code: """
  RetinaNet code borrowed from
  https://github.com/yhenon/pytorch-retinanet/blob/master/retinanet/model.py
  """
  
  import torch.nn as nn
  import torch
  import math
  import torch.utils.model_zoo as model_zoo
  from torchvision.ops import nms
  from .retinanet_utils import BasicBlock, Bottleneck, BBoxTransform, ClipBoxes, Anchors, FocalLoss
  
  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/resnet101-5d3b4d8f.pth',
      'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
  }
  
  
  class PyramidFeatures(nn.Module):
      def __init__(self, C3_size, C4_size, C5_size, feature_size=256):
          super(PyramidFeatures, self).__init__()
  
          # upsample C5 to get P5 from the FPN paper
          self.P5_1 = nn.Conv2d(C5_size, feature_size, kernel_size=1, stride=1, padding=0)
          self.P5_upsampled = nn.Upsample(scale_factor=2, mode='nearest')
          self.P5_2 = nn.Conv2d(feature_size, feature_size, kernel_size=3, stride=1, padding=1)
  
          # add P5 elementwise to C4
          self.P4_1 = nn.Conv2d(C4_size, feature_size, kernel_size=1, stride=1, padding=0)
          self.P4_upsampled = nn.Upsample(scale_factor=2, mode='nearest')
          self.P4_2 = nn.Conv2d(feature_size, feature_size, kernel_size=3, stride=1, padding=1)
  
          # add P4 elementwise to C3
          self.P3_1 = nn.Conv2d(C3_size, feature_size, kernel_size=1, stride=1, padding=0)
          self.P3_2 = nn.Conv2d(feature_size, feature_size, kernel_size=3, stride=1, padding=1)
  
          # "P6 is obtained via a 3x3 stride-2 conv on C5"
          self.P6 = nn.Conv2d(C5_size, feature_size, kernel_size=3, stride=2, padding=1)
  
          # "P7 is computed by applying ReLU followed by a 3x3 stride-2 conv on P6"
          self.P7_1 = nn.ReLU()
          self.P7_2 = nn.Conv2d(feature_size, feature_size, kernel_size=3, stride=2, padding=1)
  
      def forward(self, inputs):
          C3, C4, C5 = inputs
  
          P5_x = self.P5_1(C5)
          P5_upsampled_x = self.P5_upsampled(P5_x)
          P5_x = self.P5_2(P5_x)
  
          P4_x = self.P4_1(C4)
          P4_x = P5_upsampled_x + P4_x
          P4_upsampled_x = self.P4_upsampled(P4_x)
          P4_x = self.P4_2(P4_x)
  
          P3_x = self.P3_1(C3)
          P3_x = P3_x + P4_upsampled_x
          P3_x = self.P3_2(P3_x)
  
          P6_x = self.P6(C5)
  
          P7_x = self.P7_1(P6_x)
          P7_x = self.P7_2(P7_x)
  
          return [P3_x, P4_x, P5_x, P6_x, P7_x]
  
  
  class RegressionModel(nn.Module):
      def __init__(self, num_features_in, num_anchors=9, feature_size=256):
          super(RegressionModel, self).__init__()
  
          self.conv1 = nn.Conv2d(num_features_in, feature_size, kernel_size=3, padding=1)
          self.act1 = nn.ReLU()
  
          self.conv2 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1)
          self.act2 = nn.ReLU()
  
          self.conv3 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1)
          self.act3 = nn.ReLU()
  
          self.conv4 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1)
          self.act4 = nn.ReLU()
  
          self.output = nn.Conv2d(feature_size, num_anchors * 4, kernel_size=3, padding=1)
  
      def forward(self, x):
          out = self.conv1(x)
          out = self.act1(out)
  
          out = self.conv2(out)
          out = self.act2(out)
  
          out = self.conv3(out)
          out = self.act3(out)
  
          out = self.conv4(out)
          out = self.act4(out)
  
          out = self.output(out)
  
          # out is B x C x W x H, with C = 4*num_anchors
          out = out.permute(0, 2, 3, 1)
  
          return out.contiguous().view(out.shape[0], -1, 4)
  
  
  class ClassificationModel(nn.Module):
      def __init__(self, num_features_in, num_anchors=9, num_classes=80, prior=0.01, feature_size=256):
          super(ClassificationModel, self).__init__()
  
          self.num_classes = num_classes
          self.num_anchors = num_anchors
  
          self.conv1 = nn.Conv2d(num_features_in, feature_size, kernel_size=3, padding=1)
          self.act1 = nn.ReLU()
  
          self.conv2 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1)
          self.act2 = nn.ReLU()
  
          self.conv3 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1)
          self.act3 = nn.ReLU()
  
          self.conv4 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1)
          self.act4 = nn.ReLU()
  
          self.output = nn.Conv2d(feature_size, num_anchors * num_classes, kernel_size=3, padding=1)
          self.output_act = nn.Sigmoid()
  
      def forward(self, x):
          out = self.conv1(x)
          out = self.act1(out)
  
          out = self.conv2(out)
          out = self.act2(out)
  
          out = self.conv3(out)
          out = self.act3(out)
  
          out = self.conv4(out)
          out = self.act4(out)
  
          out = self.output(out)
          out = self.output_act(out)
  
          # out is B x C x W x H, with C = n_classes + n_anchors
          out1 = out.permute(0, 2, 3, 1)
  
          batch_size, width, height, channels = out1.shape
  
          out2 = out1.view(batch_size, width, height, self.num_anchors, self.num_classes)
  
          return out2.contiguous().view(x.shape[0], -1, self.num_classes)
  
  
  class ResNet(nn.Module):
  
      def __init__(self, num_classes, block, layers):
          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.relu = nn.ReLU(inplace=True)
          self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
          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)
  
          if block == BasicBlock:
              fpn_sizes = [self.layer2[layers[1] - 1].conv2.out_channels, self.layer3[layers[2] - 1].conv2.out_channels,
                           self.layer4[layers[3] - 1].conv2.out_channels]
          elif block == Bottleneck:
              fpn_sizes = [self.layer2[layers[1] - 1].conv3.out_channels, self.layer3[layers[2] - 1].conv3.out_channels,
                           self.layer4[layers[3] - 1].conv3.out_channels]
          else:
              raise ValueError(f"Block type {block} not understood")
  
          self.fpn = PyramidFeatures(fpn_sizes[0], fpn_sizes[1], fpn_sizes[2])
  
          self.regressionModel = RegressionModel(256)
          self.classificationModel = ClassificationModel(256, num_classes=num_classes)
  
          self.anchors = Anchors()
  
          self.regressBoxes = BBoxTransform()
  
          self.clipBoxes = ClipBoxes()
  
          self.focalLoss = FocalLoss()
  
          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_()
  
          prior = 0.01
  
          self.classificationModel.output.weight.data.fill_(0)
          self.classificationModel.output.bias.data.fill_(-math.log((1.0 - prior) / prior))
  
          self.regressionModel.output.weight.data.fill_(0)
          self.regressionModel.output.bias.data.fill_(0)
  
          self.freeze_bn()
  
      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 = [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 freeze_bn(self):
          '''Freeze BatchNorm layers.'''
          for layer in self.modules():
              if isinstance(layer, nn.BatchNorm2d):
                  layer.eval()
  
      def forward(self, inputs):
  
          if self.training:
              img_batch, annotations = inputs
          else:
              img_batch = inputs
  
          x = self.conv1(img_batch)
          x = self.bn1(x)
          x = self.relu(x)
          x = self.maxpool(x)
  
          x1 = self.layer1(x)
          x2 = self.layer2(x1)
          x3 = self.layer3(x2)
          x4 = self.layer4(x3)
  
          features = self.fpn([x2, x3, x4])
  
          regression = torch.cat([self.regressionModel(feature) for feature in features], dim=1)
  
          classification = torch.cat([self.classificationModel(feature) for feature in features], dim=1)
  
          anchors = self.anchors(img_batch)
  
          if self.training:
              return self.focalLoss(classification, regression, anchors, annotations)
          else:
              transformed_anchors = self.regressBoxes(anchors, regression)
              transformed_anchors = self.clipBoxes(transformed_anchors, img_batch)
  
              scores = torch.max(classification, dim=2, keepdim=True)[0]
  
              scores_over_thresh = (scores > 0.05)[0, :, 0]
  
              if scores_over_thresh.sum() == 0:
                  # no boxes to NMS, just return
                  return [torch.zeros(0), torch.zeros(0), torch.zeros(0, 4)]
  
              classification = classification[:, scores_over_thresh, :]
              transformed_anchors = transformed_anchors[:, scores_over_thresh, :]
              scores = scores[:, scores_over_thresh, :]
  
              anchors_nms_idx = nms(transformed_anchors[0, :, :], scores[0, :, 0], 0.5)
  
              nms_scores, nms_class = classification[0, anchors_nms_idx, :].max(dim=1)
  
              return [nms_scores, nms_class, transformed_anchors[0, anchors_nms_idx, :]]
  
  
  def resnet18(num_classes, pretrained=False, **kwargs):
      """Constructs a ResNet-18 model.
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(num_classes, BasicBlock, [2, 2, 2, 2], **kwargs)
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['resnet18'], model_dir='.'), strict=False)
      return model
  
  
  def resnet34(num_classes, pretrained=False, **kwargs):
      """Constructs a ResNet-34 model.
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(num_classes, BasicBlock, [3, 4, 6, 3], **kwargs)
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['resnet34'], model_dir='.'), strict=False)
      return model
  
  
  def resnet50(num_classes, pretrained=False, **kwargs):
      """Constructs a ResNet-50 model.
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(num_classes, Bottleneck, [3, 4, 6, 3], **kwargs)
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['resnet50'], model_dir='.'), strict=False)
      return model
  
  
  def resnet101(num_classes, pretrained=False, **kwargs):
      """Constructs a ResNet-101 model.
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(num_classes, Bottleneck, [3, 4, 23, 3], **kwargs)
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['resnet101'], model_dir='.'), strict=False)
      return model
  
  
  def resnet152(num_classes, pretrained=False, **kwargs):
      """Constructs a ResNet-152 model.
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(num_classes, Bottleneck, [3, 8, 36, 3], **kwargs)
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['resnet152'], model_dir='.'), strict=False)
      return model
  
apis: torch.nn.BatchNorm2d torch.nn.MaxPool2d torch.nn.Upsample torch.nn.Conv2d torch.nn.Sequential torch.max torch.zeros torch.utils.model_zoo.load_url torch.nn.Sigmoid torch.nn.ReLU