Method test_kernelmap_gpu

tests/python/kernel_map.py:34–78

(self)

class TestKernelMap(unittest.TestCase):
    def test_kernelmap_gpu(self):
        print(f"{self.__class__.__name__}: test_kernelmap_gpu")
        if not torch.cuda.is_available():
            return

        in_channels, out_channels, D = 2, 3, 2
        coords, feats, labels = data_loader(in_channels)
        feats = feats.double()
        feats.requires_grad_()
        input = SparseTensor(
            feats,
            coordinates=coords,
            minkowski_algorithm=MinkowskiAlgorithm.SPEED_OPTIMIZED,
            device="cuda",
        )

        # Initialize context
        conv = (
            MinkowskiConvolution(
                in_channels,
                out_channels,
                kernel_size=3,
                stride=2,
                bias=True,
                dimension=D,
            )
            .double()
            .cuda()
        )
        output = conv(input)

        iC = input.C.cpu().numpy()
        oC = output.C.cpu().numpy()
        print(iC)
        print(oC)
        kernel_maps = output.coordinate_manager.kernel_map(
            1,
            2,
            stride=2,
            kernel_size=3,
        )
        for kernel_index, in_out_map in kernel_maps.items():
            for i, o in zip(in_out_map[0], in_out_map[1]):
                print(kernel_index, iC[i], "->", oC[o])
        self.assertTrue(sum(len(in_map[0]) for k, in_map in kernel_maps.items()) == 16)

    def test_kernelmap(self):
        print(f"{self.__class__.__name__}: test_kernelmap")