Pytorch中torch.nn和torch.nn.functional的区别及实例详解

文章目录

0. 两者的区别

Pytorch中，nn与nn.functional有哪些区别？

相同之处：

• 两者都继承于nn.Module
• nn.x与nn.functional.x的实际功能相同，比如nn.Conv3d和nn.functional.conv3d都是进行3d卷积
• 运行效率几乎相同

不同之处：

• nn.x是nn.functional.x的类封装，nn.functional.x是具体的函数接口
• nn.x除了具有nn.functional.x功能之外，还具有nn.Module相关的属性和方法，比如：train(),eval()等
• nn.functional.x直接传入参数调用，nn.x需要先实例化再传参调用
• nn.x能很好的与nn.Sequential结合使用，而nn.functional.x无法与nn.Sequential结合使用
• nn.x不需要自定义和管理weight,而nn.functional.x需自定义weight,作为传入的参数

1. 创建CNN实例

import torch
import torch.nn as nn
import torch.nn.functional as F

# 使用nn.x定义一个cnn
class nn_cnn(nn.Module):
    
    def __init__(self):
        super(nn_cnn, self).__init__()
        # 第一层卷积，第一层的relu，第一层的最大池化层
        self.cnn1 = nn.Conv2d(in_channels = 1, out_channels = 16, kernel_size = 5, padding = 0)
        self.relu1 = nn.Relu()
        self.maxpool1 = nn.MaxPool2d(kernel_size = 2)
        # 第二层卷积层，第二层的relu,第二层的最大池化层
        self.cnn2 = nn.Conv2d(in_channels = 16, out_channels = 32, kernel_size = 5, padding = 0)
        self.relu2 = nn.Relu()
        self.maxpool2 = nn.MaxPool2d(kernel_size = 2)
        # 全连接层
        self.linear1 = nn.Linear(4 * 4 * 32, 10)
    
    # 前向传播函数    
    def forward(self, x):
        x = x.view(x.size(0), -1)
        # 第一层卷积计算
        out = self.maxpool1(self.relu1(self.cnn1(x)))
        # 第二层卷积计算
        out = self.maxpool2(self.relu2(self.cnn2(out)))
        # 全连接层计算
        out = self.linear1(out.view(x.size(0), -1))
        return out

# 使用nn.functional.x定义一个cnn
class F_cnn(nn.Module):
    
    def __init__(self):
        super(F_cnn, self).__init__()
        # nn.functional.x需要自己定义weight和bias
        # 定义第一层卷积的weight和bias
        self.cnn1_weight = nn.Parameter(torch.rand(16, 1, 5, 5))
        self.cnn1_bias = nn.Parameter(torch.rand(16))
        # 定义第二层卷积的weight和bias
        self.cnn2_weight = nn.Parameter(torch.rand(32, 16, 5, 5))
        self.cnn2_bias = nn.Parameter(torch.rand(32))
        # 定义全连接层的weight和bias
        self.linear1_weight = nn.Parameter(torch.rand(4 * 4 * 32, 10))
        self.linear1_bias = nn.Parameter(torch.rand(10))
    
    # 前向传播函数    
    def forward(self, x):
        x = x.view(x.size(0), -1)
        # 第一层卷积计算
        out = F.conv2d(x, self.cnn1_weight, self.cnn1_bias)
        out = F.relu(out)
        out = F.max_pool2d(out)
        # 第二层卷积计算
        out = F.conv2d(x, self.cnn2_weight, self.cnn2_bias)
        out = F.relu(out)
        out = F.max_pool2d(out)
        # 全连接层计算
        out = F.linear(x, self.linear1_weight, self.linear1_bias)
        return out

很明显看出，利用这两者定义的CNN都能实现相同的功能，但两者实现细节上不同，毕竟一个是类，一个是函数。

2. dropout的不同

import torch
import torch.nn as nn
import torch.nn.functional as F

# nn.x实现dropout
class Model1(nn.Module):
    
    def __init__(self, p = 0.0):
        super(Model1, self).__init__()
        self.dropout = nn.Dropout(p = p)
        
    def forward(self, inputs):
        return self.dropout(inputs)
    
# nn.functional.x实现dropout   
class Model2(nn.Module):

    def __init__(self, p = 0.0):
        super(Model2, self).__init__()
        self.p = p
    # training等于False,可关闭train的dropout,默认为True
    def forward(self, inputs):
        return F.dropout(inputs, p = self.p, training = True)

# 调用测试
print(10 * '-' + "load inputs and train:" + 10 * '-' + '\r\n')
# 初始化
m1 = Model1(p = 0.5)
m2 = Model2(p = 0.5)
# 随机创建inputs
inputs = torch.rand(10)
print('Model1', m1(inputs))
print('Model2', m2(inputs))
# eval模型
print(10 * '-' + "after eval model:" + 10 * '-' + '\r\n')
m1.eval()
m2.eval()
print('Model1', m1(inputs))
print('Model2', m2(inputs))

----------load inputs and train:----------

Model1 tensor([0.5136, 0.0000, 1.8053, 1.5378, 1.4294, 0.5862, 0.0000, 0.6605, 1.1898,
        0.0000])
Model2 tensor([0.5136, 1.1499, 1.8053, 1.5378, 1.4294, 0.0000, 0.0000, 0.6605, 1.1898,
        0.0000])
----------after eval model:----------

Model1 tensor([0.2568, 0.5749, 0.9026, 0.7689, 0.7147, 0.2931, 0.6212, 0.3303, 0.5949,
        0.8018])
Model2 tensor([0.5136, 0.0000, 0.0000, 1.5378, 0.0000, 0.5862, 1.2424, 0.0000, 0.0000,
        0.0000])

这里可以看出，两者在dropout上的不同，nn.dropout是在train默认True, eval时关闭

而nn.functional.dropout在train和eval都默认开启（eval Model2中还有一半的0.0000）

但CNN中一般train使用dropout, eval不使用，所以如Pytroch官方一样建议使用nn.x

3. nn.functional.x的优势

如上所述，nn.functional.x在参数定义和管理方面不如nn.x,但凡事都有两面性，

如果我们设计的CNN要共享一组weight，这时用nn.functional.x就更容易实现：

import torch
import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    
    def __init__(self):
        super(Model, self).__init__()
        self.weight = nn.Parameter(torch.rand(32, 16, 5, 5))
        self.bias = nn.Parameter(torch.rand(32))
        
    def forward(self, x):
        x1 = F.conv2d(x, self.weight, self.bias)
        x2 = F.conv2d(x, self.weight, self.bias)
        return x1 + x2

总的来说，建议使用nn.x， 如需真的需要手动操作weight, bias, stride这些中间量的值，或者共享parameter时，可采用nn.functional.x

reference:

• https://pytorch.org/docs/stable/nn.html
• https://pytorch.org/docs/stable/nn.functional.html
• https://*.com/questions/53419474/using-dropout-in-pytorch-nn-dropout-vs-f-dropout

须知少时凌云志，曾许人间第一流。