import torch import torch.nn as nn from collections import OrderedDict class UNet1d(nn.Module): def __init__(self, in_channels=3, out_channels=1, init_features=32): super(UNet1d, self).__init__() features = init_features self.encoder1 = UNet1d._block(in_channels, features, name="enc1") self.pool1 = nn.MaxPool1d(kernel_size=2, stride=2) self.encoder2 = UNet1d._block(features, features * 2, name="enc2") self.pool2 = nn.MaxPool1d(kernel_size=2, stride=2) self.encoder3 = UNet1d._block(features * 2, features * 4, name="enc3") self.pool3 = nn.MaxPool1d(kernel_size=2, stride=2) self.encoder4 = UNet1d._block(features * 4, features * 8, name="enc4") self.pool4 = nn.MaxPool1d(kernel_size=2, stride=2) self.bottleneck = UNet1d._block(features * 8, features * 16, name="bottleneck") self.upconv4 = nn.ConvTranspose1d(features * 16, features * 8, kernel_size=2, stride=2) self.decoder4 = UNet1d._block((features * 8) * 2, features * 8, name="dec4") self.upconv3 = nn.ConvTranspose1d(features * 8, features * 4, kernel_size=2, stride=2) self.decoder3 = UNet1d._block((features * 4) * 2, features * 4, name="dec3") self.upconv2 = nn.ConvTranspose1d(features * 4, features * 2, kernel_size=2, stride=2) self.decoder2 = UNet1d._block((features * 2) * 2, features * 2, name="dec2") self.upconv1 = nn.ConvTranspose1d(features * 2, features, kernel_size=2, stride=2) self.decoder1 = UNet1d._block(features * 2, features, name="dec1") self.conv = nn.Conv1d(in_channels=features, out_channels=out_channels, kernel_size=1) def forward(self, x): enc1 = self.encoder1(x) enc2 = self.encoder2(self.pool1(enc1)) enc3 = self.encoder3(self.pool2(enc2)) enc4 = self.encoder4(self.pool3(enc3)) bottleneck = self.bottleneck(self.pool4(enc4)) dec4 = self.upconv4(bottleneck) dec4 = torch.cat((dec4, enc4), dim=1) dec4 = self.decoder4(dec4) dec3 = self.upconv3(dec4) dec3 = torch.cat((dec3, enc3), dim=1) dec3 = self.decoder3(dec3) dec2 = self.upconv2(dec3) dec2 = torch.cat((dec2, enc2), dim=1) dec2 = self.decoder2(dec2) dec1 = self.upconv1(dec2) dec1 = torch.cat((dec1, enc1), dim=1) dec1 = self.decoder1(dec1) out = self.conv(dec1) return out @staticmethod def _block(in_channels, features, name): return nn.Sequential( OrderedDict( [ ( name + "conv1", nn.Conv1d( in_channels=in_channels, out_channels=features, kernel_size=3, padding=1, bias=False, ), ), (name + "norm1", nn.BatchNorm1d(num_features=features)), (name + "tanh1", nn.Tanh()), ( name + "conv2", nn.Conv1d( in_channels=features, out_channels=features, kernel_size=3, padding=1, bias=False, ), ), (name + "norm2", nn.BatchNorm1d(num_features=features)), (name + "tanh2", nn.Tanh()), ] ) ) class UNet2d(nn.Module): def __init__(self, in_channels=3, out_channels=1, init_features=32): super(UNet2d, self).__init__() features = init_features self.encoder1 = UNet2d._block(in_channels, features, name="enc1") self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2) self.encoder2 = UNet2d._block(features, features * 2, name="enc2") self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2) self.encoder3 = UNet2d._block(features * 2, features * 4, name="enc3") self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2) self.encoder4 = UNet2d._block(features * 4, features * 8, name="enc4") self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2) self.bottleneck = UNet2d._block(features * 8, features * 16, name="bottleneck") self.upconv4 = nn.ConvTranspose2d(features * 16, features * 8, kernel_size=2, stride=2) self.decoder4 = UNet2d._block((features * 8) * 2, features * 8, name="dec4") self.upconv3 = nn.ConvTranspose2d(features * 8, features * 4, kernel_size=2, stride=2) self.decoder3 = UNet2d._block((features * 4) * 2, features * 4, name="dec3") self.upconv2 = nn.ConvTranspose2d(features * 4, features * 2, kernel_size=2, stride=2) self.decoder2 = UNet2d._block((features * 2) * 2, features * 2, name="dec2") self.upconv1 = nn.ConvTranspose2d(features * 2, features, kernel_size=2, stride=2) self.decoder1 = UNet2d._block(features * 2, features, name="dec1") self.conv = nn.Conv2d(in_channels=features, out_channels=out_channels, kernel_size=1) def forward(self, x): enc1 = self.encoder1(x) enc2 = self.encoder2(self.pool1(enc1)) enc3 = self.encoder3(self.pool2(enc2)) enc4 = self.encoder4(self.pool3(enc3)) bottleneck = self.bottleneck(self.pool4(enc4)) dec4 = self.upconv4(bottleneck) dec4 = torch.cat((dec4, enc4), dim=1) dec4 = self.decoder4(dec4) dec3 = self.upconv3(dec4) dec3 = torch.cat((dec3, enc3), dim=1) dec3 = self.decoder3(dec3) dec2 = self.upconv2(dec3) dec2 = torch.cat((dec2, enc2), dim=1) dec2 = self.decoder2(dec2) dec1 = self.upconv1(dec2) dec1 = torch.cat((dec1, enc1), dim=1) dec1 = self.decoder1(dec1) return self.conv(dec1) @staticmethod def _block(in_channels, features, name): return nn.Sequential( OrderedDict( [ ( name + "conv1", nn.Conv2d( in_channels=in_channels, out_channels=features, kernel_size=3, padding=1, bias=False, ), ), (name + "norm1", nn.BatchNorm2d(num_features=features)), (name + "tanh1", nn.Tanh()), ( name + "conv2", nn.Conv2d( in_channels=features, out_channels=features, kernel_size=3, padding=1, bias=False, ), ), (name + "norm2", nn.BatchNorm2d(num_features=features)), (name + "tanh2", nn.Tanh()), ] ) ) class UNet3d(nn.Module): def __init__(self, in_channels=3, out_channels=1, init_features=32): super(UNet3d, self).__init__() features = init_features self.encoder1 = UNet3d._block(in_channels, features, name="enc1") self.pool1 = nn.MaxPool3d(kernel_size=2, stride=2) self.encoder2 = UNet3d._block(features, features * 2, name="enc2") self.pool2 = nn.MaxPool3d(kernel_size=2, stride=2) self.encoder3 = UNet3d._block(features * 2, features * 4, name="enc3") self.pool3 = nn.MaxPool3d(kernel_size=2, stride=2) self.encoder4 = UNet3d._block(features * 4, features * 8, name="enc4") self.pool4 = nn.MaxPool3d(kernel_size=2, stride=2) self.bottleneck = UNet3d._block(features * 8, features * 16, name="bottleneck") self.upconv4 = nn.ConvTranspose3d(features * 16, features * 8, kernel_size=2, stride=2) self.decoder4 = UNet3d._block((features * 8) * 2, features * 8, name="dec4") self.upconv3 = nn.ConvTranspose3d(features * 8, features * 4, kernel_size=2, stride=2) self.decoder3 = UNet3d._block((features * 4) * 2, features * 4, name="dec3") self.upconv2 = nn.ConvTranspose3d(features * 4, features * 2, kernel_size=2, stride=2) self.decoder2 = UNet3d._block((features * 2) * 2, features * 2, name="dec2") self.upconv1 = nn.ConvTranspose3d(features * 2, features, kernel_size=2, stride=2) self.decoder1 = UNet3d._block(features * 2, features, name="dec1") self.conv = nn.Conv3d(in_channels=features, out_channels=out_channels, kernel_size=1) def forward(self, x): enc1 = self.encoder1(x) enc2 = self.encoder2(self.pool1(enc1)) enc3 = self.encoder3(self.pool2(enc2)) enc4 = self.encoder4(self.pool3(enc3)) bottleneck = self.bottleneck(self.pool4(enc4)) dec4 = self.upconv4(bottleneck) dec4 = torch.cat((dec4, enc4), dim=1) dec4 = self.decoder4(dec4) dec3 = self.upconv3(dec4) dec3 = torch.cat((dec3, enc3), dim=1) dec3 = self.decoder3(dec3) dec2 = self.upconv2(dec3) dec2 = torch.cat((dec2, enc2), dim=1) dec2 = self.decoder2(dec2) dec1 = self.upconv1(dec2) dec1 = torch.cat((dec1, enc1), dim=1) dec1 = self.decoder1(dec1) return self.conv(dec1) @staticmethod def _block(in_channels, features, name): return nn.Sequential( OrderedDict( [ ( name + "conv1", nn.Conv3d( in_channels=in_channels, out_channels=features, kernel_size=3, padding=1, bias=False, ), ), (name + "norm1", nn.BatchNorm3d(num_features=features)), (name + "tanh1", nn.Tanh()), ( name + "conv2", nn.Conv3d( in_channels=features, out_channels=features, kernel_size=3, padding=1, bias=False, ), ), (name + "norm2", nn.BatchNorm3d(num_features=features)), (name + "tanh2", nn.Tanh()), ] ) )