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# 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()
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