Function process_cv2_inputs

timesformer/visualization/utils.py:303–321

Normalize and prepare inputs as a list of tensors. Each tensor correspond to a unique pathway. Args: frames (list of array): list of input images (correspond to one clip) in range [0, 255]. cfg (CfgNode): configs. Details can be found in slowfast/config/defau

(frames, cfg)

def process_cv2_inputs(frames, cfg):
    """
    Normalize and prepare inputs as a list of tensors. Each tensor
    correspond to a unique pathway.
    Args:
        frames (list of array): list of input images (correspond to one clip) in range [0, 255].
        cfg (CfgNode): configs. Details can be found in
            slowfast/config/defaults.py
    """
    inputs = torch.from_numpy(np.array(frames)).float() / 255
    inputs = tensor_normalize(inputs, cfg.DATA.MEAN, cfg.DATA.STD)
    # T H W C -> C T H W.
    inputs = inputs.permute(3, 0, 1, 2)
    # Sample frames for num_frames specified.
    index = torch.linspace(0, inputs.shape[1] - 1, cfg.DATA.NUM_FRAMES).long()
    inputs = torch.index_select(inputs, 1, index)
    inputs = pack_pathway_output(cfg, inputs)
    inputs = [inp.unsqueeze(0) for inp in inputs]
    return inputs


def get_layer(model, layer_name):