Fix load pretrain weight issue in ResNet (#7924)

Fixes #7923


### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [ ] Breaking change (fix or new feature that would cause existing
functionality to change).
- [ ] New tests added to cover the changes.
- [ ] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [ ] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [ ] In-line docstrings updated.
- [ ] Documentation updated, tested `make html` command in the `docs/`
folder.

---------

Signed-off-by: YunLiu <[email protected]>
Co-authored-by: Eric Kerfoot <[email protected]>

Author: KumoLiu

monai/networks/nets/resnet.py

@@ -510,16 +510,15 @@ def _resnet(
                     # Check model bias_downsample and shortcut_type
                     bias_downsample, shortcut_type = get_medicalnet_pretrained_resnet_args(resnet_depth)
                     if shortcut_type == kwargs.get("shortcut_type", "B") and (
-                        bool(bias_downsample) == kwargs.get("bias_downsample", False) if bias_downsample != -1 else True
+                        bias_downsample == kwargs.get("bias_downsample", True)
                     ):
                         # Download the MedicalNet pretrained model
                         model_state_dict = get_pretrained_resnet_medicalnet(
                             resnet_depth, device=device, datasets23=True
                         )
                     else:
                         raise NotImplementedError(
-                            f"Please set shortcut_type to {shortcut_type} and bias_downsample to"
-                            f"{bool(bias_downsample) if bias_downsample!=-1 else 'True or False'}"
+                            f"Please set shortcut_type to {shortcut_type} and bias_downsample to {bias_downsample} "
                             f"when using pretrained MedicalNet resnet{resnet_depth}"
                         )
                 else:
@@ -681,7 +680,7 @@ def get_medicalnet_pretrained_resnet_args(resnet_depth: int):
     # 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
+    bias_downsample = resnet_depth in (18, 34)
     shortcut_type = "A" if resnet_depth in [18, 34] else "B"
     return bias_downsample, shortcut_type
 

tests/test_resnet.py

@@ -266,7 +266,7 @@ def test_resnet_shape(self, model, input_param, input_shape, expected_shape):
     @parameterized.expand(PRETRAINED_TEST_CASES)
     @skip_if_quick
     @skip_if_no_cuda
-    def test_resnet_pretrained(self, model, input_param, input_shape, expected_shape):
+    def test_resnet_pretrained(self, model, input_param, _input_shape, _expected_shape):
         net = model(**input_param).to(device)
         # Save ckpt
         torch.save(net.state_dict(), self.tmp_ckpt_filename)
@@ -290,9 +290,7 @@ def test_resnet_pretrained(self, model, input_param, input_shape, expected_shape
                 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
-                )
+                and (input_param.get("bias_downsample", True) == bias_downsample)
             ):
                 model(**cp_input_param)
             else:
@@ -303,7 +301,7 @@ def test_resnet_pretrained(self, model, input_param, input_shape, expected_shape
             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
+            cp_input_param["bias_downsample"] = bias_downsample
             if cp_input_param.get("spatial_dims", 3) == 3:
                 with skip_if_downloading_fails():
                     pretrained_net = model(**cp_input_param).to(device)