Spaces:
Running
Running
File size: 3,636 Bytes
7088d16 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import os
from itertools import product
import torch
from fvcore.common.benchmark import benchmark
from tests.test_rasterize_meshes import TestRasterizeMeshes
BM_RASTERIZE_MESHES_N_THREADS = os.getenv("BM_RASTERIZE_MESHES_N_THREADS", 1)
torch.set_num_threads(int(BM_RASTERIZE_MESHES_N_THREADS))
# ico levels:
# 0: (12 verts, 20 faces)
# 1: (42 verts, 80 faces)
# 3: (642 verts, 1280 faces)
# 4: (2562 verts, 5120 faces)
# 5: (10242 verts, 20480 faces)
# 6: (40962 verts, 81920 faces)
def bm_rasterize_meshes() -> None:
kwargs_list = [
{
"num_meshes": 1,
"ico_level": 0,
"image_size": 10, # very slow with large image size
"blur_radius": 0.0,
"faces_per_pixel": 3,
}
]
benchmark(
TestRasterizeMeshes.rasterize_meshes_python_with_init,
"RASTERIZE_MESHES",
kwargs_list,
warmup_iters=1,
)
kwargs_list = []
num_meshes = [1]
ico_level = [1]
image_size = [64, 128, 512]
blur = [1e-6]
faces_per_pixel = [3, 50]
test_cases = product(num_meshes, ico_level, image_size, blur, faces_per_pixel)
for case in test_cases:
n, ic, im, b, f = case
kwargs_list.append(
{
"num_meshes": n,
"ico_level": ic,
"image_size": im,
"blur_radius": b,
"faces_per_pixel": f,
}
)
benchmark(
TestRasterizeMeshes.rasterize_meshes_cpu_with_init,
"RASTERIZE_MESHES",
kwargs_list,
warmup_iters=1,
)
if torch.cuda.is_available():
kwargs_list = []
num_meshes = [8, 16]
ico_level = [4, 5, 6]
# Square and non square cases
image_size = [64, 128, 512, (512, 256), (256, 512)]
blur = [1e-6]
faces_per_pixel = [40]
test_cases = product(num_meshes, ico_level, image_size, blur, faces_per_pixel)
for case in test_cases:
n, ic, im, b, f = case
kwargs_list.append(
{
"num_meshes": n,
"ico_level": ic,
"image_size": im,
"blur_radius": b,
"faces_per_pixel": f,
}
)
benchmark(
TestRasterizeMeshes.rasterize_meshes_cuda_with_init,
"RASTERIZE_MESHES_CUDA",
kwargs_list,
warmup_iters=1,
)
# Test a subset of the cases with the
# image plane intersecting the mesh.
kwargs_list = []
num_meshes = [8, 16]
# Square and non square cases
image_size = [64, 128, 512, (512, 256), (256, 512)]
dist = [3, 0.8, 0.5]
test_cases = product(num_meshes, dist, image_size)
for case in test_cases:
n, d, im = case
kwargs_list.append(
{
"num_meshes": n,
"ico_level": 4,
"image_size": im,
"blur_radius": 1e-6,
"faces_per_pixel": 40,
"dist": d,
}
)
benchmark(
TestRasterizeMeshes.bm_rasterize_meshes_with_clipping,
"RASTERIZE_MESHES_CUDA_CLIPPING",
kwargs_list,
warmup_iters=1,
)
if __name__ == "__main__":
bm_rasterize_meshes()
|