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https://github.com/facebookresearch/pytorch3d.git
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8b8291830e
Summary: Torch CUDA extension for Marching Cubes - MC involving 3 steps: - 1st forward pass to collect vertices and occupied state for each voxel - Compute compactVoxelArray to skip non-empty voxels - 2nd pass to genereate interpolated vertex positions and faces by marching through the grid - In contrast to existing MC: - Bind each interpolated vertex with a global edge_id to address floating-point precision - Added deduplication process to remove redundant vertices and faces Benchmarks (ms): | N / V(^3) | python | C++ | CUDA | Speedup | | 2 / 20 | 12176873 | 24338 | 4363 | 2790x/5x| | 1 / 100 | - | 3070511 | 27126 | 113x | | 2 / 100 | - | 5968934 | 53129 | 112x | | 1 / 256 | - | 61278092 | 430900 | 142x | | 2 / 256 | - |125687930 | 856941 | 146x | Reviewed By: kjchalup Differential Revision: D39644248 fbshipit-source-id: d679c0c79d67b98b235d12296f383d760a00042a
36 lines
902 B
Python
36 lines
902 B
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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from fvcore.common.benchmark import benchmark
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from tests.test_marching_cubes import TestMarchingCubes
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def bm_marching_cubes() -> None:
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case_grid = {
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"algo_type": [
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"naive",
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"extension",
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],
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"batch_size": [1, 2],
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"V": [5, 10, 20, 100, 512],
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"device": ["cpu", "cuda:0"],
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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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TestMarchingCubes.marching_cubes_with_init,
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"MARCHING_CUBES",
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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_marching_cubes()
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