(obj, return_numpy)
| 653 | |
| 654 | |
| 655 | def _parse_load_result(obj, return_numpy): |
| 656 | def is_layer(obj): |
| 657 | return isinstance(obj, paddle.nn.Layer) |
| 658 | |
| 659 | def parse_layer(obj): |
| 660 | temp_dict = _parse_load_result(obj.__dict__, False) |
| 661 | obj.__dict__.update(temp_dict) |
| 662 | return obj |
| 663 | |
| 664 | if _contain_x(obj, is_layer): |
| 665 | if not in_dygraph_mode(): |
| 666 | raise ValueError( |
| 667 | "Layer can only be loaded in dynamic graph mode, but now in static graph mode." |
| 668 | ) |
| 669 | |
| 670 | _parse_every_object(obj, is_layer, parse_layer) |
| 671 | |
| 672 | def tuple_to_tensor(obj): |
| 673 | return _tuple_to_tensor(obj, return_numpy=return_numpy) |
| 674 | |
| 675 | def ndarray_to_tensor(obj): |
| 676 | return _ndarray_to_tensor(obj, return_numpy=return_numpy) |
| 677 | |
| 678 | # tuple(name, ndarray) was converted from varbase of paddle2.1, |
| 679 | # and all tuple(name, ndarray) are converted to tensor. |
| 680 | if _contain_x(obj, _transformed_from_varbase): |
| 681 | return _parse_every_object( |
| 682 | obj, _transformed_from_varbase, tuple_to_tensor |
| 683 | ) |
| 684 | # If there is no tuple(name, ndarray), it is considered to be saved by paddle2.0 |
| 685 | # or converted from DenseTensor, and all ndarrays are converted to tensor. |
| 686 | else: |
| 687 | return _parse_every_object( |
| 688 | obj, _transformed_from_lodtensor, ndarray_to_tensor |
| 689 | ) |
| 690 | |
| 691 | |
| 692 | def _save_dense_tensor(tensor, file_name): |
no test coverage detected