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December 2, 2020 00:16
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""" Full assembly of the parts to form the complete network """ | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
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 | |
""" Parts of the U-Net model """ | |
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), | |
nn.BatchNorm2d(mid_channels), | |
nn.ReLU(inplace=True), | |
nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1), | |
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='nearest') #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) |
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