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model.py
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model.py
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# A simple backbone U-Net implementation adpated from https://github.com/milesial/Pytorch-UNet
# Can be replaced by other network architectures
import torch
import torch.nn as nn
import torch.nn.functional as F
class DoubleConv(nn.Module):
def __init__(self, in_channels, out_channels, mid_channels=None):
super(DoubleConv, self).__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.ReLU(inplace=True),
nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1),
nn.ReLU(inplace=True)
)
def forward(self, x):
return self.double_conv(x)
class Down(nn.Module):
def __init__(self, in_channels, out_channels):
super(Down, self).__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):
def __init__(self, in_channels, out_channels, bilinear=True):
super(Up, self).__init__()
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)
def weights_init(modules):
for m in modules:
if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear) or isinstance(m, nn.ConvTranspose2d):
nn.init.kaiming_normal_(m.weight)
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1.0)
m.bias.data.zero_()
class UNet(nn.Module):
def __init__(self, n_channels, bilinear=True):
super(UNet, self).__init__()
self.n_channels = n_channels
self.bilinear = bilinear
self.inc = DoubleConv(n_channels, 32)
self.down1 = Down(32, 64)
self.down2 = Down(64, 128)
self.down3 = Down(128, 256)
factor = 2 if bilinear else 1
self.down4 = Down(256, 512//factor)
self.up1 = Up(512, 256//factor, bilinear)
self.up2 = Up(256, 128//factor, bilinear)
self.up3 = Up(128, 64//factor, bilinear)
self.up4 = Up(64, 32, bilinear)
self.outc = OutConv(32, 3)
weights_init(self.modules())
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)
out = self.outc(x)
return out