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| import torch | |
| import torch.nn as nn | |
| class DownSampling(nn.Module): | |
| def __init__(self, in_channels, out_channels, max_pool): | |
| """ | |
| DownSampling block in the U-Net architecture. | |
| Args: | |
| in_channels (int): Number of input channels. | |
| out_channels (int): Number of output channels. | |
| max_pool (bool): Whether to use max pooling. | |
| """ | |
| super(DownSampling, self).__init__() | |
| self.max_pool = max_pool | |
| self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1) | |
| self.conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1) | |
| self.batchnorm2d = nn.BatchNorm2d(out_channels) | |
| self.relu = nn.ReLU() | |
| self.maxpool2d = nn.MaxPool2d(kernel_size=2, stride=2) | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| x = self.conv2(x) | |
| x = self.relu(self.batchnorm2d(x)) | |
| skip_connection = x | |
| if self.max_pool: | |
| next_layer = self.maxpool2d(x) | |
| else: | |
| return x | |
| return next_layer, skip_connection | |
| class UpSampling(nn.Module): | |
| def __init__(self, in_channels, out_channels): | |
| """ | |
| UpSampling block in the U-Net architecture. | |
| Args: | |
| in_channels (int): Number of input channels. | |
| out_channels (int): Number of output channels. | |
| """ | |
| super(UpSampling, self).__init__() | |
| self.up = nn.ConvTranspose2d(in_channels, out_channels=out_channels, kernel_size=2, stride=2) | |
| self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1) | |
| self.relu = nn.ReLU() | |
| self.conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1) | |
| self.batchnorm = nn.BatchNorm2d(out_channels) | |
| def forward(self, x, prev_skip): | |
| x = self.up(x) | |
| x = torch.cat((x, prev_skip), dim=1) | |
| x = self.conv1(x) | |
| x = self.conv2(x) | |
| next_layer = self.relu(self.batchnorm(x)) | |
| return next_layer | |
| class UNet(nn.Module): | |
| """ | |
| U-Net architecture. | |
| Args: | |
| in_channels (int): Number of input channels. | |
| out_channels (int): Number of output channels. | |
| features (list): List of feature sizes for downsampling and upsampling. | |
| """ | |
| def __init__(self, in_channels, out_channels, features): | |
| super(UNet, self).__init__() | |
| self.ups = nn.ModuleList() | |
| self.downs = nn.ModuleList() | |
| for feature in features: | |
| self.downs.append(DownSampling(in_channels, feature, True)) | |
| in_channels = feature | |
| for feature in reversed(features): | |
| self.ups.append(UpSampling(2 * feature, feature)) | |
| self.bottleneck = DownSampling(features[-1], 2 * features[-1], False) | |
| self.final_conv = nn.Conv2d(features[0], out_channels, kernel_size=1) | |
| def forward(self, x): | |
| skip_connections = [] | |
| for down in self.downs: | |
| x, skip_connection = down(x) | |
| skip_connections.append(skip_connection) | |
| skip_connections = skip_connections[::-1] | |
| x = self.bottleneck(x) | |
| for i, up in enumerate(self.ups): | |
| x = up(x, skip_connections[i]) | |
| return self.final_conv(x) | |
| if __name__ == "__main__": | |
| #Example Usage | |
| device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| features = [64, 128, 256, 512] | |
| model = UNet(1, 1, features=features).to(device) | |
| print(model(torch.rand(1, 1, 512, 512)).shape) |