Add benchmark for diffuse and specular lighting

Summary: I was trying to speed up the lighting computations, but my ideas didn't work. Even if that didn't work, we can at least commit the benchmarking script I wrote for diffuse and specular shading.

Reviewed By: nikhilaravi

Differential Revision: D21580171

fbshipit-source-id: 8b60c0284e91ecbe258b6aae839bd5c2bbe788aa
This commit is contained in:
Justin Johnson
2020-05-16 15:53:31 -07:00
committed by Facebook GitHub Bot
parent 3fef506895
commit d8987c6f48
2 changed files with 50 additions and 0 deletions

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tests/bm_lighting.py Normal file
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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
from itertools import product
import torch
from fvcore.common.benchmark import benchmark
from pytorch3d.renderer.lighting import diffuse, specular
def _bm_diffuse_cuda_with_init(N, S, K):
device = torch.device("cuda")
normals = torch.randn(N, S, S, K, 3, device=device)
color = torch.randn(1, 3, device=device)
direction = torch.randn(N, S, S, K, 3, device=device)
args = (normals, color, direction)
torch.cuda.synchronize()
return lambda: diffuse(*args)
def _bm_specular_cuda_with_init(N, S, K):
device = torch.device("cuda")
points = torch.randn(N, S, S, K, 3, device=device)
normals = torch.randn(N, S, S, K, 3, device=device)
direction = torch.randn(N, S, S, K, 3, device=device)
color = torch.randn(1, 3, device=device)
camera_position = torch.randn(N, 3, device=device)
shininess = torch.randn(N, device=device)
args = (points, normals, direction, color, camera_position, shininess)
torch.cuda.synchronize()
return lambda: specular(*args)
def bm_lighting() -> None:
# For now only benchmark lighting on GPU
if not torch.cuda.is_available():
return
kwargs_list = []
Ns = [1, 8]
Ss = [128, 256]
Ks = [1, 10, 80]
test_cases = product(Ns, Ss, Ks)
for case in test_cases:
N, S, K = case
kwargs_list.append({"N": N, "S": S, "K": K})
benchmark(_bm_diffuse_cuda_with_init, "DIFFUSE", kwargs_list, warmup_iters=3)
benchmark(_bm_specular_cuda_with_init, "SPECULAR", kwargs_list, warmup_iters=3)