`torch.nn.functional.l1_loss` computes a criterion with the MSE, not the MAE

[RensOliemans]

The L1Loss should create a criterion that measures the MAE error, but in TorchSharp it measures the MSE.

I believe that this is incorrect. See the following screenshot comparing TorchSharp and pytorch, which I believe should get identical results, but don't:

See the call to functional::mse_loss:

TorchSharp/src/Native/LibTorchSharp/THSLoss.cpp

Lines 94 to 102 in 26240d5

	Tensor THSNN_l1_loss(const Tensor input, const Tensor target, const int64_t reduction)
	{
	CATCH_RETURN_Tensor(
	auto opts = torch::nn::functional::MSELossFuncOptions();
	ApplyReduction(opts, reduction);
	res = ResultTensor(torch::nn::functional::mse_loss(*input, *target, opts));
	)
	}

If this is indeed incorrect, I would be happy to submit a fix in a PR, but am still figuring out how to make my projects use the locally built version of TorchSharp—I'm not the biggest C# / dotnet / nuget expert.

[NiklasGustafsson]

Thank you for the report.

You are correct, the C++ implementation calls the wrong libtorch function -- mse_loss() instead of l1_loss().

I can fix it, or you can submit a PR and get yourself on our 'Contributor' board... :-)

[NiklasGustafsson]

@RensOliemans -- take a look at #1360

[RensOliemans]

Thanks, I had the same diff in RensOliemans@1f7e50b but didn't push it yet: I haven't figured out how to build the TorchSharp so that I get a .nupkg that I can test locally. Is that possible on Linux?

[NiklasGustafsson]

Does dotnet pack not work?

[NiklasGustafsson]

You can look in the azure_pipeline.yaml file for the exact CLI arguments to provide to dotnet pack

[RensOliemans]

You can look in the azure_pipeline.yaml file for the exact CLI arguments to provide to `dotnet pack`

Thanks, this works. Specifically the `/p:IncludeTorchSharpPackage=true` and `/p:IncludeLibTorchCpuPackages=true` flags for `dotnet pack`.

Closed #1359 as completed via #1360.

Thanks!