Hi there, thanks for this package, it's really helpful!

On a cluster with multiple GPUs, I have my model on device cuda:1.

When calculating FID with a passed gen function, new samples are generated during FID calculation. To that end, a model_fn(x) function is defined here:

and if use_dataparallel=True, the model will be wrapped with model = torch.nn.DataParallel(model).

Problem: DataParallel has a kwarg device_ids=None which defaults to all the available devices and then selects the first device as the "source" device, i.e., cuda:0. Later it asserts that all parameters and buffers of the model are on that device.
Now, if device_ids is not passed, this will result in an error because my model device is different from cuda:0.
I am wondering why DataParallel just hard codes everything to the first of all available devices, but there is a solution on the cleanfid side for this problem.

Solution: pass device_ids with the device of the model:

        if use_dataparallel:
            device_ids = [torch.cuda.current_device()]  # or use next(model.parameters()).device
            model = torch.nn.DataParallel(model, device_ids=device_ids)
        def model_fn(x): return model(x)

I would be happy to make a PR fixing this. Unless I am missing something?

Cheers,
Jan