8627 perceptual loss errors out after hitting the maximum number of downloads

.github/workflows/pythonapp.yml

@@ -107,6 +107,7 @@ jobs:
     runs-on: ubuntu-latest
     env:
       QUICKTEST: True
+      PIP_DISABLE_PIP_VERSION_CHECK: "1"
       shell: bash
     steps:
     - uses: actions/checkout@v6
@@ -116,15 +117,26 @@ jobs:
       uses: actions/setup-python@v6
       with:
         python-version: '3.9'
+    - name: Get pip cache dir
+      id: pip-cache
+      run: |
+        echo "dir=$(pip cache dir)" >> $GITHUB_OUTPUT
+    - name: Cache pip (packaging)
+      uses: actions/cache@v4
+      with:
+        path: ${{ steps.pip-cache.outputs.dir }}
+        key: ${{ runner.os }}-packaging-pip-${{ hashFiles('requirements*.txt', 'setup.cfg', 'pyproject.toml') }}
         cache: 'pip'
     - name: Install dependencies
       run: |
+        df -h
         find  /opt/hostedtoolcache/* -maxdepth 0 ! -name 'Python' -exec rm -rf {} \;
         python -m pip install --user --upgrade pip setuptools wheel twine packaging
-        # install the latest pytorch for testing
+        # install CPU-only pytorch for testing to keep runner disk usage manageable
         # however, "pip install monai*.tar.gz" will build cpp/cuda with an isolated
         # fresh torch installation according to pyproject.toml
-        python -m pip install torch>=2.5.1 torchvision --extra-index-url https://download.pytorch.org/whl/cpu
+        python -m pip install --no-cache-dir torch==2.5.1+cpu torchvision==0.20.1+cpu --index-url https://download.pytorch.org/whl/cpu
+        df -h
     - name: Check packages
       run: |
         pip uninstall monai
@@ -190,6 +202,7 @@ jobs:
         cache: 'pip'
     - name: Install dependencies
       run: |
+        find  /opt/hostedtoolcache/* -maxdepth 0 ! -name 'Python' -exec rm -rf {} \;
         python -m pip install --upgrade pip wheel
         python -m pip install -r docs/requirements.txt --extra-index-url https://download.pytorch.org/whl/cpu
     - name: Make html

monai/losses/perceptual.py

@@ -19,11 +19,18 @@
 from monai.utils import optional_import
 from monai.utils.enums import StrEnum
 
+# Valid model name to download from the repository
+HF_MONAI_MODELS = frozenset(
+    ("medicalnet_resnet10_23datasets", "medicalnet_resnet50_23datasets", "radimagenet_resnet50")
+)
+
 LPIPS, _ = optional_import("lpips", name="LPIPS")
 torchvision, _ = optional_import("torchvision")
 
 
-class PercetualNetworkType(StrEnum):
+class PerceptualNetworkType(StrEnum):
+    """Types of neural networks that are supported by perceptual loss."""
+
     alex = "alex"
     vgg = "vgg"
     squeeze = "squeeze"
@@ -75,7 +82,7 @@ class PerceptualLoss(nn.Module):
     def __init__(
         self,
         spatial_dims: int,
-        network_type: str = PercetualNetworkType.alex,
+        network_type: str = PerceptualNetworkType.alex,
         is_fake_3d: bool = True,
         fake_3d_ratio: float = 0.5,
         cache_dir: str | None = None,
@@ -89,18 +96,25 @@ def __init__(
         if spatial_dims not in [2, 3]:
             raise NotImplementedError("Perceptual loss is implemented only in 2D and 3D.")
 
-        if (spatial_dims == 2 or is_fake_3d) and "medicalnet_" in network_type:
-            raise ValueError(
-                "MedicalNet networks are only compatible with ``spatial_dims=3``."
-                "Argument is_fake_3d must be set to False."
-            )
-
-        if channel_wise and "medicalnet_" not in network_type:
+        # Strict validation for MedicalNet
+        if "medicalnet_" in network_type:
+            if spatial_dims == 2 or is_fake_3d:
+                raise ValueError(
+                    "MedicalNet networks are only compatible with ``spatial_dims=3``. Argument is_fake_3d must be set to False."
+                )
+            if not channel_wise:
+                warnings.warn(
+                    "MedicalNet networks supp, ort channel-wise loss. Consider setting channel_wise=True.", stacklevel=2
+                )
+
+        # Channel-wise only for MedicalNet
+        elif channel_wise:
             raise ValueError("Channel-wise loss is only compatible with MedicalNet networks.")
 
-        if network_type.lower() not in list(PercetualNetworkType):
+        if network_type.lower() not in list(PerceptualNetworkType):
             raise ValueError(
-                f"Unrecognised criterion entered for Perceptual Loss. Must be one in: {', '.join(PercetualNetworkType)}"
+                "Unrecognised criterion entered for Perceptual Loss. Must be one in: %s"
+                % ", ".join(PerceptualNetworkType)
             )
         if cache_dir:
             torch.hub.set_dir(cache_dir)
@@ -111,12 +125,16 @@ def __init__(
 
         self.spatial_dims = spatial_dims
         self.perceptual_function: nn.Module
+
+        # If spatial_dims is 3, only MedicalNet supports 3D models, otherwise, spatial_dims=2 and fake_3D must be used.
         if spatial_dims == 3 and is_fake_3d is False:
             self.perceptual_function = MedicalNetPerceptualSimilarity(
-                net=network_type, verbose=False, channel_wise=channel_wise
+                net=network_type, verbose=False, channel_wise=channel_wise, cache_dir=cache_dir
             )
         elif "radimagenet_" in network_type:
-            self.perceptual_function = RadImageNetPerceptualSimilarity(net=network_type, verbose=False)
+            self.perceptual_function = RadImageNetPerceptualSimilarity(
+                net=network_type, verbose=False, cache_dir=cache_dir
+            )
         elif network_type == "resnet50":
             self.perceptual_function = TorchvisionModelPerceptualSimilarity(
                 net=network_type,
@@ -125,7 +143,9 @@ def __init__(
                 pretrained_state_dict_key=pretrained_state_dict_key,
             )
         else:
+            # VGG, AlexNet and SqueezeNet are independently handled by LPIPS.
             self.perceptual_function = LPIPS(pretrained=pretrained, net=network_type, verbose=False)
+
         self.is_fake_3d = is_fake_3d
         self.fake_3d_ratio = fake_3d_ratio
         self.channel_wise = channel_wise
@@ -197,7 +217,7 @@ class MedicalNetPerceptualSimilarity(nn.Module):
     """
     Component to perform the perceptual evaluation with the networks pretrained by Chen, et al. "Med3D: Transfer
     Learning for 3D Medical Image Analysis". This class uses torch Hub to download the networks from
-    "Warvito/MedicalNet-models".
+    "Project-MONAI/perceptual-models".
 
     Args:
         net: {``"medicalnet_resnet10_23datasets"``, ``"medicalnet_resnet50_23datasets"``}
@@ -208,11 +228,19 @@ class MedicalNetPerceptualSimilarity(nn.Module):
     """
 
     def __init__(
-        self, net: str = "medicalnet_resnet10_23datasets", verbose: bool = False, channel_wise: bool = False
+        self,
+        net: str = "medicalnet_resnet10_23datasets",
+        verbose: bool = False,
+        channel_wise: bool = False,
+        cache_dir: str | None = None,
     ) -> None:
         super().__init__()
-        torch.hub._validate_not_a_forked_repo = lambda a, b, c: True
-        self.model = torch.hub.load("warvito/MedicalNet-models", model=net, verbose=verbose, trust_repo=True)
+        if net not in HF_MONAI_MODELS:
+            raise ValueError(f"Invalid download model name '{net}'. Must be one of: {', '.join(HF_MONAI_MODELS)}.")
+
+        self.model = torch.hub.load(
+            "Project-MONAI/perceptual-models:main", model=net, verbose=verbose, cache_dir=cache_dir, trust_repo=True
+        )
         self.eval()
 
         self.channel_wise = channel_wise
@@ -261,7 +289,7 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
             for i in range(input.shape[1]):
                 l_idx = i * feats_per_ch
                 r_idx = (i + 1) * feats_per_ch
-                results[:, i, ...] = feats_diff[:, l_idx : i + r_idx, ...].sum(dim=1)
+                results[:, i, ...] = feats_diff[:, l_idx:r_idx, ...].sum(dim=1)
         else:
             results = feats_diff.sum(dim=1, keepdim=True)
 
@@ -290,17 +318,21 @@ class RadImageNetPerceptualSimilarity(nn.Module):
     """
     Component to perform the perceptual evaluation with the networks pretrained on RadImagenet (pretrained by Mei, et
     al. "RadImageNet: An Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning"). This class
-    uses torch Hub to download the networks from "Warvito/radimagenet-models".
+    uses torch Hub to download the networks from "Project-MONAI/perceptual-models".
 
     Args:
         net: {``"radimagenet_resnet50"``}
             Specifies the network architecture to use. Defaults to ``"radimagenet_resnet50"``.
         verbose: if false, mute messages from torch Hub load function.
     """
 
-    def __init__(self, net: str = "radimagenet_resnet50", verbose: bool = False) -> None:
+    def __init__(self, net: str = "radimagenet_resnet50", verbose: bool = False, cache_dir: str | None = None) -> None:
         super().__init__()
-        self.model = torch.hub.load("Warvito/radimagenet-models", model=net, verbose=verbose, trust_repo=True)
+        if net not in HF_MONAI_MODELS:
+            raise ValueError(f"Invalid download model name '{net}'. Must be one of: {', '.join(HF_MONAI_MODELS)}.")
+        self.model = torch.hub.load(
+            "Project-MONAI/perceptual-models:main", model=net, verbose=verbose, cache_dir=cache_dir, trust_repo=True
+        )
         self.eval()
 
         for param in self.parameters():

setup.py

@@ -16,6 +16,7 @@
 import re
 import sys
 import warnings
+from typing import Any, cast
 
 from packaging import version
 from setuptools import find_packages, setup
@@ -146,6 +147,6 @@ def get_cmds():
     cmdclass=get_cmds(),
     packages=find_packages(exclude=("docs", "examples", "tests")),
     zip_safe=False,
-    package_data={"monai": ["py.typed", *jit_extension_source]},  # type: ignore[arg-type]
+    package_data=cast(Any, {"monai": ["py.typed", *jit_extension_source]}),
     ext_modules=get_extensions(),
 )