|
|
| import numpy as np
|
| import torch
|
|
|
|
|
| def bbox2result(bboxes: torch.Tensor,
|
| labels: torch.Tensor,
|
| num_classes: int,
|
| thr: float = 0.01) -> list:
|
| """Convert detection results to a list of numpy arrays.
|
|
|
| This identifies single-label classification (as opposed to multi-label)
|
| through the thr parameter which is set to a negative value.
|
|
|
| ToDo: The ideal way would be for this to be automatically set when the
|
| Currently, the way to set this is to set ``test_cfg.rcnn.action_thr=-1.0``
|
| model cfg uses multilabel=False, however this could be a breaking change
|
| and is left as a future exercise.
|
| NB - this should not interfere with the evaluation in any case.
|
|
|
| Args:
|
| bboxes (torch.Tensor): shape ``(n, 4)``.
|
| labels (torch.Tensor): shape ``(n, num_classes)``.
|
| num_classes (int): class number, including background class.
|
| thr (float): The score threshold used when converting predictions to
|
| detection results. If a single negative value, uses single-label
|
| classification.
|
| Returns:
|
| List(ndarray): bbox results of each class.
|
| """
|
| if bboxes.shape[0] == 0:
|
| return list(np.zeros((num_classes - 1, 0, 5), dtype=np.float32))
|
|
|
| bboxes = bboxes.cpu().numpy()
|
| scores = labels.cpu().numpy()
|
|
|
|
|
| assert scores.shape[-1] > 1
|
|
|
|
|
| if not hasattr(thr, '__len__'):
|
| multilabel = thr >= 0
|
| thr = (thr, ) * num_classes
|
| else:
|
| multilabel = True
|
|
|
|
|
| assert scores.shape[1] == num_classes
|
| assert len(thr) == num_classes
|
|
|
| result = []
|
| for i in range(num_classes - 1):
|
| if multilabel:
|
| where = (scores[:, i + 1] > thr[i + 1])
|
| else:
|
| where = (scores[:, 1:].argmax(axis=1) == i)
|
| result.append(
|
| np.concatenate((bboxes[where, :4], scores[where, i + 1:i + 2]),
|
| axis=1))
|
| return result
|
|
|
