You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
78 lines
2.5 KiB
78 lines
2.5 KiB
5 months ago
|
""" 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, bias=False),
|
||
|
|
nn.BatchNorm2d(mid_channels),
|
||
|
|
nn.ReLU(inplace=True),
|
||
|
|
nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False),
|
||
|
|
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, 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)
|