text: # https://www.kesci.com/home/competition/forum/5bdc5926954d6e001060d5a9
  import torch.nn as nn
  import math
  from .utils import AngleLinear
  
  
  class ReLU20(nn.Hardtanh):  # relu
      def __init__(self, inplace=False):
          super(ReLU20, self).__init__(0, 20, inplace)
  
      def extra_repr(self):
          inplace_str = 'inplace' if self.inplace else ''
          return inplace_str
  
  
  def conv3x3(in_planes, out_planes, stride=1):
      return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
  
  
  class BasicBlock(nn.Module):
  
      expansion = 1
  
      def __init__(self, in_channels, channels, stride=1, downsample=None):
          super(BasicBlock, self).__init__()
          self.conv1 = conv3x3(in_channels, channels, stride)
          self.bn1 = nn.BatchNorm2d(channels)
          self.relu = ReLU20(inplace=True)
          self.conv2 = conv3x3(channels, channels)
          self.bn2 = nn.BatchNorm2d(channels)
          self.downsample = downsample
          self.stride = stride
  
      def forward(self, x):
          residual = x
  
          out = self.conv1(x)
          out = self.bn1(out)
          out = self.relu(out)
  
          out = self.conv2(out)
          out = self.bn2(out)
  
          if self.downsample is not None:
              residual = self.downsample(x)
  
          out += residual
          out = self.relu(out)
          return out
  
  
  class ResNet(nn.Module):
      def __init__(self, layers, block=BasicBlock, embedding_size=None, n_classes=1000,
                   m=3, input_channel=1):
          super(ResNet, self).__init__()
          if embedding_size is None:
              embedding_size = n_classes
  
          self.relu = ReLU20(inplace=True)
  
          self.in_planes = 64
          self.conv1 = nn.Conv2d(input_channel, 64, kernel_size=5, stride=2, padding=2, bias=False)
          self.bn1 = nn.BatchNorm2d(64)
          self.layer1 = self._make_layer(block, 64, layers[0])
  
          self.in_planes = 128
          self.conv2 = nn.Conv2d(64, 128, kernel_size=5, stride=2, padding=2, bias=False)
          self.bn2 = nn.BatchNorm2d(128)
          self.layer2 = self._make_layer(block, 128, layers[1])
  
          self.in_planes = 256
          self.conv3 = nn.Conv2d(128, 256, kernel_size=5, stride=2, padding=2, bias=False)
          self.bn3 = nn.BatchNorm2d(256)
          self.layer3 = self._make_layer(block, 256, layers[2])
  
          # self.in_planes = 512
          # self.conv4 = nn.Conv2d(256, 512, kernel_size=5, stride=2, padding=2, bias=False)
          # self.bn4 = nn.BatchNorm2d(512)
          # self.layer4 = self._make_layer(block, 512, layers[3])
  
          self.avg_pool = nn.AdaptiveAvgPool2d([4, 1])
  
          self.fc = nn.Sequential(
              nn.Linear(self.in_planes * 4, embedding_size),
              nn.BatchNorm1d(embedding_size)
          )
  
          # cls
          self.cls = nn.Sequential(
              nn.Linear(self.in_planes * 4, n_classes),
              nn.BatchNorm1d(n_classes)
          )
  
          self.angle_linear = AngleLinear(in_features=embedding_size, out_features=n_classes, m=m)
  
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                  m.weight.data.normal_(0, math.sqrt(2. / n))
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
              elif isinstance(m, nn.BatchNorm1d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
  
      def _make_layer(self, block, planes, blocks, stride=1):
          layers = [block(self.in_planes, planes, stride)]
          self.in_planes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(block(self.in_planes, planes))
          return nn.Sequential(*layers)
  
      def forward(self, x, target=None):
          x = self.conv1(x)
          x = self.bn1(x)
          x = self.relu(x)
          x = self.layer1(x)
  
          x = self.conv2(x)
          x = self.bn2(x)
          x = self.relu(x)
          x = self.layer2(x)
  
          x = self.conv3(x)
          x = self.bn3(x)
          x = self.relu(x)
          x = self.layer3(x)
  
          # x = self.conv4(x)
          # x = self.bn4(x)
          # x = self.relu(x)
          # x = self.layer4(x)
  
          x = self.avg_pool(x)
          x = x.view(x.size(0), -1)
          embedding = self.fc(x)
          cls = self.cls(x)
  
          logit = self.angle_linear(embedding)
          return logit, cls
  