code: from collections.abc import Sequence
  
  import mmcv
  import numpy as np
  import torch
  from mmcv.parallel import DataContainer as DC
  
  from ..builder import PIPELINES
  
  
  def to_tensor(data):
      """Convert objects of various python types to :obj:`torch.Tensor`.
  
      Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,
      :class:`Sequence`, :class:`int` and :class:`float`.
  
      Args:
          data (torch.Tensor | numpy.ndarray | Sequence | int | float): Data to
              be converted.
      """
  
      if isinstance(data, torch.Tensor):
          return data
      elif isinstance(data, np.ndarray):
          return torch.from_numpy(data)
      elif isinstance(data, Sequence) and not mmcv.is_str(data):
          return torch.tensor(data)
      elif isinstance(data, int):
          return torch.LongTensor([data])
      elif isinstance(data, float):
          return torch.FloatTensor([data])
      else:
          raise TypeError(f'type {type(data)} cannot be converted to tensor.')
  
  
  @PIPELINES.register_module()
  class ToTensor:
      """Convert some results to :obj:`torch.Tensor` by given keys.
  
      Args:
          keys (Sequence[str]): Keys that need to be converted to Tensor.
      """
  
      def __init__(self, keys):
          self.keys = keys
  
      def __call__(self, results):
          """Call function to convert data in results to :obj:`torch.Tensor`.
  
          Args:
              results (dict): Result dict contains the data to convert.
  
          Returns:
              dict: The result dict contains the data converted
                  to :obj:`torch.Tensor`.
          """
          for key in self.keys:
              results[key] = to_tensor(results[key])
          return results
  
      def __repr__(self):
          return self.__class__.__name__ + f'(keys={self.keys})'
  
  
  @PIPELINES.register_module()
  class ImageToTensor:
      """Convert image to :obj:`torch.Tensor` by given keys.
  
      The dimension order of input image is (H, W, C). The pipeline will convert
      it to (C, H, W). If only 2 dimension (H, W) is given, the output would be
      (1, H, W).
  
      Args:
          keys (Sequence[str]): Key of images to be converted to Tensor.
      """
  
      def __init__(self, keys):
          self.keys = keys
  
      def __call__(self, results):
          """Call function to convert image in results to :obj:`torch.Tensor` and
          transpose the channel order.
  
          Args:
              results (dict): Result dict contains the image data to convert.
  
          Returns:
              dict: The result dict contains the image converted
                  to :obj:`torch.Tensor` and transposed to (C, H, W) order.
          """
          for key in self.keys:
              img = results[key]
              if len(img.shape) < 3:
                  img = np.expand_dims(img, -1)
              results[key] = to_tensor(img.transpose(2, 0, 1))
          return results
  
      def __repr__(self):
          return self.__class__.__name__ + f'(keys={self.keys})'
  
  
  @PIPELINES.register_module()
  class Transpose:
      """Transpose some results by given keys.
  
      Args:
          keys (Sequence[str]): Keys of results to be transposed.
          order (Sequence[int]): Order of transpose.
      """
  
      def __init__(self, keys, order):
          self.keys = keys
          self.order = order
  
      def __call__(self, results):
          """Call function to transpose the channel order of data in results.
  
          Args:
              results (dict): Result dict contains the data to transpose.
  
          Returns:
              dict: The result dict contains the data transposed to \
                  ``self.order``.
          """
          for key in self.keys:
              results[key] = results[key].transpose(self.order)
          return results
  
      def __repr__(self):
          return self.__class__.__name__ + \
              f'(keys={self.keys}, order={self.order})'
  
  
  @PIPELINES.register_module()
  class ToDataContainer:
      """Convert results to :obj:`mmcv.DataContainer` by given fields.
  
      Args:
          fields (Sequence[dict]): Each field is a dict like
              ``dict(key='xxx', **kwargs)``. The ``key`` in result will
              be converted to :obj:`mmcv.DataContainer` with ``**kwargs``.
              Default: ``(dict(key='img', stack=True), dict(key='gt_bboxes'),
              dict(key='gt_labels'))``.
      """
  
      def __init__(self,
                   fields=(dict(key='img', stack=True), dict(key='gt_bboxes'),
                           dict(key='gt_labels'))):
          self.fields = fields
  
      def __call__(self, results):
          """Call function to convert data in results to
          :obj:`mmcv.DataContainer`.
  
          Args:
              results (dict): Result dict contains the data to convert.
  
          Returns:
              dict: The result dict contains the data converted to \
                  :obj:`mmcv.DataContainer`.
          """
  
          for field in self.fields:
              field = field.copy()
              key = field.pop('key')
              results[key] = DC(results[key], **field)
          return results
  
      def __repr__(self):
          return self.__class__.__name__ + f'(fields={self.fields})'
  
  
  @PIPELINES.register_module()
  class DefaultFormatBundle:
      """Default formatting bundle.
  
      It simplifies the pipeline of formatting common fields, including "img",
      "proposals", "gt_bboxes", "gt_labels", "gt_masks" and "gt_semantic_seg".
      These fields are formatted as follows.
  
      - img: (1)transpose, (2)to tensor, (3)to DataContainer (stack=True)
      - proposals: (1)to tensor, (2)to DataContainer
      - gt_bboxes: (1)to tensor, (2)to DataContainer
      - gt_bboxes_ignore: (1)to tensor, (2)to DataContainer
      - gt_labels: (1)to tensor, (2)to DataContainer
      - gt_masks: (1)to tensor, (2)to DataContainer (cpu_only=True)
      - gt_semantic_seg: (1)unsqueeze dim-0 (2)to tensor, \
                         (3)to DataContainer (stack=True)
      """
  
      def __call__(self, results):
          """Call function to transform and format common fields in results.
  
          Args:
              results (dict): Result dict contains the data to convert.
  
          Returns:
              dict: The result dict contains the data that is formatted with \
                  default bundle.
          """
  
          if 'img' in results:
              img = results['img']
              # add default meta keys
              results = self._add_default_meta_keys(results)
              if len(img.shape) < 3:
                  img = np.expand_dims(img, -1)
              img = np.ascontiguousarray(img.transpose(2, 0, 1))
              results['img'] = DC(to_tensor(img), stack=True)
          for key in ['proposals', 'gt_bboxes', 'gt_bboxes_ignore', 'gt_labels']:
              if key not in results:
                  continue
              results[key] = DC(to_tensor(results[key]))
          if 'gt_masks' in results:
              results['gt_masks'] = DC(results['gt_masks'], cpu_only=True)
          if 'gt_semantic_seg' in results:
              results['gt_semantic_seg'] = DC(
                  to_tensor(results['gt_semantic_seg'][None, ...]), stack=True)
          return results
  
      def _add_default_meta_keys(self, results):
          """Add default meta keys.
  
          We set default meta keys including `pad_shape`, `scale_factor` and
          `img_norm_cfg` to avoid the case where no `Resize`, `Normalize` and
          `Pad` are implemented during the whole pipeline.
  
          Args:
              results (dict): Result dict contains the data to convert.
  
          Returns:
              results (dict): Updated result dict contains the data to convert.
          """
          img = results['img']
          results.setdefault('pad_shape', img.shape)
          results.setdefault('scale_factor', 1.0)
          num_channels = 1 if len(img.shape) < 3 else img.shape[2]
          results.setdefault(
              'img_norm_cfg',
              dict(
                  mean=np.zeros(num_channels, dtype=np.float32),
                  std=np.ones(num_channels, dtype=np.float32),
                  to_rgb=False))
          return results
  
      def __repr__(self):
          return self.__class__.__name__
  
  
  @PIPELINES.register_module()
  class Collect:
      """Collect data from the loader relevant to the specific task.
  
      This is usually the last stage of the data loader pipeline. Typically keys
      is set to some subset of "img", "proposals", "gt_bboxes",
      "gt_bboxes_ignore", "gt_labels", and/or "gt_masks".
  
      The "img_meta" item is always populated.  The contents of the "img_meta"
      dictionary depends on "meta_keys". By default this includes:
  
          - "img_shape": shape of the image input to the network as a tuple \
              (h, w, c).  Note that images may be zero padded on the \
              bottom/right if the batch tensor is larger than this shape.
  
          - "scale_factor": a float indicating the preprocessing scale
  
          - "flip": a boolean indicating if image flip transform was used
  
          - "filename": path to the image file
  
          - "ori_shape": original shape of the image as a tuple (h, w, c)
  
          - "pad_shape": image shape after padding
  
          - "img_norm_cfg": a dict of normalization information:
  
              - mean - per channel mean subtraction
              - std - per channel std divisor
              - to_rgb - bool indicating if bgr was converted to rgb
  
      Args:
          keys (Sequence[str]): Keys of results to be collected in ``data``.
          meta_keys (Sequence[str], optional): Meta keys to be converted to
              ``mmcv.DataContainer`` and collected in ``data[img_metas]``.
              Default: ``('filename', 'ori_filename', 'ori_shape', 'img_shape',
              'pad_shape', 'scale_factor', 'flip', 'flip_direction',
              'img_norm_cfg')``
      """
  
      def __init__(self,
                   keys,
                   meta_keys=('filename', 'ori_filename', 'ori_shape',
                              'img_shape', 'pad_shape', 'scale_factor', 'flip',
                              'flip_direction', 'img_norm_cfg')):
          self.keys = keys
          self.meta_keys = meta_keys
  
      def __call__(self, results):
          """Call function to collect keys in results. The keys in ``meta_keys``
          will be converted to :obj:mmcv.DataContainer.
  
          Args:
              results (dict): Result dict contains the data to collect.
  
          Returns:
              dict: The result dict contains the following keys
  
                  - keys in``self.keys``
                  - ``img_metas``
          """
  
          data = {}
          img_meta = {}
          for key in self.meta_keys:
              img_meta[key] = results[key]
          data['img_metas'] = DC(img_meta, cpu_only=True)
          for key in self.keys:
              data[key] = results[key]
          return data
  
      def __repr__(self):
          return self.__class__.__name__ + \
              f'(keys={self.keys}, meta_keys={self.meta_keys})'
  
  
  @PIPELINES.register_module()
  class WrapFieldsToLists:
      """Wrap fields of the data dictionary into lists for evaluation.
  
      This class can be used as a last step of a test or validation
      pipeline for single image evaluation or inference.
  
      Example:
          >>> test_pipeline = [
          >>>    dict(type='LoadImageFromFile'),
          >>>    dict(type='Normalize',
                      mean=[123.675, 116.28, 103.53],
                      std=[58.395, 57.12, 57.375],
                      to_rgb=True),
          >>>    dict(type='Pad', size_divisor=32),
          >>>    dict(type='ImageToTensor', keys=['img']),
          >>>    dict(type='Collect', keys=['img']),
          >>>    dict(type='WrapFieldsToLists')
          >>> ]
      """
  
      def __call__(self, results):
          """Call function to wrap fields into lists.
  
          Args:
              results (dict): Result dict contains the data to wrap.
  
          Returns:
              dict: The result dict where value of ``self.keys`` are wrapped \
                  into list.
          """
  
          # Wrap dict fields into lists
          for key, val in results.items():
              results[key] = [val]
          return results
  
      def __repr__(self):
          return f'{self.__class__.__name__}()'
  
apis: numpy.ones torch.FloatTensor numpy.zeros torch.tensor torch.from_numpy numpy.expand_dims torch.LongTensor