| import numpy as np |
| import torch |
| from torch_geometric.data import Data |
| import networkx as nx |
|
|
|
|
| def _get_float_value(v): |
| if isinstance(v, str) and "|" in v: |
| v = v.split("|") |
|
|
| if isinstance(v, list): |
| return max([float(i) for i in v]) |
| else: |
| return float(v) |
|
|
|
|
| def to_torch_geometric_data(graph: nx.MultiDiGraph) -> Data: |
| node_ids = sorted(graph.nodes()) |
| n = len(node_ids) |
| m = graph.number_of_edges() |
|
|
| mapping = dict(zip(node_ids, range(n))) |
|
|
| nodes_attrs = torch.empty((n, 4), dtype=torch.float) |
| pos = torch.zeros((n, 2), dtype=torch.float) |
| for node_id in node_ids: |
| node_data = graph.nodes[node_id] |
| lanes = _get_float_value(node_data.get('lanes', 1.0)) |
| length = _get_float_value(node_data.get('length', 0.0)) |
| speed = _get_float_value(node_data.get('maxspeed', 30.0)) |
| bearing = _get_float_value(node_data.get('bearing', 0.0)) |
| nodes_attrs[mapping[node_id]] = torch.tensor([lanes, length, speed, bearing], dtype=torch.float) |
| pos[mapping[node_id], 0] = float(node_data.get('x')) |
| pos[mapping[node_id], 1] = float(node_data.get('y')) |
|
|
| edge_index = torch.empty((2, m), dtype=torch.long) |
| edge_feat = torch.empty((m, 2), dtype=torch.float) |
| for i, (src, dst, edge_data) in enumerate(graph.edges(data=True)): |
| edge_index[0, i] = mapping[src] |
| edge_index[1, i] = mapping[dst] |
| edge_feat[i, 0] = edge_data.get('size') |
| edge_feat[i, 1] = edge_data.get('turn_angle') |
|
|
| data = Data(x=nodes_attrs, edge_index=edge_index, edge_attr=edge_feat) |
| data.pos = pos |
| data.mapping = mapping |
| return data |
|
|
|
|
| def to_numpy_adj(graph: nx.MultiDiGraph) -> np.ndarray: |
| G: nx.MultiDiGraph = graph.copy() |
| G.add_edges_from((node, node) for node in G.nodes()) |
| adj = nx.to_numpy_array(G, nodelist=sorted(G.nodes()), weight='weight') |
| return adj |
|
|
|
|
