kopia lustrzana https://github.com/lzzcd001/MeshDiffusion
48 wiersze
1.6 KiB
Python
48 wiersze
1.6 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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RES = 4
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DTYPE = torch.float32
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def relative_loss(name, ref, cuda):
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ref = ref.float()
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cuda = cuda.float()
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print(name, torch.max(torch.abs(ref - cuda) / torch.abs(ref + 1e-7)).item())
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def test_cubemap():
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cubemap_cuda = torch.rand(6, RES, RES, 3, dtype=DTYPE, device='cuda', requires_grad=True)
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cubemap_ref = cubemap_cuda.clone().detach().requires_grad_(True)
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weights = torch.rand(3, 3, 1, dtype=DTYPE, device='cuda')
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target = torch.rand(6, RES, RES, 3, dtype=DTYPE, device='cuda')
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ref = ru.filter_cubemap(cubemap_ref, weights, use_python=True)
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ref_loss = torch.nn.MSELoss()(ref, target)
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ref_loss.backward()
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cuda = ru.filter_cubemap(cubemap_cuda, weights, use_python=False)
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cuda_loss = torch.nn.MSELoss()(cuda, target)
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cuda_loss.backward()
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print("-------------------------------------------------------------")
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print(" Cubemap:")
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print("-------------------------------------------------------------")
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relative_loss("flt:", ref, cuda)
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relative_loss("cubemap:", cubemap_ref.grad, cubemap_cuda.grad)
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test_cubemap()
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