Datasets:
jblitzar
/
github-python

Dataset card Data Studio Files
xet
Community
text
stringlengths
0
93.6k
ap.add_argument('--checkpoint', required=True, type=str, help='checkpoint to load')
ap.add_argument('--output_dir', required=True, type=str, help='where to save images')
ap.add_argument('--input_dir', default='datasets/acdc_day2night/valRC', type=str, help='directory with images to translate')
ap.add_argument('--exemplar_image', required=True, type=str, help='exemplar_image')
ap = ap.parse_args()
main(ap)
# <FILESEP>
import torch
from torch_geometric.data import Data, HeteroData
from torch_geometric.typing import OptTensor
import numpy as np
def to_adj_nodes_with_times(data):
num_nodes = data.num_nodes
timestamps = torch.zeros((data.edge_index.shape[1], 1)) if data.timestamps is None else data.timestamps.reshape((-1,1))
edges = torch.cat((data.edge_index.T, timestamps), dim=1) if not isinstance(data, HeteroData) else torch.cat((data['node', 'to', 'node'].edge_index.T, timestamps), dim=1)
adj_list_out = dict([(i, []) for i in range(num_nodes)])
adj_list_in = dict([(i, []) for i in range(num_nodes)])
for u,v,t in edges:
u,v,t = int(u), int(v), int(t)
adj_list_out[u] += [(v, t)]
adj_list_in[v] += [(u, t)]
return adj_list_in, adj_list_out
def to_adj_edges_with_times(data):
num_nodes = data.num_nodes
timestamps = torch.zeros((data.edge_index.shape[1], 1)) if data.timestamps is None else data.timestamps.reshape((-1,1))
edges = torch.cat((data.edge_index.T, timestamps), dim=1)
# calculate adjacent edges with times per node
adj_edges_out = dict([(i, []) for i in range(num_nodes)])
adj_edges_in = dict([(i, []) for i in range(num_nodes)])
for i, (u,v,t) in enumerate(edges):
u,v,t = int(u), int(v), int(t)
adj_edges_out[u] += [(i, v, t)]
adj_edges_in[v] += [(i, u, t)]
return adj_edges_in, adj_edges_out
def ports(edge_index, adj_list):
ports = torch.zeros(edge_index.shape[1], 1)
ports_dict = {}
for v, nbs in adj_list.items():
if len(nbs) < 1: continue
a = np.array(nbs)
a = a[a[:, -1].argsort()]
_, idx = np.unique(a[:,[0]],return_index=True,axis=0)
nbs_unique = a[np.sort(idx)][:,0]
for i, u in enumerate(nbs_unique):
ports_dict[(u,v)] = i
for i, e in enumerate(edge_index.T):
ports[i] = ports_dict[tuple(e.numpy())]
return ports
def time_deltas(data, adj_edges_list):
time_deltas = torch.zeros(data.edge_index.shape[1], 1)
if data.timestamps is None:
return time_deltas
for v, edges in adj_edges_list.items():
if len(edges) < 1: continue
a = np.array(edges)
a = a[a[:, -1].argsort()]
a_tds = [0] + [a[i+1,-1] - a[i,-1] for i in range(a.shape[0]-1)]
tds = np.hstack((a[:,0].reshape(-1,1), np.array(a_tds).reshape(-1,1)))
for i,td in tds:
time_deltas[i] = td
return time_deltas
class GraphData(Data):
'''This is the homogenous graph object we use for GNN training if reverse MP is not enabled'''
def __init__(
self, x: OptTensor = None, edge_index: OptTensor = None, edge_attr: OptTensor = None, y: OptTensor = None, pos: OptTensor = None,
readout: str = 'edge',
num_nodes: int = None,
timestamps: OptTensor = None,
node_timestamps: OptTensor = None,
**kwargs
):
super().__init__(x, edge_index, edge_attr, y, pos, **kwargs)
self.readout = readout
self.loss_fn = 'ce'
self.num_nodes = int(self.x.shape[0])
self.node_timestamps = node_timestamps
if timestamps is not None:
self.timestamps = timestamps
elif edge_attr is not None:
self.timestamps = edge_attr[:,0].clone()
else:
self.timestamps = None
def add_ports(self):
'''Adds port numberings to the edge features'''
reverse_ports = True
adj_list_in, adj_list_out = to_adj_nodes_with_times(self)
in_ports = ports(self.edge_index, adj_list_in)
out_ports = [ports(self.edge_index.flipud(), adj_list_out)] if reverse_ports else []
self.edge_attr = torch.cat([self.edge_attr, in_ports] + out_ports, dim=1)