torch.nn.Module.register_buffer(name, tensor)

参考链接: torch.nn.Module.register_buffer(name, tensor)
参考链接: Pytorch模型中的parameter与buffer
参考链接: Pytorch模型中的parameter与buffer
参考链接: What is the difference between register_buffer and register_parameter of nn.Module
参考链接: Pytorch模型中的parameter与buffer（torch.nn.Module的成员）

原文及翻译:

register_buffer(name, tensor)
方法: register_buffer(name, tensor)
    Adds a persistent buffer to the module.
    为模块添加一个持续性缓冲.
    This is typically used to register a buffer that should not to be considered a 
    model parameter. For example, BatchNorm’s running_mean is not a parameter, but is 
    part of the persistent state.
    这个方法主要被用来注册登记一个不需要作为模型参数的缓冲. 举个例子,BatchNorm的running_mean
    不是一个参数,但它是持续性状态的一部分.

    Buffers can be accessed as attributes using given names.
    使用给定的名字可以像访问属性一样访问这个缓冲.

    Parameters  参数

            name (string) – name of the buffer. The buffer can be accessed from this 
            module using the given name
            name (字符串类型) – 缓冲的名字.使用给定的名字,可以访问这个模型的该缓冲.
            tensor (Tensor) – buffer to be registered.
            tensor (Tensor类型) – 需要被注册登记的缓冲.

    Example:  例子:
    >>> self.register_buffer('running_mean', torch.zeros(num_features))

总结,缓冲buffer必须要登记注册才会有效,如果仅仅将张量赋值给Module模块的属性,不会被自动转为缓冲buffer.因而也无法被state_dict()、buffers()、named_buffers()访问到.此外state_dict()可以遍历缓冲buffer和参数Parameter.
总结,缓冲buffer和参数Parameter的区别是前者不需要训练优化,而后者需要训练优化.在创建方法上也有区别,前者必须要将一个张量使用方法register_buffer()来登记注册,后者比较灵活,可以直接赋值给模块的属性,也可以使用方法register_parameter()来登记注册.

代码实验:

import torch 
import torch.nn as nn
torch.manual_seed(seed=20200910)
class Model(torch.nn.Module):
    def __init__(self):
        super(Model,self).__init__()
        self.conv1=torch.nn.Sequential(  # 输入torch.Size([64, 1, 28, 28])
                torch.nn.Conv2d(1,64,kernel_size=3,stride=1,padding=1),
                torch.nn.ReLU(),  # 输出torch.Size([64, 64, 28, 28])
        )
        self.attribute_buffer_in = torch.randn(3,5)
        register_buffer_in_temp = torch.randn(4,6)
        self.register_buffer('register_buffer_in', register_buffer_in_temp)

    def forward(self,x): 
        pass

print('cuda(GPU)是否可用:',torch.cuda.is_available())
print('torch的版本:',torch.__version__)
model = Model() #.cuda()



print('初始化之后模型修改之前'.center(100,"-"))
print('调用named_buffers()'.center(100,"-"))
for name, buf in model.named_buffers():
    print(name,'-->',buf.shape)

print('调用named_parameters()'.center(100,"-"))
for name, param in model.named_parameters():
    print(name,'-->',param.shape)

print('调用buffers()'.center(100,"-"))
for buf in model.buffers():
    print(buf.shape)

print('调用parameters()'.center(100,"-"))
for param in model.parameters():
    print(param.shape)

print('调用state_dict()'.center(100,"-"))
for k, v in model.state_dict().items():
    print(k, '-->', v.shape)



model.attribute_buffer_out = torch.randn(10,10)
register_buffer_out_temp = torch.randn(15,15)
model.register_buffer('register_buffer_out', register_buffer_out_temp)
print('模型初始化以及修改之后'.center(100,"-"))
print('调用named_buffers()'.center(100,"-"))
for name, buf in model.named_buffers():
    print(name,'-->',buf.shape)

print('调用named_parameters()'.center(100,"-"))
for name, param in model.named_parameters():
    print(name,'-->',param.shape)

print('调用buffers()'.center(100,"-"))
for buf in model.buffers():
    print(buf.shape)

print('调用parameters()'.center(100,"-"))
for param in model.parameters():
    print(param.shape)

print('调用state_dict()'.center(100,"-"))
for k, v in model.state_dict().items():
    print(k, '-->', v.shape)

控制台输出结果:

Windows PowerShell
版权所有 (C) Microsoft Corporation。保留所有权利。

尝试新的跨平台 PowerShell https://aka.ms/pscore6

加载个人及系统配置文件用了 840 毫秒。
(base) PS C:\Users\chenxuqi\Desktop\News4cxq\test4cxq> conda activate ssd4pytorch1_2_0
(ssd4pytorch1_2_0) PS C:\Users\chenxuqi\Desktop\News4cxq\test4cxq>  & 'D:\Anaconda3\envs\ssd4pytorch1_2_0\python.exe' 'c:\Users\chenxuqi\.vscode\extensions\ms-python.python-2020.12.424452561\pythonFiles\lib\python\debugpy\launcher' '63490' '--' 'c:\Users\chenxuqi\Desktop\News4cxq\test4cxq\test2.py'
cuda(GPU)是否可用: True
torch的版本: 1.2.0+cu92
--------------------------------------------初始化之后模型修改之前---------------------------------------------
-----------------------------------------调用named_buffers()------------------------------------------
register_buffer_in --> torch.Size([4, 6])
----------------------------------------调用named_parameters()----------------------------------------
conv1.0.weight --> torch.Size([64, 1, 3, 3])
conv1.0.bias --> torch.Size([64])
--------------------------------------------调用buffers()---------------------------------------------
torch.Size([4, 6])
-------------------------------------------调用parameters()-------------------------------------------
torch.Size([64, 1, 3, 3])
torch.Size([64])
-------------------------------------------调用state_dict()-------------------------------------------
register_buffer_in --> torch.Size([4, 6])
conv1.0.weight --> torch.Size([64, 1, 3, 3])
conv1.0.bias --> torch.Size([64])
--------------------------------------------模型初始化以及修改之后---------------------------------------------
-----------------------------------------调用named_buffers()------------------------------------------
register_buffer_in --> torch.Size([4, 6])
register_buffer_out --> torch.Size([15, 15])
----------------------------------------调用named_parameters()----------------------------------------
conv1.0.weight --> torch.Size([64, 1, 3, 3])
conv1.0.bias --> torch.Size([64])
--------------------------------------------调用buffers()---------------------------------------------
torch.Size([4, 6])
torch.Size([15, 15])
-------------------------------------------调用parameters()-------------------------------------------
torch.Size([64, 1, 3, 3])
torch.Size([64])
-------------------------------------------调用state_dict()-------------------------------------------
register_buffer_in --> torch.Size([4, 6])
register_buffer_out --> torch.Size([15, 15])
conv1.0.weight --> torch.Size([64, 1, 3, 3])
conv1.0.bias --> torch.Size([64])
(ssd4pytorch1_2_0) PS C:\Users\chenxuqi\Desktop\News4cxq\test4cxq>