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import torch.nn as nn |
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from torchvision import models |
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class EncoderWithFeatures(nn.Module): |
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def __init__(self, encoder): |
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super().__init__() |
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self.features = encoder.features |
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self.feature_outputs = [] |
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def forward(self, x): |
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for name, layer in self.features.named_children(): |
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x = layer(x) |
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if name in ['3', '7', '11', '15']: |
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self.feature_outputs.append(x) |
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return x |
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class DoubleConv(nn.Module): |
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def __init__(self, in_channels, out_channels): |
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super().__init__() |
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self.conv = nn.Sequential( |
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nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), |
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nn.BatchNorm2d(out_channels), |
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nn.ReLU(inplace=True), |
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nn.Dropout2d(p=0.1), |
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nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), |
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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 Decoder(nn.Module): |
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def __init__(self, num_encoder_features, num_classes): |
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super().__init__() |
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self.up1 = nn.ConvTranspose2d(num_encoder_features, num_encoder_features // 2, kernel_size=2, stride=2) |
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self.conv1 = DoubleConv(num_encoder_features // 2, num_encoder_features // 2) |
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self.up2 = nn.ConvTranspose2d(num_encoder_features // 2, num_encoder_features // 4, kernel_size=2, stride=2) |
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self.conv2 = DoubleConv(num_encoder_features // 4, num_encoder_features // 4) |
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self.up3 = nn.ConvTranspose2d(num_encoder_features // 4, num_encoder_features // 8, kernel_size=2, stride=2) |
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self.conv3 = DoubleConv(num_encoder_features // 8, num_encoder_features // 8) |
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self.up4 = nn.ConvTranspose2d(num_encoder_features // 8, num_encoder_features // 16, kernel_size=2, stride=2) |
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self.conv4 = DoubleConv(num_encoder_features // 16, num_encoder_features // 16) |
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self.up5 = nn.ConvTranspose2d(num_encoder_features // 16, num_encoder_features//16, kernel_size=2, stride=2) |
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self.final_conv = nn.Conv2d(num_encoder_features // 16, num_classes, kernel_size=1) |
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def forward(self, x): |
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x1 = self.up1(x) |
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x1 = self.conv1(x1) |
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x2 = self.up2(x1) |
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x2 = self.conv2(x2) |
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x3 = self.up3(x2) |
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x3 = self.conv3(x3) |
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x4 = self.up4(x3) |
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x4 = self.conv4(x4) |
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x5 = self.up5(x4) |
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output = self.final_conv(x5) |
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return output |
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class SegmentationModel(nn.Module): |
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def __init__(self, encoder=None, decoder=None, num_classes=1,ngpu=0): |
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super().__init__() |
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self.ngpu = ngpu |
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if encoder is None: |
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base_model = models.mobilenet_v2(pretrained=True) |
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base_model.classifier = nn.Identity() |
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for param in base_model.parameters(): |
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param.requires_grad = False |
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self.encoder = EncoderWithFeatures(base_model) |
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else: |
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self.encoder = encoder |
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if decoder is None: |
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self.decoder = Decoder(num_encoder_features=1280, num_classes=num_classes) |
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else: |
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self.decoder = decoder |
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def forward(self, x): |
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x = self.encoder(x) |
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return self.decoder(x) |
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