mirror of
https://github.com/aladdinpersson/Machine-Learning-Collection.git
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76 lines
2.3 KiB
Python
76 lines
2.3 KiB
Python
import torch
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import torch.nn as nn
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import torchvision.transforms.functional as TF
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class DoubleConv(nn.Module):
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def __init__(self, in_channels, out_channels):
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super(DoubleConv, self).__init__()
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self.conv = nn.Sequential(
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nn.Conv2d(in_channels, out_channels, 3, 1, 1, bias=False),
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nn.BatchNorm2d(out_channels),
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nn.ReLU(inplace=True),
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nn.Conv2d(out_channels, out_channels, 3, 1, 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.conv(x)
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class UNET(nn.Module):
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def __init__(
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self, in_channels=3, out_channels=1, features=[64, 128, 256, 512],
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):
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super(UNET, self).__init__()
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self.ups = nn.ModuleList()
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self.downs = nn.ModuleList()
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self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
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# Down part of UNET
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for feature in features:
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self.downs.append(DoubleConv(in_channels, feature))
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in_channels = feature
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# Up part of UNET
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for feature in reversed(features):
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self.ups.append(
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nn.ConvTranspose2d(
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feature*2, feature, kernel_size=2, stride=2,
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)
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)
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self.ups.append(DoubleConv(feature*2, feature))
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self.bottleneck = DoubleConv(features[-1], features[-1]*2)
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self.final_conv = nn.Conv2d(features[0], out_channels, kernel_size=1)
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def forward(self, x):
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skip_connections = []
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for down in self.downs:
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x = down(x)
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skip_connections.append(x)
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x = self.pool(x)
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x = self.bottleneck(x)
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skip_connections = skip_connections[::-1]
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for idx in range(0, len(self.ups), 2):
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x = self.ups[idx](x)
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skip_connection = skip_connections[idx//2]
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if x.shape != skip_connection.shape:
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x = TF.resize(x, size=skip_connection.shape[2:])
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concat_skip = torch.cat((skip_connection, x), dim=1)
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x = self.ups[idx+1](concat_skip)
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return self.final_conv(x)
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def test():
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x = torch.randn((3, 1, 161, 161))
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model = UNET(in_channels=1, out_channels=1)
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preds = model(x)
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assert preds.shape == x.shape
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if __name__ == "__main__":
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test() |