pytoch-基本卷积网络结构, 参数提取,参数初始化

                    基本卷积网络结构net.py 

                    from torch import nn
                    
                    class SimpleCNN(nn.Module):
                        def __init__(self):
                            super(SimpleCNN, self).__init__()
                            layer1 = nn.Sequential() # 将网络模型进行添加
                            layer1.add_module(conv1, nn.Conv2d(3, 32, 3, 1, padding=1)) # nn.Conv
                            layer1.add_module(relu1, nn.ReLU(True))
                            layer1.add_module(pool1, nn.MaxPool2d(2, 2))
                            self.layer1 = layer1
                    
                            layer2 = nn.Sequential()
                            layer2.add_module(conv2, nn.Conv2d(32, 64, 3, 1, padding=1))
                            layer2.add_module(relu2, nn.ReLU(True))
                            layer2.add_module(pool2, nn.MaxPool2d(2, 2))
                            self.layer2 = layer2
                    
                            layer3 = nn.Sequential()
                            layer3.add_module(conv3, nn.Conv2d(64, 128, 3, 1, padding=1))
                            layer3.add_module(relu3, nn.ReLU(True))
                            layer3.add_module(pool3, nn.MaxPool2d(2, 2))
                            self.layer3 = layer3
                    
                            layer4 = nn.Sequential()
                            layer4.add_module(fc1, nn.Linear(2048, 512))
                            layer4.add_module(fc_relu1, nn.ReLU(True))
                            layer4.add_module(fc2, nn.Linear(512, 64))
                            layer4.add_module(fc_relu2, nn.ReLU(True))
                            layer4.add_module(fc3, nn.Linear(64, 10))
                            self.layer4 = layer4
                    
                        def forward(self, x):
                            conv1 = self.layer1(x)
                            conv2 = self.layer2(conv1)
                            conv3 = self.layer3(conv2)
                            fc_input = conv3.view(conv3.size(0), -1)
                            fc_out = self.layer4(fc_input)
                    
                            return fc_out
                    
                    model = SimpleCNN()
                    # print(model) # 打印输出网络结构

                    提取前两层的网络结构

                    new_model = nn.Sequential(*list(model.children())[:2])  # 提取前两层的网络结构, 构造nn.Sequential网络串接, * 表示将里面的内容一个个传进去

                    提取所有层的网络结构

                    conv_model = nn.Sequential()
                    # 提取所有的卷积层操作, model.name_modules() 提取所有层的网络结构
                    for name, layer in model.named_modules():
                        if isinstance(layer, nn.Conv2d):
                            name = name.replace(., _)
                            conv_model.add_module(name, layer)
                    print(conv_model)
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