Spaces:
Running
Running
File size: 1,924 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 |
# 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.
from itertools import product
from fvcore.common.benchmark import benchmark
from tests.test_blending import TestBlending
def bm_blending() -> None:
devices = ["cuda"]
kwargs_list = []
num_meshes = [8]
image_size = [64, 128, 256]
faces_per_pixel = [2, 50, 100]
backend = ["pytorch", "custom"]
test_cases = product(num_meshes, image_size, faces_per_pixel, devices, backend)
for case in test_cases:
n, s, k, d, b = case
kwargs_list.append(
{
"num_meshes": n,
"image_size": s,
"faces_per_pixel": k,
"device": d,
"backend": b,
}
)
benchmark(
TestBlending.bm_sigmoid_alpha_blending,
"SIGMOID_ALPHA_BLENDING_PYTORCH",
kwargs_list,
warmup_iters=1,
)
kwargs_list = [case for case in kwargs_list if case["backend"] == "pytorch"]
benchmark(
TestBlending.bm_softmax_blending,
"SOFTMAX_BLENDING_PYTORCH",
kwargs_list,
warmup_iters=1,
)
kwargs_list = []
faces_per_pixel = [2, 10]
backend = ["pytorch"]
test_cases = product(num_meshes, image_size, faces_per_pixel, devices, backend)
for case in test_cases:
n, s, k, d, b = case
kwargs_list.append(
{
"num_meshes": n,
"image_size": s,
"faces_per_pixel": k,
"device": d,
"backend": b,
}
)
benchmark(
TestBlending.bm_splatter_blending,
"SPLATTER_BLENDING_PYTORCH",
kwargs_list,
warmup_iters=1,
)
if __name__ == "__main__":
bm_blending()
|