Replace numpy transpose with torch permute to speed-up

mmdet/datasets/pipelines/formatting.py

@@ -80,20 +80,20 @@ def __init__(self, keys):
 
  def __call__(self, results):
  """Call function to convert image in results to :obj:`torch.Tensor` and
- transpose the channel order.
+ permute 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.
+ to :obj:`torch.Tensor` and permuted 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))).contiguous()
+ results[key] = to_tensor(img).permute(2, 0, 1).contiguous()
  return results
 
  def __repr__(self):
@@ -179,7 +179,7 @@ class DefaultFormatBundle:
  "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)
+ - img: (1)transpose & to tensor, (2)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
@@ -226,9 +226,20 @@ def __call__(self, results):
  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))
+ # To improve the computational speed by by 3-5 times, apply:
+ # If image is not contiguous, use
+ # `numpy.transpose()` followed by `numpy.ascontiguousarray()`
+ # If image is already contiguous, use
+ # `torch.permute()` followed by `torch.contiguous()`
+ # Refer to https://github.com/open-mmlab/mmdetection/pull/9533
+ # for more details
+ if not img.flags.c_contiguous:
+ img = np.ascontiguousarray(img.transpose(2, 0, 1))
+ img = to_tensor(img)
+ else:
+ img = to_tensor(img).permute(2, 0, 1).contiguous()
  results['img'] = DC(
- to_tensor(img), padding_value=self.pad_val['img'], stack=True)
+ img, padding_value=self.pad_val['img'], stack=True)
  for key in ['proposals', 'gt_bboxes', 'gt_bboxes_ignore', 'gt_labels']:
  if key not in results:
  continue

requirements/optional.txt

@@ -1,3 +1,3 @@
 cityscapesscripts
 imagecorruptions
-sklearn
+scikit-learn