123 lines
3.5 KiB
Python
123 lines
3.5 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 UNet(nn.Module):
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"""
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Architecture based on U-Net: Convolutional Networks for Biomedical Image Segmentation
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Link - https://arxiv.org/abs/1505.04597
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Parameters:
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num_classes: Number of output classes required (default 19 for KITTI dataset)
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num_layers: Number of layers in each side of U-net
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features_start: Number of features in first layer
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bilinear: Whether to use bilinear interpolation or transposed
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convolutions for upsampling.
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"""
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def __init__(
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self, num_classes: int = 19,
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num_layers: int = 5,
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features_start: int = 64,
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bilinear: bool = False
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):
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super().__init__()
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self.num_layers = num_layers
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layers = [DoubleConv(3, features_start)]
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feats = features_start
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for _ in range(num_layers - 1):
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layers.append(Down(feats, feats * 2))
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feats *= 2
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for _ in range(num_layers - 1):
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layers.append(Up(feats, feats // 2, bilinear))
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feats //= 2
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layers.append(nn.Conv2d(feats, num_classes, kernel_size=1))
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self.layers = nn.ModuleList(layers)
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def forward(self, x):
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xi = [self.layers[0](x)]
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# Down path
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for layer in self.layers[1:self.num_layers]:
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xi.append(layer(xi[-1]))
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# Up path
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for i, layer in enumerate(self.layers[self.num_layers:-1]):
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xi[-1] = layer(xi[-1], xi[-2 - i])
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return self.layers[-1](xi[-1])
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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: int, out_ch: int):
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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: int, out_ch: int):
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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: int, out_ch: int, bilinear: bool = 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.Sequential(
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nn.Upsample(scale_factor=2, mode="bilinear", align_corners=True),
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nn.Conv2d(in_ch, in_ch // 2, kernel_size=1),
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)
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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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