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

谁借莪１个温暖的怀抱￠ 2022-09-12 14:55 130阅读 0赞

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))