Method test_baseline

tests/python/stack.py:99–138

(self)

            layer(sinput)

    def test_baseline(self):
        coords, colors, pcd = load_file("1.ply")
        device = "cuda"

        D = 3
        batch_size = 16
        voxel_size = 0.02
        channels = [3, 64, 128]
        dcoords = torch.from_numpy(np.floor(coords / voxel_size)).int()
        bcoords = batched_coordinates([dcoords for i in range(batch_size)])
        in_feats = torch.rand(len(bcoords), 3).to(0)

        layer = nn.Sequential(
            ME.MinkowskiConvolution(
                channels[0],
                channels[1],
                kernel_size=3,
                stride=1,
                dimension=3,
            ),
            ME.MinkowskiConvolution(
                channels[1],
                channels[2],
                kernel_size=3,
                stride=2,
                dimension=3,
            ),
            ME.MinkowskiConvolutionTranspose(
                channels[2],
                channels[1],
                kernel_size=3,
                stride=1,
                dimension=3,
            ),
        ).cuda()

        for i in range(1000):
            torch.cuda.empty_cache()
            sinput = ME.SparseTensor(in_feats, coordinates=bcoords, device=device)
            layer(sinput)