def super_vertex(knns, k, th, th_step, max_sz):
pairs, scores = filter_knns(knns, k, th)
assert len(pairs) == len(scores)
if len(pairs) == 0:
return []
components = graph_clustering_dynamic_th(pairs, scores, max_sz, th_step)
return components
def test_lgcn(model, cfg, logger):
for k, v in cfg.model['kwargs'].items():
setattr(cfg.test_data, k, v)
dataset = build_dataset(cfg.test_data)
ofn_pred = os.path.join(cfg.work_dir, 'pred_edges_scores.npz')
if os.path.isfile(ofn_pred) and not cfg.force:
data = np.load(ofn_pred)
edges = data['edges']
scores = data['scores']
inst_num = data['inst_num']
if inst_num != len(dataset):
logger.warn(
'instance number in {} is different from dataset: {} vs {}'.
format(ofn_pred, inst_num, len(dataset)))
else:
edges, scores, inst_num = test(model, dataset, cfg, logger)
# produce predicted labels
clusters = graph_clustering_dynamic_th(edges,
scores,
max_sz=cfg.max_sz,
step=cfg.step,
pool=cfg.pool)
pred_idx2lb = clusters2labels(clusters)
pred_labels = intdict2ndarray(pred_idx2lb)
if cfg.save_output:
print('save predicted edges and scores to {}'.format(ofn_pred))
np.savez_compressed(ofn_pred,
edges=edges,
scores=scores,
inst_num=inst_num)
ofn_meta = os.path.join(cfg.work_dir, 'pred_labels.txt')
write_meta(ofn_meta, pred_idx2lb, inst_num=inst_num)
# evaluation
if not dataset.ignore_label:
print('==> evaluation')
gt_labels = dataset.labels
for metric in cfg.metrics:
evaluate(gt_labels, pred_labels, metric)
single_cluster_idxs = get_cluster_idxs(clusters, size=1)
print('==> evaluation (removing {} single clusters)'.format(
len(single_cluster_idxs)))
remain_idxs = np.setdiff1d(np.arange(len(dataset)),
np.array(single_cluster_idxs))
remain_idxs = np.array(remain_idxs)
for metric in cfg.metrics:
evaluate(gt_labels[remain_idxs], pred_labels[remain_idxs], metric)
def super_vertex(knn, k, th, th_step, max_sz):
pairs, scores = filter_knn(knn, k, th)
comps = graph_clustering_dynamic_th(pairs, scores, max_sz, th_step)
clusters = [sorted([n.name for n in c]) for c in comps]
return clusters
def super_vertex(knns, k, th, th_step, max_sz):
pairs, scores = filter_knns(knns, k, th)
components = graph_clustering_dynamic_th(pairs, scores, max_sz, th_step)
return components
