Spaces:
Running
Running
File size: 2,341 Bytes
7088d16 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 |
# 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.
from copy import deepcopy
from itertools import product
from fvcore.common.benchmark import benchmark
from tests.test_points_alignment import TestCorrespondingPointsAlignment, TestICP
def bm_iterative_closest_point() -> None:
case_grid = {
"batch_size": [1, 10],
"dim": [3, 20],
"n_points_X": [100, 1000],
"n_points_Y": [100, 1000],
"use_pointclouds": [False],
}
test_args = sorted(case_grid.keys())
test_cases = product(*case_grid.values())
kwargs_list = [dict(zip(test_args, case)) for case in test_cases]
# add the use_pointclouds=True test cases whenever we have dim==3
kwargs_to_add = []
for entry in kwargs_list:
if entry["dim"] == 3:
entry_add = deepcopy(entry)
entry_add["use_pointclouds"] = True
kwargs_to_add.append(entry_add)
kwargs_list.extend(kwargs_to_add)
benchmark(
TestICP.iterative_closest_point,
"IterativeClosestPoint",
kwargs_list,
warmup_iters=1,
)
def bm_corresponding_points_alignment() -> None:
case_grid = {
"allow_reflection": [True, False],
"batch_size": [1, 10, 100],
"dim": [3, 20],
"estimate_scale": [True, False],
"n_points": [100, 10000],
"random_weights": [False, True],
"use_pointclouds": [False],
}
test_args = sorted(case_grid.keys())
test_cases = product(*case_grid.values())
kwargs_list = [dict(zip(test_args, case)) for case in test_cases]
# add the use_pointclouds=True test cases whenever we have dim==3
kwargs_to_add = []
for entry in kwargs_list:
if entry["dim"] == 3:
entry_add = deepcopy(entry)
entry_add["use_pointclouds"] = True
kwargs_to_add.append(entry_add)
kwargs_list.extend(kwargs_to_add)
benchmark(
TestCorrespondingPointsAlignment.corresponding_points_alignment,
"CorrespodingPointsAlignment",
kwargs_list,
warmup_iters=1,
)
if __name__ == "__main__":
bm_corresponding_points_alignment()
bm_iterative_closest_point()
|