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https://github.com/facebookresearch/pytorch3d.git
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7c25d34d22
Summary: Benchmarking. We only use num_faces=2 for splatter, because as far as I can see one would never need to use more. Pose optimization and mesh optimization experiments (see next two diffs) showed that Splatter with 2 faces beats Softmax with 50 and 100 faces in terms of accuracy. Results: We're slower at 64px^2. At 128px and 256px, we're slower than Softmax+50faces, but faster than Softmax+100faces. We're also slower at 10 faces/pix, but expectation as well as results show that more then 2 faces shouldn't be necessary. See also more results in .https://fburl.com/gdoc/ttv7u7hp Reviewed By: jcjohnson Differential Revision: D36210575 fbshipit-source-id: c8de28c8a59ce5fe21a47263bd43d2757b15d123
75 lines
1.9 KiB
Python
75 lines
1.9 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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from itertools import product
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from fvcore.common.benchmark import benchmark
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from test_blending import TestBlending
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def bm_blending() -> None:
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devices = ["cuda"]
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kwargs_list = []
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num_meshes = [8]
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image_size = [64, 128, 256]
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faces_per_pixel = [2, 50, 100]
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backend = ["pytorch", "custom"]
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test_cases = product(num_meshes, image_size, faces_per_pixel, devices, backend)
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for case in test_cases:
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n, s, k, d, b = case
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kwargs_list.append(
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{
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"num_meshes": n,
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"image_size": s,
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"faces_per_pixel": k,
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"device": d,
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"backend": b,
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}
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)
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benchmark(
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TestBlending.bm_sigmoid_alpha_blending,
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"SIGMOID_ALPHA_BLENDING_PYTORCH",
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kwargs_list,
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warmup_iters=1,
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)
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kwargs_list = [case for case in kwargs_list if case["backend"] == "pytorch"]
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benchmark(
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TestBlending.bm_softmax_blending,
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"SOFTMAX_BLENDING_PYTORCH",
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kwargs_list,
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warmup_iters=1,
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)
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kwargs_list = []
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faces_per_pixel = [2, 10]
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backend = ["pytorch"]
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test_cases = product(num_meshes, image_size, faces_per_pixel, devices, backend)
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for case in test_cases:
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n, s, k, d, b = case
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kwargs_list.append(
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{
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"num_meshes": n,
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"image_size": s,
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"faces_per_pixel": k,
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"device": d,
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"backend": b,
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}
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)
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benchmark(
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TestBlending.bm_splatter_blending,
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"SPLATTER_BLENDING_PYTORCH",
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kwargs_list,
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warmup_iters=1,
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)
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if __name__ == "__main__":
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bm_blending()
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