mmaction/structures/bbox/transforms.py

# Copyright (c) OpenMMLab. All rights reserved.
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()  # rename for clarification

    # Although we can handle single-label classification, we still want scores
    assert scores.shape[-1] > 1

    # Robustly check for multi/single-label:
    if not hasattr(thr, '__len__'):
        multilabel = thr >= 0
        thr = (thr, ) * num_classes
    else:
        multilabel = True

    # Check Shape
    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