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Summary: The iterative closest point algorithm - point-to-point version. Output of `bm_iterative_closest_point`: Argument key: `batch_size dim n_points_X n_points_Y use_pointclouds` ``` Benchmark Avg Time(μs) Peak Time(μs) Iterations -------------------------------------------------------------------------------- IterativeClosestPoint_1_3_100_100_False 107569 111323 5 IterativeClosestPoint_1_3_100_1000_False 118972 122306 5 IterativeClosestPoint_1_3_1000_100_False 108576 110978 5 IterativeClosestPoint_1_3_1000_1000_False 331836 333515 2 IterativeClosestPoint_1_20_100_100_False 134387 137842 4 IterativeClosestPoint_1_20_100_1000_False 149218 153405 4 IterativeClosestPoint_1_20_1000_100_False 414248 416595 2 IterativeClosestPoint_1_20_1000_1000_False 374318 374662 2 IterativeClosestPoint_10_3_100_100_False 539852 539852 1 IterativeClosestPoint_10_3_100_1000_False 752784 752784 1 IterativeClosestPoint_10_3_1000_100_False 1070700 1070700 1 IterativeClosestPoint_10_3_1000_1000_False 1164020 1164020 1 IterativeClosestPoint_10_20_100_100_False 374548 377337 2 IterativeClosestPoint_10_20_100_1000_False 472764 476685 2 IterativeClosestPoint_10_20_1000_100_False 1457175 1457175 1 IterativeClosestPoint_10_20_1000_1000_False 2195820 2195820 1 IterativeClosestPoint_1_3_100_100_True 110084 115824 5 IterativeClosestPoint_1_3_100_1000_True 142728 147696 4 IterativeClosestPoint_1_3_1000_100_True 212966 213966 3 IterativeClosestPoint_1_3_1000_1000_True 369130 375114 2 IterativeClosestPoint_10_3_100_100_True 354615 355179 2 IterativeClosestPoint_10_3_100_1000_True 451815 452704 2 IterativeClosestPoint_10_3_1000_100_True 511833 511833 1 IterativeClosestPoint_10_3_1000_1000_True 798453 798453 1 -------------------------------------------------------------------------------- ``` Reviewed By: shapovalov, gkioxari Differential Revision: D19909952 fbshipit-source-id: f77fadc88fb7c53999909d594114b182ee2a3def
73 lines
2.1 KiB
Python
73 lines
2.1 KiB
Python
#!/usr/bin/env python3
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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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from copy import deepcopy
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from itertools import product
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from fvcore.common.benchmark import benchmark
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from test_points_alignment import TestCorrespondingPointsAlignment, TestICP
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def bm_iterative_closest_point() -> None:
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case_grid = {
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"batch_size": [1, 10],
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"dim": [3, 20],
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"n_points_X": [100, 1000],
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"n_points_Y": [100, 1000],
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"use_pointclouds": [False],
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}
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test_args = sorted(case_grid.keys())
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test_cases = product(*case_grid.values())
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kwargs_list = [dict(zip(test_args, case)) for case in test_cases]
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# add the use_pointclouds=True test cases whenever we have dim==3
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kwargs_to_add = []
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for entry in kwargs_list:
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if entry["dim"] == 3:
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entry_add = deepcopy(entry)
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entry_add["use_pointclouds"] = True
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kwargs_to_add.append(entry_add)
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kwargs_list.extend(kwargs_to_add)
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benchmark(
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TestICP.iterative_closest_point,
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"IterativeClosestPoint",
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kwargs_list,
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warmup_iters=1,
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)
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def bm_corresponding_points_alignment() -> None:
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case_grid = {
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"allow_reflection": [True, False],
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"batch_size": [1, 10, 100],
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"dim": [3, 20],
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"estimate_scale": [True, False],
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"n_points": [100, 10000],
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"random_weights": [False, True],
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"use_pointclouds": [False],
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}
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test_args = sorted(case_grid.keys())
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test_cases = product(*case_grid.values())
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kwargs_list = [dict(zip(test_args, case)) for case in test_cases]
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# add the use_pointclouds=True test cases whenever we have dim==3
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kwargs_to_add = []
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for entry in kwargs_list:
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if entry["dim"] == 3:
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entry_add = deepcopy(entry)
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entry_add["use_pointclouds"] = True
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kwargs_to_add.append(entry_add)
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kwargs_list.extend(kwargs_to_add)
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benchmark(
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TestCorrespondingPointsAlignment.corresponding_points_alignment,
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"CorrespodingPointsAlignment",
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kwargs_list,
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warmup_iters=1,
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)
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