How to get not normalized MNIST dataset PyTorch C++

Question

I'm trying to follow this C++ PyTorch example but I need to load the MNIST dataset with its standard values, between 0 and 255. I removed the application of the Normalize() method, but I continue getting value between 0 and 1. What am I doing wrong?

My code is:

int main(int argc, char* argv[]) {

  const int64_t batch_size = 1;

  // MNIST Dataset
  auto train_dataset = torch::data::datasets::MNIST("./mnist")
      .map(torch::data::transforms::Stack<>());

  // Number of samples in the training set
  auto num_train_samples = train_dataset.size().value();

  cout << "Number of training samples: " << num_train_samples << endl;
  
  // Data loaders
  auto train_loader = torch::data::make_data_loader<torch::data::samplers::RandomSampler>(
  std::move(train_dataset), batch_size);

  for (auto& batch : *train_loader) {
    auto data = batch.data.view({batch_size, -1}).to(device);
    auto record = data[0].clone();
    cout << "Max value: " << max(record) << endl;
    cout << "Min value: " << max(record) << endl;
    break;
  }
}

The MNIST dataset I downloaded is the original one, from the site.

Thank you in advance for your help.

Answer 1

I have looked at the source file and it appears that pytorch mnist dataset class performs the division by 255 to return only tensors within the [0,1] range. So you will have to multiply the batches by 255 yourself.

The normalize transform was not the culprit. It is used to change the mean and variance of your data