text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  import torchvision
  
  # VGG = torchvision.models.vgg19
  from .base_network import vgg19
  
  class UNet_Pretrained(nn.Module):
      def __init__(self, n_channels, n_classes):
          super(UNet_Pretrained, self).__init__()
          self.n_channels = n_channels
          self.n_classes = n_classes
          bilinear = True
  
          self.vgg = vgg19(pretrained=True)
          self.up1 = Up(1024, 256, bilinear)
          self.up2 = Up(512, 128, bilinear)
          self.up3 = Up(256, 64, bilinear)
          self.up4 = Up(128, 128, bilinear)
          self.up5 = Up(128, 64, bilinear)
        
          self.final = nn.Conv2d(64, self.n_classes*3, kernel_size=1, padding=0)
  
      def forward(self, x):
          _, features = self.vgg(x, get_features=True)
  
          x = self.up1(features[4], features[3])
          x = self.up2(x, features[2])
          x = self.up3(x, features[1])
          x = self.up4(x, features[0])
          x = self.up5(x)
  
          x = self.final(x)
          
          heatmap = F.sigmoid(x[:,:self.n_classes,:,:])
          regression_x = x[:,self.n_classes:2*self.n_classes,:,:]
          regression_y = x[:,2*self.n_classes:,:,:]
  
          return heatmap, regression_y, regression_x
  
  
  class UNet(nn.Module):
      def __init__(self, n_channels, n_classes, bilinear=True):
          super(UNet, self).__init__()
          self.n_channels = n_channels
          self.n_classes = n_classes
          self.bilinear = bilinear
  
          self.inc = DoubleConv(n_channels, 64)
          self.down0 = Down(64, 64)
          self.down1 = Down(64, 128)
          self.down2 = Down(128, 256)
          self.down3 = Down(256, 512)
          self.down4 = Down(512, 512)
          # self.up1 = Up(1024, 256, bilinear)
          # self.up2 = Up(512, 128, bilinear)
          # self.up3 = Up(256, 64, bilinear)
          # self.up4 = Up(128, 64, bilinear)
          # self.up5 = Up(128, 64, bilinear)
          self.up1 = Up(512, 256, bilinear)
          self.up2 = Up(256, 128, bilinear)
          self.up3 = Up(128, 64, bilinear)
          self.up4 = Up(64, 64, bilinear)
          self.up5 = Up(64, 64, bilinear)        
          self.outc = OutConv_Sigmoid(64, n_classes)
          self.outc_regression_x = OutConv(64, n_classes)
          self.outc_regression_y = OutConv(64, n_classes)
  
      def forward(self, x):
          x1 = self.inc(x)
          x0 = self.down0(x1)
          x2 = self.down1(x0)
          x3 = self.down2(x2)
          x4 = self.down3(x3)
          x5 = self.down4(x4)
          x = self.up1(x5, x4)
          x = self.up2(x, x3)
          x = self.up3(x, x2)
          x = self.up4(x, x0)
          x = self.up5(x, x1)
          logits = self.outc(x)
          regression_x = self.outc_regression_x(x)
          regression_y = self.outc_regression_y(x)
          return logits, regression_y, regression_x
  
  class DoubleConv(nn.Module):
      """(convolution => [BN] => ReLU) * 2"""
  
      def __init__(self, in_channels, out_channels):
          super().__init__()
          self.double_conv = nn.Sequential(
              nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
              nn.InstanceNorm2d(out_channels),
              nn.ReLU(inplace=True),
              nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
              nn.InstanceNorm2d(out_channels),
              nn.ReLU(inplace=True)
          )
  
      def forward(self, x):
          return self.double_conv(x)
  
  
  class Down(nn.Module):
      """Downscaling with maxpool then double conv"""
  
      def __init__(self, in_channels, out_channels):
          super().__init__()
          self.maxpool_conv = nn.Sequential(
              nn.MaxPool2d(2),
              DoubleConv(in_channels, out_channels)
          )
  
      def forward(self, x):
          return self.maxpool_conv(x)
  
  
  class Up(nn.Module):
      """Upscaling then double conv"""
  
      def __init__(self, in_channels, out_channels, bilinear=True):
          super().__init__()
  
          # if bilinear, use the normal convolutions to reduce the number of channels
          if bilinear:
              self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
          else:
              self.up = nn.ConvTranspose2d(in_channels // 2, in_channels // 2, kernel_size=2, stride=2)
  
          self.conv = DoubleConv(in_channels, out_channels)
  
      def forward(self, x1, x2=None):
          x1 = self.up(x1)
          # input is CHW
          if x2 is not None:
              diffY = torch.tensor([x2.size()[2] - x1.size()[2]])
              diffX = torch.tensor([x2.size()[3] - x1.size()[3]])
  
              x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
                              diffY // 2, diffY - diffY // 2])
              # if you have padding issues, see
              # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
              # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
              x1 = torch.cat([x2, x1], dim=1)
          x = x1
          return self.conv(x)
  
  
  class OutConv_Sigmoid(nn.Module):
      def __init__(self, in_channels, out_channels):
          super(OutConv_Sigmoid, self).__init__()
          self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
          self.sigmoid = nn.Sigmoid()
  
      def forward(self, x):
          return self.sigmoid(self.conv(x))
  
  class OutConv(nn.Module):
      def __init__(self, in_channels, out_channels):
          super(OutConv, self).__init__()
          self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
  
      def forward(self, x):
          return self.conv(x)
  
  if __name__ == '__main__':
      test = UNet_Pretrained(3, 57)
      wtf = torch.zeros([1, 3, 224, 224], dtype=torch.float)
      wtf = test(wtf)
      import ipdb; ipdb.set_trace()
  
  