source_code/SegMamba/monai/networks/blocks/dynunet_block.py

# 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 numpy as np
import torch
import torch.nn as nn

from monai.networks.blocks.convolutions import Convolution
from monai.networks.layers.factories import Act, Norm
from monai.networks.layers.utils import get_act_layer, get_norm_layer


class UnetResBlock(nn.Module):
    """
    A skip-connection based module that can be used for DynUNet, based on:
    `Automated Design of Deep Learning Methods for Biomedical Image Segmentation <https://arxiv.org/abs/1904.08128>`_.
    `nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation <https://arxiv.org/abs/1809.10486>`_.

    Args:
        spatial_dims: number of spatial dimensions.
        in_channels: number of input channels.
        out_channels: number of output channels.
        kernel_size: convolution kernel size.
        stride: convolution stride.
        norm_name: feature normalization type and arguments.
        act_name: activation layer type and arguments.
        dropout: dropout probability.

    """

    def __init__(
        self,
        spatial_dims: int,
        in_channels: int,
        out_channels: int,
        kernel_size: Sequence[int] | int,
        stride: Sequence[int] | int,
        norm_name: tuple | str,
        act_name: tuple | str = ("leakyrelu", {"inplace": True, "negative_slope": 0.01}),
        dropout: tuple | str | float | None = None,
    ):
        super().__init__()
        self.conv1 = get_conv_layer(
            spatial_dims,
            in_channels,
            out_channels,
            kernel_size=kernel_size,
            stride=stride,
            dropout=dropout,
            act=None,
            norm=None,
            conv_only=False,
        )
        self.conv2 = get_conv_layer(
            spatial_dims,
            out_channels,
            out_channels,
            kernel_size=kernel_size,
            stride=1,
            dropout=dropout,
            act=None,
            norm=None,
            conv_only=False,
        )
        self.lrelu = get_act_layer(name=act_name)
        self.norm1 = get_norm_layer(name=norm_name, spatial_dims=spatial_dims, channels=out_channels)
        self.norm2 = get_norm_layer(name=norm_name, spatial_dims=spatial_dims, channels=out_channels)
        self.downsample = in_channels != out_channels
        stride_np = np.atleast_1d(stride)
        if not np.all(stride_np == 1):
            self.downsample = True
        if self.downsample:
            self.conv3 = get_conv_layer(
                spatial_dims,
                in_channels,
                out_channels,
                kernel_size=1,
                stride=stride,
                dropout=dropout,
                act=None,
                norm=None,
                conv_only=False,
            )
            self.norm3 = get_norm_layer(name=norm_name, spatial_dims=spatial_dims, channels=out_channels)

    def forward(self, inp):
        residual = inp
        out = self.conv1(inp)
        out = self.norm1(out)
        out = self.lrelu(out)
        out = self.conv2(out)
        out = self.norm2(out)
        if hasattr(self, "conv3"):
            residual = self.conv3(residual)
        if hasattr(self, "norm3"):
            residual = self.norm3(residual)
        out += residual
        out = self.lrelu(out)
        return out


class UnetBasicBlock(nn.Module):
    """
    A CNN module that can be used for DynUNet, based on:
    `Automated Design of Deep Learning Methods for Biomedical Image Segmentation <https://arxiv.org/abs/1904.08128>`_.
    `nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation <https://arxiv.org/abs/1809.10486>`_.

    Args:
        spatial_dims: number of spatial dimensions.
        in_channels: number of input channels.
        out_channels: number of output channels.
        kernel_size: convolution kernel size.
        stride: convolution stride.
        norm_name: feature normalization type and arguments.
        act_name: activation layer type and arguments.
        dropout: dropout probability.

    """

    def __init__(
        self,
        spatial_dims: int,
        in_channels: int,
        out_channels: int,
        kernel_size: Sequence[int] | int,
        stride: Sequence[int] | int,
        norm_name: tuple | str,
        act_name: tuple | str = ("leakyrelu", {"inplace": True, "negative_slope": 0.01}),
        dropout: tuple | str | float | None = None,
    ):
        super().__init__()
        self.conv1 = get_conv_layer(
            spatial_dims,
            in_channels,
            out_channels,
            kernel_size=kernel_size,
            stride=stride,
            dropout=dropout,
            act=None,
            norm=None,
            conv_only=False,
        )
        self.conv2 = get_conv_layer(
            spatial_dims,
            out_channels,
            out_channels,
            kernel_size=kernel_size,
            stride=1,
            dropout=dropout,
            act=None,
            norm=None,
            conv_only=False,
        )
        self.lrelu = get_act_layer(name=act_name)
        self.norm1 = get_norm_layer(name=norm_name, spatial_dims=spatial_dims, channels=out_channels)
        self.norm2 = get_norm_layer(name=norm_name, spatial_dims=spatial_dims, channels=out_channels)

    def forward(self, inp):
        out = self.conv1(inp)
        out = self.norm1(out)
        out = self.lrelu(out)
        out = self.conv2(out)
        out = self.norm2(out)
        out = self.lrelu(out)
        return out


class UnetUpBlock(nn.Module):
    """
    An upsampling module that can be used for DynUNet, based on:
    `Automated Design of Deep Learning Methods for Biomedical Image Segmentation <https://arxiv.org/abs/1904.08128>`_.
    `nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation <https://arxiv.org/abs/1809.10486>`_.

    Args:
        spatial_dims: number of spatial dimensions.
        in_channels: number of input channels.
        out_channels: number of output channels.
        kernel_size: convolution kernel size.
        stride: convolution stride.
        upsample_kernel_size: convolution kernel size for transposed convolution layers.
        norm_name: feature normalization type and arguments.
        act_name: activation layer type and arguments.
        dropout: dropout probability.
        trans_bias: transposed convolution bias.

    """

    def __init__(
        self,
        spatial_dims: int,
        in_channels: int,
        out_channels: int,
        kernel_size: Sequence[int] | int,
        stride: Sequence[int] | int,
        upsample_kernel_size: Sequence[int] | int,
        norm_name: tuple | str,
        act_name: tuple | str = ("leakyrelu", {"inplace": True, "negative_slope": 0.01}),
        dropout: tuple | str | float | None = None,
        trans_bias: bool = False,
    ):
        super().__init__()
        upsample_stride = upsample_kernel_size
        self.transp_conv = get_conv_layer(
            spatial_dims,
            in_channels,
            out_channels,
            kernel_size=upsample_kernel_size,
            stride=upsample_stride,
            dropout=dropout,
            bias=trans_bias,
            act=None,
            norm=None,
            conv_only=False,
            is_transposed=True,
        )
        self.conv_block = UnetBasicBlock(
            spatial_dims,
            out_channels + out_channels,
            out_channels,
            kernel_size=kernel_size,
            stride=1,
            dropout=dropout,
            norm_name=norm_name,
            act_name=act_name,
        )

    def forward(self, inp, skip):
        # number of channels for skip should equals to out_channels
        out = self.transp_conv(inp)
        out = torch.cat((out, skip), dim=1)
        out = self.conv_block(out)
        return out


class UnetOutBlock(nn.Module):

    def __init__(
        self, spatial_dims: int, in_channels: int, out_channels: int, dropout: tuple | str | float | None = None
    ):
        super().__init__()
        self.conv = get_conv_layer(
            spatial_dims,
            in_channels,
            out_channels,
            kernel_size=1,
            stride=1,
            dropout=dropout,
            bias=True,
            act=None,
            norm=None,
            conv_only=False,
        )

    def forward(self, inp):
        return self.conv(inp)


def get_conv_layer(
    spatial_dims: int,
    in_channels: int,
    out_channels: int,
    kernel_size: Sequence[int] | int = 3,
    stride: Sequence[int] | int = 1,
    act: tuple | str | None = Act.PRELU,
    norm: tuple | str | None = Norm.INSTANCE,
    dropout: tuple | str | float | None = None,
    bias: bool = False,
    conv_only: bool = True,
    is_transposed: bool = False,
):
    padding = get_padding(kernel_size, stride)
    output_padding = None
    if is_transposed:
        output_padding = get_output_padding(kernel_size, stride, padding)
    return Convolution(
        spatial_dims,
        in_channels,
        out_channels,
        strides=stride,
        kernel_size=kernel_size,
        act=act,
        norm=norm,
        dropout=dropout,
        bias=bias,
        conv_only=conv_only,
        is_transposed=is_transposed,
        padding=padding,
        output_padding=output_padding,
    )


def get_padding(kernel_size: Sequence[int] | int, stride: Sequence[int] | int) -> tuple[int, ...] | int:
    kernel_size_np = np.atleast_1d(kernel_size)
    stride_np = np.atleast_1d(stride)
    padding_np = (kernel_size_np - stride_np + 1) / 2
    if np.min(padding_np) < 0:
        raise AssertionError("padding value should not be negative, please change the kernel size and/or stride.")
    padding = tuple(int(p) for p in padding_np)

    return padding if len(padding) > 1 else padding[0]


def get_output_padding(
    kernel_size: Sequence[int] | int, stride: Sequence[int] | int, padding: Sequence[int] | int
) -> tuple[int, ...] | int:
    kernel_size_np = np.atleast_1d(kernel_size)
    stride_np = np.atleast_1d(stride)
    padding_np = np.atleast_1d(padding)

    out_padding_np = 2 * padding_np + stride_np - kernel_size_np
    if np.min(out_padding_np) < 0:
        raise AssertionError("out_padding value should not be negative, please change the kernel size and/or stride.")
    out_padding = tuple(int(p) for p in out_padding_np)

    return out_padding if len(out_padding) > 1 else out_padding[0]