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Summary: New MultinomialRaysampler succeeds GridRaysampler bringing masking and subsampling. Correspondingly, NDCMultinomialRaysampler succeeds NDCGridRaysampler. Reviewed By: nikhilaravi, shapovalov Differential Revision: D33256897 fbshipit-source-id: cd80ec6f35b110d1d20a75c62f4e889ba8fa5d45
48 lines
1.3 KiB
Python
48 lines
1.3 KiB
Python
# Copyright (c) Meta Platforms, Inc. and 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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import itertools
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from fvcore.common.benchmark import benchmark
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from pytorch3d.renderer import (
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FoVOrthographicCameras,
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FoVPerspectiveCameras,
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MonteCarloRaysampler,
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MultinomialRaysampler,
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NDCMultinomialRaysampler,
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OrthographicCameras,
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PerspectiveCameras,
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)
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from test_raysampling import TestRaysampling
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def bm_raysampling() -> None:
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case_grid = {
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"raysampler_type": [
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MultinomialRaysampler,
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NDCMultinomialRaysampler,
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MonteCarloRaysampler,
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],
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"camera_type": [
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PerspectiveCameras,
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OrthographicCameras,
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FoVPerspectiveCameras,
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FoVOrthographicCameras,
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],
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"batch_size": [1, 10],
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"n_pts_per_ray": [10, 1000, 10000],
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"image_width": [10, 300],
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"image_height": [10, 300],
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}
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test_cases = itertools.product(*case_grid.values())
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kwargs_list = [dict(zip(case_grid.keys(), case)) for case in test_cases]
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benchmark(TestRaysampling.raysampler, "RAYSAMPLER", kwargs_list, warmup_iters=1)
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if __name__ == "__main__":
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bm_raysampling()
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