text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from mmcv.runner import ModuleList, force_fp32
  
  from mmdet.core import (build_anchor_generator, build_assigner,
                          build_bbox_coder, build_sampler, multi_apply)
  from ..builder import HEADS
  from ..losses import smooth_l1_loss
  from .anchor_head import AnchorHead
  
  
  # TODO: add loss evaluator for SSD
  @HEADS.register_module()
  class SSDHead(AnchorHead):
      """SSD head used in https://arxiv.org/abs/1512.02325.
  
      Args:
          num_classes (int): Number of categories excluding the background
              category.
          in_channels (int): Number of channels in the input feature map.
          anchor_generator (dict): Config dict for anchor generator
          bbox_coder (dict): Config of bounding box coder.
          reg_decoded_bbox (bool): If true, the regression loss would be
              applied directly on decoded bounding boxes, converting both
              the predicted boxes and regression targets to absolute
              coordinates format. Default False. It should be `True` when
              using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head.
          train_cfg (dict): Training config of anchor head.
          test_cfg (dict): Testing config of anchor head.
          init_cfg (dict or list[dict], optional): Initialization config dict.
      """  # noqa: W605
  
      def __init__(self,
                   num_classes=80,
                   in_channels=(512, 1024, 512, 256, 256, 256),
                   anchor_generator=dict(
                       type='SSDAnchorGenerator',
                       scale_major=False,
                       input_size=300,
                       strides=[8, 16, 32, 64, 100, 300],
                       ratios=([2], [2, 3], [2, 3], [2, 3], [2], [2]),
                       basesize_ratio_range=(0.1, 0.9)),
                   bbox_coder=dict(
                       type='DeltaXYWHBBoxCoder',
                       clip_border=True,
                       target_means=[.0, .0, .0, .0],
                       target_stds=[1.0, 1.0, 1.0, 1.0],
                   ),
                   reg_decoded_bbox=False,
                   train_cfg=None,
                   test_cfg=None,
                   init_cfg=dict(
                       type='Xavier',
                       layer='Conv2d',
                       distribution='uniform',
                       bias=0)):
          super(AnchorHead, self).__init__(init_cfg)
          self.num_classes = num_classes
          self.in_channels = in_channels
          self.cls_out_channels = num_classes + 1  # add background class
          self.anchor_generator = build_anchor_generator(anchor_generator)
          num_anchors = self.anchor_generator.num_base_anchors
  
          reg_convs = []
          cls_convs = []
          for i in range(len(in_channels)):
              reg_convs.append(
                  nn.Conv2d(
                      in_channels[i],
                      num_anchors[i] * 4,
                      kernel_size=3,
                      padding=1))
              cls_convs.append(
                  nn.Conv2d(
                      in_channels[i],
                      num_anchors[i] * (num_classes + 1),
                      kernel_size=3,
                      padding=1))
          self.reg_convs = ModuleList(reg_convs)
          self.cls_convs = ModuleList(cls_convs)
  
          self.bbox_coder = build_bbox_coder(bbox_coder)
          self.reg_decoded_bbox = reg_decoded_bbox
          self.use_sigmoid_cls = False
          self.cls_focal_loss = False
          self.train_cfg = train_cfg
          self.test_cfg = test_cfg
          # set sampling=False for archor_target
          self.sampling = False
          if self.train_cfg:
              self.assigner = build_assigner(self.train_cfg.assigner)
              # SSD sampling=False so use PseudoSampler
              sampler_cfg = dict(type='PseudoSampler')
              self.sampler = build_sampler(sampler_cfg, context=self)
          self.fp16_enabled = False
  
      def forward(self, feats):
          """Forward features from the upstream network.
  
          Args:
              feats (tuple[Tensor]): Features from the upstream network, each is
                  a 4D-tensor.
  
          Returns:
              tuple:
                  cls_scores (list[Tensor]): Classification scores for all scale
                      levels, each is a 4D-tensor, the channels number is
                      num_anchors * num_classes.
                  bbox_preds (list[Tensor]): Box energies / deltas for all scale
                      levels, each is a 4D-tensor, the channels number is
                      num_anchors * 4.
          """
          cls_scores = []
          bbox_preds = []
          for feat, reg_conv, cls_conv in zip(feats, self.reg_convs,
                                              self.cls_convs):
              cls_scores.append(cls_conv(feat))
              bbox_preds.append(reg_conv(feat))
          return cls_scores, bbox_preds
  
      def loss_single(self, cls_score, bbox_pred, anchor, labels, label_weights,
                      bbox_targets, bbox_weights, num_total_samples):
          """Compute loss of a single image.
  
          Args:
              cls_score (Tensor): Box scores for eachimage
                  Has shape (num_total_anchors, num_classes).
              bbox_pred (Tensor): Box energies / deltas for each image
                  level with shape (num_total_anchors, 4).
              anchors (Tensor): Box reference for each scale level with shape
                  (num_total_anchors, 4).
              labels (Tensor): Labels of each anchors with shape
                  (num_total_anchors,).
              label_weights (Tensor): Label weights of each anchor with shape
                  (num_total_anchors,)
              bbox_targets (Tensor): BBox regression targets of each anchor wight
                  shape (num_total_anchors, 4).
              bbox_weights (Tensor): BBox regression loss weights of each anchor
                  with shape (num_total_anchors, 4).
              num_total_samples (int): If sampling, num total samples equal to
                  the number of total anchors; Otherwise, it is the number of
                  positive anchors.
  
          Returns:
              dict[str, Tensor]: A dictionary of loss components.
          """
  
          loss_cls_all = F.cross_entropy(
              cls_score, labels, reduction='none') * label_weights
          # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
          pos_inds = ((labels >= 0) & (labels < self.num_classes)).nonzero(
              as_tuple=False).reshape(-1)
          neg_inds = (labels == self.num_classes).nonzero(
              as_tuple=False).view(-1)
  
          num_pos_samples = pos_inds.size(0)
          num_neg_samples = self.train_cfg.neg_pos_ratio * num_pos_samples
          if num_neg_samples > neg_inds.size(0):
              num_neg_samples = neg_inds.size(0)
          topk_loss_cls_neg, _ = loss_cls_all[neg_inds].topk(num_neg_samples)
          loss_cls_pos = loss_cls_all[pos_inds].sum()
          loss_cls_neg = topk_loss_cls_neg.sum()
          loss_cls = (loss_cls_pos + loss_cls_neg) / num_total_samples
  
          if self.reg_decoded_bbox:
              # When the regression loss (e.g. `IouLoss`, `GIouLoss`)
              # is applied directly on the decoded bounding boxes, it
              # decodes the already encoded coordinates to absolute format.
              bbox_pred = self.bbox_coder.decode(anchor, bbox_pred)
  
          loss_bbox = smooth_l1_loss(
              bbox_pred,
              bbox_targets,
              bbox_weights,
              beta=self.train_cfg.smoothl1_beta,
              avg_factor=num_total_samples)
          return loss_cls[None], loss_bbox
  
      @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
      def loss(self,
               cls_scores,
               bbox_preds,
               gt_bboxes,
               gt_labels,
               img_metas,
               gt_bboxes_ignore=None):
          """Compute losses of the head.
  
          Args:
              cls_scores (list[Tensor]): Box scores for each scale level
                  Has shape (N, num_anchors * num_classes, H, W)
              bbox_preds (list[Tensor]): Box energies / deltas for each scale
                  level with shape (N, num_anchors * 4, H, W)
              gt_bboxes (list[Tensor]): each item are the truth boxes for each
                  image in [tl_x, tl_y, br_x, br_y] format.
              gt_labels (list[Tensor]): class indices corresponding to each box
              img_metas (list[dict]): Meta information of each image, e.g.,
                  image size, scaling factor, etc.
              gt_bboxes_ignore (None | list[Tensor]): specify which bounding
                  boxes can be ignored when computing the loss.
  
          Returns:
              dict[str, Tensor]: A dictionary of loss components.
          """
          featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
          assert len(featmap_sizes) == self.anchor_generator.num_levels
  
          device = cls_scores[0].device
  
          anchor_list, valid_flag_list = self.get_anchors(
              featmap_sizes, img_metas, device=device)
          cls_reg_targets = self.get_targets(
              anchor_list,
              valid_flag_list,
              gt_bboxes,
              img_metas,
              gt_bboxes_ignore_list=gt_bboxes_ignore,
              gt_labels_list=gt_labels,
              label_channels=1,
              unmap_outputs=False)
          if cls_reg_targets is None:
              return None
          (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
           num_total_pos, num_total_neg) = cls_reg_targets
  
          num_images = len(img_metas)
          all_cls_scores = torch.cat([
              s.permute(0, 2, 3, 1).reshape(
                  num_images, -1, self.cls_out_channels) for s in cls_scores
          ], 1)
          all_labels = torch.cat(labels_list, -1).view(num_images, -1)
          all_label_weights = torch.cat(label_weights_list,
                                        -1).view(num_images, -1)
          all_bbox_preds = torch.cat([
              b.permute(0, 2, 3, 1).reshape(num_images, -1, 4)
              for b in bbox_preds
          ], -2)
          all_bbox_targets = torch.cat(bbox_targets_list,
                                       -2).view(num_images, -1, 4)
          all_bbox_weights = torch.cat(bbox_weights_list,
                                       -2).view(num_images, -1, 4)
  
          # concat all level anchors to a single tensor
          all_anchors = []
          for i in range(num_images):
              all_anchors.append(torch.cat(anchor_list[i]))
  
          # check NaN and Inf
          assert torch.isfinite(all_cls_scores).all().item(), \
              'classification scores become infinite or NaN!'
          assert torch.isfinite(all_bbox_preds).all().item(), \
              'bbox predications become infinite or NaN!'
  
          losses_cls, losses_bbox = multi_apply(
              self.loss_single,
              all_cls_scores,
              all_bbox_preds,
              all_anchors,
              all_labels,
              all_label_weights,
              all_bbox_targets,
              all_bbox_weights,
              num_total_samples=num_total_pos)
          return dict(loss_cls=losses_cls, loss_bbox=losses_bbox)
  