import torch import torch.nn as nn import torchvision.transforms.functional as TF class DoubleConv(nn.Module): def __init__(self, in_channels, out_channels): super(DoubleConv, self).__init__() self.conv = nn.Sequential( nn.Conv2d(in_channels, out_channels, 3, 1, 1, bias=False), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True), nn.Conv2d(out_channels, out_channels, 3, 1, 1, bias=False), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True), ) def forward(self, x): return self.conv(x) class UNET(nn.Module): def __init__( self, in_channels=3, out_channels=1, features=[64, 128, 256, 512], ): super(UNET, self).__init__() self.ups = nn.ModuleList() self.downs = nn.ModuleList() self.pool = nn.MaxPool2d(kernel_size=2, stride=2) # Down part of UNET for feature in features: self.downs.append(DoubleConv(in_channels, feature)) in_channels = feature # Up part of UNET for feature in reversed(features): self.ups.append( nn.ConvTranspose2d( feature*2, feature, kernel_size=2, stride=2, ) ) self.ups.append(DoubleConv(feature*2, feature)) self.bottleneck = DoubleConv(features[-1], features[-1]*2) self.final_conv = nn.Conv2d(features[0], out_channels, kernel_size=1) def forward(self, x): skip_connections = [] for down in self.downs: x = down(x) skip_connections.append(x) x = self.pool(x) x = self.bottleneck(x) skip_connections = skip_connections[::-1] for idx in range(0, len(self.ups), 2): x = self.ups[idx](x) skip_connection = skip_connections[idx//2] if x.shape != skip_connection.shape: x = TF.resize(x, size=skip_connection.shape[2:]) concat_skip = torch.cat((skip_connection, x), dim=1) x = self.ups[idx+1](concat_skip) return self.final_conv(x)