content: import torch
  
  # ! amazing!!!! autograd.grad with set_detect_anomaly(True) will cause memory leak
  # ! https://github.com/pytorch/pytorch/issues/51349
  # torch.autograd.set_detect_anomaly(True)
  import torch.nn as nn
  import torch.nn.functional as F
  from inplace_abn import InPlaceABN
  
  
  #############################################     MVS Net models        ################################################
  class ConvBnReLU(nn.Module):
      def __init__(self, in_channels, out_channels,
                   kernel_size=3, stride=1, pad=1,
                   norm_act=InPlaceABN):
          super(ConvBnReLU, self).__init__()
          self.conv = nn.Conv2d(in_channels, out_channels,
                                kernel_size, stride=stride, padding=pad, bias=False)
          self.bn = norm_act(out_channels)
  
      def forward(self, x):
          return self.bn(self.conv(x))
  
  
  class ConvBnReLU3D(nn.Module):
      def __init__(self, in_channels, out_channels,
                   kernel_size=3, stride=1, pad=1,
                   norm_act=InPlaceABN):
          super(ConvBnReLU3D, self).__init__()
          self.conv = nn.Conv3d(in_channels, out_channels,
                                kernel_size, stride=stride, padding=pad, bias=False)
          self.bn = norm_act(out_channels)
          # self.bn = nn.ReLU()
  
      def forward(self, x):
          return self.bn(self.conv(x))
  
  
  ###################################  feature net  ######################################
  class FeatureNet(nn.Module):
      """
      output 3 levels of features using a FPN structure
      """
  
      def __init__(self, norm_act=InPlaceABN):
          super(FeatureNet, self).__init__()
  
          self.conv0 = nn.Sequential(
              ConvBnReLU(3, 8, 3, 1, 1, norm_act=norm_act),
              ConvBnReLU(8, 8, 3, 1, 1, norm_act=norm_act))
  
          self.conv1 = nn.Sequential(
              ConvBnReLU(8, 16, 5, 2, 2, norm_act=norm_act),
              ConvBnReLU(16, 16, 3, 1, 1, norm_act=norm_act),
              ConvBnReLU(16, 16, 3, 1, 1, norm_act=norm_act))
  
          self.conv2 = nn.Sequential(
              ConvBnReLU(16, 32, 5, 2, 2, norm_act=norm_act),
              ConvBnReLU(32, 32, 3, 1, 1, norm_act=norm_act),
              ConvBnReLU(32, 32, 3, 1, 1, norm_act=norm_act))
  
          self.toplayer = nn.Conv2d(32, 32, 1)
          self.lat1 = nn.Conv2d(16, 32, 1)
          self.lat0 = nn.Conv2d(8, 32, 1)
  
          # to reduce channel size of the outputs from FPN
          self.smooth1 = nn.Conv2d(32, 16, 3, padding=1)
          self.smooth0 = nn.Conv2d(32, 8, 3, padding=1)
  
      def _upsample_add(self, x, y):
          return F.interpolate(x, scale_factor=2,
                               mode="bilinear", align_corners=True) + y
  
      def forward(self, x):
          # x: (B, 3, H, W)
          conv0 = self.conv0(x)  # (B, 8, H, W)
          conv1 = self.conv1(conv0)  # (B, 16, H//2, W//2)
          conv2 = self.conv2(conv1)  # (B, 32, H//4, W//4)
          feat2 = self.toplayer(conv2)  # (B, 32, H//4, W//4)
          feat1 = self._upsample_add(feat2, self.lat1(conv1))  # (B, 32, H//2, W//2)
          feat0 = self._upsample_add(feat1, self.lat0(conv0))  # (B, 32, H, W)
  
          # reduce output channels
          feat1 = self.smooth1(feat1)  # (B, 16, H//2, W//2)
          feat0 = self.smooth0(feat0)  # (B, 8, H, W)
  
          # feats = {"level_0": feat0,
          #          "level_1": feat1,
          #          "level_2": feat2}
  
          return [feat2, feat1, feat0]  # coarser to finer features
  