Sorry, I was meaning DataParallel instead of DataLoader. I would like to give multiple inputs to the DataParallel. I will probably need to modify my network for each input to be distributed to a DataParallel.
Can you explain this code extracted from the imagenet example :
    if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
        model.features = torch.nn.DataParallel(model.features)
        model.cuda()
    else:
        model = torch.nn.DataParallel(model).cuda()

Is there a specific reason to separate the classifier and the features in the alexnet and vgg models ?
Why not giving the whole model to DataParallel, like in the resnet model ?
Thanks