72 lines
1.9 KiB
Python
72 lines
1.9 KiB
Python
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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"""
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Double Convolution and BN and ReLU
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(3x3 conv -> BN -> ReLU) ** 2
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"""
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def __init__(self, in_ch, out_ch):
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super().__init__()
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self.net = nn.Sequential(
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nn.Conv2d(in_ch, out_ch, kernel_size=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, kernel_size=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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return self.net(x)
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class Down(nn.Module):
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"""
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Combination of MaxPool2d and DoubleConv in series
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"""
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def __init__(self, in_ch, out_ch):
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super().__init__()
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self.net = nn.Sequential(
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nn.MaxPool2d(kernel_size=2, stride=2),
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DoubleConv(in_ch, out_ch)
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)
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def forward(self, x):
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return self.net(x)
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class Up(nn.Module):
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"""
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Upsampling (by either bilinear interpolation or transpose convolutions)
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followed by concatenation of feature map from contracting path,
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followed by double 3x3 convolution.
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"""
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def __init__(self, in_ch, out_ch, bilinear=False):
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super().__init__()
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self.upsample = None
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if bilinear:
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self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
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else:
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self.upsample = nn.ConvTranspose2d(in_ch, in_ch // 2, kernel_size=2, stride=2)
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self.conv = DoubleConv(in_ch, out_ch)
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def forward(self, x1, x2):
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x1 = self.upsample(x1)
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# Pad x1 to the size of x2
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diff_h = x2.shape[2] - x1.shape[2]
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diff_w = x2.shape[3] - x1.shape[3]
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x1 = F.pad(x1, [diff_w // 2, diff_w - diff_w // 2, diff_h // 2, diff_h - diff_h // 2])
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# Concatenate along the channels axis
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x = torch.cat([x2, x1], dim=1)
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return self.conv(x)
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