new_contents: # https://arxiv.org/abs/1505.04597
  import torch
  import torch.nn as nn
  
  def double_conv(in_channels, out_channels):
      return nn.Sequential(
          nn.Conv2d(in_channels, out_channels, 3, padding=1),
          nn.ReLU(inplace=True),
          nn.Conv2d(out_channels, out_channels, 3, padding=1),
          nn.ReLU(inplace=True)
      )   
  
  class UNet(nn.Module):
  
      def __init__(self, n_class):
          super().__init__()
                  
          self.dconv_down1 = double_conv(3, 64)
          self.dconv_down2 = double_conv(64, 128)
          self.dconv_down3 = double_conv(128, 256)
          self.dconv_down4 = double_conv(256, 512)        
  
          self.maxpool = nn.MaxPool2d(2)
          self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)        
          
          self.dconv_up3 = double_conv(256 + 512, 256)
          self.dconv_up2 = double_conv(128 + 256, 128)
          self.dconv_up1 = double_conv(128 + 64, 64)
          
          self.conv_last = nn.Conv2d(64, n_class, 1)
          
          
      def forward(self, x):
          conv1 = self.dconv_down1(x)
          x = self.maxpool(conv1)
  
          conv2 = self.dconv_down2(x)
          x = self.maxpool(conv2)
          
          conv3 = self.dconv_down3(x)
          x = self.maxpool(conv3)   
          
          x = self.dconv_down4(x)
          
          x = self.upsample(x)        
          x = torch.cat([x, conv3], dim=1)
          
          x = self.dconv_up3(x)
          x = self.upsample(x)        
          x = torch.cat([x, conv2], dim=1)       
  
          x = self.dconv_up2(x)
          x = self.upsample(x)        
          x = torch.cat([x, conv1], dim=1)   
          
          x = self.dconv_up1(x)
          
          out = self.conv_last(x)
          
          return out
  
subject: Create unet_torch.py