def buffer_operation()

in python/singa/model.py [0:0]

• 40 lines of code
• 12 McCabe index (conditional complexity)

    def buffer_operation(func):

        def remove_creator(tensors):
            if not tensors:
                return

            if isinstance(tensors, Iterable):
                for item in tensors:
                    if isinstance(item, Iterable):
                        remove_creator(item)
                    elif isinstance(item, tensor.Tensor):
                        item.creator = None
            elif isinstance(tensors, tensor.Tensor):
                tensors.creator = None

        @wraps(func)
        def wrapper(self, *args, **kwargs):
            if self.graph_mode and self.training:
                if len(args) == 0:
                    raise ValueError('expect at least one input tensor')

                if isinstance(args[0], list):
                    assert isinstance(
                        args[0][0],
                        Tensor), ('function expects PlaceHolders or Tensors')
                    dev = args[0][0].device
                else:
                    assert isinstance(
                        args[0],
                        Tensor), ('function expects PlaceHolders or Tensors')
                    dev = args[0].device

                if not self._buffered:
                    # buffer operations
                    dev.EnableGraph(True)
                    self._results = func(self, *args, **kwargs)
                    dev.Sync()
                    dev.EnableGraph(False)
                    self._buffered = True

                    # deconstruct Operations before running the entire graph
                    remove_creator(self._results)

                    # make sure all Operations are deallocated
                    gc.collect()

                # run graph
                dev.RunGraph(self.sequential)
                return self._results
            else:
                return func(self, *args, **kwargs)

        return wrapper