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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) | |