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https://github.com/facebookresearch/pytorch3d.git
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1ea2b7272a
Summary: Implement the sample_pdf function from the NeRF project as compiled operators.. The binary search (in searchsorted) is replaced with a low tech linear search, but this is not a problem for the envisaged numbers of bins. Reviewed By: gkioxari Differential Revision: D26312535 fbshipit-source-id: df1c3119cd63d944380ed1b2657b6ad81d743e49
38 lines
1.0 KiB
Python
38 lines
1.0 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_sample_pdf import TestSamplePDF
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def bm_sample_pdf() -> None:
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backends = ["python_cuda", "cuda", "python_cpu", "cpu"]
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kwargs_list = []
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sample_counts = [64]
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batch_sizes = [1024, 10240]
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bin_counts = [62, 600]
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test_cases = product(backends, sample_counts, batch_sizes, bin_counts)
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for case in test_cases:
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backend, n_samples, batch_size, n_bins = case
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kwargs_list.append(
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{
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"backend": backend,
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"n_samples": n_samples,
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"batch_size": batch_size,
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"n_bins": n_bins,
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}
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)
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benchmark(TestSamplePDF.bm_fn, "SAMPLE_PDF", kwargs_list, warmup_iters=1)
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if __name__ == "__main__":
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bm_sample_pdf()
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