# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This code is refer from:
https://github.com/open-mmlab/mmocr/blob/main/mmocr/models/textdet/modules/local_graph.py
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import paddle
import paddle.nn as nn
from ppocr.ext_op import RoIAlignRotated
def normalize_adjacent_matrix(A):
assert A.ndim == 2
assert A.shape[0] == A.shape[1]
A = A + np.eye(A.shape[0])
d = np.sum(A, axis=0)
d = np.clip(d, 0, None)
d_inv = np.power(d, -0.5).flatten()
d_inv[np.isinf(d_inv)] = 0.0
d_inv = np.diag(d_inv)
G = A.dot(d_inv).transpose().dot(d_inv)
return G
def euclidean_distance_matrix(A, B):
"""Calculate the Euclidean distance matrix.
Args:
A (ndarray): The point sequence.
B (ndarray): The point sequence with the same dimensions as A.
returns:
D (ndarray): The Euclidean distance matrix.
"""
assert A.ndim == 2
assert B.ndim == 2
assert A.shape[1] == B.shape[1]
m = A.shape[0]
n = B.shape[0]
A_dots = (A * A).sum(axis=1).reshape((m, 1)) * np.ones(shape=(1, n))
B_dots = (B * B).sum(axis=1) * np.ones(shape=(m, 1))
D_squared = A_dots + B_dots - 2 * A.dot(B.T)
zero_mask = np.less(D_squared, 0.0)
D_squared[zero_mask] = 0.0
D = np.sqrt(D_squared)
return D
def feature_embedding(input_feats, out_feat_len):
"""Embed features. This code was partially adapted from
https://github.com/GXYM/DRRG licensed under the MIT license.
Args:
input_feats (ndarray): The input features of shape (N, d), where N is
the number of nodes in graph, d is the input feature vector length.
out_feat_len (int): The length of output feature vector.
Returns:
embedded_feats (ndarray): The embedded features.
"""
assert input_feats.ndim == 2
assert isinstance(out_feat_len, int)
assert out_feat_len >= input_feats.shape[1]
num_nodes = input_feats.shape[0]
feat_dim = input_feats.shape[1]
feat_repeat_times = out_feat_len // feat_dim
residue_dim = out_feat_len % feat_dim
if residue_dim > 0:
embed_wave = np.array(
[
np.power(1000, 2.0 * (j // 2) / feat_repeat_times + 1)
for j in range(feat_repeat_times + 1)
]
).reshape((feat_repeat_times + 1, 1, 1))
repeat_feats = np.repeat(
np.expand_dims(input_feats, axis=0), feat_repeat_times, axis=0
)
residue_feats = np.hstack(
[
input_feats[:, 0:residue_dim],
np.zeros((num_nodes, feat_dim - residue_dim)),
]
)
residue_feats = np.expand_dims(residue_feats, axis=0)
repeat_feats = np.concatenate([repeat_feats, residue_feats], axis=0)
embedded_feats = repeat_feats / embed_wave
embedded_feats[:, 0::2] = np.sin(embedded_feats[:, 0::2])
embedded_feats[:, 1::2] = np.cos(embedded_feats[:, 1::2])
embedded_feats = np.transpose(embedded_feats, (1, 0, 2)).reshape(
(num_nodes, -1)
)[:, 0:out_feat_len]
else:
embed_wave = np.array(
[
np.power(1000, 2.0 * (j // 2) / feat_repeat_times)
for j in range(feat_repeat_times)
]
).reshape((feat_repeat_times, 1, 1))
repeat_feats = np.repeat(
np.expand_dims(input_feats, axis=0), feat_repeat_times, axis=0
)
embedded_feats = repeat_feats / embed_wave
embedded_feats[:, 0::2] = np.sin(embedded_feats[:, 0::2])
embedded_feats[:, 1::2] = np.cos(embedded_feats[:, 1::2])
embedded_feats = (
np.transpose(embedded_feats, (1, 0, 2))
.reshape((num_nodes, -1))
.astype(np.float32)
)
return embedded_feats
class LocalGraphs:
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]
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 = paddle.to_tensor([node2ind_map[i] for i in pivot_knn[1:]])
pivot_feats = node_feats[pivot_ind]
normalized_feats = (
node_feats[paddle.to_tensor(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 = paddle.zeros((num_max_nodes, num_max_nodes))
pad_adjacent_matrix[:num_nodes, :num_nodes] = paddle.cast(
paddle.to_tensor(adjacent_matrix), "float32"
)
pad_normalized_feats = paddle.concat(
[
normalized_feats,
paddle.zeros(
(num_max_nodes - num_nodes, normalized_feats.shape[1])
),
],
axis=0,
)
local_graph_labels = node_labels[pivot_local_graph]
knn_labels = local_graph_labels[knn_inds.numpy()]
link_labels = (
(node_labels[pivot_ind] == knn_labels)
& (node_labels[pivot_ind] > 0)
).astype(np.int64)
link_labels = paddle.to_tensor(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 = paddle.stack(local_graphs_node_feat, 0)
adjacent_matrices = paddle.stack(adjacent_matrices, 0)
pivots_knn_inds = paddle.stack(pivots_knn_inds, 0)
pivots_gt_linkage = paddle.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, paddle.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 = []
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 = paddle.to_tensor(rotated_rois)
content_feats = self.pooling(feat_maps[batch_ind].unsqueeze(0), rois)
content_feats = content_feats.reshape([content_feats.shape[0], -1])
geo_feats = feature_embedding(comp_geo_attribs, self.node_geo_feat_dim)
geo_feats = paddle.to_tensor(geo_feats)
node_feats = paddle.concat([content_feats, geo_feats], axis=-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