torch.nn.Module.buffers(recurse=True)

参考链接: torch.nn.Module.buffers(recurse=True)
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）

在这里插入图片描述

原文及翻译:

buffers(recurse=True)
方法: buffers(recurse=True)
    Returns an iterator over module buffers.
    返回一个迭代器,带迭代器可以遍历模块的缓冲buffer.
    Parameters 参数
        recurse (bool) – if True, then yields buffers of this module and all submodules. 
        Otherwise, yields only buffers that are direct members of this module.
		recurse (布尔类型) – 如果该参数是True,表示递归地迭代返回,即迭代返回该模块的缓冲及其所有
		子模块的缓冲.否则,只返回作为该模块直接成员的缓冲.
    Yields  迭代返回
        torch.Tensor – module buffer
        torch.Tensor类型 – 该模块的缓冲
    
    Example:  例子:
    >>> for buf in model.buffers():
    >>>     print(type(buf.data), buf.size())
    <class 'torch.FloatTensor'> (20L,)
    <class 'torch.FloatTensor'> (20L, 1L, 5L, 5L)

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:

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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>

torch.nn.Module.buffers(recurse=True)

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