content: from abc import ABCMeta, abstractmethod
  from collections import OrderedDict
  
  import torch
  import torch.distributed as dist
  import torch.nn as nn
  
  from .. import builder
  
  
  class BaseLocalizer(nn.Module, metaclass=ABCMeta):
      """Base class for localizers.
  
      All localizers should subclass it. All subclass should overwrite:
      Methods:``forward_train``, supporting to forward when training.
      Methods:``forward_test``, supporting to forward when testing.
      """
  
      def __init__(self, backbone, cls_head, train_cfg=None, test_cfg=None):
          super().__init__()
          self.backbone = builder.build_backbone(backbone)
          self.cls_head = builder.build_head(cls_head)
  
          self.train_cfg = train_cfg
          self.test_cfg = test_cfg
          self.init_weights()
  
      def init_weights(self):
          """Weight initialization for model."""
          self.backbone.init_weights()
          self.cls_head.init_weights()
  
      def extract_feat(self, imgs):
          """Extract features through a backbone.
  
          Args:
              imgs (torch.Tensor): The input images.
          Returns:
              torch.tensor: The extracted features.
          """
          x = self.backbone(imgs)
          return x
  
      @abstractmethod
      def forward_train(self, imgs, labels):
          """Defines the computation performed at training."""
  
      @abstractmethod
      def forward_test(self, imgs):
          """Defines the computation performed at testing."""
  
      def forward(self, imgs, return_loss=True, **kwargs):
          """Define the computation performed at every call."""
          if return_loss:
              return self.forward_train(imgs, **kwargs)
  
          return self.forward_test(imgs, **kwargs)
  
      @staticmethod
      def _parse_losses(losses):
          """Parse the raw outputs (losses) of the network.
  
          Args:
              losses (dict): Raw output of the network, which usually contain
                  losses and other necessary information.
  
          Returns:
              tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor
                  which may be a weighted sum of all losses, log_vars contains
                  all the variables to be sent to the logger.
          """
          log_vars = OrderedDict()
          for loss_name, loss_value in losses.items():
              if isinstance(loss_value, torch.Tensor):
                  log_vars[loss_name] = loss_value.mean()
              elif isinstance(loss_value, list):
                  log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value)
              else:
                  raise TypeError(
                      f'{loss_name} is not a tensor or list of tensors')
  
          loss = sum(_value for _key, _value in log_vars.items()
                     if 'loss' in _key)
  
          log_vars['loss'] = loss
          for loss_name, loss_value in log_vars.items():
              # reduce loss when distributed training
              if dist.is_available() and dist.is_initialized():
                  loss_value = loss_value.data.clone()
                  dist.all_reduce(loss_value.div_(dist.get_world_size()))
              log_vars[loss_name] = loss_value.item()
  
          return loss, log_vars
  
      def train_step(self, data_batch, optimizer, **kwargs):
          """The iteration step during training.
  
          This method defines an iteration step during training, except for the
          back propagation and optimizer updating, which are done in an optimizer
          hook. Note that in some complicated cases or models, the whole process
          including back propagation and optimizer updating is also defined in
          this method, such as GAN.
  
          Args:
              data_batch (dict): The output of dataloader.
              optimizer (:obj:`torch.optim.Optimizer` | dict): The optimizer of
                  runner is passed to ``train_step()``. This argument is unused
                  and reserved.
  
          Returns:
              dict: It should contain at least 3 keys: ``loss``, ``log_vars``,
                  ``num_samples``.
                  ``loss`` is a tensor for back propagation, which can be a
                  weighted sum of multiple losses.
                  ``log_vars`` contains all the variables to be sent to the
                  logger.
                  ``num_samples`` indicates the batch size (when the model is
                  DDP, it means the batch size on each GPU), which is used for
                  averaging the logs.
          """
          losses = self.forward(**data_batch)
  
          loss, log_vars = self._parse_losses(losses)
  
          outputs = dict(
              loss=loss,
              log_vars=log_vars,
              num_samples=len(next(iter(data_batch.values()))))
  
          return outputs
  
      def val_step(self, data_batch, optimizer, **kwargs):
          """The iteration step during validation.
  
          This method shares the same signature as :func:`train_step`, but used
          during val epochs. Note that the evaluation after training epochs is
          not implemented with this method, but an evaluation hook.
          """
          results = self.forward(return_loss=False, **data_batch)
  
          outputs = dict(results=results)
  
          return outputs
  