Spaces:
Sleeping
Sleeping
| from collections import OrderedDict | |
| import torch | |
| import torch.nn as nn | |
| class UNet(nn.Module): | |
| def __init__(self, in_channels=3, out_channels=1, init_features=32): | |
| super(UNet, self).__init__() | |
| features = init_features | |
| self.encoder1 = UNet._block(in_channels, features, name="enc1") | |
| self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2) | |
| self.encoder2 = UNet._block(features, features * 2, name="enc2") | |
| self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2) | |
| self.encoder3 = UNet._block(features * 2, features * 4, name="enc3") | |
| self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2) | |
| self.encoder4 = UNet._block(features * 4, features * 8, name="enc4") | |
| self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2) | |
| self.bottleneck = UNet._block(features * 8, features * 16, name="bottleneck") | |
| self.upconv4 = nn.ConvTranspose2d( | |
| features * 16, features * 8, kernel_size=2, stride=2 | |
| ) | |
| self.decoder4 = UNet._block((features * 8) * 2, features * 8, name="dec4") | |
| self.upconv3 = nn.ConvTranspose2d( | |
| features * 8, features * 4, kernel_size=2, stride=2 | |
| ) | |
| self.decoder3 = UNet._block((features * 4) * 2, features * 4, name="dec3") | |
| self.upconv2 = nn.ConvTranspose2d( | |
| features * 4, features * 2, kernel_size=2, stride=2 | |
| ) | |
| self.decoder2 = UNet._block((features * 2) * 2, features * 2, name="dec2") | |
| self.upconv1 = nn.ConvTranspose2d( | |
| features * 2, features, kernel_size=2, stride=2 | |
| ) | |
| self.decoder1 = UNet._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 torch.sigmoid(self.conv(dec1)) | |
| 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 + "relu1", nn.ReLU(inplace=True)), | |
| ( | |
| 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 + "relu2", nn.ReLU(inplace=True)), | |
| ] | |
| ) | |
| ) |