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Fix load pretrain weight issue in ResNet #7924

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merged 7 commits into from
Jul 18, 2024
Merged

Fix load pretrain weight issue in ResNet #7924

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a230402
fix #7923
KumoLiu Jul 17, 2024
17ae709
fix error message
KumoLiu Jul 17, 2024
1d5e37e
address comments
KumoLiu Jul 17, 2024
2104192
Merge remote-tracking branch 'origin/dev' into resnet
KumoLiu Jul 17, 2024
10fa744
Update monai/networks/nets/resnet.py
KumoLiu Jul 17, 2024
ef20467
Merge branch 'dev' into resnet
KumoLiu Jul 18, 2024
7bea321
Merge branch 'dev' into resnet
KumoLiu Jul 18, 2024
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7 changes: 3 additions & 4 deletions monai/networks/nets/resnet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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:
Expand Down Expand Up @@ -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

Expand Down
8 changes: 3 additions & 5 deletions tests/test_resnet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)
Expand All @@ -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:
Expand All @@ -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)
Expand Down
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