Adds patchgan discriminator

docs/source/networks.rst

@@ -751,6 +751,14 @@ Nets
 .. autoclass:: VQVAE
    :members:
 
+`PatchGANDiscriminator`
+~~~~~~~~~~~~~~~~~~~~~~~
+.. autoclass:: PatchDiscriminator
+   :members:
+
+.. autoclass:: MultiScalePatchDiscriminator
+   :members:
+
 Utilities
 ---------
 .. automodule:: monai.networks.utils

monai/networks/nets/__init__.py

@@ -54,6 +54,7 @@
 from .hovernet import Hovernet, HoVernet, HoVerNet, HoverNet
 from .milmodel import MILModel
 from .netadapter import NetAdapter
+from .patchgan_discriminator import MultiScalePatchDiscriminator, PatchDiscriminator
 from .quicknat import Quicknat
 from .regressor import Regressor
 from .regunet import GlobalNet, LocalNet, RegUNet

monai/networks/nets/patchgan_discriminator.py

@@ -0,0 +1,249 @@
+# 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.
+
+from __future__ import annotations
+
+from collections.abc import Sequence
+
+import torch
+import torch.nn as nn
+
+from monai.networks.blocks import Convolution
+from monai.networks.layers import Act
+
+
+class MultiScalePatchDiscriminator(nn.Sequential):
+    """
+    Multi-scale Patch-GAN discriminator based on Pix2PixHD:
+
+    High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
+    Ting-Chun Wang
+
+    The Multi-scale discriminator made up of several PatchGAN discriminators, that process the images
+    at different spatial scales.
+
+    Args:
+        num_d: number of discriminators
+        num_layers_d: number of Convolution layers (Conv + activation + normalisation + [dropout]) in the first
+            discriminator. Each subsequent discriminator has one additional layer, meaning the output size is halved.
+        spatial_dims: number of spatial dimensions (1D, 2D etc.)
+        channels: number of filters in the first convolutional layer (doubled for each subsequent layer)
+        in_channels: number of input channels
+        out_channels: number of output channels in each discriminator
+        kernel_size: kernel size of the convolution layers
+        activation: activation layer type
+        norm: normalisation type
+        bias: introduction of layer bias
+        dropout: probability of dropout applied, defaults to 0.
+        minimum_size_im: minimum spatial size of the input image. Introduced to make sure the architecture
+            requested isn't going to downsample the input image beyond value of 1.
+        last_conv_kernel_size: kernel size of the last convolutional layer.
+    """
+
+    def __init__(
+        self,
+        num_d: int,
+        num_layers_d: int,
+        spatial_dims: int,
+        channels: int,
+        in_channels: int,
+        out_channels: int = 1,
+        kernel_size: int = 4,
+        activation: str | tuple = (Act.LEAKYRELU, {"negative_slope": 0.2}),
+        norm: str | tuple = "BATCH",
+        bias: bool = False,
+        dropout: float | tuple = 0.0,
+        minimum_size_im: int = 256,
+        last_conv_kernel_size: int = 1,
+    ) -> None:
+        super().__init__()
+        self.num_d = num_d
+        self.num_layers_d = num_layers_d
+        self.num_channels = channels
+        self.padding = tuple([int((kernel_size - 1) / 2)] * spatial_dims)
+        for i_ in range(self.num_d):
+            num_layers_d_i = self.num_layers_d * (i_ + 1)
+            output_size = float(minimum_size_im) / (2**num_layers_d_i)
+            if output_size < 1:
+                raise AssertionError(
+                    f"Your image size is too small to take in up to {i_} discriminators with num_layers = {num_layers_d_i}."
+                    "Please reduce num_layers, reduce num_D or enter bigger images."
+                )
+            subnet_d = PatchDiscriminator(
+                spatial_dims=spatial_dims,
+                channels=self.num_channels,
+                in_channels=in_channels,
+                out_channels=out_channels,
+                num_layers_d=num_layers_d_i,
+                kernel_size=kernel_size,
+                activation=activation,
+                norm=norm,
+                bias=bias,
+                padding=self.padding,
+                dropout=dropout,
+                last_conv_kernel_size=last_conv_kernel_size,
+            )
+
+            self.add_module("discriminator_%d" % i_, subnet_d)
+
+    def forward(self, i: torch.Tensor) -> tuple[list[torch.Tensor], list[list[torch.Tensor]]]:
+        """
+        Args:
+            i: Input tensor
+
+        Returns:
+            list of outputs and another list of lists with the intermediate features
+            of each discriminator.
+        """
+
+        out: list[torch.Tensor] = []
+        intermediate_features: list[list[torch.Tensor]] = []
+        for disc in self.children():
+            out_d: list[torch.Tensor] = disc(i)
+            out.append(out_d[-1])
+            intermediate_features.append(out_d[:-1])
+
+        return out, intermediate_features
+
+
+class PatchDiscriminator(nn.Sequential):
+    """
+    Patch-GAN discriminator based on Pix2PixHD:
+    High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs.
+    Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Andrew Tao, Jan Kautz, Bryan Catanzaro
+
+    Args:
+        spatial_dims: number of spatial dimensions (1D, 2D etc.)
+        channels: number of filters in the first convolutional layer (doubled for each subsequent layer)
+        in_channels: number of input channels
+        out_channels: number of output channels
+        num_layers_d: number of Convolution layers (Conv + activation + normalisation + [dropout]) in the discriminator.
+        kernel_size: kernel size of the convolution layers
+        activation: activation layer type
+        norm: normalisation type
+        bias: introduction of layer bias
+        padding: padding to be applied to the convolutional layers
+        dropout: proportion of dropout applied, defaults to 0.
+        last_conv_kernel_size: kernel size of the last convolutional layer.
+    """
+
+    def __init__(
+        self,
+        spatial_dims: int,
+        channels: int,
+        in_channels: int,
+        out_channels: int = 1,
+        num_layers_d: int = 3,
+        kernel_size: int = 4,
+        activation: str | tuple = (Act.LEAKYRELU, {"negative_slope": 0.2}),
+        norm: str | tuple = "BATCH",
+        bias: bool = False,
+        padding: int | Sequence[int] = 1,
+        dropout: float | tuple = 0.0,
+        last_conv_kernel_size: int | None = None,
+    ) -> None:
+        super().__init__()
+        self.num_layers_d = num_layers_d
+        self.num_channels = channels
+        if last_conv_kernel_size is None:
+            last_conv_kernel_size = kernel_size
+
+        self.add_module(
+            "initial_conv",
+            Convolution(
+                spatial_dims=spatial_dims,
+                kernel_size=kernel_size,
+                in_channels=in_channels,
+                out_channels=channels,
+                act=activation,
+                bias=True,
+                norm=None,
+                dropout=dropout,
+                padding=padding,
+                strides=2,
+            ),
+        )
+
+        input_channels = channels
+        output_channels = channels * 2
+
+        # Initial Layer
+        for l_ in range(self.num_layers_d):
+            if l_ == self.num_layers_d - 1:
+                stride = 1
+            else:
+                stride = 2
+            layer = Convolution(
+                spatial_dims=spatial_dims,
+                kernel_size=kernel_size,
+                in_channels=input_channels,
+                out_channels=output_channels,
+                act=activation,
+                bias=bias,
+                norm=norm,
+                dropout=dropout,
+                padding=padding,
+                strides=stride,
+            )
+            self.add_module("%d" % l_, layer)
+            input_channels = output_channels
+            output_channels = output_channels * 2
+
+        # Final layer
+        self.add_module(
+            "final_conv",
+            Convolution(
+                spatial_dims=spatial_dims,
+                kernel_size=last_conv_kernel_size,
+                in_channels=input_channels,
+                out_channels=out_channels,
+                bias=True,
+                conv_only=True,
+                padding=int((last_conv_kernel_size - 1) / 2),
+                dropout=0.0,
+                strides=1,
+            ),
+        )
+
+        self.apply(self.initialise_weights)
+
+    def forward(self, x: torch.Tensor) -> list[torch.Tensor]:
+        """
+        Args:
+            x: input tensor
+
+        Returns:
+            list of intermediate features, with the last element being the output.
+        """
+        out = [x]
+        for submodel in self.children():
+            intermediate_output = submodel(out[-1])
+            out.append(intermediate_output)
+
+        return out[1:]
+
+    def initialise_weights(self, m: nn.Module) -> None:
+        """
+        Initialise weights of Convolution and BatchNorm layers.
+
+        Args:
+            m: instance of torch.nn.module (or of class inheriting torch.nn.module)
+        """
+        classname = m.__class__.__name__
+        if classname.find("Conv2d") != -1:
+            nn.init.normal_(m.weight.data, 0.0, 0.02)
+        elif classname.find("Conv3d") != -1:
+            nn.init.normal_(m.weight.data, 0.0, 0.02)
+        elif classname.find("Conv1d") != -1:
+            nn.init.normal_(m.weight.data, 0.0, 0.02)
+        elif classname.find("BatchNorm") != -1:
+            nn.init.normal_(m.weight.data, 1.0, 0.02)
+            nn.init.constant_(m.bias.data, 0)