Spaces:
Runtime error
Runtime error
File size: 10,389 Bytes
01bb3bb |
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 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 |
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import
import numpy as np
import time
from util.misc import norm2
class Data(object):
def __init__(self, name):
self.__name = name
self.__links = set()
@property
def name(self):
return self.__name
@property
def links(self):
return set(self.__links)
def add_link(self, other, score):
self.__links.add(other)
other.__links.add(self)
def connected_components(nodes, score_dict, th):
'''
conventional connected components searching
'''
result = []
nodes = set(nodes)
while nodes:
n = nodes.pop()
group = {n}
queue = [n]
while queue:
n = queue.pop(0)
if th is not None:
neighbors = {l for l in n.links if score_dict[tuple(sorted([n.name, l.name]))] >= th}
else:
neighbors = n.links
neighbors.difference_update(group)
nodes.difference_update(neighbors)
group.update(neighbors)
queue.extend(neighbors)
result.append(group)
return result
def connected_components_constraint(nodes, max_sz, score_dict=None, th=None):
'''
only use edges whose scores are above `th`
if a component is larger than `max_sz`, all the nodes in this component are added into `remain` and returned for next iteration.
'''
result = []
remain = set()
nodes = set(nodes)
while nodes:
n = nodes.pop()
group = {n}
queue = [n]
valid = True
while queue:
n = queue.pop(0)
if th is not None:
neighbors = {l for l in n.links if score_dict[tuple(sorted([n.name, l.name]))] >= th}
else:
neighbors = n.links
neighbors.difference_update(group)
nodes.difference_update(neighbors)
group.update(neighbors)
queue.extend(neighbors)
if len(group) > max_sz or len(remain.intersection(neighbors)) > 0:
# if this group is larger than `max_sz`, add the nodes into `remain`
valid = False
remain.update(group)
break
if valid: # if this group is smaller than or equal to `max_sz`, finalize it.
result.append(group)
return result, remain
def graph_propagation_naive(edges, score, th, bboxs=None, dis_thresh=50, pool='avg'):
edges = np.sort(edges, axis=1)
score_dict = {} # score lookup table
if pool is None:
for i, e in enumerate(edges):
score_dict[e[0], e[1]] = score[i]
elif pool == 'avg':
for i, e in enumerate(edges):
if bboxs is not None:
box1 = bboxs[e[0]][:8].reshape(4, 2)
box2 = bboxs[e[1]][:8].reshape(4, 2)
c1 = np.mean(box1, 0); c2 = np.mean(box2, 0)
dst = norm2(c1 - c2)
if dst > dis_thresh:
score[i] = 0
if (e[0], e[1]) in score_dict:
score_dict[e[0], e[1]] = 0.5 * (score_dict[e[0], e[1]] + score[i])
else:
score_dict[e[0], e[1]] = score[i]
elif pool == 'max':
for i, e in enumerate(edges):
if (e[0], e[1]) in score_dict:
score_dict[e[0], e[1]] = max(score_dict[e[0], e[1]], score[i])
else:
score_dict[e[0], e[1]] = score[i]
else:
raise ValueError('Pooling operation not supported')
nodes = np.sort(np.unique(edges.flatten()))
mapping = -1 * np.ones((nodes.max()+1), dtype=np.int)
mapping[nodes] = np.arange(nodes.shape[0])
link_idx = mapping[edges]
vertex = [Data(n) for n in nodes]
for l, s in zip(link_idx, score):
vertex[l[0]].add_link(vertex[l[1]], s)
# first iteration
comps = connected_components(vertex, score_dict,th)
return comps
def graph_search(edges, scores, edges_num, th=None):
# graph search
scores = scores.reshape((-1, edges_num))
select_index = np.argsort(scores, axis=1)[:, -2:]
edges = np.sort(edges, axis=1).reshape((-1, edges_num, 2))
score_dict = {}
for i, ips in enumerate(select_index):
edg = edges[i]
si = scores[i]
for j, idx in enumerate(ips):
e = edg[idx, :]
if (e[0], e[1]) in score_dict:
score_dict[e[0], e[1]] = 0.5 * (score_dict[e[0], e[1]] + si[j])
else:
score_dict[e[0], e[1]] = si[j]
nodes = np.sort(np.unique(edges.flatten()))
vertex = [Data(n) for n in nodes]
for (key, value) in score_dict.items():
vertex[key[0]].add_link(vertex[key[1]], value)
comps = connected_components(vertex, score_dict, th)
return comps
def graph_propagation(edges, score, max_sz, step=0.1, beg_th=0.5, pool=None):
edges = np.sort(edges, axis=1)
th = score.min()
# th = beg_th
# construct graph
score_dict = {} # score lookup table
if pool is None:
for i,e in enumerate(edges):
score_dict[e[0], e[1]] = score[i]
elif pool == 'avg':
for i,e in enumerate(edges):
if (e[0], e[1]) in score_dict:
score_dict[e[0], e[1]] = 0.5*(score_dict[e[0], e[1]] + score[i])
else:
score_dict[e[0], e[1]] = score[i]
elif pool == 'max':
for i,e in enumerate(edges):
if (e[0],e[1]) in score_dict:
score_dict[e[0], e[1]] = max(score_dict[e[0], e[1]] , score[i])
else:
score_dict[e[0], e[1]] = score[i]
else:
raise ValueError('Pooling operation not supported')
nodes = np.sort(np.unique(edges.flatten()))
mapping = -1 * np.ones((nodes.max()+1), dtype=np.int)
mapping[nodes] = np.arange(nodes.shape[0])
link_idx = mapping[edges]
vertex = [Data(n) for n in nodes]
for l, s in zip(link_idx, score):
vertex[l[0]].add_link(vertex[l[1]], s)
# first iteration
comps, remain = connected_components_constraint(vertex, max_sz)
# iteration
components = comps[:]
while remain:
th = th + (1 - th) * step
comps, remain = connected_components_constraint(remain, max_sz, score_dict, th)
components.extend(comps)
return components
def graph_propagation_soft(edges, score, max_sz, step=0.1, **kwargs):
edges = np.sort(edges, axis=1)
th = score.min()
# construct graph
score_dict = {} # score lookup table
for i,e in enumerate(edges):
score_dict[e[0], e[1]] = score[i]
nodes = np.sort(np.unique(edges.flatten()))
mapping = -1 * np.ones((nodes.max()+1), dtype=np.int)
mapping[nodes] = np.arange(nodes.shape[0])
link_idx = mapping[edges]
vertex = [Data(n) for n in nodes]
for l, s in zip(link_idx, score):
vertex[l[0]].add_link(vertex[l[1]], s)
# first iteration
comps, remain = connected_components_constraint(vertex, max_sz)
first_vertex_idx = np.array([mapping[n.name] for c in comps for n in c])
fusion_vertex_idx = np.setdiff1d(np.arange(nodes.shape[0]), first_vertex_idx, assume_unique=True)
# iteration
components = comps[:]
while remain:
th = th + (1 - th) * step
comps, remain = connected_components_constraint(remain, max_sz, score_dict, th)
components.extend(comps)
label_dict = {}
for i,c in enumerate(components):
for n in c:
label_dict[n.name] = i
print('Propagation ...')
prop_vertex = [vertex[idx] for idx in fusion_vertex_idx]
label, label_fusion = diffusion(prop_vertex, label_dict, score_dict, **kwargs)
return label, label_fusion
def diffusion(vertex, label, score_dict, max_depth=5, weight_decay=0.6, normalize=True):
class BFSNode():
def __init__(self, node, depth, value):
self.node = node
self.depth = depth
self.value = value
label_fusion = {}
for name in label.keys():
label_fusion[name] = {label[name]: 1.0}
prog = 0
prog_step = len(vertex) // 20
start = time.time()
for root in vertex:
if prog % prog_step == 0:
print("progress: {} / {}, elapsed time: {}".format(prog, len(vertex), time.time() - start))
prog += 1
#queue = {[root, 0, 1.0]}
queue = {BFSNode(root, 0, 1.0)}
visited = [root.name]
root_label = label[root.name]
while queue:
curr = queue.pop()
if curr.depth >= max_depth: # pruning
continue
neighbors = curr.node.links
tmp_value = []
tmp_neighbor = []
for n in neighbors:
if n.name not in visited:
sub_value = score_dict[tuple(sorted([curr.node.name, n.name]))] * weight_decay * curr.value
tmp_value.append(sub_value)
tmp_neighbor.append(n)
if root_label not in label_fusion[n.name].keys():
label_fusion[n.name][root_label] = sub_value
else:
label_fusion[n.name][root_label] += sub_value
visited.append(n.name)
#queue.add([n, curr.depth+1, sub_value])
sortidx = np.argsort(tmp_value)[::-1]
for si in sortidx:
queue.add(BFSNode(tmp_neighbor[si], curr.depth+1, tmp_value[si]))
if normalize:
for name in label_fusion.keys():
summ = sum(label_fusion[name].values())
for k in label_fusion[name].keys():
label_fusion[name][k] /= summ
return label, label_fusion
def clusters2labels(clusters, n_nodes):
labels = (-1)* np.ones((n_nodes,))
for ci, c in enumerate(clusters):
for xid in c:
labels[xid.name] = ci
assert np.sum(labels < 0) < 1
return labels
def single_remove(bbox, pred):
single_idcs = np.zeros_like(pred)
pred_unique = np.unique(pred)
for u in pred_unique:
idcs = pred == u
if np.sum(idcs) == 1:
single_idcs[np.where(idcs)[0][0]] = 1
remain_idcs = [i for i in range(len(pred)) if not single_idcs[i]]
remain_idcs = np.asarray(remain_idcs)
return bbox[remain_idcs, :], pred[remain_idcs]
|