| 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()), |
| ] |
| ) |
| ) |
