kopia lustrzana https://github.com/lzzcd001/MeshDiffusion
91 wiersze
3.4 KiB
Python
91 wiersze
3.4 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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BATCH = 8
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RES = 1024
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DTYPE = torch.float32
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torch.manual_seed(0)
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def tonemap_srgb(f):
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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)
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def l1(output, target):
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x = torch.clamp(output, min=0, max=65535)
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r = torch.clamp(target, min=0, max=65535)
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x = tonemap_srgb(torch.log(x + 1))
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r = tonemap_srgb(torch.log(r + 1))
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return torch.nn.functional.l1_loss(x,r)
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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)).item())
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def test_xfm_points():
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points_cuda = torch.rand(1, RES, 3, dtype=DTYPE, device='cuda', requires_grad=True)
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points_ref = points_cuda.clone().detach().requires_grad_(True)
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mtx_cuda = torch.rand(BATCH, 4, 4, dtype=DTYPE, device='cuda', requires_grad=False)
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mtx_ref = mtx_cuda.clone().detach().requires_grad_(True)
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target = torch.rand(BATCH, RES, 4, dtype=DTYPE, device='cuda', requires_grad=True)
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ref_out = ru.xfm_points(points_ref, mtx_ref, use_python=True)
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ref_loss = torch.nn.MSELoss()(ref_out, target)
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ref_loss.backward()
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cuda_out = ru.xfm_points(points_cuda, mtx_cuda)
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cuda_loss = torch.nn.MSELoss()(cuda_out, target)
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cuda_loss.backward()
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print("-------------------------------------------------------------")
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relative_loss("res:", ref_out, cuda_out)
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relative_loss("points:", points_ref.grad, points_cuda.grad)
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def test_xfm_vectors():
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points_cuda = torch.rand(1, RES, 3, dtype=DTYPE, device='cuda', requires_grad=True)
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points_ref = points_cuda.clone().detach().requires_grad_(True)
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points_cuda_p = points_cuda.clone().detach().requires_grad_(True)
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points_ref_p = points_cuda.clone().detach().requires_grad_(True)
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mtx_cuda = torch.rand(BATCH, 4, 4, dtype=DTYPE, device='cuda', requires_grad=False)
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mtx_ref = mtx_cuda.clone().detach().requires_grad_(True)
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target = torch.rand(BATCH, RES, 4, dtype=DTYPE, device='cuda', requires_grad=True)
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ref_out = ru.xfm_vectors(points_ref.contiguous(), mtx_ref, use_python=True)
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ref_loss = torch.nn.MSELoss()(ref_out, target[..., 0:3])
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ref_loss.backward()
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cuda_out = ru.xfm_vectors(points_cuda.contiguous(), mtx_cuda)
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cuda_loss = torch.nn.MSELoss()(cuda_out, target[..., 0:3])
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cuda_loss.backward()
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ref_out_p = ru.xfm_points(points_ref_p.contiguous(), mtx_ref, use_python=True)
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ref_loss_p = torch.nn.MSELoss()(ref_out_p, target)
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ref_loss_p.backward()
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cuda_out_p = ru.xfm_points(points_cuda_p.contiguous(), mtx_cuda)
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cuda_loss_p = torch.nn.MSELoss()(cuda_out_p, target)
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cuda_loss_p.backward()
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print("-------------------------------------------------------------")
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relative_loss("res:", ref_out, cuda_out)
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relative_loss("points:", points_ref.grad, points_cuda.grad)
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relative_loss("points_p:", points_ref_p.grad, points_cuda_p.grad)
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test_xfm_points()
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test_xfm_vectors()
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