Upload hy3dgen/texgen/custom_rasterizer/custom_rasterizer/render.py with huggingface_hub

hy3dgen/texgen/custom_rasterizer/custom_rasterizer/render.py

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