GenericBlocks.py

import torch.nn as nn

class DoubleConv(nn.Module):
    def __init__(self, c_in, c_out):
        super(DoubleConv, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels=c_in, out_channels=c_out, kernel_size=3),
            nn.ReLU(inplace=True),
            nn.Conv2d(in_channels=c_out, out_channels=c_out, kernel_size=3),
            nn.ReLU(inplace=True),
        )

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


class DoubleConvSame(nn.Module):
    def __init__(self, c_in, c_out):
        super(DoubleConvSame, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels=c_in, out_channels=c_out, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(in_channels=c_out, out_channels=c_out, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
        )

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


class DoubleConvSame3D(nn.Module):
    def __init__(self, c_in, c_out):
        super(DoubleConvSame3D, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv3d(
                in_channels=c_in, out_channels=c_out, kernel_size=3, stride=1, padding=1
            ),
            nn.ReLU(inplace=True),
            nn.Conv3d(
                in_channels=c_out,
                out_channels=c_out,
                kernel_size=3,
                stride=1,
                padding=1,
            ),
            nn.ReLU(inplace=True),
        )

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


class Encoder(nn.Module):
    def __init__(self, in_channels):
        super(Encoder, self).__init__()

        self.conv = DoubleConvSame(c_in=in_channels, c_out=in_channels * 2)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)

    def forward(self, x):
        c = self.conv(x)
        p = self.pool(c)

        return c, p


class Encoder3D(nn.Module):
    def __init__(self, in_channels):
        super(Encoder3D, self).__init__()

        self.conv = DoubleConvSame3D(c_in=in_channels, c_out=in_channels * 2)
        self.pool = nn.MaxPool3d(kernel_size=2, stride=2)

    def forward(self, x):
        c = self.conv(x)
        p = self.pool(c)

        return c, p