lightning/pl_examples/full_examples/semantic_segmentation/models/unet/parts.py

import torch
import torch.nn as nn
import torch.nn.functional as F


class DoubleConv(nn.Module):
    '''
    Double Convolution and BN and ReLU
    (3x3 conv -> BN -> ReLU) ** 2
    '''

    def __init__(self, in_ch, out_ch):
        super().__init__()
        self.net = nn.Sequential(
            nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_ch),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_ch),
            nn.ReLU(inplace=True)
        )

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


class Down(nn.Module):
    '''
    Combination of MaxPool2d and DoubleConv in series
    '''

    def __init__(self, in_ch, out_ch):
        super().__init__()
        self.net = nn.Sequential(
            nn.MaxPool2d(kernel_size=2, stride=2),
            DoubleConv(in_ch, out_ch)
        )

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


class Up(nn.Module):
    '''
    Upsampling (by either bilinear interpolation or transpose convolutions)
    followed by concatenation of feature map from contracting path,
    followed by double 3x3 convolution.
    '''

    def __init__(self, in_ch, out_ch, bilinear=False):
        super().__init__()
        self.upsample = None
        if bilinear:
            self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
        else:
            self.upsample = nn.ConvTranspose2d(in_ch, in_ch // 2, kernel_size=2, stride=2)

        self.conv = DoubleConv(in_ch, out_ch)

    def forward(self, x1, x2):
        x1 = self.upsample(x1)

        # Pad x1 to the size of x2
        diff_h = x2.shape[2] - x1.shape[2]
        diff_w = x2.shape[3] - x1.shape[3]

        x1 = F.pad(x1, [diff_w // 2, diff_w - diff_w // 2, diff_h // 2, diff_h - diff_h // 2])

        # Concatenate along the channels axis
        x = torch.cat([x2, x1], dim=1)
        return self.conv(x)