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|
| 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] |
|
|
| |
| 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] |
|
|
| |
| 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() |
|
|
