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74 lines
2.5 KiB
Python
74 lines
2.5 KiB
Python
""" 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 DoubleConv(nn.Module):
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"""(convolution => [BN] => ReLU) * 2"""
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def __init__(self, in_channels, out_channels, mid_channels=None):
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super().__init__()
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if not mid_channels:
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mid_channels = out_channels
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self.double_conv = nn.Sequential(
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nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1, bias=False),
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nn.BatchNorm2d(mid_channels),
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nn.ReLU(inplace=True),
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nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False),
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nn.BatchNorm2d(out_channels),
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nn.ReLU(inplace=True),
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)
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def forward(self, x):
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return self.double_conv(x)
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class Down(nn.Module):
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"""Downscaling with maxpool then double conv"""
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def __init__(self, in_channels, out_channels):
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super().__init__()
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self.maxpool_conv = nn.Sequential(nn.MaxPool2d(2), DoubleConv(in_channels, out_channels))
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def forward(self, x):
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return self.maxpool_conv(x)
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class Up(nn.Module):
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"""Upscaling then double conv"""
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def __init__(self, in_channels, out_channels, bilinear=True):
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super().__init__()
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# if bilinear, use the normal convolutions to reduce the number of channels
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if bilinear:
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self.up = nn.Upsample(scale_factor=2, mode="bilinear", align_corners=True)
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self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
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else:
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self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)
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self.conv = DoubleConv(in_channels, out_channels)
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def forward(self, x1, x2):
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x1 = self.up(x1)
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# input is CHW
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diffY = x2.size()[2] - x1.size()[2]
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diffX = x2.size()[3] - x1.size()[3]
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x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2, diffY // 2, diffY - diffY // 2])
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# if you have padding issues, see
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# https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
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# https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
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x = torch.cat([x2, x1], dim=1)
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return self.conv(x)
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class OutConv(nn.Module):
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def __init__(self, in_channels, out_channels):
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super(OutConv, self).__init__()
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self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
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def forward(self, x):
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return self.conv(x)
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