Can you please let me know how to load the pretrained models using c++ frontend? I am trying to load
the resnet model using C++ front end as follows.
// replacement for the last layer
torch::nn::Linear lin(2048, 2); // the last layer of resnet, which you want to replace, has dimensions 512x1000
std::shared_ptr<torch::jit::script::Module> resnet;
resnet = torch::jit::load("../../PretrainModel/resnet101.pt");
torch::optim::Adam optimizer(lin->parameters(), torch::optim::AdamOptions(starting_lr).weight_decay(1e-4));
resnet->to(device);
lin->to(device);
for (size_t epoch = 1; epoch <= kNumberOfEpochs; ++epoch)
{
double running_loss = 0;
double running_corrects = 0;
lin->train();
clock_t s = (int)std::clock();
for (const auto& batch : *train_loader)
{
std::vector<torch::jit::IValue> input;
auto data = batch.data.to(device), targets = batch.target.to(device);
input.push_back(data);
// some training loop
torch::Tensor out = resnet->forward(input).toTensor().squeeze();
out = lin(out);
auto pred = out.argmax(1);
torch::Tensor loss = torch::nll_loss(out.log_softmax(1), targets);
optimizer.zero_grad();
loss.backward();
optimizer.step();
running_loss += loss.template item<float>();
running_corrects += pred.eq(targets).sum().template item<int64_t>();
}
clock_t e = (int)std::clock();
std::printf
(
"\rTrain Epoch: %ld [%5d] Loss: %.4f acc: %.4f processing time : %0.3f s",
epoch,
train_dataset_size, running_loss / train_dataset_size, running_corrects / train_dataset_size, (float)(e - s) / CLOCKS_PER_SEC);
}
But I want to create a new model using the values of all the parameters.
How to do it ?
Thank you.