e304d4ee794a4cca8c116e52f99077ee71f87af1,mushroom/approximators/parametric/pytorch_network.py,PyTorchApproximator,predict,#PyTorchApproximator#,62

Before Change

            val = self._network.forward(*torch_args, **kwargs).detach().numpy()
        else:
            torch_args = [torch.from_numpy(x).cuda() for x in args]
            val = self._network.forward(*torch_args,
                                        **kwargs).detach().cpu().numpy()

        return val

    def fit(self, *args, **kwargs):

After Change

        if not self._use_cuda:
            torch_args = [torch.from_numpy(x) for x in args]
            val = self._network.forward(*torch_args, **kwargs)
            if isinstance(val, tuple):
                val = tuple([x.detach().numpy() for x in val])
            else:
                val = val.detach().numpy()
        else:
            torch_args = [torch.from_numpy(x).cuda() for x in args]
            val = self._network.forward(*torch_args,
                                        **kwargs)

Instances