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c2862ff427
Summary: Use existing workaround for batched 3x3 symeig because it is faster than torch.symeig. Added benchmark showing speedup. True = workaround. ``` Benchmark Avg Time(μs) Peak Time(μs) Iterations -------------------------------------------------------------------------------- normals_True_3000 16237 17233 31 normals_True_6000 33028 33391 16 normals_False_3000 18623069 18623069 1 normals_False_6000 36535475 36535475 1 ``` Should help https://github.com/facebookresearch/pytorch3d/issues/988 Reviewed By: nikhilaravi Differential Revision: D33660585 fbshipit-source-id: d1162b277f5d61ed67e367057a61f25e03888dce
48 lines
1.2 KiB
Python
48 lines
1.2 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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import itertools
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import torch
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from fvcore.common.benchmark import benchmark
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from pytorch3d.ops import estimate_pointcloud_normals
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from test_points_normals import TestPCLNormals
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def to_bm(num_points, use_symeig_workaround):
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device = torch.device("cuda:0")
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points_padded, _normals = TestPCLNormals.init_spherical_pcl(
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num_points=num_points, device=device, use_pointclouds=False
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)
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torch.cuda.synchronize()
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def run():
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estimate_pointcloud_normals(
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points_padded, use_symeig_workaround=use_symeig_workaround
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)
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torch.cuda.synchronize()
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return run
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def bm_points_normals() -> None:
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case_grid = {
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"use_symeig_workaround": [True, False],
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"num_points": [3000, 6000],
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}
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test_cases = itertools.product(*case_grid.values())
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kwargs_list = [dict(zip(case_grid.keys(), case)) for case in test_cases]
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benchmark(
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to_bm,
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"normals",
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kwargs_list,
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warmup_iters=1,
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)
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if __name__ == "__main__":
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bm_points_normals()
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