| 39 | class ModelMeta(layer.LayerMeta): |
| 40 | |
| 41 | def buffer_operation(func): |
| 42 | |
| 43 | def remove_creator(tensors): |
| 44 | if not tensors: |
| 45 | return |
| 46 | |
| 47 | if isinstance(tensors, Iterable): |
| 48 | for item in tensors: |
| 49 | if isinstance(item, Iterable): |
| 50 | remove_creator(item) |
| 51 | elif isinstance(item, tensor.Tensor): |
| 52 | item.creator = None |
| 53 | elif isinstance(tensors, tensor.Tensor): |
| 54 | tensors.creator = None |
| 55 | |
| 56 | @wraps(func) |
| 57 | def wrapper(self, *args, **kwargs): |
| 58 | if self.graph_mode and self.training: |
| 59 | if len(args) == 0: |
| 60 | raise ValueError('expect at least one input tensor') |
| 61 | |
| 62 | if isinstance(args[0], list): |
| 63 | assert isinstance( |
| 64 | args[0][0], |
| 65 | Tensor), ('function expects PlaceHolders or Tensors') |
| 66 | dev = args[0][0].device |
| 67 | else: |
| 68 | assert isinstance( |
| 69 | args[0], |
| 70 | Tensor), ('function expects PlaceHolders or Tensors') |
| 71 | dev = args[0].device |
| 72 | |
| 73 | if not self._buffered: |
| 74 | # buffer operations |
| 75 | dev.EnableGraph(True) |
| 76 | self._results = func(self, *args, **kwargs) |
| 77 | dev.Sync() |
| 78 | dev.EnableGraph(False) |
| 79 | self._buffered = True |
| 80 | |
| 81 | # deconstruct Operations before running the entire graph |
| 82 | remove_creator(self._results) |
| 83 | |
| 84 | # make sure all Operations are deallocated |
| 85 | gc.collect() |
| 86 | |
| 87 | # run graph |
| 88 | dev.RunGraph(self.sequential) |
| 89 | return self._results |
| 90 | else: |
| 91 | return func(self, *args, **kwargs) |
| 92 | |
| 93 | return wrapper |
| 94 | |
| 95 | def __new__(cls, name, bases, attr): |
| 96 | if 'train_one_batch' in attr: |