python的卷积torch.nn.Conv2d的参数个数

参考：https://blog.csdn.net/u012428169/article/details/114702453

import torch
import torch.nn as nn
def get_n_params(model):
    pp=0
    for p in list(model.parameters()):
        nn=1
        for s in list(p.size()):
            nn = nn*s
        pp += nn
    return pp

class test_module(nn.Module):
    def __init__(self, ): #num_classes=1000
        super(test_module, self).__init__()
        hidden1 = nn.Sequential(
            nn.Conv2d(in_channels=3, out_channels=20,
                      kernel_size=5, padding=2, stride=2),
            nn.BatchNorm2d(num_features=20),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2, padding=1),
            nn.Dropout(0.2)
        )
        # hidden2 = nn.Sequential(
        #     nn.Conv2d(in_channels=20, out_channels=24,
        #               kernel_size=5, padding=2),
        #     nn.BatchNorm2d(num_features=24),
        #     nn.ReLU(),
        #     nn.MaxPool2d(kernel_size=2, stride=1, padding=1),
        #     nn.Dropout(0.2)
        # )

        self.features = nn.Sequential(
            hidden1,
            # hidden2,

        )
        def forward(self, x):
        	x1 = self.features(x)
        	return x1

model_conv = test_module()

print(model_conv)
print(get_n_params(model_conv))

只有nn.Conv2d使用了1520个参数，可以计算5*5*3*20 +20=1520，5*5表示kernel_size(或filter_size),1表示在进行卷积计算时的一个bias，3和20 分别表示输入输出的通道数

nn.BatchNorm2d（下式）使用了2*20个参数，gamma和beta需要得到

参数个数是13632，计算第一层的参数是1560，则第二层参数是12072=5*5*24*20 +24+2*24，

nn.Conv2d的参数个数，(卷积和为正方形时)kernel_size^2*in_channels*out_channels + out_channels
最后的out_channels表示每一个输出通道的biase