diff --git a/configs/table/table_master.yml b/configs/table/table_master.yml new file mode 100755 index 0000000000000000000000000000000000000000..cfd98346c4e71b11f9ec6b84abe9fb4ed0932704 --- /dev/null +++ b/configs/table/table_master.yml @@ -0,0 +1,136 @@ +Global: + use_gpu: true + epoch_num: 17 + log_smooth_window: 20 + print_batch_step: 100 + save_model_dir: ./output/table_master/ + save_epoch_step: 17 + eval_batch_step: [0, 6259] + cal_metric_during_train: true + pretrained_model: null + checkpoints: + save_inference_dir: output/table_master/infer + use_visualdl: false + infer_img: ppstructure/docs/table/table.jpg + save_res_path: ./output/table_master + character_dict_path: ppocr/utils/dict/table_master_structure_dict.txt + infer_mode: false + max_text_length: 500 + process_total_num: 0 + process_cut_num: 0 + + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + name: MultiStepDecay + learning_rate: 0.001 + milestones: [12, 15] + gamma: 0.1 + warmup_epoch: 0.02 + regularizer: + name: L2 + factor: 0.0 + +Architecture: + model_type: table + algorithm: TableMaster + Backbone: + name: TableResNetExtra + gcb_config: + ratio: 0.0625 + headers: 1 + att_scale: False + fusion_type: channel_add + layers: [False, True, True, True] + layers: [1,2,5,3] + Head: + name: TableMasterHead + hidden_size: 512 + headers: 8 + dropout: 0 + d_ff: 2024 + max_text_length: 500 + +Loss: + name: TableMasterLoss + ignore_index: 42 # set to len of dict + 3 + +PostProcess: + name: TableMasterLabelDecode + box_shape: pad + +Metric: + name: TableMetric + main_indicator: acc + compute_bbox_metric: False + +Train: + dataset: + name: PubTabDataSet + data_dir: train_data/table/pubtabnet/train/ + label_file_list: [train_data/table/pubtabnet/PubTabNet_2.0.0_train.jsonl] + transforms: + - DecodeImage: + img_mode: BGR + channel_first: False + - TableMasterLabelEncode: + learn_empty_box: False + merge_no_span_structure: True + replace_empty_cell_token: True + - ResizeTableImage: + max_len: 480 + resize_bboxes: True + - PaddingTableImage: + size: [480, 480] + - TableBoxEncode: + use_xywh: True + - NormalizeImage: + scale: 1./255. + mean: [0.5, 0.5, 0.5] + std: [0.5, 0.5, 0.5] + order: hwc + - ToCHWImage: null + - KeepKeys: + keep_keys: [image, structure, bboxes, bbox_masks, shape] + loader: + shuffle: True + batch_size_per_card: 10 + drop_last: True + num_workers: 8 + +Eval: + dataset: + name: PubTabDataSet + data_dir: /home/zhoujun20/table/PubTabNe/pubtabnet/val/ + label_file_list: [/home/zhoujun20/table/PubTabNe/pubtabnet/val_500.jsonl] + transforms: + - DecodeImage: + img_mode: BGR + channel_first: False + - TableMasterLabelEncode: + learn_empty_box: False + merge_no_span_structure: True + replace_empty_cell_token: True + - ResizeTableImage: + max_len: 480 + resize_bboxes: True + - PaddingTableImage: + size: [480, 480] + - TableBoxEncode: + use_xywh: True + - NormalizeImage: + scale: 1./255. + mean: [0.5, 0.5, 0.5] + std: [0.5, 0.5, 0.5] + order: hwc + - ToCHWImage: null + - KeepKeys: + keep_keys: [image, structure, bboxes, bbox_masks, shape] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 10 + num_workers: 8 \ No newline at end of file diff --git a/configs/table/table_mv3.yml b/configs/table/table_mv3.yml index 1a91ea95afb4ff91d3fd68fe0df6afaac9304661..66c1c83e124d4e94e1f4036a494dfd80c840f229 100755 --- a/configs/table/table_mv3.yml +++ b/configs/table/table_mv3.yml @@ -4,21 +4,20 @@ Global: log_smooth_window: 20 print_batch_step: 5 save_model_dir: ./output/table_mv3/ - save_epoch_step: 3 + save_epoch_step: 400 # evaluation is run every 400 iterations after the 0th iteration eval_batch_step: [0, 400] cal_metric_during_train: True pretrained_model: - checkpoints: + checkpoints: save_inference_dir: use_visualdl: False - infer_img: doc/table/table.jpg + infer_img: ppstructure/docs/table/table.jpg + save_res_path: output/table_mv3 # for data or label process character_dict_path: ppocr/utils/dict/table_structure_dict.txt character_type: en - max_text_length: 100 - max_elem_length: 800 - max_cell_num: 500 + max_text_length: 800 infer_mode: False process_total_num: 0 process_cut_num: 0 @@ -44,11 +43,8 @@ Architecture: Head: name: TableAttentionHead hidden_size: 256 - l2_decay: 0.00001 loc_type: 2 - max_text_length: 100 - max_elem_length: 800 - max_cell_num: 500 + max_text_length: 800 Loss: name: TableAttentionLoss @@ -61,28 +57,34 @@ PostProcess: Metric: name: TableMetric main_indicator: acc + compute_bbox_metric: false # cost many time, set False for training Train: dataset: name: PubTabDataSet data_dir: train_data/table/pubtabnet/train/ - label_file_path: train_data/table/pubtabnet/PubTabNet_2.0.0_train.jsonl + label_file_list: [train_data/table/pubtabnet/PubTabNet_2.0.0_train.jsonl] transforms: - DecodeImage: # load image img_mode: BGR channel_first: False + - TableLabelEncode: + learn_empty_box: False + merge_no_span_structure: False + replace_empty_cell_token: False + - TableBoxEncode: - ResizeTableImage: max_len: 488 - - TableLabelEncode: - NormalizeImage: scale: 1./255. mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] order: 'hwc' - PaddingTableImage: + size: [488, 488] - ToCHWImage: - KeepKeys: - keep_keys: ['image', 'structure', 'bbox_list', 'sp_tokens', 'bbox_list_mask'] + keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ] loader: shuffle: True batch_size_per_card: 32 @@ -92,24 +94,29 @@ Train: Eval: dataset: name: PubTabDataSet - data_dir: train_data/table/pubtabnet/val/ - label_file_path: train_data/table/pubtabnet/PubTabNet_2.0.0_val.jsonl + data_dir: /home/zhoujun20/table/PubTabNe/pubtabnet/val/ + label_file_list: [/home/zhoujun20/table/PubTabNe/pubtabnet/val_500.jsonl] transforms: - DecodeImage: # load image img_mode: BGR channel_first: False + - TableLabelEncode: + learn_empty_box: False + merge_no_span_structure: False + replace_empty_cell_token: False + - TableBoxEncode: - ResizeTableImage: max_len: 488 - - TableLabelEncode: - NormalizeImage: scale: 1./255. mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] order: 'hwc' - PaddingTableImage: + size: [488, 488] - ToCHWImage: - KeepKeys: - keep_keys: ['image', 'structure', 'bbox_list', 'sp_tokens', 'bbox_list_mask'] + keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ] loader: shuffle: False drop_last: False diff --git a/doc/doc_ch/algorithm_det_fcenet.md b/doc/doc_ch/algorithm_det_fcenet.md index bd2e734204d32bbf575ddea9f889953a72582c59..a70caa29fb590c7b7bf8d587a40676757a2ba4ce 100644 --- a/doc/doc_ch/algorithm_det_fcenet.md +++ b/doc/doc_ch/algorithm_det_fcenet.md @@ -1,17 +1,15 @@ # FCENet -- [1. 算法简介](#1) -- [2. 环境配置](#2) -- [3. 模型训练、评估、预测](#3) - - [3.1 训练](#3-1) - - [3.2 评估](#3-2) - - [3.3 预测](#3-3) -- [4. 推理部署](#4) - - [4.1 Python推理](#4-1) - - [4.2 C++推理](#4-2) - - [4.3 Serving服务化部署](#4-3) - - [4.4 更多推理部署](#4-4) -- [5. FAQ](#5) +- [1. 算法简介](#1-算法简介) +- [2. 环境配置](#2-环境配置) +- [3. 模型训练、评估、预测](#3-模型训练评估预测) +- [4. 推理部署](#4-推理部署) + - [4.1 Python推理](#41-python推理) + - [4.2 C++推理](#42-c推理) + - [4.3 Serving服务化部署](#43-serving服务化部署) + - [4.4 更多推理部署](#44-更多推理部署) +- [5. FAQ](#5-faq) +- [引用](#引用) ## 1. 算法简介 diff --git a/doc/doc_ch/algorithm_overview.md b/doc/doc_ch/algorithm_overview.md index ef96f6ec122594afd115b333ffc18fb836253b79..1efd564ce10ac4d92dccda0b844e6d6d07a17906 100755 --- a/doc/doc_ch/algorithm_overview.md +++ b/doc/doc_ch/algorithm_overview.md @@ -1,9 +1,10 @@ # OCR算法 - [1. 两阶段算法](#1-两阶段算法) - - [1.1 文本检测算法](#11-文本检测算法) - - [1.2 文本识别算法](#12-文本识别算法) + - [1.1 文本检测算法](#11-文本检测算法) + - [1.2 文本识别算法](#12-文本识别算法) - [2. 端到端算法](#2-端到端算法) +- [3. 表格识别算法](#3-表格识别算法) 本文给出了PaddleOCR已支持的OCR算法列表，以及每个算法在**英文公开数据集**上的模型和指标，主要用于算法简介和算法性能对比，更多包括中文在内的其他数据集上的模型请参考[PP-OCR v2.0 系列模型下载](./models_list.md)。 @@ -96,3 +97,14 @@ 已支持的端到端OCR算法列表（戳链接获取使用教程）： - [x] [PGNet](./algorithm_e2e_pgnet.md) + +## 3. 表格识别算法 + +已支持的表格识别算法列表（戳链接获取使用教程）： +- [x] [TableMaster](./algorithm_table_master.md) + +在PubTabNet表格识别公开数据集上，算法效果如下： + +|模型|骨干网络|配置文件|acc|下载链接| +|---|---|---|---|---| +|TableMaster|TableResNetExtra|[configs/table/table_master.yml](../../configs/table/table_master.yml)|77.47%|[训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar) / [推理模型](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_infer.tar)| diff --git a/doc/doc_ch/algorithm_table_master.md b/doc/doc_ch/algorithm_table_master.md new file mode 100644 index 0000000000000000000000000000000000000000..36455ed9f94581c31fb849ebe121a3d7ecdb7acb --- /dev/null +++ b/doc/doc_ch/algorithm_table_master.md @@ -0,0 +1,114 @@ +# 表格识别算法-TableMASTER + +- [1. 算法简介](#1-算法简介) +- [2. 环境配置](#2-环境配置) +- [3. 模型训练、评估、预测](#3-模型训练评估预测) +- [4. 推理部署](#4-推理部署) + - [4.1 Python推理](#41-python推理) + - [4.2 C++推理部署](#42-c推理部署) + - [4.3 Serving服务化部署](#43-serving服务化部署) + - [4.4 更多推理部署](#44-更多推理部署) +- [5. FAQ](#5-faq) +- [引用](#引用) + + +## 1. 算法简介 + +论文信息： +> [TableMaster: PINGAN-VCGROUP’S SOLUTION FOR ICDAR 2021 COMPETITION ON SCIENTIFIC LITERATURE PARSING TASK B: TABLE RECOGNITION TO HTML](https://arxiv.org/pdf/2105.01848.pdf) +> Ye, Jiaquan and Qi, Xianbiao and He, Yelin and Chen, Yihao and Gu, Dengyi and Gao, Peng and Xiao, Rong +> 2021 + +在PubTabNet表格识别公开数据集上，算法复现效果如下： + +|模型|骨干网络|配置文件|acc|下载链接| +| --- | --- | --- | --- | --- | +|TableMaster|TableResNetExtra|[configs/table/table_master.yml](../../configs/table/table_master.yml)|77.47%|[训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar)/[推理模型](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_infer.tar)| + + + +## 2. 环境配置 +请先参考[《运行环境准备》](./environment.md)配置PaddleOCR运行环境，参考[《项目克隆》](./clone.md)克隆项目代码。 + + + +## 3. 模型训练、评估、预测 + +上述TableMaster模型使用PubTabNet表格识别公开数据集训练得到，数据集下载可参考 [table_datasets](./dataset/table_datasets.md)。 + +数据下载完成后，请参考[文本识别教程](./recognition.md)进行训练。PaddleOCR对代码进行了模块化，训练不同的模型只需要**更换配置文件**即可。 + + +## 4. 推理部署 + + +### 4.1 Python推理 +首先将训练得到best模型，转换成inference model。以基于TableResNetExtra骨干网络，在PubTabNet数据集训练的模型为例([模型下载地址](https://paddleocr.bj.bcebos.com/contribution/table_master.tar))，可以使用如下命令进行转换： + +```shell +# 注意将pretrained_model的路径设置为本地路径。 +python3 tools/export_model.py -c configs/table/table_master.yml -o Global.pretrained_model=output/table_master/best_accuracy Global.save_inference_dir=./inference/table_master +``` + +**注意：** +- 如果您是在自己的数据集上训练的模型，并且调整了字典文件，请注意修改配置文件中的`character_dict_path`是否为所正确的字典文件。 + +转换成功后，在目录下有三个文件： +``` +./inference/table_master/ + ├── inference.pdiparams # 识别inference模型的参数文件 + ├── inference.pdiparams.info # 识别inference模型的参数信息，可忽略 + └── inference.pdmodel # 识别inference模型的program文件 +``` + + +执行如下命令进行模型推理： + +```shell +cd ppstructure/ +python3.7 table/predict_structure.py --table_model_dir=../output/table_master/table_structure_tablemaster_infer/ --table_algorithm=TableMaster --table_char_dict_path=../ppocr/utils/dict/table_master_structure_dict.txt --table_max_len=480 --image_dir=docs/table/table.jpg +# 预测文件夹下所有图像时，可修改image_dir为文件夹，如 --image_dir='docs/table'。 +``` + +执行命令后，上面图像的预测结果（结构信息和表格中每个单元格的坐标）会打印到屏幕上，同时会保存单元格坐标的可视化结果。示例如下： +结果如下： +```shell +[2022/06/16 13:06:54] ppocr INFO: result: ['', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '
', '', ''], [[72.17591094970703, 10.759100914001465, 60.29658508300781, 16.6805362701416], [161.85562133789062, 10.884308815002441, 14.9495210647583, 16.727018356323242], [277.79876708984375, 29.54340362548828, 31.490320205688477, 18.143272399902344], +... +[336.11724853515625, 280.3601989746094, 39.456939697265625, 18.121286392211914]] +[2022/06/16 13:06:54] ppocr INFO: save vis result to ./output/table.jpg +[2022/06/16 13:06:54] ppocr INFO: Predict time of docs/table/table.jpg: 17.36806297302246 +``` + +**注意**： + +- TableMaster在推理时比较慢，建议使用GPU进行使用。 + + +### 4.2 C++推理部署 + +由于C++预处理后处理还未支持TableMaster，所以暂未支持 + + +### 4.3 Serving服务化部署 + +暂不支持 + + +### 4.4 更多推理部署 + +暂不支持 + + +## 5. FAQ + +## 引用 + +```bibtex +@article{ye2021pingan, + title={PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTML}, + author={Ye, Jiaquan and Qi, Xianbiao and He, Yelin and Chen, Yihao and Gu, Dengyi and Gao, Peng and Xiao, Rong}, + journal={arXiv preprint arXiv:2105.01848}, + year={2021} +} +``` diff --git a/doc/doc_en/algorithm_overview_en.md b/doc/doc_en/algorithm_overview_en.md index bc96cdf2351f10454441e20d319e485019bbec00..8b9b3f7b0bf08b8dbfc7b50edca501c35319ee06 100755 --- a/doc/doc_en/algorithm_overview_en.md +++ b/doc/doc_en/algorithm_overview_en.md @@ -1,9 +1,10 @@ # OCR Algorithms -- [1. Two-stage Algorithms](#1) - * [1.1 Text Detection Algorithms](#11) - * [1.2 Text Recognition Algorithms](#12) -- [2. End-to-end Algorithms](#2) +- [1. Two-stage Algorithms](#1-two-stage-algorithms) + - [1.1 Text Detection Algorithms](#11-text-detection-algorithms) + - [1.2 Text Recognition Algorithms](#12-text-recognition-algorithms) +- [2. End-to-end Algorithms](#2-end-to-end-algorithms) +- [3. Table Recognition Algorithms](#3-table-recognition-algorithms) This tutorial lists the OCR algorithms supported by PaddleOCR, as well as the models and metrics of each algorithm on **English public datasets**. It is mainly used for algorithm introduction and algorithm performance comparison. For more models on other datasets including Chinese, please refer to [PP-OCR v2.0 models list](./models_list_en.md). @@ -95,3 +96,15 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r Supported end-to-end algorithms (Click the link to get the tutorial): - [x] [PGNet](./algorithm_e2e_pgnet_en.md) + + +## 3. Table Recognition Algorithms + +Supported table recognition algorithms (Click the link to get the tutorial): +- [x] [TableMaster](./algorithm_table_master_en.md) + +On the PubTabNet dataset, the algorithm result is as follows: + +|Model|Backbone|Config|Acc|Download link| +|---|---|---|---|---| +|TableMaster|TableResNetExtra|[configs/table/table_master.yml](../../configs/table/table_master.yml)|77.47%|[trained](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar) / [inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_infer.tar)| diff --git a/doc/doc_en/algorithm_table_master_en.md b/doc/doc_en/algorithm_table_master_en.md new file mode 100644 index 0000000000000000000000000000000000000000..e9249a2a05d3e79f4358366d46cf02a14a223f5f --- /dev/null +++ b/doc/doc_en/algorithm_table_master_en.md @@ -0,0 +1,112 @@ +# Table Recognition Algorithm-TableMASTER + +- [1. Introduction](#1-introduction) +- [2. Environment](#2-environment) +- [3. Model Training / Evaluation / Prediction](#3-model-training--evaluation--prediction) +- [4. Inference and Deployment](#4-inference-and-deployment) + - [4.1 Python Inference](#41-python-inference) + - [4.2 C++ Inference](#42-c-inference) + - [4.3 Serving](#43-serving) + - [4.4 More](#44-more) +- [5. FAQ](#5-faq) +- [Citation](#citation) + + +## 1. Introduction + +Paper: +> [TableMaster: PINGAN-VCGROUP’S SOLUTION FOR ICDAR 2021 COMPETITION ON SCIENTIFIC LITERATURE PARSING TASK B: TABLE RECOGNITION TO HTML](https://arxiv.org/pdf/2105.01848.pdf) +> Ye, Jiaquan and Qi, Xianbiao and He, Yelin and Chen, Yihao and Gu, Dengyi and Gao, Peng and Xiao, Rong +> 2021 + + +On the PubTabNet table recognition public data set, the algorithm reproduction acc is as follows: + +|Model|Backbone|Cnnfig|Acc|Download link| +| --- | --- | --- | --- | --- | +|TableMaster|TableResNetExtra|[configs/table/table_master.yml](../../configs/table/table_master.yml)|77.47%|[trained model](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar)/[inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_infer.tar)| + + + +## 2. Environment +Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code. + + + +## 3. Model Training / Evaluation / Prediction + +The above TableMaster model is trained using the PubTabNet table recognition public dataset. For the download of the dataset, please refer to [table_datasets](./dataset/table_datasets_en.md). + +After the data download is complete, please refer to [Text Recognition Training Tutorial](./recognition_en.md) for training. PaddleOCR has modularized the code structure, so that you only need to **replace the configuration file** to train different models. + + + +## 4. Inference and Deployment + + +### 4.1 Python Inference + +First, convert the model saved in the TableMaster table recognition training process into an inference model. Taking the model based on the TableResNetExtra backbone network and trained on the PubTabNet dataset as example ([model download link](https://paddleocr.bj.bcebos.com/contribution/table_master.tar)), you can use the following command to convert: + + +```shell +python3 tools/export_model.py -c configs/table/table_master.yml -o Global.pretrained_model=output/table_master/best_accuracy Global.save_inference_dir=./inference/table_master +``` + +**Note: ** +- If you trained the model on your own dataset and adjusted the dictionary file, please pay attention to whether the `character_dict_path` in the modified configuration file is the correct dictionary file + + +Execute the following command for model inference: + +```shell +cd ppstructure/ +# When predicting all images in a folder, you can modify image_dir to a folder, such as --image_dir='docs/table'. +python3.7 table/predict_structure.py --table_model_dir=../output/table_master/table_structure_tablemaster_infer/ --table_algorithm=TableMaster --table_char_dict_path=../ppocr/utils/dict/table_master_structure_dict.txt --table_max_len=480 --image_dir=docs/table/table.jpg + +``` + +After executing the command, the prediction results of the above image (structural information and the coordinates of each cell in the table) are printed to the screen, and the visualization of the cell coordinates is also saved. An example is as follows: + +result： +```shell +[2022/06/16 13:06:54] ppocr INFO: result: ['', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '
', '', ''], [[72.17591094970703, 10.759100914001465, 60.29658508300781, 16.6805362701416], [161.85562133789062, 10.884308815002441, 14.9495210647583, 16.727018356323242], [277.79876708984375, 29.54340362548828, 31.490320205688477, 18.143272399902344], +... +[336.11724853515625, 280.3601989746094, 39.456939697265625, 18.121286392211914]] +[2022/06/16 13:06:54] ppocr INFO: save vis result to ./output/table.jpg +[2022/06/16 13:06:54] ppocr INFO: Predict time of docs/table/table.jpg: 17.36806297302246 +``` + +**Note**: + +- TableMaster is relatively slow during inference, and it is recommended to use GPU for use. + + +### 4.2 C++ Inference + +Since the post-processing is not written in CPP, the TableMaster does not support CPP inference. + + + +### 4.3 Serving + +Not supported + + +### 4.4 More + +Not supported + + +## 5. FAQ + +## Citation + +```bibtex +@article{ye2021pingan, + title={PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTML}, + author={Ye, Jiaquan and Qi, Xianbiao and He, Yelin and Chen, Yihao and Gu, Dengyi and Gao, Peng and Xiao, Rong}, + journal={arXiv preprint arXiv:2105.01848}, + year={2021} +} +``` diff --git a/ppocr/data/imaug/__init__.py b/ppocr/data/imaug/__init__.py index 8d8f4e1ffab75473315425c030a1576e19d46e24..d82176282839bf76d34ed8a60d5e2e13ac6bbce6 100644 --- a/ppocr/data/imaug/__init__.py +++ b/ppocr/data/imaug/__init__.py @@ -37,7 +37,7 @@ from .label_ops import * from .east_process import * from .sast_process import * from .pg_process import * -from .gen_table_mask import * +from .table_ops import * from .vqa import * diff --git a/ppocr/data/imaug/label_ops.py b/ppocr/data/imaug/label_ops.py index 0723e97ae719690ef2e6a500b327b039c7a46f66..7cc4cef46a8298001ac4089dbd5e32dbca009caf 100644 --- a/ppocr/data/imaug/label_ops.py +++ b/ppocr/data/imaug/label_ops.py @@ -562,171 +562,210 @@ class SRNLabelEncode(BaseRecLabelEncode): return idx -class TableLabelEncode(object): +class TableLabelEncode(AttnLabelEncode): """ Convert between text-label and text-index """ def __init__(self, max_text_length, - max_elem_length, - max_cell_num, character_dict_path, - span_weight=1.0, + replace_empty_cell_token=False, + merge_no_span_structure=False, + learn_empty_box=False, + point_num=2, **kwargs): - self.max_text_length = max_text_length - self.max_elem_length = max_elem_length - self.max_cell_num = max_cell_num - list_character, list_elem = self.load_char_elem_dict( - character_dict_path) - list_character = self.add_special_char(list_character) - list_elem = self.add_special_char(list_elem) - self.dict_character = {} - for i, char in enumerate(list_character): - self.dict_character[char] = i - self.dict_elem = {} - for i, elem in enumerate(list_elem): - self.dict_elem[elem] = i - self.span_weight = span_weight - - def load_char_elem_dict(self, character_dict_path): - list_character = [] - list_elem = [] + self.max_text_len = max_text_length + self.lower = False + self.learn_empty_box = learn_empty_box + self.merge_no_span_structure = merge_no_span_structure + self.replace_empty_cell_token = replace_empty_cell_token + + dict_character = [] with open(character_dict_path, "rb") as fin: lines = fin.readlines() - substr = lines[0].decode('utf-8').strip("\r\n").split("\t") - character_num = int(substr[0]) - elem_num = int(substr[1]) - for cno in range(1, 1 + character_num): - character = lines[cno].decode('utf-8').strip("\r\n") - list_character.append(character) - for eno in range(1 + character_num, 1 + character_num + elem_num): - elem = lines[eno].decode('utf-8').strip("\r\n") - list_elem.append(elem) - return list_character, list_elem - - def add_special_char(self, list_character): - self.beg_str = "sos" - self.end_str = "eos" - list_character = [self.beg_str] + list_character + [self.end_str] - return list_character + for line in lines: + line = line.decode('utf-8').strip("\n").strip("\r\n") + dict_character.append(line) - def get_span_idx_list(self): - span_idx_list = [] - for elem in self.dict_elem: - if 'span' in elem: - span_idx_list.append(self.dict_elem[elem]) - return span_idx_list + dict_character = self.add_special_char(dict_character) + self.dict = {} + for i, char in enumerate(dict_character): + self.dict[char] = i + self.idx2char = {v: k for k, v in self.dict.items()} + + self.character = dict_character + self.point_num = point_num + self.pad_idx = self.dict[self.beg_str] + self.start_idx = self.dict[self.beg_str] + self.end_idx = self.dict[self.end_str] + + self.td_token = ['', '', ''] + self.empty_bbox_token_dict = { + "[]": '', + "[' ']": '', + "['', ' ', '']": '', + "['\\u2028', '\\u2028']": '', + "['', ' ', '']": '', + "['', '']": '', + "['', ' ', '']": '', + "['', '', '', '']": '', + "['', '', ' ', '', '']": '', + "['', '']": '', + "['', ' ', '\\u2028', ' ', '\\u2028', ' ', '']": + '', + } + + @property + def _max_text_len(self): + return self.max_text_len + 2 def __call__(self, data): cells = data['cells'] - structure = data['structure']['tokens'] - structure = self.encode(structure, 'elem') + structure = data['structure'] + if self.merge_no_span_structure: + structure = self._merge_no_span_structure(structure) + if self.replace_empty_cell_token: + structure = self._replace_empty_cell_token(structure, cells) + # remove empty token and add " " to span token + new_structure = [] + for token in structure: + if token != '': + if 'span' in token and token[0] != ' ': + token = ' ' + token + new_structure.append(token) + # encode structure + structure = self.encode(new_structure) if structure is None: return None - elem_num = len(structure) - structure = [0] + structure + [len(self.dict_elem) - 1] - structure = structure + [0] * (self.max_elem_length + 2 - len(structure) - ) + + structure = [self.start_idx] + structure + [self.end_idx + ] # add sos abd eos + structure = structure + [self.pad_idx] * (self._max_text_len - + len(structure)) # pad structure = np.array(structure) data['structure'] = structure - elem_char_idx1 = self.dict_elem[''] - elem_char_idx2 = self.dict_elem[' 0: - span_weight = len(td_idx_list) * 1.0 / len(span_idx_list) - span_weight = min(max(span_weight, 1.0), self.span_weight) - for cno in range(len(cells)): - if 'bbox' in cells[cno]: - bbox = cells[cno]['bbox'].copy() - bbox[0] = bbox[0] * 1.0 / img_width - bbox[1] = bbox[1] * 1.0 / img_height - bbox[2] = bbox[2] * 1.0 / img_width - bbox[3] = bbox[3] * 1.0 / img_height - td_idx = td_idx_list[cno] - bbox_list[td_idx] = bbox - bbox_list_mask[td_idx] = 1.0 - cand_span_idx = td_idx + 1 - if cand_span_idx < (self.max_elem_length + 2): - if structure[cand_span_idx] in span_idx_list: - structure_mask[cand_span_idx] = span_weight - - data['bbox_list'] = bbox_list - data['bbox_list_mask'] = bbox_list_mask - data['structure_mask'] = structure_mask - char_beg_idx = self.get_beg_end_flag_idx('beg', 'char') - char_end_idx = self.get_beg_end_flag_idx('end', 'char') - elem_beg_idx = self.get_beg_end_flag_idx('beg', 'elem') - elem_end_idx = self.get_beg_end_flag_idx('end', 'elem') - data['sp_tokens'] = np.array([ - char_beg_idx, char_end_idx, elem_beg_idx, elem_end_idx, - elem_char_idx1, elem_char_idx2, self.max_text_length, - self.max_elem_length, self.max_cell_num, elem_num - ]) + + if len(structure) > self._max_text_len: + return None + + # encode box + bboxes = np.zeros( + (self._max_text_len, self.point_num * 2), dtype=np.float32) + bbox_masks = np.zeros((self._max_text_len, 1), dtype=np.float32) + + bbox_idx = 0 + + for i, token in enumerate(structure): + if self.idx2char[token] in self.td_token: + if 'bbox' in cells[bbox_idx] and len(cells[bbox_idx][ + 'tokens']) > 0: + bbox = cells[bbox_idx]['bbox'].copy() + bbox = np.array(bbox, dtype=np.float32).reshape(-1) + bboxes[i] = bbox + bbox_masks[i] = 1.0 + if self.learn_empty_box: + bbox_masks[i] = 1.0 + bbox_idx += 1 + data['bboxes'] = bboxes + data['bbox_masks'] = bbox_masks return data - def encode(self, text, char_or_elem): - """convert text-label into text-index. + def _merge_no_span_structure(self, structure): """ - if char_or_elem == "char": - max_len = self.max_text_length - current_dict = self.dict_character - else: - max_len = self.max_elem_length - current_dict = self.dict_elem - if len(text) > max_len: - return None - if len(text) == 0: - if char_or_elem == "char": - return [self.dict_character['space']] - else: - return None - text_list = [] - for char in text: - if char not in current_dict: - return None - text_list.append(current_dict[char]) - if len(text_list) == 0: - if char_or_elem == "char": - return [self.dict_character['space']] + This code is refer from: + https://github.com/JiaquanYe/TableMASTER-mmocr/blob/master/table_recognition/data_preprocess.py + """ + new_structure = [] + i = 0 + while i < len(structure): + token = structure[i] + if token == '': + token = '' + i += 1 + new_structure.append(token) + i += 1 + return new_structure + + def _replace_empty_cell_token(self, token_list, cells): + """ + This fun code is refer from: + https://github.com/JiaquanYe/TableMASTER-mmocr/blob/master/table_recognition/data_preprocess.py + """ + + bbox_idx = 0 + add_empty_bbox_token_list = [] + for token in token_list: + if token in ['', '']: + if 'bbox' not in cells[bbox_idx].keys(): + content = str(cells[bbox_idx]['tokens']) + token = self.empty_bbox_token_dict[content] + add_empty_bbox_token_list.append(token) + bbox_idx += 1 else: - return None - return text_list + add_empty_bbox_token_list.append(token) + return add_empty_bbox_token_list - def get_ignored_tokens(self, char_or_elem): - beg_idx = self.get_beg_end_flag_idx("beg", char_or_elem) - end_idx = self.get_beg_end_flag_idx("end", char_or_elem) - return [beg_idx, end_idx] - def get_beg_end_flag_idx(self, beg_or_end, char_or_elem): - if char_or_elem == "char": - if beg_or_end == "beg": - idx = np.array(self.dict_character[self.beg_str]) - elif beg_or_end == "end": - idx = np.array(self.dict_character[self.end_str]) - else: - assert False, "Unsupport type %s in get_beg_end_flag_idx of char" \ - % beg_or_end - elif char_or_elem == "elem": - if beg_or_end == "beg": - idx = np.array(self.dict_elem[self.beg_str]) - elif beg_or_end == "end": - idx = np.array(self.dict_elem[self.end_str]) - else: - assert False, "Unsupport type %s in get_beg_end_flag_idx of elem" \ - % beg_or_end - else: - assert False, "Unsupport type %s in char_or_elem" \ - % char_or_elem - return idx +class TableMasterLabelEncode(TableLabelEncode): + """ Convert between text-label and text-index """ + + def __init__(self, + max_text_length, + character_dict_path, + replace_empty_cell_token=False, + merge_no_span_structure=False, + learn_empty_box=False, + point_num=2, + **kwargs): + super(TableMasterLabelEncode, self).__init__( + max_text_length, character_dict_path, replace_empty_cell_token, + merge_no_span_structure, learn_empty_box, point_num, **kwargs) + self.pad_idx = self.dict[self.pad_str] + self.unknown_idx = self.dict[self.unknown_str] + + @property + def _max_text_len(self): + return self.max_text_len + + def add_special_char(self, dict_character): + self.beg_str = '' + self.end_str = '' + self.unknown_str = '' + self.pad_str = '' + dict_character = dict_character + dict_character = dict_character + [ + self.unknown_str, self.beg_str, self.end_str, self.pad_str + ] + return dict_character + + +class TableBoxEncode(object): + def __init__(self, use_xywh=False, **kwargs): + self.use_xywh = use_xywh + + def __call__(self, data): + img_height, img_width = data['image'].shape[:2] + bboxes = data['bboxes'] + if self.use_xywh and bboxes.shape[1] == 4: + bboxes = self.xyxy2xywh(bboxes) + bboxes[:, 0::2] /= img_width + bboxes[:, 1::2] /= img_height + data['bboxes'] = bboxes + return data + + def xyxy2xywh(self, bboxes): + """ + Convert coord (x1,y1,x2,y2) to (x,y,w,h). + where (x1,y1) is top-left, (x2,y2) is bottom-right. + (x,y) is bbox center and (w,h) is width and height. + :param bboxes: (x1, y1, x2, y2) + :return: + """ + new_bboxes = np.empty_like(bboxes) + new_bboxes[:, 0] = (bboxes[:, 0] + bboxes[:, 2]) / 2 # x center + new_bboxes[:, 1] = (bboxes[:, 1] + bboxes[:, 3]) / 2 # y center + new_bboxes[:, 2] = bboxes[:, 2] - bboxes[:, 0] # width + new_bboxes[:, 3] = bboxes[:, 3] - bboxes[:, 1] # height + return new_bboxes class SARLabelEncode(BaseRecLabelEncode): @@ -1013,7 +1052,6 @@ class MultiLabelEncode(BaseRecLabelEncode): use_space_char, **kwargs) def __call__(self, data): - data_ctc = copy.deepcopy(data) data_sar = copy.deepcopy(data) data_out = dict() diff --git a/ppocr/data/imaug/gen_table_mask.py b/ppocr/data/imaug/table_ops.py similarity index 77% rename from ppocr/data/imaug/gen_table_mask.py rename to ppocr/data/imaug/table_ops.py index 08e35d5d1df7f9663b4e008451308d0ee409cf5a..8d139190ab4b22c553036ddc8e31cfbc7ec3423d 100644 --- a/ppocr/data/imaug/gen_table_mask.py +++ b/ppocr/data/imaug/table_ops.py @@ -32,7 +32,7 @@ class GenTableMask(object): self.shrink_h_max = 5 self.shrink_w_max = 5 self.mask_type = mask_type - + def projection(self, erosion, h, w, spilt_threshold=0): # 水平投影 projection_map = np.ones_like(erosion) @@ -48,10 +48,12 @@ class GenTableMask(object): in_text = False # 是否遍历到了字符区内 box_list = [] for i in range(len(project_val_array)): - if in_text == False and project_val_array[i] > spilt_threshold: # 进入字符区了 + if in_text == False and project_val_array[ + i] > spilt_threshold: # 进入字符区了 in_text = True start_idx = i - elif project_val_array[i] <= spilt_threshold and in_text == True: # 进入空白区了 + elif project_val_array[ + i] <= spilt_threshold and in_text == True: # 进入空白区了 end_idx = i in_text = False if end_idx - start_idx <= 2: @@ -70,7 +72,8 @@ class GenTableMask(object): box_gray_img = cv2.cvtColor(box_img, cv2.COLOR_BGR2GRAY) h, w = box_gray_img.shape # 灰度图片进行二值化处理 - ret, thresh1 = cv2.threshold(box_gray_img, 200, 255, cv2.THRESH_BINARY_INV) + ret, thresh1 = cv2.threshold(box_gray_img, 200, 255, + cv2.THRESH_BINARY_INV) # 纵向腐蚀 if h < w: kernel = np.ones((2, 1), np.uint8) @@ -95,10 +98,12 @@ class GenTableMask(object): box_list = [] spilt_threshold = 0 for i in range(len(project_val_array)): - if in_text == False and project_val_array[i] > spilt_threshold: # 进入字符区了 + if in_text == False and project_val_array[ + i] > spilt_threshold: # 进入字符区了 in_text = True start_idx = i - elif project_val_array[i] <= spilt_threshold and in_text == True: # 进入空白区了 + elif project_val_array[ + i] <= spilt_threshold and in_text == True: # 进入空白区了 end_idx = i in_text = False if end_idx - start_idx <= 2: @@ -120,7 +125,8 @@ class GenTableMask(object): h_end = h word_img = erosion[h_start:h_end + 1, :] word_h, word_w = word_img.shape - w_split_list, w_projection_map = self.projection(word_img.T, word_w, word_h) + w_split_list, w_projection_map = self.projection(word_img.T, + word_w, word_h) w_start, w_end = w_split_list[0][0], w_split_list[-1][1] if h_start > 0: h_start -= 1 @@ -170,75 +176,54 @@ class GenTableMask(object): for sno in range(len(split_bbox_list)): left, top, right, bottom = split_bbox_list[sno] - left, top, right, bottom = self.shrink_bbox([left, top, right, bottom]) + left, top, right, bottom = self.shrink_bbox( + [left, top, right, bottom]) if self.mask_type == 1: mask_img[top:bottom, left:right] = 1.0 data['mask_img'] = mask_img else: - mask_img[top:bottom, left:right, :] = (255, 255, 255) + mask_img[top:bottom, left:right, :] = (255, 255, 255) data['image'] = mask_img return data + class ResizeTableImage(object): - def __init__(self, max_len, **kwargs): + def __init__(self, max_len, resize_bboxes=False, infer_mode=False, + **kwargs): super(ResizeTableImage, self).__init__() self.max_len = max_len + self.resize_bboxes = resize_bboxes + self.infer_mode = infer_mode - def get_img_bbox(self, cells): - bbox_list = [] - if len(cells) == 0: - return bbox_list - cell_num = len(cells) - for cno in range(cell_num): - if "bbox" in cells[cno]: - bbox = cells[cno]['bbox'] - bbox_list.append(bbox) - return bbox_list - - def resize_img_table(self, img, bbox_list, max_len): + def __call__(self, data): + img = data['image'] height, width = img.shape[0:2] - ratio = max_len / (max(height, width) * 1.0) + ratio = self.max_len / (max(height, width) * 1.0) resize_h = int(height * ratio) resize_w = int(width * ratio) - img_new = cv2.resize(img, (resize_w, resize_h)) - bbox_list_new = [] - for bno in range(len(bbox_list)): - left, top, right, bottom = bbox_list[bno].copy() - left = int(left * ratio) - top = int(top * ratio) - right = int(right * ratio) - bottom = int(bottom * ratio) - bbox_list_new.append([left, top, right, bottom]) - return img_new, bbox_list_new - - def __call__(self, data): - img = data['image'] - if 'cells' not in data: - cells = [] - else: - cells = data['cells'] - bbox_list = self.get_img_bbox(cells) - img_new, bbox_list_new = self.resize_img_table(img, bbox_list, self.max_len) - data['image'] = img_new - cell_num = len(cells) - bno = 0 - for cno in range(cell_num): - if "bbox" in data['cells'][cno]: - data['cells'][cno]['bbox'] = bbox_list_new[bno] - bno += 1 + resize_img = cv2.resize(img, (resize_w, resize_h)) + if self.resize_bboxes and not self.infer_mode: + data['bboxes'] = data['bboxes'] * ratio + data['image'] = resize_img + data['src_img'] = img + data['shape'] = np.array([resize_h, resize_w, ratio, ratio]) data['max_len'] = self.max_len return data + class PaddingTableImage(object): - def __init__(self, **kwargs): + def __init__(self, size, **kwargs): super(PaddingTableImage, self).__init__() - + self.size = size + def __call__(self, data): img = data['image'] - max_len = data['max_len'] - padding_img = np.zeros((max_len, max_len, 3), dtype=np.float32) + pad_h, pad_w = self.size + padding_img = np.zeros((pad_h, pad_w, 3), dtype=np.float32) height, width = img.shape[0:2] padding_img[0:height, 0:width, :] = img.copy() data['image'] = padding_img + shape = data['shape'].tolist() + shape.extend([pad_h, pad_w]) + data['shape'] = np.array(shape) return data - \ No newline at end of file diff --git a/ppocr/data/pubtab_dataset.py b/ppocr/data/pubtab_dataset.py index 671cda76fb4c36f3ac6bcc7da5a7fc4de241c0e2..642d3eb1961cbf0e829e6fb122f38c6af99df1c5 100644 --- a/ppocr/data/pubtab_dataset.py +++ b/ppocr/data/pubtab_dataset.py @@ -16,6 +16,7 @@ import os import random from paddle.io import Dataset import json +from copy import deepcopy from .imaug import transform, create_operators @@ -29,33 +30,63 @@ class PubTabDataSet(Dataset): dataset_config = config[mode]['dataset'] loader_config = config[mode]['loader'] - label_file_path = dataset_config.pop('label_file_path') + label_file_list = dataset_config.pop('label_file_list') + data_source_num = len(label_file_list) + ratio_list = dataset_config.get("ratio_list", [1.0]) + if isinstance(ratio_list, (float, int)): + ratio_list = [float(ratio_list)] * int(data_source_num) + + assert len( + ratio_list + ) == data_source_num, "The length of ratio_list should be the same as the file_list." self.data_dir = dataset_config['data_dir'] self.do_shuffle = loader_config['shuffle'] - self.do_hard_select = False - if 'hard_select' in loader_config: - self.do_hard_select = loader_config['hard_select'] - self.hard_prob = loader_config['hard_prob'] - if self.do_hard_select: - self.img_select_prob = self.load_hard_select_prob() - self.table_select_type = None - if 'table_select_type' in loader_config: - self.table_select_type = loader_config['table_select_type'] - self.table_select_prob = loader_config['table_select_prob'] self.seed = seed - logger.info("Initialize indexs of datasets:%s" % label_file_path) - with open(label_file_path, "rb") as f: - self.data_lines = f.readlines() - self.data_idx_order_list = list(range(len(self.data_lines))) - if mode.lower() == "train": + self.mode = mode.lower() + logger.info("Initialize indexs of datasets:%s" % label_file_list) + self.data_lines = self.get_image_info_list(label_file_list, ratio_list) + # self.check(config['Global']['max_text_length']) + + if mode.lower() == "train" and self.do_shuffle: self.shuffle_data_random() self.ops = create_operators(dataset_config['transforms'], global_config) - - ratio_list = dataset_config.get("ratio_list", [1.0]) self.need_reset = True in [x < 1 for x in ratio_list] + def get_image_info_list(self, file_list, ratio_list): + if isinstance(file_list, str): + file_list = [file_list] + data_lines = [] + for idx, file in enumerate(file_list): + with open(file, "rb") as f: + lines = f.readlines() + if self.mode == "train" or ratio_list[idx] < 1.0: + random.seed(self.seed) + lines = random.sample(lines, + round(len(lines) * ratio_list[idx])) + data_lines.extend(lines) + return data_lines + + def check(self, max_text_length): + data_lines = [] + for line in self.data_lines: + data_line = line.decode('utf-8').strip("\n") + info = json.loads(data_line) + file_name = info['filename'] + cells = info['html']['cells'].copy() + structure = info['html']['structure']['tokens'].copy() + + img_path = os.path.join(self.data_dir, file_name) + if not os.path.exists(img_path): + self.logger.warning("{} does not exist!".format(img_path)) + continue + if len(structure) == 0 or len(structure) > max_text_length: + continue + # data = {'img_path': img_path, 'cells': cells, 'structure':structure,'file_name':file_name} + data_lines.append(line) + self.data_lines = data_lines + def shuffle_data_random(self): if self.do_shuffle: random.seed(self.seed) @@ -68,47 +99,35 @@ class PubTabDataSet(Dataset): data_line = data_line.decode('utf-8').strip("\n") info = json.loads(data_line) file_name = info['filename'] - select_flag = True - if self.do_hard_select: - prob = self.img_select_prob[file_name] - if prob < random.uniform(0, 1): - select_flag = False - - if self.table_select_type: - structure = info['html']['structure']['tokens'].copy() - structure_str = ''.join(structure) - table_type = "simple" - if 'colspan' in structure_str or 'rowspan' in structure_str: - table_type = "complex" - if table_type == "complex": - if self.table_select_prob < random.uniform(0, 1): - select_flag = False - - if select_flag: - cells = info['html']['cells'].copy() - structure = info['html']['structure'].copy() - img_path = os.path.join(self.data_dir, file_name) - data = { - 'img_path': img_path, - 'cells': cells, - 'structure': structure - } - if not os.path.exists(img_path): - raise Exception("{} does not exist!".format(img_path)) - with open(data['img_path'], 'rb') as f: - img = f.read() - data['image'] = img - outs = transform(data, self.ops) - else: - outs = None - except Exception as e: + cells = info['html']['cells'].copy() + structure = info['html']['structure']['tokens'].copy() + + img_path = os.path.join(self.data_dir, file_name) + if not os.path.exists(img_path): + raise Exception("{} does not exist!".format(img_path)) + data = { + 'img_path': img_path, + 'cells': cells, + 'structure': structure, + 'file_name': file_name + } + + with open(data['img_path'], 'rb') as f: + img = f.read() + data['image'] = img + outs = transform(data, self.ops) + except: + import traceback + err = traceback.format_exc() self.logger.error( "When parsing line {}, error happened with msg: {}".format( - data_line, e)) + data_line, err)) outs = None if outs is None: - return self.__getitem__(np.random.randint(self.__len__())) + rnd_idx = np.random.randint(self.__len__( + )) if self.mode == "train" else (idx + 1) % self.__len__() + return self.__getitem__(rnd_idx) return outs def __len__(self): - return len(self.data_idx_order_list) + return len(self.data_lines) diff --git a/ppocr/losses/__init__.py b/ppocr/losses/__init__.py index 7bea87f62f335a9a47c881d4bc789ce34aaa734a..62e0544ea94daaaff7d019e6a48e65a2d508aca0 100755 --- a/ppocr/losses/__init__.py +++ b/ppocr/losses/__init__.py @@ -51,7 +51,7 @@ from .combined_loss import CombinedLoss # table loss from .table_att_loss import TableAttentionLoss - +from .table_master_loss import TableMasterLoss # vqa token loss from .vqa_token_layoutlm_loss import VQASerTokenLayoutLMLoss @@ -61,7 +61,8 @@ def build_loss(config): 'DBLoss', 'PSELoss', 'EASTLoss', 'SASTLoss', 'FCELoss', 'CTCLoss', 'ClsLoss', 'AttentionLoss', 'SRNLoss', 'PGLoss', 'CombinedLoss', 'CELoss', 'TableAttentionLoss', 'SARLoss', 'AsterLoss', 'SDMGRLoss', - 'VQASerTokenLayoutLMLoss', 'LossFromOutput', 'PRENLoss', 'MultiLoss' + 'VQASerTokenLayoutLMLoss', 'LossFromOutput', 'PRENLoss', 'MultiLoss', + 'TableMasterLoss' ] config = copy.deepcopy(config) module_name = config.pop('name') diff --git a/ppocr/losses/table_att_loss.py b/ppocr/losses/table_att_loss.py index 51377efa2b5e802fe9f9fc1973c74deb00fc4816..3496c9072553d839017eaa017fe47dfb66fb9d3b 100644 --- a/ppocr/losses/table_att_loss.py +++ b/ppocr/losses/table_att_loss.py @@ -20,15 +20,21 @@ import paddle from paddle import nn from paddle.nn import functional as F + class TableAttentionLoss(nn.Layer): - def __init__(self, structure_weight, loc_weight, use_giou=False, giou_weight=1.0, **kwargs): + def __init__(self, + structure_weight, + loc_weight, + use_giou=False, + giou_weight=1.0, + **kwargs): super(TableAttentionLoss, self).__init__() self.loss_func = nn.CrossEntropyLoss(weight=None, reduction='none') self.structure_weight = structure_weight self.loc_weight = loc_weight self.use_giou = use_giou self.giou_weight = giou_weight - + def giou_loss(self, preds, bbox, eps=1e-7, reduction='mean'): ''' :param preds:[[x1,y1,x2,y2], [x1,y1,x2,y2],,,] @@ -47,9 +53,10 @@ class TableAttentionLoss(nn.Layer): inters = iw * ih # union - uni = (preds[:, 2] - preds[:, 0] + 1e-3) * (preds[:, 3] - preds[:, 1] + 1e-3 - ) + (bbox[:, 2] - bbox[:, 0] + 1e-3) * ( - bbox[:, 3] - bbox[:, 1] + 1e-3) - inters + eps + uni = (preds[:, 2] - preds[:, 0] + 1e-3) * ( + preds[:, 3] - preds[:, 1] + 1e-3) + (bbox[:, 2] - bbox[:, 0] + 1e-3 + ) * (bbox[:, 3] - bbox[:, 1] + + 1e-3) - inters + eps # ious ious = inters / uni @@ -79,30 +86,34 @@ class TableAttentionLoss(nn.Layer): structure_probs = predicts['structure_probs'] structure_targets = batch[1].astype("int64") structure_targets = structure_targets[:, 1:] - if len(batch) == 6: - structure_mask = batch[5].astype("int64") - structure_mask = structure_mask[:, 1:] - structure_mask = paddle.reshape(structure_mask, [-1]) - structure_probs = paddle.reshape(structure_probs, [-1, structure_probs.shape[-1]]) + structure_probs = paddle.reshape(structure_probs, + [-1, structure_probs.shape[-1]]) structure_targets = paddle.reshape(structure_targets, [-1]) structure_loss = self.loss_func(structure_probs, structure_targets) - - if len(batch) == 6: - structure_loss = structure_loss * structure_mask - -# structure_loss = paddle.sum(structure_loss) * self.structure_weight + structure_loss = paddle.mean(structure_loss) * self.structure_weight - + loc_preds = predicts['loc_preds'] loc_targets = batch[2].astype("float32") - loc_targets_mask = batch[4].astype("float32") + loc_targets_mask = batch[3].astype("float32") loc_targets = loc_targets[:, 1:, :] loc_targets_mask = loc_targets_mask[:, 1:, :] - loc_loss = F.mse_loss(loc_preds * loc_targets_mask, loc_targets) * self.loc_weight + loc_loss = F.mse_loss(loc_preds * loc_targets_mask, + loc_targets) * self.loc_weight if self.use_giou: - loc_loss_giou = self.giou_loss(loc_preds * loc_targets_mask, loc_targets) * self.giou_weight + loc_loss_giou = self.giou_loss(loc_preds * loc_targets_mask, + loc_targets) * self.giou_weight total_loss = structure_loss + loc_loss + loc_loss_giou - return {'loss':total_loss, "structure_loss":structure_loss, "loc_loss":loc_loss, "loc_loss_giou":loc_loss_giou} + return { + 'loss': total_loss, + "structure_loss": structure_loss, + "loc_loss": loc_loss, + "loc_loss_giou": loc_loss_giou + } else: - total_loss = structure_loss + loc_loss - return {'loss':total_loss, "structure_loss":structure_loss, "loc_loss":loc_loss} \ No newline at end of file + total_loss = structure_loss + loc_loss + return { + 'loss': total_loss, + "structure_loss": structure_loss, + "loc_loss": loc_loss + } diff --git a/ppocr/losses/table_master_loss.py b/ppocr/losses/table_master_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..dca982dbd43e2c14f15503e1e98d6fe6c18878c5 --- /dev/null +++ b/ppocr/losses/table_master_loss.py @@ -0,0 +1,70 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# 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/JiaquanYe/TableMASTER-mmocr/tree/master/mmocr/models/textrecog/losses +""" + +import paddle +from paddle import nn + + +class TableMasterLoss(nn.Layer): + def __init__(self, ignore_index=-1): + super(TableMasterLoss, self).__init__() + self.structure_loss = nn.CrossEntropyLoss( + ignore_index=ignore_index, reduction='mean') + self.box_loss = nn.L1Loss(reduction='sum') + self.eps = 1e-12 + + def forward(self, predicts, batch): + # structure_loss + structure_probs = predicts['structure_probs'] + structure_targets = batch[1] + structure_targets = structure_targets[:, 1:] + structure_probs = structure_probs.reshape( + [-1, structure_probs.shape[-1]]) + structure_targets = structure_targets.reshape([-1]) + + structure_loss = self.structure_loss(structure_probs, structure_targets) + structure_loss = structure_loss.mean() + losses = dict(structure_loss=structure_loss) + + # box loss + bboxes_preds = predicts['loc_preds'] + bboxes_targets = batch[2][:, 1:, :] + bbox_masks = batch[3][:, 1:] + # mask empty-bbox or non-bbox structure token's bbox. + + masked_bboxes_preds = bboxes_preds * bbox_masks + masked_bboxes_targets = bboxes_targets * bbox_masks + + # horizon loss (x and width) + horizon_sum_loss = self.box_loss(masked_bboxes_preds[:, :, 0::2], + masked_bboxes_targets[:, :, 0::2]) + horizon_loss = horizon_sum_loss / (bbox_masks.sum() + self.eps) + # vertical loss (y and height) + vertical_sum_loss = self.box_loss(masked_bboxes_preds[:, :, 1::2], + masked_bboxes_targets[:, :, 1::2]) + vertical_loss = vertical_sum_loss / (bbox_masks.sum() + self.eps) + + horizon_loss = horizon_loss.mean() + vertical_loss = vertical_loss.mean() + all_loss = structure_loss + horizon_loss + vertical_loss + losses.update({ + 'loss': all_loss, + 'horizon_bbox_loss': horizon_loss, + 'vertical_bbox_loss': vertical_loss + }) + return losses diff --git a/ppocr/metrics/table_metric.py b/ppocr/metrics/table_metric.py index ca4d6474202b4e85cadf86ccb2fe2726c7fa9aeb..fd2631e442b8d111c64d5cf4b34ea9063d8c60dd 100644 --- a/ppocr/metrics/table_metric.py +++ b/ppocr/metrics/table_metric.py @@ -12,29 +12,30 @@ # See the License for the specific language governing permissions and # limitations under the License. import numpy as np +from ppocr.metrics.det_metric import DetMetric -class TableMetric(object): - def __init__(self, main_indicator='acc', **kwargs): +class TableStructureMetric(object): + def __init__(self, main_indicator='acc', eps=1e-6, **kwargs): self.main_indicator = main_indicator - self.eps = 1e-5 + self.eps = eps self.reset() - def __call__(self, pred, batch, *args, **kwargs): - structure_probs = pred['structure_probs'].numpy() - structure_labels = batch[1] + def __call__(self, pred_label, batch=None, *args, **kwargs): + preds, labels = pred_label + pred_structure_batch_list = preds['structure_batch_list'] + gt_structure_batch_list = labels['structure_batch_list'] correct_num = 0 all_num = 0 - structure_probs = np.argmax(structure_probs, axis=2) - structure_labels = structure_labels[:, 1:] - batch_size = structure_probs.shape[0] - for bno in range(batch_size): - all_num += 1 - if (structure_probs[bno] == structure_labels[bno]).all(): + for (pred, pred_conf), target in zip(pred_structure_batch_list, + gt_structure_batch_list): + pred_str = ''.join(pred) + target_str = ''.join(target) + if pred_str == target_str: correct_num += 1 + all_num += 1 self.correct_num += correct_num self.all_num += all_num - return {'acc': correct_num * 1.0 / (all_num + self.eps), } def get_metric(self): """ @@ -49,3 +50,89 @@ class TableMetric(object): def reset(self): self.correct_num = 0 self.all_num = 0 + self.len_acc_num = 0 + self.token_nums = 0 + self.anys_dict = dict() + + +class TableMetric(object): + def __init__(self, + main_indicator='acc', + compute_bbox_metric=False, + point_num=2, + **kwargs): + """ + + @param sub_metrics: configs of sub_metric + @param main_matric: main_matric for save best_model + @param kwargs: + """ + self.structure_metric = TableStructureMetric() + self.bbox_metric = DetMetric() if compute_bbox_metric else None + self.main_indicator = main_indicator + self.point_num = point_num + self.reset() + + def __call__(self, pred_label, batch=None, *args, **kwargs): + self.structure_metric(pred_label) + if self.bbox_metric is not None: + self.bbox_metric(*self.prepare_bbox_metric_input(pred_label)) + + def prepare_bbox_metric_input(self, pred_label): + pred_bbox_batch_list = [] + gt_ignore_tags_batch_list = [] + gt_bbox_batch_list = [] + preds, labels = pred_label + + batch_num = len(preds['bbox_batch_list']) + for batch_idx in range(batch_num): + # pred + pred_bbox_list = [ + self.format_box(pred_box) + for pred_box in preds['bbox_batch_list'][batch_idx] + ] + pred_bbox_batch_list.append({'points': pred_bbox_list}) + + # gt + gt_bbox_list = [] + gt_ignore_tags_list = [] + for gt_box in labels['bbox_batch_list'][batch_idx]: + gt_bbox_list.append(self.format_box(gt_box)) + gt_ignore_tags_list.append(0) + gt_bbox_batch_list.append(gt_bbox_list) + gt_ignore_tags_batch_list.append(gt_ignore_tags_list) + + return [ + pred_bbox_batch_list, + [0, 0, gt_bbox_batch_list, gt_ignore_tags_batch_list] + ] + + def get_metric(self): + structure_metric = self.structure_metric.get_metric() + if self.bbox_metric is None: + return structure_metric + bbox_metric = self.bbox_metric.get_metric() + if self.main_indicator == self.bbox_metric.main_indicator: + output = bbox_metric + for sub_key in structure_metric: + output["structure_metric_{}".format( + sub_key)] = structure_metric[sub_key] + else: + output = structure_metric + for sub_key in bbox_metric: + output["bbox_metric_{}".format(sub_key)] = bbox_metric[sub_key] + return output + + def reset(self): + self.structure_metric.reset() + if self.bbox_metric is not None: + self.bbox_metric.reset() + + def format_box(self, box): + if self.point_num == 2: + x1, y1, x2, y2 = box + box = [[x1, y1], [x2, y1], [x2, y2], [x1, y2]] + elif self.point_num == 4: + x1, y1, x2, y2, x3, y3, x4, y4 = box + box = [[x1, y1], [x2, y2], [x3, y3], [x4, y4]] + return box diff --git a/ppocr/modeling/backbones/__init__.py b/ppocr/modeling/backbones/__init__.py index ab2939c24d92fcebcd9afe574cb02a6b113190fa..f4094d796b1f14c955e5962936e86bd6b3f5ec78 100755 --- a/ppocr/modeling/backbones/__init__.py +++ b/ppocr/modeling/backbones/__init__.py @@ -22,6 +22,9 @@ def build_backbone(config, model_type): from .det_resnet_vd import ResNet_vd from .det_resnet_vd_sast import ResNet_SAST support_dict = ["MobileNetV3", "ResNet", "ResNet_vd", "ResNet_SAST"] + if model_type == "table": + from .table_master_resnet import TableResNetExtra + support_dict.append('TableResNetExtra') elif model_type == "rec" or model_type == "cls": from .rec_mobilenet_v3 import MobileNetV3 from .rec_resnet_vd import ResNet diff --git a/ppocr/modeling/backbones/table_master_resnet.py b/ppocr/modeling/backbones/table_master_resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..dacf5ed26e5374b3c93c1a983be1d7b5b4c471fc --- /dev/null +++ b/ppocr/modeling/backbones/table_master_resnet.py @@ -0,0 +1,369 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# 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/JiaquanYe/TableMASTER-mmocr/blob/master/mmocr/models/textrecog/backbones/table_resnet_extra.py +""" + +import paddle +import paddle.nn as nn +import paddle.nn.functional as F + + +class BasicBlock(nn.Layer): + expansion = 1 + + def __init__(self, + inplanes, + planes, + stride=1, + downsample=None, + gcb_config=None): + super(BasicBlock, self).__init__() + self.conv1 = nn.Conv2D( + inplanes, + planes, + kernel_size=3, + stride=stride, + padding=1, + bias_attr=False) + self.bn1 = nn.BatchNorm2D(planes, momentum=0.9) + self.relu = nn.ReLU() + self.conv2 = nn.Conv2D( + planes, planes, kernel_size=3, stride=1, padding=1, bias_attr=False) + self.bn2 = nn.BatchNorm2D(planes, momentum=0.9) + self.downsample = downsample + self.stride = stride + self.gcb_config = gcb_config + + if self.gcb_config is not None: + gcb_ratio = gcb_config['ratio'] + gcb_headers = gcb_config['headers'] + att_scale = gcb_config['att_scale'] + fusion_type = gcb_config['fusion_type'] + self.context_block = MultiAspectGCAttention( + inplanes=planes, + ratio=gcb_ratio, + headers=gcb_headers, + att_scale=att_scale, + fusion_type=fusion_type) + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.gcb_config is not None: + out = self.context_block(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +def get_gcb_config(gcb_config, layer): + if gcb_config is None or not gcb_config['layers'][layer]: + return None + else: + return gcb_config + + +class TableResNetExtra(nn.Layer): + def __init__(self, layers, in_channels=3, gcb_config=None): + assert len(layers) >= 4 + + super(TableResNetExtra, self).__init__() + self.inplanes = 128 + self.conv1 = nn.Conv2D( + in_channels, + 64, + kernel_size=3, + stride=1, + padding=1, + bias_attr=False) + self.bn1 = nn.BatchNorm2D(64) + self.relu1 = nn.ReLU() + + self.conv2 = nn.Conv2D( + 64, 128, kernel_size=3, stride=1, padding=1, bias_attr=False) + self.bn2 = nn.BatchNorm2D(128) + self.relu2 = nn.ReLU() + + self.maxpool1 = nn.MaxPool2D(kernel_size=2, stride=2) + + self.layer1 = self._make_layer( + BasicBlock, + 256, + layers[0], + stride=1, + gcb_config=get_gcb_config(gcb_config, 0)) + + self.conv3 = nn.Conv2D( + 256, 256, kernel_size=3, stride=1, padding=1, bias_attr=False) + self.bn3 = nn.BatchNorm2D(256) + self.relu3 = nn.ReLU() + + self.maxpool2 = nn.MaxPool2D(kernel_size=2, stride=2) + + self.layer2 = self._make_layer( + BasicBlock, + 256, + layers[1], + stride=1, + gcb_config=get_gcb_config(gcb_config, 1)) + + self.conv4 = nn.Conv2D( + 256, 256, kernel_size=3, stride=1, padding=1, bias_attr=False) + self.bn4 = nn.BatchNorm2D(256) + self.relu4 = nn.ReLU() + + self.maxpool3 = nn.MaxPool2D(kernel_size=2, stride=2) + + self.layer3 = self._make_layer( + BasicBlock, + 512, + layers[2], + stride=1, + gcb_config=get_gcb_config(gcb_config, 2)) + + self.conv5 = nn.Conv2D( + 512, 512, kernel_size=3, stride=1, padding=1, bias_attr=False) + self.bn5 = nn.BatchNorm2D(512) + self.relu5 = nn.ReLU() + + self.layer4 = self._make_layer( + BasicBlock, + 512, + layers[3], + stride=1, + gcb_config=get_gcb_config(gcb_config, 3)) + + self.conv6 = nn.Conv2D( + 512, 512, kernel_size=3, stride=1, padding=1, bias_attr=False) + self.bn6 = nn.BatchNorm2D(512) + self.relu6 = nn.ReLU() + + self.out_channels = [256, 256, 512] + + def _make_layer(self, block, planes, blocks, stride=1, gcb_config=None): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2D( + self.inplanes, + planes * block.expansion, + kernel_size=1, + stride=stride, + bias_attr=False), + nn.BatchNorm2D(planes * block.expansion), ) + + layers = [] + layers.append( + block( + self.inplanes, + planes, + stride, + downsample, + gcb_config=gcb_config)) + self.inplanes = planes * block.expansion + for _ in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x): + f = [] + x = self.conv1(x) + + x = self.bn1(x) + x = self.relu1(x) + + x = self.conv2(x) + x = self.bn2(x) + x = self.relu2(x) + + x = self.maxpool1(x) + x = self.layer1(x) + + x = self.conv3(x) + x = self.bn3(x) + x = self.relu3(x) + f.append(x) + + x = self.maxpool2(x) + x = self.layer2(x) + + x = self.conv4(x) + x = self.bn4(x) + x = self.relu4(x) + f.append(x) + + x = self.maxpool3(x) + + x = self.layer3(x) + x = self.conv5(x) + x = self.bn5(x) + x = self.relu5(x) + + x = self.layer4(x) + x = self.conv6(x) + x = self.bn6(x) + x = self.relu6(x) + f.append(x) + return f + + +class MultiAspectGCAttention(nn.Layer): + def __init__(self, + inplanes, + ratio, + headers, + pooling_type='att', + att_scale=False, + fusion_type='channel_add'): + super(MultiAspectGCAttention, self).__init__() + assert pooling_type in ['avg', 'att'] + + assert fusion_type in ['channel_add', 'channel_mul', 'channel_concat'] + assert inplanes % headers == 0 and inplanes >= 8 # inplanes must be divided by headers evenly + + self.headers = headers + self.inplanes = inplanes + self.ratio = ratio + self.planes = int(inplanes * ratio) + self.pooling_type = pooling_type + self.fusion_type = fusion_type + self.att_scale = False + + self.single_header_inplanes = int(inplanes / headers) + + if pooling_type == 'att': + self.conv_mask = nn.Conv2D( + self.single_header_inplanes, 1, kernel_size=1) + self.softmax = nn.Softmax(axis=2) + else: + self.avg_pool = nn.AdaptiveAvgPool2D(1) + + if fusion_type == 'channel_add': + self.channel_add_conv = nn.Sequential( + nn.Conv2D( + self.inplanes, self.planes, kernel_size=1), + nn.LayerNorm([self.planes, 1, 1]), + nn.ReLU(), + nn.Conv2D( + self.planes, self.inplanes, kernel_size=1)) + elif fusion_type == 'channel_concat': + self.channel_concat_conv = nn.Sequential( + nn.Conv2D( + self.inplanes, self.planes, kernel_size=1), + nn.LayerNorm([self.planes, 1, 1]), + nn.ReLU(), + nn.Conv2D( + self.planes, self.inplanes, kernel_size=1)) + # for concat + self.cat_conv = nn.Conv2D( + 2 * self.inplanes, self.inplanes, kernel_size=1) + elif fusion_type == 'channel_mul': + self.channel_mul_conv = nn.Sequential( + nn.Conv2D( + self.inplanes, self.planes, kernel_size=1), + nn.LayerNorm([self.planes, 1, 1]), + nn.ReLU(), + nn.Conv2D( + self.planes, self.inplanes, kernel_size=1)) + + def spatial_pool(self, x): + batch, channel, height, width = x.shape + if self.pooling_type == 'att': + # [N*headers, C', H , W] C = headers * C' + x = x.reshape([ + batch * self.headers, self.single_header_inplanes, height, width + ]) + input_x = x + + # [N*headers, C', H * W] C = headers * C' + # input_x = input_x.view(batch, channel, height * width) + input_x = input_x.reshape([ + batch * self.headers, self.single_header_inplanes, + height * width + ]) + + # [N*headers, 1, C', H * W] + input_x = input_x.unsqueeze(1) + # [N*headers, 1, H, W] + context_mask = self.conv_mask(x) + # [N*headers, 1, H * W] + context_mask = context_mask.reshape( + [batch * self.headers, 1, height * width]) + + # scale variance + if self.att_scale and self.headers > 1: + context_mask = context_mask / paddle.sqrt( + self.single_header_inplanes) + + # [N*headers, 1, H * W] + context_mask = self.softmax(context_mask) + + # [N*headers, 1, H * W, 1] + context_mask = context_mask.unsqueeze(-1) + # [N*headers, 1, C', 1] = [N*headers, 1, C', H * W] * [N*headers, 1, H * W, 1] + context = paddle.matmul(input_x, context_mask) + + # [N, headers * C', 1, 1] + context = context.reshape( + [batch, self.headers * self.single_header_inplanes, 1, 1]) + else: + # [N, C, 1, 1] + context = self.avg_pool(x) + + return context + + def forward(self, x): + # [N, C, 1, 1] + context = self.spatial_pool(x) + + out = x + + if self.fusion_type == 'channel_mul': + # [N, C, 1, 1] + channel_mul_term = F.sigmoid(self.channel_mul_conv(context)) + out = out * channel_mul_term + elif self.fusion_type == 'channel_add': + # [N, C, 1, 1] + channel_add_term = self.channel_add_conv(context) + out = out + channel_add_term + else: + # [N, C, 1, 1] + channel_concat_term = self.channel_concat_conv(context) + + # use concat + _, C1, _, _ = channel_concat_term.shape + N, C2, H, W = out.shape + + out = paddle.concat( + [out, channel_concat_term.expand([-1, -1, H, W])], axis=1) + out = self.cat_conv(out) + out = F.layer_norm(out, [self.inplanes, H, W]) + out = F.relu(out) + + return out diff --git a/ppocr/modeling/heads/__init__.py b/ppocr/modeling/heads/__init__.py index 14e6aab854d8942083f1e3466f554c0876bcf403..fcd146efbc378faeebd42534a994836789974c32 100755 --- a/ppocr/modeling/heads/__init__.py +++ b/ppocr/modeling/heads/__init__.py @@ -42,12 +42,13 @@ def build_head(config): from .kie_sdmgr_head import SDMGRHead from .table_att_head import TableAttentionHead + from .table_master_head import TableMasterHead support_dict = [ 'DBHead', 'PSEHead', 'FCEHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead', 'AttentionHead', 'SRNHead', 'PGHead', 'Transformer', 'TableAttentionHead', 'SARHead', 'AsterHead', 'SDMGRHead', 'PRENHead', - 'MultiHead', 'ABINetHead' + 'MultiHead', 'ABINetHead', 'TableMasterHead' ] #table head diff --git a/ppocr/modeling/heads/table_att_head.py b/ppocr/modeling/heads/table_att_head.py index e354f40d6518c1f7ca22e93694b1c6668fc003d2..4f39d6253d8d596fecdc4736666a6d3106601a82 100644 --- a/ppocr/modeling/heads/table_att_head.py +++ b/ppocr/modeling/heads/table_att_head.py @@ -21,6 +21,8 @@ import paddle.nn as nn import paddle.nn.functional as F import numpy as np +from .rec_att_head import AttentionGRUCell + class TableAttentionHead(nn.Layer): def __init__(self, @@ -28,21 +30,19 @@ class TableAttentionHead(nn.Layer): hidden_size, loc_type, in_max_len=488, - max_text_length=100, - max_elem_length=800, - max_cell_num=500, + max_text_length=800, + out_channels=30, + point_num=2, **kwargs): super(TableAttentionHead, self).__init__() self.input_size = in_channels[-1] self.hidden_size = hidden_size - self.elem_num = 30 + self.out_channels = out_channels self.max_text_length = max_text_length - self.max_elem_length = max_elem_length - self.max_cell_num = max_cell_num self.structure_attention_cell = AttentionGRUCell( - self.input_size, hidden_size, self.elem_num, use_gru=False) - self.structure_generator = nn.Linear(hidden_size, self.elem_num) + self.input_size, hidden_size, self.out_channels, use_gru=False) + self.structure_generator = nn.Linear(hidden_size, self.out_channels) self.loc_type = loc_type self.in_max_len = in_max_len @@ -50,12 +50,13 @@ class TableAttentionHead(nn.Layer): self.loc_generator = nn.Linear(hidden_size, 4) else: if self.in_max_len == 640: - self.loc_fea_trans = nn.Linear(400, self.max_elem_length + 1) + self.loc_fea_trans = nn.Linear(400, self.max_text_length + 1) elif self.in_max_len == 800: - self.loc_fea_trans = nn.Linear(625, self.max_elem_length + 1) + self.loc_fea_trans = nn.Linear(625, self.max_text_length + 1) else: - self.loc_fea_trans = nn.Linear(256, self.max_elem_length + 1) - self.loc_generator = nn.Linear(self.input_size + hidden_size, 4) + self.loc_fea_trans = nn.Linear(256, self.max_text_length + 1) + self.loc_generator = nn.Linear(self.input_size + hidden_size, + point_num * 2) def _char_to_onehot(self, input_char, onehot_dim): input_ont_hot = F.one_hot(input_char, onehot_dim) @@ -77,9 +78,9 @@ class TableAttentionHead(nn.Layer): output_hiddens = [] if self.training and targets is not None: structure = targets[0] - for i in range(self.max_elem_length + 1): + for i in range(self.max_text_length + 1): elem_onehots = self._char_to_onehot( - structure[:, i], onehot_dim=self.elem_num) + structure[:, i], onehot_dim=self.out_channels) (outputs, hidden), alpha = self.structure_attention_cell( hidden, fea, elem_onehots) output_hiddens.append(paddle.unsqueeze(outputs, axis=1)) @@ -102,11 +103,11 @@ class TableAttentionHead(nn.Layer): elem_onehots = None outputs = None alpha = None - max_elem_length = paddle.to_tensor(self.max_elem_length) + max_text_length = paddle.to_tensor(self.max_text_length) i = 0 - while i < max_elem_length + 1: + while i < max_text_length + 1: elem_onehots = self._char_to_onehot( - temp_elem, onehot_dim=self.elem_num) + temp_elem, onehot_dim=self.out_channels) (outputs, hidden), alpha = self.structure_attention_cell( hidden, fea, elem_onehots) output_hiddens.append(paddle.unsqueeze(outputs, axis=1)) @@ -128,119 +129,3 @@ class TableAttentionHead(nn.Layer): loc_preds = self.loc_generator(loc_concat) loc_preds = F.sigmoid(loc_preds) return {'structure_probs': structure_probs, 'loc_preds': loc_preds} - - -class AttentionGRUCell(nn.Layer): - def __init__(self, input_size, hidden_size, num_embeddings, use_gru=False): - super(AttentionGRUCell, self).__init__() - self.i2h = nn.Linear(input_size, hidden_size, bias_attr=False) - self.h2h = nn.Linear(hidden_size, hidden_size) - self.score = nn.Linear(hidden_size, 1, bias_attr=False) - self.rnn = nn.GRUCell( - input_size=input_size + num_embeddings, hidden_size=hidden_size) - self.hidden_size = hidden_size - - def forward(self, prev_hidden, batch_H, char_onehots): - batch_H_proj = self.i2h(batch_H) - prev_hidden_proj = paddle.unsqueeze(self.h2h(prev_hidden), axis=1) - res = paddle.add(batch_H_proj, prev_hidden_proj) - res = paddle.tanh(res) - e = self.score(res) - alpha = F.softmax(e, axis=1) - alpha = paddle.transpose(alpha, [0, 2, 1]) - context = paddle.squeeze(paddle.mm(alpha, batch_H), axis=1) - concat_context = paddle.concat([context, char_onehots], 1) - cur_hidden = self.rnn(concat_context, prev_hidden) - return cur_hidden, alpha - - -class AttentionLSTM(nn.Layer): - def __init__(self, in_channels, out_channels, hidden_size, **kwargs): - super(AttentionLSTM, self).__init__() - self.input_size = in_channels - self.hidden_size = hidden_size - self.num_classes = out_channels - - self.attention_cell = AttentionLSTMCell( - in_channels, hidden_size, out_channels, use_gru=False) - self.generator = nn.Linear(hidden_size, out_channels) - - def _char_to_onehot(self, input_char, onehot_dim): - input_ont_hot = F.one_hot(input_char, onehot_dim) - return input_ont_hot - - def forward(self, inputs, targets=None, batch_max_length=25): - batch_size = inputs.shape[0] - num_steps = batch_max_length - - hidden = (paddle.zeros((batch_size, self.hidden_size)), paddle.zeros( - (batch_size, self.hidden_size))) - output_hiddens = [] - - if targets is not None: - for i in range(num_steps): - # one-hot vectors for a i-th char - char_onehots = self._char_to_onehot( - targets[:, i], onehot_dim=self.num_classes) - hidden, alpha = self.attention_cell(hidden, inputs, - char_onehots) - - hidden = (hidden[1][0], hidden[1][1]) - output_hiddens.append(paddle.unsqueeze(hidden[0], axis=1)) - output = paddle.concat(output_hiddens, axis=1) - probs = self.generator(output) - - else: - targets = paddle.zeros(shape=[batch_size], dtype="int32") - probs = None - - for i in range(num_steps): - char_onehots = self._char_to_onehot( - targets, onehot_dim=self.num_classes) - hidden, alpha = self.attention_cell(hidden, inputs, - char_onehots) - probs_step = self.generator(hidden[0]) - hidden = (hidden[1][0], hidden[1][1]) - if probs is None: - probs = paddle.unsqueeze(probs_step, axis=1) - else: - probs = paddle.concat( - [probs, paddle.unsqueeze( - probs_step, axis=1)], axis=1) - - next_input = probs_step.argmax(axis=1) - - targets = next_input - - return probs - - -class AttentionLSTMCell(nn.Layer): - def __init__(self, input_size, hidden_size, num_embeddings, use_gru=False): - super(AttentionLSTMCell, self).__init__() - self.i2h = nn.Linear(input_size, hidden_size, bias_attr=False) - self.h2h = nn.Linear(hidden_size, hidden_size) - self.score = nn.Linear(hidden_size, 1, bias_attr=False) - if not use_gru: - self.rnn = nn.LSTMCell( - input_size=input_size + num_embeddings, hidden_size=hidden_size) - else: - self.rnn = nn.GRUCell( - input_size=input_size + num_embeddings, hidden_size=hidden_size) - - self.hidden_size = hidden_size - - def forward(self, prev_hidden, batch_H, char_onehots): - batch_H_proj = self.i2h(batch_H) - prev_hidden_proj = paddle.unsqueeze(self.h2h(prev_hidden[0]), axis=1) - res = paddle.add(batch_H_proj, prev_hidden_proj) - res = paddle.tanh(res) - e = self.score(res) - - alpha = F.softmax(e, axis=1) - alpha = paddle.transpose(alpha, [0, 2, 1]) - context = paddle.squeeze(paddle.mm(alpha, batch_H), axis=1) - concat_context = paddle.concat([context, char_onehots], 1) - cur_hidden = self.rnn(concat_context, prev_hidden) - - return cur_hidden, alpha diff --git a/ppocr/modeling/heads/table_master_head.py b/ppocr/modeling/heads/table_master_head.py new file mode 100644 index 0000000000000000000000000000000000000000..fddbcc63fcd6d5380f9fdd96f9ca85756d666442 --- /dev/null +++ b/ppocr/modeling/heads/table_master_head.py @@ -0,0 +1,281 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# 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/JiaquanYe/TableMASTER-mmocr/blob/master/mmocr/models/textrecog/decoders/master_decoder.py +""" + +import copy +import math +import paddle +from paddle import nn +from paddle.nn import functional as F + + +class TableMasterHead(nn.Layer): + """ + Split to two transformer header at the last layer. + Cls_layer is used to structure token classification. + Bbox_layer is used to regress bbox coord. + """ + + def __init__(self, + in_channels, + out_channels=30, + headers=8, + d_ff=2048, + dropout=0, + max_text_length=500, + point_num=2, + **kwargs): + super(TableMasterHead, self).__init__() + hidden_size = in_channels[-1] + self.layers = clones( + DecoderLayer(headers, hidden_size, dropout, d_ff), 2) + self.cls_layer = clones( + DecoderLayer(headers, hidden_size, dropout, d_ff), 1) + self.bbox_layer = clones( + DecoderLayer(headers, hidden_size, dropout, d_ff), 1) + self.cls_fc = nn.Linear(hidden_size, out_channels) + self.bbox_fc = nn.Sequential( + # nn.Linear(hidden_size, hidden_size), + nn.Linear(hidden_size, point_num * 2), + nn.Sigmoid()) + self.norm = nn.LayerNorm(hidden_size) + self.embedding = Embeddings(d_model=hidden_size, vocab=out_channels) + self.positional_encoding = PositionalEncoding(d_model=hidden_size) + + self.SOS = out_channels - 3 + self.PAD = out_channels - 1 + self.out_channels = out_channels + self.point_num = point_num + self.max_text_length = max_text_length + + def make_mask(self, tgt): + """ + Make mask for self attention. + :param src: [b, c, h, l_src] + :param tgt: [b, l_tgt] + :return: + """ + trg_pad_mask = (tgt != self.PAD).unsqueeze(1).unsqueeze(3) + + tgt_len = paddle.shape(tgt)[1] + trg_sub_mask = paddle.tril( + paddle.ones( + ([tgt_len, tgt_len]), dtype=paddle.float32)) + + tgt_mask = paddle.logical_and( + trg_pad_mask.astype(paddle.float32), trg_sub_mask) + return tgt_mask.astype(paddle.float32) + + def decode(self, input, feature, src_mask, tgt_mask): + # main process of transformer decoder. + x = self.embedding(input) # x: 1*x*512, feature: 1*3600,512 + x = self.positional_encoding(x) + + # origin transformer layers + for i, layer in enumerate(self.layers): + x = layer(x, feature, src_mask, tgt_mask) + + # cls head + for layer in self.cls_layer: + cls_x = layer(x, feature, src_mask, tgt_mask) + cls_x = self.norm(cls_x) + + # bbox head + for layer in self.bbox_layer: + bbox_x = layer(x, feature, src_mask, tgt_mask) + bbox_x = self.norm(bbox_x) + return self.cls_fc(cls_x), self.bbox_fc(bbox_x) + + def greedy_forward(self, SOS, feature): + input = SOS + output = paddle.zeros( + [input.shape[0], self.max_text_length + 1, self.out_channels]) + bbox_output = paddle.zeros( + [input.shape[0], self.max_text_length + 1, self.point_num * 2]) + max_text_length = paddle.to_tensor(self.max_text_length) + for i in range(max_text_length + 1): + target_mask = self.make_mask(input) + out_step, bbox_output_step = self.decode(input, feature, None, + target_mask) + prob = F.softmax(out_step, axis=-1) + next_word = prob.argmax(axis=2, dtype="int64") + input = paddle.concat( + [input, next_word[:, -1].unsqueeze(-1)], axis=1) + if i == self.max_text_length: + output = out_step + bbox_output = bbox_output_step + return output, bbox_output + + def forward_train(self, out_enc, targets): + # x is token of label + # feat is feature after backbone before pe. + # out_enc is feature after pe. + padded_targets = targets[0] + src_mask = None + tgt_mask = self.make_mask(padded_targets[:, :-1]) + output, bbox_output = self.decode(padded_targets[:, :-1], out_enc, + src_mask, tgt_mask) + return {'structure_probs': output, 'loc_preds': bbox_output} + + def forward_test(self, out_enc): + batch_size = out_enc.shape[0] + SOS = paddle.zeros([batch_size, 1], dtype='int64') + self.SOS + output, bbox_output = self.greedy_forward(SOS, out_enc) + output = F.softmax(output) + return {'structure_probs': output, 'loc_preds': bbox_output} + + def forward(self, feat, targets=None): + feat = feat[-1] + b, c, h, w = feat.shape + feat = feat.reshape([b, c, h * w]) # flatten 2D feature map + feat = feat.transpose((0, 2, 1)) + out_enc = self.positional_encoding(feat) + if self.training: + return self.forward_train(out_enc, targets) + + return self.forward_test(out_enc) + + +class DecoderLayer(nn.Layer): + """ + Decoder is made of self attention, srouce attention and feed forward. + """ + + def __init__(self, headers, d_model, dropout, d_ff): + super(DecoderLayer, self).__init__() + self.self_attn = MultiHeadAttention(headers, d_model, dropout) + self.src_attn = MultiHeadAttention(headers, d_model, dropout) + self.feed_forward = FeedForward(d_model, d_ff, dropout) + self.sublayer = clones(SubLayerConnection(d_model, dropout), 3) + + def forward(self, x, feature, src_mask, tgt_mask): + x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, tgt_mask)) + x = self.sublayer[1]( + x, lambda x: self.src_attn(x, feature, feature, src_mask)) + return self.sublayer[2](x, self.feed_forward) + + +class MultiHeadAttention(nn.Layer): + def __init__(self, headers, d_model, dropout): + super(MultiHeadAttention, self).__init__() + + assert d_model % headers == 0 + self.d_k = int(d_model / headers) + self.headers = headers + self.linears = clones(nn.Linear(d_model, d_model), 4) + self.attn = None + self.dropout = nn.Dropout(dropout) + + def forward(self, query, key, value, mask=None): + B = query.shape[0] + + # 1) Do all the linear projections in batch from d_model => h x d_k + query, key, value = \ + [l(x).reshape([B, 0, self.headers, self.d_k]).transpose([0, 2, 1, 3]) + for l, x in zip(self.linears, (query, key, value))] + # 2) Apply attention on all the projected vectors in batch + x, self.attn = self_attention( + query, key, value, mask=mask, dropout=self.dropout) + x = x.transpose([0, 2, 1, 3]).reshape([B, 0, self.headers * self.d_k]) + return self.linears[-1](x) + + +class FeedForward(nn.Layer): + def __init__(self, d_model, d_ff, dropout): + super(FeedForward, self).__init__() + self.w_1 = nn.Linear(d_model, d_ff) + self.w_2 = nn.Linear(d_ff, d_model) + self.dropout = nn.Dropout(dropout) + + def forward(self, x): + return self.w_2(self.dropout(F.relu(self.w_1(x)))) + + +class SubLayerConnection(nn.Layer): + """ + A residual connection followed by a layer norm. + Note for code simplicity the norm is first as opposed to last. + """ + + def __init__(self, size, dropout): + super(SubLayerConnection, self).__init__() + self.norm = nn.LayerNorm(size) + self.dropout = nn.Dropout(dropout) + + def forward(self, x, sublayer): + return x + self.dropout(sublayer(self.norm(x))) + + +def masked_fill(x, mask, value): + mask = mask.astype(x.dtype) + return x * paddle.logical_not(mask).astype(x.dtype) + mask * value + + +def self_attention(query, key, value, mask=None, dropout=None): + """ + Compute 'Scale Dot Product Attention' + """ + d_k = value.shape[-1] + + score = paddle.matmul(query, key.transpose([0, 1, 3, 2]) / math.sqrt(d_k)) + if mask is not None: + # score = score.masked_fill(mask == 0, -1e9) # b, h, L, L + score = masked_fill(score, mask == 0, -6.55e4) # for fp16 + + p_attn = F.softmax(score, axis=-1) + + if dropout is not None: + p_attn = dropout(p_attn) + return paddle.matmul(p_attn, value), p_attn + + +def clones(module, N): + """ Produce N identical layers """ + return nn.LayerList([copy.deepcopy(module) for _ in range(N)]) + + +class Embeddings(nn.Layer): + def __init__(self, d_model, vocab): + super(Embeddings, self).__init__() + self.lut = nn.Embedding(vocab, d_model) + self.d_model = d_model + + def forward(self, *input): + x = input[0] + return self.lut(x) * math.sqrt(self.d_model) + + +class PositionalEncoding(nn.Layer): + """ Implement the PE function. """ + + def __init__(self, d_model, dropout=0., max_len=5000): + super(PositionalEncoding, self).__init__() + self.dropout = nn.Dropout(p=dropout) + + # Compute the positional encodings once in log space. + pe = paddle.zeros([max_len, d_model]) + position = paddle.arange(0, max_len).unsqueeze(1).astype('float32') + div_term = paddle.exp( + paddle.arange(0, d_model, 2) * -math.log(10000.0) / d_model) + pe[:, 0::2] = paddle.sin(position * div_term) + pe[:, 1::2] = paddle.cos(position * div_term) + pe = pe.unsqueeze(0) + self.register_buffer('pe', pe) + + def forward(self, feat, **kwargs): + feat = feat + self.pe[:, :paddle.shape(feat)[1]] # pe 1*5000*512 + return self.dropout(feat) diff --git a/ppocr/optimizer/learning_rate.py b/ppocr/optimizer/learning_rate.py index 8e05a84011e3c463dd15a3b6bd76f24fa3ab81ef..7d45109b4857871f52764c64d6d32e5322fc7c57 100644 --- a/ppocr/optimizer/learning_rate.py +++ b/ppocr/optimizer/learning_rate.py @@ -343,3 +343,46 @@ class DecayLearningRate(object): power=self.factor, end_lr=self.end_lr) return learning_rate + + +class MultiStepDecay(object): + """ + Piecewise learning rate decay + Args: + step_each_epoch(int): steps each epoch + learning_rate (float): The initial learning rate. It is a python float number. + step_size (int): the interval to update. + gamma (float, optional): The Ratio that the learning rate will be reduced. ``new_lr = origin_lr * gamma`` . + It should be less than 1.0. Default: 0.1. + last_epoch (int, optional): The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate. + """ + + def __init__(self, + learning_rate, + milestones, + step_each_epoch, + gamma, + warmup_epoch=0, + last_epoch=-1, + **kwargs): + super(MultiStepDecay, self).__init__() + self.milestones = [step_each_epoch * e for e in milestones] + self.learning_rate = learning_rate + self.gamma = gamma + self.last_epoch = last_epoch + self.warmup_epoch = round(warmup_epoch * step_each_epoch) + + def __call__(self): + learning_rate = lr.MultiStepDecay( + learning_rate=self.learning_rate, + milestones=self.milestones, + gamma=self.gamma, + last_epoch=self.last_epoch) + if self.warmup_epoch > 0: + learning_rate = lr.LinearWarmup( + learning_rate=learning_rate, + warmup_steps=self.warmup_epoch, + start_lr=0.0, + end_lr=self.learning_rate, + last_epoch=self.last_epoch) + return learning_rate diff --git a/ppocr/postprocess/__init__.py b/ppocr/postprocess/__init__.py index 2635117c84298bcf77a77cdcab553278f2df642b..1d414eb2e8562925f461b0c6f6ce15774b81bb8f 100644 --- a/ppocr/postprocess/__init__.py +++ b/ppocr/postprocess/__init__.py @@ -26,12 +26,13 @@ from .east_postprocess import EASTPostProcess from .sast_postprocess import SASTPostProcess from .fce_postprocess import FCEPostProcess from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode, \ - DistillationCTCLabelDecode, TableLabelDecode, NRTRLabelDecode, SARLabelDecode, \ + DistillationCTCLabelDecode, NRTRLabelDecode, SARLabelDecode, \ SEEDLabelDecode, PRENLabelDecode, ViTSTRLabelDecode, ABINetLabelDecode from .cls_postprocess import ClsPostProcess from .pg_postprocess import PGPostProcess from .vqa_token_ser_layoutlm_postprocess import VQASerTokenLayoutLMPostProcess from .vqa_token_re_layoutlm_postprocess import VQAReTokenLayoutLMPostProcess +from .table_postprocess import TableMasterLabelDecode, TableLabelDecode def build_post_process(config, global_config=None): @@ -42,7 +43,8 @@ def build_post_process(config, global_config=None): 'DistillationDBPostProcess', 'NRTRLabelDecode', 'SARLabelDecode', 'SEEDLabelDecode', 'VQASerTokenLayoutLMPostProcess', 'VQAReTokenLayoutLMPostProcess', 'PRENLabelDecode', - 'DistillationSARLabelDecode', 'ViTSTRLabelDecode', 'ABINetLabelDecode' + 'DistillationSARLabelDecode', 'ViTSTRLabelDecode', 'ABINetLabelDecode', + 'TableMasterLabelDecode' ] if config['name'] == 'PSEPostProcess': diff --git a/ppocr/postprocess/rec_postprocess.py b/ppocr/postprocess/rec_postprocess.py index c77420ad19b76e203262568ba77f06ea248a1655..cc7c2cb379cc476943152507569f0b0066189c46 100644 --- a/ppocr/postprocess/rec_postprocess.py +++ b/ppocr/postprocess/rec_postprocess.py @@ -380,146 +380,6 @@ class SRNLabelDecode(BaseRecLabelDecode): return idx -class TableLabelDecode(object): - """ """ - - def __init__(self, character_dict_path, **kwargs): - list_character, list_elem = self.load_char_elem_dict( - character_dict_path) - list_character = self.add_special_char(list_character) - list_elem = self.add_special_char(list_elem) - self.dict_character = {} - self.dict_idx_character = {} - for i, char in enumerate(list_character): - self.dict_idx_character[i] = char - self.dict_character[char] = i - self.dict_elem = {} - self.dict_idx_elem = {} - for i, elem in enumerate(list_elem): - self.dict_idx_elem[i] = elem - self.dict_elem[elem] = i - - def load_char_elem_dict(self, character_dict_path): - list_character = [] - list_elem = [] - with open(character_dict_path, "rb") as fin: - lines = fin.readlines() - substr = lines[0].decode('utf-8').strip("\n").strip("\r\n").split( - "\t") - character_num = int(substr[0]) - elem_num = int(substr[1]) - for cno in range(1, 1 + character_num): - character = lines[cno].decode('utf-8').strip("\n").strip("\r\n") - list_character.append(character) - for eno in range(1 + character_num, 1 + character_num + elem_num): - elem = lines[eno].decode('utf-8').strip("\n").strip("\r\n") - list_elem.append(elem) - return list_character, list_elem - - def add_special_char(self, list_character): - self.beg_str = "sos" - self.end_str = "eos" - list_character = [self.beg_str] + list_character + [self.end_str] - return list_character - - def __call__(self, preds): - structure_probs = preds['structure_probs'] - loc_preds = preds['loc_preds'] - if isinstance(structure_probs, paddle.Tensor): - structure_probs = structure_probs.numpy() - if isinstance(loc_preds, paddle.Tensor): - loc_preds = loc_preds.numpy() - structure_idx = structure_probs.argmax(axis=2) - structure_probs = structure_probs.max(axis=2) - structure_str, structure_pos, result_score_list, result_elem_idx_list = self.decode( - structure_idx, structure_probs, 'elem') - res_html_code_list = [] - res_loc_list = [] - batch_num = len(structure_str) - for bno in range(batch_num): - res_loc = [] - for sno in range(len(structure_str[bno])): - text = structure_str[bno][sno] - if text in ['', ' 0 and tmp_elem_idx == end_idx: - break - if tmp_elem_idx in ignored_tokens: - continue - - char_list.append(current_dict[tmp_elem_idx]) - elem_pos_list.append(idx) - score_list.append(structure_probs[batch_idx, idx]) - elem_idx_list.append(tmp_elem_idx) - result_list.append(char_list) - result_pos_list.append(elem_pos_list) - result_score_list.append(score_list) - result_elem_idx_list.append(elem_idx_list) - return result_list, result_pos_list, result_score_list, result_elem_idx_list - - def get_ignored_tokens(self, char_or_elem): - beg_idx = self.get_beg_end_flag_idx("beg", char_or_elem) - end_idx = self.get_beg_end_flag_idx("end", char_or_elem) - return [beg_idx, end_idx] - - def get_beg_end_flag_idx(self, beg_or_end, char_or_elem): - if char_or_elem == "char": - if beg_or_end == "beg": - idx = self.dict_character[self.beg_str] - elif beg_or_end == "end": - idx = self.dict_character[self.end_str] - else: - assert False, "Unsupport type %s in get_beg_end_flag_idx of char" \ - % beg_or_end - elif char_or_elem == "elem": - if beg_or_end == "beg": - idx = self.dict_elem[self.beg_str] - elif beg_or_end == "end": - idx = self.dict_elem[self.end_str] - else: - assert False, "Unsupport type %s in get_beg_end_flag_idx of elem" \ - % beg_or_end - else: - assert False, "Unsupport type %s in char_or_elem" \ - % char_or_elem - return idx - - class SARLabelDecode(BaseRecLabelDecode): """ Convert between text-label and text-index """ diff --git a/ppocr/postprocess/table_postprocess.py b/ppocr/postprocess/table_postprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..4396ec4f701478e7bdcdd8c7752738c5c8ef148d --- /dev/null +++ b/ppocr/postprocess/table_postprocess.py @@ -0,0 +1,160 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# 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. + +import numpy as np +import paddle + +from .rec_postprocess import AttnLabelDecode + + +class TableLabelDecode(AttnLabelDecode): + """ """ + + def __init__(self, character_dict_path, **kwargs): + super(TableLabelDecode, self).__init__(character_dict_path) + self.td_token = ['', '', ''] + + def __call__(self, preds, batch=None): + structure_probs = preds['structure_probs'] + bbox_preds = preds['loc_preds'] + if isinstance(structure_probs, paddle.Tensor): + structure_probs = structure_probs.numpy() + if isinstance(bbox_preds, paddle.Tensor): + bbox_preds = bbox_preds.numpy() + shape_list = batch[-1] + result = self.decode(structure_probs, bbox_preds, shape_list) + if len(batch) == 1: # only contains shape + return result + + label_decode_result = self.decode_label(batch) + return result, label_decode_result + + def decode(self, structure_probs, bbox_preds, shape_list): + """convert text-label into text-index. + """ + ignored_tokens = self.get_ignored_tokens() + end_idx = self.dict[self.end_str] + + structure_idx = structure_probs.argmax(axis=2) + structure_probs = structure_probs.max(axis=2) + + structure_batch_list = [] + bbox_batch_list = [] + batch_size = len(structure_idx) + for batch_idx in range(batch_size): + structure_list = [] + bbox_list = [] + score_list = [] + for idx in range(len(structure_idx[batch_idx])): + char_idx = int(structure_idx[batch_idx][idx]) + if idx > 0 and char_idx == end_idx: + break + if char_idx in ignored_tokens: + continue + text = self.character[char_idx] + if text in self.td_token: + bbox = bbox_preds[batch_idx, idx] + bbox = self._bbox_decode(bbox, shape_list[batch_idx]) + bbox_list.append(bbox) + structure_list.append(text) + score_list.append(structure_probs[batch_idx, idx]) + structure_batch_list.append([structure_list, np.mean(score_list)]) + bbox_batch_list.append(np.array(bbox_list)) + result = { + 'bbox_batch_list': bbox_batch_list, + 'structure_batch_list': structure_batch_list, + } + return result + + def decode_label(self, batch): + """convert text-label into text-index. + """ + structure_idx = batch[1] + gt_bbox_list = batch[2] + shape_list = batch[-1] + ignored_tokens = self.get_ignored_tokens() + end_idx = self.dict[self.end_str] + + structure_batch_list = [] + bbox_batch_list = [] + batch_size = len(structure_idx) + for batch_idx in range(batch_size): + structure_list = [] + bbox_list = [] + for idx in range(len(structure_idx[batch_idx])): + char_idx = int(structure_idx[batch_idx][idx]) + if idx > 0 and char_idx == end_idx: + break + if char_idx in ignored_tokens: + continue + structure_list.append(self.character[char_idx]) + + bbox = gt_bbox_list[batch_idx][idx] + if bbox.sum() != 0: + bbox = self._bbox_decode(bbox, shape_list[batch_idx]) + bbox_list.append(bbox) + structure_batch_list.append(structure_list) + bbox_batch_list.append(bbox_list) + result = { + 'bbox_batch_list': bbox_batch_list, + 'structure_batch_list': structure_batch_list, + } + return result + + def _bbox_decode(self, bbox, shape): + h, w, ratio_h, ratio_w, pad_h, pad_w = shape + src_h = h / ratio_h + src_w = w / ratio_w + bbox[0::2] *= src_w + bbox[1::2] *= src_h + return bbox + + +class TableMasterLabelDecode(TableLabelDecode): + """ """ + + def __init__(self, character_dict_path, box_shape='ori', **kwargs): + super(TableMasterLabelDecode, self).__init__(character_dict_path) + self.box_shape = box_shape + assert box_shape in [ + 'ori', 'pad' + ], 'The shape used for box normalization must be ori or pad' + + def add_special_char(self, dict_character): + self.beg_str = '' + self.end_str = '' + self.unknown_str = '' + self.pad_str = '' + dict_character = dict_character + dict_character = dict_character + [ + self.unknown_str, self.beg_str, self.end_str, self.pad_str + ] + return dict_character + + def get_ignored_tokens(self): + pad_idx = self.dict[self.pad_str] + start_idx = self.dict[self.beg_str] + end_idx = self.dict[self.end_str] + unknown_idx = self.dict[self.unknown_str] + return [start_idx, end_idx, pad_idx, unknown_idx] + + def _bbox_decode(self, bbox, shape): + h, w, ratio_h, ratio_w, pad_h, pad_w = shape + if self.box_shape == 'pad': + h, w = pad_h, pad_w + bbox[0::2] *= w + bbox[1::2] *= h + bbox[0::2] /= ratio_w + bbox[1::2] /= ratio_h + return bbox diff --git a/ppocr/utils/dict/table_master_structure_dict.txt b/ppocr/utils/dict/table_master_structure_dict.txt new file mode 100644 index 0000000000000000000000000000000000000000..95ab2539a70aca4f695c53a38cdc1c3e164fcfb3 --- /dev/null +++ b/ppocr/utils/dict/table_master_structure_dict.txt @@ -0,0 +1,39 @@ + + + + + + + + + + + colspan="2" + colspan="3" + + + rowspan="2" + colspan="4" + colspan="6" + rowspan="3" + colspan="9" + colspan="10" + colspan="7" + rowspan="4" + rowspan="5" + rowspan="9" + colspan="8" + rowspan="8" + rowspan="6" + rowspan="7" + rowspan="10" + + + + + + + + diff --git a/ppocr/utils/dict/table_structure_dict.txt b/ppocr/utils/dict/table_structure_dict.txt index 9c4531e5f3b8c498e70d3c2ea0471e5e746a2c30..8edb10b8817ad596af6c63b6b8fc5eb2349b7464 100644 --- a/ppocr/utils/dict/table_structure_dict.txt +++ b/ppocr/utils/dict/table_structure_dict.txt @@ -1,281 +1,3 @@ -277 28 1267 1186 - -V -a -r -i -b -l -e - -H -z -d - -t -o -9 -5 -% -C -I - -p - -v -u -* -A -g -( -m -n -) -0 -. -7 -1 -6 -≤ -> -8 -3 -– -2 -G -4 -M -F -T -y -f -s -L -w -c -U -h -D -S -Q -R -x -P -- -E -O -/ -k -, -+ -N -K -q -′ -[ -] -< -≥ - -− - -μ -± -J -j -W -_ -Δ -B -“ -: -Y -α -λ -; - - -? -∼ -= -° -# -̊ -̈ -̂ -’ -Z -X -∗ -— -β -' -† -~ -@ -" -γ -↓ -↑ -& -‡ -χ -” -σ -§ -| -¶ -‐ -× -$ -→ -√ -✓ -‘ -\ -∞ -π -• -® -^ -∆ -≧ - - -́ -♀ -♂ -‒ -⁎ -▲ -· -£ -φ -Ψ -ß -△ -☆ -▪ -η -€ -∧ -̃ -Φ -ρ -̄ -δ -‰ -̧ -Ω -♦ -{ -} -̀ -∑ -∫ -ø -κ -ε -¥ -※ -` -ω -Σ -➔ -‖ -Β -̸ -
 -─ -● -⩾ -Χ -Α -⋅ -◆ -★ -■ -ψ -ǂ -□ -ζ -! -Γ -↔ -θ -⁄ -〈 -〉 -― -υ -τ -⋆ -Ø -© -∥ -С -˂ -➢ -ɛ -⁡ -✗ -← -○ -¢ -⩽ -∖ -˃ -­ -≈ -Π -̌ -≦ -∅ -ᅟ - - -∣ -¤ -♯ -̆ -ξ -÷ -▼ - -ι -ν -║ - - -◦ -​ -◊ -∙ -« -» -ł -ı -Θ -∈ -„ -∘ -✔ -̇ -æ -ʹ -ˆ -♣ -⇓ -∩ -⊕ -⇒ -⇑ -̨ -Ι -Λ -⋯ -А -⋮ @@ -303,2457 +25,4 @@ $ rowspan="8" rowspan="6" rowspan="7" - rowspan="10" -0 2924682 -1 3405345 -2 2363468 -3 2709165 -4 4078680 -5 3250792 -6 1923159 -7 1617890 -8 1450532 -9 1717624 -10 1477550 -11 1489223 -12 915528 -13 819193 -14 593660 -15 518924 -16 682065 -17 494584 -18 400591 -19 396421 -20 340994 -21 280688 -22 250328 -23 226786 -24 199927 -25 182707 -26 164629 -27 141613 -28 127554 -29 116286 -30 107682 -31 96367 -32 88002 -33 79234 -34 72186 -35 65921 -36 60374 -37 55976 -38 52166 -39 47414 -40 44932 -41 41279 -42 38232 -43 35463 -44 33703 -45 30557 -46 29639 -47 27000 -48 25447 -49 23186 -50 22093 -51 20412 -52 19844 -53 18261 -54 17561 -55 16499 -56 15597 -57 14558 -58 14372 -59 13445 -60 13514 -61 12058 -62 11145 -63 10767 -64 10370 -65 9630 -66 9337 -67 8881 -68 8727 -69 8060 -70 7994 -71 7740 -72 7189 -73 6729 -74 6749 -75 6548 -76 6321 -77 5957 -78 5740 -79 5407 -80 5370 -81 5035 -82 4921 -83 4656 -84 4600 -85 4519 -86 4277 -87 4023 -88 3939 -89 3910 -90 3861 -91 3560 -92 3483 -93 3406 -94 3346 -95 3229 -96 3122 -97 3086 -98 3001 -99 2884 -100 2822 -101 2677 -102 2670 -103 2610 -104 2452 -105 2446 -106 2400 -107 2300 -108 2316 -109 2196 -110 2089 -111 2083 -112 2041 -113 1881 -114 1838 -115 1896 -116 1795 -117 1786 -118 1743 -119 1765 -120 1750 -121 1683 -122 1563 -123 1499 -124 1513 -125 1462 -126 1388 -127 1441 -128 1417 -129 1392 -130 1306 -131 1321 -132 1274 -133 1294 -134 1240 -135 1126 -136 1157 -137 1130 -138 1084 -139 1130 -140 1083 -141 1040 -142 980 -143 1031 -144 974 -145 980 -146 932 -147 898 -148 960 -149 907 -150 852 -151 912 -152 859 -153 847 -154 876 -155 792 -156 791 -157 765 -158 788 -159 787 -160 744 -161 673 -162 683 -163 697 -164 666 -165 680 -166 632 -167 677 -168 657 -169 618 -170 587 -171 585 -172 567 -173 549 -174 562 -175 548 -176 542 -177 539 -178 542 -179 549 -180 547 -181 526 -182 525 -183 514 -184 512 -185 505 -186 515 -187 467 -188 475 -189 458 -190 435 -191 443 -192 427 -193 424 -194 404 -195 389 -196 429 -197 404 -198 386 -199 351 -200 388 -201 408 -202 361 -203 346 -204 324 -205 361 -206 363 -207 364 -208 323 -209 336 -210 342 -211 315 -212 325 -213 328 -214 314 -215 327 -216 320 -217 300 -218 295 -219 315 -220 310 -221 295 -222 275 -223 248 -224 274 -225 232 -226 293 -227 259 -228 286 -229 263 -230 242 -231 214 -232 261 -233 231 -234 211 -235 250 -236 233 -237 206 -238 224 -239 210 -240 233 -241 223 -242 216 -243 222 -244 207 -245 212 -246 196 -247 205 -248 201 -249 202 -250 211 -251 201 -252 215 -253 179 -254 163 -255 179 -256 191 -257 188 -258 196 -259 150 -260 154 -261 176 -262 211 -263 166 -264 171 -265 165 -266 149 -267 182 -268 159 -269 161 -270 164 -271 161 -272 141 -273 151 -274 127 -275 129 -276 142 -277 158 -278 148 -279 135 -280 127 -281 134 -282 138 -283 131 -284 126 -285 125 -286 130 -287 126 -288 135 -289 125 -290 135 -291 131 -292 95 -293 135 -294 106 -295 117 -296 136 -297 128 -298 128 -299 118 -300 109 -301 112 -302 117 -303 108 -304 120 -305 100 -306 95 -307 108 -308 112 -309 77 -310 120 -311 104 -312 109 -313 89 -314 98 -315 82 -316 98 -317 93 -318 77 -319 93 -320 77 -321 98 -322 93 -323 86 -324 89 -325 73 -326 70 -327 71 -328 77 -329 87 -330 77 -331 93 -332 100 -333 83 -334 72 -335 74 -336 69 -337 77 -338 68 -339 78 -340 90 -341 98 -342 75 -343 80 -344 63 -345 71 -346 83 -347 66 -348 71 -349 70 -350 62 -351 62 -352 59 -353 63 -354 62 -355 52 -356 64 -357 64 -358 56 -359 49 -360 57 -361 63 -362 60 -363 68 -364 62 -365 55 -366 54 -367 40 -368 75 -369 70 -370 53 -371 58 -372 57 -373 55 -374 69 -375 57 -376 53 -377 43 -378 45 -379 47 -380 56 -381 51 -382 59 -383 51 -384 43 -385 34 -386 57 -387 49 -388 39 -389 46 -390 48 -391 43 -392 40 -393 54 -394 50 -395 41 -396 43 -397 33 -398 27 -399 49 -400 44 -401 44 -402 38 -403 30 -404 32 -405 37 -406 39 -407 42 -408 53 -409 39 -410 34 -411 31 -412 32 -413 52 -414 27 -415 41 -416 34 -417 36 -418 50 -419 35 -420 32 -421 33 -422 45 -423 35 -424 40 -425 29 -426 41 -427 40 -428 39 -429 32 -430 31 -431 34 -432 29 -433 27 -434 26 -435 22 -436 34 -437 28 -438 30 -439 38 -440 35 -441 36 -442 36 -443 27 -444 24 -445 33 -446 31 -447 25 -448 33 -449 27 -450 32 -451 46 -452 31 -453 35 -454 35 -455 34 -456 26 -457 21 -458 25 -459 26 -460 24 -461 27 -462 33 -463 30 -464 35 -465 21 -466 32 -467 19 -468 27 -469 16 -470 28 -471 26 -472 27 -473 26 -474 25 -475 25 -476 27 -477 20 -478 28 -479 22 -480 23 -481 16 -482 25 -483 27 -484 19 -485 23 -486 19 -487 15 -488 15 -489 23 -490 24 -491 19 -492 20 -493 18 -494 17 -495 30 -496 28 -497 20 -498 29 -499 17 -500 19 -501 21 -502 15 -503 24 -504 15 -505 19 -506 25 -507 16 -508 23 -509 26 -510 21 -511 15 -512 12 -513 16 -514 18 -515 24 -516 26 -517 18 -518 8 -519 25 -520 14 -521 8 -522 24 -523 20 -524 18 -525 15 -526 13 -527 17 -528 18 -529 22 -530 21 -531 9 -532 16 -533 17 -534 13 -535 17 -536 15 -537 13 -538 20 -539 13 -540 19 -541 29 -542 10 -543 8 -544 18 -545 13 -546 9 -547 18 -548 10 -549 18 -550 18 -551 9 -552 9 -553 15 -554 13 -555 15 -556 14 -557 14 -558 18 -559 8 -560 13 -561 9 -562 7 -563 12 -564 6 -565 9 -566 9 -567 18 -568 9 -569 10 -570 13 -571 14 -572 13 -573 21 -574 8 -575 16 -576 12 -577 9 -578 16 -579 17 -580 22 -581 6 -582 14 -583 13 -584 15 -585 11 -586 13 -587 5 -588 12 -589 13 -590 15 -591 13 -592 15 -593 12 -594 7 -595 18 -596 12 -597 13 -598 13 -599 13 -600 12 -601 12 -602 10 -603 11 -604 6 -605 6 -606 2 -607 9 -608 8 -609 12 -610 9 -611 12 -612 13 -613 12 -614 14 -615 9 -616 8 -617 9 -618 14 -619 13 -620 12 -621 6 -622 8 -623 8 -624 8 -625 12 -626 8 -627 7 -628 5 -629 8 -630 12 -631 6 -632 10 -633 10 -634 7 -635 8 -636 9 -637 6 -638 9 -639 4 -640 12 -641 4 -642 3 -643 11 -644 10 -645 6 -646 12 -647 12 -648 4 -649 4 -650 9 -651 8 -652 6 -653 5 -654 14 -655 10 -656 11 -657 8 -658 5 -659 5 -660 9 -661 13 -662 4 -663 5 -664 9 -665 11 -666 12 -667 7 -668 13 -669 2 -670 1 -671 7 -672 7 -673 7 -674 10 -675 9 -676 6 -677 5 -678 7 -679 6 -680 3 -681 3 -682 4 -683 9 -684 8 -685 5 -686 3 -687 11 -688 9 -689 2 -690 6 -691 5 -692 9 -693 5 -694 6 -695 5 -696 9 -697 8 -698 3 -699 7 -700 5 -701 9 -702 8 -703 7 -704 2 -705 3 -706 7 -707 6 -708 6 -709 10 -710 2 -711 10 -712 6 -713 7 -714 5 -715 6 -716 4 -717 6 -718 8 -719 4 -720 6 -721 7 -722 5 -723 7 -724 3 -725 10 -726 10 -727 3 -728 7 -729 7 -730 5 -731 2 -732 1 -733 5 -734 1 -735 5 -736 6 -737 2 -738 2 -739 3 -740 7 -741 2 -742 7 -743 4 -744 5 -745 4 -746 5 -747 3 -748 1 -749 4 -750 4 -751 2 -752 4 -753 6 -754 6 -755 6 -756 3 -757 2 -758 5 -759 5 -760 3 -761 4 -762 2 -763 1 -764 8 -765 3 -766 4 -767 3 -768 1 -769 5 -770 3 -771 3 -772 4 -773 4 -774 1 -775 3 -776 2 -777 2 -778 3 -779 3 -780 1 -781 4 -782 3 -783 4 -784 6 -785 3 -786 5 -787 4 -788 2 -789 4 -790 5 -791 4 -792 6 -794 4 -795 1 -796 1 -797 4 -798 2 -799 3 -800 3 -801 1 -802 5 -803 5 -804 3 -805 3 -806 3 -807 4 -808 4 -809 2 -811 5 -812 4 -813 6 -814 3 -815 2 -816 2 -817 3 -818 5 -819 3 -820 1 -821 1 -822 4 -823 3 -824 4 -825 8 -826 3 -827 5 -828 5 -829 3 -830 6 -831 3 -832 4 -833 8 -834 5 -835 3 -836 3 -837 2 -838 4 -839 2 -840 1 -841 3 -842 2 -843 1 -844 3 -846 4 -847 4 -848 3 -849 3 -850 2 -851 3 -853 1 -854 4 -855 4 -856 2 -857 4 -858 1 -859 2 -860 5 -861 1 -862 1 -863 4 -864 2 -865 2 -867 5 -868 1 -869 4 -870 1 -871 1 -872 1 -873 2 -875 5 -876 3 -877 1 -878 3 -879 3 -880 3 -881 2 -882 1 -883 6 -884 2 -885 2 -886 1 -887 1 -888 3 -889 2 -890 2 -891 3 -892 1 -893 3 -894 1 -895 5 -896 1 -897 3 -899 2 -900 2 -902 1 -903 2 -904 4 -905 4 -906 3 -907 1 -908 1 -909 2 -910 5 -911 2 -912 3 -914 1 -915 1 -916 2 -918 2 -919 2 -920 4 -921 4 -922 1 -923 1 -924 4 -925 5 -926 1 -928 2 -929 1 -930 1 -931 1 -932 1 -933 1 -934 2 -935 1 -936 1 -937 1 -938 2 -939 1 -941 1 -942 4 -944 2 -945 2 -946 2 -947 1 -948 1 -950 1 -951 2 -953 1 -954 2 -955 1 -956 1 -957 2 -958 1 -960 3 -962 4 -963 1 -964 1 -965 3 -966 2 -967 2 -968 1 -969 3 -970 3 -972 1 -974 4 -975 3 -976 3 -977 2 -979 2 -980 1 -981 1 -983 5 -984 1 -985 3 -986 1 -987 2 -988 4 -989 2 -991 2 -992 2 -993 1 -994 1 -996 2 -997 2 -998 1 -999 3 -1000 2 -1001 1 -1002 3 -1003 3 -1004 2 -1005 3 -1006 1 -1007 2 -1009 1 -1011 1 -1013 3 -1014 1 -1016 2 -1017 1 -1018 1 -1019 1 -1020 4 -1021 1 -1022 2 -1025 1 -1026 1 -1027 2 -1028 1 -1030 1 -1031 2 -1032 4 -1034 3 -1035 2 -1036 1 -1038 1 -1039 1 -1040 1 -1041 1 -1042 2 -1043 1 -1044 2 -1045 4 -1048 1 -1050 1 -1051 1 -1052 2 -1054 1 -1055 3 -1056 2 -1057 1 -1059 1 -1061 2 -1063 1 -1064 1 -1065 1 -1066 1 -1067 1 -1068 1 -1069 2 -1074 1 -1075 1 -1077 1 -1078 1 -1079 1 -1082 1 -1085 1 -1088 1 -1090 1 -1091 1 -1092 2 -1094 2 -1097 2 -1098 1 -1099 2 -1101 2 -1102 1 -1104 1 -1105 1 -1107 1 -1109 1 -1111 2 -1112 1 -1114 2 -1115 2 -1116 2 -1117 1 -1118 1 -1119 1 -1120 1 -1122 1 -1123 1 -1127 1 -1128 3 -1132 2 -1138 3 -1142 1 -1145 4 -1150 1 -1153 2 -1154 1 -1158 1 -1159 1 -1163 1 -1165 1 -1169 2 -1174 1 -1176 1 -1177 1 -1178 2 -1179 1 -1180 2 -1181 1 -1182 1 -1183 2 -1185 1 -1187 1 -1191 2 -1193 1 -1195 3 -1196 1 -1201 3 -1203 1 -1206 1 -1210 1 -1213 1 -1214 1 -1215 2 -1218 1 -1220 1 -1221 1 -1225 1 -1226 1 -1233 2 -1241 1 -1243 1 -1249 1 -1250 2 -1251 1 -1254 1 -1255 2 -1260 1 -1268 1 -1270 1 -1273 1 -1274 1 -1277 1 -1284 1 -1287 1 -1291 1 -1292 2 -1294 1 -1295 2 -1297 1 -1298 1 -1301 1 -1307 1 -1308 3 -1311 2 -1313 1 -1316 1 -1321 1 -1324 1 -1325 1 -1330 1 -1333 1 -1334 1 -1338 2 -1340 1 -1341 1 -1342 1 -1343 1 -1345 1 -1355 1 -1357 1 -1360 2 -1375 1 -1376 1 -1380 1 -1383 1 -1387 1 -1389 1 -1393 1 -1394 1 -1396 1 -1398 1 -1410 1 -1414 1 -1419 1 -1425 1 -1434 1 -1435 1 -1438 1 -1439 1 -1447 1 -1455 2 -1460 1 -1461 1 -1463 1 -1466 1 -1470 1 -1473 1 -1478 1 -1480 1 -1483 1 -1484 1 -1485 2 -1492 2 -1499 1 -1509 1 -1512 1 -1513 1 -1523 1 -1524 1 -1525 2 -1529 1 -1539 1 -1544 1 -1568 1 -1584 1 -1591 1 -1598 1 -1600 1 -1604 1 -1614 1 -1617 1 -1621 1 -1622 1 -1626 1 -1638 1 -1648 1 -1658 1 -1661 1 -1679 1 -1682 1 -1693 1 -1700 1 -1705 1 -1707 1 -1722 1 -1728 1 -1758 1 -1762 1 -1763 1 -1775 1 -1776 1 -1801 1 -1810 1 -1812 1 -1827 1 -1834 1 -1846 1 -1847 1 -1848 1 -1851 1 -1862 1 -1866 1 -1877 2 -1884 1 -1888 1 -1903 1 -1912 1 -1925 1 -1938 1 -1955 1 -1998 1 -2054 1 -2058 1 -2065 1 -2069 1 -2076 1 -2089 1 -2104 1 -2111 1 -2133 1 -2138 1 -2156 1 -2204 1 -2212 1 -2237 1 -2246 2 -2298 1 -2304 1 -2360 1 -2400 1 -2481 1 -2544 1 -2586 1 -2622 1 -2666 1 -2682 1 -2725 1 -2920 1 -3997 1 -4019 1 -5211 1 -12 19 -14 1 -16 401 -18 2 -20 421 -22 557 -24 625 -26 50 -28 4481 -30 52 -32 550 -34 5840 -36 4644 -38 87 -40 5794 -41 33 -42 571 -44 11805 -46 4711 -47 7 -48 597 -49 12 -50 678 -51 2 -52 14715 -53 3 -54 7322 -55 3 -56 508 -57 39 -58 3486 -59 11 -60 8974 -61 45 -62 1276 -63 4 -64 15693 -65 15 -66 657 -67 13 -68 6409 -69 10 -70 3188 -71 25 -72 1889 -73 27 -74 10370 -75 9 -76 12432 -77 23 -78 520 -79 15 -80 1534 -81 29 -82 2944 -83 23 -84 12071 -85 36 -86 1502 -87 10 -88 10978 -89 11 -90 889 -91 16 -92 4571 -93 17 -94 7855 -95 21 -96 2271 -97 33 -98 1423 -99 15 -100 11096 -101 21 -102 4082 -103 13 -104 5442 -105 25 -106 2113 -107 26 -108 3779 -109 43 -110 1294 -111 29 -112 7860 -113 29 -114 4965 -115 22 -116 7898 -117 25 -118 1772 -119 28 -120 1149 -121 38 -122 1483 -123 32 -124 10572 -125 25 -126 1147 -127 31 -128 1699 -129 22 -130 5533 -131 22 -132 4669 -133 34 -134 3777 -135 10 -136 5412 -137 21 -138 855 -139 26 -140 2485 -141 46 -142 1970 -143 27 -144 6565 -145 40 -146 933 -147 15 -148 7923 -149 16 -150 735 -151 23 -152 1111 -153 33 -154 3714 -155 27 -156 2445 -157 30 -158 3367 -159 10 -160 4646 -161 27 -162 990 -163 23 -164 5679 -165 25 -166 2186 -167 17 -168 899 -169 32 -170 1034 -171 22 -172 6185 -173 32 -174 2685 -175 17 -176 1354 -177 38 -178 1460 -179 15 -180 3478 -181 20 -182 958 -183 20 -184 6055 -185 23 -186 2180 -187 15 -188 1416 -189 30 -190 1284 -191 22 -192 1341 -193 21 -194 2413 -195 18 -196 4984 -197 13 -198 830 -199 22 -200 1834 -201 19 -202 2238 -203 9 -204 3050 -205 22 -206 616 -207 17 -208 2892 -209 22 -210 711 -211 30 -212 2631 -213 19 -214 3341 -215 21 -216 987 -217 26 -218 823 -219 9 -220 3588 -221 20 -222 692 -223 7 -224 2925 -225 31 -226 1075 -227 16 -228 2909 -229 18 -230 673 -231 20 -232 2215 -233 14 -234 1584 -235 21 -236 1292 -237 29 -238 1647 -239 25 -240 1014 -241 30 -242 1648 -243 19 -244 4465 -245 10 -246 787 -247 11 -248 480 -249 25 -250 842 -251 15 -252 1219 -253 23 -254 1508 -255 8 -256 3525 -257 16 -258 490 -259 12 -260 1678 -261 14 -262 822 -263 16 -264 1729 -265 28 -266 604 -267 11 -268 2572 -269 7 -270 1242 -271 15 -272 725 -273 18 -274 1983 -275 13 -276 1662 -277 19 -278 491 -279 12 -280 1586 -281 14 -282 563 -283 10 -284 2363 -285 10 -286 656 -287 14 -288 725 -289 28 -290 871 -291 9 -292 2606 -293 12 -294 961 -295 9 -296 478 -297 13 -298 1252 -299 10 -300 736 -301 19 -302 466 -303 13 -304 2254 -305 12 -306 486 -307 14 -308 1145 -309 13 -310 955 -311 13 -312 1235 -313 13 -314 931 -315 14 -316 1768 -317 11 -318 330 -319 10 -320 539 -321 23 -322 570 -323 12 -324 1789 -325 13 -326 884 -327 5 -328 1422 -329 14 -330 317 -331 11 -332 509 -333 13 -334 1062 -335 12 -336 577 -337 27 -338 378 -339 10 -340 2313 -341 9 -342 391 -343 13 -344 894 -345 17 -346 664 -347 9 -348 453 -349 6 -350 363 -351 15 -352 1115 -353 13 -354 1054 -355 8 -356 1108 -357 12 -358 354 -359 7 -360 363 -361 16 -362 344 -363 11 -364 1734 -365 12 -366 265 -367 10 -368 969 -369 16 -370 316 -371 12 -372 757 -373 7 -374 563 -375 15 -376 857 -377 9 -378 469 -379 9 -380 385 -381 12 -382 921 -383 15 -384 764 -385 14 -386 246 -387 6 -388 1108 -389 14 -390 230 -391 8 -392 266 -393 11 -394 641 -395 8 -396 719 -397 9 -398 243 -399 4 -400 1108 -401 7 -402 229 -403 7 -404 903 -405 7 -406 257 -407 12 -408 244 -409 3 -410 541 -411 6 -412 744 -413 8 -414 419 -415 8 -416 388 -417 19 -418 470 -419 14 -420 612 -421 6 -422 342 -423 3 -424 1179 -425 3 -426 116 -427 14 -428 207 -429 6 -430 255 -431 4 -432 288 -433 12 -434 343 -435 6 -436 1015 -437 3 -438 538 -439 10 -440 194 -441 6 -442 188 -443 15 -444 524 -445 7 -446 214 -447 7 -448 574 -449 6 -450 214 -451 5 -452 635 -453 9 -454 464 -455 5 -456 205 -457 9 -458 163 -459 2 -460 558 -461 4 -462 171 -463 14 -464 444 -465 11 -466 543 -467 5 -468 388 -469 6 -470 141 -471 4 -472 647 -473 3 -474 210 -475 4 -476 193 -477 7 -478 195 -479 7 -480 443 -481 10 -482 198 -483 3 -484 816 -485 6 -486 128 -487 9 -488 215 -489 9 -490 328 -491 7 -492 158 -493 11 -494 335 -495 8 -496 435 -497 6 -498 174 -499 1 -500 373 -501 5 -502 140 -503 7 -504 330 -505 9 -506 149 -507 5 -508 642 -509 3 -510 179 -511 3 -512 159 -513 8 -514 204 -515 7 -516 306 -517 4 -518 110 -519 5 -520 326 -521 6 -522 305 -523 6 -524 294 -525 7 -526 268 -527 5 -528 149 -529 4 -530 133 -531 2 -532 513 -533 10 -534 116 -535 5 -536 258 -537 4 -538 113 -539 4 -540 138 -541 6 -542 116 -544 485 -545 4 -546 93 -547 9 -548 299 -549 3 -550 256 -551 6 -552 92 -553 3 -554 175 -555 6 -556 253 -557 7 -558 95 -559 2 -560 128 -561 4 -562 206 -563 2 -564 465 -565 3 -566 69 -567 3 -568 157 -569 7 -570 97 -571 8 -572 118 -573 5 -574 130 -575 4 -576 301 -577 6 -578 177 -579 2 -580 397 -581 3 -582 80 -583 1 -584 128 -585 5 -586 52 -587 2 -588 72 -589 1 -590 84 -591 6 -592 323 -593 11 -594 77 -595 5 -596 205 -597 1 -598 244 -599 4 -600 69 -601 3 -602 89 -603 5 -604 254 -605 6 -606 147 -607 3 -608 83 -609 3 -610 77 -611 3 -612 194 -613 1 -614 98 -615 3 -616 243 -617 3 -618 50 -619 8 -620 188 -621 4 -622 67 -623 4 -624 123 -625 2 -626 50 -627 1 -628 239 -629 2 -630 51 -631 4 -632 65 -633 5 -634 188 -636 81 -637 3 -638 46 -639 3 -640 103 -641 1 -642 136 -643 3 -644 188 -645 3 -646 58 -648 122 -649 4 -650 47 -651 2 -652 155 -653 4 -654 71 -655 1 -656 71 -657 3 -658 50 -659 2 -660 177 -661 5 -662 66 -663 2 -664 183 -665 3 -666 50 -667 2 -668 53 -669 2 -670 115 -672 66 -673 2 -674 47 -675 1 -676 197 -677 2 -678 46 -679 3 -680 95 -681 3 -682 46 -683 3 -684 107 -685 1 -686 86 -687 2 -688 158 -689 4 -690 51 -691 1 -692 80 -694 56 -695 4 -696 40 -698 43 -699 3 -700 95 -701 2 -702 51 -703 2 -704 133 -705 1 -706 100 -707 2 -708 121 -709 2 -710 15 -711 3 -712 35 -713 2 -714 20 -715 3 -716 37 -717 2 -718 78 -720 55 -721 1 -722 42 -723 2 -724 218 -725 3 -726 23 -727 2 -728 26 -729 1 -730 64 -731 2 -732 65 -734 24 -735 2 -736 53 -737 1 -738 32 -739 1 -740 60 -742 81 -743 1 -744 77 -745 1 -746 47 -747 1 -748 62 -749 1 -750 19 -751 1 -752 86 -753 3 -754 40 -756 55 -757 2 -758 38 -759 1 -760 101 -761 1 -762 22 -764 67 -765 2 -766 35 -767 1 -768 38 -769 1 -770 22 -771 1 -772 82 -773 1 -774 73 -776 29 -777 1 -778 55 -780 23 -781 1 -782 16 -784 84 -785 3 -786 28 -788 59 -789 1 -790 33 -791 3 -792 24 -794 13 -795 1 -796 110 -797 2 -798 15 -800 22 -801 3 -802 29 -803 1 -804 87 -806 21 -808 29 -810 48 -812 28 -813 1 -814 58 -815 1 -816 48 -817 1 -818 31 -819 1 -820 66 -822 17 -823 2 -824 58 -826 10 -827 2 -828 25 -829 1 -830 29 -831 1 -832 63 -833 1 -834 26 -835 3 -836 52 -837 1 -838 18 -840 27 -841 2 -842 12 -843 1 -844 83 -845 1 -846 7 -847 1 -848 10 -850 26 -852 25 -853 1 -854 15 -856 27 -858 32 -859 1 -860 15 -862 43 -864 32 -865 1 -866 6 -868 39 -870 11 -872 25 -873 1 -874 10 -875 1 -876 20 -877 2 -878 19 -879 1 -880 30 -882 11 -884 53 -886 25 -887 1 -888 28 -890 6 -892 36 -894 10 -896 13 -898 14 -900 31 -902 14 -903 2 -904 43 -906 25 -908 9 -910 11 -911 1 -912 16 -913 1 -914 24 -916 27 -918 6 -920 15 -922 27 -923 1 -924 23 -926 13 -928 42 -929 1 -930 3 -932 27 -934 17 -936 8 -937 1 -938 11 -940 33 -942 4 -943 1 -944 18 -946 15 -948 13 -950 18 -952 12 -954 11 -956 21 -958 10 -960 13 -962 5 -964 32 -966 13 -968 8 -970 8 -971 1 -972 23 -973 2 -974 12 -975 1 -976 22 -978 7 -979 1 -980 14 -982 8 -984 22 -985 1 -986 6 -988 17 -989 1 -990 6 -992 13 -994 19 -996 11 -998 4 -1000 9 -1002 2 -1004 14 -1006 5 -1008 3 -1010 9 -1012 29 -1014 6 -1016 22 -1017 1 -1018 8 -1019 1 -1020 7 -1022 6 -1023 1 -1024 10 -1026 2 -1028 8 -1030 11 -1031 2 -1032 8 -1034 9 -1036 13 -1038 12 -1040 12 -1042 3 -1044 12 -1046 3 -1048 11 -1050 2 -1051 1 -1052 2 -1054 11 -1056 6 -1058 8 -1059 1 -1060 23 -1062 6 -1063 1 -1064 8 -1066 3 -1068 6 -1070 8 -1071 1 -1072 5 -1074 3 -1076 5 -1078 3 -1080 11 -1081 1 -1082 7 -1084 18 -1086 4 -1087 1 -1088 3 -1090 3 -1092 7 -1094 3 -1096 12 -1098 6 -1099 1 -1100 2 -1102 6 -1104 14 -1106 3 -1108 6 -1110 5 -1112 2 -1114 8 -1116 3 -1118 3 -1120 7 -1122 10 -1124 6 -1126 8 -1128 1 -1130 4 -1132 3 -1134 2 -1136 5 -1138 5 -1140 8 -1142 3 -1144 7 -1146 3 -1148 11 -1150 1 -1152 5 -1154 1 -1156 5 -1158 1 -1160 5 -1162 3 -1164 6 -1165 1 -1166 1 -1168 4 -1169 1 -1170 3 -1171 1 -1172 2 -1174 5 -1176 3 -1177 1 -1180 8 -1182 2 -1184 4 -1186 2 -1188 3 -1190 2 -1192 5 -1194 6 -1196 1 -1198 2 -1200 2 -1204 10 -1206 2 -1208 9 -1210 1 -1214 6 -1216 3 -1218 4 -1220 9 -1221 2 -1222 1 -1224 5 -1226 4 -1228 8 -1230 1 -1232 1 -1234 3 -1236 5 -1240 3 -1242 1 -1244 3 -1245 1 -1246 4 -1248 6 -1250 2 -1252 7 -1256 3 -1258 2 -1260 2 -1262 3 -1264 4 -1265 1 -1266 1 -1270 1 -1271 1 -1272 2 -1274 3 -1276 3 -1278 1 -1280 3 -1284 1 -1286 1 -1290 1 -1292 3 -1294 1 -1296 7 -1300 2 -1302 4 -1304 3 -1306 2 -1308 2 -1312 1 -1314 1 -1316 3 -1318 2 -1320 1 -1324 8 -1326 1 -1330 1 -1331 1 -1336 2 -1338 1 -1340 3 -1341 1 -1344 1 -1346 2 -1347 1 -1348 3 -1352 1 -1354 2 -1356 1 -1358 1 -1360 3 -1362 1 -1364 4 -1366 1 -1370 1 -1372 3 -1380 2 -1384 2 -1388 2 -1390 2 -1392 2 -1394 1 -1396 1 -1398 1 -1400 2 -1402 1 -1404 1 -1406 1 -1410 1 -1412 5 -1418 1 -1420 1 -1424 1 -1432 2 -1434 2 -1442 3 -1444 5 -1448 1 -1454 1 -1456 1 -1460 3 -1462 4 -1468 1 -1474 1 -1476 1 -1478 2 -1480 1 -1486 2 -1488 1 -1492 1 -1496 1 -1500 3 -1503 1 -1506 1 -1512 2 -1516 1 -1522 1 -1524 2 -1534 4 -1536 1 -1538 1 -1540 2 -1544 2 -1548 1 -1556 1 -1560 1 -1562 1 -1564 2 -1566 1 -1568 1 -1570 1 -1572 1 -1576 1 -1590 1 -1594 1 -1604 1 -1608 1 -1614 1 -1622 1 -1624 2 -1628 1 -1629 1 -1636 1 -1642 1 -1654 2 -1660 1 -1664 1 -1670 1 -1684 4 -1698 1 -1732 3 -1742 1 -1752 1 -1760 1 -1764 1 -1772 2 -1798 1 -1808 1 -1820 1 -1852 1 -1856 1 -1874 1 -1902 1 -1908 1 -1952 1 -2004 1 -2018 1 -2020 1 -2028 1 -2174 1 -2233 1 -2244 1 -2280 1 -2290 1 -2352 1 -2604 1 -4190 1 + rowspan="10" \ No newline at end of file diff --git a/ppocr/utils/visual.py b/ppocr/utils/visual.py index 235eb572a3975b4446ae2f2c9ad9c8558d5c5ad8..e0fbf06abb471c294cb268520fb99bca1a6b1d61 100644 --- a/ppocr/utils/visual.py +++ b/ppocr/utils/visual.py @@ -11,6 +11,7 @@ # 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. +import cv2 import os import numpy as np from PIL import Image, ImageDraw, ImageFont @@ -110,3 +111,16 @@ def draw_re_results(image, img_new = Image.blend(image, img_new, 0.5) return np.array(img_new) + + +def draw_rectangle(img_path, boxes, use_xywh=False): + img = cv2.imread(img_path) + img_show = img.copy() + for box in boxes.astype(int): + if use_xywh: + x, y, w, h = box + x1, y1, x2, y2 = x - w // 2, y - h // 2, x + w // 2, y + h // 2 + else: + x1, y1, x2, y2 = box + cv2.rectangle(img_show, (x1, y1), (x2, y2), (255, 0, 0), 2) + return img_show \ No newline at end of file diff --git a/ppstructure/docs/models_list.md b/ppstructure/docs/models_list.md index dabce3a5149a88833d38a4395e31ac1f82306c4f..42d44009dad1ba1b07bb410c199993c6f79f3d5d 100644 --- a/ppstructure/docs/models_list.md +++ b/ppstructure/docs/models_list.md @@ -35,7 +35,7 @@ |模型名称|模型简介|推理模型大小|下载地址| | --- | --- | --- | --- | -|en_ppocr_mobile_v2.0_table_structure|PubLayNet数据集训练的英文表格场景的表格结构预测|18.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_structure_train.tar) | +|en_ppocr_mobile_v2.0_table_structure|PubTabNet数据集训练的英文表格场景的表格结构预测|18.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_structure_train.tar) | ## 3. VQA模型 diff --git a/ppstructure/table/predict_structure.py b/ppstructure/table/predict_structure.py index 0179c614ae4864677576f6073f291282fb772988..00385c76658ebd65843e351fda7da8807561d518 100755 --- a/ppstructure/table/predict_structure.py +++ b/ppstructure/table/predict_structure.py @@ -23,43 +23,63 @@ os.environ["FLAGS_allocator_strategy"] = 'auto_growth' import cv2 import numpy as np import time +import json import tools.infer.utility as utility from ppocr.data import create_operators, transform from ppocr.postprocess import build_post_process from ppocr.utils.logging import get_logger from ppocr.utils.utility import get_image_file_list, check_and_read_gif +from ppocr.utils.visual import draw_rectangle from ppstructure.utility import parse_args logger = get_logger() +def build_pre_process_list(args): + resize_op = {'ResizeTableImage': {'max_len': args.table_max_len, }} + pad_op = { + 'PaddingTableImage': { + 'size': [args.table_max_len, args.table_max_len] + } + } + normalize_op = { + 'NormalizeImage': { + 'std': [0.229, 0.224, 0.225] if + args.table_algorithm not in ['TableMaster'] else [0.5, 0.5, 0.5], + 'mean': [0.485, 0.456, 0.406] if + args.table_algorithm not in ['TableMaster'] else [0.5, 0.5, 0.5], + 'scale': '1./255.', + 'order': 'hwc' + } + } + to_chw_op = {'ToCHWImage': None} + keep_keys_op = {'KeepKeys': {'keep_keys': ['image', 'shape']}} + if args.table_algorithm not in ['TableMaster']: + pre_process_list = [ + resize_op, normalize_op, pad_op, to_chw_op, keep_keys_op + ] + else: + pre_process_list = [ + resize_op, pad_op, normalize_op, to_chw_op, keep_keys_op + ] + return pre_process_list + + class TableStructurer(object): def __init__(self, args): - pre_process_list = [{ - 'ResizeTableImage': { - 'max_len': args.table_max_len - } - }, { - 'NormalizeImage': { - 'std': [0.229, 0.224, 0.225], - 'mean': [0.485, 0.456, 0.406], - 'scale': '1./255.', - 'order': 'hwc' + pre_process_list = build_pre_process_list(args) + if args.table_algorithm not in ['TableMaster']: + postprocess_params = { + 'name': 'TableLabelDecode', + "character_dict_path": args.table_char_dict_path, } - }, { - 'PaddingTableImage': None - }, { - 'ToCHWImage': None - }, { - 'KeepKeys': { - 'keep_keys': ['image'] + else: + postprocess_params = { + 'name': 'TableMasterLabelDecode', + "character_dict_path": args.table_char_dict_path, + 'box_shape': 'pad' } - }] - postprocess_params = { - 'name': 'TableLabelDecode', - "character_dict_path": args.table_char_dict_path, - } self.preprocess_op = create_operators(pre_process_list) self.postprocess_op = build_post_process(postprocess_params) @@ -88,27 +108,17 @@ class TableStructurer(object): preds['structure_probs'] = outputs[1] preds['loc_preds'] = outputs[0] - post_result = self.postprocess_op(preds) - - structure_str_list = post_result['structure_str_list'] - res_loc = post_result['res_loc'] - imgh, imgw = ori_im.shape[0:2] - res_loc_final = [] - for rno in range(len(res_loc[0])): - x0, y0, x1, y1 = res_loc[0][rno] - left = max(int(imgw * x0), 0) - top = max(int(imgh * y0), 0) - right = min(int(imgw * x1), imgw - 1) - bottom = min(int(imgh * y1), imgh - 1) - res_loc_final.append([left, top, right, bottom]) - - structure_str_list = structure_str_list[0][:-1] + shape_list = np.expand_dims(data[-1], axis=0) + post_result = self.postprocess_op(preds, [shape_list]) + + structure_str_list = post_result['structure_batch_list'][0] + bbox_list = post_result['bbox_batch_list'][0] + structure_str_list = structure_str_list[0] structure_str_list = [ '', '', '' ] + structure_str_list + ['
', '', ''] - elapse = time.time() - starttime - return (structure_str_list, res_loc_final), elapse + return structure_str_list, bbox_list, elapse def main(args): @@ -116,21 +126,35 @@ def main(args): table_structurer = TableStructurer(args) count = 0 total_time = 0 - for image_file in image_file_list: - img, flag = check_and_read_gif(image_file) - if not flag: - img = cv2.imread(image_file) - if img is None: - logger.info("error in loading image:{}".format(image_file)) - continue - structure_res, elapse = table_structurer(img) - - logger.info("result: {}".format(structure_res)) - - if count > 0: - total_time += elapse - count += 1 - logger.info("Predict time of {}: {}".format(image_file, elapse)) + use_xywh = args.table_algorithm in ['TableMaster'] + os.makedirs(args.output, exist_ok=True) + with open( + os.path.join(args.output, 'infer.txt'), mode='w', + encoding='utf-8') as f_w: + for image_file in image_file_list: + img, flag = check_and_read_gif(image_file) + if not flag: + img = cv2.imread(image_file) + if img is None: + logger.info("error in loading image:{}".format(image_file)) + continue + structure_str_list, bbox_list, elapse = table_structurer(img) + + bbox_list_str = json.dumps(bbox_list.tolist()) + logger.info("result: {}, {}".format(structure_str_list, + bbox_list_str)) + f_w.write("result: {}, {}\n".format(structure_str_list, + bbox_list_str)) + + img = draw_rectangle(image_file, bbox_list, use_xywh) + img_save_path = os.path.join(args.output, + os.path.basename(image_file)) + cv2.imwrite(img_save_path, img) + logger.info("save vis result to {}".format(img_save_path)) + if count > 0: + total_time += elapse + count += 1 + logger.info("Predict time of {}: {}".format(image_file, elapse)) if __name__ == "__main__": diff --git a/ppstructure/utility.py b/ppstructure/utility.py index 4ae56099b83a46c85ce2dc362c1c6417b324dbe1..af0616239b167ff9ca5f6e1222015d51338d6bab 100644 --- a/ppstructure/utility.py +++ b/ppstructure/utility.py @@ -25,6 +25,7 @@ def init_args(): parser.add_argument("--output", type=str, default='./output') # params for table structure parser.add_argument("--table_max_len", type=int, default=488) + parser.add_argument("--table_algorithm", type=str, default='TableAttn') parser.add_argument("--table_model_dir", type=str) parser.add_argument( "--table_char_dict_path", diff --git a/tools/export_model.py b/tools/export_model.py index 65573cf46a9d650b8f833fdec43235de57faf5ac..afecbff8cbb834a5aa5ef3ea1448cf04fbd8c3bb 100755 --- a/tools/export_model.py +++ b/tools/export_model.py @@ -126,6 +126,8 @@ def export_single_model(model, infer_shape[-1] = 100 elif arch_config["model_type"] == "table": infer_shape = [3, 488, 488] + if arch_config["algorithm"] == "TableMaster": + infer_shape = [3, 480, 480] model = to_static( model, input_spec=[ diff --git a/tools/infer_table.py b/tools/infer_table.py index 66c2da4421a313c634d27eb7a1013638a7c005ed..6c02dd8640c9345c267e56d6e5a0c14bde121b7e 100644 --- a/tools/infer_table.py +++ b/tools/infer_table.py @@ -36,10 +36,12 @@ from ppocr.modeling.architectures import build_model from ppocr.postprocess import build_post_process from ppocr.utils.save_load import load_model from ppocr.utils.utility import get_image_file_list +from ppocr.utils.visual import draw_rectangle import tools.program as program import cv2 +@paddle.no_grad() def main(config, device, logger, vdl_writer): global_config = config['Global'] @@ -53,53 +55,61 @@ def main(config, device, logger, vdl_writer): getattr(post_process_class, 'character')) model = build_model(config['Architecture']) + algorithm = config['Architecture']['algorithm'] + use_xywh = algorithm in ['TableMaster'] load_model(config, model) # create data ops transforms = [] - use_padding = False for op in config['Eval']['dataset']['transforms']: op_name = list(op)[0] - if 'Label' in op_name: + if 'Encode' in op_name: continue if op_name == 'KeepKeys': - op[op_name]['keep_keys'] = ['image'] - if op_name == "ResizeTableImage": - use_padding = True - padding_max_len = op['ResizeTableImage']['max_len'] + op[op_name]['keep_keys'] = ['image', 'shape'] transforms.append(op) global_config['infer_mode'] = True ops = create_operators(transforms, global_config) + save_res_path = config['Global']['save_res_path'] + os.makedirs(save_res_path, exist_ok=True) + model.eval() - for file in get_image_file_list(config['Global']['infer_img']): - logger.info("infer_img: {}".format(file)) - with open(file, 'rb') as f: - img = f.read() - data = {'image': img} - batch = transform(data, ops) - images = np.expand_dims(batch[0], axis=0) - images = paddle.to_tensor(images) - preds = model(images) - post_result = post_process_class(preds) - res_html_code = post_result['res_html_code'] - res_loc = post_result['res_loc'] - img = cv2.imread(file) - imgh, imgw = img.shape[0:2] - res_loc_final = [] - for rno in range(len(res_loc[0])): - x0, y0, x1, y1 = res_loc[0][rno] - left = max(int(imgw * x0), 0) - top = max(int(imgh * y0), 0) - right = min(int(imgw * x1), imgw - 1) - bottom = min(int(imgh * y1), imgh - 1) - cv2.rectangle(img, (left, top), (right, bottom), (0, 0, 255), 2) - res_loc_final.append([left, top, right, bottom]) - res_loc_str = json.dumps(res_loc_final) - logger.info("result: {}, {}".format(res_html_code, res_loc_final)) - logger.info("success!") + with open( + os.path.join(save_res_path, 'infer.txt'), mode='w', + encoding='utf-8') as f_w: + for file in get_image_file_list(config['Global']['infer_img']): + logger.info("infer_img: {}".format(file)) + with open(file, 'rb') as f: + img = f.read() + data = {'image': img} + batch = transform(data, ops) + images = np.expand_dims(batch[0], axis=0) + shape_list = np.expand_dims(batch[1], axis=0) + + images = paddle.to_tensor(images) + preds = model(images) + post_result = post_process_class(preds, [shape_list]) + + structure_str_list = post_result['structure_batch_list'][0] + bbox_list = post_result['bbox_batch_list'][0] + structure_str_list = structure_str_list[0] + structure_str_list = [ + '', '', '' + ] + structure_str_list + ['
', '', ''] + bbox_list_str = json.dumps(bbox_list.tolist()) + + logger.info("result: {}, {}".format(structure_str_list, + bbox_list_str)) + f_w.write("result: {}, {}\n".format(structure_str_list, + bbox_list_str)) + + img = draw_rectangle(file, bbox_list, use_xywh) + cv2.imwrite( + os.path.join(save_res_path, os.path.basename(file)), img) + logger.info("success!") if __name__ == '__main__': diff --git a/tools/program.py b/tools/program.py index 7bd54ba083b912bd489efb1a763a8169685f2d9a..1d83b46216ad62d59e7123c1b2d590d2a1aae5ac 100755 --- a/tools/program.py +++ b/tools/program.py @@ -281,8 +281,11 @@ def train(config, if cal_metric_during_train and epoch % calc_epoch_interval == 0: # only rec and cls need batch = [item.numpy() for item in batch] - if model_type in ['table', 'kie']: + if model_type in ['kie']: eval_class(preds, batch) + elif model_type in ['table']: + post_result = post_process_class(preds, batch) + eval_class(post_result, batch) else: if config['Loss']['name'] in ['MultiLoss', 'MultiLoss_v2' ]: # for multi head loss @@ -463,7 +466,6 @@ def eval(model, preds = model(batch) else: preds = model(images) - batch_numpy = [] for item in batch: if isinstance(item, paddle.Tensor): @@ -473,9 +475,9 @@ def eval(model, # Obtain usable results from post-processing methods total_time += time.time() - start # Evaluate the results of the current batch - if model_type in ['table', 'kie']: + if model_type in ['kie']: eval_class(preds, batch_numpy) - elif model_type in ['vqa']: + elif model_type in ['table', 'vqa']: post_result = post_process_class(preds, batch_numpy) eval_class(post_result, batch_numpy) else: @@ -577,7 +579,7 @@ def preprocess(is_train=False): 'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN', 'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR', 'PSE', 'SEED', 'SDMGR', 'LayoutXLM', 'LayoutLM', 'LayoutLMv2', 'PREN', 'FCE', - 'SVTR', 'ViTSTR', 'ABINet', 'DB++' + 'SVTR', 'ViTSTR', 'ABINet', 'DB++', 'TableMaster' ] if use_xpu: