165 update check all examples

examples/classification_3d/densenet_evaluation_array.py

@@ -0,0 +1,97 @@
+# Copyright 2020 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import sys
+import logging
+import numpy as np
+import torch
+from ignite.engine import create_supervised_evaluator, _prepare_batch
+from torch.utils.data import DataLoader
+
+# assumes the framework is found here, change as necessary
+sys.path.append("../..")
+import monai
+import monai.transforms.compose as transforms
+from monai.data.nifti_reader import NiftiDataset
+from monai.transforms import (AddChannel, Rescale, Resize)
+from monai.handlers.stats_handler import StatsHandler
+from monai.handlers.classification_saver import ClassificationSaver
+from monai.handlers.checkpoint_loader import CheckpointLoader
+from ignite.metrics import Accuracy
+
+monai.config.print_config()
+logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+
+# IXI dataset as a demo, dowloadable from https://brain-development.org/ixi-dataset/
+images = [
+    "/workspace/data/medical/ixi/IXI-T1/IXI607-Guys-1097-T1.nii.gz",
+    "/workspace/data/medical/ixi/IXI-T1/IXI175-HH-1570-T1.nii.gz",
+    "/workspace/data/medical/ixi/IXI-T1/IXI385-HH-2078-T1.nii.gz",
+    "/workspace/data/medical/ixi/IXI-T1/IXI344-Guys-0905-T1.nii.gz",
+    "/workspace/data/medical/ixi/IXI-T1/IXI409-Guys-0960-T1.nii.gz",
+    "/workspace/data/medical/ixi/IXI-T1/IXI584-Guys-1129-T1.nii.gz",
+    "/workspace/data/medical/ixi/IXI-T1/IXI253-HH-1694-T1.nii.gz",
+    "/workspace/data/medical/ixi/IXI-T1/IXI092-HH-1436-T1.nii.gz",
+    "/workspace/data/medical/ixi/IXI-T1/IXI574-IOP-1156-T1.nii.gz",
+    "/workspace/data/medical/ixi/IXI-T1/IXI585-Guys-1130-T1.nii.gz"
+]
+# 2 binary labels for gender classification: man and woman
+labels = np.array([
+    0, 0, 1, 0, 1, 0, 1, 0, 1, 0
+])
+
+# Define transforms for image
+val_transforms = transforms.Compose([
+    Rescale(),
+    AddChannel(),
+    Resize((96, 96, 96))
+])
+# Define nifti dataset
+val_ds = NiftiDataset(image_files=images, labels=labels, transform=val_transforms, image_only=False)
+# Create DenseNet121
+net = monai.networks.nets.densenet3d.densenet121(
+    in_channels=1,
+    out_channels=2,
+)
+device = torch.device("cuda:0")
+
+metric_name = 'Accuracy'
+# add evaluation metric to the evaluator engine
+val_metrics = {metric_name: Accuracy()}
+
+
+def prepare_batch(batch, device=None, non_blocking=False):
+    return _prepare_batch((batch[0], batch[1]), device, non_blocking)
+
+
+# ignite evaluator expects batch=(img, label) and returns output=(y_pred, y) at every iteration,
+# user can add output_transform to return other values
+evaluator = create_supervised_evaluator(net, val_metrics, device, True, prepare_batch=prepare_batch)
+
+# Add stats event handler to print validation stats via evaluator
+val_stats_handler = StatsHandler(
+    name='evaluator',
+    output_transform=lambda x: None  # no need to print loss value, so disable per iteration output
+)
+val_stats_handler.attach(evaluator)
+
+# for the arrary data format, assume the 3rd item of batch data is the meta_data
+prediction_saver = ClassificationSaver(output_dir='tempdir', batch_transform=lambda batch: batch[2],
+                                       output_transform=lambda output: output[0].argmax(1))
+prediction_saver.attach(evaluator)
+
+# the model was trained by "densenet_training_array" exmple
+CheckpointLoader(load_path='./runs/net_checkpoint_40.pth', load_dict={'net': net}).attach(evaluator)
+
+# create a validation data loader
+val_loader = DataLoader(val_ds, batch_size=2, num_workers=4, pin_memory=torch.cuda.is_available())
+
+state = evaluator.run(val_loader)

examples/classification_3d/densenet_evaluation_dict.py

@@ -0,0 +1,98 @@
+# Copyright 2020 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from ignite.metrics import Accuracy
+import sys
+import logging
+import numpy as np
+import torch
+from ignite.engine import create_supervised_evaluator, _prepare_batch
+from torch.utils.data import DataLoader
+
+# assumes the framework is found here, change as necessary
+sys.path.append("../..")
+from monai.handlers.classification_saver import ClassificationSaver
+from monai.handlers.checkpoint_loader import CheckpointLoader
+from monai.handlers.stats_handler import StatsHandler
+from monai.transforms.composables import LoadNiftid, AddChanneld, Rescaled, Resized
+import monai.transforms.compose as transforms
+import monai
+
+monai.config.print_config()
+logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+
+# IXI dataset as a demo, dowloadable from https://brain-development.org/ixi-dataset/
+images = [
+    "/workspace/data/medical/ixi/IXI-T1/IXI607-Guys-1097-T1.nii.gz",
+    "/workspace/data/medical/ixi/IXI-T1/IXI175-HH-1570-T1.nii.gz",
+    "/workspace/data/medical/ixi/IXI-T1/IXI385-HH-2078-T1.nii.gz",
+    "/workspace/data/medical/ixi/IXI-T1/IXI344-Guys-0905-T1.nii.gz",
+    "/workspace/data/medical/ixi/IXI-T1/IXI409-Guys-0960-T1.nii.gz",
+    "/workspace/data/medical/ixi/IXI-T1/IXI584-Guys-1129-T1.nii.gz",
+    "/workspace/data/medical/ixi/IXI-T1/IXI253-HH-1694-T1.nii.gz",
+    "/workspace/data/medical/ixi/IXI-T1/IXI092-HH-1436-T1.nii.gz",
+    "/workspace/data/medical/ixi/IXI-T1/IXI574-IOP-1156-T1.nii.gz",
+    "/workspace/data/medical/ixi/IXI-T1/IXI585-Guys-1130-T1.nii.gz"
+]
+# 2 binary labels for gender classification: man and woman
+labels = np.array([
+    0, 0, 1, 0, 1, 0, 1, 0, 1, 0
+])
+val_files = [{'img': img, 'label': label} for img, label in zip(images, labels)]
+
+# Define transforms for image
+val_transforms = transforms.Compose([
+    LoadNiftid(keys=['img']),
+    AddChanneld(keys=['img']),
+    Rescaled(keys=['img']),
+    Resized(keys=['img'], output_spatial_shape=(96, 96, 96))
+])
+
+# Create DenseNet121
+net = monai.networks.nets.densenet3d.densenet121(
+    in_channels=1,
+    out_channels=2,
+)
+device = torch.device("cuda:0")
+
+
+def prepare_batch(batch, device=None, non_blocking=False):
+    return _prepare_batch((batch['img'], batch['label']), device, non_blocking)
+
+
+metric_name = 'Accuracy'
+# add evaluation metric to the evaluator engine
+val_metrics = {metric_name: Accuracy()}
+# ignite evaluator expects batch=(img, label) and returns output=(y_pred, y) at every iteration,
+# user can add output_transform to return other values
+evaluator = create_supervised_evaluator(net, val_metrics, device, True, prepare_batch=prepare_batch)
+
+# Add stats event handler to print validation stats via evaluator
+val_stats_handler = StatsHandler(
+    name='evaluator',
+    output_transform=lambda x: None  # no need to print loss value, so disable per iteration output
+)
+val_stats_handler.attach(evaluator)
+
+# for the arrary data format, assume the 3rd item of batch data is the meta_data
+prediction_saver = ClassificationSaver(output_dir='tempdir', name='evaluator',
+                                       batch_transform=lambda batch: {'filename_or_obj': batch['img.filename_or_obj']},
+                                       output_transform=lambda output: output[0].argmax(1))
+prediction_saver.attach(evaluator)
+
+# the model was trained by "densenet_training_dict" exmple
+CheckpointLoader(load_path='./runs/net_checkpoint_40.pth', load_dict={'net': net}).attach(evaluator)
+
+# create a validation data loader
+val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
+val_loader = DataLoader(val_ds, batch_size=2, num_workers=4, pin_memory=torch.cuda.is_available())
+
+state = evaluator.run(val_loader)

examples/densenet_classification_3d.py → examples/classification_3d/densenet_training_array.py

@@ -18,19 +18,20 @@
 from torch.utils.data import DataLoader
 
 # assumes the framework is found here, change as necessary
-sys.path.append("..")
+sys.path.append("../..")
 import monai
 import monai.transforms.compose as transforms
-
 from monai.data.nifti_reader import NiftiDataset
-from monai.transforms import (AddChannel, Rescale, ToTensor, UniformRandomPatch)
+from monai.transforms import (AddChannel, Rescale, Resize, RandRotate90)
 from monai.handlers.stats_handler import StatsHandler
+from monai.handlers.tensorboard_handlers import TensorBoardStatsHandler
 from ignite.metrics import Accuracy
 from monai.handlers.utils import stopping_fn_from_metric
 
 monai.config.print_config()
+logging.basicConfig(stream=sys.stdout, level=logging.INFO)
 
-# FIXME: temp test dataset, Wenqi will replace later
+# IXI dataset as a demo, dowloadable from https://brain-development.org/ixi-dataset/
 images = [
     "/workspace/data/medical/ixi/IXI-T1/IXI314-IOP-0889-T1.nii.gz",
     "/workspace/data/medical/ixi/IXI-T1/IXI249-Guys-1072-T1.nii.gz",
@@ -53,83 +54,101 @@
     "/workspace/data/medical/ixi/IXI-T1/IXI574-IOP-1156-T1.nii.gz",
     "/workspace/data/medical/ixi/IXI-T1/IXI585-Guys-1130-T1.nii.gz"
 ]
+# 2 binary labels for gender classification: man and woman
 labels = np.array([
     0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0
 ])
 
-# Define transforms for image and segmentation
-imtrans = transforms.Compose([
+# Define transforms
+train_transforms = transforms.Compose([
+    Rescale(),
+    AddChannel(),
+    Resize((96, 96, 96)),
+    RandRotate90()
+])
+val_transforms = transforms.Compose([
     Rescale(),
     AddChannel(),
-    UniformRandomPatch((96, 96, 96)),
-    ToTensor()
+    Resize((96, 96, 96))
 ])
 
-# Define nifti dataset, dataloader.
-ds = NiftiDataset(image_files=images, labels=labels, transform=imtrans)
-loader = DataLoader(ds, batch_size=2, num_workers=2, pin_memory=torch.cuda.is_available())
-im, label = monai.utils.misc.first(loader)
+# Define nifti dataset, dataloader
+check_ds = NiftiDataset(image_files=images, labels=labels, transform=train_transforms)
+check_loader = DataLoader(check_ds, batch_size=2, num_workers=2, pin_memory=torch.cuda.is_available())
+im, label = monai.utils.misc.first(check_loader)
 print(type(im), im.shape, label)
 
-lr = 1e-5
-
-# Create DenseNet121, CrossEntropyLoss and Adam optimizer.
+# Create DenseNet121, CrossEntropyLoss and Adam optimizer
 net = monai.networks.nets.densenet3d.densenet121(
     in_channels=1,
     out_channels=2,
 )
-
 loss = torch.nn.CrossEntropyLoss()
+lr = 1e-5
 opt = torch.optim.Adam(net.parameters(), lr)
-
-# Create trainer
 device = torch.device("cuda:0")
+
+# ignite trainer expects batch=(img, label) and returns output=loss at every iteration,
+# user can add output_transform to return other values, like: y_pred, y, etc.
 trainer = create_supervised_trainer(net, opt, loss, device, False)
 
 # adding checkpoint handler to save models (network params and optimizer stats) during training
 checkpoint_handler = ModelCheckpoint('./runs/', 'net', n_saved=10, require_empty=False)
 trainer.add_event_handler(event_name=Events.EPOCH_COMPLETED,
                           handler=checkpoint_handler,
                           to_save={'net': net, 'opt': opt})
-train_stats_handler = StatsHandler(output_transform=lambda x: x[3])
+
+# StatsHandler prints loss at every iteration and print metrics at every epoch,
+# we don't set metrics for trainer here, so just print loss, user can also customize print functions
+# and can use output_transform to convert engine.state.output if it's not loss value
+train_stats_handler = StatsHandler(name='trainer')
 train_stats_handler.attach(trainer)
 
-@trainer.on(Events.EPOCH_COMPLETED)
-def log_training_loss(engine):
-    engine.logger.info("Epoch[%s] Loss: %s", engine.state.epoch, engine.state.output)
+# TensorBoardStatsHandler plots loss at every iteration and plots metrics at every epoch, same as StatsHandler
+train_tensorboard_stats_handler = TensorBoardStatsHandler()
+train_tensorboard_stats_handler.attach(trainer)
 
 # Set parameters for validation
 validation_every_n_epochs = 1
-metric_name = 'Accuracy'
 
+metric_name = 'Accuracy'
 # add evaluation metric to the evaluator engine
 val_metrics = {metric_name: Accuracy()}
+# ignite evaluator expects batch=(img, label) and returns output=(y_pred, y) at every iteration,
+# user can add output_transform to return other values
 evaluator = create_supervised_evaluator(net, val_metrics, device, True)
 
 # Add stats event handler to print validation stats via evaluator
-logging.basicConfig(stream=sys.stdout, level=logging.INFO)
-val_stats_handler = StatsHandler(output_transform=lambda x: None)
+val_stats_handler = StatsHandler(
+    name='evaluator',
+    output_transform=lambda x: None,  # no need to print loss value, so disable per iteration output
+    global_epoch_transform=lambda x: trainer.state.epoch)  # fetch global epoch number from trainer
 val_stats_handler.attach(evaluator)
 
-# Add early stopping handler to evaluator.
+# add handler to record metrics to TensorBoard at every epoch
+val_tensorboard_stats_handler = TensorBoardStatsHandler(
+    output_transform=lambda x: None,  # no need to plot loss value, so disable per iteration output
+    global_epoch_transform=lambda x: trainer.state.epoch)  # fetch global epoch number from trainer
+val_tensorboard_stats_handler.attach(evaluator)
+
+# Add early stopping handler to evaluator
 early_stopper = EarlyStopping(patience=4,
                               score_function=stopping_fn_from_metric(metric_name),
                               trainer=trainer)
 evaluator.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=early_stopper)
 
 # create a validation data loader
-val_ds = NiftiDataset(image_files=images[-5:], labels=labels[-5:], transform=imtrans)
-val_loader = DataLoader(ds, batch_size=2, num_workers=2, pin_memory=torch.cuda.is_available())
+val_ds = NiftiDataset(image_files=images[-10:], labels=labels[-10:], transform=val_transforms)
+val_loader = DataLoader(val_ds, batch_size=2, num_workers=2, pin_memory=torch.cuda.is_available())
 
 
 @trainer.on(Events.EPOCH_COMPLETED(every=validation_every_n_epochs))
 def run_validation(engine):
     evaluator.run(val_loader)
 
-# create a training data loader
-logging.basicConfig(stream=sys.stdout, level=logging.INFO)
 
-train_ds = NiftiDataset(image_files=images[:15], labels=labels[:15], transform=imtrans)
+# create a training data loader
+train_ds = NiftiDataset(image_files=images[:10], labels=labels[:10], transform=train_transforms)
 train_loader = DataLoader(train_ds, batch_size=2, num_workers=2, pin_memory=torch.cuda.is_available())
 
 train_epochs = 30