mirror of
https://github.com/facebookresearch/pytorch3d.git
synced 2025-08-01 03:12:49 +08:00
6471893f59
Summary:
Threaded the for loop:
```
for (int yi = 0; yi < H; ++yi) {...}
```
in function `RasterizeMeshesNaiveCpu()`.
Chunk size is approx equal.
Reviewed By: bottler
Differential Revision: D40063604
fbshipit-source-id: 09150269405538119b0f1b029892179501421e68
128 lines
3.6 KiB
Python
128 lines
3.6 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 os
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from itertools import product
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import torch
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from fvcore.common.benchmark import benchmark
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from tests.test_rasterize_meshes import TestRasterizeMeshes
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BM_RASTERIZE_MESHES_N_THREADS = os.getenv("BM_RASTERIZE_MESHES_N_THREADS", 1)
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torch.set_num_threads(int(BM_RASTERIZE_MESHES_N_THREADS))
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# ico levels:
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# 0: (12 verts, 20 faces)
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# 1: (42 verts, 80 faces)
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# 3: (642 verts, 1280 faces)
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# 4: (2562 verts, 5120 faces)
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# 5: (10242 verts, 20480 faces)
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# 6: (40962 verts, 81920 faces)
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def bm_rasterize_meshes() -> None:
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kwargs_list = [
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{
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"num_meshes": 1,
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"ico_level": 0,
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"image_size": 10, # very slow with large image size
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"blur_radius": 0.0,
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"faces_per_pixel": 3,
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}
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]
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benchmark(
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TestRasterizeMeshes.rasterize_meshes_python_with_init,
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"RASTERIZE_MESHES",
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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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num_meshes = [1]
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ico_level = [1]
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image_size = [64, 128, 512]
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blur = [1e-6]
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faces_per_pixel = [3, 50]
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test_cases = product(num_meshes, ico_level, image_size, blur, faces_per_pixel)
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for case in test_cases:
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n, ic, im, b, f = case
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kwargs_list.append(
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{
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"num_meshes": n,
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"ico_level": ic,
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"image_size": im,
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"blur_radius": b,
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"faces_per_pixel": f,
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}
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)
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benchmark(
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TestRasterizeMeshes.rasterize_meshes_cpu_with_init,
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"RASTERIZE_MESHES",
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kwargs_list,
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warmup_iters=1,
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)
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if torch.cuda.is_available():
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kwargs_list = []
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num_meshes = [8, 16]
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ico_level = [4, 5, 6]
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# Square and non square cases
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image_size = [64, 128, 512, (512, 256), (256, 512)]
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blur = [1e-6]
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faces_per_pixel = [40]
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test_cases = product(num_meshes, ico_level, image_size, blur, faces_per_pixel)
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for case in test_cases:
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n, ic, im, b, f = case
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kwargs_list.append(
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{
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"num_meshes": n,
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"ico_level": ic,
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"image_size": im,
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"blur_radius": b,
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"faces_per_pixel": f,
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}
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)
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benchmark(
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TestRasterizeMeshes.rasterize_meshes_cuda_with_init,
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"RASTERIZE_MESHES_CUDA",
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kwargs_list,
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warmup_iters=1,
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)
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# Test a subset of the cases with the
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# image plane intersecting the mesh.
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kwargs_list = []
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num_meshes = [8, 16]
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# Square and non square cases
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image_size = [64, 128, 512, (512, 256), (256, 512)]
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dist = [3, 0.8, 0.5]
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test_cases = product(num_meshes, dist, image_size)
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for case in test_cases:
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n, d, im = case
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kwargs_list.append(
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{
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"num_meshes": n,
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"ico_level": 4,
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"image_size": im,
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"blur_radius": 1e-6,
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"faces_per_pixel": 40,
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"dist": d,
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}
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)
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benchmark(
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TestRasterizeMeshes.bm_rasterize_meshes_with_clipping,
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"RASTERIZE_MESHES_CUDA_CLIPPING",
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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_rasterize_meshes()
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