Pytorch children()、modules()、named_children()、named_modules()、named_parameters()、parameters()使用说明

children()：返回包含直接子模块的迭代器
modules()：（递归）返回包含所有子模块（直接、间接）的迭代器
named_children() ：返回包含直接子模块的迭代器，同时产生模块的名称以及模块本身
named_modules()：返回包含所有子模块（直接、间接）的迭代器，同时产生模块的名称以及模块本身
named_parameters()：返回模块参数上的迭代器，产生参数的名称和参数本身
parameters()： 返回模块参数上的迭代器，不包括名称

import torch.nn as nn


class AlexNet(nn.Module):
    def __init__(self, num_classes=1000, init_weights=False):
        super(AlexNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 48, kernel_size=11, stride=4, padding=2),  # input[3, 224, 224]  output[48, 55, 55]
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),                  # output[48, 27, 27]
            nn.Conv2d(48, 128, kernel_size=5, padding=2),           # output[128, 27, 27]
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),                  # output[128, 13, 13]
            nn.Conv2d(128, 192, kernel_size=3, padding=1),          # output[192, 13, 13]
            nn.ReLU(inplace=True),
            nn.Conv2d(192, 192, kernel_size=3, padding=1),          # output[192, 13, 13]
            nn.ReLU(inplace=True),
            nn.Conv2d(192, 128, kernel_size=3, padding=1),          # output[128, 13, 13]
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),                  # output[128, 6, 6]
        )
        self.classifier = nn.Sequential(
            nn.Dropout(p=0.5),
            nn.Linear(128 * 6 * 6, 2048),
            nn.ReLU(inplace=True),
            nn.Dropout(p=0.5),
            nn.Linear(2048, 2048),
            nn.ReLU(inplace=True),
            nn.Linear(2048, num_classes),
        )
        if init_weights:
            self._initialize_weights()

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

    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                nn.init.constant_(m.bias, 0)

if __name__ == '__main__':
    
    model = AlexNet()

    print('model children: ')
    for module in model.children():
        print(module)
    
    print('model modules: ')
    for module in model.modules():
        print(module)

    print('model named children: ')
    for name, module in model.named_children():
        print('name: {}, module: {}'.format(name, module))
    
    print('model named modules: ')
    for name, module in  model.named_modules():
        print('name: {}, module: {}'.format(name, module))

    print('model named parameters: ')
    for name, parameter in model.named_parameters():
         print('name: {}, parameter: {}'.format(name, parameter))

    print('parameters: ')
    for parameter in model.parameters():
        print('parameter: {}'.format(parameter))