Inconsistent parameters

[iris0329]

Hi

As you mentioned in issues 17

For the Number of Parameters we used the built-in functions of Pytorch like so: sum(p.numel() for p in model.parameters() if p.requires_grad)
For the FLOPs we used this package: https://github.com/sovrasov/flops-counter.pytorch
Originally posted by @TiagoCortinhal in #17 (comment)

Following your advice, I also calculated FLOPs and parameters myself.

But what is strange is that the parameter I calculated is 6.71M instead of 6.73M in the paper. At the same time, FLOPs are the same as the results in the paper.

Attach my code:

    from ptflops import get_model_complexity_info

    with torch.cuda.device(0):
        model = SalsaNext(nclasses=20)
        macs, params = get_model_complexity_info(model, (5, 64, 2048), as_strings=True,
                                                 print_per_layer_stat=True, verbose=True)
        print('{:<30}  {:<8}'.format('Computational complexity: ', macs))
        print('{:<30}  {:<8}'.format('Number of parameters: ', params))

    # Computational complexity:       62.84 GMac   1 Mac = 2 FLOPs
    # Number of parameters:           6.71 M

Do you have any suggestions for reproducing the results in the paper?

Best,
Iris