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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()
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