Repository: Project-MONAI/MONAI

Issue:
Add pretrain option for DenseNet and SENet
So far, Class DenseNet and Senet do not have an option to load pretrained weights for specific arch. Users need to call a function, such as `densenet121` and `se_resnet50` to load pretrained weights. It may not be very convenient for some cases that only `Class` type are supported to call.
Let me submit a PR to improve it.


Discussion:

Resolving patch:
diff --git a/monai/networks/nets/densenet.py b/monai/networks/nets/densenet.py
--- a/monai/networks/nets/densenet.py
+++ b/monai/networks/nets/densenet.py
@@ -115,6 +115,11 @@ class DenseNet(nn.Module):
         bn_size: multiplicative factor for number of bottle neck layers.
             (i.e. bn_size * k features in the bottleneck layer)
         dropout_prob: dropout rate after each dense layer.
+        pretrained: whether to load ImageNet pretrained weights when `spatial_dims == 2`.
+            In order to load weights correctly, Please ensure that the `block_config`
+            is consistent with the corresponding arch.
+        pretrained_arch: the arch name for pretrained weights.
+        progress: If True, displays a progress bar of the download to stderr.
     """
 
     def __init__(
@@ -127,6 +132,9 @@ def __init__(
         block_config: Sequence[int] = (6, 12, 24, 16),
         bn_size: int = 4,
         dropout_prob: float = 0.0,
+        pretrained: bool = False,
+        pretrained_arch: str = "densenet121",
+        progress: bool = True,
     ) -> None:
 
         super(DenseNet, self).__init__()
@@ -190,43 +198,49 @@ def __init__(
             elif isinstance(m, nn.Linear):
                 nn.init.constant_(torch.as_tensor(m.bias), 0)
 
+        if pretrained:
+            self._load_state_dict(pretrained_arch, progress)
+
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         x = self.features(x)
         x = self.class_layers(x)
         return x
 
+    def _load_state_dict(self, arch, progress):
+        """
+        This function is used to load pretrained models.
+        Adapted from `PyTorch Hub 2D version
+        <https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py>`_
+        """
+        model_urls = {
+            "densenet121": "https://download.pytorch.org/models/densenet121-a639ec97.pth",
+            "densenet169": "https://download.pytorch.org/models/densenet169-b2777c0a.pth",
+            "densenet201": "https://download.pytorch.org/models/densenet201-c1103571.pth",
+        }
+        if arch in model_urls.keys():
+            model_url = model_urls[arch]
+        else:
+            raise ValueError(
+                "only 'densenet121', 'densenet169' and 'densenet201' are supported to load pretrained weights."
+            )
+        pattern = re.compile(
+            r"^(.*denselayer\d+)(\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$"
+        )
 
-model_urls = {
-    "densenet121": "https://download.pytorch.org/models/densenet121-a639ec97.pth",
-    "densenet169": "https://download.pytorch.org/models/densenet169-b2777c0a.pth",
-    "densenet201": "https://download.pytorch.org/models/densenet201-c1103571.pth",
-}
-
-
-def _load_state_dict(model, model_url, progress):
-    """
-    This function is used to load pretrained models.
-    Adapted from `PyTorch Hub 2D version
-    <https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py>`_
-    """
-    pattern = re.compile(
-        r"^(.*denselayer\d+)(\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$"
-    )
-
-    state_dict = load_state_dict_from_url(model_url, progress=progress)
-    for key in list(state_dict.keys()):
-        res = pattern.match(key)
-        if res:
-            new_key = res.group(1) + ".layers" + res.group(2) + res.group(3)
-            state_dict[new_key] = state_dict[key]
-            del state_dict[key]
+        state_dict = load_state_dict_from_url(model_url, progress=progress)
+        for key in list(state_dict.keys()):
+            res = pattern.match(key)
+            if res:
+                new_key = res.group(1) + ".layers" + res.group(2) + res.group(3)
+                state_dict[new_key] = state_dict[key]
+                del state_dict[key]
 
-    model_dict = model.state_dict()
-    state_dict = {
-        k: v for k, v in state_dict.items() if (k in model_dict) and (model_dict[k].shape == state_dict[k].shape)
-    }
-    model_dict.update(state_dict)
-    model.load_state_dict(model_dict)
+        model_dict = self.state_dict()
+        state_dict = {
+            k: v for k, v in state_dict.items() if (k in model_dict) and (model_dict[k].shape == state_dict[k].shape)
+        }
+        model_dict.update(state_dict)
+        self.load_state_dict(model_dict)
 
 
 def densenet121(pretrained: bool = False, progress: bool = True, **kwargs) -> DenseNet:
@@ -235,10 +249,15 @@ def densenet121(pretrained: bool = False, progress: bool = True, **kwargs) -> De
     from `PyTorch Hub 2D version
     <https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py>`_
     """
-    model = DenseNet(init_features=64, growth_rate=32, block_config=(6, 12, 24, 16), **kwargs)
-    if pretrained:
-        arch = "densenet121"
-        _load_state_dict(model, model_urls[arch], progress)
+    model = DenseNet(
+        init_features=64,
+        growth_rate=32,
+        block_config=(6, 12, 24, 16),
+        pretrained=pretrained,
+        pretrained_arch="densenet121",
+        progress=progress,
+        **kwargs,
+    )
     return model
 
 
@@ -248,10 +267,15 @@ def densenet169(pretrained: bool = False, progress: bool = True, **kwargs) -> De
     from `PyTorch Hub 2D version
     <https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py>`_
     """
-    model = DenseNet(init_features=64, growth_rate=32, block_config=(6, 12, 32, 32), **kwargs)
-    if pretrained:
-        arch = "densenet169"
-        _load_state_dict(model, model_urls[arch], progress)
+    model = DenseNet(
+        init_features=64,
+        growth_rate=32,
+        block_config=(6, 12, 32, 32),
+        pretrained=pretrained,
+        pretrained_arch="densenet169",
+        progress=progress,
+        **kwargs,
+    )
     return model
 
 
@@ -261,10 +285,15 @@ def densenet201(pretrained: bool = False, progress: bool = True, **kwargs) -> De
     from `PyTorch Hub 2D version
     <https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py>`_
     """
-    model = DenseNet(init_features=64, growth_rate=32, block_config=(6, 12, 48, 32), **kwargs)
-    if pretrained:
-        arch = "densenet201"
-        _load_state_dict(model, model_urls[arch], progress)
+    model = DenseNet(
+        init_features=64,
+        growth_rate=32,
+        block_config=(6, 12, 48, 32),
+        pretrained=pretrained,
+        pretrained_arch="densenet201",
+        progress=progress,
+        **kwargs,
+    )
     return model
 
 
diff --git a/monai/networks/nets/senet.py b/monai/networks/nets/senet.py
--- a/monai/networks/nets/senet.py
+++ b/monai/networks/nets/senet.py
@@ -66,7 +66,11 @@ class SENet(nn.Module):
             - For SE-ResNeXt models: False
         num_classes: number of outputs in `last_linear` layer.
             for all models: 1000
-
+        pretrained: whether to load ImageNet pretrained weights when `spatial_dims == 2`.
+            In order to load weights correctly, Please ensure that the `block_config`
+            is consistent with the corresponding arch.
+        pretrained_arch: the arch name for pretrained weights.
+        progress: If True, displays a progress bar of the download to stderr.
     """
 
     def __init__(
@@ -83,6 +87,9 @@ def __init__(
         downsample_kernel_size: int = 3,
         input_3x3: bool = True,
         num_classes: int = 1000,
+        pretrained: bool = False,
+        pretrained_arch: str = "se_resnet50",
+        progress: bool = True,
     ) -> None:
 
         super(SENet, self).__init__()
@@ -176,6 +183,64 @@ def __init__(
             elif isinstance(m, nn.Linear):
                 nn.init.constant_(torch.as_tensor(m.bias), 0)
 
+        if pretrained:
+            self._load_state_dict(pretrained_arch, progress)
+
+    def _load_state_dict(self, arch, progress):
+        """
+        This function is used to load pretrained models.
+        """
+        model_urls = {
+            "senet154": "http://data.lip6.fr/cadene/pretrainedmodels/senet154-c7b49a05.pth",
+            "se_resnet50": "http://data.lip6.fr/cadene/pretrainedmodels/se_resnet50-ce0d4300.pth",
+            "se_resnet101": "http://data.lip6.fr/cadene/pretrainedmodels/se_resnet101-7e38fcc6.pth",
+            "se_resnet152": "http://data.lip6.fr/cadene/pretrainedmodels/se_resnet152-d17c99b7.pth",
+            "se_resnext50_32x4d": "http://data.lip6.fr/cadene/pretrainedmodels/se_resnext50_32x4d-a260b3a4.pth",
+            "se_resnext101_32x4d": "http://data.lip6.fr/cadene/pretrainedmodels/se_resnext101_32x4d-3b2fe3d8.pth",
+        }
+        if arch in model_urls.keys():
+            model_url = model_urls[arch]
+        else:
+            raise ValueError(
+                "only 'senet154', 'se_resnet50', 'se_resnet101',  'se_resnet152', 'se_resnext50_32x4d', \
+                and se_resnext101_32x4d are supported to load pretrained weights."
+            )
+
+        pattern_conv = re.compile(r"^(layer[1-4]\.\d\.(?:conv)\d\.)(\w*)$")
+        pattern_bn = re.compile(r"^(layer[1-4]\.\d\.)(?:bn)(\d\.)(\w*)$")
+        pattern_se = re.compile(r"^(layer[1-4]\.\d\.)(?:se_module.fc1.)(\w*)$")
+        pattern_se2 = re.compile(r"^(layer[1-4]\.\d\.)(?:se_module.fc2.)(\w*)$")
+        pattern_down_conv = re.compile(r"^(layer[1-4]\.\d\.)(?:downsample.0.)(\w*)$")
+        pattern_down_bn = re.compile(r"^(layer[1-4]\.\d\.)(?:downsample.1.)(\w*)$")
+
+        state_dict = load_state_dict_from_url(model_url, progress=progress)
+        for key in list(state_dict.keys()):
+            new_key = None
+            if pattern_conv.match(key):
+                new_key = re.sub(pattern_conv, r"\1conv.\2", key)
+            elif pattern_bn.match(key):
+                new_key = re.sub(pattern_bn, r"\1conv\2adn.N.\3", key)
+            elif pattern_se.match(key):
+                state_dict[key] = state_dict[key].squeeze()
+                new_key = re.sub(pattern_se, r"\1se_layer.fc.0.\2", key)
+            elif pattern_se2.match(key):
+                state_dict[key] = state_dict[key].squeeze()
+                new_key = re.sub(pattern_se2, r"\1se_layer.fc.2.\2", key)
+            elif pattern_down_conv.match(key):
+                new_key = re.sub(pattern_down_conv, r"\1project.conv.\2", key)
+            elif pattern_down_bn.match(key):
+                new_key = re.sub(pattern_down_bn, r"\1project.adn.N.\2", key)
+            if new_key:
+                state_dict[new_key] = state_dict[key]
+                del state_dict[key]
+
+        model_dict = self.state_dict()
+        state_dict = {
+            k: v for k, v in state_dict.items() if (k in model_dict) and (model_dict[k].shape == state_dict[k].shape)
+        }
+        model_dict.update(state_dict)
+        self.load_state_dict(model_dict)
+
     def _make_layer(
         self,
         block: Type[Union[SEBottleneck, SEResNetBottleneck, SEResNeXtBottleneck]],
@@ -248,56 +313,6 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         return x
 
 
-model_urls = {
-    "senet154": "http://data.lip6.fr/cadene/pretrainedmodels/senet154-c7b49a05.pth",
-    "se_resnet50": "http://data.lip6.fr/cadene/pretrainedmodels/se_resnet50-ce0d4300.pth",
-    "se_resnet101": "http://data.lip6.fr/cadene/pretrainedmodels/se_resnet101-7e38fcc6.pth",
-    "se_resnet152": "http://data.lip6.fr/cadene/pretrainedmodels/se_resnet152-d17c99b7.pth",
-    "se_resnext50_32x4d": "http://data.lip6.fr/cadene/pretrainedmodels/se_resnext50_32x4d-a260b3a4.pth",
-    "se_resnext101_32x4d": "http://data.lip6.fr/cadene/pretrainedmodels/se_resnext101_32x4d-3b2fe3d8.pth",
-}
-
-
-def _load_state_dict(model, model_url, progress):
-    """
-    This function is used to load pretrained models.
-    """
-    pattern_conv = re.compile(r"^(layer[1-4]\.\d\.(?:conv)\d\.)(\w*)$")
-    pattern_bn = re.compile(r"^(layer[1-4]\.\d\.)(?:bn)(\d\.)(\w*)$")
-    pattern_se = re.compile(r"^(layer[1-4]\.\d\.)(?:se_module.fc1.)(\w*)$")
-    pattern_se2 = re.compile(r"^(layer[1-4]\.\d\.)(?:se_module.fc2.)(\w*)$")
-    pattern_down_conv = re.compile(r"^(layer[1-4]\.\d\.)(?:downsample.0.)(\w*)$")
-    pattern_down_bn = re.compile(r"^(layer[1-4]\.\d\.)(?:downsample.1.)(\w*)$")
-
-    state_dict = load_state_dict_from_url(model_url, progress=progress)
-    for key in list(state_dict.keys()):
-        new_key = None
-        if pattern_conv.match(key):
-            new_key = re.sub(pattern_conv, r"\1conv.\2", key)
-        elif pattern_bn.match(key):
-            new_key = re.sub(pattern_bn, r"\1conv\2adn.N.\3", key)
-        elif pattern_se.match(key):
-            state_dict[key] = state_dict[key].squeeze()
-            new_key = re.sub(pattern_se, r"\1se_layer.fc.0.\2", key)
-        elif pattern_se2.match(key):
-            state_dict[key] = state_dict[key].squeeze()
-            new_key = re.sub(pattern_se2, r"\1se_layer.fc.2.\2", key)
-        elif pattern_down_conv.match(key):
-            new_key = re.sub(pattern_down_conv, r"\1project.conv.\2", key)
-        elif pattern_down_bn.match(key):
-            new_key = re.sub(pattern_down_bn, r"\1project.adn.N.\2", key)
-        if new_key:
-            state_dict[new_key] = state_dict[key]
-            del state_dict[key]
-
-    model_dict = model.state_dict()
-    state_dict = {
-        k: v for k, v in state_dict.items() if (k in model_dict) and (model_dict[k].shape == state_dict[k].shape)
-    }
-    model_dict.update(state_dict)
-    model.load_state_dict(model_dict)
-
-
 def senet154(
     spatial_dims: int,
     in_channels: int,
@@ -320,10 +335,10 @@ def senet154(
         dropout_prob=0.2,
         dropout_dim=1,
         num_classes=num_classes,
+        pretrained=pretrained,
+        pretrained_arch="senet154",
+        progress=progress,
     )
-    if pretrained:
-        arch = "senet154"
-        _load_state_dict(model, model_urls[arch], progress)
     return model
 
 
@@ -347,10 +362,10 @@ def se_resnet50(
         input_3x3=False,
         downsample_kernel_size=1,
         num_classes=num_classes,
+        pretrained=pretrained,
+        pretrained_arch="se_resnet50",
+        progress=progress,
     )
-    if pretrained:
-        arch = "se_resnet50"
-        _load_state_dict(model, model_urls[arch], progress)
     return model
 
 
@@ -375,10 +390,10 @@ def se_resnet101(
         input_3x3=False,
         downsample_kernel_size=1,
         num_classes=num_classes,
+        pretrained=pretrained,
+        pretrained_arch="se_resnet101",
+        progress=progress,
     )
-    if pretrained:
-        arch = "se_resnet101"
-        _load_state_dict(model, model_urls[arch], progress)
     return model
 
 
@@ -403,10 +418,10 @@ def se_resnet152(
         input_3x3=False,
         downsample_kernel_size=1,
         num_classes=num_classes,
+        pretrained=pretrained,
+        pretrained_arch="se_resnet152",
+        progress=progress,
     )
-    if pretrained:
-        arch = "se_resnet152"
-        _load_state_dict(model, model_urls[arch], progress)
     return model
 
 
@@ -430,10 +445,10 @@ def se_resnext50_32x4d(
         input_3x3=False,
         downsample_kernel_size=1,
         num_classes=num_classes,
+        pretrained=pretrained,
+        pretrained_arch="se_resnext50_32x4d",
+        progress=progress,
     )
-    if pretrained:
-        arch = "se_resnext50_32x4d"
-        _load_state_dict(model, model_urls[arch], progress)
     return model
 
 
@@ -457,8 +472,8 @@ def se_resnext101_32x4d(
         input_3x3=False,
         downsample_kernel_size=1,
         num_classes=num_classes,
+        pretrained=pretrained,
+        pretrained_arch="se_resnext101_32x4d",
+        progress=progress,
     )
-    if pretrained:
-        arch = "se_resnext101_32x4d"
-        _load_state_dict(model, model_urls[arch], progress)
     return model


Test patch:
diff --git a/tests/test_densenet.py b/tests/test_densenet.py
--- a/tests/test_densenet.py
+++ b/tests/test_densenet.py
@@ -17,7 +17,7 @@
 from parameterized import parameterized
 
 from monai.networks import eval_mode
-from monai.networks.nets import densenet121, densenet169, densenet201, densenet264
+from monai.networks.nets import DenseNet, densenet121, densenet169, densenet201, densenet264
 from monai.utils import optional_import
 from tests.utils import skip_if_quick, test_pretrained_networks, test_script_save
 
@@ -78,6 +78,17 @@
     (1, 3, 32, 32),
 ]
 
+TEST_PRETRAINED_2D_CASE_4 = [
+    {
+        "pretrained": True,
+        "pretrained_arch": "densenet264",
+        "progress": False,
+        "spatial_dims": 2,
+        "in_channels": 3,
+        "out_channels": 1,
+    },
+]
+
 
 class TestPretrainedDENSENET(unittest.TestCase):
     @parameterized.expand([TEST_PRETRAINED_2D_CASE_1, TEST_PRETRAINED_2D_CASE_2])
@@ -100,6 +111,11 @@ def test_pretrain_consistency(self, model, input_param, input_shape):
             expected_result = torchvision_net.features.forward(example)
         self.assertTrue(torch.all(result == expected_result))
 
+    @parameterized.expand([TEST_PRETRAINED_2D_CASE_4])
+    def test_ill_pretrain(self, input_param):
+        with self.assertRaisesRegex(ValueError, ""):
+            net = DenseNet(**input_param)
+
 
 class TestDENSENET(unittest.TestCase):
     @parameterized.expand(TEST_CASES)
diff --git a/tests/test_senet.py b/tests/test_senet.py
--- a/tests/test_senet.py
+++ b/tests/test_senet.py
@@ -17,7 +17,9 @@
 from parameterized import parameterized
 
 from monai.networks import eval_mode
+from monai.networks.blocks.squeeze_and_excitation import SEBottleneck
 from monai.networks.nets import (
+    SENet,
     se_resnet50,
     se_resnet101,
     se_resnet152,
@@ -46,7 +48,20 @@
 TEST_CASE_5 = [se_resnext50_32x4d, NET_ARGS]
 TEST_CASE_6 = [se_resnext101_32x4d, NET_ARGS]
 
-TEST_CASE_PRETRAINED = [se_resnet50, {"spatial_dims": 2, "in_channels": 3, "num_classes": 2, "pretrained": True}]
+TEST_CASE_PRETRAINED_1 = [se_resnet50, {"spatial_dims": 2, "in_channels": 3, "num_classes": 2, "pretrained": True}]
+TEST_CASE_PRETRAINED_2 = [
+    {
+        "spatial_dims": 2,
+        "in_channels": 3,
+        "block": SEBottleneck,
+        "layers": [3, 8, 36, 3],
+        "groups": 64,
+        "reduction": 16,
+        "num_classes": 2,
+        "pretrained": True,
+        "pretrained_arch": "resnet50",
+    }
+]
 
 
 class TestSENET(unittest.TestCase):
@@ -67,7 +82,7 @@ def test_script(self, net, net_args):
 
 
 class TestPretrainedSENET(unittest.TestCase):
-    @parameterized.expand([TEST_CASE_PRETRAINED])
+    @parameterized.expand([TEST_CASE_PRETRAINED_1])
     def test_senet_shape(self, model, input_param):
         net = test_pretrained_networks(model, input_param, device)
         input_data = torch.randn(3, 3, 64, 64).to(device)
@@ -77,7 +92,7 @@ def test_senet_shape(self, model, input_param):
             result = net(input_data)
             self.assertEqual(result.shape, expected_shape)
 
-    @parameterized.expand([TEST_CASE_PRETRAINED])
+    @parameterized.expand([TEST_CASE_PRETRAINED_1])
     @skipUnless(has_cadene_pretrain, "Requires `pretrainedmodels` package.")
     def test_pretrain_consistency(self, model, input_param):
         input_data = torch.randn(1, 3, 64, 64).to(device)
@@ -92,6 +107,11 @@ def test_pretrain_consistency(self, model, input_param):
         # a conv layer with kernel size equals to 1. It may bring a little difference.
         self.assertTrue(torch.allclose(result, expected_result, rtol=1e-5, atol=1e-5))
 
+    @parameterized.expand([TEST_CASE_PRETRAINED_2])
+    def test_ill_pretrain(self, input_param):
+        with self.assertRaisesRegex(ValueError, ""):
+            net = SENet(**input_param)
+
 
 if __name__ == "__main__":
     unittest.main()