Method test_gpu

tests/python/chwise_conv.py:74–98

(self)

        print(input.F.grad)

    def test_gpu(self):
        print(f"{self.__class__.__name__}: test_gpu")
        if not torch.cuda.is_available():
            return

        device = torch.device('cuda')
        in_channels, D = 3, 2
        coords, feats, labels = data_loader(in_channels, batch_size=2)

        # Create random coordinates with tensor stride == 2
        out_coords, tensor_stride = get_random_coords()

        feats = feats.double()
        feats.requires_grad_()
        input = SparseTensor(feats, coords=coords).to(device)
        conv = MinkowskiChannelwiseConvolution(
            in_channels,
            kernel_size=3,
            stride=1,
            has_bias=False,
            dimension=D).double().to(device)

        print('Initial input: ', input)
        output = conv(input)
        print('Conv output: ', output)


if __name__ == '__main__':