update resnet tests and deployment files

Signed-off-by: vgrau98 <[email protected]>

docs/requirements.txt

@@ -39,3 +39,4 @@ opencv-python-headless
 onnx>=1.13.0
 onnxruntime; python_version <= '3.10'
 zarr
+huggingface_hub

requirements-dev.txt

@@ -56,3 +56,4 @@ filelock!=3.12.0  # https://github.com/microsoft/nni/issues/5523
 zarr
 lpips==0.1.4
 nvidia-ml-py
+huggingface_hub

setup.cfg

@@ -83,6 +83,7 @@ all =
     zarr
     lpips==0.1.4
     nvidia-ml-py
+    huggingface_hub
 nibabel =
     nibabel
 ninja =

tests/test_resnet.py

@@ -14,6 +14,7 @@
 import unittest
 from typing import TYPE_CHECKING
 import os
+import sys
 import re
 import copy
 
@@ -33,6 +34,8 @@
     has_torchvision = True
 else:
     torchvision, has_torchvision = optional_import("torchvision")
+
+has_hf_modules = ("huggingface_hub" in sys.modules and "huggingface_hub.utils._errors" in sys.modules)
 
 # from torchvision.models import ResNet50_Weights, resnet50
 
@@ -202,46 +205,47 @@ def test_resnet_pretrained(self, model, input_param, input_shape, expected_shape
         pretrained_net = model(**cp_input_param)
         assert (equal_state_dict(net.state_dict(), pretrained_net.state_dict()))
 
-        # True flag
-        cp_input_param["pretrained"] = True
-        resnet_depth = int(re.search(r"resnet(\d+)", model.__name__).group(1))
-
-        # Duplicate. see monai/networks/nets/resnet.py
-        def get_medicalnet_pretrained_resnet_args(resnet_depth: int) :
-            """
-            Return correct shortcut_type and bias_downsample for pretrained MedicalNet weights according to rensnet depth
-            """
-            # After testing
-            # False: 10, 50, 101, 152, 200
-            # Any: 18, 34
-            bias_downsample = -1 if resnet_depth in [18, 34] else 0  # 18, 10, 34
-            shortcut_type = "A" if resnet_depth in [18, 34] else "B"
-            return bias_downsample, shortcut_type
-
-        bias_downsample, shortcut_type = get_medicalnet_pretrained_resnet_args(resnet_depth)
-
-        # With orig. test cases
-        if (input_param.get("spatial_dims", 3) == 3 and
-            input_param.get("n_input_channels", 3)==1 and
-            input_param.get("feed_forward", True) is False and
-            input_param.get("shortcut_type", "B") == shortcut_type and
-            (input_param.get("bias_downsample", True) == bool(bias_downsample) if bias_downsample != -1 else True)
-            ):
-            model(**cp_input_param)
-        else:
-            with self.assertRaises(NotImplementedError):
+        if has_hf_modules:
+            # True flag
+            cp_input_param["pretrained"] = True
+            resnet_depth = int(re.search(r"resnet(\d+)", model.__name__).group(1))
+
+            # Duplicate. see monai/networks/nets/resnet.py
+            def get_medicalnet_pretrained_resnet_args(resnet_depth: int) :
+                """
+                Return correct shortcut_type and bias_downsample for pretrained MedicalNet weights according to rensnet depth
+                """
+                # After testing
+                # False: 10, 50, 101, 152, 200
+                # Any: 18, 34
+                bias_downsample = -1 if resnet_depth in [18, 34] else 0  # 18, 10, 34
+                shortcut_type = "A" if resnet_depth in [18, 34] else "B"
+                return bias_downsample, shortcut_type
+
+            bias_downsample, shortcut_type = get_medicalnet_pretrained_resnet_args(resnet_depth)
+
+            # With orig. test cases
+            if (input_param.get("spatial_dims", 3) == 3 and
+                input_param.get("n_input_channels", 3)==1 and
+                input_param.get("feed_forward", True) is False and
+                input_param.get("shortcut_type", "B") == shortcut_type and
+                (input_param.get("bias_downsample", True) == bool(bias_downsample) if bias_downsample != -1 else True)
+                ):
                 model(**cp_input_param)
-
-        # forcing MedicalNet pretrained download for 3D tests cases
-        cp_input_param["n_input_channels"] = 1
-        cp_input_param["feed_forward"] = False
-        cp_input_param["shortcut_type"] = shortcut_type
-        cp_input_param["bias_downsample"] = bool(bias_downsample) if bias_downsample!=-1 else True
-        if cp_input_param.get("spatial_dims", 3)==3:
-            pretrained_net = model(**cp_input_param).to(device)
-            medicalnet_state_dict = get_pretrained_resnet_medicalnet(resnet_depth, device = device)
-            medicalnet_state_dict = {key.replace("module.", ""): value for key, value in medicalnet_state_dict.items()}
-            assert(equal_state_dict(pretrained_net.state_dict(), medicalnet_state_dict))
+            else:
+                with self.assertRaises(NotImplementedError):
+                    model(**cp_input_param)
+
+            # forcing MedicalNet pretrained download for 3D tests cases
+            cp_input_param["n_input_channels"] = 1
+            cp_input_param["feed_forward"] = False
+            cp_input_param["shortcut_type"] = shortcut_type
+            cp_input_param["bias_downsample"] = bool(bias_downsample) if bias_downsample!=-1 else True
+            if cp_input_param.get("spatial_dims", 3)==3:
+                pretrained_net = model(**cp_input_param).to(device)
+                medicalnet_state_dict = get_pretrained_resnet_medicalnet(resnet_depth, device = device)
+                medicalnet_state_dict = {key.replace("module.", ""): value for key, value in medicalnet_state_dict.items()}
+                assert(equal_state_dict(pretrained_net.state_dict(), medicalnet_state_dict))
 
         # clean
         os.remove(tmp_ckpt_filename)