Spaces:
Runtime error
Runtime error
| # 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 | |