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

View File

@ -61,6 +61,7 @@ def diffuse(normals, color, direction) -> torch.Tensor:
color = color.view(expand_dims)
# Renormalize the normals in case they have been interpolated.
# We tried to replace the following with F.cosine_similarity, but it wasn't faster.
normals = F.normalize(normals, p=2, dim=-1, eps=1e-6)
direction = F.normalize(direction, p=2, dim=-1, eps=1e-6)
angle = F.relu(torch.sum(normals * direction, dim=-1))
@ -132,6 +133,8 @@ def specular(
shininess = shininess.view(expand_dims)
# Renormalize the normals in case they have been interpolated.
# We tried a version that uses F.cosine_similarity instead of renormalizing,
# but it was slower.
normals = F.normalize(normals, p=2, dim=-1, eps=1e-6)
direction = F.normalize(direction, p=2, dim=-1, eps=1e-6)
cos_angle = torch.sum(normals * direction, dim=-1)

47
tests/bm_lighting.py Normal file
View File

@ -0,0 +1,47 @@
# 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)