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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 | |
RES = 4 | |
DTYPE = torch.float32 | |
def relative_loss(name, ref, cuda): | |
ref = ref.float() | |
cuda = cuda.float() | |
print(name, torch.max(torch.abs(ref - cuda) / torch.abs(ref + 1e-7)).item()) | |
def test_cubemap(): | |
cubemap_cuda = torch.rand(6, RES, RES, 3, dtype=DTYPE, device='cuda', requires_grad=True) | |
cubemap_ref = cubemap_cuda.clone().detach().requires_grad_(True) | |
weights = torch.rand(3, 3, 1, dtype=DTYPE, device='cuda') | |
target = torch.rand(6, RES, RES, 3, dtype=DTYPE, device='cuda') | |
ref = ru.filter_cubemap(cubemap_ref, weights, use_python=True) | |
ref_loss = torch.nn.MSELoss()(ref, target) | |
ref_loss.backward() | |
cuda = ru.filter_cubemap(cubemap_cuda, weights, use_python=False) | |
cuda_loss = torch.nn.MSELoss()(cuda, target) | |
cuda_loss.backward() | |
print("-------------------------------------------------------------") | |
print(" Cubemap:") | |
print("-------------------------------------------------------------") | |
relative_loss("flt:", ref, cuda) | |
relative_loss("cubemap:", cubemap_ref.grad, cubemap_cuda.grad) | |
test_cubemap() | |