register_buffer vs register_parameter

先来看下nn.Module的成员：

    def __init__(self):
        """
        Initializes internal Module state, shared by both nn.Module and ScriptModule.
        """
        torch._C._log_api_usage_once("python.nn_module")

        self.training = True
        self._parameters = OrderedDict()
        self._buffers = OrderedDict()
        self._backward_hooks = OrderedDict()
        self._forward_hooks = OrderedDict()
        self._forward_pre_hooks = OrderedDict()
        self._state_dict_hooks = OrderedDict()
        self._load_state_dict_pre_hooks = OrderedDict()
        self._modules = OrderedDict()

register_buffer和register_parameter只涉及到_buffer和_parameters，调用这两个函数分别会向两个成员写入数据。

_buffer和_parameter都会被state_dict返回，且可以通过.cpu()和.cuda()在设备间进行转换。
_buffer中的元素不会被优化器更新，如果在模型中需要需要一些参数，并且要通过state_dict返回，且不需要被优化器训练，那么这些参数可以注册在_buffer中。
例如在maskrcnn_benchmark中的anchor_generator生成中就用到了register_buffer，以及detectron2中的BatchNorm2d。

如果定义self.param1=torch.randn(2,2)，那么param1是不会被state_dict返回的，且不会被.cpu()和.cuda()在设备间进行转换。

import torch
from torch import nn

class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        print('before register buffer:
', self._buffers, end='

')
        self.register_buffer('mybuffer1', torch.randn(2, 2))
        print('after register buffer:
', self._buffers, end='

')

        print('before register parameter:
', self._parameters, end='

')
        self.register_parameter('my_param1', nn.Parameter(torch.randn(3, 3)))
        print('after register parameter:
', self._parameters, end='

')
        self.param1 = torch.randn(3, 3)

    def forward(self, x):
        return x

mymodel = MyModel()
mymodel.cuda()
print(list(mymodel.parameters()))
print(list(mymodel.buffers()))
print(mymodel.param1)

返回如下

before register buffer:
 OrderedDict()

after register buffer:
 OrderedDict([('mybuffer1', tensor([[-0.4997, -1.0214],
        [ 0.5604, -2.3252]]))])

before register parameter:
 OrderedDict()

after register parameter:
 OrderedDict([('my_param1', Parameter containing:
tensor([[ 0.1465,  1.1252, -0.2854],
        [ 2.2109, -0.3919,  0.0385],
        [ 0.3347,  0.1597,  0.7505]], requires_grad=True))])

[Parameter containing:
tensor([[ 0.1465,  1.1252, -0.2854],
        [ 2.2109, -0.3919,  0.0385],
        [ 0.3347,  0.1597,  0.7505]], device='cuda:0', requires_grad=True)]
[tensor([[-0.4997, -1.0214],
        [ 0.5604, -2.3252]], device='cuda:0')]
tensor([[ 0.6994, -2.6078,  2.0409],
        [-0.1210,  1.0048, -1.3913],
        [-1.3752, -1.3748, -2.4478]])