kopia lustrzana https://github.com/lzzcd001/MeshDiffusion
58 wiersze
2.3 KiB
Python
58 wiersze
2.3 KiB
Python
# Copyright (c) 2020-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
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# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
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# property and proprietary rights in and to this material, related
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# documentation and any modifications thereto. Any use, reproduction,
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# disclosure or distribution of this material and related documentation
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# without an express license agreement from NVIDIA CORPORATION or
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# its affiliates is strictly prohibited.
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import torch
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import os
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import sys
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sys.path.insert(0, os.path.join(sys.path[0], '../..'))
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import renderutils as ru
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DTYPE=torch.float32
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def test_bsdf(BATCH, RES, ITR):
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kd_cuda = torch.rand(BATCH, RES, RES, 3, dtype=DTYPE, device='cuda', requires_grad=True)
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kd_ref = kd_cuda.clone().detach().requires_grad_(True)
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arm_cuda = torch.rand(BATCH, RES, RES, 3, dtype=DTYPE, device='cuda', requires_grad=True)
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arm_ref = arm_cuda.clone().detach().requires_grad_(True)
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pos_cuda = torch.rand(BATCH, RES, RES, 3, dtype=DTYPE, device='cuda', requires_grad=True)
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pos_ref = pos_cuda.clone().detach().requires_grad_(True)
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nrm_cuda = torch.rand(BATCH, RES, RES, 3, dtype=DTYPE, device='cuda', requires_grad=True)
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nrm_ref = nrm_cuda.clone().detach().requires_grad_(True)
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view_cuda = torch.rand(BATCH, RES, RES, 3, dtype=DTYPE, device='cuda', requires_grad=True)
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view_ref = view_cuda.clone().detach().requires_grad_(True)
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light_cuda = torch.rand(BATCH, RES, RES, 3, dtype=DTYPE, device='cuda', requires_grad=True)
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light_ref = light_cuda.clone().detach().requires_grad_(True)
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target = torch.rand(BATCH, RES, RES, 3, device='cuda')
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start = torch.cuda.Event(enable_timing=True)
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end = torch.cuda.Event(enable_timing=True)
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ru.pbr_bsdf(kd_cuda, arm_cuda, pos_cuda, nrm_cuda, view_cuda, light_cuda)
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print("--- Testing: [%d, %d, %d] ---" % (BATCH, RES, RES))
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start.record()
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for i in range(ITR):
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ref = ru.pbr_bsdf(kd_ref, arm_ref, pos_ref, nrm_ref, view_ref, light_ref, use_python=True)
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end.record()
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torch.cuda.synchronize()
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print("Pbr BSDF python:", start.elapsed_time(end))
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start.record()
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for i in range(ITR):
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cuda = ru.pbr_bsdf(kd_cuda, arm_cuda, pos_cuda, nrm_cuda, view_cuda, light_cuda)
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end.record()
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torch.cuda.synchronize()
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print("Pbr BSDF cuda:", start.elapsed_time(end))
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test_bsdf(1, 512, 1000)
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test_bsdf(16, 512, 1000)
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test_bsdf(1, 2048, 1000)
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