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d8987c6f48
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
48 lines
1.6 KiB
Python
48 lines
1.6 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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from itertools import product
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import torch
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from fvcore.common.benchmark import benchmark
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from pytorch3d.renderer.lighting import diffuse, specular
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def _bm_diffuse_cuda_with_init(N, S, K):
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device = torch.device("cuda")
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normals = torch.randn(N, S, S, K, 3, device=device)
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color = torch.randn(1, 3, device=device)
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direction = torch.randn(N, S, S, K, 3, device=device)
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args = (normals, color, direction)
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torch.cuda.synchronize()
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return lambda: diffuse(*args)
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def _bm_specular_cuda_with_init(N, S, K):
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device = torch.device("cuda")
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points = torch.randn(N, S, S, K, 3, device=device)
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normals = torch.randn(N, S, S, K, 3, device=device)
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direction = torch.randn(N, S, S, K, 3, device=device)
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color = torch.randn(1, 3, device=device)
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camera_position = torch.randn(N, 3, device=device)
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shininess = torch.randn(N, device=device)
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args = (points, normals, direction, color, camera_position, shininess)
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torch.cuda.synchronize()
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return lambda: specular(*args)
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def bm_lighting() -> None:
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# For now only benchmark lighting on GPU
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if not torch.cuda.is_available():
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return
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kwargs_list = []
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Ns = [1, 8]
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Ss = [128, 256]
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Ks = [1, 10, 80]
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test_cases = product(Ns, Ss, Ks)
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for case in test_cases:
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N, S, K = case
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kwargs_list.append({"N": N, "S": S, "K": K})
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benchmark(_bm_diffuse_cuda_with_init, "DIFFUSE", kwargs_list, warmup_iters=3)
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benchmark(_bm_specular_cuda_with_init, "SPECULAR", kwargs_list, warmup_iters=3)
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