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Guocheng Qian
2023-08-02 19:51:43 -07:00
parent c2891c38cc
commit 13e18567fa
202 changed files with 43362 additions and 17 deletions
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render/renderutils/tests/test_perf.py Normal file
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# Copyright (c) 2020-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
import torch
import os
import sys
sys.path.insert(0, os.path.join(sys.path[0], '../..'))
import renderutils as ru
DTYPE=torch.float32
def test_bsdf(BATCH, RES, ITR):
kd_cuda = torch.rand(BATCH, RES, RES, 3, dtype=DTYPE, device='cuda', requires_grad=True)
kd_ref = kd_cuda.clone().detach().requires_grad_(True)
arm_cuda = torch.rand(BATCH, RES, RES, 3, dtype=DTYPE, device='cuda', requires_grad=True)
arm_ref = arm_cuda.clone().detach().requires_grad_(True)
pos_cuda = torch.rand(BATCH, RES, RES, 3, dtype=DTYPE, device='cuda', requires_grad=True)
pos_ref = pos_cuda.clone().detach().requires_grad_(True)
nrm_cuda = torch.rand(BATCH, RES, RES, 3, dtype=DTYPE, device='cuda', requires_grad=True)
nrm_ref = nrm_cuda.clone().detach().requires_grad_(True)
view_cuda = torch.rand(BATCH, RES, RES, 3, dtype=DTYPE, device='cuda', requires_grad=True)
view_ref = view_cuda.clone().detach().requires_grad_(True)
light_cuda = torch.rand(BATCH, RES, RES, 3, dtype=DTYPE, device='cuda', requires_grad=True)
light_ref = light_cuda.clone().detach().requires_grad_(True)
target = torch.rand(BATCH, RES, RES, 3, device='cuda')
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
ru.pbr_bsdf(kd_cuda, arm_cuda, pos_cuda, nrm_cuda, view_cuda, light_cuda)
print("--- Testing: [%d, %d, %d] ---" % (BATCH, RES, RES))
start.record()
for i in range(ITR):
ref = ru.pbr_bsdf(kd_ref, arm_ref, pos_ref, nrm_ref, view_ref, light_ref, use_python=True)
end.record()
torch.cuda.synchronize()
print("Pbr BSDF python:", start.elapsed_time(end))
start.record()
for i in range(ITR):
cuda = ru.pbr_bsdf(kd_cuda, arm_cuda, pos_cuda, nrm_cuda, view_cuda, light_cuda)
end.record()
torch.cuda.synchronize()
print("Pbr BSDF cuda:", start.elapsed_time(end))
test_bsdf(1, 512, 1000)
test_bsdf(16, 512, 1000)
test_bsdf(1, 2048, 1000)