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unet_model.py
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unet_model.py
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""" Parts of the U-Net model """
import torch
import torch.nn as nn
import torch.nn.functional as F
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='bilinear', align_corners=True)
self.conv = DoubleConv(in_channels, out_channels // 2, 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 = torch.tensor([x2.size()[2] - x1.size()[2]])
diffX = torch.tensor([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 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)
class UNet(nn.Module):
def __init__(self, n_channels = 3, out_channels_id = 9, out_channels_uv = 256, bilinear=True):
super(UNet, self).__init__()
self.n_channels = n_channels
self.out_channels_id = out_channels_id
self.out_channels_uv = out_channels_uv
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)
#ID MASK
self.up1_id = Up(1024, 512, bilinear)
self.up2_id = Up(512, 256, bilinear)
self.up3_id = Up(256, 128, bilinear)
self.up4_id = Up(128, 64 * factor, bilinear)
self.outc_id = OutConv(64, out_channels_id)
#U Mask
self.up1_u = Up(1024, 512, bilinear)
self.up2_u = Up(512,512,bilinear)
self.outc_u1 = OutConv(256, out_channels_uv)
self.outc_u2 = OutConv(256, out_channels_uv)
self.outc_u3 = OutConv(256, out_channels_uv)
self.outc_u4 = OutConv(256, out_channels_uv)
self.up3_u = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
self.up4_u = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
#V Mask
self.up1_v = Up(1024, 512, bilinear)
self.up2_v = Up(512,512,bilinear)
self.outc_v1 = OutConv(256, out_channels_uv)
self.outc_v2 = OutConv(256, out_channels_uv)
self.outc_v3 = OutConv(256, out_channels_uv)
self.outc_v4 = OutConv(256, out_channels_uv)
self.up3_v = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
self.up4_v = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
def forward(self, x):
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x5 = self.down4(x4)
# ID mask
x_id = self.up1_id(x5, x4)
x_id = self.up2_id(x_id, x3)
x_id = self.up3_id(x_id, x2)
x_id = self.up4_id(x_id, x1)
logits_id = self.outc_id(x_id)
# U mask
x_u = self.up1_u(x5, x4)
x_u = self.up2_u(x_u,x3)
x_u = self.outc_u1(x_u)
x_u = self.outc_u2(x_u)
x_u = self.outc_u3(x_u)
x_u = self.up3_u(x_u)
x_u = self.up4_u(x_u)
logits_u = self.outc_u4(x_u)
# V mask
x_v = self.up1_v(x5, x4)
x_v = self.up2_v(x_v,x3)
x_v = self.outc_v1(x_v)
x_v = self.outc_v2(x_v)
x_v = self.outc_v3(x_v)
x_v = self.up3_v(x_v)
x_v = self.up4_v(x_v)
logits_v = self.outc_v4(x_v)
return logits_id,logits_u, logits_v