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| import torch |
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| import os |
| import sys |
| sys.path.insert(0, os.path.join(sys.path[0], '../..')) |
| import renderutils as ru |
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| DTYPE=torch.float32 |
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| 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') |
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| start = torch.cuda.Event(enable_timing=True) |
| 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() |
| 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)) |
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| 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)) |
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| test_bsdf(1, 512, 1000) |
| test_bsdf(16, 512, 1000) |
| test_bsdf(1, 2048, 1000) |
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