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https://github.com/Laurent2916/REVA-QCAV.git
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83 lines
2.1 KiB
Python
83 lines
2.1 KiB
Python
#!/usr/bin/python
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# sub-parts of the U-Net model
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class double_conv(nn.Module):
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'''(conv => BN => ReLU) * 2'''
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def __init__(self, in_ch, out_ch):
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super(double_conv, self).__init__()
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self.conv = nn.Sequential(
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nn.Conv2d(in_ch, out_ch, 3, padding=1),
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nn.BatchNorm2d(out_ch),
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nn.ReLU(inplace=True),
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nn.Conv2d(out_ch, out_ch, 3, padding=1),
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nn.BatchNorm2d(out_ch),
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nn.ReLU(inplace=True)
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)
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def forward(self, x):
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x = self.conv(x)
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return x
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class inconv(nn.Module):
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def __init__(self, in_ch, out_ch):
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super(inconv, self).__init__()
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self.conv = double_conv(in_ch, out_ch)
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def forward(self, x):
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x = self.conv(x)
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return x
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class down(nn.Module):
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def __init__(self, in_ch, out_ch):
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super(down, self).__init__()
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self.mpconv = nn.Sequential(
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nn.MaxPool2d(2),
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double_conv(in_ch, out_ch)
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)
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def forward(self, x):
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x = self.mpconv(x)
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return x
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class up(nn.Module):
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def __init__(self, in_ch, out_ch, bilinear=True):
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super(up, self).__init__()
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# would be a nice idea if the upsampling could be learned too,
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# but my machine do not have enough memory to handle all those weights
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if bilinear:
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self.up = nn.UpsamplingBilinear2d(scale_factor=2)
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else:
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self.up = nn.ConvTranspose2d(in_ch//2, in_ch//2, 2, stride=2)
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self.conv = double_conv(in_ch, out_ch)
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def forward(self, x1, x2):
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x1 = self.up(x1)
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diffX = x1.size()[2] - x2.size()[2]
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diffY = x1.size()[3] - x2.size()[3]
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x2 = F.pad(x2, (diffX // 2, int(diffX / 2),
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diffY // 2, int(diffY / 2)))
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x = torch.cat([x2, x1], dim=1)
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x = self.conv(x)
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return x
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class outconv(nn.Module):
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def __init__(self, in_ch, out_ch):
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super(outconv, self).__init__()
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self.conv = nn.Conv2d(in_ch, out_ch, 1)
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def forward(self, x):
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x = self.conv(x)
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return x
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