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| #https://www.kaggle.com/c/tgs-salt-identification-challenge/discussion/67693 | |
| class ConvBn2d(nn.Module): | |
| def __init__(self, in_channels, out_channels, kernel_size=(3,3), stride=(1,1), padding=(1,1)): | |
| super(ConvBn2d, self).__init__() | |
| self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, bias=False) | |
| self.bn = nn.BatchNorm2d(out_channels) | |
| def forward(self, z): | |
| x = self.conv(z) | |
| x = self.bn(x) | |
| return x | |
| class Decoder(nn.Module): | |
| def __init__(self, in_channels, channels, out_channels ): | |
| super(Decoder, self).__init__() | |
| self.conv1 = ConvBn2d(in_channels, channels, kernel_size=3, padding=1) | |
| self.conv2 = ConvBn2d(channels, out_channels, kernel_size=3, padding=1) | |
| def forward(self, x ): | |
| x = F.upsample(x, scale_factor=2, mode='bilinear', align_corners=True)#False | |
| x = F.relu(self.conv1(x),inplace=True) | |
| x = F.relu(self.conv2(x),inplace=True) | |
| return x | |
| class Baseline(nn.Module): | |
| def __init__(self ): | |
| super().__init__() | |
| self.resnet = torchvision.models.resnet34(pretrained=True) | |
| self.conv1 = nn.Sequential( | |
| self.resnet.conv1, | |
| self.resnet.bn1, | |
| self.resnet.relu, | |
| )# 64 | |
| self.encoder2 = self.resnet.layer1 # 64 | |
| self.encoder3 = self.resnet.layer2 #128 | |
| self.encoder4 = self.resnet.layer3 #256 | |
| self.encoder5= self.resnet.layer4 #512 | |
| self.center = nn.Sequential( | |
| nn.Conv2d(512, 64, kernel_size=3, padding=1), | |
| nn.ReLU(inplace=True), | |
| ) | |
| self.decoder5 = Decoder(512+64, 512, 64) | |
| self.decoder4 = Decoder(256+64, 256, 64) | |
| self.decoder3 = Decoder(128+64, 128, 64) | |
| self.decoder2 = Decoder(64+64 , 64, 64) | |
| self.logit = nn.Sequential( | |
| nn.Conv2d(64, 32, kernel_size=3, padding=1), | |
| nn.ReLU(inplace=True), | |
| nn.Conv2d(32, 1, kernel_size=1, padding=0), | |
| ) | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| e2 = self.encoder2( x) #; print('e2',e2.size()) | |
| e3 = self.encoder3(e2) #; print('e3',e3.size()) | |
| e4 = self.encoder4(e3) #; print('e4',e4.size()) | |
| e5 = self.encoder5(e4) #; print('e5',e5.size()) | |
| f = self.center(e5) | |
| f = self.decoder5(torch.cat([f, e5], 1)) #; print('d5',f.size()) | |
| f = self.decoder4(torch.cat([f, e4], 1)) #; print('d4',f.size()) | |
| f = self.decoder3(torch.cat([f, e3], 1)) #; print('d3',f.size()) | |
| f = self.decoder2(torch.cat([f, e2], 1)) #; print('d2',f.size()) | |
| logit = self.logit(f) #; print('logit',logit.size()) | |
| return logit | |
| model=Baseline() |
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