Code formating for Blake and Flake8 checks to pass + integration of MedNext variants (S, B, M, L) + integration of remarks from @johnzilke (Project-MONAI#8004 (review)) for renaming class arguments - removal of self defined LayerNorm - linked residual connection for encoder and decoder

Signed-off-by: Robin CREMESE <[email protected]>

Author: rcremese

monai/networks/blocks/__init__.py

@@ -26,6 +26,7 @@
 from .fcn import FCN, GCN, MCFCN, Refine
 from .feature_pyramid_network import ExtraFPNBlock, FeaturePyramidNetwork, LastLevelMaxPool, LastLevelP6P7
 from .localnet_block import LocalNetDownSampleBlock, LocalNetFeatureExtractorBlock, LocalNetUpSampleBlock
+from .mednext_block import MedNeXtBlock, MedNeXtDownBlock, MedNeXtOutBlock, MedNeXtUpBlock
 from .mlp import MLPBlock
 from .patchembedding import PatchEmbed, PatchEmbeddingBlock
 from .regunet_block import RegistrationDownSampleBlock, RegistrationExtractionBlock, RegistrationResidualConvBlock

monai/networks/blocks/mednext_block.py

@@ -0,0 +1,238 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Portions of this code are derived from the original repository at:
+# https://github.com/MIC-DKFZ/MedNeXt
+# and are used under the terms of the Apache License, Version 2.0.
+
+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+
+all = ["MedNeXtBlock", "MedNeXtDownBlock", "MedNeXtUpBlock", "MedNeXtOutBlock"]
+
+
+class MedNeXtBlock(nn.Module):
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        expansion_ratio: int = 4,
+        kernel_size: int = 7,
+        use_residual_connection: int = True,
+        norm_type: str = "group",
+        dim="3d",
+        grn=False,
+    ):
+
+        super().__init__()
+
+        self.do_res = use_residual_connection
+
+        assert dim in ["2d", "3d"]
+        self.dim = dim
+        if self.dim == "2d":
+            conv = nn.Conv2d
+            normalized_shape = [in_channels, kernel_size, kernel_size]
+            grn_parameter_shape = (1, 1)
+        elif self.dim == "3d":
+            conv = nn.Conv3d
+            normalized_shape = [in_channels, kernel_size, kernel_size, kernel_size]
+            grn_parameter_shape = (1, 1, 1)
+        else:
+            raise ValueError("dim must be either '2d' or '3d'")
+        # First convolution layer with DepthWise Convolutions
+        self.conv1 = conv(
+            in_channels=in_channels,
+            out_channels=in_channels,
+            kernel_size=kernel_size,
+            stride=1,
+            padding=kernel_size // 2,
+            groups=in_channels,
+        )
+
+        # Normalization Layer. GroupNorm is used by default.
+        if norm_type == "group":
+            self.norm = nn.GroupNorm(num_groups=in_channels, num_channels=in_channels)
+        elif norm_type == "layer":
+            self.norm = nn.LayerNorm(normalized_shape=normalized_shape)
+        # Second convolution (Expansion) layer with Conv3D 1x1x1
+        self.conv2 = conv(
+            in_channels=in_channels, out_channels=expansion_ratio * in_channels, kernel_size=1, stride=1, padding=0
+        )
+
+        # GeLU activations
+        self.act = nn.GELU()
+
+        # Third convolution (Compression) layer with Conv3D 1x1x1
+        self.conv3 = conv(
+            in_channels=expansion_ratio * in_channels, out_channels=out_channels, kernel_size=1, stride=1, padding=0
+        )
+
+        self.grn = grn
+        if self.grn:
+            grn_parameter_shape = (1, expansion_ratio * in_channels) + grn_parameter_shape
+            self.grn_beta = nn.Parameter(torch.zeros(grn_parameter_shape), requires_grad=True)
+            self.grn_gamma = nn.Parameter(torch.zeros(grn_parameter_shape), requires_grad=True)
+
+    def forward(self, x):
+
+        x1 = x
+        x1 = self.conv1(x1)
+        x1 = self.act(self.conv2(self.norm(x1)))
+
+        if self.grn:
+            # gamma, beta: learnable affine transform parameters
+            # X: input of shape (N,C,H,W,D)
+            if self.dim == "2d":
+                gx = torch.norm(x1, p=2, dim=(-2, -1), keepdim=True)
+            else:
+                gx = torch.norm(x1, p=2, dim=(-3, -2, -1), keepdim=True)
+            nx = gx / (gx.mean(dim=1, keepdim=True) + 1e-6)
+            x1 = self.grn_gamma * (x1 * nx) + self.grn_beta + x1
+        x1 = self.conv3(x1)
+        if self.do_res:
+            x1 = x + x1
+        return x1
+
+
+class MedNeXtDownBlock(MedNeXtBlock):
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        expansion_ratio: int = 4,
+        kernel_size: int = 7,
+        use_residual_connection: bool = False,
+        norm_type: str = "group",
+        dim: str = "3d",
+        grn: bool = False,
+    ):
+
+        super().__init__(
+            in_channels,
+            out_channels,
+            expansion_ratio,
+            kernel_size,
+            use_residual_connection=False,
+            norm_type=norm_type,
+            dim=dim,
+            grn=grn,
+        )
+
+        if dim == "2d":
+            conv = nn.Conv2d
+        else:
+            conv = nn.Conv3d
+        self.resample_do_res = use_residual_connection
+        if use_residual_connection:
+            self.res_conv = conv(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=2)
+
+        self.conv1 = conv(
+            in_channels=in_channels,
+            out_channels=in_channels,
+            kernel_size=kernel_size,
+            stride=2,
+            padding=kernel_size // 2,
+            groups=in_channels,
+        )
+
+    def forward(self, x):
+
+        x1 = super().forward(x)
+
+        if self.resample_do_res:
+            res = self.res_conv(x)
+            x1 = x1 + res
+
+        return x1
+
+
+class MedNeXtUpBlock(MedNeXtBlock):
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        expansion_ratio: int = 4,
+        kernel_size: int = 7,
+        use_residual_connection: bool = False,
+        norm_type: str = "group",
+        dim: str = "3d",
+        grn: bool = False,
+    ):
+        super().__init__(
+            in_channels,
+            out_channels,
+            expansion_ratio,
+            kernel_size,
+            use_residual_connection=False,
+            norm_type=norm_type,
+            dim=dim,
+            grn=grn,
+        )
+
+        self.resample_do_res = use_residual_connection
+
+        self.dim = dim
+        if dim == "2d":
+            conv = nn.ConvTranspose2d
+        else:
+            conv = nn.ConvTranspose3d
+        if use_residual_connection:
+            self.res_conv = conv(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=2)
+
+        self.conv1 = conv(
+            in_channels=in_channels,
+            out_channels=in_channels,
+            kernel_size=kernel_size,
+            stride=2,
+            padding=kernel_size // 2,
+            groups=in_channels,
+        )
+
+    def forward(self, x):
+
+        x1 = super().forward(x)
+        # Asymmetry but necessary to match shape
+
+        if self.dim == "2d":
+            x1 = torch.nn.functional.pad(x1, (1, 0, 1, 0))
+        else:
+            x1 = torch.nn.functional.pad(x1, (1, 0, 1, 0, 1, 0))
+
+        if self.resample_do_res:
+            res = self.res_conv(x)
+            if self.dim == "2d":
+                res = torch.nn.functional.pad(res, (1, 0, 1, 0))
+            else:
+                res = torch.nn.functional.pad(res, (1, 0, 1, 0, 1, 0))
+            x1 = x1 + res
+
+        return x1
+
+
+class MedNeXtOutBlock(nn.Module):
+
+    def __init__(self, in_channels, n_classes, dim):
+        super().__init__()
+
+        if dim == "2d":
+            conv = nn.ConvTranspose2d
+        else:
+            conv = nn.ConvTranspose3d
+        self.conv_out = conv(in_channels, n_classes, kernel_size=1)
+
+    def forward(self, x):
+        return self.conv_out(x)

monai/networks/nets/__init__.py

@@ -53,6 +53,25 @@
 from .generator import Generator
 from .highresnet import HighResBlock, HighResNet
 from .hovernet import Hovernet, HoVernet, HoVerNet, HoverNet
+from .mednext import (
+    MedNeXt,
+    MedNext,
+    MedNextB,
+    MedNeXtB,
+    MedNextBase,
+    MedNextL,
+    MedNeXtL,
+    MedNeXtLarge,
+    MedNextLarge,
+    MedNextM,
+    MedNeXtM,
+    MedNeXtMedium,
+    MedNextMedium,
+    MedNextS,
+    MedNeXtS,
+    MedNeXtSmall,
+    MedNextSmall,
+)
 from .milmodel import MILModel
 from .netadapter import NetAdapter
 from .patchgan_discriminator import MultiScalePatchDiscriminator, PatchDiscriminator