Datasets:
File size: 1,897 Bytes
1e36484 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 |
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
|