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unet.py
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unet.py
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# Based on https://github.com/milesial/Pytorch-UNet
import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
"""Very simple CNN"""
def __init__(self):
super(Net, self).__init__()
self.classifier = nn.Sequential(
nn.Conv2d(3, 6, 3, padding=1),
nn.BatchNorm2d(6),
nn.ReLU(),
nn.Conv2d(6, 3, 3, padding=1),
nn.BatchNorm2d(3),
nn.ReLU(),
nn.Conv2d(3, 1, 3, padding=1)
)
nn.init.xavier_uniform_(self.classifier[0].weight)
nn.init.xavier_uniform_(self.classifier[3].weight)
nn.init.xavier_uniform_(self.classifier[6].weight)
def forward(self, x):
x = self.classifier(x)
return x
class UNet(nn.Module):
"""U-Net implementation"""
def __init__(self, n_channels, n_classes, depth=1, bilinear=True):
super(UNet, self).__init__()
self.n_channels = n_channels
self.n_classes = n_classes
self.bilinear = bilinear
self.depth = depth
# Testing for different depths. Could have been
# more elegant but this unet is discarded eventually.
if self.depth == 0:
# Patched unet
# print('Not scaled Unet created')
self.inc = DoubleConv(n_channels, 64)
self.down1 = Down(64, 128)
self.down2 = Down(128, 256)
self.down3 = Down(256, 512)
self.down4 = Down(512, 512)
self.up1 = UpNoScale(512, 128, bilinear)
self.up2 = UpNoScale(128, 64, bilinear)
self.outc = OutConv(64, n_classes)
elif self.depth == 1:
# Original UNet
self.inc = DoubleConv(n_channels, 64)
self.down1 = Down(64, 128)
self.down2 = Down(128, 256)
self.down3 = Down(256, 512)
self.down4 = Down(512, 512)
self.up1 = Up(1024, 256, bilinear)
self.up2 = Up(512, 128, bilinear)
self.up3 = Up(256, 64, bilinear)
self.up4 = Up(128, 64, bilinear)
self.outc = OutConv(64, n_classes)
elif self.depth == 2:
# Original + 32 channel in beginning
self.inc = DoubleConv(n_channels, 32)
self.down1 = Down(32, 64)
self.down2 = Down(64, 128)
self.down3 = Down(128, 256)
self.down4 = Down(256, 512)
self.down5 = Down(512, 512)
self.up1 = Up(1024, 256, bilinear)
self.up2 = Up(512, 128, bilinear)
self.up3 = Up(256, 64, bilinear)
self.up4 = Up(128, 32, bilinear)
self.up5 = Up(64, 32, bilinear)
self.outc = OutConv(32, n_classes)
elif self.depth == 3:
# Original + 32 channel in beginning + 2056 channel at the end
self.inc = DoubleConv(n_channels, 32)
self.down1 = Down(32, 64)
self.down2 = Down(64, 128)
self.down3 = Down(128, 256)
self.down4 = Down(256, 512)
self.down5 = Down(512, 1024)
self.down6 = Down(1024, 1024)
self.up1 = Up(2048, 512, bilinear)
self.up2 = Up(1024, 256, bilinear)
self.up3 = Up(512, 128, bilinear)
self.up4 = Up(256, 64, bilinear)
self.up5 = Up(128, 32, bilinear)
self.up6 = Up(64, 32, bilinear)
self.outc = OutConv(32, n_classes)
elif self.depth == 4:
self.inc = DoubleConv(n_channels, 32)
self.down1 = Down(32, 64)
self.down2 = Down(64, 128)
self.down3 = Down(128, 256)
self.down4 = Down(256, 512)
self.down5 = Down(512, 1024)
self.down6 = Down(1024, 2048)
self.down7 = Down(2048, 2048)
self.up1 = Up(4096, 1024, bilinear)
self.up2 = Up(2048, 512, bilinear)
self.up3 = Up(1024, 256, bilinear)
self.up4 = Up(512, 128, bilinear)
self.up5 = Up(256, 64, bilinear)
self.up6 = Up(128, 32, bilinear)
self.up7 = Up(64, 32, bilinear)
self.outc = OutConv(32, n_classes)
else:
raise Exception("Unknown Depth")
def forward(self, x):
if self.depth == 0:
# Original UNet
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x5 = self.down4(x4)
# print(x5.size())
x = self.up1(x5)
x = self.up2(x)
logits = self.outc(x)
# print(logits.size())
elif self.depth == 1:
# Original UNet
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x5 = self.down4(x4)
# print(x5.size())
x = self.up1(x5, x4)
x = self.up2(x, x3)
x = self.up3(x, x2)
x = self.up4(x, x1)
logits = self.outc(x)
elif self.depth == 2:
# Original + 32 channel in beginning
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x5 = self.down4(x4)
x6 = self.down5(x5)
# print(x6.size())
x = self.up1(x6, x5)
x = self.up2(x, x4)
x = self.up3(x, x3)
x = self.up4(x, x2)
x = self.up5(x, x1)
logits = self.outc(x)
elif self.depth == 3:
# Original + 32 channel in beginning + 2056 channel at the end
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x5 = self.down4(x4)
x6 = self.down5(x5)
x7 = self.down6(x6)
x = self.up1(x7, x6)
x = self.up2(x, x5)
x = self.up3(x, x4)
x = self.up4(x, x3)
x = self.up5(x, x2)
x = self.up6(x, x1)
logits = self.outc(x)
elif self.depth == 4:
# Original + 32 channel in beginning + 2056 channel at the end
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x5 = self.down4(x4)
x6 = self.down5(x5)
x7 = self.down6(x6)
x8 = self.down7(x7)
x = self.up1(x8, x7)
x = self.up2(x, x6)
x = self.up3(x, x5)
x = self.up4(x, x4)
x = self.up5(x, x3)
x = self.up6(x, x2)
x = self.up7(x, x1)
logits = self.outc(x)
else:
raise Exception("Unknown depth")
return logits
class DoubleConv(nn.Module):
"""(convolution => [BN] => ReLU) * 2"""
def __init__(self, in_channels, out_channels):
super().__init__()
self.double_conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(out_channels),
nn.LeakyReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(out_channels),
nn.LeakyReLU(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)
else:
self.up = nn.ConvTranspose2d(in_channels // 2, 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])
x = torch.cat([x2, x1], dim=1)
return self.conv(x)
class UpNoScale(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=1, mode='bilinear', align_corners=True)
else:
self.up = nn.ConvTranspose2d(in_channels // 2, in_channels // 2, kernel_size=2, stride=2)
self.conv = DoubleConv(in_channels, out_channels)
def forward(self, x):
x = self.up(x)
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)