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https://github.com/facebookresearch/pytorch3d.git
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b2b0c5a442
Summary: Adds knn backward to return `grad_pts1` and `grad_pts2`. Adds `knn_gather` to return the nearest neighbors in pts2. The BM tests include backward pass and are ran on an M40. ``` Benchmark Avg Time(μs) Peak Time(μs) Iterations -------------------------------------------------------------------------------- KNN_SQUARE_32_256_128_3_24_cpu 39558 43485 13 KNN_SQUARE_32_256_128_3_24_cuda:0 1080 1404 463 KNN_SQUARE_32_256_512_3_24_cpu 81950 85781 7 KNN_SQUARE_32_256_512_3_24_cuda:0 1519 1641 330 -------------------------------------------------------------------------------- Benchmark Avg Time(μs) Peak Time(μs) Iterations -------------------------------------------------------------------------------- KNN_RAGGED_32_256_128_3_24_cpu 13798 14650 37 KNN_RAGGED_32_256_128_3_24_cuda:0 1576 1713 318 KNN_RAGGED_32_256_512_3_24_cpu 31255 32210 16 KNN_RAGGED_32_256_512_3_24_cuda:0 2024 2162 248 -------------------------------------------------------------------------------- ``` Reviewed By: jcjohnson Differential Revision: D20945556 fbshipit-source-id: a16f616029c6b5f8c2afceb5f2bc12c5c20d2f3c
27 lines
698 B
Python
27 lines
698 B
Python
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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from itertools import product
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from fvcore.common.benchmark import benchmark
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from test_knn import TestKNN
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def bm_knn() -> None:
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backends = ["cpu", "cuda:0"]
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kwargs_list = []
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Ns = [32]
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P1s = [256]
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P2s = [128, 512]
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Ds = [3]
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Ks = [24]
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test_cases = product(Ns, P1s, P2s, Ds, Ks, backends)
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for case in test_cases:
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N, P1, P2, D, K, b = case
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kwargs_list.append({"N": N, "P1": P1, "P2": P2, "D": D, "K": K, "device": b})
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benchmark(TestKNN.knn_square, "KNN_SQUARE", kwargs_list, warmup_iters=1)
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benchmark(TestKNN.knn_ragged, "KNN_RAGGED", kwargs_list, warmup_iters=1)
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