93 lines
3.9 KiB
Python
93 lines
3.9 KiB
Python
# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
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# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
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# and proprietary rights in and to this software, related documentation
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# and any modifications thereto. Any use, reproduction, disclosure or
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# distribution of this software and related documentation without an express
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# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
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import numpy as np
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import torch
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import nvdiffrast.torch as dr
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###############################################################################
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# Functions adapted from https://github.com/NVlabs/nvdiffrec
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###############################################################################
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def dot(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
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return torch.sum(x*y, -1, keepdim=True)
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def length(x: torch.Tensor, eps: float =1e-8) -> torch.Tensor:
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return torch.sqrt(torch.clamp(dot(x,x), min=eps)) # Clamp to avoid nan gradients because grad(sqrt(0)) = NaN
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def safe_normalize(x: torch.Tensor, eps: float =1e-8) -> torch.Tensor:
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return x / length(x, eps)
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def perspective(fovy=0.7854, aspect=1.0, n=0.1, f=1000.0, device=None):
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y = np.tan(fovy / 2)
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return torch.tensor([[1/(y*aspect), 0, 0, 0],
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[ 0, 1/-y, 0, 0],
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[ 0, 0, -(f+n)/(f-n), -(2*f*n)/(f-n)],
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[ 0, 0, -1, 0]], dtype=torch.float32, device=device)
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def translate(x, y, z, device=None):
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return torch.tensor([[1, 0, 0, x],
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[0, 1, 0, y],
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[0, 0, 1, z],
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[0, 0, 0, 1]], dtype=torch.float32, device=device)
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@torch.no_grad()
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def random_rotation_translation(t, device=None):
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m = np.random.normal(size=[3, 3])
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m[1] = np.cross(m[0], m[2])
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m[2] = np.cross(m[0], m[1])
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m = m / np.linalg.norm(m, axis=1, keepdims=True)
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m = np.pad(m, [[0, 1], [0, 1]], mode='constant')
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m[3, 3] = 1.0
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m[:3, 3] = np.random.uniform(-t, t, size=[3])
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return torch.tensor(m, dtype=torch.float32, device=device)
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def rotate_x(a, device=None):
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s, c = np.sin(a), np.cos(a)
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return torch.tensor([[1, 0, 0, 0],
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[0, c, s, 0],
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[0, -s, c, 0],
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[0, 0, 0, 1]], dtype=torch.float32, device=device)
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def rotate_y(a, device=None):
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s, c = np.sin(a), np.cos(a)
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return torch.tensor([[ c, 0, s, 0],
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[ 0, 1, 0, 0],
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[-s, 0, c, 0],
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[ 0, 0, 0, 1]], dtype=torch.float32, device=device)
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class SimpleMesh:
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def __init__(self, vertices, faces):
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self.vertices = vertices
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self.faces = faces
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def auto_normals(self):
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v0 = self.vertices[self.faces[:, 0], :]
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v1 = self.vertices[self.faces[:, 1], :]
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v2 = self.vertices[self.faces[:, 2], :]
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nrm = safe_normalize(torch.cross(v1 - v0, v2 - v0, dim=-1))
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self.nrm = nrm
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def xfm_points(points, matrix):
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'''Transform points.
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Args:
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points: Tensor containing 3D points with shape [minibatch_size, num_vertices, 3] or [1, num_vertices, 3]
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matrix: A 4x4 transform matrix with shape [minibatch_size, 4, 4]
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use_python: Use PyTorch's torch.matmul (for validation)
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Returns:
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Transformed points in homogeneous 4D with shape [minibatch_size, num_vertices, 4].
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'''
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out = torch.matmul(
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torch.nn.functional.pad(points, pad=(0, 1), mode='constant', value=1.0), torch.transpose(matrix, 1, 2))
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if torch.is_anomaly_enabled():
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assert torch.all(torch.isfinite(out)), "Output of xfm_points contains inf or NaN"
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return out
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def interpolate(attr, rast, attr_idx, rast_db=None):
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return dr.interpolate(
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attr, rast, attr_idx, rast_db=rast_db,
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diff_attrs=None if rast_db is None else 'all') |