| # Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. | |
| # | |
| # Permission is hereby granted, free of charge, to any person obtaining a | |
| # copy of this software and associated documentation files (the "Software"), | |
| # to deal in the Software without restriction, including without limitation | |
| # the rights to use, copy, modify, merge, publish, distribute, sublicense, | |
| # and/or sell copies of the Software, and to permit persons to whom the | |
| # Software is furnished to do so, subject to the following conditions: | |
| # | |
| # The above copyright notice and this permission notice shall be included in | |
| # all copies or substantial portions of the Software. | |
| # | |
| # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
| # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
| # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL | |
| # THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
| # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING | |
| # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER | |
| # DEALINGS IN THE SOFTWARE. | |
| # | |
| # SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES | |
| # SPDX-License-Identifier: MIT | |
| import dgl | |
| import torch | |
| def get_random_graph(N, num_edges_factor=18): | |
| graph = dgl.transform.remove_self_loop(dgl.rand_graph(N, N * num_edges_factor)) | |
| return graph | |
| def assign_relative_pos(graph, coords): | |
| src, dst = graph.edges() | |
| graph.edata['rel_pos'] = coords[src] - coords[dst] | |
| return graph | |
| def get_max_diff(a, b): | |
| return (a - b).abs().max().item() | |
| def rot_z(gamma): | |
| return torch.tensor([ | |
| [torch.cos(gamma), -torch.sin(gamma), 0], | |
| [torch.sin(gamma), torch.cos(gamma), 0], | |
| [0, 0, 1] | |
| ], dtype=gamma.dtype) | |
| def rot_y(beta): | |
| return torch.tensor([ | |
| [torch.cos(beta), 0, torch.sin(beta)], | |
| [0, 1, 0], | |
| [-torch.sin(beta), 0, torch.cos(beta)] | |
| ], dtype=beta.dtype) | |
| def rot(alpha, beta, gamma): | |
| return rot_z(alpha) @ rot_y(beta) @ rot_z(gamma) | |