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Running
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L40S
| """ Parts of the U-Net model """ | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| class DoubleConv(nn.Module): | |
| """(convolution => [BN] => ReLU) * 2""" | |
| def __init__(self, in_channels, out_channels, mid_channels=None): | |
| super().__init__() | |
| if not mid_channels: | |
| mid_channels = out_channels | |
| self.double_conv = nn.Sequential( | |
| nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1, bias=False), | |
| nn.BatchNorm2d(mid_channels), | |
| nn.ReLU(inplace=True), | |
| nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False), | |
| nn.BatchNorm2d(out_channels), | |
| nn.ReLU(inplace=True) | |
| ) | |
| def forward(self, x): | |
| return self.double_conv(x) | |
| class Down(nn.Module): | |
| """Downscaling with maxpool then double conv""" | |
| def __init__(self, in_channels, out_channels): | |
| super().__init__() | |
| self.maxpool_conv = nn.Sequential( | |
| nn.MaxPool2d(2), | |
| DoubleConv(in_channels, out_channels) | |
| ) | |
| def forward(self, x): | |
| return self.maxpool_conv(x) | |
| class Up(nn.Module): | |
| """Upscaling then double conv""" | |
| def __init__(self, in_channels, out_channels, bilinear=True): | |
| super().__init__() | |
| # if bilinear, use the normal convolutions to reduce the number of channels | |
| if bilinear: | |
| self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) | |
| self.conv = DoubleConv(in_channels, out_channels, in_channels // 2) | |
| else: | |
| self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2) | |
| self.conv = DoubleConv(in_channels, out_channels) | |
| def forward(self, x1, x2): | |
| x1 = self.up(x1) | |
| # input is CHW | |
| diffY = x2.size()[2] - x1.size()[2] | |
| diffX = x2.size()[3] - x1.size()[3] | |
| x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2, | |
| diffY // 2, diffY - diffY // 2]) | |
| # if you have padding issues, see | |
| # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a | |
| # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd | |
| x = torch.cat([x2, x1], dim=1) | |
| return self.conv(x) | |
| class OutConv(nn.Module): | |
| def __init__(self, in_channels, out_channels): | |
| super(OutConv, self).__init__() | |
| self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1) | |
| def forward(self, x): | |
| return self.conv(x) | |
| """ Full assembly of the parts to form the complete network """ | |
| class UNet(nn.Module): | |
| def __init__(self, n_channels, n_classes, bilinear=False): | |
| super(UNet, self).__init__() | |
| self.n_channels = n_channels | |
| self.n_classes = n_classes | |
| self.bilinear = bilinear | |
| self.inc = (DoubleConv(n_channels, 64)) | |
| self.down1 = (Down(64, 128)) | |
| self.down2 = (Down(128, 256)) | |
| self.down3 = (Down(256, 512)) | |
| factor = 2 if bilinear else 1 | |
| self.down4 = (Down(512, 1024 // factor)) | |
| self.up1 = (Up(1024, 512 // factor, bilinear)) | |
| self.up2 = (Up(512, 256 // factor, bilinear)) | |
| self.up3 = (Up(256, 128 // factor, bilinear)) | |
| self.up4 = (Up(128, 64, bilinear)) | |
| self.outc = (OutConv(64, n_classes)) | |
| def forward(self, x): | |
| x1 = self.inc(x) | |
| x2 = self.down1(x1) | |
| x3 = self.down2(x2) | |
| x4 = self.down3(x3) | |
| x5 = self.down4(x4) | |
| x = self.up1(x5, x4) | |
| x = self.up2(x, x3) | |
| x = self.up3(x, x2) | |
| x = self.up4(x, x1) | |
| logits = self.outc(x) | |
| return logits | |
| def use_checkpointing(self): | |
| self.inc = torch.utils.checkpoint(self.inc) | |
| self.down1 = torch.utils.checkpoint(self.down1) | |
| self.down2 = torch.utils.checkpoint(self.down2) | |
| self.down3 = torch.utils.checkpoint(self.down3) | |
| self.down4 = torch.utils.checkpoint(self.down4) | |
| self.up1 = torch.utils.checkpoint(self.up1) | |
| self.up2 = torch.utils.checkpoint(self.up2) | |
| self.up3 = torch.utils.checkpoint(self.up3) | |
| self.up4 = torch.utils.checkpoint(self.up4) | |
| self.outc = torch.utils.checkpoint(self.outc) |