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977213426f
when custom_rasterizer_kernel is imported before torch under cu128/python 3.12, the following error will raise: ImportError: DLL load failed while importing custom_rasterizer_kernel: 找不到指定的模块。 simply swapping these two imports will solve this issue
42 lines
2.1 KiB
Python
Executable File
42 lines
2.1 KiB
Python
Executable File
# Open Source Model Licensed under the Apache License Version 2.0
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# and Other Licenses of the Third-Party Components therein:
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# The below Model in this distribution may have been modified by THL A29 Limited
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# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
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# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
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# The below software and/or models in this distribution may have been
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# modified by THL A29 Limited ("Tencent Modifications").
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# All Tencent Modifications are Copyright (C) THL A29 Limited.
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# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
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# except for the third-party components listed below.
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# Hunyuan 3D does not impose any additional limitations beyond what is outlined
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# in the repsective licenses of these third-party components.
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# Users must comply with all terms and conditions of original licenses of these third-party
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# components and must ensure that the usage of the third party components adheres to
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# all relevant laws and regulations.
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# For avoidance of doubts, Hunyuan 3D means the large language models and
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# their software and algorithms, including trained model weights, parameters (including
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# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
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# fine-tuning enabling code and other elements of the foregoing made publicly available
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# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
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import torch
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import custom_rasterizer_kernel
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def rasterize(pos, tri, resolution, clamp_depth=torch.zeros(0), use_depth_prior=0):
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assert (pos.device == tri.device)
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findices, barycentric = custom_rasterizer_kernel.rasterize_image(pos[0], tri, clamp_depth, resolution[1],
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resolution[0], 1e-6, use_depth_prior)
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return findices, barycentric
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def interpolate(col, findices, barycentric, tri):
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f = findices - 1 + (findices == 0)
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vcol = col[0, tri.long()[f.long()]]
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result = barycentric.view(*barycentric.shape, 1) * vcol
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result = torch.sum(result, axis=-2)
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return result.view(1, *result.shape)
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