# 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)