mirror of
https://github.com/Laurent2916/REVA-QCAV.git
synced 2024-11-08 14:39:00 +00:00
89 lines
2.4 KiB
Python
89 lines
2.4 KiB
Python
# sub-parts of the U-Net model
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
|
|
|
|
class double_conv(nn.Module):
|
|
'''(conv => BN => ReLU) * 2'''
|
|
def __init__(self, in_ch, out_ch):
|
|
super(double_conv, self).__init__()
|
|
self.conv = nn.Sequential(
|
|
nn.Conv2d(in_ch, out_ch, 3, padding=1),
|
|
nn.BatchNorm2d(out_ch),
|
|
nn.ReLU(inplace=True),
|
|
nn.Conv2d(out_ch, out_ch, 3, padding=1),
|
|
nn.BatchNorm2d(out_ch),
|
|
nn.ReLU(inplace=True)
|
|
)
|
|
|
|
def forward(self, x):
|
|
x = self.conv(x)
|
|
return x
|
|
|
|
|
|
class inconv(nn.Module):
|
|
def __init__(self, in_ch, out_ch):
|
|
super(inconv, self).__init__()
|
|
self.conv = double_conv(in_ch, out_ch)
|
|
|
|
def forward(self, x):
|
|
x = self.conv(x)
|
|
return x
|
|
|
|
|
|
class down(nn.Module):
|
|
def __init__(self, in_ch, out_ch):
|
|
super(down, self).__init__()
|
|
self.mpconv = nn.Sequential(
|
|
nn.MaxPool2d(2),
|
|
double_conv(in_ch, out_ch)
|
|
)
|
|
|
|
def forward(self, x):
|
|
x = self.mpconv(x)
|
|
return x
|
|
|
|
|
|
class up(nn.Module):
|
|
def __init__(self, in_ch, out_ch, bilinear=True):
|
|
super(up, self).__init__()
|
|
|
|
# would be a nice idea if the upsampling could be learned too,
|
|
# but my machine do not have enough memory to handle all those weights
|
|
if bilinear:
|
|
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
|
|
else:
|
|
self.up = nn.ConvTranspose2d(in_ch//2, in_ch//2, 2, stride=2)
|
|
|
|
self.conv = double_conv(in_ch, out_ch)
|
|
|
|
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))
|
|
|
|
# for 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)
|
|
x = self.conv(x)
|
|
return x
|
|
|
|
|
|
class outconv(nn.Module):
|
|
def __init__(self, in_ch, out_ch):
|
|
super(outconv, self).__init__()
|
|
self.conv = nn.Conv2d(in_ch, out_ch, 1)
|
|
|
|
def forward(self, x):
|
|
x = self.conv(x)
|
|
return x
|