Hi all,
due to IT reasons, I cannot run the inference on my dataset using pytorch (long story).
I have created a C++11 code that loads weights and biases from a pytorch training and uses them to run the inference.
So far, the C++ inference works fine.
However, the model I’m currently using for my dataset relies on Batch Normalisation techniques (BatchNorm1D); I managed to extract the following normalisation variables from my training:

normalise.weight (vector)
normalise.bias (vector)
normalise.running_mean (vector)
normalise.running_var (vector)
normalise.num_batches_tracked (int number)

I have some troubles understanding how to use these values to normalise the inputs during the inference process. To be clear, I need to understand the underlying vector/matrix algebra of the normalization process.
I know that, given X as input:
X_norm = (X - mean)/sqrt{var + 1e-5}
out = gamma * X_norm + beta

but it is not clear to me which of the previous values are gamma and beta. My guess would be:
gamma = normalise.weights
and
beta = normalise.bias
I had a look at the original Batch Normalization paper, but that focuses more on the normalisation in batches during the training.
Any idea?
Thanks