code: '''
  ResNet-based model to map an image from pixel space to a features space.
  Need to be pretrained on the dataset.
  
  if isometric_map = True, there is an extra step (elf.classifier_1 = nn.Linear(512, 32*32*3)) to increase the dimension of the feature map from 512 to 32*32*3. This selection is for desity-ratio estimation in feature space.
  
  codes are based on
  @article{
  zhang2018mixup,
  title={mixup: Beyond Empirical Risk Minimization},
  author={Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz},
  journal={International Conference on Learning Representations},
  year={2018},
  url={https://openreview.net/forum?id=r1Ddp1-Rb},
  }
  '''
  
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  NC = 3
  IMG_SIZE = 64
  DIM_EMBED = 128
  
  
  #------------------------------------------------------------------------------
  class BasicBlock(nn.Module):
      expansion = 1
  
      def __init__(self, in_planes, planes, stride=1):
          super(BasicBlock, self).__init__()
          self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
          self.bn1 = nn.BatchNorm2d(planes)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
          self.bn2 = nn.BatchNorm2d(planes)
  
          self.shortcut = nn.Sequential()
          if stride != 1 or in_planes != self.expansion*planes:
              self.shortcut = nn.Sequential(
                  nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                  nn.BatchNorm2d(self.expansion*planes)
              )
  
      def forward(self, x):
          out = F.relu(self.bn1(self.conv1(x)))
          out = self.bn2(self.conv2(out))
          out += self.shortcut(x)
          out = F.relu(out)
          return out
  
  
  class Bottleneck(nn.Module):
      expansion = 4
  
      def __init__(self, in_planes, planes, stride=1):
          super(Bottleneck, self).__init__()
          self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
          self.bn1 = nn.BatchNorm2d(planes)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
          self.bn2 = nn.BatchNorm2d(planes)
          self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)
          self.bn3 = nn.BatchNorm2d(self.expansion*planes)
  
          self.shortcut = nn.Sequential()
          if stride != 1 or in_planes != self.expansion*planes:
              self.shortcut = nn.Sequential(
                  nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                  nn.BatchNorm2d(self.expansion*planes)
              )
  
      def forward(self, x):
          out = F.relu(self.bn1(self.conv1(x)))
          out = F.relu(self.bn2(self.conv2(out)))
          out = self.bn3(self.conv3(out))
          out += self.shortcut(x)
          out = F.relu(out)
          return out
  
  
  class ResNet_embed(nn.Module):
      def __init__(self, block, num_blocks, nc=NC, dim_embed=DIM_EMBED, ngpu = 1):
          super(ResNet_embed, self).__init__()
          self.in_planes = 64
          self.ngpu = ngpu
  
          self.main = nn.Sequential(
              nn.Conv2d(nc, 64, kernel_size=3, stride=1, padding=1, bias=False),  # h=h
              # nn.Conv2d(nc, 64, kernel_size=4, stride=2, padding=1, bias=False),  # h=h/2
              nn.BatchNorm2d(64),
              nn.ReLU(),
              # self._make_layer(block, 64, num_blocks[0], stride=1),  # h=h
              self._make_layer(block, 64, num_blocks[0], stride=2),  # h=h/2 32
              self._make_layer(block, 128, num_blocks[1], stride=2), # h=h/2 16
              self._make_layer(block, 256, num_blocks[2], stride=2), # h=h/2 8
              self._make_layer(block, 512, num_blocks[3], stride=2), # h=h/2 4
              # nn.AvgPool2d(kernel_size=4)
              nn.AdaptiveAvgPool2d((1, 1))
          )
  
          self.x2h_res = nn.Sequential(
              nn.Linear(512, 512),
              nn.BatchNorm1d(512),
              nn.ReLU(),
  
              nn.Linear(512, dim_embed),
              nn.BatchNorm1d(dim_embed),
              nn.ReLU(),
          )
  
          self.h2y = nn.Sequential(
              nn.Linear(dim_embed, 1),
              nn.ReLU()
          )
  
      def _make_layer(self, block, planes, num_blocks, stride):
          strides = [stride] + [1]*(num_blocks-1)
          layers = []
          for stride in strides:
              layers.append(block(self.in_planes, planes, stride))
              self.in_planes = planes * block.expansion
          return nn.Sequential(*layers)
  
      def forward(self, x):
  
          if x.is_cuda and self.ngpu > 1:
              features = nn.parallel.data_parallel(self.main, x, range(self.ngpu))
              features = features.view(features.size(0), -1)
              features = nn.parallel.data_parallel(self.x2h_res, features, range(self.ngpu))
              out = nn.parallel.data_parallel(self.h2y, features, range(self.ngpu))
          else:
              features = self.main(x)
              features = features.view(features.size(0), -1)
              features = self.x2h_res(features)
              out = self.h2y(features)
  
          return out, features
  
  
  def ResNet18_embed(dim_embed=DIM_EMBED, ngpu = 1):
      return ResNet_embed(BasicBlock, [2,2,2,2], dim_embed=dim_embed, ngpu = ngpu)
  
  def ResNet34_embed(dim_embed=DIM_EMBED, ngpu = 1):
      return ResNet_embed(BasicBlock, [3,4,6,3], dim_embed=dim_embed, ngpu = ngpu)
  
  def ResNet50_embed(dim_embed=DIM_EMBED, ngpu = 1):
      return ResNet_embed(Bottleneck, [3,4,6,3], dim_embed=dim_embed, ngpu = ngpu)
  
  #------------------------------------------------------------------------------
  # map labels to the embedding space
  class model_y2h(nn.Module):
      def __init__(self, dim_embed=DIM_EMBED):
          super(model_y2h, self).__init__()
          self.main = nn.Sequential(
              nn.Linear(1, dim_embed),
              # nn.BatchNorm1d(dim_embed),
              nn.GroupNorm(8, dim_embed),
              nn.ReLU(),
  
              nn.Linear(dim_embed, dim_embed),
              # nn.BatchNorm1d(dim_embed),
              nn.GroupNorm(8, dim_embed),
              nn.ReLU(),
  
              nn.Linear(dim_embed, dim_embed),
              # nn.BatchNorm1d(dim_embed),
              nn.GroupNorm(8, dim_embed),
              nn.ReLU(),
  
              nn.Linear(dim_embed, dim_embed),
              # nn.BatchNorm1d(dim_embed),
              nn.GroupNorm(8, dim_embed),
              nn.ReLU(),
  
              nn.Linear(dim_embed, dim_embed),
              nn.ReLU()
          )
  
      def forward(self, y):
          y = y.view(-1, 1) +1e-8
          # y = torch.exp(y.view(-1, 1))
          return self.main(y)
  
  
  
  if __name__ == "__main__":
      net = ResNet34_embed(ngpu = 1).cuda()
      x = torch.randn(16,NC,IMG_SIZE,IMG_SIZE).cuda()
      out, features = net(x)
      print(out.size())
      print(features.size())
  
      net_y2h = model_y2h()
  
apis: torch.nn.BatchNorm2d torch.nn.GroupNorm torch.nn.Linear torch.nn.BatchNorm1d torch.randn torch.nn.AdaptiveAvgPool2d torch.nn.functional.relu torch.nn.Conv2d torch.nn.Sequential torch.nn.ReLU