Function numpify_tensor_nested

modelscope/utils/regress_test_utils.py:463–490

(tensors, reduction=None, clip_value=10000)

def numpify_tensor_nested(tensors, reduction=None, clip_value=10000):
    try:
        from modelscope.outputs import ModelOutputBase
    except ImportError:
        ModelOutputBase = dict
    "Numpify `tensors` (even if it's a nested list/tuple of tensors)."
    if isinstance(tensors, (Mapping, ModelOutputBase)):
        return OrderedDict({
            k: numpify_tensor_nested(t, reduction, clip_value)
            for k, t in tensors.items()
        })
    if isinstance(tensors, list):
        return list(
            numpify_tensor_nested(t, reduction, clip_value) for t in tensors)
    if isinstance(tensors, tuple):
        return tuple(
            numpify_tensor_nested(t, reduction, clip_value) for t in tensors)
    if isinstance(tensors, torch.Tensor):
        t: np.ndarray = tensors.cpu().numpy()
        if clip_value is not None:
            t = np.where(t > clip_value, clip_value, t)
            t = np.where(t < -clip_value, -clip_value, t)
        if reduction == 'sum':
            return t.sum(dtype=float)
        elif reduction == 'mean':
            return t.mean(dtype=float)
        return t
    return tensors


def detach_tensor_nested(tensors):