text: # sub-parts of the U-Net model
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  
  class double_conv(nn.Module):
      '''(conv => BN => ReLU) * 2'''
  
      def __init__(self, in_ch, out_ch):
          super(double_conv, self).__init__()
          self.conv = nn.Sequential(
              nn.Conv3d(in_ch, out_ch, 3, padding=1),
              nn.BatchNorm3d(out_ch),
              nn.LeakyReLU(inplace=True),
              nn.Conv3d(out_ch, out_ch, 3, padding=1),
              nn.BatchNorm3d(out_ch),
              nn.LeakyReLU(inplace=True)
          )
  
      def forward(self, x):
          x = self.conv(x)
          return x
  
  
  class inconv(nn.Module):
      def __init__(self, in_ch, out_ch):
          super(inconv, self).__init__()
          self.conv = double_conv(in_ch, out_ch)
  
      def forward(self, x):
          x = self.conv(x)
          return x
  
  
  class down(nn.Module):
      def __init__(self, in_ch, out_ch):
          super(down, self).__init__()
          self.mpconv = nn.Sequential(
              nn.MaxPool3d(2),
              double_conv(in_ch, out_ch)
          )
  
      def forward(self, x):
          x = self.mpconv(x)
          return x
  
  
  class up(nn.Module):
      def __init__(self, in_ch, out_ch, bilinear=True):
          super(up, self).__init__()
  
          #  would be a nice idea if the upsampling could be learned too,
          #  but my machine do not have enough memory to handle all those weights
          if bilinear:
              self.up = nn.Upsample(scale_factor=2, mode='trilinear', align_corners=True)
          else:
              self.up = nn.ConvTranspose3d(in_ch // 2, in_ch // 2, 2, stride=2)
  
          self.conv = double_conv(in_ch, out_ch)
  
      def forward(self, x1, x2):
          x1 = self.up(x1)
  
          # input is CHW
          diffY = x2.size()[2] - x1.size()[2]
          diffX = x2.size()[3] - x1.size()[3]
  
          x1 = F.pad(x1, (diffX // 2, diffX - diffX // 2,
                          diffY // 2, diffY - diffY // 2))
  
          # for padding issues, see
          # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
          # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
  
          x = torch.cat([x2, x1], dim=1)
          x = self.conv(x)
          return x
  
  
  class outconv(nn.Module):
      def __init__(self, in_ch, out_ch):
          super(outconv, self).__init__()
          # self.conv = nn.Sequential(nn.Conv3d(in_ch, out_ch, 1),
          #                           nn.Tanh())
          self.conv = nn.Conv3d(in_ch, out_ch, 1)
  
      def forward(self, x):
          x = self.conv(x)
          return x
  
  
  class conv_block(nn.Module):
      """
      Convolution Block
      """
  
      def __init__(self, in_ch, out_ch):
          super(conv_block, self).__init__()
  
          self.conv = nn.Sequential(
              nn.Conv3d(in_ch, out_ch, kernel_size=3, stride=1, padding=1, bias=True),
              nn.BatchNorm3d(out_ch),
              nn.LeakyReLU(inplace=True),
              # nn.Conv3d(out_ch, out_ch, kernel_size=3, stride=1, padding=1, bias=True),
              # nn.BatchNorm3d(out_ch),
              # nn.LeakyReLU(inplace=True)
          )
  
      def forward(self, x):
          x = self.conv(x)
          return x
  
  
  class up_conv(nn.Module):
      """
      Up Convolution Block
      """
  
      def __init__(self, in_ch, out_ch):
          super(up_conv, self).__init__()
          self.up = nn.Sequential(
              nn.Upsample(scale_factor=2),
              nn.Conv3d(in_ch, out_ch, kernel_size=3, stride=1, padding=1, bias=True),
              nn.BatchNorm3d(out_ch),
              nn.LeakyReLU(inplace=True)
          )
  
      def forward(self, x):
          x = self.up(x)
          return x
  