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  #     Copyright 2020 Huawei Technologies Co., Ltd
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  #
  #
  #     Licensed under the BSD 3-Clause License (the "License");
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  import math
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import torch.utils.model_zoo as model_zoo
  
  __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
             'resnet152']
  
  model_urls = {
      'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
      'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
      'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
      'resnet101': 'https://download.pytorch.org/models/resnet101-5d3mb4d8f.pth',
      'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
  }
  
  
  def conv3x3(in_planes, out_planes, stride=1):
      """3x3 convolution with padding"""
      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, inplanes, planes, stride=1, downsample=None):
          super(BasicBlock, self).__init__()
          self.conv1 = conv3x3(inplanes, planes, stride)
          self.bn1 = nn.BatchNorm2d(planes)
          self.relu = nn.ReLU(inplace=True)
          self.conv2 = conv3x3(planes, planes)
          self.bn2 = nn.BatchNorm2d(planes)
          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 Bottleneck(nn.Module):
      expansion = 4
  
      def __init__(self, inplanes, planes, stride=1, downsample=None):
          super(Bottleneck, self).__init__()
          self.conv1 = nn.Conv2d(inplanes, 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, planes * 4, kernel_size=1, bias=False)
          self.bn3 = nn.BatchNorm2d(planes * 4)
          self.relu = nn.ReLU(inplace=True)
          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)
          out = self.relu(out)
  
          out = self.conv3(out)
          out = self.bn3(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, block, layers, num_classes=7, scale=1):
          self.inplanes = 64
          super(ResNet, self).__init__()
          self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                                 bias=False)
          self.bn1 = nn.BatchNorm2d(64)
          self.relu1 = nn.ReLU(inplace=True)
          self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, return_indices=False)
          self.layer1 = self._make_layer(block, 64, layers[0])
          self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
          self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
          self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
  
          # Top layer
          self.toplayer = nn.Conv2d(2048, 256, kernel_size=1, stride=1, padding=0)  # Reduce channels
          self.toplayer_bn = nn.BatchNorm2d(256)
          self.toplayer_relu = nn.ReLU(inplace=True)
  
          # Smooth layers
          self.smooth1 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
          self.smooth1_bn = nn.BatchNorm2d(256)
          self.smooth1_relu = nn.ReLU(inplace=True)
  
          self.smooth2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
          self.smooth2_bn = nn.BatchNorm2d(256)
          self.smooth2_relu = nn.ReLU(inplace=True)
  
          self.smooth3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
          self.smooth3_bn = nn.BatchNorm2d(256)
          self.smooth3_relu = nn.ReLU(inplace=True)
  
          # Lateral layers
          self.latlayer1 = nn.Conv2d(1024, 256, kernel_size=1, stride=1, padding=0)
          self.latlayer1_bn = nn.BatchNorm2d(256)
          self.latlayer1_relu = nn.ReLU(inplace=True)
  
          self.latlayer2 = nn.Conv2d(512, 256, kernel_size=1, stride=1, padding=0)
          self.latlayer2_bn = nn.BatchNorm2d(256)
          self.latlayer2_relu = nn.ReLU(inplace=True)
  
          self.latlayer3 = nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)
          self.latlayer3_bn = nn.BatchNorm2d(256)
          self.latlayer3_relu = nn.ReLU(inplace=True)
  
          self.conv2 = nn.Conv2d(1024, 256, kernel_size=3, stride=1, padding=1)
          self.bn2 = nn.BatchNorm2d(256)
          self.relu2 = nn.ReLU(inplace=True)
          self.conv3 = nn.Conv2d(256, num_classes, kernel_size=1, stride=1, padding=0)
          self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
  
          self.scale = scale
  
          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_()
  
      def _make_layer(self, block, planes, blocks, stride=1):
          downsample = None
          if stride != 1 or self.inplanes != planes * block.expansion:
              downsample = nn.Sequential(
                  nn.Conv2d(self.inplanes, planes * block.expansion,
                            kernel_size=1, stride=stride, bias=False),
                  nn.BatchNorm2d(planes * block.expansion),
              )
  
          layers = []
          layers.append(block(self.inplanes, planes, stride, downsample))
          self.inplanes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(block(self.inplanes, planes))
  
          return nn.Sequential(*layers)
  
      def _upsample(self, x, y, scale=1):
          _, _, H, W = y.size()
          # return self.upsample(x)
          return F.interpolate(x, size=(H // scale, W // scale), mode='nearest')
  
      def _upsample_add(self, x, y):
          _, _, H, W = y.size()
          return F.interpolate(x, size=(H, W), mode='nearest') + y
  
      def forward(self, x):
          h = x
          h = self.conv1(h)
          h = self.bn1(h)
          h = self.relu1(h)
  
          h = h.float()
          h = self.maxpool(h)
          h = h.half()
  
          h = self.layer1(h)
          c2 = h
          h = self.layer2(h)
          c3 = h
          h = self.layer3(h)
          c4 = h
          h = self.layer4(h)
          c5 = h
  
          # Top-down
          p5 = self.toplayer(c5)
          p5 = self.toplayer_relu(self.toplayer_bn(p5))
  
          c4 = self.latlayer1(c4)
          c4 = self.latlayer1_relu(self.latlayer1_bn(c4))
          p4 = self._upsample_add(p5, c4)
          p4 = self.smooth1(p4)
          p4 = self.smooth1_relu(self.smooth1_bn(p4))
  
          c3 = self.latlayer2(c3)
          c3 = self.latlayer2_relu(self.latlayer2_bn(c3))
          p3 = self._upsample_add(p4, c3)
          p3 = self.smooth2(p3)
          p3 = self.smooth2_relu(self.smooth2_bn(p3))
  
          c2 = self.latlayer3(c2)
          c2 = self.latlayer3_relu(self.latlayer3_bn(c2))
          p2 = self._upsample_add(p3, c2)
          p2 = self.smooth3(p2)
          p2 = self.smooth3_relu(self.smooth3_bn(p2))
  
          p3 = self._upsample(p3, p2)
          p4 = self._upsample(p4, p2)
          p5 = self._upsample(p5, p2)
  
          out = torch.cat((p2, p3, p4, p5), 1)
  
          out = self.conv2(out)
  
          out = self.relu2(self.bn2(out))
  
          out = self.conv3(out)
          out = self._upsample(out, x, scale=self.scale)
  
          return out
  
  
  def resnet18(pretrained=False, **kwargs):
      """Constructs a ResNet-18 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
      return model
  
  
  def resnet34(pretrained=False, **kwargs):
      """Constructs a ResNet-34 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
      return model
  
  
  def resnet50(pretrained=False, **kwargs):
      """Constructs a ResNet-50 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
      if pretrained:
          pretrained_model = model_zoo.load_url(model_urls['resnet50'])
          state = model.state_dict()
          for key in state.keys():
              if key in pretrained_model.keys():
                  state[key] = pretrained_model[key]
          model.load_state_dict(state)
      return model
  
  
  def resnet101(pretrained=False, **kwargs):
      """Constructs a ResNet-101 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
      if pretrained:
          pretrained_model = model_zoo.load_url(model_urls['resnet101'])
          state = model.state_dict()
          for key in state.keys():
              if key in pretrained_model.keys():
                  state[key] = pretrained_model[key]
          model.load_state_dict(state)
      return model
  
  
  def resnet152(pretrained=False, **kwargs):
      """Constructs a ResNet-152 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
      if pretrained:
          pretrained_model = model_zoo.load_url(model_urls['resnet152'])
          state = model.state_dict()
          for key in state.keys():
              if key in pretrained_model.keys():
                  state[key] = pretrained_model[key]
          model.load_state_dict(state)
      return model
  