mirror of
https://github.com/facebookresearch/pytorch3d.git
synced 2025-08-02 03:42:50 +08:00
34f648ede0
Summary: Move testing targets from pytorch3d/tests/TARGETS to pytorch3d/TARGETS. Reviewed By: shapovalov Differential Revision: D36186940 fbshipit-source-id: a4c52c4d99351f885e2b0bf870532d530324039b
55 lines
1.6 KiB
Python
55 lines
1.6 KiB
Python
# 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 itertools import product
|
|
|
|
from fvcore.common.benchmark import benchmark
|
|
from tests.test_iou_box3d import TestIoU3D
|
|
|
|
|
|
def bm_iou_box3d() -> None:
|
|
# Realistic use cases
|
|
N = [30, 100]
|
|
M = [5, 10, 100]
|
|
kwargs_list = []
|
|
test_cases = product(N, M)
|
|
for case in test_cases:
|
|
n, m = case
|
|
kwargs_list.append({"N": n, "M": m, "device": "cuda:0"})
|
|
benchmark(TestIoU3D.iou, "3D_IOU", kwargs_list, warmup_iters=1)
|
|
|
|
# Comparison of C++/CUDA
|
|
kwargs_list = []
|
|
N = [1, 4, 8, 16]
|
|
devices = ["cpu", "cuda:0"]
|
|
test_cases = product(N, N, devices)
|
|
for case in test_cases:
|
|
n, m, d = case
|
|
kwargs_list.append({"N": n, "M": m, "device": d})
|
|
benchmark(TestIoU3D.iou, "3D_IOU", kwargs_list, warmup_iters=1)
|
|
|
|
# Naive PyTorch
|
|
N = [1, 4]
|
|
kwargs_list = []
|
|
test_cases = product(N, N)
|
|
for case in test_cases:
|
|
n, m = case
|
|
kwargs_list.append({"N": n, "M": m, "device": "cuda:0"})
|
|
benchmark(TestIoU3D.iou_naive, "3D_IOU_NAIVE", kwargs_list, warmup_iters=1)
|
|
|
|
# Sampling based method
|
|
num_samples = [2000, 5000]
|
|
kwargs_list = []
|
|
test_cases = product(N, N, num_samples)
|
|
for case in test_cases:
|
|
n, m, s = case
|
|
kwargs_list.append({"N": n, "M": m, "num_samples": s, "device": "cuda:0"})
|
|
benchmark(TestIoU3D.iou_sampling, "3D_IOU_SAMPLING", kwargs_list, warmup_iters=1)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
bm_iou_box3d()
|