Source code for locpix.img_processing.models.unet

"""UNET model

Adapted from https://github.com/milesial/Pytorch-UNet"""

import torch
import torch.nn as nn
import torch.nn.functional as F


[docs] class DoubleConv(nn.Module): """(convolution => [BN] => ReLU) * 2"""
[docs] 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), )
[docs] def forward(self, x): return self.double_conv(x)
[docs] class Down(nn.Module): """Downscaling with maxpool then double conv"""
[docs] def __init__(self, in_channels, out_channels): super().__init__() self.maxpool_conv = nn.Sequential( nn.MaxPool2d(2), DoubleConv(in_channels, out_channels) )
[docs] def forward(self, x): return self.maxpool_conv(x)
[docs] class Up(nn.Module): """Upscaling then double conv"""
[docs] 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)
[docs] 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]) x = torch.cat([x2, x1], dim=1) return self.conv(x)
[docs] class OutConv(nn.Module):
[docs] def __init__(self, in_channels, out_channels): super(OutConv, self).__init__() self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
[docs] def forward(self, x): return self.conv(x)
[docs] class two_d_UNet(nn.Module):
[docs] def __init__(self, n_channels, n_classes, bilinear=False): super(two_d_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)
[docs] 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)