Spaces:
Runtime error
Runtime error
File size: 13,094 Bytes
2366e36 |
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 |
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
from mmcv.ops import RoIAlignRotated
from .utils import (euclidean_distance_matrix, feature_embedding,
normalize_adjacent_matrix)
class LocalGraphs:
"""Generate local graphs for GCN to classify the neighbors of a pivot for
DRRG: Deep Relational Reasoning Graph Network for Arbitrary Shape Text
Detection.
[https://arxiv.org/abs/2003.07493]. This code was partially adapted from
https://github.com/GXYM/DRRG licensed under the MIT license.
Args:
k_at_hops (tuple(int)): The number of i-hop neighbors, i = 1, 2.
num_adjacent_linkages (int): The number of linkages when constructing
adjacent matrix.
node_geo_feat_len (int): The length of embedded geometric feature
vector of a text component.
pooling_scale (float): The spatial scale of rotated RoI-Align.
pooling_output_size (tuple(int)): The output size of rotated RoI-Align.
local_graph_thr(float): The threshold for filtering out identical local
graphs.
"""
def __init__(self, k_at_hops, num_adjacent_linkages, node_geo_feat_len,
pooling_scale, pooling_output_size, local_graph_thr):
assert len(k_at_hops) == 2
assert all(isinstance(n, int) for n in k_at_hops)
assert isinstance(num_adjacent_linkages, int)
assert isinstance(node_geo_feat_len, int)
assert isinstance(pooling_scale, float)
assert all(isinstance(n, int) for n in pooling_output_size)
assert isinstance(local_graph_thr, float)
self.k_at_hops = k_at_hops
self.num_adjacent_linkages = num_adjacent_linkages
self.node_geo_feat_dim = node_geo_feat_len
self.pooling = RoIAlignRotated(pooling_output_size, pooling_scale)
self.local_graph_thr = local_graph_thr
def generate_local_graphs(self, sorted_dist_inds, gt_comp_labels):
"""Generate local graphs for GCN to predict which instance a text
component belongs to.
Args:
sorted_dist_inds (ndarray): The complete graph node indices, which
is sorted according to the Euclidean distance.
gt_comp_labels(ndarray): The ground truth labels define the
instance to which the text components (nodes in graphs) belong.
Returns:
pivot_local_graphs(list[list[int]]): The list of local graph
neighbor indices of pivots.
pivot_knns(list[list[int]]): The list of k-nearest neighbor indices
of pivots.
"""
assert sorted_dist_inds.ndim == 2
assert (sorted_dist_inds.shape[0] == sorted_dist_inds.shape[1] ==
gt_comp_labels.shape[0])
knn_graph = sorted_dist_inds[:, 1:self.k_at_hops[0] + 1]
pivot_local_graphs = []
pivot_knns = []
for pivot_ind, knn in enumerate(knn_graph):
local_graph_neighbors = set(knn)
for neighbor_ind in knn:
local_graph_neighbors.update(
set(sorted_dist_inds[neighbor_ind,
1:self.k_at_hops[1] + 1]))
local_graph_neighbors.discard(pivot_ind)
pivot_local_graph = list(local_graph_neighbors)
pivot_local_graph.insert(0, pivot_ind)
pivot_knn = [pivot_ind] + list(knn)
if pivot_ind < 1:
pivot_local_graphs.append(pivot_local_graph)
pivot_knns.append(pivot_knn)
else:
add_flag = True
for graph_ind, added_knn in enumerate(pivot_knns):
added_pivot_ind = added_knn[0]
added_local_graph = pivot_local_graphs[graph_ind]
union = len(
set(pivot_local_graph[1:]).union(
set(added_local_graph[1:])))
intersect = len(
set(pivot_local_graph[1:]).intersection(
set(added_local_graph[1:])))
local_graph_iou = intersect / (union + 1e-8)
if (local_graph_iou > self.local_graph_thr
and pivot_ind in added_knn
and gt_comp_labels[added_pivot_ind]
== gt_comp_labels[pivot_ind]
and gt_comp_labels[pivot_ind] != 0):
add_flag = False
break
if add_flag:
pivot_local_graphs.append(pivot_local_graph)
pivot_knns.append(pivot_knn)
return pivot_local_graphs, pivot_knns
def generate_gcn_input(self, node_feat_batch, node_label_batch,
local_graph_batch, knn_batch,
sorted_dist_ind_batch):
"""Generate graph convolution network input data.
Args:
node_feat_batch (List[Tensor]): The batched graph node features.
node_label_batch (List[ndarray]): The batched text component
labels.
local_graph_batch (List[List[list[int]]]): The local graph node
indices of image batch.
knn_batch (List[List[list[int]]]): The knn graph node indices of
image batch.
sorted_dist_ind_batch (list[ndarray]): The node indices sorted
according to the Euclidean distance.
Returns:
local_graphs_node_feat (Tensor): The node features of graph.
adjacent_matrices (Tensor): The adjacent matrices of local graphs.
pivots_knn_inds (Tensor): The k-nearest neighbor indices in
local graph.
gt_linkage (Tensor): The surpervision signal of GCN for linkage
prediction.
"""
assert isinstance(node_feat_batch, list)
assert isinstance(node_label_batch, list)
assert isinstance(local_graph_batch, list)
assert isinstance(knn_batch, list)
assert isinstance(sorted_dist_ind_batch, list)
num_max_nodes = max([
len(pivot_local_graph) for pivot_local_graphs in local_graph_batch
for pivot_local_graph in pivot_local_graphs
])
local_graphs_node_feat = []
adjacent_matrices = []
pivots_knn_inds = []
pivots_gt_linkage = []
for batch_ind, sorted_dist_inds in enumerate(sorted_dist_ind_batch):
node_feats = node_feat_batch[batch_ind]
pivot_local_graphs = local_graph_batch[batch_ind]
pivot_knns = knn_batch[batch_ind]
node_labels = node_label_batch[batch_ind]
device = node_feats.device
for graph_ind, pivot_knn in enumerate(pivot_knns):
pivot_local_graph = pivot_local_graphs[graph_ind]
num_nodes = len(pivot_local_graph)
pivot_ind = pivot_local_graph[0]
node2ind_map = {j: i for i, j in enumerate(pivot_local_graph)}
knn_inds = torch.tensor(
[node2ind_map[i] for i in pivot_knn[1:]])
pivot_feats = node_feats[pivot_ind]
normalized_feats = node_feats[pivot_local_graph] - pivot_feats
adjacent_matrix = np.zeros((num_nodes, num_nodes),
dtype=np.float32)
for node in pivot_local_graph:
neighbors = sorted_dist_inds[node,
1:self.num_adjacent_linkages +
1]
for neighbor in neighbors:
if neighbor in pivot_local_graph:
adjacent_matrix[node2ind_map[node],
node2ind_map[neighbor]] = 1
adjacent_matrix[node2ind_map[neighbor],
node2ind_map[node]] = 1
adjacent_matrix = normalize_adjacent_matrix(adjacent_matrix)
pad_adjacent_matrix = torch.zeros(
(num_max_nodes, num_max_nodes),
dtype=torch.float,
device=device)
pad_adjacent_matrix[:num_nodes, :num_nodes] = torch.from_numpy(
adjacent_matrix)
pad_normalized_feats = torch.cat([
normalized_feats,
torch.zeros(
(num_max_nodes - num_nodes, normalized_feats.shape[1]),
dtype=torch.float,
device=device)
],
dim=0)
local_graph_labels = node_labels[pivot_local_graph]
knn_labels = local_graph_labels[knn_inds]
link_labels = ((node_labels[pivot_ind] == knn_labels) &
(node_labels[pivot_ind] > 0)).astype(np.int64)
link_labels = torch.from_numpy(link_labels)
local_graphs_node_feat.append(pad_normalized_feats)
adjacent_matrices.append(pad_adjacent_matrix)
pivots_knn_inds.append(knn_inds)
pivots_gt_linkage.append(link_labels)
local_graphs_node_feat = torch.stack(local_graphs_node_feat, 0)
adjacent_matrices = torch.stack(adjacent_matrices, 0)
pivots_knn_inds = torch.stack(pivots_knn_inds, 0)
pivots_gt_linkage = torch.stack(pivots_gt_linkage, 0)
return (local_graphs_node_feat, adjacent_matrices, pivots_knn_inds,
pivots_gt_linkage)
def __call__(self, feat_maps, comp_attribs):
"""Generate local graphs as GCN input.
Args:
feat_maps (Tensor): The feature maps to extract the content
features of text components.
comp_attribs (ndarray): The text component attributes.
Returns:
local_graphs_node_feat (Tensor): The node features of graph.
adjacent_matrices (Tensor): The adjacent matrices of local graphs.
pivots_knn_inds (Tensor): The k-nearest neighbor indices in local
graph.
gt_linkage (Tensor): The surpervision signal of GCN for linkage
prediction.
"""
assert isinstance(feat_maps, torch.Tensor)
assert comp_attribs.ndim == 3
assert comp_attribs.shape[2] == 8
sorted_dist_inds_batch = []
local_graph_batch = []
knn_batch = []
node_feat_batch = []
node_label_batch = []
device = feat_maps.device
for batch_ind in range(comp_attribs.shape[0]):
num_comps = int(comp_attribs[batch_ind, 0, 0])
comp_geo_attribs = comp_attribs[batch_ind, :num_comps, 1:7]
node_labels = comp_attribs[batch_ind, :num_comps,
7].astype(np.int32)
comp_centers = comp_geo_attribs[:, 0:2]
distance_matrix = euclidean_distance_matrix(
comp_centers, comp_centers)
batch_id = np.zeros(
(comp_geo_attribs.shape[0], 1), dtype=np.float32) * batch_ind
comp_geo_attribs[:, -2] = np.clip(comp_geo_attribs[:, -2], -1, 1)
angle = np.arccos(comp_geo_attribs[:, -2]) * np.sign(
comp_geo_attribs[:, -1])
angle = angle.reshape((-1, 1))
rotated_rois = np.hstack(
[batch_id, comp_geo_attribs[:, :-2], angle])
rois = torch.from_numpy(rotated_rois).to(device)
content_feats = self.pooling(feat_maps[batch_ind].unsqueeze(0),
rois)
content_feats = content_feats.view(content_feats.shape[0],
-1).to(feat_maps.device)
geo_feats = feature_embedding(comp_geo_attribs,
self.node_geo_feat_dim)
geo_feats = torch.from_numpy(geo_feats).to(device)
node_feats = torch.cat([content_feats, geo_feats], dim=-1)
sorted_dist_inds = np.argsort(distance_matrix, axis=1)
pivot_local_graphs, pivot_knns = self.generate_local_graphs(
sorted_dist_inds, node_labels)
node_feat_batch.append(node_feats)
node_label_batch.append(node_labels)
local_graph_batch.append(pivot_local_graphs)
knn_batch.append(pivot_knns)
sorted_dist_inds_batch.append(sorted_dist_inds)
(node_feats, adjacent_matrices, knn_inds, gt_linkage) = \
self.generate_gcn_input(node_feat_batch,
node_label_batch,
local_graph_batch,
knn_batch,
sorted_dist_inds_batch)
return node_feats, adjacent_matrices, knn_inds, gt_linkage
|