Add ResNet backbones for FlexibleUNet

docs/source/networks.rst

@@ -491,6 +491,11 @@ Nets
 .. autoclass:: ResNet
   :members:
 
+`ResNetFeatures`
+~~~~~~~~~~~~~~~~
+.. autoclass:: ResNetFeatures
+  :members:
+
 `SENet`
 ~~~~~~~
 .. autoclass:: SENet

monai/networks/nets/__init__.py

@@ -59,6 +59,8 @@
     ResNet,
     ResNetBlock,
     ResNetBottleneck,
+    ResNetEncoder,
+    ResNetFeatures,
     get_medicalnet_pretrained_resnet_args,
     get_pretrained_resnet_medicalnet,
     resnet10,

monai/networks/nets/flexible_unet.py

@@ -24,6 +24,7 @@
 from monai.networks.layers.utils import get_act_layer
 from monai.networks.nets import EfficientNetEncoder
 from monai.networks.nets.basic_unet import UpCat
+from monai.networks.nets.resnet import ResNetEncoder
 from monai.utils import InterpolateMode, optional_import
 
 __all__ = ["FlexibleUNet", "FlexUNet", "FLEXUNET_BACKBONE", "FlexUNetEncoderRegister"]
@@ -78,6 +79,7 @@ def register_class(self, name: type[Any] | str):
 
 FLEXUNET_BACKBONE = FlexUNetEncoderRegister()
 FLEXUNET_BACKBONE.register_class(EfficientNetEncoder)
+FLEXUNET_BACKBONE.register_class(ResNetEncoder)
 
 
 class UNetDecoder(nn.Module):
@@ -238,7 +240,7 @@ def __init__(
     ) -> None:
         """
         A flexible implement of UNet, in which the backbone/encoder can be replaced with
-        any efficient network. Currently the input must have a 2 or 3 spatial dimension
+        any efficient or residual network. Currently the input must have a 2 or 3 spatial dimension
         and the spatial size of each dimension must be a multiple of 32 if is_pad parameter
         is False.
         Please notice each output of backbone must be 2x downsample in spatial dimension
@@ -248,10 +250,11 @@ def __init__(
         Args:
             in_channels: number of input channels.
             out_channels: number of output channels.
-            backbone: name of backbones to initialize, only support efficientnet right now,
-                can be from [efficientnet-b0,..., efficientnet-b8, efficientnet-l2].
-            pretrained: whether to initialize pretrained ImageNet weights, only available
-                for spatial_dims=2 and batch norm is used, default to False.
+            backbone: name of backbones to initialize, only support efficientnet and resnet right now,
+                can be from [efficientnet-b0, ..., efficientnet-b8, efficientnet-l2, resnet10, ..., resnet200].
+            pretrained: whether to initialize pretrained weights. ImageNet weights are available for efficient networks
+                if spatial_dims=2 and batch norm is used. MedicalNet weights are available for residual networks
+                if spatial_dims=3 and in_channels=1. Default to False.
             decoder_channels: number of output channels for all feature maps in decoder.
                 `len(decoder_channels)` should equal to `len(encoder_channels) - 1`,default
                 to (256, 128, 64, 32, 16).

monai/networks/nets/resnet.py

@@ -21,6 +21,7 @@
 import torch
 import torch.nn as nn
 
+from monai.networks.blocks.encoder import BaseEncoder
 from monai.networks.layers.factories import Conv, Norm, Pool
 from monai.networks.layers.utils import get_pool_layer
 from monai.utils import ensure_tuple_rep
@@ -45,6 +46,19 @@
     "resnet200",
 ]
 
+
+resnet_params = {
+    # model_name: (block, layers, shortcut_type, bias_downsample, datasets23)
+    "resnet10": ("basic", [1, 1, 1, 1], "B", False, True),
+    "resnet18": ("basic", [2, 2, 2, 2], "A", True, True),
+    "resnet34": ("basic", [3, 4, 6, 3], "A", True, True),
+    "resnet50": ("bottleneck", [3, 4, 6, 3], "B", False, True),
+    "resnet101": ("bottleneck", [3, 4, 23, 3], "B", False, False),
+    "resnet152": ("bottleneck", [3, 8, 36, 3], "B", False, False),
+    "resnet200": ("bottleneck", [3, 24, 36, 3], "B", False, False),
+}
+
+
 logger = logging.getLogger(__name__)
 
 
@@ -335,6 +349,120 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         return x
 
 
+class ResNetFeatures(ResNet):
+
+    def __init__(self, model_name: str, pretrained: bool = True, spatial_dims: int = 3, in_channels: int = 1) -> None:
+        """Initialize resnet18 to resnet200 models as a backbone, the backbone can be used as an encoder for
+        segmentation and objection models.
+
+        Compared with the class `ResNet`, the only different place is the forward function.
+
+        Args:
+            model_name: name of model to initialize, can be from [resnet10, ..., resnet200].
+            pretrained: whether to initialize pretrained MedicalNet weights,
+                only available for spatial_dims=3 and in_channels=1.
+            spatial_dims: number of spatial dimensions of the input image.
+            in_channels: number of input channels for first convolutional layer.
+        """
+        if model_name not in resnet_params:
+            model_name_string = ", ".join(resnet_params.keys())
+            raise ValueError(f"invalid model_name {model_name} found, must be one of {model_name_string} ")
+
+        block, layers, shortcut_type, bias_downsample, datasets23 = resnet_params[model_name]
+
+        super().__init__(
+            block=block,
+            layers=layers,
+            block_inplanes=get_inplanes(),
+            spatial_dims=spatial_dims,
+            n_input_channels=in_channels,
+            conv1_t_stride=2,
+            shortcut_type=shortcut_type,
+            feed_forward=False,
+            bias_downsample=bias_downsample,
+        )
+        if pretrained:
+            if spatial_dims == 3 and in_channels == 1:
+                _load_state_dict(self, model_name, datasets23=datasets23)
+            else:
+                raise ValueError("Pretrained resnet models are only available for in_channels=1 and spatial_dims=3.")
+
+    def forward(self, inputs: torch.Tensor):
+        """
+        Args:
+            inputs: input should have spatially N dimensions
+            ``(Batch, in_channels, dim_0[, dim_1, ..., dim_N])``, N is defined by `dimensions`.
+
+        Returns:
+            a list of torch Tensors.
+        """
+        x = self.conv1(inputs)
+        x = self.bn1(x)
+        x = self.relu(x)
+
+        features = []
+        features.append(x)
+
+        if not self.no_max_pool:
+            x = self.maxpool(x)
+
+        x = self.layer1(x)
+        features.append(x)
+
+        x = self.layer2(x)
+        features.append(x)
+
+        x = self.layer3(x)
+        features.append(x)
+
+        x = self.layer4(x)
+        features.append(x)
+
+        return features
+
+
+class ResNetEncoder(ResNetFeatures, BaseEncoder):
+    """Wrap the original resnet to an encoder for flexible-unet."""
+
+    backbone_names = ["resnet10", "resnet18", "resnet34", "resnet50", "resnet101", "resnet152", "resnet200"]
+
+    @classmethod
+    def get_encoder_parameters(cls) -> list[dict]:
+        """Get the initialization parameter for resnet backbones."""
+        parameter_list = []
+        for backbone_name in cls.backbone_names:
+            parameter_list.append(
+                {"model_name": backbone_name, "pretrained": True, "spatial_dims": 3, "in_channels": 1}
+            )
+        return parameter_list
+
+    @classmethod
+    def num_channels_per_output(cls) -> list[tuple[int, ...]]:
+        """Get number of resnet backbone output feature maps channel."""
+        return [
+            (64, 64, 128, 256, 512),
+            (64, 64, 128, 256, 512),
+            (64, 64, 128, 256, 512),
+            (64, 256, 512, 1024, 2048),
+            (64, 256, 512, 1024, 2048),
+            (64, 256, 512, 1024, 2048),
+            (64, 256, 512, 1024, 2048),
+        ]
+
+    @classmethod
+    def num_outputs(cls) -> list[int]:
+        """Get number of resnet backbone output feature maps.
+
+        Since every backbone contains the same 5 output feature maps, the number list should be `[5] * 7`.
+        """
+        return [5] * 7
+
+    @classmethod
+    def get_encoder_names(cls) -> list[str]:
+        """Get names of resnet backbones."""
+        return cls.backbone_names
+
+
 def _resnet(
     arch: str,
     block: type[ResNetBlock | ResNetBottleneck],
@@ -477,7 +605,7 @@ def resnet200(pretrained: bool = False, progress: bool = True, **kwargs: Any) ->
 
 def get_pretrained_resnet_medicalnet(resnet_depth: int, device: str = "cpu", datasets23: bool = True):
     """
-    Donwlad resnet pretrained weights from https://huggingface.co/TencentMedicalNet
+    Download resnet pretrained weights from https://huggingface.co/TencentMedicalNet
 
     Args:
         resnet_depth: depth of the pretrained model. Supported values are 10, 18, 34, 50, 101, 152 and 200
@@ -533,11 +661,24 @@ def get_pretrained_resnet_medicalnet(resnet_depth: int, device: str = "cpu", dat
 def get_medicalnet_pretrained_resnet_args(resnet_depth: int):
     """
     Return correct shortcut_type and bias_downsample
-    for pretrained MedicalNet weights according to resnet depth
+    for pretrained MedicalNet weights according to resnet 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
+
+
+def _load_state_dict(model: nn.Module, model_name: str, datasets23: bool = True) -> None:
+    search_res = re.search(r"resnet(\d+)", model_name)
+    if search_res:
+        resnet_depth = int(search_res.group(1))
+        datasets23 = model_name.endswith("_23datasets")
+    else:
+        raise ValueError("model_name argument should contain resnet depth. Example: resnet18 or resnet18_23datasets.")
+
+    model_state_dict = get_pretrained_resnet_medicalnet(resnet_depth, device="cpu", datasets23=datasets23)
+    model_state_dict = {key.replace("module.", ""): value for key, value in model_state_dict.items()}
+    model.load_state_dict(model_state_dict)