@user_123454321 Thank you for your quick response.

I am using 4 workers as follows:

torch.utils.data.DataLoader(dataset,
                            batch_size=64,
                            sampler=SubsetRandomSampler(train_idx), 
                            num_workers=4, 
                            worker_init_fn=init_worker)


I wrote, previously training phase, a torch version of samples on disk with:

torch.save(transforms.ToTensor(img), path / 'data.pt')

I load 256 torch samples (with interpolation applied) in ~0.26 seconds. Without interpolation, it takes ~0.17 seconds. I used the same params (batch_size=64 & num_workers=4) in DataLoader.
Anyway, I observed that increasing number of workers does not improve the loading time.

As transformation, I apply just an interpolation to resize the torch input. I can say from point 2 that there is a 0.09 seconds cost for interpolation on a pool of 256 samples.