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| # Copyright (c) 2020-2021, NVIDIA CORPORATION. All rights reserved. | |
| # | |
| # NVIDIA CORPORATION and its licensors retain all intellectual property | |
| # and proprietary rights in and to this software, related documentation | |
| # and any modifications thereto. Any use, reproduction, disclosure or | |
| # distribution of this software and related documentation without an express | |
| # license agreement from NVIDIA CORPORATION is strictly prohibited. | |
| import numpy as np | |
| import torch | |
| import os | |
| import sys | |
| sys.path.insert(0, os.path.join(sys.path[0], '../..')) | |
| import renderutils as ru | |
| BATCH = 8 | |
| RES = 1024 | |
| DTYPE = torch.float32 | |
| torch.manual_seed(0) | |
| def tonemap_srgb(f): | |
| return torch.where(f > 0.0031308, torch.pow(torch.clamp(f, min=0.0031308), 1.0/2.4)*1.055 - 0.055, 12.92*f) | |
| def l1(output, target): | |
| x = torch.clamp(output, min=0, max=65535) | |
| r = torch.clamp(target, min=0, max=65535) | |
| x = tonemap_srgb(torch.log(x + 1)) | |
| r = tonemap_srgb(torch.log(r + 1)) | |
| return torch.nn.functional.l1_loss(x,r) | |
| def relative_loss(name, ref, cuda): | |
| ref = ref.float() | |
| cuda = cuda.float() | |
| print(name, torch.max(torch.abs(ref - cuda) / torch.abs(ref)).item()) | |
| def test_xfm_points(): | |
| points_cuda = torch.rand(1, RES, 3, dtype=DTYPE, device='cuda', requires_grad=True) | |
| points_ref = points_cuda.clone().detach().requires_grad_(True) | |
| mtx_cuda = torch.rand(BATCH, 4, 4, dtype=DTYPE, device='cuda', requires_grad=False) | |
| mtx_ref = mtx_cuda.clone().detach().requires_grad_(True) | |
| target = torch.rand(BATCH, RES, 4, dtype=DTYPE, device='cuda', requires_grad=True) | |
| ref_out = ru.xfm_points(points_ref, mtx_ref, use_python=True) | |
| ref_loss = torch.nn.MSELoss()(ref_out, target) | |
| ref_loss.backward() | |
| cuda_out = ru.xfm_points(points_cuda, mtx_cuda) | |
| cuda_loss = torch.nn.MSELoss()(cuda_out, target) | |
| cuda_loss.backward() | |
| print("-------------------------------------------------------------") | |
| relative_loss("res:", ref_out, cuda_out) | |
| relative_loss("points:", points_ref.grad, points_cuda.grad) | |
| def test_xfm_vectors(): | |
| points_cuda = torch.rand(1, RES, 3, dtype=DTYPE, device='cuda', requires_grad=True) | |
| points_ref = points_cuda.clone().detach().requires_grad_(True) | |
| points_cuda_p = points_cuda.clone().detach().requires_grad_(True) | |
| points_ref_p = points_cuda.clone().detach().requires_grad_(True) | |
| mtx_cuda = torch.rand(BATCH, 4, 4, dtype=DTYPE, device='cuda', requires_grad=False) | |
| mtx_ref = mtx_cuda.clone().detach().requires_grad_(True) | |
| target = torch.rand(BATCH, RES, 4, dtype=DTYPE, device='cuda', requires_grad=True) | |
| ref_out = ru.xfm_vectors(points_ref.contiguous(), mtx_ref, use_python=True) | |
| ref_loss = torch.nn.MSELoss()(ref_out, target[..., 0:3]) | |
| ref_loss.backward() | |
| cuda_out = ru.xfm_vectors(points_cuda.contiguous(), mtx_cuda) | |
| cuda_loss = torch.nn.MSELoss()(cuda_out, target[..., 0:3]) | |
| cuda_loss.backward() | |
| ref_out_p = ru.xfm_points(points_ref_p.contiguous(), mtx_ref, use_python=True) | |
| ref_loss_p = torch.nn.MSELoss()(ref_out_p, target) | |
| ref_loss_p.backward() | |
| cuda_out_p = ru.xfm_points(points_cuda_p.contiguous(), mtx_cuda) | |
| cuda_loss_p = torch.nn.MSELoss()(cuda_out_p, target) | |
| cuda_loss_p.backward() | |
| print("-------------------------------------------------------------") | |
| relative_loss("res:", ref_out, cuda_out) | |
| relative_loss("points:", points_ref.grad, points_cuda.grad) | |
| relative_loss("points_p:", points_ref_p.grad, points_cuda_p.grad) | |
| test_xfm_points() | |
| test_xfm_vectors() | |