Method test_pcd2

tests/cpp/convolution_cpu_test.py:133–181

(self)

                print(f"{batch_size}\t{voxel_size}\t{manager.size(in_key)}\t{min_time}")

    def test_pcd2(self):
        IC, OC = 3, 16
        coords, colors, pcd = load_file("1.ply")
        kernel_size = [3, 3, 3]
        kernel_stride = [2, 2, 2]
        kernel_dilation = [1, 1, 1]

        for IC in [3, 8, 16, 32, 64, 128]:
            for OC in [16, 32, 64, 128, 256]:
                # size, in, out
                kernel = torch.rand(np.prod(kernel_size), IC, OC)
                for batch_size in [1]:
                    for voxel_size in [0.02]:

                        min_time = 100000

                        dcoords = torch.from_numpy(np.floor(coords / voxel_size)).int()
                        bcoords = batched_coordinates(
                            [dcoords for i in range(batch_size)]
                        )

                        for i in range(10):
                            manager = _C.CoordinateMapManager()

                            # batch insert
                            in_key, (unique_map, inverse_map) = manager.insert_and_map(
                                bcoords, [1, 1, 1], ""
                            )
                            in_feats = torch.rand(manager.size(in_key), IC)
                            out_key = _C.CoordinateMapKey(4)

                            stime = time.time()
                            out_features = _C.ConvolutionForwardCPU(
                                in_feats,
                                kernel,
                                kernel_size,
                                kernel_stride,
                                kernel_dilation,
                                _C.RegionType.HYPER_CUBE,
                                torch.IntTensor(),
                                in_key,
                                out_key,
                                manager,
                            )
                            min_time = min(time.time() - stime, min_time)

                        print(
                            f"{batch_size}\t{manager.size(in_key)}\t{manager.size(out_key)}\t{IC}\t{OC}\t{min_time}"
                        )