Neater use off nn.Sequential in controlnet

docs/source/conf.py

@@ -13,6 +13,8 @@
 import os
 import subprocess
 import sys
+import importlib
+import inspect
 
 sys.path.insert(0, os.path.abspath(".."))
 sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")))
@@ -137,7 +139,7 @@ def generate_apidocs(*args):
     "github_user": "Project-MONAI",
     "github_repo": "MONAI",
     "github_version": "dev",
-    "doc_path": "docs/",
+    "doc_path": "docs/source",
     "conf_py_path": "/docs/",
     "VERSION": version,
 }
@@ -162,3 +164,47 @@ def setup(app):
     # Hook to allow for automatic generation of API docs
     # before doc deployment begins.
     app.connect("builder-inited", generate_apidocs)
+
+
+# -- Linkcode configuration --------------------------------------------------
+DEFAULT_REPOSITORY = "Project-MONAI/MONAI"
+repository = os.environ.get("GITHUB_REPOSITORY", DEFAULT_REPOSITORY)
+
+base_code_url = f"https://github.com/{repository}/blob/{version}"
+MODULE_ROOT_FOLDER = "monai"
+
+
+# Adjusted from https://github.com/python-websockets/websockets/blob/main/docs/conf.py
+def linkcode_resolve(domain, info):
+    if domain != "py":
+        raise ValueError(
+            f"expected domain to be 'py', got {domain}."
+            "Please adjust linkcode_resolve to either handle this domain or ignore it."
+        )
+
+    mod = importlib.import_module(info["module"])
+    if "." in info["fullname"]:
+        objname, attrname = info["fullname"].split(".")
+        obj = getattr(mod, objname)
+        try:
+            # object is a method of a class
+            obj = getattr(obj, attrname)
+        except AttributeError:
+            # object is an attribute of a class
+            return None
+    else:
+        obj = getattr(mod, info["fullname"])
+
+    try:
+        file = inspect.getsourcefile(obj)
+        source, lineno = inspect.getsourcelines(obj)
+    except TypeError:
+        # e.g. object is a typing.Union
+        return None
+    file = os.path.relpath(file, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")))
+    if not file.startswith(MODULE_ROOT_FOLDER):
+        # e.g. object is a typing.NewType
+        return None
+    start, end = lineno, lineno + len(source) - 1
+    url = f"{base_code_url}/{file}#L{start}-L{end}"
+    return url

monai/networks/nets/controlnet.py

@@ -34,7 +34,6 @@
 from collections.abc import Sequence
 
 import torch
-import torch.nn.functional as F
 from torch import nn
 
 from monai.networks.blocks import Convolution
@@ -57,7 +56,8 @@ def __init__(self, spatial_dims: int, in_channels: int, out_channels: int, chann
             strides=1,
             kernel_size=3,
             padding=1,
-            conv_only=True,
+            adn_ordering="A",
+            act="SWISH",
         )
 
         self.blocks = nn.ModuleList([])
@@ -73,7 +73,8 @@ def __init__(self, spatial_dims: int, in_channels: int, out_channels: int, chann
                     strides=1,
                     kernel_size=3,
                     padding=1,
-                    conv_only=True,
+                    adn_ordering="A",
+                    act="SWISH",
                 )
             )
 
@@ -85,7 +86,8 @@ def __init__(self, spatial_dims: int, in_channels: int, out_channels: int, chann
                     strides=2,
                     kernel_size=3,
                     padding=1,
-                    conv_only=True,
+                    adn_ordering="A",
+                    act="SWISH",
                 )
             )
 
@@ -103,11 +105,9 @@ def __init__(self, spatial_dims: int, in_channels: int, out_channels: int, chann
 
     def forward(self, conditioning):
         embedding = self.conv_in(conditioning)
-        embedding = F.silu(embedding)
 
         for block in self.blocks:
             embedding = block(embedding)
-            embedding = F.silu(embedding)
 
         embedding = self.conv_out(embedding)
 
@@ -410,3 +410,56 @@ def forward(
         mid_block_res_sample *= conditioning_scale
 
         return down_block_res_samples, mid_block_res_sample
+
+    def load_old_state_dict(self, old_state_dict: dict, verbose=False) -> None:
+        """
+        Load a state dict from a ControlNet trained with
+        [MONAI Generative](https://github.com/Project-MONAI/GenerativeModels).
+
+        Args:
+            old_state_dict: state dict from the old ControlNet model.
+        """
+
+        new_state_dict = self.state_dict()
+        # if all keys match, just load the state dict
+        if all(k in new_state_dict for k in old_state_dict):
+            print("All keys match, loading state dict.")
+            self.load_state_dict(old_state_dict)
+            return
+
+        if verbose:
+            # print all new_state_dict keys that are not in old_state_dict
+            for k in new_state_dict:
+                if k not in old_state_dict:
+                    print(f"key {k} not found in old state dict")
+            # and vice versa
+            print("----------------------------------------------")
+            for k in old_state_dict:
+                if k not in new_state_dict:
+                    print(f"key {k} not found in new state dict")
+
+        # copy over all matching keys
+        for k in new_state_dict:
+            if k in old_state_dict:
+                new_state_dict[k] = old_state_dict[k]
+
+        # fix the attention blocks
+        attention_blocks = [k.replace(".attn1.qkv.weight", "") for k in new_state_dict if "attn1.qkv.weight" in k]
+        for block in attention_blocks:
+            new_state_dict[f"{block}.attn1.qkv.weight"] = torch.concat(
+                [
+                    old_state_dict[f"{block}.attn1.to_q.weight"],
+                    old_state_dict[f"{block}.attn1.to_k.weight"],
+                    old_state_dict[f"{block}.attn1.to_v.weight"],
+                ],
+                dim=0,
+            )
+
+            # projection
+            new_state_dict[f"{block}.attn1.out_proj.weight"] = old_state_dict[f"{block}.attn1.to_out.0.weight"]
+            new_state_dict[f"{block}.attn1.out_proj.bias"] = old_state_dict[f"{block}.attn1.to_out.0.bias"]
+
+            new_state_dict[f"{block}.attn2.out_proj.weight"] = old_state_dict[f"{block}.attn2.to_out.0.weight"]
+            new_state_dict[f"{block}.attn2.out_proj.bias"] = old_state_dict[f"{block}.attn2.to_out.0.bias"]
+
+        self.load_state_dict(new_state_dict)

monai/visualize/utils.py

@@ -24,11 +24,9 @@
 from monai.utils.type_conversion import convert_data_type, convert_to_dst_type
 
 if TYPE_CHECKING:
-    from matplotlib import cm
     from matplotlib import pyplot as plt
 else:
     plt, _ = optional_import("matplotlib", name="pyplot")
-    cm, _ = optional_import("matplotlib", name="cm")
 
 __all__ = ["matshow3d", "blend_images"]
 
@@ -210,7 +208,7 @@ def blend_images(
         image = repeat(image, 3, axis=0)
 
     def get_label_rgb(cmap: str, label: NdarrayOrTensor) -> NdarrayOrTensor:
-        _cmap = cm.get_cmap(cmap)
+        _cmap = plt.colormaps.get_cmap(cmap)
         label_np, *_ = convert_data_type(label, np.ndarray)
         label_rgb_np = _cmap(label_np[0])
         label_rgb_np = np.moveaxis(label_rgb_np, -1, 0)[:3]

requirements-dev.txt

@@ -36,7 +36,7 @@ einops
 transformers>=4.36.0
 mlflow>=1.28.0, <=2.11.3
 clearml>=1.10.0rc0
-matplotlib!=3.5.0
+matplotlib>=3.6.3
 tensorboardX
 types-PyYAML
 pyyaml

setup.cfg

@@ -68,7 +68,7 @@ all =
     transformers<4.22; python_version <= '3.10'
     mlflow>=1.28.0, <=2.11.3
     clearml>=1.10.0rc0
-    matplotlib
+    matplotlib>=3.6.3
     tensorboardX
     pyyaml
     fire
@@ -127,7 +127,7 @@ transformers =
 mlflow =
     mlflow>=1.28.0, <=2.11.3
 matplotlib =
-    matplotlib
+    matplotlib>=3.6.3
 clearml =
     clearml
 tensorboardX =

tests/test_controlnet.py

@@ -11,15 +11,19 @@
 
 from __future__ import annotations
 
+import os
+import tempfile
 import unittest
 from unittest import skipUnless
 
 import torch
 from parameterized import parameterized
 
+from monai.apps import download_url
 from monai.networks import eval_mode
 from monai.networks.nets.controlnet import ControlNet
 from monai.utils import optional_import
+from tests.utils import skip_if_downloading_fails, testing_data_config
 
 _, has_einops = optional_import("einops")
 UNCOND_CASES_2D = [
@@ -177,6 +181,35 @@ def test_shape_conditioned_models(self, input_param, expected_output_shape):
             self.assertEqual(len(result[0]), 2 * len(input_param["channels"]))
             self.assertEqual(result[1].shape, expected_output_shape)
 
+    @skipUnless(has_einops, "Requires einops")
+    def test_compatibility_with_monai_generative(self):
+        # test loading weights from a model saved in MONAI Generative, version 0.2.3
+        with skip_if_downloading_fails():
+            net = ControlNet(
+                spatial_dims=2,
+                in_channels=1,
+                num_res_blocks=1,
+                channels=(8, 8, 8),
+                attention_levels=(False, False, True),
+                norm_num_groups=8,
+                with_conditioning=True,
+                transformer_num_layers=1,
+                cross_attention_dim=3,
+                resblock_updown=True,
+            )
+
+            tmpdir = tempfile.mkdtemp()
+            key = "controlnet_monai_generative_weights"
+            url = testing_data_config("models", key, "url")
+            hash_type = testing_data_config("models", key, "hash_type")
+            hash_val = testing_data_config("models", key, "hash_val")
+            filename = "controlnet_monai_generative_weights.pt"
+
+            weight_path = os.path.join(tmpdir, filename)
+            download_url(url=url, filepath=weight_path, hash_val=hash_val, hash_type=hash_type)
+
+            net.load_old_state_dict(torch.load(weight_path), verbose=False)
+
 
 if __name__ == "__main__":
     unittest.main()

tests/test_matshow3d.py

@@ -114,6 +114,7 @@ def test_3d_rgb(self):
             every_n=2,
             frame_dim=-1,
             channel_dim=0,
+            fill_value=0,
             show=False,
         )
 

tests/testing_data/data_config.json

@@ -153,6 +153,11 @@
             "url": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/autoencoderkl.pth",
             "hash_type": "sha256",
             "hash_val": "6e02c9540c51b16b9ba98b5c0c75d6b84b430afe9a3237df1d67a520f8d34184"
+        },
+        "controlnet_monai_generative_weights": {
+            "url": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/controlnet.pth",
+            "hash_type": "sha256",
+            "hash_val": "cd100d0c69f47569ae5b4b7df653a1cb19f5e02eff1630db3210e2646fb1ab2e"
         }
     },
     "configs": {