common.py

# Lint as: python3
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or  implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Commonly used data structures and functions."""

import enum
# import tensorflow.compat.v1 as tf
import torch


class NodeType(enum.IntEnum):
    NORMAL = 0
    OBSTACLE = 1
    AIRFOIL = 2
    HANDLE = 3
    INFLOW = 4
    OUTFLOW = 5
    WALL_BOUNDARY = 6
    SIZE = 9


def triangles_to_edges(faces, deform=False):
    """Computes mesh edges from triangles."""
    if not deform:
        # collect edges from triangles
        edges = torch.cat((faces[:, 0:2],
                           faces[:, 1:3],
                           torch.stack((faces[:, 2], faces[:, 0]), dim=1)), dim=0)
        # those edges are sometimes duplicated (within the mesh) and sometimes
        # single (at the mesh boundary).
        # sort & pack edges as single tf.int64
        receivers, _ = torch.min(edges, dim=1)
        senders, _ = torch.max(edges, dim=1)

        packed_edges = torch.stack((senders, receivers), dim=1)
        unique_edges = torch.unique(packed_edges, return_inverse=False, return_counts=False, dim=0)
        senders, receivers = torch.unbind(unique_edges, dim=1)
        senders = senders.to(torch.int64)
        receivers = receivers.to(torch.int64)

        two_way_connectivity = (torch.cat((senders, receivers), dim=0), torch.cat((receivers, senders), dim=0))
        return {'two_way_connectivity': two_way_connectivity, 'senders': senders, 'receivers': receivers}
    else:
        edges = torch.cat((faces[:, 0:2],
                           faces[:, 1:3],
                           faces[:, 2:4],
                           torch.stack((faces[:, 3], faces[:, 0]), dim=1)), dim=0)
        # those edges are sometimes duplicated (within the mesh) and sometimes
        # single (at the mesh boundary).
        # sort & pack edges as single tf.int64
        receivers, _ = torch.min(edges, dim=1)
        senders, _ = torch.max(edges, dim=1)

        packed_edges = torch.stack((senders, receivers), dim=1)
        unique_edges = torch.unique(packed_edges, return_inverse=False, return_counts=False, dim=0)
        senders, receivers = torch.unbind(unique_edges, dim=1)
        senders = senders.to(torch.int64)
        receivers = receivers.to(torch.int64)

        two_way_connectivity = (torch.cat((senders, receivers), dim=0), torch.cat((receivers, senders), dim=0))
        return {'two_way_connectivity': two_way_connectivity, 'senders': senders, 'receivers': receivers}