python - Pytorch 中的缓冲区是什么？

Question

我明白了register_buffer和 register_buffer and register_parameters 之间的区别.

但是 PyTorch 中缓冲区的精确定义是什么？

Answer 1

这可以通过查看 implementation 来回答。 :

def register_buffer(self, name, tensor):
    if '_buffers' not in self.__dict__:
        raise AttributeError(
            "cannot assign buffer before Module.__init__() call")
    elif not isinstance(name, torch._six.string_classes):
        raise TypeError("buffer name should be a string. "
                        "Got {}".format(torch.typename(name)))
    elif '.' in name:
        raise KeyError("buffer name can't contain \".\"")
    elif name == '':
        raise KeyError("buffer name can't be empty string \"\"")
    elif hasattr(self, name) and name not in self._buffers:
        raise KeyError("attribute '{}' already exists".format(name))
    elif tensor is not None and not isinstance(tensor, torch.Tensor):
        raise TypeError("cannot assign '{}' object to buffer '{}' "
                        "(torch Tensor or None required)"
                        .format(torch.typename(tensor), name))
    else:
        self._buffers[name] = tensor

也就是说，缓冲区的名称:

必须是字符串:not isinstance(name, torch._six.string_classes)

不能包含 . (点):'.' in name

不能为空字符串:name == ''

不能是模块的属性:hasattr(self, name)

应该是唯一的:name not in self._buffers

和 tensor (你猜怎么了？):

应该是张量:isinstance(tensor, torch.Tensor)

因此，缓冲区只是具有这些属性的张量，在 _buffers 中注册。 Module 的属性;