如何使用 libtorch 实现 VGG16 网络？
2024-09-04 14:16:31
参考地址：https://ethereon.github.io/netscope/#/preset/vgg-16
按照上面的图来写即可。
论文地址：https://arxiv.org/pdf/1409.1556.pdf
// Define a new Module.
struct Net : torch::nn::Module {
Net() {
conv1_1 = torch::nn::Conv2d(torch::nn::Conv2dOptions(3, 64, { 3,3 }).padding(1));
conv1_2 = torch::nn::Conv2d(torch::nn::Conv2dOptions(64, 64, { 3,3 }).padding(1));
conv2_1 = torch::nn::Conv2d(torch::nn::Conv2dOptions(64, 128, { 3,3 }).padding(1));
conv2_2 = torch::nn::Conv2d(torch::nn::Conv2dOptions(128, 128, { 3,3 }).padding(1));
conv3_1 = torch::nn::Conv2d(torch::nn::Conv2dOptions(128, 256, { 3,3 }).padding(1));
conv3_2 = torch::nn::Conv2d(torch::nn::Conv2dOptions(256, 256, { 3,3 }).padding(1));
conv3_3 = torch::nn::Conv2d(torch::nn::Conv2dOptions(256, 256, { 3,3 }).padding(1));
conv4_1 = torch::nn::Conv2d(torch::nn::Conv2dOptions(256, 512, { 3,3 }).padding(1));
conv4_2 = torch::nn::Conv2d(torch::nn::Conv2dOptions(512, 512, { 3,3 }).padding(1));
conv4_3 = torch::nn::Conv2d(torch::nn::Conv2dOptions(512, 512, { 3,3 }).padding(1));
conv5_1 = torch::nn::Conv2d(torch::nn::Conv2dOptions(512, 512, { 3,3 }).padding(1));
conv5_2 = torch::nn::Conv2d(torch::nn::Conv2dOptions(512, 512, { 3,3 }).padding(1));
conv5_3 = torch::nn::Conv2d(torch::nn::Conv2dOptions(512, 512, { 3,3 }).padding(1));
fc1 = torch::nn::Linear(512*7*7,4096);
fc2 = torch::nn::Linear(4096, 4096);
fc3 = torch::nn::Linear(4096, 1000);
}
// Implement the Net's algorithm.
torch::Tensor forward(torch::Tensor x) {
x = conv1_1->forward(x);
x = torch::relu(x);
x = conv1_2->forward(x);
x = torch::relu(x);
x = torch::max_pool2d(x, { 2,2 }, { 2,2 });
x = conv2_1->forward(x);
x = torch::relu(x);
x = conv2_2->forward(x);
x = torch::relu(x);
x = torch::max_pool2d(x, { 2,2 }, { 2,2 });
x = conv3_1->forward(x);
x = torch::relu(x);
x = conv3_2->forward(x);
x = torch::relu(x);
x = conv3_3->forward(x);
x = torch::relu(x);
x = torch::max_pool2d(x, { 2,2 }, { 2,2 });
x = conv4_1->forward(x);
x = torch::relu(x);
x = conv4_2->forward(x);
x = torch::relu(x);
x = conv4_3->forward(x);
x = torch::relu(x);
x = torch::max_pool2d(x, { 2,2 }, { 2,2 });
x = conv5_1->forward(x);
x = torch::relu(x);
x = conv5_2->forward(x);
x = torch::relu(x);
x = conv5_3->forward(x);
x = torch::relu(x);
x = torch::max_pool2d(x, { 2,2 }, { 2,2 });
x = x.view({ x.size(0), -1 });//512x7x7 = 25088
x = fc1->forward(x);
x = torch::relu(x);
x = torch::dropout(x, 0.5, is_training());
x = fc2->forward(x);
x = torch::relu(x);
x = torch::dropout(x, 0.5, is_training());
x = fc3->forward(x);
x = torch::log_softmax(x, 1);
return x;
}
// Use one of many "standard library" modules.
torch::nn::Conv2d conv1_1{ nullptr };
torch::nn::Conv2d conv1_2{ nullptr };
torch::nn::Conv2d conv2_1{ nullptr };
torch::nn::Conv2d conv2_2{ nullptr };
torch::nn::Conv2d conv3_1{ nullptr };
torch::nn::Conv2d conv3_2{ nullptr };
torch::nn::Conv2d conv3_3{ nullptr };
torch::nn::Conv2d conv4_1{ nullptr };
torch::nn::Conv2d conv4_2{ nullptr };
torch::nn::Conv2d conv4_3{ nullptr };
torch::nn::Conv2d conv5_1{ nullptr };
torch::nn::Conv2d conv5_2{ nullptr };
torch::nn::Conv2d conv5_3{ nullptr };
torch::nn::Linear fc1{ nullptr };
torch::nn::Linear fc2{ nullptr };
torch::nn::Linear fc3{ nullptr };
};
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