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unet_3d.py
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unet_3d.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
class UNet(nn.Module):
def __init__(self, in_channels=1, squeeze=False):
super(UNet, self).__init__()
self.conv1 = Conv(in_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(512, 256)
self.up2 = Up(256, 128)
self.up3 = Up(128, 64)
self.up4 = Up(64, 64)
self.out = OutConv(64, 1)
self.squeeze = squeeze
def forward(self, x):
if self.squeeze:
x = x.unsqueeze(1)
x1 = self.conv1(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)
x = self.out(x)
if self.squeeze:
x = x.squeeze(1)
return x
class OutConv(nn.Module):
def __init__(self, in_size, out_size):
super(OutConv, self).__init__()
self.conv = nn.Sequential(nn.Conv3d(in_size, out_size, kernel_size=3, padding=1))
def forward(self, x):
x = self.conv(x)
return x
class Conv(nn.Module):
def __init__(self, in_size, out_size):
super(Conv, self).__init__()
self.conv = nn.Sequential(nn.Conv3d(in_size, out_size, kernel_size=3, padding=1),
nn.BatchNorm3d(out_size),
nn.ReLU(inplace=True),
nn.Conv3d(out_size, out_size, kernel_size=3, padding=1),
nn.BatchNorm3d(out_size),
nn.ReLU(inplace=True)
)
def forward(self, x):
x = self.conv(x)
return x
class Down(nn.Module):
def __init__(self, in_size, out_size):
super(Down, self).__init__()
self.down = nn.Sequential(
nn.MaxPool3d(2),
Conv(in_size, out_size)
)
def forward(self, x):
x = self.down(x)
return x
class Up(nn.Module):
def __init__(self, in_size, out_size):
super(Up, self).__init__()
self.up = nn.ConvTranspose3d(in_size, in_size, kernel_size=2, stride=2)
self.conv = Conv(in_size * 2, out_size)
def forward(self, x1, x2):
up = self.up(x1)
out = torch.cat([up, x2], dim=1)
out = self.conv(out)
return out