text: import warnings
  from abc import ABCMeta, abstractmethod
  from collections import OrderedDict
  
  import mmcv
  import torch
  import torch.distributed as dist
  import torch.nn as nn
  from mmcv.runner import auto_fp16
  from mmcv.utils import print_log
  
  from mmocr.core import imshow_text_label
  from mmocr.utils import get_root_logger
  
  
  class BaseRecognizer(nn.Module, metaclass=ABCMeta):
      """Base class for text recognition."""
  
      def __init__(self):
          super().__init__()
          self.fp16_enabled = False
  
      @abstractmethod
      def extract_feat(self, imgs):
          """Extract features from images."""
          pass
  
      @abstractmethod
      def forward_train(self, imgs, img_metas, **kwargs):
          """
          Args:
              img (tensor): tensors with shape (N, C, H, W).
                  Typically should be mean centered and std scaled.
              img_metas (list[dict]): List of image info dict where each dict
                  has: 'img_shape', 'scale_factor', 'flip', and may also contain
                  'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
                  For details of the values of these keys, see
                  :class:`mmdet.datasets.pipelines.Collect`.
              kwargs (keyword arguments): Specific to concrete implementation.
          """
          pass
  
      @abstractmethod
      def simple_test(self, img, img_metas, **kwargs):
          pass
  
      @abstractmethod
      def aug_test(self, imgs, img_metas, **kwargs):
          """Test function with test time augmentation.
  
          Args:
              imgs (list[tensor]): Tensor should have shape NxCxHxW,
                  which contains all images in the batch.
              img_metas (list[list[dict]]): The metadata of images.
          """
          pass
  
      def init_weights(self, pretrained=None):
          """Initialize the weights for detector.
  
          Args:
              pretrained (str, optional): Path to pre-trained weights.
                  Defaults to None.
          """
          if pretrained is not None:
              logger = get_root_logger()
              print_log(f'load model from: {pretrained}', logger=logger)
  
      def forward_test(self, imgs):
          """
          Args:
              imgs (tensor | list[tensor]): Tensor should have shape NxCxHxW,
                  which contains all images in the batch.
              img_metas (list[dict] | list[list[dict]]):
                  The outer list indicates images in a batch.
          """
          if isinstance(imgs, list):
              assert len(imgs) > 0
              assert imgs[0].size(0) == 1, ('aug test does not support '
                                            f'inference with batch size '
                                            f'{imgs[0].size(0)}')
              # assert len(imgs) == len(img_metas)
              # return self.aug_test(imgs, img_metas, **kwargs)
  
          return self.simple_test(imgs)
  
      @auto_fp16(apply_to=('img', ))
      def forward(self, img):
          """Calls either :func:`forward_train` or :func:`forward_test` depending
          on whether ``return_loss`` is ``True``.
  
          Note that img and img_meta are single-nested (i.e. tensor and
          list[dict]).
          """
  
          if isinstance(img, list):
              for idx, each_img in enumerate(img):
                  if each_img.dim() == 3:
                      img[idx] = each_img.unsqueeze(0)
          # else:
          #     if len(img_metas) == 1 and isinstance(img_metas[0], list):
          #         img_metas = img_metas[0]
  
          return self.forward_test(img)#, img_metas, **kwargs)
  
      def _parse_losses(self, losses):
          """Parse the raw outputs (losses) of the network.
  
          Args:
              losses (dict): Raw outputs of the network, which usually contain
                  losses and other necessary infomation.
  
          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, optimizer):
          """The iteration step during training.
  
          This method defines an iteration step during training, except for the
          back propagation and optimizer update, which are done by an optimizer
          hook. Note that in some complicated cases or models (e.g. GAN),
          the whole process (including the back propagation and optimizer update)
          is also defined by this method.
  
          Args:
              data (dict): The outputs 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 is a
                  weighted sum of multiple losses.
                  - ``log_vars`` contains all the variables to be sent to the
                  logger.
                  - ``num_samples`` indicates the batch size used for
                  averaging the logs (Note: for the
                  DDP model, num_samples refers to the batch size for each GPU).
          """
          losses = self(**data)
          loss, log_vars = self._parse_losses(losses)
  
          outputs = dict(
              loss=loss, log_vars=log_vars, num_samples=len(data['img_metas']))
  
          return outputs
  
      def val_step(self, data, optimizer):
          """The iteration step during validation.
  
          This method shares the same signature as :func:`train_step`, but is
          used during val epochs. Note that the evaluation after training epochs
          is not implemented by this method, but by an evaluation hook.
          """
          losses = self(**data)
          loss, log_vars = self._parse_losses(losses)
  
          outputs = dict(
              loss=loss, log_vars=log_vars, num_samples=len(data['img_metas']))
  
          return outputs
  
      def show_result(self,
                      img,
                      result,
                      gt_label='',
                      win_name='',
                      show=False,
                      wait_time=0,
                      out_file=None,
                      **kwargs):
          """Draw `result` on `img`.
  
          Args:
              img (str or tensor): The image to be displayed.
              result (dict): The results to draw on `img`.
              gt_label (str): Ground truth label of img.
              win_name (str): The window name.
              wait_time (int): Value of waitKey param.
                  Default: 0.
              show (bool): Whether to show the image.
                  Default: False.
              out_file (str or None): The output filename.
                  Default: None.
  
          Returns:
              img (tensor): Only if not `show` or `out_file`.
          """
          img = mmcv.imread(img)
          img = img.copy()
          pred_label = None
          if 'text' in result.keys():
              pred_label = result['text']
  
          # if out_file specified, do not show image in window
          if out_file is not None:
              show = False
          # draw text label
          if pred_label is not None:
              img = imshow_text_label(
                  img,
                  pred_label,
                  gt_label,
                  show=show,
                  win_name=win_name,
                  wait_time=wait_time,
                  out_file=out_file)
  
          if not (show or out_file):
              warnings.warn('show==False and out_file is not specified, only '
                            'result image will be returned')
              return img
  
          return img
  