| # Copyright (c) Meta Platforms, Inc. and affiliates. | |
| # All rights reserved. | |
| # | |
| # This source code is licensed under the BSD-style license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import itertools | |
| import torch | |
| from fvcore.common.benchmark import benchmark | |
| from pytorch3d.ops import estimate_pointcloud_normals | |
| from tests.test_points_normals import TestPCLNormals | |
| def to_bm(num_points, use_symeig_workaround): | |
| device = torch.device("cuda:0") | |
| points_padded, _normals = TestPCLNormals.init_spherical_pcl( | |
| num_points=num_points, device=device, use_pointclouds=False | |
| ) | |
| torch.cuda.synchronize() | |
| def run(): | |
| estimate_pointcloud_normals( | |
| points_padded, use_symeig_workaround=use_symeig_workaround | |
| ) | |
| torch.cuda.synchronize() | |
| return run | |
| def bm_points_normals() -> None: | |
| case_grid = { | |
| "use_symeig_workaround": [True, False], | |
| "num_points": [3000, 6000], | |
| } | |
| test_cases = itertools.product(*case_grid.values()) | |
| kwargs_list = [dict(zip(case_grid.keys(), case)) for case in test_cases] | |
| benchmark( | |
| to_bm, | |
| "normals", | |
| kwargs_list, | |
| warmup_iters=1, | |
| ) | |
| if __name__ == "__main__": | |
| bm_points_normals() | |