def __call__(self, results):
        """Performs the FormatShape formating.

        Args:
            results (dict): The resulting dict to be modified and passed
                to the next transform in pipeline.
        """
        if not isinstance(results['imgs'], np.ndarray):
            results['imgs'] = np.array(results['imgs'])
        imgs = results['imgs']
        # [M x H x W x C]
        # M = 1 * N_crops * N_clips * L
        if self.collapse:
            assert results['num_clips'] == 1

        if self.input_format == 'NCTHW':
            num_clips = results['num_clips']
            clip_len = results['clip_len']

            imgs = imgs.reshape((-1, num_clips, clip_len) + imgs.shape[1:])
            # N_crops x N_clips x L x H x W x C
            imgs = np.transpose(imgs, (0, 1, 5, 2, 3, 4))
            # N_crops x N_clips x C x L x H x W
            imgs = imgs.reshape((-1,) + imgs.shape[2:])
            # M' x C x L x H x W
            # M' = N_crops x N_clips
        elif self.input_format == 'NCHW':
            imgs = np.transpose(imgs, (0, 3, 1, 2))
            # M x C x H x W
        elif self.input_format == 'NCHW_Flow':
            num_clips = results['num_clips']
            clip_len = results['clip_len']
            imgs = imgs.reshape((-1, num_clips, clip_len) + imgs.shape[1:])
            # N_crops x N_clips x L x H x W x C
            imgs = np.transpose(imgs, (0, 1, 2, 5, 3, 4))
            # N_crops x N_clips x L x C x H x W
            imgs = imgs.reshape((-1, imgs.shape[2] * imgs.shape[3]) +
                                imgs.shape[4:])
            # M' x C' x H x W
            # M' = N_crops x N_clips
            # C' = L x C
        elif self.input_format == 'NPTCHW':
            num_proposals = results['num_proposals']
            num_clips = results['num_clips']
            clip_len = results['clip_len']
            imgs = imgs.reshape((num_proposals, num_clips * clip_len) +
                                imgs.shape[1:])
            # P x M x H x W x C
            # M = N_clips x L
            imgs = np.transpose(imgs, (0, 1, 4, 2, 3))
            # P x M x C x H x W
        if self.collapse:
            assert imgs.shape[0] == 1
            imgs = imgs.squeeze(0)

        results['imgs'] = imgs
        results['input_shape'] = imgs.shape
        return results
Performs the FormatShape formating.

        Args:
            results (dict): The resulting dict to be modified and passed
                to the next transform in pipeline.
        
EVA-01/video/datasets/pipeline.py