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# 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)
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