7 @@ def _set_worker_config(self) -> config.InferenceWorkerConfig: self.instance_widgets.method_choice.currentText() ], ) + self.instance_config.method.record_parameters() # keep parameters set when Start is clicked self.post_process_config = config.PostProcessConfig( zoom=zoom_config, @@ -803,7 +837,7 @@ def _set_worker_config(self) -> config.InferenceWorkerConfig: return self.worker_config def on_start(self): - """Catches start signal from worker to call :py:func:`~display_status_report`""" + """Catches start signal from worker to call :py:func:`~display_status_report`.""" self.display_status_report() self._set_self_config() self.log.print_and_log(f"Worker started at {utils.get_time()}") @@ -831,15 +865,13 @@ def on_finish(self): return True # signal clean exit def on_yield(self, result: InferenceResult): - """ - Displays the inference results in napari as long as data["image_id"] is lower than nbr_to_show, - and updates the status report docked widget (namely the progress bar) + """Displays the inference results in napari. + + Works as long as data["image_id"] is lower than nbr_to_show, and updates the status report docked widget (namely the progress bar). Args: - data (dict): dict yielded by :py:func:`~inference()`, contains : "image_id" : index of the returned image, "original" : original volume used for inference, "result" : inference result - widget (QWidget): widget for accessing attributes + result (InferenceResult): results from the worker """ - if isinstance(result, Exception): self.on_error(result) # raise result diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py index 766c2c89..5d727956 100644 --- a/napari_cellseg3d/code_plugins/plugin_model_training.py +++ b/napari_cellseg3d/code_plugins/plugin_model_training.py @@ -1,3 +1,5 @@ +"""Training plugin for napari_cellseg3d.""" + import shutil import warnings from functools import partial @@ -36,8 +38,10 @@ class Trainer(ModelFramework, metaclass=ui.QWidgetSingleton): """A plugin to train pre-defined PyTorch models for one-channel segmentation directly in napari. + Features parameter selection for training, dynamic loss plotting and automatic saving of the best weights during - training through validation.""" + training through validation. + """ default_config = config.SupervisedTrainingWorkerConfig() @@ -45,7 +49,7 @@ def __init__( self, viewer: "napari.viewer.Viewer", ): - """Creates a Trainer tab widget with the following functionalities : + """Creates a Trainer tab widget with the following functionalities. * First tab : Dataset parameters * A choice for the file extension of images to be loaded @@ -104,7 +108,6 @@ def __init__( val_interval (uint) : epoch interval for validation """ - super().__init__(viewer) # self.master = parent @@ -183,10 +186,10 @@ def __init__( ########### self.zip_choice = ui.CheckBox("Compress results") - self.validation_percent_choice = ui.Slider( + self.train_split_percent_choice = ui.Slider( lower=10, upper=90, - default=self.default_config.validation_percent * 100, + default=self.default_config.training_percent * 100, step=5, parent=self, ) @@ -328,7 +331,7 @@ def _set_tooltips(self): self.zip_choice.setToolTip( "Save a copy of the results as a zip folder" ) - self.validation_percent_choice.tooltips = "The percentage of images to retain for training.\nThe remaining images will be used for validation" + self.train_split_percent_choice.tooltips = "The percentage of images to retain for training.\nThe remaining images will be used for validation" self.epoch_choice.tooltips = "The number of epochs to train for.\nThe more you train, the better the model will fit the training data" self.loss_choice.setToolTip( "The loss function to use for training.\nSee the list in the training guide for more info" @@ -424,11 +427,9 @@ def _toggle_deterministic_param(self): self.container_seed.setVisible(False) def check_ready(self): - """ - Checks that the paths to the images and labels are correctly set + """Checks that the paths to the images and labels are correctly set. Returns: - * True if paths are set correctly * False and displays a warning if not @@ -449,7 +450,7 @@ def check_ready(self): return True def _toggle_unsupervised_mode(self, enabled=False): - """Change all the UI elements needed for unsupervised learning mode""" + """Change all the UI elements needed for unsupervised learning mode.""" if self.model_choice.currentText() == "WNet" or enabled: unsupervised = True self.start_btn = self.start_button_unsupervised @@ -494,7 +495,7 @@ def _toggle_unsupervised_mode(self, enabled=False): self._check_all_filepaths() def _build(self): - """Builds the layout of the widget and creates the following tabs and prompts: + """Builds the layout of the widget and creates the following tabs and prompts. * Model parameters : @@ -526,8 +527,8 @@ def _build(self): * Previous tab - * Start (see :py:func:`~start`)""" - + * Start (see :py:func:`~start`) + """ # for w in self.children(): # w.setToolTip(f"{w}") @@ -681,8 +682,8 @@ def _build(self): ui.add_blank(data_tab_w, data_tab_l) ####################### self.validation_group = ui.GroupedWidget.create_single_widget_group( - "Validation (%)", - self.validation_percent_choice.container, + "Training split (%)", + self.train_split_percent_choice.container, data_tab_l, ) ####################### @@ -919,12 +920,11 @@ def _build(self): self.results_path = default_results_path def send_log(self, text): - """Sends a message via the Log attribute""" + """Sends a message via the Log attribute.""" self.log.print_and_log(text) def start(self): - """ - Initiates the :py:func:`train` function as a worker and does the following : + """Initiates the :py:func:`train` function as a worker and does the following. * Checks that filepaths are set correctly using :py:func:`check_ready` @@ -936,8 +936,7 @@ def start(self): * When the worker finishes, clears the memory (tries to for now) - TODO: - + Todo: * Fix memory allocation from torch @@ -948,6 +947,8 @@ def start(self): if self._stop_requested: self.log.print_and_log("Worker is already stopping !") + if self.worker is None: + self._stop_requested = False return if not self.check_ready(): # issues a warning if not ready @@ -1042,9 +1043,10 @@ def _set_worker_config( self, additional_description=None, ) -> config.TrainingWorkerConfig: - """Creates a worker config for supervised or unsupervised training + """Creates a worker config for supervised or unsupervised training. + Args: - additional_description: Additional description to add to the results folder name + additional_description: Additional description to add to the results folder name. Returns: A worker config @@ -1053,11 +1055,14 @@ def _set_worker_config( model_config = config.ModelInfo(name=self.model_choice.currentText()) self.weights_config.path = self.weights_config.path - self.weights_config.custom = self.custom_weights_choice.isChecked() + self.weights_config.use_custom = self.custom_weights_choice.isChecked() + self.weights_config.use_pretrained = ( self.use_transfer_choice.isChecked() and not self.custom_weights_choice.isChecked() ) + self.weights_config.use_custom = self.custom_weights_choice.isChecked() + deterministic_config = config.DeterministicConfig( enabled=self.use_deterministic_choice.isChecked(), seed=self.box_seed.value(), @@ -1113,23 +1118,24 @@ def _set_supervised_worker_config( patch_size, deterministic_config, ): - """Sets the worker config for supervised training + """Sets the worker config for supervised training. + Args: model_config: Model config results_path_folder: Path to results folder patch_size: Patch size - deterministic_config: Deterministic config + deterministic_config: Deterministic config. Returns: A worker config """ - validation_percent = self.validation_percent_choice.slider_value / 100 + validation_percent = self.train_split_percent_choice.slider_value / 100 self.worker_config = config.SupervisedTrainingWorkerConfig( device=self.check_device_choice(), model_info=model_config, weights_info=self.weights_config, train_data_dict=self.data, - validation_percent=validation_percent, + training_percent=validation_percent, max_epochs=self.epoch_choice.value(), loss_function=self.loss_choice.currentText(), learning_rate=self.learning_rate_choice.get_learning_rate(), @@ -1154,12 +1160,13 @@ def _set_unsupervised_worker_config( deterministic_config, eval_volume_dict, ) -> config.WNetTrainingWorkerConfig: - """Sets the worker config for unsupervised training + """Sets the worker config for unsupervised training. + Args: results_path_folder: Path to results folder patch_size: Patch size deterministic_config: Deterministic config - eval_volume_dict: Evaluation volume dictionary + eval_volume_dict: Evaluation volume dictionary. Returns: A worker config @@ -1205,8 +1212,7 @@ def _is_current_job_supervised( return True def on_start(self): - """Catches started signal from worker""" - + """Catches started signal from worker.""" self.remove_docked_widgets() self.display_status_report() self.log.clear() @@ -1216,7 +1222,7 @@ def on_start(self): self.log.print_and_log("\nWorker is running...") def on_finish(self): - """Catches finished signal from worker""" + """Catches finished signal from worker.""" self.log.print_and_log("*" * 20) self.log.print_and_log(f"\nWorker finished at {utils.get_time()}") @@ -1256,11 +1262,12 @@ def on_finish(self): self.worker = None def on_error(self): - """Catches errored signal from worker""" + """Catches errored signal from worker.""" self.log.print_and_log(f"WORKER ERRORED at {utils.get_time()}") self.worker = None def on_stop(self): + """Catches stop signal from worker.""" self._remove_result_layers() self.worker = None self._stop_requested = False @@ -1269,11 +1276,15 @@ def on_stop(self): def _remove_result_layers(self): for layer in self.result_layers: - self._viewer.layers.remove(layer) + try: + self._viewer.layers.remove(layer) + except ValueError: + logger.debug("Layer already removed ?") + pass self.result_layers = [] def _display_results(self, images_dict, complete_missing=False): - """Show various model input/outputs in napari viewer as a list of layers""" + """Show various model input/outputs in napari viewer as a list of layers.""" layer_list = [] if not complete_missing: for layer_name in list(images_dict.keys()): @@ -1309,6 +1320,7 @@ def _display_results(self, images_dict, complete_missing=False): self.result_layers[i].reset_contrast_limits() def on_yield(self, report: TrainingReport): + """Catches yielded signal from worker and plots the loss.""" if report == TrainingReport(): return # skip empty reports @@ -1433,7 +1445,7 @@ def _plot_loss( loss_values_2: list, show_plot_2_max: bool = True, ): - """Creates two subplots to plot the training loss and validation metric""" + """Creates two subplots to plot the training loss and validation metric.""" plot_key = ( "supervised" if self._is_current_job_supervised() @@ -1465,11 +1477,11 @@ def _plot_loss( # update plot 2 if self._is_current_job_supervised(): x = [ - self.worker_config.validation_interval * (i + 1) + int(self.worker_config.validation_interval * (i + 1)) for i in range(len(loss_values_2)) ] else: - x = [i + 1 for i in range(len(loss_values_2))] + x = [int(i + 1) for i in range(len(loss_values_2))] y = loss_values_2 self.plot_2.plot(x, y, zorder=1) @@ -1484,15 +1496,14 @@ def _plot_loss( self.canvas.draw_idle() def update_loss_plot(self, loss_1: dict, loss_2: list): - """ - Updates the plots on subsequent validation steps. + """Updates the plots on subsequent validation steps. + Creates the plot on the second validation step (epoch == val_interval*2). Updates the plot on subsequent validation steps. Epoch is obtained from the length of the loss vector. Returns: returns empty if the epoch is < than 2 * validation interval. """ - epoch = len(loss_1[list(loss_1.keys())[0]]) logger.debug(f"Updating loss plot for epoch {epoch}") plot_max = self._is_current_job_supervised() @@ -1554,7 +1565,10 @@ def _reset_loss_plot(self): class LearningRateWidget(ui.ContainerWidget): + """A widget to choose the learning rate.""" + def __init__(self, parent=None): + """Creates a widget to choose the learning rate.""" super().__init__(vertical=False, parent=parent) self.lr_exponent_dict = { @@ -1596,6 +1610,7 @@ def _build(self): ) def get_learning_rate(self) -> float: + """Return the learning rate as a float.""" return float( self.lr_value_choice.value() * self.lr_exponent_dict[self.lr_exponent_choice.currentText()] @@ -1603,11 +1618,12 @@ def get_learning_rate(self) -> float: class WNetWidgets: - """A collection of widgets for the WNet training GUI""" + """A collection of widgets for the WNet training GUI.""" default_config = config.WNetTrainingWorkerConfig() def __init__(self, parent): + """Creates a collection of widgets for the WNet training GUI.""" self.num_classes_choice = ui.DropdownMenu( entries=["2", "3", "4"], parent=parent, @@ -1689,6 +1705,7 @@ def _set_tooltips(self): ) def get_reconstruction_weight(self): + """Returns the reconstruction weight as a float.""" return float( self.reconstruction_weight_choice.value() / self.reconstruction_weight_divide_factor_choice.value() diff --git a/napari_cellseg3d/code_plugins/plugin_review.py b/napari_cellseg3d/code_plugins/plugin_review.py index 712b3193..cbdd3dd0 100644 --- a/napari_cellseg3d/code_plugins/plugin_review.py +++ b/napari_cellseg3d/code_plugins/plugin_review.py @@ -1,3 +1,4 @@ +"""Review plugin for 3D labeling of volumes.""" from pathlib import Path import matplotlib.pyplot as plt @@ -24,10 +25,12 @@ class Reviewer(BasePluginSingleImage, metaclass=ui.QWidgetSingleton): """A plugin for selecting volumes and labels file and launching the review process. - Inherits from : :doc:`plugin_base`""" + + Inherits from : :doc:`plugin_base`. + """ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): - """Creates a Reviewer plugin with several buttons : + """Creates a Reviewer plugin with several buttons. * Open file prompt to select volumes directory @@ -39,7 +42,6 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): * A button to launch the review process """ - super().__init__( viewer, parent, @@ -95,13 +97,12 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None): print(f"{self}") def _update_results_path(self): - p = self.image_filewidget.text_field.text() + p = self.labels_filewidget.text_field.text() if p is not None and Path(p).is_file(): self.results_filewidget.text_field.setText(str(Path(p).parent)) def _build(self): - """Build buttons in a layout and add them to the napari Viewer""" - + """Build buttons in a layout and add them to the napari Viewer.""" self.setSizePolicy(QSizePolicy.Maximum, QSizePolicy.MinimumExpanding) tab = ui.ContainerWidget(0, 0, 1, 1) @@ -168,8 +169,7 @@ def _build(self): self.results_path = self.results_filewidget.text_field.text() def check_image_data(self): - """Checks that images are present and that sizes match""" - + """Checks that images are present and that sizes match.""" cfg = self.config if cfg.image is None: @@ -195,8 +195,8 @@ def _prepare_data(self): self.check_image_data() self._check_results_path(self.results_filewidget.text_field.text()) - self.config.csv_path = self.results_filewidget.text_field.text() self.config.model_name = self.csv_textbox.text() + self.config.csv_path = self.results_filewidget.text_field.text() self.config.new_csv = self.new_csv_choice.isChecked() self.config.filetype = self.filetype @@ -208,13 +208,12 @@ def _prepare_data(self): self.config.zoom_factor = zoom def run_review(self): - """Launches review process by loading the files from the chosen folders, - and adds several widgets to the napari Viewer. + """Launches review process by loading the files from the chosen folders, and adds several widgets to the napari Viewer. + If the review process has been launched once before, closes the window entirely and launches the review process in a fresh window. - TODO: - + Todo: * Save work done before leaving See launch_review @@ -222,7 +221,6 @@ def run_review(self): Returns: napari.viewer.Viewer: self.viewer """ - print("New review session\n" + "*" * 20) previous_viewer = self._viewer try: @@ -240,8 +238,7 @@ def _reset(self): self.remove_docked_widgets() def launch_review(self): - """Launch the review process, loading the original image, the labels & the raw labels (from prediction) - in the viewer. + """Launch the review process, loading the original image, the labels & the raw labels (from prediction) in the viewer. Adds several widgets to the viewer : @@ -329,19 +326,19 @@ def quicksave(): "Shift-click on image for plot \n", fontsize=8 ) xy_axes.imshow(np.zeros((100, 100), np.int16)) - xy_axes.scatter(50, 50, s=10, c="green", alpha=0.25) - xy_axes.set_xlabel("x axis") - xy_axes.set_ylabel("y axis") + xy_axes.scatter(50, 50, s=30, c="green", alpha=0.6, marker="+") + xy_axes.set_xlabel("X axis") + xy_axes.set_ylabel("Y axis") yz_axes = canvas.figure.add_subplot(3, 1, 2) yz_axes.imshow(np.zeros((100, 100), np.int16)) - yz_axes.scatter(50, 50, s=10, c="green", alpha=0.25) - yz_axes.set_xlabel("y axis") - yz_axes.set_ylabel("z axis") + yz_axes.scatter(50, 50, s=30, c="green", alpha=0.6, marker="+") + yz_axes.set_xlabel("Y axis") + yz_axes.set_ylabel("Z axis") zx_axes = canvas.figure.add_subplot(3, 1, 3) zx_axes.imshow(np.zeros((100, 100), np.int16)) - zx_axes.scatter(50, 50, s=10, c="green", alpha=0.25) - zx_axes.set_xlabel("x axis") - zx_axes.set_ylabel("z axis") + zx_axes.scatter(50, 50, s=30, c="green", alpha=0.6, marker="+") + zx_axes.set_xlabel("X axis") + zx_axes.set_ylabel("Z axis") # canvas.figure.tight_layout() canvas.figure.subplots_adjust( @@ -419,6 +416,13 @@ def update_button(axis_event): viewer.dims.events.current_step.connect(update_button) def crop_volume_around_point(points, layer, zoom_factor): + """Crops a volume around a point. + + Args: + points (list): list of 3 integers, the coordinates of the point to crop around + layer (napari.layers.Image): the layer to crop + zoom_factor (list): list of 3 floats, the zoom factor to apply to the layer before cropping + """ if zoom_factor != [1, 1, 1]: data = np.array(layer.data, dtype=np.int16) volume = utils.resize(data, zoom_factor) diff --git a/napari_cellseg3d/code_plugins/plugin_review_dock.py b/napari_cellseg3d/code_plugins/plugin_review_dock.py index f634d117..e485b0a5 100644 --- a/napari_cellseg3d/code_plugins/plugin_review_dock.py +++ b/napari_cellseg3d/code_plugins/plugin_review_dock.py @@ -1,3 +1,4 @@ +"""Widget opened when a new Review session is started.""" from datetime import datetime, timedelta from pathlib import Path from typing import TYPE_CHECKING @@ -27,10 +28,7 @@ class Datamanager(QWidget): - """A widget with a single checkbox that allows to store the status of - a slice in csv file (checked/not checked) - - """ + """A widget with a single checkbox that allows to store the status of a slice in csv file (checked/not checked).""" def __init__(self, parent: "napari.viewer.Viewer"): """Creates the datamanager widget in the specified viewer window. @@ -38,7 +36,6 @@ def __init__(self, parent: "napari.viewer.Viewer"): Args: parent (napari.viewer.Viewer): napari Viewer for the widget to be displayed in """ - super().__init__() layout = QVBoxLayout() @@ -46,7 +43,9 @@ def __init__(self, parent: "napari.viewer.Viewer"): """napari.viewer.Viewer: viewer in which the widget is displayed""" # add some buttons - self.button = ui.Button("1", self.button_func, parent=self, fixed=True) + self.button = ui.Button( + "1", self._button_func, parent=self, fixed=True + ) self.time_label = ui.make_label("", self) self.time_label.setVisible(False) @@ -90,7 +89,7 @@ def __init__(self, parent: "napari.viewer.Viewer"): # self.pause_time = None def pause_timer(self): - """Pause the timer for the review time""" + """Pause the timer for the review time.""" if self.pause_box.isChecked(): self.time_label.setVisible(True) @@ -108,51 +107,48 @@ def pause_timer(self): # self.pause_time = datetime.now() - self.pause_start self.start_time = datetime.now() self.is_paused = False - self.update_time_csv() + self._update_time_csv() - def update_time_csv(self): + def _update_time_csv(self): if not self.is_paused: self.time_elapsed += datetime.now() - self.start_time self.start_time = datetime.now() str_time = utils.time_difference(timedelta(), self.time_elapsed) - print(f"Time elapsed : {str_time}") + logger.info(f"Time elapsed : {str_time}") self.df.at[0, "time"] = str_time self.df.to_csv(self.csv_path) def prepare(self, label_dir, filetype, model_type, checkbox): - """Initialize the Datamanager, which loads the csv file and updates it - with the index of the current slice. + """Initialize the Datamanager, which loads the csv file and updates it with the index of the current slice. Args: - label_dir (str): label path - filetype (str) : file extension - model_type (str): model type - checkbox (bool): create new dataset or not - as_folder (bool) : load as folder or as single file + label_dir (str): label path + filetype (str) : file extension + model_type (str): model type + checkbox (bool): create new dataset or not + as_folder (bool) : load as folder or as single file """ - # label_dir = os.path.dirname(label_dir) - print("csv path try :") - print(label_dir) + logger.debug("csv path try :") + logger.debug(label_dir) self.filetype = filetype if not self.as_folder: p = Path(label_dir) self.filename = p.name - label_dir = p.parent - print("Loading single image") - print(self.filename) - print(label_dir) + label_dir = p.parents[0] + logger.info(f"Loading single image : {self.filename}") + logger.debug(label_dir) self.df, self.csv_path = self.load_csv(label_dir, model_type, checkbox) - print(self.csv_path, checkbox) - # print(self.viewer.dims.current_step[0]) + logger.debug(f"csv path : {self.csv_path}") + logger.debug(f"Create new dataset : {checkbox}") + # logger.debug(self.viewer.dims.current_step[0]) self.update_dm(self.viewer.dims.current_step[0]) def load_csv(self, label_dir, model_type, checkbox): - """ - Loads newest csv or create new csv + """Loads newest csv or create new csv. Args: label_dir (str): label path @@ -164,8 +160,8 @@ def load_csv(self, label_dir, model_type, checkbox): """ # if not self.as_folder : # label_dir = os.path.dirname(label_dir) - print("label dir") - print(label_dir) + logger.debug("label dir") + logger.debug(label_dir) csvs = sorted(list(Path(str(label_dir)).glob(f"{model_type}*.csv"))) if len(csvs) == 0: df, csv_path = self.create_csv( @@ -186,25 +182,24 @@ def load_csv(self, label_dir, model_type, checkbox): pass recorded_time = df.at[0, "time"] - # print("csv load time") - # print(recorded_time) + # logger.debug("csv load time") + # logger.debug(recorded_time) t = datetime.strptime(recorded_time, TIMER_FORMAT) self.time_elapsed = timedelta( hours=t.hour, minutes=t.minute, seconds=t.second ) - # print(self.time_elapsed) + # logger.debug(self.time_elapsed) return df, csv_path - def create_csv(self, label_dir, model_type, filename=None): - """ - Create a new dataframe and save the csv + def create_csv(self, label_dir, model_type): + """Create a new dataframe and save the csv. + Args: label_dir (str): label path model_type (str): model type Returns: - (pandas.DataFrame, str): dataframe, csv path + (pandas.DataFrame, str): dataframe, csv path. """ - if self.as_folder: labels = sorted( list( @@ -215,7 +210,7 @@ def create_csv(self, label_dir, model_type, filename=None): ) ) else: - # print(self.image_dims[0]) + # logger.debug(self.image_dims[0]) filename = self.filename if self.filename is not None else "image" labels = [str(filename) for i in range(self.image_dims[0])] @@ -228,45 +223,47 @@ def create_csv(self, label_dir, model_type, filename=None): ) df.at[0, "time"] = "00:00:00" - csv_path = str(Path(label_dir) / Path(f"{model_type}_train0.csv")) - print("csv path for create") - print(csv_path) - df.to_csv(csv_path) + csv_path = Path(label_dir) / Path(f"{model_type}_train0.csv") + if not csv_path.parent.exists(): + csv_path.parent.mkdir(parents=True) + logger.debug(f"CSV path : {csv_path}") + df.to_csv(str(csv_path)) return df, csv_path - def update_button(self): + def _update_button(self): if len(self.df) > 1: self.button.setText( self.df.at[self.df.index[self.slice_num], "train"] ) # puts button values at value of 1st csv item def update_dm(self, slice_num): - """Updates the Datamanager with the index of the current slice, and updates - the text with the status contained in the csv (e.g. checked/not checked). + """Updates the Datamanager with the index of the current slice. + + Also updates the text with the status contained in the csv (e.g. checked/not checked). Args: slice_num (int): index of the current slice """ self.slice_num = slice_num - self.update_time_csv() + self._update_time_csv() - print(f"New slice review started at {utils.get_time()}") - # print(self.df) + logger.info(f"New slice review started at {utils.get_time()}") + # logger.debug(self.df) try: - self.update_button() + self._update_button() except IndexError: self.slice_num -= 1 - self.update_button() + self._update_button() - def button_func(self): # updates csv every time you press button... + def _button_func(self): # updates csv every time you press button... if self.viewer.dims.ndisplay != 2: # TODO test if undefined behaviour or if okay logger.warning("Please switch back to 2D mode !") return - self.update_time_csv() + self._update_time_csv() if self.button.text() == "Not checked": self.button.setText("Checked") diff --git a/napari_cellseg3d/code_plugins/plugin_utilities.py b/napari_cellseg3d/code_plugins/plugin_utilities.py index abe56699..c90734ca 100644 --- a/napari_cellseg3d/code_plugins/plugin_utilities.py +++ b/napari_cellseg3d/code_plugins/plugin_utilities.py @@ -1,3 +1,4 @@ +"""Central plugin for all utilities.""" from typing import TYPE_CHECKING if TYPE_CHECKING: @@ -15,6 +16,7 @@ AnisoUtils, FragmentUtils, RemoveSmallUtils, + StatsUtils, ThresholdUtils, ToInstanceUtils, ToSemanticUtils, @@ -32,11 +34,15 @@ "Convert to semantic labels": ToSemanticUtils, "Threshold": ThresholdUtils, "CRF": CRFWidget, + "Label statistics": StatsUtils, } class Utilities(QWidget, metaclass=ui.QWidgetSingleton): + """Central plugin for all utilities.""" + def __init__(self, viewer: "napari.viewer.Viewer"): + """Creates a widget with all utilities.""" super().__init__() self._viewer = viewer self.current_widget = None @@ -50,6 +56,7 @@ def __init__(self, viewer: "napari.viewer.Viewer"): "sem", "thresh", "crf", + "stats", ] self._create_utils_widgets(attr_names) self.utils_choice = ui.DropdownMenu( diff --git a/napari_cellseg3d/config.py b/napari_cellseg3d/config.py index 422fdc70..d3aa0eb1 100644 --- a/napari_cellseg3d/config.py +++ b/napari_cellseg3d/config.py @@ -1,3 +1,4 @@ +"""Module to store configuration parameters for napari_cellseg3d.""" import datetime from dataclasses import dataclass from pathlib import Path @@ -18,7 +19,6 @@ logger = LOGGER -# TODO(cyril) DOCUMENT !!! and add default values # TODO(cyril) add JSON load/save MODEL_LIST = { @@ -43,6 +43,18 @@ @dataclass class ReviewConfig: + """Class to record params for Review plugin. + + Args: + image (np.array): image to review + labels (np.array): labels to review + csv_path (str): path to csv to save results + model_name (str): name of the model (added to csv name) + new_csv (bool): whether to create a new csv + filetype (str): filetype to read & write review images + zoom_factor (List[int]): zoom factor to apply to image & labels, if selected + """ + image: np.array = None labels: np.array = None csv_path: str = Path.home() / "cellseg3d" / "review" @@ -54,6 +66,17 @@ class ReviewConfig: @dataclass # TODO create custom reader for JSON to load project class ReviewSession: + """Class to record params for Review session. + + Args: + project_name (str): name of the project + image_path (str): path to images + labels_path (str): path to labels + csv_path (str): path to csv + aniso_zoom (List[int]): anisotropy zoom + time_taken (datetime.timedelta): time taken to review + """ + project_name: str image_path: str labels_path: str @@ -69,18 +92,22 @@ class ReviewSession: @dataclass class ModelInfo: - """Dataclass recording supervised models info + """Dataclass recording supervised models info. + Args: name (str): name of the model model_input_size (Optional[List[int]]): input size of the model - num_classes (int): number of classes for the model + num_classes (int): number of classes for the model. """ name: str = next(iter(MODEL_LIST)) - model_input_size: Optional[List[int]] = None - num_classes: int = 2 + model_input_size: Optional[ + List[int] + ] = None # only used by SegResNet and SwinUNETR + num_classes: int = 2 # only used by WNets def get_model(self): + """Return model from model list.""" try: return MODEL_LIST[self.name] except KeyError as e: @@ -91,6 +118,7 @@ def get_model(self): @staticmethod def get_model_name_list(): + """Return list of model names.""" logger.info("Model list :") for model_name in MODEL_LIST: logger.info(f" * {model_name}") @@ -99,37 +127,54 @@ def get_model_name_list(): @dataclass class WeightsInfo: - path: str = PRETRAINED_WEIGHTS_DIR - custom: bool = False + """Class to record params for weights. + + Args: + path (Optional[str]): path to weights + use_custom (Optional[bool]): whether to use custom weights + use_pretrained (Optional[bool]): whether to use pretrained weights + """ + + path: Optional[str] = PRETRAINED_WEIGHTS_DIR use_pretrained: Optional[bool] = False + use_custom: Optional[bool] = False ############################################# # Post processing & instance segmentation # ############################################# +# Utils + @dataclass class Thresholding: - enabled: bool = True + """Class to record params for thresholding.""" + + enabled: bool = False threshold_value: float = 0.8 @dataclass class Zoom: - enabled: bool = True + """Class to record params for zoom.""" + + enabled: bool = False zoom_values: List[float] = None @dataclass class InstanceSegConfig: + """Class to record params for instance segmentation.""" + enabled: bool = False method: InstanceMethod = None +# Workers @dataclass class PostProcessConfig: - """Class to record params for post processing + """Class to record params for post processing. Args: zoom (Zoom): zoom config @@ -144,8 +189,15 @@ class PostProcessConfig: @dataclass class CRFConfig: - """ - Class to record params for CRF + """Class to record params for CRF. + + Args: + sa (float): alpha standard deviation, the scale of the spatial part of the appearance/bilateral kernel. + sb (float): beta standard deviation, the scale of the color part of the appearance/bilateral kernel. + sg (float): gamma standard deviation, the scale of the smoothness/gaussian kernel. + w1 (float): weight of the appearance/bilateral kernel. + w2 (float): weight of the smoothness/gaussian kernel. + n_iter (int, optional): Number of iterations for the CRF post-processing step. Defaults to 5. """ sa: float = 10 @@ -163,16 +215,19 @@ class CRFConfig: @dataclass class SlidingWindowConfig: + """Class to record params for sliding window inference.""" + window_size: int = None window_overlap: float = 0.25 def is_enabled(self): + """Return True if sliding window is enabled.""" return self.window_size is not None @dataclass class InfererConfig: - """Class to record params for Inferer plugin + """Class to record params for Inferer plugin. Args: model_info (ModelInfo): model info @@ -191,7 +246,7 @@ class InfererConfig: @dataclass class InferenceWorkerConfig: - """Class to record configuration for Inference job + """Class to record configuration for Inference job. Args: device (str): device to use for inference @@ -219,7 +274,7 @@ class InferenceWorkerConfig: use_crf: bool = False crf_config: CRFConfig = CRFConfig() - images_filepaths: str = None + images_filepaths: List[str] = None layer: napari.layers.Layer = None @@ -230,7 +285,7 @@ class InferenceWorkerConfig: @dataclass class DeterministicConfig: - """Class to record deterministic config""" + """Class to record deterministic config.""" enabled: bool = True seed: int = 34936339 # default seed from NP_MAX @@ -238,14 +293,33 @@ class DeterministicConfig: @dataclass class TrainerConfig: - """Class to record trainer plugin config""" + """Class to record trainer plugin config.""" save_as_zip: bool = False @dataclass class TrainingWorkerConfig: - """General class to record config for training""" + """General class to record config for training. + + Args: + device (str): device to use for training + max_epochs (int): max number of epochs + learning_rate (np.float64): learning rate + validation_interval (int): validation interval + batch_size (int): batch size + deterministic_config (DeterministicConfig): deterministic config + scheduler_factor (float): scheduler factor + scheduler_patience (int): scheduler patience + weights_info (WeightsInfo): weights info + results_path_folder (str): path to save results + sampling (bool): whether to sample data into patches + num_samples (int): number of patches + sample_size (List[int]): patch size + do_augmentation (bool): whether to do augmentation + num_workers (int): number of workers + train_data_dict (dict): dict of train data as {"image": np.array, "labels": np.array} + """ # model params device: str = "cpu" @@ -269,37 +343,61 @@ class TrainingWorkerConfig: @dataclass class SupervisedTrainingWorkerConfig(TrainingWorkerConfig): - """Class to record config for Trainer plugin""" + """Class to record config for Trainer plugin. + + Args: + model_info (ModelInfo): model info + loss_function (callable): loss function + validation_percent (float): validation percent + """ model_info: ModelInfo = None loss_function: callable = None - validation_percent: float = 0.8 + training_percent: float = 0.8 @dataclass class WNetTrainingWorkerConfig(TrainingWorkerConfig): - """Class to record config for WNet worker""" + """Class to record config for WNet worker. + + Args: + in_channels (int): encoder input channels + out_channels (int): decoder (reconstruction) output channels + num_classes (int): encoder output channels + dropout (float): dropout + learning_rate (np.float64): learning rate + use_clipping (bool): use gradient clipping + clipping (float): clipping value + weight_decay (float): weight decay + intensity_sigma (float): intensity sigma + spatial_sigma (float): spatial sigma + radius (int): pixel radius for loss computation; might be overriden depending on data shape + reconstruction_loss (str): reconstruction loss (MSE or BCE) + n_cuts_weight (float): weight for NCuts loss + rec_loss_weight (float): weight for reconstruction loss. Must be adjusted depending on images; compare to NCuts loss value + train_data_dict (dict): dict of train data as {"image": np.array, "labels": np.array} + eval_volume_dict (str): dict of eval volume (optional) + eval_batch_size (int): eval batch size (optional) + """ # model params - in_channels: int = 1 # encoder input channels - out_channels: int = 1 # decoder (reconstruction) output channels - num_classes: int = 2 # encoder output channels + in_channels: int = 1 + out_channels: int = 1 + num_classes: int = 2 dropout: float = 0.65 learning_rate: np.float64 = 2e-5 - use_clipping: bool = False # use gradient clipping - clipping: float = 1.0 # clipping value - weight_decay: float = 0.01 # 1e-5 # weight decay (used 0.01 historically) + use_clipping: bool = False + clipping: float = 1.0 + weight_decay: float = 0.01 # 1e-5 # NCuts loss params intensity_sigma: float = 1.0 spatial_sigma: float = 4.0 - radius: int = 2 # pixel radius for loss computation; might be overriden depending on data shape + radius: int = 2 # reconstruction loss params reconstruction_loss: str = "MSE" # or "BCE" # summed losses weights n_cuts_weight: float = 0.5 - rec_loss_weight: float = ( - 0.5 / 100 - ) # must be adjusted depending on images; compare to NCuts loss value + rec_loss_weight: float = 0.5 / 100 # normalization params # normalizing_function: callable = remap_image # FIXME: call directly in worker, not a param # data params @@ -313,25 +411,7 @@ class WNetTrainingWorkerConfig(TrainingWorkerConfig): ################ @dataclass class WandBConfig: - """Class to record parameters for WandB""" + """Class to record parameters for WandB.""" - mode: str = "disabled" # disabled, online, enabled + mode: str = "online" # disabled, online, offline save_model_artifact: bool = False - - -################ -# CRF config for WNet -################ - - -@dataclass -class WNetCRFConfig: - """Class to store parameters of WNet CRF post-processing""" - - # CRF - sa = 10 # 50 - sb = 10 - sg = 1 - w1 = 10 # 50 - w2 = 10 - n_iter = 5 diff --git a/napari_cellseg3d/dev_scripts/artefact_labeling.py b/napari_cellseg3d/dev_scripts/artefact_labeling.py index 93746eb6..a2935354 100644 --- a/napari_cellseg3d/dev_scripts/artefact_labeling.py +++ b/napari_cellseg3d/dev_scripts/artefact_labeling.py @@ -21,18 +21,20 @@ def map_labels(labels, artefacts): """Map the artefacts labels to the neurons labels. + Parameters ---------- labels : ndarray Label image with neurons labelled as mulitple values. artefacts : ndarray Label image with artefacts labelled as mulitple values. - Returns + + Returns: ------- map_labels_existing: numpy array The label value of the artefact and the label value of the neurone associated or the neurons associated new_labels: list - The labels of the artefacts that are not labelled in the neurons + The labels of the artefacts that are not labelled in the neurons. """ map_labels_existing = [] new_labels = [] @@ -75,6 +77,7 @@ def make_labels( augment_contrast_factor=2, ): """Detect nucleus. using a binary watershed algorithm and otsu thresholding. + Parameters ---------- image : str @@ -91,12 +94,12 @@ def make_labels( If True, use watershed algorithm to detect nucleus. augment_contrast_factor : int, optional Factor to augment the contrast of the image. - Returns + + Returns: ------- ndarray Label image with nucleus labelled with 1 value per nucleus. """ - # image = imread(image) image = (image - np.min(image)) / (np.max(image) - np.min(image)) @@ -129,6 +132,7 @@ def make_labels( def select_image_by_labels(image, labels, path_image_out, label_values): """Select image by labels. + Parameters ---------- image : np.array @@ -166,6 +170,7 @@ def crop_image(img): def crop_image_path(image, path_image_out): """Crop image. + Parameters ---------- image : np.array @@ -188,6 +193,7 @@ def make_artefact_labels( remove_true_labels=True, ): """Detect pseudo nucleus. + Parameters ---------- image : ndarray @@ -206,12 +212,12 @@ def make_artefact_labels( If True, each different artefact will be labelled as a different value. remove_true_labels : bool, optional If True, the true labels will be removed from the artefacts. - Returns + + Returns: ------- ndarray Label image with pseudo nucleus labelled with 1 value per artefact. """ - neurons = np.array(labels > 0) non_neurons = np.array(labels == 0) @@ -277,6 +283,7 @@ def make_artefact_labels( def select_artefacts_by_size(artefacts, min_size, is_labeled=False): """Select artefacts by size. + Parameters ---------- artefacts : ndarray @@ -285,7 +292,8 @@ def select_artefacts_by_size(artefacts, min_size, is_labeled=False): Minimum size of artefacts to keep is_labeled : bool, optional If True, the artefacts are already labelled. - Returns + + Returns: ------- ndarray Label image with artefacts labelled and small artefacts removed. @@ -308,6 +316,7 @@ def create_artefact_labels( contrast_power=20, ): """Create a new label image with artefacts labelled as 2 and neurons labelled as 1. + Parameters ---------- image : np.array @@ -339,6 +348,7 @@ def create_artefact_labels( def visualize_images(paths): """Visualize images. + Parameters ---------- paths : list @@ -360,6 +370,7 @@ def create_artefact_labels_from_folder( contrast_power=20, ): """Create a new label image with artefacts labelled as 2 and neurons labelled as 1 for all images in a folder. The images created are stored in a folder artefact_neurons. + Parameters ---------- path : str diff --git a/napari_cellseg3d/dev_scripts/classifier_test.ipynb b/napari_cellseg3d/dev_scripts/classifier_test.ipynb new file mode 100644 index 00000000..80486f5e --- /dev/null +++ b/napari_cellseg3d/dev_scripts/classifier_test.ipynb @@ -0,0 +1,587 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from tifffile import imread\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from pathlib import Path\n", + "import napari\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded 162 images\n" + ] + } + ], + "source": [ + "DATA_PATH = Path.home() / \"Desktop/Code/CELLSEG_BENCHMARK/classifier_test/train\"\n", + "images_p = list(DATA_PATH.glob(\"*.tif\"))\n", + "images_p.sort()\n", + "print(f\"Loaded {len(images_p)} images\")\n", + "images = [imread(str(image)) for image in images_p]\n", + "\n", + "DATA_DF_PATH = DATA_PATH / \"train_data_df.csv\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0.002281 0.001797 0.001286 0.000664 0.000019 0.000015 1 \n", + "2 0.004478 0.003887 0.002670 0.000980 0.000137 0.000103 0.000137 1 \n", + "3 0.007359 0.005615 0.001610 0.000328 0.000080 0.000103 0.000065 1 \n", + "4 0.004478 0.003887 0.002670 0.000980 0.000137 0.000103 0.000137 1 \n", + "\n", + "[5 rows x 102 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "if not DATA_DF_PATH.exists():\n", + " data_df = pd.DataFrame([i.name[:-4] for i in images_p], columns=[\"image_path\"])\n", + " # add 100 bins of the hist of each image\n", + " hists = []\n", + " for i, image in enumerate(images):\n", + " # hists.append(np.histogram(image, bins=100)[0])\n", + " hist = np.histogram(image, bins=100)[0]\n", + " hist = hist / hist.sum()\n", + " hists.append(hist)\n", + " \n", + " data_df = pd.concat([data_df, pd.DataFrame(hists)], axis=1)\n", + "else:\n", + " data_df = pd.read_csv(DATA_DF_PATH)\n", + " # make the hist a density\n", + " # data_df.iloc[:, 1:-1] = data_df.iloc[:, 1:-1].div(data_df.iloc[:, 1:-1].sum(axis=1), axis=0)\n", + " # make the hist a percentage\n", + " # data_df.iloc[:, 1:-1] = data_df.iloc[:, 1:-1] / 1000\n", + "data_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# # plot all histograms\n", + "# fig, axs = plt.subplots(8, 8, figsize=(16, 16))\n", + "# for i, ax in enumerate(axs.flatten()):\n", + "# ax.plot(data_df.iloc[i, 1:])\n", + "# ax.set_yticklabels([])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from napari.settings import get_settings\n", + "settings = get_settings()\n", + "settings.application.ipy_interactive = False\n", + "\n", + "# labels = []\n", + "\n", + "# for i, im in enumerate(images):\n", + "# viewer = napari.Viewer()\n", + " \n", + "# @viewer.bind_key(\"o\")\n", + "# def next_label(event):\n", + "# if len(labels) == i+1:\n", + "# print(\"You have labeled all images already!\")\n", + "# return\n", + "# labels.append(0)\n", + "# print(\"Added label 0\")\n", + " \n", + "# @viewer.bind_key(\"p\")\n", + "# def next_label(event):\n", + "# if len(labels) == i+1:\n", + "# print(\"You have labeled all images already!\")\n", + "# return\n", + "# labels.append(1)\n", + "# print(\"Added label 1\")\n", + " \n", + "# @viewer.bind_key(\"q\")\n", + "# def next_label(event):\n", + "# if len(labels) != i+1:\n", + "# print(\"You need to label the current image first!\")\n", + "# return\n", + "# else:\n", + "# viewer.close()\n", + "# @viewer.bind_key(\"u\")\n", + "# def undo_label(event):\n", + "# labels.pop()\n", + "# print(\"Removed last label\")\n", + "# print(labels)\n", + " \n", + "# print(f\"Image {i+1}/{len(images)} :\")\n", + "# viewer.add_image(im, colormap=\"turbo\")\n", + "# viewer.dims.ndisplay = 3\n", + "# napari.run()\n", + "\n", + "# data_df[\"label\"] = labels\n", + "# # save data_df as csv\n", + "# data_df.to_csv(DATA_PATH / \"train_data_df.csv\", index=False)\n", + "# display(data_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    RandomForestClassifier()
    In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
    On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
    " + ], + "text/plain": [ + "RandomForestClassifier()" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "classifier = RandomForestClassifier()\n", + "classifier.fit(data_df.iloc[:, 1:-1].values, data_df[\"label\"].values)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# test performance on training data\n", + "classifier.score(data_df.iloc[:, 1:-1].values, data_df[\"label\"].values)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot decision tree\n", + "from sklearn import tree\n", + "plt.figure(figsize=(20, 20))\n", + "tree.plot_tree(classifier.estimators_[5], filled=True,class_names=[\"0\", \"1\"])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# TEST_CROPS_PATH = Path.home() / \"Desktop/Code/CELLSEG_BENCHMARK/classifier_test/test_crops_128\"\n", + "# test_images_p = list(TEST_CROPS_PATH.glob(\"*.tif\"))\n", + "# test_images_p.sort()\n", + "# print(f\"Loaded {len(test_images_p)} images\")\n", + "\n", + "# test_images = [imread(str(image)) for image in test_images_p]\n", + "\n", + "# for i, im in enumerate(test_images):\n", + "# # norm_im = (im - im.min()) / (im.max() - im.min())\n", + "# hist = np.histogram(im, bins=100)[0]\n", + "# hist = hist / hist.sum()\n", + "# pred = classifier.predict(hist.reshape(1, -1))\n", + "# print(f\"Image {i} is {pred}\")\n", + "# # view = napari.view_image(im,colormap=\"turbo\")\n", + "# # view.dims.ndisplay = 3\n", + "# # napari.run()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# TEST_CROPS_PATH = Path.home() / \"Desktop/Code/CELLSEG_BENCHMARK/classifier_test/crops_64\"\n", + "# test_images_p = list(TEST_CROPS_PATH.glob(\"*.tif\"))\n", + "# test_images_p.sort()\n", + "# print(f\"Loaded {len(test_images_p)} images\")\n", + "\n", + "# test_images = [imread(str(image)) for image in test_images_p]\n", + "\n", + "# for i, im in enumerate(test_images):\n", + "# # norm_im = (im - im.min()) / (im.max() - im.min())\n", + "# hist = np.histogram(im, bins=100)[0]\n", + "# hist = hist / hist.sum()\n", + "# pred = classifier.predict(hist.reshape(1, -1))\n", + "# print(f\"Image {i} is {pred}\")\n", + "# # view = napari.view_image(im,colormap=\"turbo\")\n", + "# # view.dims.ndisplay = 3\n", + "# # napari.run()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "TEST_DATA_PATH = Path.home() / \"Desktop/Code/CELLSEG_BENCHMARK/classifier_test/TEST/TEST_preds_128_overlap_0.tif\"\n", + "test_image = imread(str(TEST_DATA_PATH))\n", + "test_image = test_image.swapaxes(0, 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32mc:\\Users\\Cyril\\Desktop\\Code\\CellSeg3d\\napari_cellseg3d\\dev_scripts\\classifier_test.ipynb Cell 12\u001b[0m line \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 31\u001b[0m preds[crop_location_i:crop_location_i\u001b[39m+\u001b[39mcube_size, crop_location_j:crop_location_j\u001b[39m+\u001b[39mcube_size, crop_location_k:crop_location_k\u001b[39m+\u001b[39mcube_size] \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[0;32m 32\u001b[0m rejected[crop_location_i:crop_location_i\u001b[39m+\u001b[39mcube_size, crop_location_j:crop_location_j\u001b[39m+\u001b[39mcube_size, crop_location_k:crop_location_k\u001b[39m+\u001b[39mcube_size] \u001b[39m=\u001b[39m crop\n\u001b[1;32m---> 34\u001b[0m view \u001b[39m=\u001b[39m napari\u001b[39m.\u001b[39;49mview_image(preds, colormap\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mturbo\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[0;32m 35\u001b[0m view\u001b[39m.\u001b[39madd_image(test_image, colormap\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mturbo\u001b[39m\u001b[39m\"\u001b[39m, blending\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39madditive\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m 36\u001b[0m view\u001b[39m.\u001b[39madd_image(rejected, colormap\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mturbo\u001b[39m\u001b[39m\"\u001b[39m, blending\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39madditive\u001b[39m\u001b[39m\"\u001b[39m)\n", + "File \u001b[1;32mc:\\Users\\Cyril\\anaconda3\\envs\\cellseg3d\\lib\\site-packages\\napari\\view_layers.py:178\u001b[0m, in \u001b[0;36mview_image\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 176\u001b[0m \u001b[39m@_merge_layer_viewer_sigs_docs\u001b[39m\n\u001b[0;32m 177\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mview_image\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m--> 178\u001b[0m \u001b[39mreturn\u001b[39;00m _make_viewer_then(\u001b[39m'\u001b[39;49m\u001b[39madd_image\u001b[39;49m\u001b[39m'\u001b[39;49m, args, kwargs)[\u001b[39m0\u001b[39m]\n", + "File \u001b[1;32mc:\\Users\\Cyril\\anaconda3\\envs\\cellseg3d\\lib\\site-packages\\napari\\view_layers.py:156\u001b[0m, in \u001b[0;36m_make_viewer_then\u001b[1;34m(add_method, args, kwargs)\u001b[0m\n\u001b[0;32m 154\u001b[0m viewer \u001b[39m=\u001b[39m kwargs\u001b[39m.\u001b[39mpop(\u001b[39m\"\u001b[39m\u001b[39mviewer\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m)\n\u001b[0;32m 155\u001b[0m \u001b[39mif\u001b[39;00m viewer \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m--> 156\u001b[0m viewer \u001b[39m=\u001b[39m Viewer(\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mvkwargs)\n\u001b[0;32m 157\u001b[0m kwargs\u001b[39m.\u001b[39mupdate(kwargs\u001b[39m.\u001b[39mpop(\u001b[39m\"\u001b[39m\u001b[39mkwargs\u001b[39m\u001b[39m\"\u001b[39m, {}))\n\u001b[0;32m 158\u001b[0m method \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(viewer, add_method)\n", + "File \u001b[1;32mc:\\Users\\Cyril\\anaconda3\\envs\\cellseg3d\\lib\\site-packages\\napari\\viewer.py:67\u001b[0m, in \u001b[0;36mViewer.__init__\u001b[1;34m(self, title, ndisplay, order, axis_labels, show)\u001b[0m\n\u001b[0;32m 63\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mnapari\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mwindow\u001b[39;00m \u001b[39mimport\u001b[39;00m Window\n\u001b[0;32m 65\u001b[0m _initialize_plugins()\n\u001b[1;32m---> 67\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_window \u001b[39m=\u001b[39m Window(\u001b[39mself\u001b[39;49m, show\u001b[39m=\u001b[39;49mshow)\n\u001b[0;32m 68\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_instances\u001b[39m.\u001b[39madd(\u001b[39mself\u001b[39m)\n", + "File \u001b[1;32mc:\\Users\\Cyril\\anaconda3\\envs\\cellseg3d\\lib\\site-packages\\napari\\_qt\\qt_main_window.py:552\u001b[0m, in \u001b[0;36mWindow.__init__\u001b[1;34m(self, viewer, show)\u001b[0m\n\u001b[0;32m 549\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_unnamed_dockwidget_count \u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[0;32m 551\u001b[0m \u001b[39m# Connect the Viewer and create the Main Window\u001b[39;00m\n\u001b[1;32m--> 552\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_qt_window \u001b[39m=\u001b[39m _QtMainWindow(viewer, \u001b[39mself\u001b[39;49m)\n\u001b[0;32m 553\u001b[0m qapp\u001b[39m.\u001b[39minstallEventFilter(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_qt_window)\n\u001b[0;32m 555\u001b[0m \u001b[39m# connect theme events before collecting plugin-provided themes\u001b[39;00m\n\u001b[0;32m 556\u001b[0m \u001b[39m# to ensure icons from the plugins are generated correctly.\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\Cyril\\anaconda3\\envs\\cellseg3d\\lib\\site-packages\\napari\\_qt\\qt_main_window.py:114\u001b[0m, in \u001b[0;36m_QtMainWindow.__init__\u001b[1;34m(self, viewer, window, parent)\u001b[0m\n\u001b[0;32m 112\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_ev \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m 113\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_window \u001b[39m=\u001b[39m window\n\u001b[1;32m--> 114\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_qt_viewer \u001b[39m=\u001b[39m QtViewer(viewer, show_welcome_screen\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n\u001b[0;32m 115\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_quit_app \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[0;32m 117\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msetWindowIcon(QIcon(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_window_icon))\n", + "File \u001b[1;32mc:\\Users\\Cyril\\anaconda3\\envs\\cellseg3d\\lib\\site-packages\\napari\\_qt\\qt_viewer.py:205\u001b[0m, in \u001b[0;36mQtViewer.__init__\u001b[1;34m(self, viewer, show_welcome_screen)\u001b[0m\n\u001b[0;32m 200\u001b[0m QCoreApplication\u001b[39m.\u001b[39msetAttribute(\n\u001b[0;32m 201\u001b[0m Qt\u001b[39m.\u001b[39mAA_UseStyleSheetPropagationInWidgetStyles, \u001b[39mTrue\u001b[39;00m\n\u001b[0;32m 202\u001b[0m )\n\u001b[0;32m 204\u001b[0m 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have been added\u001b[39;00m\n\u001b[1;32m---> 59\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_update_nsliders()\n\u001b[0;32m 60\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdims\u001b[39m.\u001b[39mevents\u001b[39m.\u001b[39mndim\u001b[39m.\u001b[39mconnect(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_update_nsliders)\n\u001b[0;32m 61\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdims\u001b[39m.\u001b[39mevents\u001b[39m.\u001b[39mcurrent_step\u001b[39m.\u001b[39mconnect(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_update_slider)\n", + "File \u001b[1;32mc:\\Users\\Cyril\\anaconda3\\envs\\cellseg3d\\lib\\site-packages\\napari\\_qt\\widgets\\qt_dims.py:125\u001b[0m, in \u001b[0;36mQtDims._update_nsliders\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 123\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstop()\n\u001b[0;32m 124\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_trim_sliders(\u001b[39m0\u001b[39m)\n\u001b[1;32m--> 125\u001b[0m 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\u001b[39m1\u001b[39m\n\u001b[1;32m--> 190\u001b[0m slider_widget \u001b[39m=\u001b[39m QtDimSliderWidget(\u001b[39mself\u001b[39;49m, dim_axis)\n\u001b[0;32m 191\u001b[0m slider_widget\u001b[39m.\u001b[39maxis_label\u001b[39m.\u001b[39mtextChanged\u001b[39m.\u001b[39mconnect(\n\u001b[0;32m 192\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_resize_axis_labels\n\u001b[0;32m 193\u001b[0m )\n\u001b[0;32m 194\u001b[0m slider_widget\u001b[39m.\u001b[39mplay_button\u001b[39m.\u001b[39mplay_requested\u001b[39m.\u001b[39mconnect(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mplay)\n", + "File \u001b[1;32mc:\\Users\\Cyril\\anaconda3\\envs\\cellseg3d\\lib\\site-packages\\napari\\_qt\\widgets\\qt_dims_slider.py:89\u001b[0m, in \u001b[0;36mQtDimSliderWidget.__init__\u001b[1;34m(self, parent, axis)\u001b[0m\n\u001b[0;32m 84\u001b[0m connect_setattr_value(\n\u001b[0;32m 85\u001b[0m settings\u001b[39m.\u001b[39mapplication\u001b[39m.\u001b[39mevents\u001b[39m.\u001b[39mplayback_mode, \u001b[39mself\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mloop_mode\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 86\u001b[0m )\n\u001b[0;32m 88\u001b[0m layout \u001b[39m=\u001b[39m QHBoxLayout()\n\u001b[1;32m---> 89\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_create_axis_label_widget()\n\u001b[0;32m 90\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_create_range_slider_widget()\n\u001b[0;32m 91\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_create_play_button_widget()\n", + "File \u001b[1;32mc:\\Users\\Cyril\\anaconda3\\envs\\cellseg3d\\lib\\site-packages\\napari\\_qt\\widgets\\qt_dims_slider.py:127\u001b[0m, in \u001b[0;36mQtDimSliderWidget._create_axis_label_widget\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 125\u001b[0m label\u001b[39m.\u001b[39msetObjectName(\u001b[39m'\u001b[39m\u001b[39maxis_label\u001b[39m\u001b[39m'\u001b[39m) \u001b[39m# needed for _update_label\u001b[39;00m\n\u001b[0;32m 126\u001b[0m label\u001b[39m.\u001b[39msetText(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdims\u001b[39m.\u001b[39maxis_labels[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxis])\n\u001b[1;32m--> 127\u001b[0m label\u001b[39m.\u001b[39;49mhome(\u001b[39mFalse\u001b[39;00m)\n\u001b[0;32m 128\u001b[0m label\u001b[39m.\u001b[39msetToolTip(trans\u001b[39m.\u001b[39m_(\u001b[39m'\u001b[39m\u001b[39mEdit to change axis label\u001b[39m\u001b[39m'\u001b[39m))\n\u001b[0;32m 129\u001b[0m label\u001b[39m.\u001b[39msetAcceptDrops(\u001b[39mFalse\u001b[39;00m)\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "cube_size = 128\n", + "preds = np.zeros(test_image.shape)\n", + "rejected = np.zeros(test_image.shape)\n", + "\n", + "for i in range(0, test_image.shape[0], cube_size):\n", + " for j in range(0, test_image.shape[1], cube_size):\n", + " for k in range(0, test_image.shape[2], cube_size):\n", + " if i + cube_size >= test_image.shape[0]:\n", + " crop_location_i = test_image.shape[0] - cube_size\n", + " else:\n", + " crop_location_i = i\n", + " if j + cube_size >= test_image.shape[1]:\n", + " crop_location_j = test_image.shape[1] - cube_size\n", + " else:\n", + " crop_location_j = j\n", + " if k + cube_size >= test_image.shape[2]:\n", + " crop_location_k = test_image.shape[2] - cube_size\n", + " else:\n", + " crop_location_k = k\n", + " \n", + " crop = test_image[crop_location_i:crop_location_i+cube_size, crop_location_j:crop_location_j+cube_size, crop_location_k:crop_location_k+cube_size]\n", + " # crop_normalized = (crop - crop.min()) / (crop.max() - crop.min())\n", + " hist = np.histogram(crop, bins=100)[0]\n", + " hist = hist / hist.sum()\n", + " pred = classifier.predict(hist.reshape(1, -1))[0]\n", + " \n", + " if pred == 0:\n", + " preds[crop_location_i:crop_location_i+cube_size, crop_location_j:crop_location_j+cube_size, crop_location_k:crop_location_k+cube_size] = crop\n", + " rejected[crop_location_i:crop_location_i+cube_size, crop_location_j:crop_location_j+cube_size, crop_location_k:crop_location_k+cube_size] = 0\n", + " else:\n", + " preds[crop_location_i:crop_location_i+cube_size, crop_location_j:crop_location_j+cube_size, crop_location_k:crop_location_k+cube_size] = 0\n", + " rejected[crop_location_i:crop_location_i+cube_size, crop_location_j:crop_location_j+cube_size, crop_location_k:crop_location_k+cube_size] = crop\n", + "\n", + "view = napari.view_image(preds, colormap=\"turbo\")\n", + "view.add_image(test_image, colormap=\"turbo\", blending=\"additive\")\n", + "view.add_image(rejected, colormap=\"turbo\", blending=\"additive\")\n", + "view.grid.enabled = True\n", + "view.dims.ndisplay = 3\n", + "napari.run()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['C:\\\\Users\\\\Cyril\\\\Desktop\\\\Code\\\\CELLSEG_BENCHMARK\\\\classifier_test\\\\train\\\\classifier_new.joblib']" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import joblib\n", + "joblib.dump(classifier, DATA_PATH / \"classifier_new.joblib\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cellseg3d", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/napari_cellseg3d/dev_scripts/colab_training.py b/napari_cellseg3d/dev_scripts/colab_training.py index 413cb25b..21c85f32 100644 --- a/napari_cellseg3d/dev_scripts/colab_training.py +++ b/napari_cellseg3d/dev_scripts/colab_training.py @@ -1,3 +1,4 @@ +"""Script to run WNet training in Google Colab.""" import time from pathlib import Path @@ -59,7 +60,8 @@ class WNetTrainingWorkerColab(TrainingWorkerBase): """A custom worker to run WNet (unsupervised) training jobs in. - Inherits from :py:class:`napari.qt.threading.GeneratorWorker` via :py:class:`TrainingWorkerBase` + + Inherits from :py:class:`napari.qt.threading.GeneratorWorker` via :py:class:`TrainingWorkerBase`. """ def __init__( @@ -67,6 +69,12 @@ def __init__( worker_config: config.WNetTrainingWorkerConfig, wandb_config: config.WandBConfig = None, ): + """Create a WNet training worker for Google Colab. + + Args: + worker_config: worker configuration + wandb_config: optional wandb configuration + """ super().__init__() self.config = worker_config self.wandb_config = ( @@ -74,7 +82,7 @@ def __init__( ) self.dice_metric = DiceMetric( - include_background=True, reduction="mean", get_not_nans=False + include_background=False, reduction="mean", get_not_nans=False ) self.normalize_function = utils.remap_image self.start_time = time.time() @@ -89,18 +97,18 @@ def __init__( self.data_shape = None def log(self, text): + """Log a message to the logger and to wandb if installed.""" logger.info(text) def get_patch_dataset(self, train_transforms): - """Creates a Dataset from the original data using the tifffile library + """Creates a Dataset from the original data using the tifffile library. Args: - train_data_dict (dict): dict with the Paths to the directory containing the data + train_transforms (Compose): The transforms to apply to the data Returns: (tuple): A tuple containing the shape of the data and the dataset """ - patch_func = Compose( [ LoadImaged(keys=["image"], image_only=True), @@ -134,6 +142,7 @@ def get_patch_dataset(self, train_transforms): return self.config.sample_size, dataset def get_dataset_eval(self, eval_dataset_dict): + """Creates a Dataset applying some transforms/augmentation on the data using the MONAI library.""" eval_transforms = Compose( [ LoadImaged(keys=["image", "label"]), @@ -166,10 +175,10 @@ def get_dataset_eval(self, eval_dataset_dict): ) def get_dataset(self, train_transforms): - """Creates a Dataset applying some transforms/augmentation on the data using the MONAI library + """Creates a Dataset applying some transforms/augmentation on the data using the MONAI library. Args: - config (WNetTrainingWorkerConfig): The configuration object + train_transforms (Compose): The transforms to apply to the data Returns: (tuple): A tuple containing the shape of the data and the dataset @@ -257,6 +266,7 @@ def _get_data(self): return self.dataloader, self.eval_dataloader, self.data_shape def log_parameters(self): + """Log the parameters of the training.""" self.log("*" * 20) self.log("-- Parameters --") self.log(f"Device: {self.config.device}") @@ -264,7 +274,7 @@ def log_parameters(self): self.log(f"Epochs: {self.config.max_epochs}") self.log(f"Learning rate: {self.config.learning_rate}") self.log(f"Validation interval: {self.config.validation_interval}") - if self.config.weights_info.custom: + if self.config.weights_info.use_custom: self.log(f"Custom weights: {self.config.weights_info.path}") elif self.config.weights_info.use_pretrained: self.log(f"Pretrained weights: {self.config.weights_info.path}") @@ -306,6 +316,7 @@ def log_parameters(self): def train( self, provided_model=None, provided_optimizer=None, provided_loss=None ): + """Train the model.""" try: if self.config is None: self.config = config.WNetTrainingWorkerConfig() @@ -318,7 +329,7 @@ def train( logger.debug(f"wandb config : {config_dict}") wandb.init( config=config_dict, - project="CellSeg3D WNet (Colab)", + project="CellSeg3D (Colab)", mode=self.wandb_config.mode, ) @@ -363,7 +374,7 @@ def train( if WANDB_INSTALLED: wandb.watch(model, log_freq=100) - if self.config.weights_info.custom: + if self.config.weights_info.use_custom: if self.config.weights_info.use_pretrained: weights_file = "wnet.pth" self.downloader.download_weights("WNet", weights_file) @@ -599,6 +610,7 @@ def train( raise e def eval(self, model, _): + """Evaluate the model on the validation set.""" with torch.no_grad(): device = self.config.device for _k, val_data in enumerate(self.eval_dataloader): @@ -712,6 +724,7 @@ def get_colab_worker( Args: worker_config (config.WNetTrainingWorkerConfig): config for the training worker + wandb_config (config.WandBConfig): config for wandb """ worker = WNetTrainingWorkerColab(worker_config) worker.wandb_config = wandb_config @@ -742,7 +755,6 @@ def create_eval_dataset_dict(image_directory, label_directory): * "image": image * "label" : corresponding label """ - images_filepaths = sorted(Path.glob(image_directory, "*.tif")) labels_filepaths = sorted(Path.glob(label_directory, "*.tif")) diff --git a/napari_cellseg3d/dev_scripts/correct_labels.py b/napari_cellseg3d/dev_scripts/correct_labels.py index 4f529abe..572ca429 100644 --- a/napari_cellseg3d/dev_scripts/correct_labels.py +++ b/napari_cellseg3d/dev_scripts/correct_labels.py @@ -25,13 +25,14 @@ def relabel_non_unique_i(label, save_path, go_fast=False): - """relabel the image labelled with different label for each neuron and save it in the save_path location + """Relabel the image labelled with different label for each neuron and save it in the save_path location. + Parameters ---------- label : np.array the label image save_path : str - the path to save the relabeld image + the path to save the relabeld image. """ value_label = 0 new_labels = np.zeros_like(label) @@ -67,7 +68,8 @@ def relabel_non_unique_i(label, save_path, go_fast=False): def add_label(old_label, artefact, new_label_path, i_labels_to_add): - """add the label to the label image + """Add the label to the label image. + Parameters ---------- old_label : np.array @@ -75,7 +77,7 @@ def add_label(old_label, artefact, new_label_path, i_labels_to_add): artefact : np.array the artefact image that contains some neurons new_label_path : str - the path to save the new label image + the path to save the new label image. """ new_label = old_label.copy() max_label = np.max(old_label) @@ -148,7 +150,8 @@ def relabel( viewer=None, test=False, ): - """relabel the image labelled with different label for each neuron and save it in the save_path location + """Relabel the image labelled with different label for each neuron and save it in the save_path location. + Parameters ---------- image_path : str @@ -162,7 +165,7 @@ def relabel( delay : float, optional the delay between each image for the visualization, by default 0.3 viewer : napari.Viewer, optional - the napari viewer, by default None + the napari viewer, by default None. """ global returns @@ -255,7 +258,8 @@ def relabel( def modify_viewer(old_label, new_label, args): - """modify the viewer to show the relabeling + """Modify the viewer to show the relabeling. + Parameters ---------- old_label : napari.layers.Labels @@ -263,7 +267,7 @@ def modify_viewer(old_label, new_label, args): new_label : napari.layers.Labels the layer of the new label args : list - the first element is the old label and the second element is the new label + the first element is the old label and the second element is the new label. """ if args == "hide new label": new_label.visible = False @@ -277,13 +281,14 @@ def modify_viewer(old_label, new_label, args): @thread_worker def to_show(map_labels_existing, delay=0.5): - """modify the viewer to show the relabeling + """Modify the viewer to show the relabeling. + Parameters ---------- map_labels_existing : list the list of the of the map between the old label and the new label delay : float, optional - the delay between each image for the visualization, by default 0.3 + the delay between each image for the visualization, by default 0.3. """ time.sleep(2) for i in map_labels_existing: @@ -303,7 +308,6 @@ def create_connected_widget( old_label, new_label, map_labels_existing, delay=0.5 ): """Builds a widget that can control a function in another thread.""" - worker = to_show(map_labels_existing, delay) worker.start() worker.yielded.connect( @@ -312,11 +316,12 @@ def create_connected_widget( def visualize_map(map_labels_existing, label_path, relabel_path, delay=0.5): - """visualize the map of the relabeling + """Visualize the map of the relabeling. + Parameters ---------- map_labels_existing : list - the list of the relabeling + the list of the relabeling. """ label = imread(label_path) relabel = imread(relabel_path) @@ -346,13 +351,14 @@ def visualize_map(map_labels_existing, label_path, relabel_path, delay=0.5): def relabel_non_unique_i_folder(folder_path, end_of_new_name="relabeled"): - """relabel the image labelled with different label for each neuron and save it in the save_path location + """Relabel the image labelled with different label for each neuron and save it in the save_path location. + Parameters ---------- folder_path : str the path to the folder containing the label images end_of_new_name : str - thename to add at the end of the relabled image + thename to add at the end of the relabled image. """ for file in Path.iterdir(folder_path): if file.suffix == ".tif": diff --git a/napari_cellseg3d/dev_scripts/evaluate_labels.py b/napari_cellseg3d/dev_scripts/evaluate_labels.py index 2830f4e7..48861dcd 100644 --- a/napari_cellseg3d/dev_scripts/evaluate_labels.py +++ b/napari_cellseg3d/dev_scripts/evaluate_labels.py @@ -16,6 +16,7 @@ def evaluate_model_performance( visualize=False, ): """Evaluate the model performance. + Parameters ---------- labels : ndarray @@ -26,7 +27,8 @@ def evaluate_model_performance( If True, print the results. visualize : bool If True, visualize the results. - Returns + + Returns: ------- neuron_found : float The number of neurons found by the model @@ -45,7 +47,7 @@ def evaluate_model_performance( mean_true_positive_ratio_model_fused: float The mean (over the model's labels that correspond to multiple true label) of (correctly labelled pixels in any fused neurons of this model's label)/(total number of pixels of the model's label) mean_ratio_false_pixel_artefact: float - The mean (over the model's labels that are not labelled in the neurons) of (wrongly labelled pixels)/(total number of pixels of the model's label) + The mean (over the model's labels that are not labelled in the neurons) of (wrongly labelled pixels)/(total number of pixels of the model's label). """ log.debug("Mapping labels...") map_labels_existing, map_fused_neurons, new_labels = map_labels( @@ -185,20 +187,22 @@ def evaluate_model_performance( def map_labels(gt_labels, model_labels, threshold_correct=PERCENT_CORRECT): """Map the model's labels to the neurons labels. + Parameters ---------- gt_labels : ndarray Label image with neurons labelled as mulitple values. model_labels : ndarray Label image from the model labelled as mulitple values. - Returns + + Returns: ------- map_labels_existing: numpy array The label value of the model and the label value of the neuron associated, the ratio of the pixels of the true label correctly labelled, the ratio of the pixels of the model's label correctly labelled map_fused_neurons: numpy array The neurones are considered fused if they are labelled by the same model's label, in this case we will return The label value of the model and the label value of the neurone associated, the ratio of the pixels of the true label correctly labelled, the ratio of the pixels of the model's label that are in one of the fused neurones new_labels: list - The labels of the model that are not labelled in the neurons, the ratio of the pixels of the model's label that are an artefact + The labels of the model that are not labelled in the neurons, the ratio of the pixels of the model's label that are an artefact. """ map_labels_existing = [] map_fused_neurons = [] @@ -259,8 +263,7 @@ def map_labels(gt_labels, model_labels, threshold_correct=PERCENT_CORRECT): def save_as_csv(results, path): - """ - Save the results as a csv file + """Save the results as a csv file. Parameters ---------- @@ -285,222 +288,3 @@ def save_as_csv(results, path): ], ) df.to_csv(path, index=False) - - -####################### -# Slower version that was used for debugging -####################### - -# from collections import Counter -# from dataclasses import dataclass -# from typing import Dict -# @dataclass -# class LabelInfo: -# gt_index: int -# model_labels_id_and_status: Dict = None # for each model label id present on gt_index in gt labels, contains status (correct/wrong) -# best_model_label_coverage: float = ( -# 0.0 # ratio of pixels of the gt label correctly labelled -# ) -# overall_gt_label_coverage: float = 0.0 # true positive ration of the model -# -# def get_correct_ratio(self): -# for model_label, status in self.model_labels_id_and_status.items(): -# if status == "correct": -# return self.best_model_label_coverage -# else: -# return None - - -# def eval_model(gt_labels, model_labels, print_report=False): -# -# report_list, new_labels, fused_labels = create_label_report( -# gt_labels, model_labels -# ) -# per_label_perfs = [] -# for report in report_list: -# if print_report: -# log.info( -# f"Label {report.gt_index} : {report.model_labels_id_and_status}" -# ) -# log.info( -# f"Best model label coverage : {report.best_model_label_coverage}" -# ) -# log.info( -# f"Overall gt label coverage : {report.overall_gt_label_coverage}" -# ) -# -# perf = report.get_correct_ratio() -# if perf is not None: -# per_label_perfs.append(perf) -# -# per_label_perfs = np.array(per_label_perfs) -# return per_label_perfs.mean(), new_labels, fused_labels - - -# def create_label_report(gt_labels, model_labels): -# """Map the model's labels to the neurons labels. -# Parameters -# ---------- -# gt_labels : ndarray -# Label image with neurons labelled as mulitple values. -# model_labels : ndarray -# Label image from the model labelled as mulitple values. -# Returns -# ------- -# map_labels_existing: numpy array -# The label value of the model and the label value of the neurone associated, the ratio of the pixels of the true label correctly labelled, the ratio of the pixels of the model's label correctly labelled -# map_fused_neurons: numpy array -# The neurones are considered fused if they are labelled by the same model's label, in this case we will return The label value of the model and the label value of the neurone associated, the ratio of the pixels of the true label correctly labelled, the ratio of the pixels of the model's label that are in one of the fused neurones -# new_labels: list -# The labels of the model that are not labelled in the neurons, the ratio of the pixels of the model's label that are an artefact -# """ -# -# map_labels_existing = [] -# map_fused_neurons = {} -# "background_labels contains all model labels where gt_labels is 0 and model_labels is not 0" -# background_labels = model_labels[np.where((gt_labels == 0))] -# "new_labels contains all labels in model_labels for which more than PERCENT_CORRECT% of the pixels are not labelled in gt_labels" -# new_labels = [] -# for lab in np.unique(background_labels): -# if lab == 0: -# continue -# gt_background_size_at_lab = ( -# gt_labels[np.where((model_labels == lab) & (gt_labels == 0))] -# .flatten() -# .shape[0] -# ) -# gt_lab_size = ( -# gt_labels[np.where(model_labels == lab)].flatten().shape[0] -# ) -# if gt_background_size_at_lab / gt_lab_size > PERCENT_CORRECT: -# new_labels.append(lab) -# -# label_report_list = [] -# # label_report = {} # contains a dict saying which labels are correct or wrong for each gt label -# # model_label_values = {} # contains the model labels value assigned to each unique gt label -# not_found_id = 0 -# -# for i in tqdm(np.unique(gt_labels)): -# if i == 0: -# continue -# -# gt_label = gt_labels[np.where(gt_labels == i)] # get a single gt label -# -# model_lab_on_gt = model_labels[ -# np.where(((gt_labels == i) & (model_labels != 0))) -# ] # all models labels on single gt_label -# info = LabelInfo(i) -# -# info.model_labels_id_and_status = { -# label_id: "" for label_id in np.unique(model_lab_on_gt) -# } -# -# if model_lab_on_gt.shape[0] == 0: -# info.model_labels_id_and_status[ -# f"not_found_{not_found_id}" -# ] = "not found" -# not_found_id += 1 -# label_report_list.append(info) -# continue -# -# log.debug(f"model_lab_on_gt : {np.unique(model_lab_on_gt)}") -# -# # create LabelInfo object and init model_labels_id_and_status with all unique model labels on gt_label -# log.debug( -# f"info.model_labels_id_and_status : {info.model_labels_id_and_status}" -# ) -# -# ratio = [] -# for model_lab_id in info.model_labels_id_and_status.keys(): -# size_model_label = ( -# model_lab_on_gt[np.where(model_lab_on_gt == model_lab_id)] -# .flatten() -# .shape[0] -# ) -# size_gt_label = gt_label.flatten().shape[0] -# -# log.debug(f"size_model_label : {size_model_label}") -# log.debug(f"size_gt_label : {size_gt_label}") -# -# ratio.append(size_model_label / size_gt_label) -# -# # log.debug(ratio) -# ratio_model_lab_for_given_gt_lab = np.array(ratio) -# info.best_model_label_coverage = ratio_model_lab_for_given_gt_lab.max() -# -# best_model_lab_id = model_lab_on_gt[ -# np.argmax(ratio_model_lab_for_given_gt_lab) -# ] -# log.debug(f"best_model_lab_id : {best_model_lab_id}") -# -# info.overall_gt_label_coverage = ( -# ratio_model_lab_for_given_gt_lab.sum() -# ) # the ratio of the pixels of the true label correctly labelled -# -# if info.best_model_label_coverage > PERCENT_CORRECT: -# info.model_labels_id_and_status[best_model_lab_id] = "correct" -# # info.model_labels_id_and_size[best_model_lab_id] = model_labels[np.where(model_labels == best_model_lab_id)].flatten().shape[0] -# else: -# info.model_labels_id_and_status[best_model_lab_id] = "wrong" -# for model_lab_id in np.unique(model_lab_on_gt): -# if model_lab_id != best_model_lab_id: -# log.debug(model_lab_id, "is wrong") -# info.model_labels_id_and_status[model_lab_id] = "wrong" -# -# label_report_list.append(info) -# -# correct_labels_id = [] -# for report in label_report_list: -# for i_lab in report.model_labels_id_and_status.keys(): -# if report.model_labels_id_and_status[i_lab] == "correct": -# correct_labels_id.append(i_lab) -# """Find all labels in label_report_list that are correct more than once""" -# duplicated_labels = [ -# item for item, count in Counter(correct_labels_id).items() if count > 1 -# ] -# "Sum up the size of all duplicated labels" -# for i in duplicated_labels: -# for report in label_report_list: -# if ( -# i in report.model_labels_id_and_status.keys() -# and report.model_labels_id_and_status[i] == "correct" -# ): -# size = ( -# model_labels[np.where(model_labels == i)] -# .flatten() -# .shape[0] -# ) -# map_fused_neurons[i] = size -# -# return label_report_list, new_labels, map_fused_neurons - -# if __name__ == "__main__": -# """ -# # Example of how to use the functions in this module. -# a = np.array([[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]]) -# -# b = np.array([[5, 5, 0, 0], [5, 5, 2, 0], [0, 2, 2, 0], [0, 0, 2, 0]]) -# evaluate_model_performance(a, b) -# -# c = np.array([[2, 2, 0, 0], [2, 2, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]]) -# -# d = np.array([[4, 0, 4, 0], [4, 4, 4, 0], [0, 4, 4, 0], [0, 0, 4, 0]]) -# -# evaluate_model_performance(c, d) -# -# from tifffile import imread -# labels=imread("dataset/visual_tif/labels/testing_im_new_label.tif") -# labels_model=imread("dataset/visual_tif/artefact_neurones/basic_model.tif") -# evaluate_model_performance(labels, labels_model,visualize=True) -# """ -# from tifffile import imread -# -# labels = imread("dataset_clean/VALIDATION/validation_labels.tif") -# try: -# labels_model = imread("results/watershed_based_model/instance_labels.tif") -# except: -# raise Exception( -# "you should download the model's label that are under results (output and statistics)/watershed_based_model/instance_labels.tif and put it in the folder results/watershed_based_model/" -# ) -# -# evaluate_model_performance(labels, labels_model, visualize=True) diff --git a/napari_cellseg3d/dev_scripts/remote_training.py b/napari_cellseg3d/dev_scripts/remote_training.py new file mode 100644 index 00000000..e2d05d12 --- /dev/null +++ b/napari_cellseg3d/dev_scripts/remote_training.py @@ -0,0 +1,138 @@ +"""Showcases how to train a model without napari.""" + +from pathlib import Path + +from napari_cellseg3d import config as cfg +from napari_cellseg3d.code_models.worker_training import ( + SupervisedTrainingWorker, +) +from napari_cellseg3d.utils import LOGGER as logger + +TRAINING_SPLIT = 0.2 # 0.4, 0.8 +MODEL_NAME = "SegResNet" # "SwinUNetR" +BATCH_SIZE = 10 if MODEL_NAME == "SegResNet" else 5 +# BATCH_SIZE = 1 + +SPLIT_FOLDER = "1_c15" # "2_c1_c4_visual" "3_c1245_visual" +RESULTS_PATH = ( + Path("/data/cyril") + / "CELLSEG_BENCHMARK/cellseg3d_train" + / f"{MODEL_NAME}_{SPLIT_FOLDER}_{int(TRAINING_SPLIT*100)}" +) + +IMAGES = ( + Path("/data/cyril") + / f"CELLSEG_BENCHMARK/TPH2_mesospim/SPLITS/{SPLIT_FOLDER}" +) +LABELS = ( + Path("/data/cyril") + / f"CELLSEG_BENCHMARK/TPH2_mesospim/SPLITS/{SPLIT_FOLDER}/labels/semantic" +) + + +class LogFixture: + """Fixture for napari-less logging, replaces napari_cellseg3d.interface.Log in model_workers. + + This allows to redirect the output of the workers to stdout instead of a specialized widget. + """ + + def __init__(self): + """Creates a LogFixture object.""" + super(LogFixture, self).__init__() + + def print_and_log(self, text, printing=None): + """Prints and logs text.""" + print(text) + + def warn(self, warning): + """Logs warning.""" + logger.warning(warning) + + def error(self, e): + """Logs error.""" + raise (e) + + +def prepare_data(images_path, labels_path): + """Prepares data for training.""" + assert images_path.exists(), f"Images path does not exist: {images_path}" + assert labels_path.exists(), f"Labels path does not exist: {labels_path}" + if not RESULTS_PATH.exists(): + RESULTS_PATH.mkdir(parents=True, exist_ok=True) + + images = sorted(Path.glob(images_path, "*.tif")) + labels = sorted(Path.glob(labels_path, "*.tif")) + + print(f"Images paths: {images}") + print(f"Labels paths: {labels}") + + logger.info("Images :\n") + for file in images: + logger.info(Path(file).name) + logger.info("*" * 10) + logger.info("Labels :\n") + for file in images: + logger.info(Path(file).name) + + assert len(images) == len( + labels + ), "Number of images and labels must be the same" + + return [ + {"image": str(image_path), "label": str(label_path)} + for image_path, label_path in zip(images, labels) + ] + + +def remote_training(): + """Function to train a model without napari.""" + # print(f"Results path: {RESULTS_PATH.resolve()}") + + wandb_config = cfg.WandBConfig( + mode="online", + save_model_artifact=True, + ) + + deterministic_config = cfg.DeterministicConfig( + seed=34936339, + ) + + worker_config = cfg.SupervisedTrainingWorkerConfig( + device="cuda:0", + max_epochs=50, + learning_rate=0.001, # 1e-3 + validation_interval=2, + batch_size=BATCH_SIZE, # 10 for SegResNet + deterministic_config=deterministic_config, + scheduler_factor=0.5, + scheduler_patience=10, # use default scheduler + weights_info=cfg.WeightsInfo(), # no pretrained weights + results_path_folder=str(RESULTS_PATH), + sampling=False, + do_augmentation=True, + train_data_dict=prepare_data(IMAGES, LABELS), + # supervised specific + model_info=cfg.ModelInfo( + name=MODEL_NAME, + model_input_size=(64, 64, 64), + ), + loss_function="Generalized Dice", + training_percent=TRAINING_SPLIT, + ) + + worker = SupervisedTrainingWorker(worker_config) + worker.wandb_config = wandb_config + ######### SET LOG + log = LogFixture() + worker.log_signal.connect(log.print_and_log) + worker.warn_signal.connect(log.warn) + worker.error_signal.connect(log.error) + + results = [] + for result in worker.train(): + results.append(result) + print("Training finished") + + +if __name__ == "__main__": + results = remote_training() diff --git a/napari_cellseg3d/dev_scripts/thread_test.py b/napari_cellseg3d/dev_scripts/thread_test.py index 82782ef8..cdaa368e 100644 --- a/napari_cellseg3d/dev_scripts/thread_test.py +++ b/napari_cellseg3d/dev_scripts/thread_test.py @@ -23,11 +23,11 @@ @thread_worker def two_way_communication_with_args(start, end): """Both sends and receives values to & from the main thread. + Accepts arguments, puts them on the worker object. Receives values from main thread with ``incoming = yield`` - Optionally returns a value at the end + Optionally returns a value at the end. """ - # do computationally intensive work here i = start while i < end: @@ -43,7 +43,10 @@ def two_way_communication_with_args(start, end): class Controller(QWidget): + """Widget that controls a function running in another thread.""" + def __init__(self, viewer): + """Build the widget.""" super().__init__() self.viewer = viewer diff --git a/napari_cellseg3d/dev_scripts/whole_brain_utils.py b/napari_cellseg3d/dev_scripts/whole_brain_utils.py new file mode 100644 index 00000000..e36be7cf --- /dev/null +++ b/napari_cellseg3d/dev_scripts/whole_brain_utils.py @@ -0,0 +1,50 @@ +"""Utilities to improve whole-brain regions segmentation.""" +import numpy as np +from skimage.measure import label +from skimage.segmentation import find_boundaries + + +def extract_continuous_region(image): + """Extract continuous region from image.""" + image = np.where(image > 0, 1, 0) + return label(image) + + +def get_boundaries(image_regions, num_iters=1): + """Obtain boundaries from image regions.""" + boundaries = np.zeros_like(image_regions) + label_values = np.unique(image_regions) + iter_n = 0 + new_labels = image_regions + while iter_n < num_iters: + for i in label_values: + if i == 0: + continue + boundary = find_boundaries(new_labels == i) + boundaries += np.where(boundary > 0, i, 0) + new_labels = np.where(boundary > 0, 0, new_labels) + iter_n += 1 + return boundaries + + +def remove_boundaries_from_segmentation( + image_segmentation, image_labels=None, image=None, thickness_num_iters=1 +): + """Remove boundaries from segmentation. + + Args: + image_segmentation (np.ndarray): 3D image segmentation. + image_labels (np.ndarray): 3D integer labels of image segmentation. Use output from extract_continuous_region. + image (np.ndarray): Additional 3D image used to extract continuous region. + thickness_num_iters (int): Number of iterations to remove boundaries. A greater number will remove more boundary pixels. + """ + if image_labels is None: + image_regions = extract_continuous_region(image_segmentation) + elif image is not None: + image_regions = extract_continuous_region(image) + else: + image_regions = image_labels + boundaries = get_boundaries(image_regions, num_iters=thickness_num_iters) + + seg_in = np.where(image_regions > 0, image_segmentation, 0) + return np.where(boundaries > 0, 0, seg_in) diff --git a/napari_cellseg3d/interface.py b/napari_cellseg3d/interface.py index 6f832e23..05baf64c 100644 --- a/napari_cellseg3d/interface.py +++ b/napari_cellseg3d/interface.py @@ -1,3 +1,4 @@ +"""User interface functions and aliases.""" import threading from functools import partial from typing import List, Optional @@ -36,10 +37,6 @@ # Local from napari_cellseg3d import utils -""" -User interface functions and aliases""" - - ############### # show debug tooltips SHOW_LABELS_DEBUG_TOOLTIP = False @@ -79,19 +76,15 @@ class QWidgetSingleton(type(QObject)): - """ - To be used as a metaclass when making a singleton QWidget, - meaning only one instance exists at a time. - Avoids unnecessary memory overhead and keeps user parameters even when a widget is closed + """To be used as a metaclass when making a singleton QWidget, meaning only one instance exists at a time. + + Avoids unnecessary memory overhead and keeps user parameters even when a widget is closed. """ _instances = {} def __call__(cls, *args, **kwargs): - """ - Ensure only one instance of a QWidget with QWidgetSingleton as a metaclass exists at a time - - """ + """Ensure only one instance of a QWidget with QWidgetSingleton as a metaclass exists at a time.""" if cls not in cls._instances: cls._instances[cls] = super(QWidgetSingleton, cls).__call__( *args, **kwargs @@ -105,8 +98,7 @@ def __call__(cls, *args, **kwargs): def handle_adjust_errors(widget, warning_type, context, msg: str): - """Qt message handler that attempts to react to errors when setting the window size - and resizes the main window""" + """Qt message handler that attempts to react to errors when setting the window size and resizes the main window.""" pass # head = msg.split(": ")[0] # if warning_type == QtWarningMsg and head == "QWindowsWindow::setGeometry": @@ -129,7 +121,7 @@ def handle_adjust_errors(widget, warning_type, context, msg: str): def handle_adjust_errors_wrapper(widget): - """Returns a callable that can be used with qInstallMessageHandler directly""" + """Returns a callable that can be used with qInstallMessageHandler directly.""" return partial(handle_adjust_errors, widget) @@ -144,7 +136,7 @@ class UtilsDropdown(metaclass=utils.Singleton): caller_widget = None def dropdown_menu_call(self, widget, event): - """Calls the utility dropdown menu at the location of a CTRL+right-click""" + """Calls the utility dropdown menu at the location of a CTRL+right-click.""" # ### DEBUG ### # # print(event.modifiers) # print("menu call") @@ -176,8 +168,8 @@ def dropdown_menu_call(self, widget, event): # print(f"blocked widget {widget} from opening utils") def show_utils_menu(self, widget, event): - """ - Shows the context menu for utilities. Use with dropdown_menu_call. + """Shows the context menu for utilities. Use with dropdown_menu_call. + Args: widget: widget to show context menu in event: mouse press event @@ -212,7 +204,7 @@ class Log(QTextEdit): """Class to implement a log for important user info. Should be thread-safe.""" def __init__(self, parent=None): - """Creates a log with a lock for multithreading + """Creates a log with a lock for multithreading. Args: parent (QWidget): parent widget to add Log instance to. @@ -227,8 +219,8 @@ def __init__(self, parent=None): # def receive_log(self, text): # self.print_and_log(text) def write(self, message): - """ - Write message to log in a thread-safe manner + """Write message to log in a thread-safe manner. + Args: message: string to be printed """ @@ -254,7 +246,11 @@ def write(self, message): @QtCore.Slot(str) def replace_last_line(self, text): - """Replace last line. For use in progress bar""" + """Replace last line. For use in progress bar. + + Args: + text: string to be printed + """ self.lock.acquire() try: cursor = self.textCursor() @@ -268,8 +264,7 @@ def replace_last_line(self, text): self.lock.release() def print_and_log(self, text, printing=True): - """Utility used to both print to terminal and log text to a QTextEdit - item in a thread-safe manner. Use only for important user info. + """Utility used to both print to terminal and log text to a QTextEdit item in a thread-safe manner. Use only for important user info. Args: text (str): Text to be printed and logged @@ -290,7 +285,11 @@ def print_and_log(self, text, printing=True): self.lock.release() def warn(self, warning): - """Show logger.warning from another thread""" + """Show logger.warning from another thread. + + Args: + warning: warning to be printed + """ self.lock.acquire() try: logger.warning(warning) @@ -298,7 +297,12 @@ def warn(self, warning): self.lock.release() def error(self, error, msg=None): - """Show exception and message from another thread""" + """Show exception and message from another thread. + + Args: + error: error to be printed + msg: message to be printed + """ self.lock.acquire() try: logger.error(error, exc_info=True) @@ -306,7 +310,7 @@ def error(self, error, msg=None): self.print_and_log(f"{msg} : {error}", printing=False) else: self.print_and_log( - f"Excepetion caught in another thread : {error}", + f"Exception caught in another thread : {error}", printing=False, ) raise error @@ -330,17 +334,27 @@ def toggle_visibility(checkbox, widget): def add_label(widget, label, label_before=True, horizontal=True): + """Adds a label to a widget. + + Args: + widget: The widget to add the label to + label: The label to add + label_before: If True, the label is added before the widget. If False, the label is added after the widget + horizontal: If True, the label and widget are added horizontally. If False, they are added vertically + """ if label_before: return combine_blocks(widget, label, horizontal=horizontal) return combine_blocks(label, widget, horizontal=horizontal) class ContainerWidget(QWidget): + """Class for a container widget that can contain other widgets.""" + def __init__( self, l=0, t=0, r=1, b=11, vertical=True, parent=None, fixed=True ): - """ - Creates a container widget that can contain other widgets + """Creates a container widget that can contain other widgets. + Args: l: left margin in pixels t: top margin in pixels @@ -350,7 +364,6 @@ def __init__( parent: parent QWidget fixed: uses QLayout.SetFixedSize if True """ - super().__init__(parent) self.layout = None @@ -365,7 +378,10 @@ def __init__( class RadioButton(QRadioButton): + """Class for a radio button with a title and connected to a function when clicked. Inherits from QRadioButton.""" + def __init__(self, text: str = None, parent=None): + """Creates a radio button with a title and a function to execute when toggled.""" super().__init__(text, parent) @@ -387,6 +403,7 @@ def __init__( parent: Optional[QWidget] = None, fixed: Optional[bool] = True, ): + """Creates a button with a title and a function to execute when clicked.""" super().__init__(parent) if title is not None: self.setText(title) @@ -398,12 +415,12 @@ def __init__( self.setSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) def visibility_condition(self, checkbox): - """Provide a QCheckBox to use to determine whether to show the button or not""" + """Provide a QCheckBox to use to determine whether to show the button or not.""" toggle_visibility(checkbox, self) class DropdownMenu(QComboBox): - """Creates a dropdown menu with a title and adds specified entries to it""" + """Creates a dropdown menu with a title and adds specified entries to it.""" def __init__( self, @@ -412,11 +429,13 @@ def __init__( text_label: Optional[str] = None, fixed: Optional[bool] = True, ): - """Args: - entries (array(str)): Entries to add to the dropdown menu. Defaults to None, no entries if None - parent (QWidget): parent QWidget to add dropdown menu to. Defaults to None, no parent is set if None - text_label (str) : if not None, creates a QLabel with the contents of 'label', and returns the label as well - fixed (bool): if True, will set the size policy of the dropdown menu to Fixed in h and w. Defaults to True. + """Creates a dropdown menu with a title and adds specified entries to it. + + Args: + entries (array(str)): Entries to add to the dropdown menu. Defaults to None, no entries if None + parent (QWidget): parent QWidget to add dropdown menu to. Defaults to None, no parent is set if None + text_label (str) : if not None, creates a QLabel with the contents of 'label', and returns the label as well + fixed (bool): if True, will set the size policy of the dropdown menu to Fixed in h and w. Defaults to True. """ super().__init__(parent) self.label = None @@ -428,11 +447,12 @@ def __init__( self.setSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) def get_items(self): + """Returns the items in the dropdown menu.""" return [self.itemText(i) for i in range(self.count())] class CheckBox(QCheckBox): - """Shortcut class for creating QCheckBox with a title and a function""" + """Shortcut class for creating QCheckBox with a title and a function.""" def __init__( self, @@ -441,7 +461,8 @@ def __init__( parent: Optional[QWidget] = None, fixed: Optional[bool] = True, ): - """ + """Creates a checkbox with a title and a function to execute when toggled. + Args: title (str-like): title of the checkbox. Defaults to None, if None no title is set func (callable): function to execute when checkbox is toggled. Defaults to None, no binding is made if None @@ -456,7 +477,7 @@ def __init__( class Slider(QSlider): - """Shortcut class to create a Slider widget""" + """Shortcut class to create a Slider widget.""" def __init__( self, @@ -469,6 +490,7 @@ def __init__( orientation=Qt.Horizontal, text_label: str = None, ): + """Creates a slider to select a value between lower and upper, with a step.""" super().__init__(orientation, parent) if upper <= lower: @@ -527,6 +549,7 @@ def __init__( self._build_container() def set_visibility(self, visible: bool): + """Sets the visibility of the slider and its label.""" self.container.setVisible(visible) self.setVisible(visible) self.label.setVisible(visible) @@ -552,7 +575,7 @@ def _warn_outside_bounds(self, default): ) def _update_slider(self): - """Update slider when value is changed""" + """Update slider when value is changed.""" try: if self._value_label.text() == "": return @@ -571,7 +594,7 @@ def _update_slider(self): logger.error(e) def _update_value_label(self): - """Update label, to connect to when slider is dragged""" + """Update label, to connect to when slider is dragged.""" try: self._value_label.setText(str(self.value_text)) except Exception as e: @@ -579,10 +602,12 @@ def _update_value_label(self): @property def tooltips(self): + """Get the tooltip of the slider.""" return self.toolTip() @tooltips.setter def tooltips(self, tooltip: str): + """Set the tooltip of the slider and label.""" self.setToolTip(tooltip) self._value_label.setToolTip(tooltip) @@ -591,25 +616,24 @@ def tooltips(self, tooltip: str): @property def slider_value(self): - """Get value of the slider divided by self._divide_factor to implement floats in Slider""" + """Get value of the slider divided by self._divide_factor to implement floats in Slider.""" if self._divide_factor == 1.0: return self.value() try: return self.value() / self._divide_factor except ZeroDivisionError as e: - raise ZeroDivisionError from ( - f"Divide factor cannot be 0 for Slider : {e}" - ) + logger.error(f"Divide factor cannot be 0 for Slider : {e}") + raise ZeroDivisionError from (e) @property def value_text(self): - """Get value of the slide bar as string""" + """Get value of the slide bar as string.""" return str(self.slider_value) @slider_value.setter def slider_value(self, value: int): - """Set a value (int) divided by self._divide_factor""" + """Set a value (int) divided by self._divide_factor.""" if value < self.minimum() or value > self.maximum(): logger.error( ValueError( @@ -629,9 +653,12 @@ def slider_value(self, value: int): class AnisotropyWidgets(QWidget): - """Class that creates widgets for anisotropy handling. Includes : - - A checkbox to hides or shows the controls - - Three spinboxes to enter resolution for each dimension""" + """Class that creates widgets for anisotropy handling. + + Includes : + A checkbox to hides or shows the controls + Three spinboxes to enter resolution for each dimension. + """ def __init__( self, @@ -642,12 +669,15 @@ def __init__( always_visible: Optional[bool] = False, use_integer_counter: Optional[bool] = False, ): - """Creates an instance of AnisotropyWidgets + """Creates an instance of AnisotropyWidgets. + Args: - - parent: parent QWidget - - default_x: default resolution to use for x axis in microns - - default_y: default resolution to use for y axis in microns - - default_z: default resolution to use for z axis in microns + parent: parent QWidget + default_x: default resolution to use for x axis in microns + default_y: default resolution to use for y axis in microns + default_z: default resolution to use for z axis in microns + always_visible: if True, the checkbox is hidden and the spinboxes are always visible + use_integer_counter: if True, the spinboxes are QSpinBoxes instead of QDoubleSpinBoxes """ super().__init__(parent) @@ -697,13 +727,11 @@ def __init__( self._toggle_permanent_visibility() def _toggle_display_aniso(self): - """Shows the choices for correcting anisotropy - when viewing results depending on whether :py:attr:`self.checkbox` is checked - """ + """Shows the choices for correcting anisotropy when viewing results depending on whether :py:attr:`self.checkbox` is checked.""" toggle_visibility(self.checkbox, self.container) def build(self): - """Builds the layout of the widget""" + """Builds the layout of the widget.""" [ self.container.layout.addWidget(widget, alignment=HCENTER_AL) for widgets in zip(self.box_widgets_lbl, self.box_widgets) @@ -717,50 +745,52 @@ def build(self): self.setLayout(self._layout) def resolution_xyz(self): - """The resolution selected for each of the three dimensions. XYZ order (for MONAI)""" + """The resolution selected for each of the three dimensions. XYZ order (for MONAI).""" return [w.value() for w in self.box_widgets] def scaling_xyz(self): - """The scaling factors for each of the three dimensions. XYZ order (for MONAI)""" + """The scaling factors for each of the three dimensions. XYZ order (for MONAI).""" return self.anisotropy_zoom_factor(self.resolution_xyz()) def resolution_zyx(self): - """The resolution selected for each of the three dimensions. ZYX order (for napari)""" + """The resolution selected for each of the three dimensions. ZYX order (for napari).""" res = self.resolution_xyz() return [res[2], res[1], res[0]] def scaling_zyx(self): - """The scaling factors for each of the three dimensions. ZYX order (for napari)""" + """The scaling factors for each of the three dimensions. ZYX order (for napari).""" return self.anisotropy_zoom_factor(self.resolution_zyx()) @staticmethod def anisotropy_zoom_factor(aniso_res): - """Computes a zoom factor to correct anisotropy, based on anisotropy resolutions + """Computes a zoom factor to correct anisotropy, based on anisotropy resolutions. - Args: - aniso_res: array for anisotropic resolution (float) in microns for each axis + Args: + aniso_res: array for anisotropic resolution (float) in microns for each axis Returns: an array with the corresponding zoom factors for each axis (all values divided by min) """ - base = min(aniso_res) return [base / res for res in aniso_res] def enabled(self): - """Returns : whether anisotropy correction has been enabled or not""" + """Returns : whether anisotropy correction has been enabled or not.""" return self.checkbox.isChecked() def _toggle_permanent_visibility(self): - """Hides the checkbox and always display resolution spinboxes""" + """Hides the checkbox and always display resolution spinboxes.""" self.checkbox.toggle() self.checkbox.setVisible(False) class LayerSelecter(ContainerWidget): + """Class that creates a dropdown menu to select a layer from a napari viewer.""" + def __init__( self, viewer, name="Layer", layer_type=napari.layers.Layer, parent=None ): + """Creates an instance of LayerSelecter.""" super().__init__(parent=parent, fixed=False) self._viewer = viewer self.layer_type = layer_type @@ -821,31 +851,38 @@ def _remove_layer(self, event): self.layer_list.removeItem(index) def set_layer_type(self, layer_type): # no @property due to Qt constraint + """Sets the layer type to be selected in the dropdown menu.""" self.layer_type = layer_type [self.layer_list.removeItem(i) for i in range(self.layer_list.count())] self._check_for_layers() def layer(self): + """Returns the layer selected in the dropdown menu.""" try: return self._viewer.layers[self.layer_name()] except ValueError: return None def layer_name(self): + """Returns the name of the layer selected in the dropdown menu.""" return self.layer_list.currentText() def layer_data(self): + """Returns the data of the layer selected in the dropdown menu.""" if self.layer_list.count() < 1: - logger.warning("Please select a valid layer !") + logger.debug("Layer list is empty") return None return self.layer().data class FilePathWidget(QWidget): # TODO include load as folder - """Widget to handle the choice of file paths for data throughout the plugin. Provides the following elements : - - An "Open" button to show a file dialog (defined externally) - - A QLineEdit in read only to display the chosen path/file""" + """Widget to handle the choice of file paths for data throughout the plugin. + + Provides the following elements : + An "Open" button to show a file dialog (defined externally) + A QLineEdit in read only to display the chosen path/file + """ def __init__( self, @@ -856,11 +893,13 @@ def __init__( default: Optional[str] = None, ): """Creates a FilePathWidget. + Args: description (str): Initial text to add to the text box file_function (callable): Function to handle the file dialog parent (Optional[QWidget]): parent QWidget required (Optional[bool]): if True, field will be highlighted in red if empty. Defaults to False. + default (Optional[str]): default path to use. Defaults to None. """ super().__init__(parent) self._layout = QHBoxLayout() @@ -883,7 +922,7 @@ def __init__( self.text_field.textChanged.connect(self.check_ready) def build(self): - """Builds the layout of the widget""" + """Builds the layout of the widget.""" add_widgets( self._layout, [combine_blocks(self.button, self.text_field, min_spacing=5, b=0)], @@ -893,32 +932,34 @@ def build(self): @property def tooltips(self): + """Get the tooltip of the text field and button.""" return self._text_field.toolTip() @tooltips.setter def tooltips(self, tooltip: str): + """Set the tooltip of the text field and button.""" self._text_field.setToolTip(tooltip) self._button.setToolTip(tooltip) @property def text_field(self): - """Get text field with file path""" + """Get text field with file path.""" return self._text_field @text_field.setter def text_field(self, text: str): - """Sets the initial description in the text field, makes it the new default path""" + """Sets the initial description in the text field, makes it the new default path.""" self._initial_desc = text self.tooltips = text self._text_field.setText(text) @property def button(self): - """Get "Open" button""" + """Get "Open" button.""" return self._button def check_ready(self): - """Check if a path is correctly set""" + """Check if a path is correctly set.""" if ( self.text_field.text() in ["", self._initial_desc] and self.required @@ -932,16 +973,17 @@ def check_ready(self): @property def required(self): + """Get whether the field is required or not.""" return self._required @required.setter def required(self, is_required): - """If set to True, will be colored red if incorrectly set""" + """If set to True, will be colored red if incorrectly set.""" self.check_ready() self._required = is_required def update_field_color(self, color: str): - """Updates the background of the text field""" + """Updates the background of the text field.""" self.text_field.setStyleSheet(f"background-color : {color}") self.text_field.style().unpolish(self.text_field) self.text_field.style().polish(self.text_field) @@ -958,13 +1000,14 @@ def __init__( base_wh: Optional[List[int]] = None, parent: Optional[QWidget] = None, ): - """ + """Initializes a QScrollArea and sets it up, then adds the contained_layout to it. + Args: - contained_layout (QLayout): the layout of widgets to be made scrollable - min_wh (Optional[List[int]]): array of two ints for respectively the minimum width and minimum height of the scrollable area. Defaults to None, lets Qt decide if None - max_wh (Optional[List[int]]): array of two ints for respectively the maximum width and maximum height of the scrollable area. Defaults to None, lets Qt decide if None - base_wh (Optional[List[int]]): array of two ints for respectively the initial width and initial height of the scrollable area. Defaults to None, lets Qt decide if None - parent (Optional[QWidget]): array of two ints for respectively the initial width and initial height of the scrollable area. Defaults to None, lets Qt decide if None + contained_layout (QLayout): the layout of widgets to be made scrollable + min_wh (Optional[List[int]]): array of two ints for respectively the minimum width and minimum height of the scrollable area. Defaults to None, lets Qt decide if None + max_wh (Optional[List[int]]): array of two ints for respectively the maximum width and maximum height of the scrollable area. Defaults to None, lets Qt decide if None + base_wh (Optional[List[int]]): array of two ints for respectively the initial width and initial height of the scrollable area. Defaults to None, lets Qt decide if None + parent (Optional[QWidget]): array of two ints for respectively the initial width and initial height of the scrollable area. Defaults to None, lets Qt decide if None """ super().__init__(parent) @@ -1004,15 +1047,15 @@ def make_scrollable( max_wh: Optional[List[int]] = None, base_wh: Optional[List[int]] = None, ): - """Factory method to create a scroll area in a widget + """Factory method to create a scroll area in a widget. + Args: - contained_layout (QLayout): the widget to be made scrollable - parent (QWidget): the parent widget to add the resulting scroll area in - min_wh (Optional[List[int]]): array of two ints for respectively the minimum width and minimum height of the scrollable area. Defaults to None, lets Qt decide if None - max_wh (Optional[List[int]]): array of two ints for respectively the maximum width and maximum height of the scrollable area. Defaults to None, lets Qt decide if None - base_wh (Optional[List[int]]): array of two ints for respectively the initial width and initial height of the scrollable area. Defaults to None, lets Qt decide if None + contained_layout (QLayout): the widget to be made scrollable + parent (QWidget): the parent widget to add the resulting scroll area in + min_wh (Optional[List[int]]): array of two ints for respectively the minimum width and minimum height of the scrollable area. Defaults to None, lets Qt decide if None + max_wh (Optional[List[int]]): array of two ints for respectively the maximum width and maximum height of the scrollable area. Defaults to None, lets Qt decide if None + base_wh (Optional[List[int]]): array of two ints for respectively the initial width and initial height of the scrollable area. Defaults to None, lets Qt decide if None """ - scroll = cls(contained_layout, min_wh, max_wh, base_wh) layout = QVBoxLayout(parent) # layout.setContentsMargins(0,0,1,1) @@ -1029,14 +1072,16 @@ def set_spinbox( step=1, fixed: Optional[bool] = True, ): - """Args: - box : QSpinBox or QDoubleSpinBox - min_value : minimum value, defaults to 0 - max_value : maximum value, defaults to 10 - default : default value, defaults to 0 - step : step value, defaults to 1 - fixed (bool): if True, sets the QSizePolicy of the spinbox to Fixed""" + """Sets the parameters of a QSpinBox or QDoubleSpinBox. + Args: + box : QSpinBox or QDoubleSpinBox + min_value : minimum value, defaults to 0 + max_value : maximum value, defaults to 10 + default : default value, defaults to 0 + step : step value, defaults to 1 + fixed (bool): if True, sets the QSizePolicy of the spinbox to Fixed + """ box.setMinimum(min_value) box.setMaximum(max_value) box.setSingleStep(step) @@ -1056,7 +1101,7 @@ def make_n_spinboxes( parent: Optional[QWidget] = None, fixed: Optional[bool] = True, ): - """Creates n increment counters with the specified parameters : + """Creates n increment counters with the specified parameters. Args: class_ : QSpinBox or QDoubleSpinbox @@ -1091,16 +1136,17 @@ def __init__( fixed: Optional[bool] = True, text_label: Optional[str] = None, ): - """Args: - lower (Optional[float]): minimum value, defaults to 0 - upper (Optional[float]): maximum value, defaults to 10 - default (Optional[float]): default value, defaults to 0 - step (Optional[float]): step value, defaults to 1 - parent: parent widget, defaults to None - fixed (bool): if True, sets the QSizePolicy of the spinbox to Fixed - text_label (Optional[str]): if provided, creates a label with the chosen title to use with the counter - """ + """Creates a DoubleIncrementCounter. + Args: + lower (Optional[float]): minimum value, defaults to 0 + upper (Optional[float]): maximum value, defaults to 10 + default (Optional[float]): default value, defaults to 0 + step (Optional[float]): step value, defaults to 1 + parent: parent widget, defaults to None + fixed (bool): if True, sets the QSizePolicy of the spinbox to Fixed + text_label (Optional[str]): if provided, creates a label with the chosen title to use with the counter + """ super().__init__(parent) set_spinbox(self, lower, upper, default, step, fixed) @@ -1119,22 +1165,24 @@ def __init__( @property def tooltips(self): + """Gets the tooltip of both the DoubleIncrementCounter and its label.""" return self.toolTip() @tooltips.setter def tooltips(self, tooltip: str): - """Sets the tooltip of both the DoubleIncrementCounter and its label""" + """Sets the tooltip of both the DoubleIncrementCounter and its label.""" self.setToolTip(tooltip) if self.label is not None: self.label.setToolTip(tooltip) @property def precision(self): + """Get current precision of the box.""" return self.decimals() @precision.setter def precision(self, decimals: int): - """Sets the precision of the box to the specified number of decimals""" + """Sets the precision of the box to the specified number of decimals.""" self.setDecimals(decimals) @classmethod @@ -1148,11 +1196,13 @@ def make_n( parent: Optional[QWidget] = None, fixed: Optional[bool] = True, ): + """Creates n increment counters with the specified parameters.""" return make_n_spinboxes( cls, n, lower, upper, default, step, parent, fixed ) def set_visibility(self, visible: bool): + """Sets the visibility of the counter and its label.""" self.setVisible(visible) self.label.setVisible(visible) @@ -1170,14 +1220,17 @@ def __init__( fixed: Optional[bool] = True, text_label: Optional[str] = None, ): - """Args: - lower (Optional[int]): minimum value, defaults to 0 - upper (Optional[int]): maximum value, defaults to 10 - default (Optional[int]): default value, defaults to 0 - step (Optional[int]): step value, defaults to 1 - parent: parent widget, defaults to None - fixed (bool): if True, sets the QSizePolicy of the spinbox to Fixed""" + """Creates an IntIncrementCounter. + Args: + lower (Optional[int]): minimum value, defaults to 0 + upper (Optional[int]): maximum value, defaults to 10 + default (Optional[int]): default value, defaults to 0 + step (Optional[int]): step value, defaults to 1 + parent: parent widget, defaults to None + fixed (bool): if True, sets the QSizePolicy of the spinbox to Fixed + text_label (Optional[str]): if provided, creates a label with the chosen title to use with the counter + """ super().__init__(parent) set_spinbox(self, lower, upper, default, step, fixed) @@ -1189,10 +1242,12 @@ def __init__( @property def tooltips(self): + """Gets the tooltip of both the IntIncrementCounter and its label.""" return self.toolTip() @tooltips.setter def tooltips(self, tooltip): + """Sets the tooltip of both the IntIncrementCounter and its label.""" self.setToolTip(tooltip) self.label.setToolTip(tooltip) @@ -1207,14 +1262,14 @@ def make_n( parent: Optional[QWidget] = None, fixed: Optional[bool] = True, ): + """Creates n increment counters with the specified parameters.""" return make_n_spinboxes( cls, n, lower, upper, default, step, parent, fixed ) def add_blank(widget, layout=None): - """ - Adds a space between consecutive buttons/labels in a layout when building a widget + """Adds a space between consecutive buttons/labels in a layout when building a widget. Args: widget (QWidget): widget to add blank in @@ -1243,8 +1298,9 @@ def open_file_dialog( load_as_folder (bool): Whether to open a folder or a single file. If True, will allow opening folder as a single file (2D stack interpreted as 3D) file_extension (str): The description and file extension to load (format : ``"Description (*.example1 *.example2)"``). Default ``"Image file (*.tif *.tiff)"`` + Returns: + str : chosen file path """ - default_path = utils.parse_default_path(possible_paths) return QFileDialog.getOpenFileName( @@ -1256,6 +1312,16 @@ def open_folder_dialog( widget, possible_paths: list = (), ): + """Opens a window to choose a directory using QFileDialog. + + Args: + widget (QWidget): Widget to display file dialog in + possible_paths (str): Paths that may have been chosen before, can be a string + or an array of strings containing the paths + + Returns: + str : chosen directory path + """ default_path = utils.parse_default_path(possible_paths) logger.debug(f"Default : {default_path}") @@ -1265,7 +1331,7 @@ def open_folder_dialog( def make_label(name, parent=None): # TODO update to child class - """Creates a QLabel + """Creates a QLabel. Args: name: string with name @@ -1286,7 +1352,8 @@ def make_label(name, parent=None): # TODO update to child class def make_group(title, l=7, t=20, r=7, b=11, parent=None): - """Creates a group widget and layout, with a header (`title`) and content margins for top/left/right/bottom `L, T, R, B` (in pixels) + """Creates a group widget and layout, with a header (`title`) and content margins for top/left/right/bottom `L, T, R, B` (in pixels). + Group widget and layout returned will have a Fixed size policy. Args: @@ -1304,9 +1371,10 @@ def make_group(title, l=7, t=20, r=7, b=11, parent=None): class GroupedWidget(QGroupBox): - """Subclass of QGroupBox designed to easily group widgets belonging to a same category""" + """Subclass of QGroupBox designed to easily group widgets belonging to a same category.""" def __init__(self, title, l=7, t=20, r=7, b=11, parent=None): + """Creates a GroupedWidget.""" super().__init__(title, parent) self.setSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) @@ -1315,7 +1383,8 @@ def __init__(self, title, l=7, t=20, r=7, b=11, parent=None): self.layout.setContentsMargins(l, t, r, b) self.layout.setSizeConstraint(QLayout.SetFixedSize) - def set_layout(self): + def _set_layout(self): + """Sets the layout of the widget.""" self.setLayout(self.layout) @classmethod @@ -1330,6 +1399,7 @@ def create_single_widget_group( b=11, alignment=LEFT_AL, ): + """Creates a group with a single widget in it.""" group = cls(title, l, t, r, b) group.layout.addWidget(widget, alignment=alignment) group.setLayout(group.layout) @@ -1339,7 +1409,9 @@ def create_single_widget_group( def add_widgets(layout, widgets, alignment=LEFT_AL): """Adds all widgets in the list to layout, with the specified alignment. + If alignment is None, no alignment is set. + Args: layout: layout to add widgets in widgets: list of QWidgets to add to layout @@ -1363,14 +1435,15 @@ def combine_blocks( # TODO FIXME PLEASE this is a horrible design r=11, b=11, ): - """Combines two QWidget objects and puts them side by side (first on the left/top and second on the right/bottom depending on "horizontal") - Weird argument names due the initial implementation of it. # TODO maybe fix arg names or refactor + """Combines two QWidget objects and puts them side by side (first on the left/top and second on the right/bottom depending on "horizontal"). + + Weird argument names due the initial implementation of it. # TODO maybe fix arg names or refactor. Args: left_or_above (QWidget): First widget, to be added on the left/above of "second" right_or_below (QWidget): Second widget, to be displayed right/below of "first" min_spacing (int): Minimum spacing between the two widgets (from the start of label to the start of button) - horizontal (bool): whether to stack widgets vertically (False) or horizontally (True) + horizontal (bool): whether to stack widgets vertically (False) or horizontally (True) l (int): left spacing in pixels t (int): top spacing in pixels r (int): right spacing in pixels diff --git a/napari_cellseg3d/plugins.py b/napari_cellseg3d/plugins.py index f0d74386..74d867c7 100644 --- a/napari_cellseg3d/plugins.py +++ b/napari_cellseg3d/plugins.py @@ -1,3 +1,7 @@ +"""napari-cellseg3d: napari plugin for 3D cell segmentation. + +Main plugins menu for napari-cellseg3d. +""" from napari_cellseg3d.code_plugins.plugin_helper import Helper from napari_cellseg3d.code_plugins.plugin_model_inference import Inferer from napari_cellseg3d.code_plugins.plugin_model_training import Trainer diff --git a/napari_cellseg3d/utils.py b/napari_cellseg3d/utils.py index dfd6866e..f665230f 100644 --- a/napari_cellseg3d/utils.py +++ b/napari_cellseg3d/utils.py @@ -1,3 +1,4 @@ +"""Utilities functions, classes, and variables.""" import logging import math from datetime import datetime @@ -31,14 +32,14 @@ def save_folder( results_path, folder_name, images, image_paths, exist_ok=False ): - """ - Saves a list of images in a folder + """Saves a list of images in a folder. Args: results_path: Path to the folder containing results folder_name: Name of the folder containing results images: List of images to save image_paths: list of filenames of images + exist_ok: whether to check for existing files. If False, will raise an error if the folder already exists. """ results_folder = results_path / Path(folder_name) results_folder.mkdir(exist_ok=exist_ok, parents=True) @@ -54,8 +55,7 @@ def save_folder( def save_layer(results_path, image_name, image): - """ - Saves an image layer at the specified path + """Saves an image layer at the specified path. Args: results_path: path to folder containing result @@ -76,14 +76,15 @@ def show_result( existing_layer: napari.layers.Layer = None, colormap="bop orange", ) -> napari.layers.Layer: - """ - Adds layers to a viewer to show result to user + """Adds layers to a viewer to show result to user. Args: viewer: viewer to add layer in layer: original layer the operation was run on, to determine whether it should be an Image or Labels layer image: the data array containing the image name: name of the added layer + existing_layer: existing layer to update, if any + colormap: colormap to use for the layer Returns: napari.layers.Layer: the layer added to the viewer @@ -118,22 +119,25 @@ def show_result( class Singleton(type): - """ - Singleton class that can only be instantiated once at a time, - with said unique instance always being accessed on call. - Should be used as a metaclass for classes without inheritance (object type) + """Singleton class that can only be instantiated once at a time, with said unique instance always being accessed on call. + + Should be used as a metaclass for classes without inheritance (object type). """ _instances = {} def __call__(cls, *args, **kwargs): + """Call method for Singleton class. + + Ensures that only one instance of the class is created at a time, and that it is always the same instance that is returned. + """ if cls not in cls._instances: cls._instances[cls] = super().__call__(*args, **kwargs) return cls._instances[cls] def normalize_x(image): - """Normalizes the values of an image array to be between [-1;1] rather than [0;255] + """Normalizes the values of an image array to be between [-1;1] rather than [0;255]. Args: image (array): Image to process @@ -145,11 +149,12 @@ def normalize_x(image): def mkdir_from_str(path: str, exist_ok=True, parents=True): + """Creates a directory from a string path.""" Path(path).resolve().mkdir(exist_ok=exist_ok, parents=parents) def normalize_y(image): - """Normalizes the values of an image array to be between [0;1] rather than [0;255] + """Normalizes the values of an image array to be between [0;1] rather than [0;255]. Args: image (array): Image to process @@ -161,7 +166,7 @@ def normalize_y(image): def sphericity_volume_area(volume, surface_area): - """Computes the sphericity from volume and area + r"""Computes the sphericity from volume and area. .. math:: sphericity =\\frac {\\pi^\\frac{1}{3} (6 V_{p})^\\frac{2}{3}} {A_p} @@ -171,7 +176,7 @@ def sphericity_volume_area(volume, surface_area): def sphericity_axis(semi_major, semi_minor): - """Computes the sphericity from volume semi major (a) and semi minor (b) axes. + r"""Computes the sphericity from volume semi major (a) and semi minor (b) axes. .. math:: sphericity = \\frac {2 \\sqrt[3]{ab^2}} {a+ \\frac {b^2} {\\sqrt{a^2-b^2}}ln( \\frac {a+ \\sqrt{a^2-b^2}} {b} )} @@ -188,14 +193,16 @@ def sphericity_axis(semi_major, semi_minor): / (a + (b**2) / root * np.log((a + root) / b)) ) except ZeroDivisionError: - print("Zero division in sphericity calculation was replaced by 0") + LOGGER.warning( + "Zero division in sphericity calculation was replaced by 0" + ) result = 0 except ValueError as e: - print(f"Error encountered in calculation : {e}") + LOGGER.warning(f"Error encountered in calculation : {e}") result = "Error in calculation" if math.isnan(result): - print("NaN in sphericity calculation was replaced by 0") + LOGGER.warning("NaN in sphericity calculation was replaced by 0") result = 0 return result @@ -206,11 +213,14 @@ def dice_coeff( y_pred: Union[torch.Tensor, np.ndarray], smooth: float = 1.0, ) -> Union[torch.Tensor, np.float64]: - """Compute Dice-Sorensen coefficient between two numpy arrays + """Compute Dice-Sorensen coefficient between two numpy arrays. + Args: y_true: Ground truth label y_pred: Prediction label - Returns: dice coefficient + smooth: Smoothing factor to avoid division by zero + + Returns: dice coefficient. """ if isinstance(y_true, np.ndarray) and isinstance(y_pred, np.ndarray): sum_tensor = np.sum @@ -230,12 +240,12 @@ def dice_coeff( def seek_best_dice_coeff_channel(y_pred, y_true) -> torch.Tensor: - """Compute Dice-Sorensen coefficient between unsupervised model output and ground truth labels; - returns the channel with the highest dice coefficient. + """Compute Dice-Sorensen coefficient between unsupervised model output and ground truth labels; returns the channel with the highest dice coefficient. + Args: y_true: Ground truth label y_pred: Prediction label - Returns: best Dice coefficient channel + Returns: best Dice coefficient channel. """ dices = [] # Find in which channel the labels are (to avoid background) @@ -253,13 +263,13 @@ def seek_best_dice_coeff_channel(y_pred, y_true) -> torch.Tensor: def correct_rotation(image): - """Rotates the exes 0 and 2 in [DHW] section of image array""" + """Rotates the axes 0 and 2 in [DHW] section of image array.""" extra_dims = len(image.shape) - 3 return np.swapaxes(image, 0 + extra_dims, 2 + extra_dims) def normalize_max(image): - """Normalizes an image using the max and min value""" + """Normalizes an image using the max and min value.""" shape = image.shape image = image.flatten() image = (image - image.min()) / (image.max() - image.min()) @@ -274,7 +284,7 @@ def remap_image( prev_max=None, prev_min=None, ): - """Normalizes a numpy array or Tensor using the max and min value""" + """Normalizes a numpy array or Tensor using the max and min value.""" shape = image.shape image = image.flatten() im_max = prev_max if prev_max is not None else image.max() @@ -286,6 +296,7 @@ def remap_image( def resize(image, zoom_factors): + """Resizes an image using the zoom_factors.""" isotropic_image = Zoom( zoom_factors, keep_size=False, @@ -296,6 +307,7 @@ def resize(image, zoom_factors): def align_array_sizes(array_shape, target_shape): + """Aligns the sizes of two arrays by adding zeros to the smaller one.""" index_differences = [] for i in range(len(target_shape)): if target_shape[i] != array_shape[i]: @@ -303,7 +315,7 @@ def align_array_sizes(array_shape, target_shape): if array_shape[i] == target_shape[j] and j != i: index_differences.append({"origin": i, "target": j}) - # print(index_differences) + # LOGGER.debug(index_differences) if len(index_differences) == 0: return [0, 1, 2], [-3, -2, -1] @@ -319,7 +331,7 @@ def align_array_sizes(array_shape, target_shape): targets[i] = reverse_mapping[targets[i]] infos = np.unique(origins, return_index=True, return_counts=True) {"origins": infos[0], "index": infos[1], "counts": infos[2]} - # print(info_dict) + # LOGGER.debug(info_dict) final_orig = [] final_targ = [] @@ -327,19 +339,19 @@ def align_array_sizes(array_shape, target_shape): if infos[2][i] == 1: final_orig.append(infos[0][i]) final_targ.append(targets[infos[1][i]]) - # print(final_orig, final_targ) + # LOGGER.debug(final_orig, final_targ) return final_orig, final_targ def time_difference(time_start, time_finish, as_string=True): - """ + """Computes the time difference between two datetime objects. + Args: - time_start (datetime): time to subtract to time_finish - time_finish (datetime): time to add to subtract time_start to - as_string (bool): if True, returns a string with the full time diff. Otherwise, returns as a list [hours,minutes,seconds] + time_start (datetime): time to subtract to time_finish + time_finish (datetime): time to add to subtract time_start to + as_string (bool): if True, returns a string with the full time diff. Otherwise, returns as a list [hours,minutes,seconds]. """ - time_taken = time_finish - time_start days = divmod(time_taken.total_seconds(), 86400) # Get days (without [0]!) hours = divmod(days[1], 3600) # Use remainder of days to calc hours @@ -358,13 +370,14 @@ def time_difference(time_start, time_finish, as_string=True): def get_padding_dim(image_shape, anisotropy_factor=None): - """ - Finds the nearest and superior power of two for each image dimension to zero-pad it for CNN processing, - accepts either 2D or 3D images shapes. E.g. an image size of 30x40x100 will result in a padding of 32x64x128. + """Finds the nearest and superior power of two for each image dimension to zero-pad it for CNN processing. + + Accepts either 2D or 3D images shapes. E.g. an image size of 30x40x100 will result in a padding of 32x64x128. Shows a warning if the padding dimensions are very large. Args: image_shape (torch.size): an array of the dimensions of the image in D/H/W if 3D or H/W if 2D + anisotropy_factor (list): anisotropy factor for each dimension Returns: array(int): padding value for each dim @@ -409,7 +422,7 @@ def get_padding_dim(image_shape, anisotropy_factor=None): def denormalize_y(image): - """De-normalizes the values of an image array to be between [0;255] rather than [0;1] + """De-normalizes the values of an image array to be between [0;255] rather than [0;1]. Args: image (array): Image to process @@ -422,7 +435,8 @@ def denormalize_y(image): def fill_list_in_between(lst, n, fill_value): """Fills a list with n * elem between each member of list. - Example with list = [1,2,3], n=2, elem='&' : returns [1, &, &,2,&,&,3,&,&] + + Example with list = [1,2,3], n=2, elem='&' : returns [1, &, &,2,&,&,3,&,&]. Args: lst: list to fill @@ -452,6 +466,7 @@ def parse_default_path(possible_paths, check_existence=True): Args: possible_paths: array of paths + check_existence: whether to check if the path exists. Returns: the chosen default path @@ -476,25 +491,24 @@ def parse_default_path(possible_paths, check_existence=True): def get_date_time(): - """Get date and time in the following format : year_month_day_hour_minute_second""" + """Get date and time in the following format : year_month_day_hour_minute_second.""" return f"{datetime.now():%Y_%m_%d_%H_%M_%S}" def get_time(): - """Get time in the following format : hour:minute:second. NOT COMPATIBLE with file paths (saving with ":" is invalid)""" + """Get time in the following format : hour:minute:second. NOT COMPATIBLE with file paths (saving with ":" is invalid).""" return f"{datetime.now():%H:%M:%S}" def get_time_filepath(): - """Get time in the following format : hour_minute_second. Compatible with saving""" + """Get time in the following format : hour_minute_second. Compatible with saving.""" return f"{datetime.now():%H_%M_%S}" def load_images(dir_or_path, filetype="", as_folder: bool = False): - """Loads the images in ``directory``, with different behaviour depending on ``filetype`` and ``as_folder`` + """Loads the images in ``directory``, with different behaviour depending on ``filetype`` and ``as_folder``. * If ``as_folder`` is **False**, will load the path as a single 3D **.tif** image. - * If **True**, it will try to load a folder as stack of images. In this case ``filetype`` must be specified. If **True** : @@ -512,22 +526,9 @@ def load_images(dir_or_path, filetype="", as_folder: bool = False): Returns: np.array: array with loaded images """ - # if not as_folder: filename_pattern_original = Path(dir_or_path) return imread(str(filename_pattern_original)) # tifffile imread - # print(filename_pattern_original) - # elif as_folder and filetype != "": - # filename_pattern_original = Path(dir_or_path + "/*" + filetype) - # print(filename_pattern_original) - # else: - # raise ValueError("If loading as a folder, filetype must be specified") - - # if as_folder: - # raise NotImplementedError( - # "Loading as folder not implemented yet. Use napari to load as folder" - # images_original = dask_imread(filename_pattern_original) - # ) def quantile_normalization( @@ -535,7 +536,7 @@ def quantile_normalization( quantile_high=0.99, quantile_low=0.01, ): - """Normalizes an image using the quantiles""" + """Normalizes an image using the quantiles.""" if quantile_high < quantile_low: raise ValueError( f"quantile_high must be greater than quantile_low, got {quantile_high} and {quantile_low}" @@ -560,7 +561,7 @@ def quantile_normalization( def channels_fraction_above_threshold(volume: np.array, threshold=0.5) -> list: - """Computes the fraction of pixels above a certain value in a 4D volume for each channel + """Computes the fraction of pixels above a certain value in a 4D volume for each channel. Args: volume (np.ndarray): Array of shape (C, H, W, D) containing the input volume @@ -569,7 +570,7 @@ def channels_fraction_above_threshold(volume: np.array, threshold=0.5) -> list: Returns: list: List of length C containing the fraction of pixels above the threshold for each channel """ - if volume.shape != 4: + if len(volume.shape) != 4: raise ValueError( f"Volume shape {volume.shape} is not 4D. Expecting CxHxWxD." ) @@ -582,11 +583,11 @@ def channels_fraction_above_threshold(volume: np.array, threshold=0.5) -> list: def fraction_above_threshold(volume: np.array, threshold=0.5) -> float: - """ - Computes the fraction of pixels above a certain value in a volume + """Computes the fraction of pixels above a certain value in a volume. + Args: volume (np.ndarray): Array containing the input volume - threshold (float): Threshold value to use for the computation + threshold (float): Threshold value to use for the computation. Returns: float: Fraction of pixels above the threshold diff --git a/pyproject.toml b/pyproject.toml index 094166fb..2be8ce1d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -73,6 +73,7 @@ select = [ "E", "F", "W", "A", "B", + "D", "G", "I", "PT", @@ -84,6 +85,7 @@ select = [ ] # Never enforce `E501` (line length violations) and 'E741' (ambiguous variable names) # and 'G004' (do not use f-strings in logging) +# and 'A003' (Shadowing python builtins) ignore = ["E501", "E741", "G004", "A003"] exclude = [ ".bzr", @@ -111,6 +113,9 @@ exclude = [ "napari_cellseg3d/_tests/conftest.py", ] +[tool.ruff.pydocstyle] +convention = "google" + [tool.black] line-length = 79 @@ -139,12 +144,10 @@ dev = [ "ruff", "pre-commit", "tuna", + "twine", ] docs = [ - "sphinx", - "sphinx_autodoc_typehints", - "sphinx_rtd_theme", - "twine", + "jupyter-book", ] test = [ "pytest", diff --git a/requirements.txt b/requirements.txt index ada03ae4..3f8e64ea 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,11 +3,9 @@ coverage imageio-ffmpeg>=0.4.5 isort itk +jupyter-book pytest pytest-qt -sphinx -sphinx-autodoc-typehints -sphinx-rtd-theme tox twine numpy From 0c68c31cb895bfd4f5ff71016becb5e7cdda5413 Mon Sep 17 00:00:00 2001 From: C-Achard Date: Wed, 15 Nov 2023 12:57:46 +0100 Subject: [PATCH 250/250] Update .gitignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 6ee49040..60241038 100644 --- a/.gitignore +++ b/.gitignore @@ -110,6 +110,7 @@ notebooks/full_plot.html *.png notebooks/instance_test.ipynb *.prof +/docs/source/code/_autosummary/*.rst #include test data !napari_cellseg3d/_tests/res/test.tif

    3. 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