def build_graph(self, fn_node, fn_edge):
''' build graph from graph file
- nodes: NxD,
each row represents the feature of a node
- adj: NxN,
a symmetric similarity matrix with self-connection
'''
node = load_data(fn_node)
edge = load_data(fn_edge)
assert len(node) > 1, '#node of {}: {}'.format(fn_node, len(node))
# take majority as label of the graph
if not self.dataset.ignore_label:
lb2cnt = {}
for idx in node:
if idx not in self.dataset.idx2lb:
continue
lb = self.dataset.idx2lb[idx]
if lb not in lb2cnt:
lb2cnt[lb] = 0
lb2cnt[lb] += 1
gt_lb, _ = get_majority(lb2cnt)
gt_node = self.dataset.lb2idxs[gt_lb]
if self.dataset.det_label == 'iou':
label = compute_iou(node, gt_node)
elif self.dataset.det_label == 'iop':
label = compute_iop(node, gt_node)
else:
raise KeyError('Unknown det_label type: {}'.format(
self.dataset.det_label))
else:
label = -1.
adj, _, _ = self.build_adj(node, edge)
features = self.build_features(node)
return features, adj, label
def build_graph(self, fn_node, fn_edge):
""" build graph from graph file
- nodes: NxD,
each row represents the feature of a node
- adj: NxN,
a symmetric similarity matrix with self-connection
"""
node = load_data(fn_node)
edge = load_data(fn_edge)
assert len(node) > 1, '#node of {}: {}'.format(fn_node, len(node))
# take majority as label of the graph
if not self.dataset.ignore_label:
lb2cnt = {}
for idx in node:
if idx not in self.dataset.idx2lb:
continue
lb = self.dataset.idx2lb[idx]
if lb not in lb2cnt:
lb2cnt[lb] = 0
lb2cnt[lb] += 1
gt_lb, _ = get_majority(lb2cnt)
gt_node = self.dataset.lb2idxs[gt_lb]
iou = compute_iou(node, gt_node)
else:
iou = -1.
# compute adj
node = list(node)
abs2rel = {}
for i, n in enumerate(node):
abs2rel[n] = i
size = len(node)
adj = np.eye(size)
for e in edge:
w = 1.
if len(e) == 2:
e1, e2 = e
elif len(e) == 3:
e1, e2, dist = e
if not self.dataset.wo_weight:
w = 1. - dist
else:
raise ValueError('Unknown length of e: {}'.format(e))
v1 = abs2rel[e1]
v2 = abs2rel[e2]
adj[v1][v2] = w
adj[v2][v1] = w
if self.dataset.featureless:
vertices = adj.sum(axis=1, keepdims=True)
vertices /= vertices.sum(axis=1, keepdims=True)
else:
vertices = self.dataset.features[node, :]
if self.dataset.is_norm_adj:
adj /= adj.sum(axis=1, keepdims=True)
return vertices, adj, iou
def nms(clusters, th=1.):
# nms
t0 = time.time()
suppressed = set()
if th < 1:
start_idx = 0
tot_size = len(clusters)
while start_idx < tot_size:
if start_idx in suppressed:
start_idx += 1
continue
cluster = clusters[start_idx]
for j in range(start_idx + 1, tot_size):
if j in suppressed:
continue
if compute_iou(cluster, clusters[j]) > th:
suppressed.add(j)
start_idx += 1
else:
print('th={} >= 1, skip the nms'.format(th))
print('nms consumes {} s'.format(time.time() - t0))
# assign label
lb = 0
idx2lbs = {}
for i, cluster in enumerate(clusters):
if i in suppressed:
continue
for v in cluster:
if v not in idx2lbs:
idx2lbs[v] = []
idx2lbs[v].append(lb)
lb += 1
# deoverlap (choose the one belongs to the highest predicted iou)
idx2lb = {}
for idx, lbs in idx2lbs.items():
idx2lb[idx] = lbs[0]
return idx2lb, idx2lbs
cluster = set(cluster)
# take majority as label of the graph
for idx in cluster:
if idx not in idx2lb:
print('[warn] {} is not found'.format(idx))
continue
lb = idx2lb[idx]
if lb not in lb2cnt:
lb2cnt[lb] = 0
lb2cnt[lb] += 1
lb, _ = get_majority(lb2cnt)
if lb is None:
iou = -1e6
else:
idxs = lb2idxs[lb]
iou = compute_iou(cluster, idxs)
ious.append(iou)
ious = np.array(ious)
# rank by iou
pos_g_labels = np.where(ious > args.th_pos)[0]
clusters = [[clusters[i], ious[i]] for i in pos_g_labels]
clusters = sorted(clusters, key=lambda x: x[1], reverse=True)
clusters = [n for n, _ in clusters]
inst_num = len(idx2lb)
pos_idx_set = set()
for c in clusters:
pos_idx_set |= set(c)
print('inst-coverage before nms: {}'.format(1. * len(pos_idx_set) /
inst_num))