pytorch3d/tests/bm_raysampling.py
David Novotny e6bc960fb5 Raysampling
Summary: Implements 3 basic raysamplers.

Reviewed By: nikhilaravi

Differential Revision: D24110643

fbshipit-source-id: eb67d0e56773c7871ebdcb23e7e520302dc1b3c9
2021-01-06 04:01:29 -08:00

40 lines
1.1 KiB
Python

# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import itertools
from fvcore.common.benchmark import benchmark
from pytorch3d.renderer import (
GridRaysampler,
MonteCarloRaysampler,
NDCGridRaysampler,
FoVOrthographicCameras,
FoVPerspectiveCameras,
OrthographicCameras,
PerspectiveCameras,
)
from test_raysampling import TestRaysampling
def bm_raysampling() -> None:
case_grid = {
"raysampler_type": [GridRaysampler, NDCGridRaysampler, MonteCarloRaysampler],
"camera_type": [
PerspectiveCameras,
OrthographicCameras,
FoVPerspectiveCameras,
FoVOrthographicCameras,
],
"batch_size": [1, 10],
"n_pts_per_ray": [10, 1000, 10000],
"image_width": [10, 300],
"image_height": [10, 300],
}
test_cases = itertools.product(*case_grid.values())
kwargs_list = [dict(zip(case_grid.keys(), case)) for case in test_cases]
benchmark(TestRaysampling.raysampler, "RAYSAMPLER", kwargs_list, warmup_iters=1)
if __name__ == "__main__":
bm_raysampling()