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det_drrg_head.py
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# 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/dense_heads/drrg_head.py
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import warnings
import cv2
import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from .gcn import GCN
from .local_graph import LocalGraphs
from .proposal_local_graph import ProposalLocalGraphs
class DRRGHead(nn.Layer):
def __init__(self,
in_channels,
k_at_hops=(8, 4),
num_adjacent_linkages=3,
node_geo_feat_len=120,
pooling_scale=1.0,
pooling_output_size=(4, 3),
nms_thr=0.3,
min_width=8.0,
max_width=24.0,
comp_shrink_ratio=1.03,
comp_ratio=0.4,
comp_score_thr=0.3,
text_region_thr=0.2,
center_region_thr=0.2,
center_region_area_thr=50,
local_graph_thr=0.7,
**kwargs):
super().__init__()
assert isinstance(in_channels, int)
assert isinstance(k_at_hops, tuple)
assert isinstance(num_adjacent_linkages, int)
assert isinstance(node_geo_feat_len, int)
assert isinstance(pooling_scale, float)
assert isinstance(pooling_output_size, tuple)
assert isinstance(comp_shrink_ratio, float)
assert isinstance(nms_thr, float)
assert isinstance(min_width, float)
assert isinstance(max_width, float)
assert isinstance(comp_ratio, float)
assert isinstance(comp_score_thr, float)
assert isinstance(text_region_thr, float)
assert isinstance(center_region_thr, float)
assert isinstance(center_region_area_thr, int)
assert isinstance(local_graph_thr, float)
self.in_channels = in_channels
self.out_channels = 6
self.downsample_ratio = 1.0
self.k_at_hops = k_at_hops
self.num_adjacent_linkages = num_adjacent_linkages
self.node_geo_feat_len = node_geo_feat_len
self.pooling_scale = pooling_scale
self.pooling_output_size = pooling_output_size
self.comp_shrink_ratio = comp_shrink_ratio
self.nms_thr = nms_thr
self.min_width = min_width
self.max_width = max_width
self.comp_ratio = comp_ratio
self.comp_score_thr = comp_score_thr
self.text_region_thr = text_region_thr
self.center_region_thr = center_region_thr
self.center_region_area_thr = center_region_area_thr
self.local_graph_thr = local_graph_thr
self.out_conv = nn.Conv2D(
in_channels=self.in_channels,
out_channels=self.out_channels,
kernel_size=1,
stride=1,
padding=0)
self.graph_train = LocalGraphs(
self.k_at_hops, self.num_adjacent_linkages, self.node_geo_feat_len,
self.pooling_scale, self.pooling_output_size, self.local_graph_thr)
self.graph_test = ProposalLocalGraphs(
self.k_at_hops, self.num_adjacent_linkages, self.node_geo_feat_len,
self.pooling_scale, self.pooling_output_size, self.nms_thr,
self.min_width, self.max_width, self.comp_shrink_ratio,
self.comp_ratio, self.comp_score_thr, self.text_region_thr,
self.center_region_thr, self.center_region_area_thr)
pool_w, pool_h = self.pooling_output_size
node_feat_len = (pool_w * pool_h) * (
self.in_channels + self.out_channels) + self.node_geo_feat_len
self.gcn = GCN(node_feat_len)
def forward(self, inputs, targets=None):
"""
Args:
inputs (Tensor): Shape of :math:`(N, C, H, W)`.
gt_comp_attribs (list[ndarray]): The padded text component
attributes. Shape: (num_component, 8).
Returns:
tuple: Returns (pred_maps, (gcn_pred, gt_labels)).
- | pred_maps (Tensor): Prediction map with shape
:math:`(N, C_{out}, H, W)`.
- | gcn_pred (Tensor): Prediction from GCN module, with
shape :math:`(N, 2)`.
- | gt_labels (Tensor): Ground-truth label with shape
:math:`(N, 8)`.
"""
if self.training:
assert targets is not None
gt_comp_attribs = targets[7]
pred_maps = self.out_conv(inputs)
feat_maps = paddle.concat([inputs, pred_maps], axis=1)
node_feats, adjacent_matrices, knn_inds, gt_labels = self.graph_train(
feat_maps, np.stack(gt_comp_attribs))
gcn_pred = self.gcn(node_feats, adjacent_matrices, knn_inds)
return pred_maps, (gcn_pred, gt_labels)
else:
return self.single_test(inputs)
def single_test(self, feat_maps):
r"""
Args:
feat_maps (Tensor): Shape of :math:`(N, C, H, W)`.
Returns:
tuple: Returns (edge, score, text_comps).
- | edge (ndarray): The edge array of shape :math:`(N, 2)`
where each row is a pair of text component indices
that makes up an edge in graph.
- | score (ndarray): The score array of shape :math:`(N,)`,
corresponding to the edge above.
- | text_comps (ndarray): The text components of shape
:math:`(N, 9)` where each row corresponds to one box and
its score: (x1, y1, x2, y2, x3, y3, x4, y4, score).
"""
pred_maps = self.out_conv(feat_maps)
feat_maps = paddle.concat([feat_maps, pred_maps], axis=1)
none_flag, graph_data = self.graph_test(pred_maps, feat_maps)
(local_graphs_node_feat, adjacent_matrices, pivots_knn_inds,
pivot_local_graphs, text_comps) = graph_data
if none_flag:
return None, None, None
gcn_pred = self.gcn(local_graphs_node_feat, adjacent_matrices,
pivots_knn_inds)
pred_labels = F.softmax(gcn_pred, axis=1)
edges = []
scores = []
pivot_local_graphs = pivot_local_graphs.squeeze().numpy()
for pivot_ind, pivot_local_graph in enumerate(pivot_local_graphs):
pivot = pivot_local_graph[0]
for k_ind, neighbor_ind in enumerate(pivots_knn_inds[pivot_ind]):
neighbor = pivot_local_graph[neighbor_ind.item()]
edges.append([pivot, neighbor])
scores.append(pred_labels[pivot_ind * pivots_knn_inds.shape[1] +
k_ind, 1].item())
edges = np.asarray(edges)
scores = np.asarray(scores)
return edges, scores, text_comps