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https://github.com/facebookresearch/pytorch3d.git
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ff8d4762f4
Summary: CUDA implementation of 3D bounding box overlap calculation. Reviewed By: gkioxari Differential Revision: D31157919 fbshipit-source-id: 5dc89805d01fef2d6779f00a33226131e39c43ed
55 lines
1.6 KiB
Python
55 lines
1.6 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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from itertools import product
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from fvcore.common.benchmark import benchmark
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from test_iou_box3d import TestIoU3D
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def bm_iou_box3d() -> None:
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# Realistic use cases
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N = [30, 100]
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M = [5, 10, 100]
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kwargs_list = []
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test_cases = product(N, M)
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for case in test_cases:
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n, m = case
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kwargs_list.append({"N": n, "M": m, "device": "cuda:0"})
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benchmark(TestIoU3D.iou, "3D_IOU", kwargs_list, warmup_iters=1)
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# Comparison of C++/CUDA
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kwargs_list = []
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N = [1, 4, 8, 16]
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devices = ["cpu", "cuda:0"]
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test_cases = product(N, N, devices)
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for case in test_cases:
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n, m, d = case
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kwargs_list.append({"N": n, "M": m, "device": d})
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benchmark(TestIoU3D.iou, "3D_IOU", kwargs_list, warmup_iters=1)
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# Naive PyTorch
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N = [1, 4]
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kwargs_list = []
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test_cases = product(N, N)
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for case in test_cases:
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n, m = case
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kwargs_list.append({"N": n, "M": m, "device": "cuda:0"})
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benchmark(TestIoU3D.iou_naive, "3D_IOU_NAIVE", kwargs_list, warmup_iters=1)
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# Sampling based method
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num_samples = [2000, 5000]
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kwargs_list = []
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test_cases = product(N, N, num_samples)
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for case in test_cases:
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n, m, s = case
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kwargs_list.append({"N": n, "M": m, "num_samples": s, "device": "cuda:0"})
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benchmark(TestIoU3D.iou_sampling, "3D_IOU_SAMPLING", kwargs_list, warmup_iters=1)
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if __name__ == "__main__":
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bm_iou_box3d()
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