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https://github.com/facebookresearch/pytorch3d.git
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53266ec9ff
Summary: C++ Implementation of algorithm to compute 3D bounding boxes for batches of bboxes of shape (N, 8, 3) and (M, 8, 3). Reviewed By: gkioxari Differential Revision: D30905190 fbshipit-source-id: 02e2cf025cd4fa3ff706ce5cf9b82c0fb5443f96
38 lines
1.1 KiB
Python
38 lines
1.1 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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N = [1, 4, 8, 16]
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num_samples = [2000, 5000, 10000, 20000]
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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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[k.update({"device": "cpu"}) for k in kwargs_list]
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benchmark(TestIoU3D.iou, "3D_IOU", kwargs_list, warmup_iters=1)
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kwargs_list = []
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test_cases = product([1, 4], [1, 4], 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})
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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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