content: from abc import ABCMeta, abstractmethod
  from collections import OrderedDict
  
  import torch
  import torch.distributed as dist
  import torch.nn as nn
  import torch.nn.functional as F
  from mmcv.runner import auto_fp16
  
  from .. import builder
  
  
  class BaseRecognizer(nn.Module, metaclass=ABCMeta):
      """Base class for recognizers.
  
      All recognizers should subclass it.
      All subclass should overwrite:
  
      - Methods:``forward_train``, supporting to forward when training.
      - Methods:``forward_test``, supporting to forward when testing.
  
      Args:
          backbone (dict): Backbone modules to extract feature.
          cls_head (dict): Classification head to process feature.
          train_cfg (dict | None): Config for training. Default: None.
          test_cfg (dict | None): Config for testing. Default: None.
      """
  
      def __init__(self,
                   backbone,
                   cls_head,
                   neck=None,
                   debias_head=None,
                   train_cfg=None,
                   test_cfg=None):
          super().__init__()
          self.backbone = builder.build_backbone(backbone)
          if neck is not None:
              self.neck = builder.build_neck(neck)
          self.cls_head = builder.build_head(cls_head)
          if debias_head is not None:
              self.debias_head = builder.build_head(debias_head)
  
          self.train_cfg = train_cfg
          self.test_cfg = test_cfg
  
          # aux_info is the list of tensor names beyond 'imgs' and 'label' which
          # will be used in train_step and val_step, data_batch should contain
          # these tensors
          self.aux_info = []
          if train_cfg is not None and 'aux_info' in train_cfg:
              self.aux_info = train_cfg['aux_info']
  
          self.init_weights()
  
          self.fp16_enabled = False
  
      def init_weights(self):
          """Initialize the model network weights."""
          self.backbone.init_weights()
          self.cls_head.init_weights()
          if hasattr(self, 'neck'):
              self.neck.init_weights()
          if hasattr(self, 'debias_head'):
              self.debias_head.init_weights()
  
      @auto_fp16()
      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
  
      def evidence_to_prob(self, output, evidence_type):
          if evidence_type == 'relu':
              from ..losses.edl_loss import relu_evidence as evidence
          elif evidence_type == 'exp':
              from ..losses.edl_loss import exp_evidence as evidence
          elif evidence_type == 'softplus':
              from ..losses.edl_loss import softplus_evidence as evidence
          alpha = evidence(output) + 1
          S = torch.sum(alpha, dim=-1, keepdim=True)
          prob = alpha / S
          return prob
  
  
      def average_clip(self, cls_score, num_segs=1):
          """Averaging class score over multiple clips.
  
          Using different averaging types ('score' or 'prob' or None,
          which defined in test_cfg) to computed the final averaged
          class score. Only called in test mode.
  
          Args:
              cls_score (torch.Tensor): Class score to be averaged.
              num_segs (int): Number of clips for each input sample.
  
          Returns:
              torch.Tensor: Averaged class score.
          """
          if 'average_clips' not in self.test_cfg.keys():
              raise KeyError('"average_clips" must defined in test_cfg\'s keys')
  
          average_clips = self.test_cfg['average_clips']
          if average_clips not in ['score', 'prob', 'evidence', None]:
              raise ValueError(f'{average_clips} is not supported. '
                               f'Currently supported ones are '
                               f'["score", "prob", "evidence", None]')
  
          if average_clips is None:
              return cls_score
  
          batch_size = cls_score.shape[0]
          cls_score = cls_score.view(batch_size // num_segs, num_segs, -1)
  
          if average_clips == 'prob':
              cls_score = F.softmax(cls_score, dim=2).mean(dim=1)
          elif average_clips == 'score':
              cls_score = cls_score.mean(dim=1)
          elif average_clips == 'evidence':
              assert 'evidence_type' in self.test_cfg.keys()
              cls_score = self.evidence_to_prob(cls_score, self.test_cfg['evidence_type'])
              cls_score = cls_score.mean(dim=1)
  
          return cls_score
  
      @abstractmethod
      def forward_train(self, imgs, labels, **kwargs):
          """Defines the computation performed at every call when training."""
  
      @abstractmethod
      def forward_test(self, imgs):
          """Defines the computation performed at every call when evaluation and
          testing."""
  
      @abstractmethod
      def forward_gradcam(self, imgs):
          """Defines the computation performed at every all when using gradcam
          utils."""
  
      @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().to(log_vars['loss'].device)
                  dist.all_reduce(loss_value.div_(dist.get_world_size()))
              log_vars[loss_name] = loss_value.item()
  
          return loss, log_vars
  
      def forward(self, imgs, label=None, return_loss=True, **kwargs):
          """Define the computation performed at every call."""
          if kwargs.get('gradcam', False):
              del kwargs['gradcam']
              return self.forward_gradcam(imgs, **kwargs)
          if kwargs.get('get_feat', False):
              del kwargs['get_feat']
              return self.get_feat(imgs, **kwargs)
          if return_loss:
              if label is None:
                  raise ValueError('Label should not be None.')
              return self.forward_train(imgs, label, **kwargs)
  
          return self.forward_test(imgs, **kwargs)
  
      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.
          """
          imgs = data_batch['imgs']
          label = data_batch['label']
  
          aux_info = {}
          for item in self.aux_info:
              assert item in data_batch
              aux_info[item] = data_batch[item]
          aux_info.update(kwargs)
  
          losses = self(imgs, label, return_loss=True, **aux_info)
  
          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.
          """
          imgs = data_batch['imgs']
          label = data_batch['label']
  
          aux_info = {}
          for item in self.aux_info:
              aux_info[item] = data_batch[item]
  
          losses = self(imgs, label, return_loss=True, **aux_info)
  
          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
  