text: from abc import ABCMeta, abstractmethod
  from collections import OrderedDict
  
  import mmcv
  import numpy as np
  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 mmdet.utils import get_root_logger
  
  
  class BaseDetector(nn.Module, metaclass=ABCMeta):
      """Base class for detectors."""
  
      def __init__(self):
          super(BaseDetector, self).__init__()
          self.fp16_enabled = False
  
      @property
      def with_neck(self):
          """bool: whether the detector has a neck"""
          return hasattr(self, 'neck') and self.neck is not None
  
      # TODO: these properties need to be carefully handled
      # for both single stage & two stage detectors
      @property
      def with_shared_head(self):
          """bool: whether the detector has a shared head in the RoI Head"""
          return hasattr(self, 'roi_head') and self.roi_head.with_shared_head
  
      @property
      def with_bbox(self):
          """bool: whether the detector has a bbox head"""
          return ((hasattr(self, 'roi_head') and self.roi_head.with_bbox)
                  or (hasattr(self, 'bbox_head') and self.bbox_head is not None))
  
      @property
      def with_mask(self):
          """bool: whether the detector has a mask head"""
          return ((hasattr(self, 'roi_head') and self.roi_head.with_mask)
                  or (hasattr(self, 'mask_head') and self.mask_head is not None))
  
      @abstractmethod
      def extract_feat(self, imgs):
          """Extract features from images."""
          pass
  
      def extract_feats(self, imgs):
          """Extract features from multiple images.
  
          Args:
              imgs (list[torch.Tensor]): A list of images. The images are
                  augmented from the same image but in different ways.
  
          Returns:
              list[torch.Tensor]: Features of different images
          """
          assert isinstance(imgs, list)
          return [self.extract_feat(img) for img in imgs]
  
      @abstractmethod
      def forward_train(self, imgs, img_metas, **kwargs):
          """
          Args:
              img (list[Tensor]): List of tensors of shape (1, C, H, W).
                  Typically these 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 on the values of these keys, see
                  :class:`mmdet.datasets.pipelines.Collect`.
              kwargs (keyword arguments): Specific to concrete implementation.
          """
          pass
  
      async def async_simple_test(self, img, img_metas, **kwargs):
          raise NotImplementedError
  
      @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."""
          pass
  
      def init_weights(self, pretrained=None):
          """Initialize the weights in 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)
  
      async def aforward_test(self, *, img, img_metas, **kwargs):
          for var, name in [(img, 'img'), (img_metas, 'img_metas')]:
              if not isinstance(var, list):
                  raise TypeError(f'{name} must be a list, but got {type(var)}')
  
          num_augs = len(img)
          if num_augs != len(img_metas):
              raise ValueError(f'num of augmentations ({len(img)}) '
                               f'!= num of image metas ({len(img_metas)})')
          # TODO: remove the restriction of samples_per_gpu == 1 when prepared
          samples_per_gpu = img[0].size(0)
          assert samples_per_gpu == 1
  
          if num_augs == 1:
              return await self.async_simple_test(img[0], img_metas[0], **kwargs)
          else:
              raise NotImplementedError
  
      def forward_test(self, imgs, img_metas, **kwargs):
          """
          Args:
              imgs (List[Tensor]): the outer list indicates test-time
                  augmentations and inner Tensor should have a shape NxCxHxW,
                  which contains all images in the batch.
              img_metas (List[List[dict]]): the outer list indicates test-time
                  augs (multiscale, flip, etc.) and the inner list indicates
                  images in a batch.
          """
          #imgs = [imgs]
          for var, name in [(imgs, 'imgs'), (img_metas, 'img_metas')]:
              #print ("+++++++++++!!!!!!!!!!",var)
              if not isinstance(var, list):
                  raise TypeError(f'{name} must be a list, but got {type(var)}')
  
          num_augs = len(imgs)
          if num_augs != len(img_metas):
              raise ValueError(f'num of augmentations ({len(imgs)}) '
                               f'!= num of image meta ({len(img_metas)})')
  
          if num_augs == 1:
              # proposals (List[List[Tensor]]): the outer list indicates
              # test-time augs (multiscale, flip, etc.) and the inner list
              # indicates images in a batch.
              # The Tensor should have a shape Px4, where P is the number of
              # proposals.
              if 'proposals' in kwargs:
                  kwargs['proposals'] = kwargs['proposals'][0]
              return self.simple_test(imgs[0], img_metas[0], **kwargs)
          else:
              assert imgs[0].size(0) == 1, 'aug test does not support ' \
                                           'inference with batch size ' \
                                           f'{imgs[0].size(0)}'
              # TODO: support test augmentation for predefined proposals
              assert 'proposals' not in kwargs
              return self.aug_test(imgs, img_metas, **kwargs)
  
      @auto_fp16(apply_to=('img', ))
      def forward(self, img, gt_boxes, return_loss=True):
          """Calls either :func:`forward_train` or :func:`forward_test` depending
          on whether ``return_loss`` is ``True``.
  
          Note this setting will change the expected inputs. When
          ``return_loss=True``, img and img_meta are single-nested (i.e. Tensor
          and List[dict]), and when ``resturn_loss=False``, img and img_meta
          should be double nested (i.e.  List[Tensor], List[List[dict]]), with
          the outer list indicating test time augmentations.
          """
          if return_loss:
              return self.forward_train(img, gt_boxes)
          else:
              return self.forward_test(img, gt_boxes)
  
      def _parse_losses(self, losses):
          """Parse the raw outputs (losses) of the network.
  
          Args:
              losses (dict): Raw output 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()))
              #print (loss_name,loss_value)
              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 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 (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(**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 used
          during val epochs. Note that the evaluation after training epochs is
          not implemented with this method, but 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,
                      score_thr=0.3,
                      bbox_color='green',
                      text_color='green',
                      thickness=1,
                      font_scale=0.5,
                      win_name='',
                      show=False,
                      wait_time=0,
                      out_file=None):
          """Draw `result` over `img`.
  
          Args:
              img (str or Tensor): The image to be displayed.
              result (Tensor or tuple): The results to draw over `img`
                  bbox_result or (bbox_result, segm_result).
              score_thr (float, optional): Minimum score of bboxes to be shown.
                  Default: 0.3.
              bbox_color (str or tuple or :obj:`Color`): Color of bbox lines.
              text_color (str or tuple or :obj:`Color`): Color of texts.
              thickness (int): Thickness of lines.
              font_scale (float): Font scales of texts.
              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 filename to write the image.
                  Default: None.
  
          Returns:
              img (Tensor): Only if not `show` or `out_file`
          """
          img = mmcv.imread(img)
          img = img.copy()
          if isinstance(result, tuple):
              bbox_result, segm_result = result
              if isinstance(segm_result, tuple):
                  segm_result = segm_result[0]  # ms rcnn
          else:
              bbox_result, segm_result = result, None
          bboxes = np.vstack(bbox_result)
          labels = [
              np.full(bbox.shape[0], i, dtype=np.int32)
              for i, bbox in enumerate(bbox_result)
          ]
          labels = np.concatenate(labels)
          # draw segmentation masks
          if segm_result is not None and len(labels) > 0:  # non empty
              segms = mmcv.concat_list(segm_result)
              inds = np.where(bboxes[:, -1] > score_thr)[0]
              np.random.seed(42)
              color_masks = [
                  np.random.randint(0, 256, (1, 3), dtype=np.uint8)
                  for _ in range(max(labels) + 1)
              ]
              for i in inds:
                  i = int(i)
                  color_mask = color_masks[labels[i]]
                  mask = segms[i].astype(bool)
                  img[mask] = img[mask] * 0.5 + color_mask * 0.5
          # if out_file specified, do not show image in window
          if out_file is not None:
              show = False
          # draw bounding boxes
          mmcv.imshow_det_bboxes(
              img,
              bboxes,
              labels,
              class_names=self.CLASSES,
              score_thr=score_thr,
              bbox_color=bbox_color,
              text_color=text_color,
              thickness=thickness,
              font_scale=font_scale,
              win_name=win_name,
              show=show,
              wait_time=wait_time,
              out_file=out_file)
  
          if not (show or out_file):
              return img
  