rec_times;
RunRecModel(boxes, srcimg, rec_predictor, rec_text, rec_text_score,
- charactor_dict, cls_predictor, use_direction_classify, &rec_times);
+ charactor_dict, cls_predictor, use_direction_classify, &rec_times, rec_image_height);
//// visualization
auto img_vis = Visualization(srcimg, boxes);
diff --git a/doc/doc_ch/algorithm.md b/doc/doc_ch/algorithm.md
index 3056f35d5260812686447367f7cbddc1e1cad531..d50a5aa4e80336036424bddace9579db98c699c3 100644
--- a/doc/doc_ch/algorithm.md
+++ b/doc/doc_ch/algorithm.md
@@ -5,9 +5,10 @@ PaddleOCR将**持续新增**支持OCR领域前沿算法与模型,已支持的
- [文本检测算法](./algorithm_overview.md#11-%E6%96%87%E6%9C%AC%E6%A3%80%E6%B5%8B%E7%AE%97%E6%B3%95)
- [文本识别算法](./algorithm_overview.md#12-%E6%96%87%E6%9C%AC%E8%AF%86%E5%88%AB%E7%AE%97%E6%B3%95)
- [端到端算法](./algorithm_overview.md#2-%E6%96%87%E6%9C%AC%E8%AF%86%E5%88%AB%E7%AE%97%E6%B3%95)
+- [表格识别]](./algorithm_overview.md#3-%E8%A1%A8%E6%A0%BC%E8%AF%86%E5%88%AB%E7%AE%97%E6%B3%95)
**欢迎广大开发者合作共建,贡献更多算法,合入有奖🎁!具体可查看[社区常规赛](https://github.com/PaddlePaddle/PaddleOCR/issues/4982)。**
新增算法可参考如下教程:
-- [使用PaddleOCR架构添加新算法](./add_new_algorithm.md)
\ No newline at end of file
+- [使用PaddleOCR架构添加新算法](./add_new_algorithm.md)
diff --git a/doc/doc_ch/algorithm_det_db.md b/doc/doc_ch/algorithm_det_db.md
index 90837c2ac1ebbc04ee47cbb74ed6466352710e88..5401132061e507773ae77be49555ba754d1cba15 100644
--- a/doc/doc_ch/algorithm_det_db.md
+++ b/doc/doc_ch/algorithm_det_db.md
@@ -1,4 +1,4 @@
-# DB
+# DB与DB++
- [1. 算法简介](#1)
- [2. 环境配置](#2)
@@ -21,12 +21,24 @@
> Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang
> AAAI, 2020
+> [Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion](https://arxiv.org/abs/2202.10304)
+> Liao, Minghui and Zou, Zhisheng and Wan, Zhaoyi and Yao, Cong and Bai, Xiang
+> TPAMI, 2022
+
+
在ICDAR2015文本检测公开数据集上,算法复现效果如下:
|模型|骨干网络|配置文件|precision|recall|Hmean|下载链接|
| --- | --- | --- | --- | --- | --- | --- |
|DB|ResNet50_vd|[configs/det/det_r50_vd_db.yml](../../configs/det/det_r50_vd_db.yml)|86.41%|78.72%|82.38%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
|DB|MobileNetV3|[configs/det/det_mv3_db.yml](../../configs/det/det_mv3_db.yml)|77.29%|73.08%|75.12%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
+|DB++|ResNet50|[configs/det/det_r50_db++_icdar15.yml](../../configs/det/det_r50_db++_icdar15.yml)|90.89%|82.66%|86.58%|[合成数据预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/ResNet50_dcn_asf_synthtext_pretrained.pdparams)/[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_db%2B%2B_icdar15_train.tar)|
+
+在TD_TR文本检测公开数据集上,算法复现效果如下:
+
+|模型|骨干网络|配置文件|precision|recall|Hmean|下载链接|
+| --- | --- | --- | --- | --- | --- | --- |
+|DB++|ResNet50|[configs/det/det_r50_db++_td_tr.yml](../../configs/det/det_r50_db++_td_tr.yml)|92.92%|86.48%|89.58%|[合成数据预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/ResNet50_dcn_asf_synthtext_pretrained.pdparams)/[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_db%2B%2B_td_tr_train.tar)|
@@ -54,7 +66,7 @@ python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.pretrai
DB文本检测模型推理,可以执行如下命令:
```shell
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_db/"
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_db/" --det_algorithm="DB"
```
可视化文本检测结果默认保存到`./inference_results`文件夹里面,结果文件的名称前缀为'det_res'。结果示例如下:
@@ -96,4 +108,12 @@ DB模型还支持以下推理部署方式:
pages={11474--11481},
year={2020}
}
-```
\ No newline at end of file
+
+@article{liao2022real,
+ title={Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion},
+ author={Liao, Minghui and Zou, Zhisheng and Wan, Zhaoyi and Yao, Cong and Bai, Xiang},
+ journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
+ year={2022},
+ publisher={IEEE}
+}
+```
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 eb81e4cd6dae2542dd07d0e25fe543419f798c9b..5c7adc715f1a5e728d9320c62dc15c578d9f18bf 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)
-- [2. 端到端算法](#2-端到端算法)
+- [1. 两阶段算法](#1)
+ - [1.1 文本检测算法](#11)
+ - [1.2 文本识别算法](#12)
+- [2. 端到端算法](#2)
+- [3. 表格识别算法](#3)
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)。
@@ -86,8 +87,9 @@
|SAR|Resnet31| 87.20% | rec_r31_sar | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_sar_train.tar) |
|SEED|Aster_Resnet| 85.35% | rec_resnet_stn_bilstm_att | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) |
|SVTR|SVTR-Tiny| 89.25% | rec_svtr_tiny_none_ctc_en | [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_tiny_none_ctc_en_train.tar) |
-|ViTSTR|ViTSTR| 79.82% | rec_vitstr_none_ce_en | [训练模型](https://paddleocr.bj.bcebos.com/rec_vitstr_none_ce_train.tar) |
-|ABINet|Resnet45| 90.75% | rec_r45_abinet_en | [训练模型](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar) |
+|ViTSTR|ViTSTR| 79.82% | rec_vitstr_none_ce | [训练模型](https://paddleocr.bj.bcebos.com/rec_vitstr_none_ce_train.tar) |
+|ABINet|Resnet45| 90.75% | rec_r45_abinet | [训练模型](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar) |
+
@@ -95,3 +97,16 @@
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_ch/dataset/ocr_datasets.md b/doc/doc_ch/dataset/ocr_datasets.md
index c6ff2e170f7c30a29e98ed2b1349cae2b84cf441..b7666fd63e6f17b734a17e2d11a0c8614d225964 100644
--- a/doc/doc_ch/dataset/ocr_datasets.md
+++ b/doc/doc_ch/dataset/ocr_datasets.md
@@ -34,6 +34,7 @@ json.dumps编码前的图像标注信息是包含多个字典的list,字典中
| ICDAR 2015 |https://rrc.cvc.uab.es/?ch=4&com=downloads| [train](https://paddleocr.bj.bcebos.com/dataset/train_icdar2015_label.txt) / [test](https://paddleocr.bj.bcebos.com/dataset/test_icdar2015_label.txt) |
| ctw1500 |https://paddleocr.bj.bcebos.com/dataset/ctw1500.zip| 图片下载地址中已包含 |
| total text |https://paddleocr.bj.bcebos.com/dataset/total_text.tar| 图片下载地址中已包含 |
+| td tr |https://paddleocr.bj.bcebos.com/dataset/TD_TR.tar| 图片下载地址中已包含 |
#### 1.2.1 ICDAR 2015
ICDAR 2015 数据集包含1000张训练图像和500张测试图像。ICDAR 2015 数据集可以从上表中链接下载,首次下载需注册。
diff --git a/doc/doc_ch/inference_ppocr.md b/doc/doc_ch/inference_ppocr.md
index 472c0003f963d6f65fa2d0babbf6b9c7d0ec9b80..622ac995d37ce290ee51af06164b0c2aef8b5a14 100644
--- a/doc/doc_ch/inference_ppocr.md
+++ b/doc/doc_ch/inference_ppocr.md
@@ -7,7 +7,8 @@
- [1. 文本检测模型推理](#1-文本检测模型推理)
- [2. 文本识别模型推理](#2-文本识别模型推理)
- [2.1 超轻量中文识别模型推理](#21-超轻量中文识别模型推理)
- - [2.2 多语言模型的推理](#22-多语言模型的推理)
+ - [2.2 英文识别模型推理](#22-英文识别模型推理)
+ - [2.3 多语言模型的推理](#23-多语言模型的推理)
- [3. 方向分类模型推理](#3-方向分类模型推理)
- [4. 文本检测、方向分类和文字识别串联推理](#4-文本检测方向分类和文字识别串联推理)
@@ -78,9 +79,29 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg"
Predicts of ./doc/imgs_words/ch/word_4.jpg:('实力活力', 0.9956803321838379)
```
+
+
+### 2.2 英文识别模型推理
+
+英文识别模型推理,可以执行如下命令, 注意修改字典路径:
+
+```
+# 下载英文数字识别模型:
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar
+tar xf en_PP-OCRv3_det_infer.tar
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./en_PP-OCRv3_det_infer/" --rec_char_dict_path="ppocr/utils/en_dict.txt"
+```
+
+![](../imgs_words/en/word_1.png)
+
+执行命令后,上图的预测结果为:
+
+```
+Predicts of ./doc/imgs_words/en/word_1.png: ('JOINT', 0.998160719871521)
+```
-### 2.2 多语言模型的推理
+### 2.3 多语言模型的推理
如果您需要预测的是其他语言模型,可以在[此链接](./models_list.md#%E5%A4%9A%E8%AF%AD%E8%A8%80%E8%AF%86%E5%88%AB%E6%A8%A1%E5%9E%8B)中找到对应语言的inference模型,在使用inference模型预测时,需要通过`--rec_char_dict_path`指定使用的字典路径, 同时为了得到正确的可视化结果,需要通过 `--vis_font_path` 指定可视化的字体路径,`doc/fonts/` 路径下有默认提供的小语种字体,例如韩文识别:
```
diff --git a/doc/doc_en/algorithm_en.md b/doc/doc_en/algorithm_en.md
index fa7887eb2681271f2b02296516221b00f9cf4626..c880336b4ad528eab2cce479edf11fce0b43f435 100644
--- a/doc/doc_en/algorithm_en.md
+++ b/doc/doc_en/algorithm_en.md
@@ -6,5 +6,6 @@ PaddleOCR will add cutting-edge OCR algorithms and models continuously. Check ou
- [text detection algorithms](./algorithm_overview_en.md#11)
- [text recognition algorithms](./algorithm_overview_en.md#12)
- [end-to-end algorithms](./algorithm_overview_en.md#2)
+- [table recognition algorithms](./algorithm_overview_en.md#3)
-Developers are welcome to contribute more algorithms! Please refer to [add new algorithm](./add_new_algorithm_en.md) guideline.
\ No newline at end of file
+Developers are welcome to contribute more algorithms! Please refer to [add new algorithm](./add_new_algorithm_en.md) guideline.
diff --git a/doc/doc_en/algorithm_overview_en.md b/doc/doc_en/algorithm_overview_en.md
index 28aca7c0d171008156104fbcc786707538fd49ef..f3c96b620c94c3b5f795b6117a7c6bcfcfa43b7a 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)
+ - [1.1 Text Detection Algorithms](#11)
+ - [1.2 Text Recognition Algorithms](#12)
- [2. End-to-end Algorithms](#2)
+- [3. Table Recognition Algorithms](#3)
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).
@@ -85,8 +86,9 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r
|SAR|Resnet31| 87.20% | rec_r31_sar | [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_sar_train.tar) |
|SEED|Aster_Resnet| 85.35% | rec_resnet_stn_bilstm_att | [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) |
|SVTR|SVTR-Tiny| 89.25% | rec_svtr_tiny_none_ctc_en | [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_tiny_none_ctc_en_train.tar) |
-|ViTSTR|ViTSTR| 79.82% | rec_vitstr_none_ce_en | [trained model](https://paddleocr.bj.bcebos.com/rec_vitstr_none_none_train.tar) |
-|ABINet|Resnet45| 90.75% | rec_r45_abinet_en | [trained model](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar) |
+|ViTSTR|ViTSTR| 79.82% | rec_vitstr_none_ce | [trained model](https://paddleocr.bj.bcebos.com/rec_vitstr_none_none_train.tar) |
+|ABINet|Resnet45| 90.75% | rec_r45_abinet | [trained model](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar) |
+
@@ -94,3 +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/doc/doc_en/inference_ppocr_en.md b/doc/doc_en/inference_ppocr_en.md
index 935f92f5144f582630a45edcc886b609ecdc82da..0f57b0ba6b226c19ecb1e0b60afdfa34302b8e78 100755
--- a/doc/doc_en/inference_ppocr_en.md
+++ b/doc/doc_en/inference_ppocr_en.md
@@ -8,7 +8,8 @@ This article introduces the use of the Python inference engine for the PP-OCR mo
- [Text Detection Model Inference](#text-detection-model-inference)
- [Text Recognition Model Inference](#text-recognition-model-inference)
- [1. Lightweight Chinese Recognition Model Inference](#1-lightweight-chinese-recognition-model-inference)
- - [2. Multilingual Model Inference](#2-multilingual-model-inference)
+ - [2. English Recognition Model Inference](#2-english-recognition-model-inference)
+ - [3. Multilingual Model Inference](#3-multilingual-model-inference)
- [Angle Classification Model Inference](#angle-classification-model-inference)
- [Text Detection Angle Classification and Recognition Inference Concatenation](#text-detection-angle-classification-and-recognition-inference-concatenation)
@@ -76,10 +77,31 @@ After executing the command, the prediction results (recognized text and score)
```bash
Predicts of ./doc/imgs_words_en/word_10.png:('PAIN', 0.988671)
```
+
+### 2. English Recognition Model Inference
-
+For English recognition model inference, you can execute the following commands,you need to specify the dictionary path used by `--rec_char_dict_path`:
-### 2. Multilingual Model Inference
+```
+# download en model:
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar
+tar xf en_PP-OCRv3_det_infer.tar
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./en_PP-OCRv3_det_infer/" --rec_char_dict_path="ppocr/utils/en_dict.txt"
+```
+
+![](../imgs_words/en/word_1.png)
+
+
+After executing the command, the prediction result of the above figure is:
+
+```
+Predicts of ./doc/imgs_words/en/word_1.png: ('JOINT', 0.998160719871521)
+```
+
+
+
+
+### 3. Multilingual Model Inference
If you need to predict [other language models](./models_list_en.md#Multilingual), when using inference model prediction, you need to specify the dictionary path used by `--rec_char_dict_path`. At the same time, in order to get the correct visualization results,
You need to specify the visual font path through `--vis_font_path`. There are small language fonts provided by default under the `doc/fonts` path, such as Korean recognition:
diff --git a/ppocr/data/imaug/__init__.py b/ppocr/data/imaug/__init__.py
index 63dfda91f8d0eb200d3c635fda43670039375784..d82176282839bf76d34ed8a60d5e2e13ac6bbce6 100644
--- a/ppocr/data/imaug/__init__.py
+++ b/ppocr/data/imaug/__init__.py
@@ -23,9 +23,10 @@ from .random_crop_data import EastRandomCropData, RandomCropImgMask
from .make_pse_gt import MakePseGt
-from .rec_img_aug import RecAug, RecConAug, RecResizeImg, ClsResizeImg, \
- SRNRecResizeImg, GrayRecResizeImg, SARRecResizeImg, PRENResizeImg, \
- ABINetRecResizeImg, SVTRRecResizeImg, ABINetRecAug
+
+from .rec_img_aug import BaseDataAugmentation, RecAug, RecConAug, RecResizeImg, ClsResizeImg, \
+ SRNRecResizeImg, GrayRecResizeImg, SARRecResizeImg, PRENResizeImg, \
+ ABINetRecResizeImg, SVTRRecResizeImg, ABINetRecAug
from .ssl_img_aug import SSLRotateResize
from .randaugment import RandAugment
from .copy_paste import CopyPaste
@@ -36,7 +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 312d6dc9ad25bfa73aa9009f932fe6f3d3ca7644..a4087d53287fcd57f9c4992ba712c700f33b9981 100644
--- a/ppocr/data/imaug/label_ops.py
+++ b/ppocr/data/imaug/label_ops.py
@@ -259,15 +259,26 @@ class E2ELabelEncodeTrain(object):
class KieLabelEncode(object):
- def __init__(self, character_dict_path, norm=10, directed=False, **kwargs):
+ def __init__(self,
+ character_dict_path,
+ class_path,
+ norm=10,
+ directed=False,
+ **kwargs):
super(KieLabelEncode, self).__init__()
self.dict = dict({'': 0})
+ self.label2classid_map = dict()
with open(character_dict_path, 'r', encoding='utf-8') as fr:
idx = 1
for line in fr:
char = line.strip()
self.dict[char] = idx
idx += 1
+ with open(class_path, "r") as fin:
+ lines = fin.readlines()
+ for idx, line in enumerate(lines):
+ line = line.strip("\n")
+ self.label2classid_map[line] = idx
self.norm = norm
self.directed = directed
@@ -408,7 +419,7 @@ class KieLabelEncode(object):
text_ind = [self.dict[c] for c in text if c in self.dict]
text_inds.append(text_ind)
if 'label' in ann.keys():
- labels.append(ann['label'])
+ labels.append(self.label2classid_map[ann['label']])
elif 'key_cls' in ann.keys():
labels.append(ann['key_cls'])
else:
@@ -551,171 +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):
@@ -819,6 +869,7 @@ class VQATokenLabelEncode(object):
contains_re=False,
add_special_ids=False,
algorithm='LayoutXLM',
+ use_textline_bbox_info=True,
infer_mode=False,
ocr_engine=None,
**kwargs):
@@ -847,11 +898,51 @@ class VQATokenLabelEncode(object):
self.add_special_ids = add_special_ids
self.infer_mode = infer_mode
self.ocr_engine = ocr_engine
+ self.use_textline_bbox_info = use_textline_bbox_info
+
+ def split_bbox(self, bbox, text, tokenizer):
+ words = text.split()
+ token_bboxes = []
+ curr_word_idx = 0
+ x1, y1, x2, y2 = bbox
+ unit_w = (x2 - x1) / len(text)
+ for idx, word in enumerate(words):
+ curr_w = len(word) * unit_w
+ word_bbox = [x1, y1, x1 + curr_w, y2]
+ token_bboxes.extend([word_bbox] * len(tokenizer.tokenize(word)))
+ x1 += (len(word) + 1) * unit_w
+ return token_bboxes
+
+ def filter_empty_contents(self, ocr_info):
+ """
+ find out the empty texts and remove the links
+ """
+ new_ocr_info = []
+ empty_index = []
+ for idx, info in enumerate(ocr_info):
+ if len(info["transcription"]) > 0:
+ new_ocr_info.append(copy.deepcopy(info))
+ else:
+ empty_index.append(info["id"])
+
+ for idx, info in enumerate(new_ocr_info):
+ new_link = []
+ for link in info["linking"]:
+ if link[0] in empty_index or link[1] in empty_index:
+ continue
+ new_link.append(link)
+ new_ocr_info[idx]["linking"] = new_link
+ return new_ocr_info
def __call__(self, data):
# load bbox and label info
ocr_info = self._load_ocr_info(data)
+ # for re
+ train_re = self.contains_re and not self.infer_mode
+ if train_re:
+ ocr_info = self.filter_empty_contents(ocr_info)
+
height, width, _ = data['image'].shape
words_list = []
@@ -863,8 +954,6 @@ class VQATokenLabelEncode(object):
entities = []
- # for re
- train_re = self.contains_re and not self.infer_mode
if train_re:
relations = []
id2label = {}
@@ -874,17 +963,19 @@ class VQATokenLabelEncode(object):
data['ocr_info'] = copy.deepcopy(ocr_info)
for info in ocr_info:
+ text = info["transcription"]
+ if len(text) <= 0:
+ continue
if train_re:
# for re
- if len(info["text"]) == 0:
+ if len(text) == 0:
empty_entity.add(info["id"])
continue
id2label[info["id"]] = info["label"]
relations.extend([tuple(sorted(l)) for l in info["linking"]])
# smooth_box
- bbox = self._smooth_box(info["bbox"], height, width)
+ info["bbox"] = self.trans_poly_to_bbox(info["points"])
- text = info["text"]
encode_res = self.tokenizer.encode(
text, pad_to_max_seq_len=False, return_attention_mask=True)
@@ -895,6 +986,19 @@ class VQATokenLabelEncode(object):
-1]
encode_res["attention_mask"] = encode_res["attention_mask"][1:
-1]
+
+ if self.use_textline_bbox_info:
+ bbox = [info["bbox"]] * len(encode_res["input_ids"])
+ else:
+ bbox = self.split_bbox(info["bbox"], info["transcription"],
+ self.tokenizer)
+ if len(bbox) <= 0:
+ continue
+ bbox = self._smooth_box(bbox, height, width)
+ if self.add_special_ids:
+ bbox.insert(0, [0, 0, 0, 0])
+ bbox.append([0, 0, 0, 0])
+
# parse label
if not self.infer_mode:
label = info['label']
@@ -919,7 +1023,7 @@ class VQATokenLabelEncode(object):
})
input_ids_list.extend(encode_res["input_ids"])
token_type_ids_list.extend(encode_res["token_type_ids"])
- bbox_list.extend([bbox] * len(encode_res["input_ids"]))
+ bbox_list.extend(bbox)
words_list.append(text)
segment_offset_id.append(len(input_ids_list))
if not self.infer_mode:
@@ -944,40 +1048,42 @@ class VQATokenLabelEncode(object):
data['entity_id_to_index_map'] = entity_id_to_index_map
return data
- def _load_ocr_info(self, data):
- def trans_poly_to_bbox(poly):
- x1 = np.min([p[0] for p in poly])
- x2 = np.max([p[0] for p in poly])
- y1 = np.min([p[1] for p in poly])
- y2 = np.max([p[1] for p in poly])
- return [x1, y1, x2, y2]
+ def trans_poly_to_bbox(self, poly):
+ x1 = np.min([p[0] for p in poly])
+ x2 = np.max([p[0] for p in poly])
+ y1 = np.min([p[1] for p in poly])
+ y2 = np.max([p[1] for p in poly])
+ return [x1, y1, x2, y2]
+ def _load_ocr_info(self, data):
if self.infer_mode:
ocr_result = self.ocr_engine.ocr(data['image'], cls=False)
ocr_info = []
for res in ocr_result:
ocr_info.append({
- "text": res[1][0],
- "bbox": trans_poly_to_bbox(res[0]),
- "poly": res[0],
+ "transcription": res[1][0],
+ "bbox": self.trans_poly_to_bbox(res[0]),
+ "points": res[0],
})
return ocr_info
else:
info = data['label']
# read text info
info_dict = json.loads(info)
- return info_dict["ocr_info"]
+ return info_dict
- def _smooth_box(self, bbox, height, width):
- bbox[0] = int(bbox[0] * 1000.0 / width)
- bbox[2] = int(bbox[2] * 1000.0 / width)
- bbox[1] = int(bbox[1] * 1000.0 / height)
- bbox[3] = int(bbox[3] * 1000.0 / height)
- return bbox
+ def _smooth_box(self, bboxes, height, width):
+ bboxes = np.array(bboxes)
+ bboxes[:, 0] = bboxes[:, 0] * 1000 / width
+ bboxes[:, 2] = bboxes[:, 2] * 1000 / width
+ bboxes[:, 1] = bboxes[:, 1] * 1000 / height
+ bboxes[:, 3] = bboxes[:, 3] * 1000 / height
+ bboxes = bboxes.astype("int64").tolist()
+ return bboxes
def _parse_label(self, label, encode_res):
gt_label = []
- if label.lower() == "other":
+ if label.lower() in ["other", "others", "ignore"]:
gt_label.extend([0] * len(encode_res["input_ids"]))
else:
gt_label.append(self.label2id_map[("b-" + label).upper()])
@@ -1001,7 +1107,6 @@ 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/operators.py b/ppocr/data/imaug/operators.py
index 5397d71ccb466235e64f85e1eb9365ba03d2aa17..04cc2848fb4d25baaf553c6eda235ddb0e86511f 100644
--- a/ppocr/data/imaug/operators.py
+++ b/ppocr/data/imaug/operators.py
@@ -205,9 +205,12 @@ class DetResizeForTest(object):
def __init__(self, **kwargs):
super(DetResizeForTest, self).__init__()
self.resize_type = 0
+ self.keep_ratio = False
if 'image_shape' in kwargs:
self.image_shape = kwargs['image_shape']
self.resize_type = 1
+ if 'keep_ratio' in kwargs:
+ self.keep_ratio = kwargs['keep_ratio']
elif 'limit_side_len' in kwargs:
self.limit_side_len = kwargs['limit_side_len']
self.limit_type = kwargs.get('limit_type', 'min')
@@ -237,6 +240,10 @@ class DetResizeForTest(object):
def resize_image_type1(self, img):
resize_h, resize_w = self.image_shape
ori_h, ori_w = img.shape[:2] # (h, w, c)
+ if self.keep_ratio is True:
+ resize_w = ori_w * resize_h / ori_h
+ N = math.ceil(resize_w / 32)
+ resize_w = N * 32
ratio_h = float(resize_h) / ori_h
ratio_w = float(resize_w) / ori_w
img = cv2.resize(img, (int(resize_w), int(resize_h)))
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/imaug/vqa/__init__.py b/ppocr/data/imaug/vqa/__init__.py
index a5025e7985198e7ee40d6c92d8e1814eb1797032..bde175115536a3f644750260082204fe5f10dc05 100644
--- a/ppocr/data/imaug/vqa/__init__.py
+++ b/ppocr/data/imaug/vqa/__init__.py
@@ -13,7 +13,12 @@
# limitations under the License.
from .token import VQATokenPad, VQASerTokenChunk, VQAReTokenChunk, VQAReTokenRelation
+from .augment import DistortBBox
__all__ = [
- 'VQATokenPad', 'VQASerTokenChunk', 'VQAReTokenChunk', 'VQAReTokenRelation'
+ 'VQATokenPad',
+ 'VQASerTokenChunk',
+ 'VQAReTokenChunk',
+ 'VQAReTokenRelation',
+ 'DistortBBox',
]
diff --git a/ppocr/data/imaug/vqa/augment.py b/ppocr/data/imaug/vqa/augment.py
new file mode 100644
index 0000000000000000000000000000000000000000..fcdc9685e9855c3a2d8e9f6f5add270f95f15a6c
--- /dev/null
+++ b/ppocr/data/imaug/vqa/augment.py
@@ -0,0 +1,37 @@
+# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import sys
+import numpy as np
+import random
+
+
+class DistortBBox:
+ def __init__(self, prob=0.5, max_scale=1, **kwargs):
+ """Random distort bbox
+ """
+ self.prob = prob
+ self.max_scale = max_scale
+
+ def __call__(self, data):
+ if random.random() > self.prob:
+ return data
+ bbox = np.array(data['bbox'])
+ rnd_scale = (np.random.rand(*bbox.shape) - 0.5) * 2 * self.max_scale
+ bbox = np.round(bbox + rnd_scale).astype(bbox.dtype)
+ data['bbox'] = np.clip(data['bbox'], 0, 1000)
+ data['bbox'] = bbox.tolist()
+ sys.stdout.flush()
+ return data
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/basic_loss.py b/ppocr/losses/basic_loss.py
index 2df96ea2642d10a50eb892d738f89318dc5e0f4c..74490791c2af0be54dab8ab30ac323790fcac657 100644
--- a/ppocr/losses/basic_loss.py
+++ b/ppocr/losses/basic_loss.py
@@ -57,17 +57,24 @@ class CELoss(nn.Layer):
class KLJSLoss(object):
def __init__(self, mode='kl'):
assert mode in ['kl', 'js', 'KL', 'JS'
- ], "mode can only be one of ['kl', 'js', 'KL', 'JS']"
+ ], "mode can only be one of ['kl', 'KL', 'js', 'JS']"
self.mode = mode
def __call__(self, p1, p2, reduction="mean"):
- loss = paddle.multiply(p2, paddle.log((p2 + 1e-5) / (p1 + 1e-5) + 1e-5))
-
- if self.mode.lower() == "js":
+ if self.mode.lower() == 'kl':
+ loss = paddle.multiply(p2, paddle.log((p2 + 1e-5) / (p1 + 1e-5) + 1e-5))
+ loss += paddle.multiply(
+ p1, paddle.log((p1 + 1e-5) / (p2 + 1e-5) + 1e-5))
+ loss *= 0.5
+ elif self.mode.lower() == "js":
+ loss = paddle.multiply(p2, paddle.log((2*p2 + 1e-5) / (p1 + p2 + 1e-5) + 1e-5))
loss += paddle.multiply(
- p1, paddle.log((p1 + 1e-5) / (p2 + 1e-5) + 1e-5))
+ p1, paddle.log((2*p1 + 1e-5) / (p1 + p2 + 1e-5) + 1e-5))
loss *= 0.5
+ else:
+ raise ValueError("The mode.lower() if KLJSLoss should be one of ['kl', 'js']")
+
if reduction == "mean":
loss = paddle.mean(loss, axis=[1, 2])
elif reduction == "none" or reduction is None:
@@ -95,7 +102,7 @@ class DMLLoss(nn.Layer):
self.act = None
self.use_log = use_log
- self.jskl_loss = KLJSLoss(mode="js")
+ self.jskl_loss = KLJSLoss(mode="kl")
def _kldiv(self, x, target):
eps = 1.0e-10
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/losses/vqa_token_layoutlm_loss.py b/ppocr/losses/vqa_token_layoutlm_loss.py
index 244893d97d0e422c5ca270bdece689e13aba2b07..f9cd4634731a26dd990d6ffac3d8defc8cdf7e97 100755
--- a/ppocr/losses/vqa_token_layoutlm_loss.py
+++ b/ppocr/losses/vqa_token_layoutlm_loss.py
@@ -27,8 +27,8 @@ class VQASerTokenLayoutLMLoss(nn.Layer):
self.ignore_index = self.loss_class.ignore_index
def forward(self, predicts, batch):
- labels = batch[1]
- attention_mask = batch[4]
+ labels = batch[5]
+ attention_mask = batch[2]
if attention_mask is not None:
active_loss = attention_mask.reshape([-1, ]) == 1
active_outputs = predicts.reshape(
diff --git a/ppocr/metrics/eval_det_iou.py b/ppocr/metrics/eval_det_iou.py
index bc05e7df7d1d21abfb9d9fbd224ecd7254d9f393..c144886b3f84a458a88931d6beb2153054eba7d0 100644
--- a/ppocr/metrics/eval_det_iou.py
+++ b/ppocr/metrics/eval_det_iou.py
@@ -83,14 +83,10 @@ class DetectionIoUEvaluator(object):
evaluationLog = ""
- # print(len(gt))
for n in range(len(gt)):
points = gt[n]['points']
- # transcription = gt[n]['text']
dontCare = gt[n]['ignore']
- # points = Polygon(points)
- # points = points.buffer(0)
- if not Polygon(points).is_valid or not Polygon(points).is_simple:
+ if not Polygon(points).is_valid:
continue
gtPol = points
@@ -105,9 +101,7 @@ class DetectionIoUEvaluator(object):
for n in range(len(pred)):
points = pred[n]['points']
- # points = Polygon(points)
- # points = points.buffer(0)
- if not Polygon(points).is_valid or not Polygon(points).is_simple:
+ if not Polygon(points).is_valid:
continue
detPol = points
@@ -191,8 +185,6 @@ class DetectionIoUEvaluator(object):
methodHmean = 0 if methodRecall + methodPrecision == 0 else 2 * \
methodRecall * methodPrecision / (
methodRecall + methodPrecision)
- # print(methodRecall, methodPrecision, methodHmean)
- # sys.exit(-1)
methodMetrics = {
'precision': methodPrecision,
'recall': methodRecall,
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/metrics/vqa_token_re_metric.py b/ppocr/metrics/vqa_token_re_metric.py
index 8a13bc081298284194d365933cd67d5633957ee8..f84387d8beb729bcc4b420ceea24a5e9b2993c64 100644
--- a/ppocr/metrics/vqa_token_re_metric.py
+++ b/ppocr/metrics/vqa_token_re_metric.py
@@ -37,23 +37,26 @@ class VQAReTokenMetric(object):
gt_relations = []
for b in range(len(self.relations_list)):
rel_sent = []
- for head, tail in zip(self.relations_list[b]["head"],
- self.relations_list[b]["tail"]):
- rel = {}
- rel["head_id"] = head
- rel["head"] = (self.entities_list[b]["start"][rel["head_id"]],
- self.entities_list[b]["end"][rel["head_id"]])
- rel["head_type"] = self.entities_list[b]["label"][rel[
- "head_id"]]
-
- rel["tail_id"] = tail
- rel["tail"] = (self.entities_list[b]["start"][rel["tail_id"]],
- self.entities_list[b]["end"][rel["tail_id"]])
- rel["tail_type"] = self.entities_list[b]["label"][rel[
- "tail_id"]]
-
- rel["type"] = 1
- rel_sent.append(rel)
+ if "head" in self.relations_list[b]:
+ for head, tail in zip(self.relations_list[b]["head"],
+ self.relations_list[b]["tail"]):
+ rel = {}
+ rel["head_id"] = head
+ rel["head"] = (
+ self.entities_list[b]["start"][rel["head_id"]],
+ self.entities_list[b]["end"][rel["head_id"]])
+ rel["head_type"] = self.entities_list[b]["label"][rel[
+ "head_id"]]
+
+ rel["tail_id"] = tail
+ rel["tail"] = (
+ self.entities_list[b]["start"][rel["tail_id"]],
+ self.entities_list[b]["end"][rel["tail_id"]])
+ rel["tail_type"] = self.entities_list[b]["label"][rel[
+ "tail_id"]]
+
+ rel["type"] = 1
+ rel_sent.append(rel)
gt_relations.append(rel_sent)
re_metrics = self.re_score(
self.pred_relations_list, gt_relations, mode="boundaries")
diff --git a/ppocr/modeling/backbones/__init__.py b/ppocr/modeling/backbones/__init__.py
index f8959e263ecffb301dff227ff22e5e913375f919..f4094d796b1f14c955e5962936e86bd6b3f5ec78 100755
--- a/ppocr/modeling/backbones/__init__.py
+++ b/ppocr/modeling/backbones/__init__.py
@@ -18,9 +18,13 @@ __all__ = ["build_backbone"]
def build_backbone(config, model_type):
if model_type == "det" or model_type == "table":
from .det_mobilenet_v3 import MobileNetV3
- from .det_resnet_vd import ResNet
+ from .det_resnet import ResNet
+ from .det_resnet_vd import ResNet_vd
from .det_resnet_vd_sast import ResNet_SAST
- support_dict = ["MobileNetV3", "ResNet", "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/det_resnet.py b/ppocr/modeling/backbones/det_resnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..87eef11cf0e33c24c0f539c8074b21f589345282
--- /dev/null
+++ b/ppocr/modeling/backbones/det_resnet.py
@@ -0,0 +1,236 @@
+# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import paddle
+from paddle import ParamAttr
+import paddle.nn as nn
+import paddle.nn.functional as F
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
+from paddle.nn.initializer import Uniform
+
+import math
+
+from paddle.vision.ops import DeformConv2D
+from paddle.regularizer import L2Decay
+from paddle.nn.initializer import Normal, Constant, XavierUniform
+from .det_resnet_vd import DeformableConvV2, ConvBNLayer
+
+
+class BottleneckBlock(nn.Layer):
+ def __init__(self,
+ num_channels,
+ num_filters,
+ stride,
+ shortcut=True,
+ is_dcn=False):
+ super(BottleneckBlock, self).__init__()
+
+ self.conv0 = ConvBNLayer(
+ in_channels=num_channels,
+ out_channels=num_filters,
+ kernel_size=1,
+ act="relu", )
+ self.conv1 = ConvBNLayer(
+ in_channels=num_filters,
+ out_channels=num_filters,
+ kernel_size=3,
+ stride=stride,
+ act="relu",
+ is_dcn=is_dcn,
+ dcn_groups=1, )
+ self.conv2 = ConvBNLayer(
+ in_channels=num_filters,
+ out_channels=num_filters * 4,
+ kernel_size=1,
+ act=None, )
+
+ if not shortcut:
+ self.short = ConvBNLayer(
+ in_channels=num_channels,
+ out_channels=num_filters * 4,
+ kernel_size=1,
+ stride=stride, )
+
+ self.shortcut = shortcut
+
+ self._num_channels_out = num_filters * 4
+
+ def forward(self, inputs):
+ y = self.conv0(inputs)
+ conv1 = self.conv1(y)
+ conv2 = self.conv2(conv1)
+
+ if self.shortcut:
+ short = inputs
+ else:
+ short = self.short(inputs)
+
+ y = paddle.add(x=short, y=conv2)
+ y = F.relu(y)
+ return y
+
+
+class BasicBlock(nn.Layer):
+ def __init__(self,
+ num_channels,
+ num_filters,
+ stride,
+ shortcut=True,
+ name=None):
+ super(BasicBlock, self).__init__()
+ self.stride = stride
+ self.conv0 = ConvBNLayer(
+ in_channels=num_channels,
+ out_channels=num_filters,
+ kernel_size=3,
+ stride=stride,
+ act="relu")
+ self.conv1 = ConvBNLayer(
+ in_channels=num_filters,
+ out_channels=num_filters,
+ kernel_size=3,
+ act=None)
+
+ if not shortcut:
+ self.short = ConvBNLayer(
+ in_channels=num_channels,
+ out_channels=num_filters,
+ kernel_size=1,
+ stride=stride)
+
+ self.shortcut = shortcut
+
+ def forward(self, inputs):
+ y = self.conv0(inputs)
+ conv1 = self.conv1(y)
+
+ if self.shortcut:
+ short = inputs
+ else:
+ short = self.short(inputs)
+ y = paddle.add(x=short, y=conv1)
+ y = F.relu(y)
+ return y
+
+
+class ResNet(nn.Layer):
+ def __init__(self,
+ in_channels=3,
+ layers=50,
+ out_indices=None,
+ dcn_stage=None):
+ super(ResNet, self).__init__()
+
+ self.layers = layers
+ self.input_image_channel = in_channels
+
+ supported_layers = [18, 34, 50, 101, 152]
+ assert layers in supported_layers, \
+ "supported layers are {} but input layer is {}".format(
+ supported_layers, layers)
+
+ if layers == 18:
+ depth = [2, 2, 2, 2]
+ elif layers == 34 or layers == 50:
+ depth = [3, 4, 6, 3]
+ elif layers == 101:
+ depth = [3, 4, 23, 3]
+ elif layers == 152:
+ depth = [3, 8, 36, 3]
+ num_channels = [64, 256, 512,
+ 1024] if layers >= 50 else [64, 64, 128, 256]
+ num_filters = [64, 128, 256, 512]
+
+ self.dcn_stage = dcn_stage if dcn_stage is not None else [
+ False, False, False, False
+ ]
+ self.out_indices = out_indices if out_indices is not None else [
+ 0, 1, 2, 3
+ ]
+
+ self.conv = ConvBNLayer(
+ in_channels=self.input_image_channel,
+ out_channels=64,
+ kernel_size=7,
+ stride=2,
+ act="relu", )
+ self.pool2d_max = MaxPool2D(
+ kernel_size=3,
+ stride=2,
+ padding=1, )
+
+ self.stages = []
+ self.out_channels = []
+ if layers >= 50:
+ for block in range(len(depth)):
+ shortcut = False
+ block_list = []
+ is_dcn = self.dcn_stage[block]
+ for i in range(depth[block]):
+ if layers in [101, 152] and block == 2:
+ if i == 0:
+ conv_name = "res" + str(block + 2) + "a"
+ else:
+ conv_name = "res" + str(block + 2) + "b" + str(i)
+ else:
+ conv_name = "res" + str(block + 2) + chr(97 + i)
+ bottleneck_block = self.add_sublayer(
+ conv_name,
+ BottleneckBlock(
+ num_channels=num_channels[block]
+ if i == 0 else num_filters[block] * 4,
+ num_filters=num_filters[block],
+ stride=2 if i == 0 and block != 0 else 1,
+ shortcut=shortcut,
+ is_dcn=is_dcn))
+ block_list.append(bottleneck_block)
+ shortcut = True
+ if block in self.out_indices:
+ self.out_channels.append(num_filters[block] * 4)
+ self.stages.append(nn.Sequential(*block_list))
+ else:
+ for block in range(len(depth)):
+ shortcut = False
+ block_list = []
+ for i in range(depth[block]):
+ conv_name = "res" + str(block + 2) + chr(97 + i)
+ basic_block = self.add_sublayer(
+ conv_name,
+ BasicBlock(
+ num_channels=num_channels[block]
+ if i == 0 else num_filters[block],
+ num_filters=num_filters[block],
+ stride=2 if i == 0 and block != 0 else 1,
+ shortcut=shortcut))
+ block_list.append(basic_block)
+ shortcut = True
+ if block in self.out_indices:
+ self.out_channels.append(num_filters[block])
+ self.stages.append(nn.Sequential(*block_list))
+
+ def forward(self, inputs):
+ y = self.conv(inputs)
+ y = self.pool2d_max(y)
+ out = []
+ for i, block in enumerate(self.stages):
+ y = block(y)
+ if i in self.out_indices:
+ out.append(y)
+ return out
diff --git a/ppocr/modeling/backbones/det_resnet_vd.py b/ppocr/modeling/backbones/det_resnet_vd.py
index 8c955a4af377374f21e7c09f0d10952f2fe1ceed..a421da0ab440e9b87c1c7efc7d2448f8f76ad205 100644
--- a/ppocr/modeling/backbones/det_resnet_vd.py
+++ b/ppocr/modeling/backbones/det_resnet_vd.py
@@ -25,7 +25,7 @@ from paddle.vision.ops import DeformConv2D
from paddle.regularizer import L2Decay
from paddle.nn.initializer import Normal, Constant, XavierUniform
-__all__ = ["ResNet"]
+__all__ = ["ResNet_vd", "ConvBNLayer", "DeformableConvV2"]
class DeformableConvV2(nn.Layer):
@@ -104,6 +104,7 @@ class ConvBNLayer(nn.Layer):
kernel_size,
stride=1,
groups=1,
+ dcn_groups=1,
is_vd_mode=False,
act=None,
is_dcn=False):
@@ -128,7 +129,7 @@ class ConvBNLayer(nn.Layer):
kernel_size=kernel_size,
stride=stride,
padding=(kernel_size - 1) // 2,
- groups=2, #groups,
+ groups=dcn_groups, #groups,
bias_attr=False)
self._batch_norm = nn.BatchNorm(out_channels, act=act)
@@ -162,7 +163,8 @@ class BottleneckBlock(nn.Layer):
kernel_size=3,
stride=stride,
act='relu',
- is_dcn=is_dcn)
+ is_dcn=is_dcn,
+ dcn_groups=2)
self.conv2 = ConvBNLayer(
in_channels=out_channels,
out_channels=out_channels * 4,
@@ -238,14 +240,14 @@ class BasicBlock(nn.Layer):
return y
-class ResNet(nn.Layer):
+class ResNet_vd(nn.Layer):
def __init__(self,
in_channels=3,
layers=50,
dcn_stage=None,
out_indices=None,
**kwargs):
- super(ResNet, self).__init__()
+ super(ResNet_vd, self).__init__()
self.layers = layers
supported_layers = [18, 34, 50, 101, 152, 200]
@@ -321,7 +323,6 @@ class ResNet(nn.Layer):
for block in range(len(depth)):
block_list = []
shortcut = False
- # is_dcn = self.dcn_stage[block]
for i in range(depth[block]):
basic_block = self.add_sublayer(
'bb_%d_%d' % (block, i),
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/backbones/vqa_layoutlm.py b/ppocr/modeling/backbones/vqa_layoutlm.py
index ede5b7a35af65fac351277cefccd89b251f5cdb7..34dd9d10ea36758059448d96674d4d2c249d3ad0 100644
--- a/ppocr/modeling/backbones/vqa_layoutlm.py
+++ b/ppocr/modeling/backbones/vqa_layoutlm.py
@@ -43,9 +43,11 @@ class NLPBaseModel(nn.Layer):
super(NLPBaseModel, self).__init__()
if checkpoints is not None:
self.model = model_class.from_pretrained(checkpoints)
+ elif isinstance(pretrained, (str, )) and os.path.exists(pretrained):
+ self.model = model_class.from_pretrained(pretrained)
else:
pretrained_model_name = pretrained_model_dict[base_model_class]
- if pretrained:
+ if pretrained is True:
base_model = base_model_class.from_pretrained(
pretrained_model_name)
else:
@@ -74,9 +76,9 @@ class LayoutLMForSer(NLPBaseModel):
def forward(self, x):
x = self.model(
input_ids=x[0],
- bbox=x[2],
- attention_mask=x[4],
- token_type_ids=x[5],
+ bbox=x[1],
+ attention_mask=x[2],
+ token_type_ids=x[3],
position_ids=None,
output_hidden_states=False)
return x
@@ -96,13 +98,15 @@ class LayoutLMv2ForSer(NLPBaseModel):
def forward(self, x):
x = self.model(
input_ids=x[0],
- bbox=x[2],
- image=x[3],
- attention_mask=x[4],
- token_type_ids=x[5],
+ bbox=x[1],
+ attention_mask=x[2],
+ token_type_ids=x[3],
+ image=x[4],
position_ids=None,
head_mask=None,
labels=None)
+ if not self.training:
+ return x
return x[0]
@@ -120,13 +124,15 @@ class LayoutXLMForSer(NLPBaseModel):
def forward(self, x):
x = self.model(
input_ids=x[0],
- bbox=x[2],
- image=x[3],
- attention_mask=x[4],
- token_type_ids=x[5],
+ bbox=x[1],
+ attention_mask=x[2],
+ token_type_ids=x[3],
+ image=x[4],
position_ids=None,
head_mask=None,
labels=None)
+ if not self.training:
+ return x
return x[0]
@@ -140,12 +146,12 @@ class LayoutLMv2ForRe(NLPBaseModel):
x = self.model(
input_ids=x[0],
bbox=x[1],
- labels=None,
- image=x[2],
- attention_mask=x[3],
- token_type_ids=x[4],
+ attention_mask=x[2],
+ token_type_ids=x[3],
+ image=x[4],
position_ids=None,
head_mask=None,
+ labels=None,
entities=x[5],
relations=x[6])
return x
@@ -161,12 +167,12 @@ class LayoutXLMForRe(NLPBaseModel):
x = self.model(
input_ids=x[0],
bbox=x[1],
- labels=None,
- image=x[2],
- attention_mask=x[3],
- token_type_ids=x[4],
+ attention_mask=x[2],
+ token_type_ids=x[3],
+ image=x[4],
position_ids=None,
head_mask=None,
+ labels=None,
entities=x[5],
relations=x[6])
return x
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/modeling/necks/db_fpn.py b/ppocr/modeling/necks/db_fpn.py
index 93ed2dbfd1fac9bf2d163c54d23a20e16b537981..8c3f52a331db5daafab2a38c0a441edd44eb141d 100644
--- a/ppocr/modeling/necks/db_fpn.py
+++ b/ppocr/modeling/necks/db_fpn.py
@@ -105,9 +105,10 @@ class DSConv(nn.Layer):
class DBFPN(nn.Layer):
- def __init__(self, in_channels, out_channels, **kwargs):
+ def __init__(self, in_channels, out_channels, use_asf=False, **kwargs):
super(DBFPN, self).__init__()
self.out_channels = out_channels
+ self.use_asf = use_asf
weight_attr = paddle.nn.initializer.KaimingUniform()
self.in2_conv = nn.Conv2D(
@@ -163,6 +164,9 @@ class DBFPN(nn.Layer):
weight_attr=ParamAttr(initializer=weight_attr),
bias_attr=False)
+ if self.use_asf is True:
+ self.asf = ASFBlock(self.out_channels, self.out_channels // 4)
+
def forward(self, x):
c2, c3, c4, c5 = x
@@ -187,6 +191,10 @@ class DBFPN(nn.Layer):
p3 = F.upsample(p3, scale_factor=2, mode="nearest", align_mode=1)
fuse = paddle.concat([p5, p4, p3, p2], axis=1)
+
+ if self.use_asf is True:
+ fuse = self.asf(fuse, [p5, p4, p3, p2])
+
return fuse
@@ -356,3 +364,64 @@ class LKPAN(nn.Layer):
fuse = paddle.concat([p5, p4, p3, p2], axis=1)
return fuse
+
+
+class ASFBlock(nn.Layer):
+ """
+ This code is refered from:
+ https://github.com/MhLiao/DB/blob/master/decoders/feature_attention.py
+ """
+
+ def __init__(self, in_channels, inter_channels, out_features_num=4):
+ """
+ Adaptive Scale Fusion (ASF) block of DBNet++
+ Args:
+ in_channels: the number of channels in the input data
+ inter_channels: the number of middle channels
+ out_features_num: the number of fused stages
+ """
+ super(ASFBlock, self).__init__()
+ weight_attr = paddle.nn.initializer.KaimingUniform()
+ self.in_channels = in_channels
+ self.inter_channels = inter_channels
+ self.out_features_num = out_features_num
+ self.conv = nn.Conv2D(in_channels, inter_channels, 3, padding=1)
+
+ self.spatial_scale = nn.Sequential(
+ #Nx1xHxW
+ nn.Conv2D(
+ in_channels=1,
+ out_channels=1,
+ kernel_size=3,
+ bias_attr=False,
+ padding=1,
+ weight_attr=ParamAttr(initializer=weight_attr)),
+ nn.ReLU(),
+ nn.Conv2D(
+ in_channels=1,
+ out_channels=1,
+ kernel_size=1,
+ bias_attr=False,
+ weight_attr=ParamAttr(initializer=weight_attr)),
+ nn.Sigmoid())
+
+ self.channel_scale = nn.Sequential(
+ nn.Conv2D(
+ in_channels=inter_channels,
+ out_channels=out_features_num,
+ kernel_size=1,
+ bias_attr=False,
+ weight_attr=ParamAttr(initializer=weight_attr)),
+ nn.Sigmoid())
+
+ def forward(self, fuse_features, features_list):
+ fuse_features = self.conv(fuse_features)
+ spatial_x = paddle.mean(fuse_features, axis=1, keepdim=True)
+ attention_scores = self.spatial_scale(spatial_x) + fuse_features
+ attention_scores = self.channel_scale(attention_scores)
+ assert len(features_list) == self.out_features_num
+
+ out_list = []
+ for i in range(self.out_features_num):
+ out_list.append(attention_scores[:, i:i + 1] * features_list[i])
+ return paddle.concat(out_list, axis=1)
diff --git a/ppocr/optimizer/learning_rate.py b/ppocr/optimizer/learning_rate.py
index fe251f36e736bb1eac8a71a8115c941cbd7443e6..7d45109b4857871f52764c64d6d32e5322fc7c57 100644
--- a/ppocr/optimizer/learning_rate.py
+++ b/ppocr/optimizer/learning_rate.py
@@ -308,3 +308,81 @@ class Const(object):
end_lr=self.learning_rate,
last_epoch=self.last_epoch)
return learning_rate
+
+
+class DecayLearningRate(object):
+ """
+ DecayLearningRate learning rate decay
+ new_lr = (lr - end_lr) * (1 - epoch/decay_steps)**power + end_lr
+ Args:
+ learning_rate(float): initial learning rate
+ step_each_epoch(int): steps each epoch
+ epochs(int): total training epochs
+ factor(float): Power of polynomial, should greater than 0.0 to get learning rate decay. Default: 0.9
+ end_lr(float): The minimum final learning rate. Default: 0.0.
+ """
+
+ def __init__(self,
+ learning_rate,
+ step_each_epoch,
+ epochs,
+ factor=0.9,
+ end_lr=0,
+ **kwargs):
+ super(DecayLearningRate, self).__init__()
+ self.learning_rate = learning_rate
+ self.epochs = epochs + 1
+ self.factor = factor
+ self.end_lr = 0
+ self.decay_steps = step_each_epoch * epochs
+
+ def __call__(self):
+ learning_rate = lr.PolynomialDecay(
+ learning_rate=self.learning_rate,
+ decay_steps=self.decay_steps,
+ 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/db_postprocess.py b/ppocr/postprocess/db_postprocess.py
index 27b428ef2e73c9abf81d3881b23979343c8595b2..5e2553c3a09f8359d1641d2d49b1bfb84df695ac 100755
--- a/ppocr/postprocess/db_postprocess.py
+++ b/ppocr/postprocess/db_postprocess.py
@@ -38,6 +38,7 @@ class DBPostProcess(object):
unclip_ratio=2.0,
use_dilation=False,
score_mode="fast",
+ use_polygon=False,
**kwargs):
self.thresh = thresh
self.box_thresh = box_thresh
@@ -45,6 +46,7 @@ class DBPostProcess(object):
self.unclip_ratio = unclip_ratio
self.min_size = 3
self.score_mode = score_mode
+ self.use_polygon = use_polygon
assert score_mode in [
"slow", "fast"
], "Score mode must be in [slow, fast] but got: {}".format(score_mode)
@@ -52,6 +54,53 @@ class DBPostProcess(object):
self.dilation_kernel = None if not use_dilation else np.array(
[[1, 1], [1, 1]])
+ def polygons_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
+ '''
+ _bitmap: single map with shape (1, H, W),
+ whose values are binarized as {0, 1}
+ '''
+
+ bitmap = _bitmap
+ height, width = bitmap.shape
+
+ boxes = []
+ scores = []
+
+ contours, _ = cv2.findContours((bitmap * 255).astype(np.uint8),
+ cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
+
+ for contour in contours[:self.max_candidates]:
+ epsilon = 0.002 * cv2.arcLength(contour, True)
+ approx = cv2.approxPolyDP(contour, epsilon, True)
+ points = approx.reshape((-1, 2))
+ if points.shape[0] < 4:
+ continue
+
+ score = self.box_score_fast(pred, points.reshape(-1, 2))
+ if self.box_thresh > score:
+ continue
+
+ if points.shape[0] > 2:
+ box = self.unclip(points, self.unclip_ratio)
+ if len(box) > 1:
+ continue
+ else:
+ continue
+ box = box.reshape(-1, 2)
+
+ _, sside = self.get_mini_boxes(box.reshape((-1, 1, 2)))
+ if sside < self.min_size + 2:
+ continue
+
+ box = np.array(box)
+ box[:, 0] = np.clip(
+ np.round(box[:, 0] / width * dest_width), 0, dest_width)
+ box[:, 1] = np.clip(
+ np.round(box[:, 1] / height * dest_height), 0, dest_height)
+ boxes.append(box.tolist())
+ scores.append(score)
+ return boxes, scores
+
def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
'''
_bitmap: single map with shape (1, H, W),
@@ -85,7 +134,7 @@ class DBPostProcess(object):
if self.box_thresh > score:
continue
- box = self.unclip(points).reshape(-1, 1, 2)
+ box = self.unclip(points, self.unclip_ratio).reshape(-1, 1, 2)
box, sside = self.get_mini_boxes(box)
if sside < self.min_size + 2:
continue
@@ -99,8 +148,7 @@ class DBPostProcess(object):
scores.append(score)
return np.array(boxes, dtype=np.int16), scores
- def unclip(self, box):
- unclip_ratio = self.unclip_ratio
+ def unclip(self, box, unclip_ratio):
poly = Polygon(box)
distance = poly.area * unclip_ratio / poly.length
offset = pyclipper.PyclipperOffset()
@@ -185,8 +233,12 @@ class DBPostProcess(object):
self.dilation_kernel)
else:
mask = segmentation[batch_index]
- boxes, scores = self.boxes_from_bitmap(pred[batch_index], mask,
- src_w, src_h)
+ if self.use_polygon is True:
+ boxes, scores = self.polygons_from_bitmap(pred[batch_index],
+ mask, src_w, src_h)
+ else:
+ boxes, scores = self.boxes_from_bitmap(pred[batch_index], mask,
+ src_w, src_h)
boxes_batch.append({'points': boxes})
return boxes_batch
@@ -202,6 +254,7 @@ class DistillationDBPostProcess(object):
unclip_ratio=1.5,
use_dilation=False,
score_mode="fast",
+ use_polygon=False,
**kwargs):
self.model_name = model_name
self.key = key
@@ -211,7 +264,8 @@ class DistillationDBPostProcess(object):
max_candidates=max_candidates,
unclip_ratio=unclip_ratio,
use_dilation=use_dilation,
- score_mode=score_mode)
+ score_mode=score_mode,
+ use_polygon=use_polygon)
def __call__(self, predicts, shape_list):
results = {}
diff --git a/ppocr/postprocess/pse_postprocess/pse_postprocess.py b/ppocr/postprocess/pse_postprocess/pse_postprocess.py
index 34f1b8c9b5397a5513462468a9ee3d8530389607..962f3efe922c4a2656e0f44f478e1baf301a5542 100755
--- a/ppocr/postprocess/pse_postprocess/pse_postprocess.py
+++ b/ppocr/postprocess/pse_postprocess/pse_postprocess.py
@@ -58,6 +58,8 @@ class PSEPostProcess(object):
kernels = (pred > self.thresh).astype('float32')
text_mask = kernels[:, 0, :, :]
+ text_mask = paddle.unsqueeze(text_mask, axis=1)
+
kernels[:, 0:, :, :] = kernels[:, 0:, :, :] * text_mask
score = score.numpy()
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/postprocess/vqa_token_ser_layoutlm_postprocess.py b/ppocr/postprocess/vqa_token_ser_layoutlm_postprocess.py
index 782cdea6c58c69e0d728787e0e21e200c9e13790..8a6669f71f5ae6a7a16931e565b43355de5928d9 100644
--- a/ppocr/postprocess/vqa_token_ser_layoutlm_postprocess.py
+++ b/ppocr/postprocess/vqa_token_ser_layoutlm_postprocess.py
@@ -41,11 +41,13 @@ class VQASerTokenLayoutLMPostProcess(object):
self.id2label_map_for_show[val] = key
def __call__(self, preds, batch=None, *args, **kwargs):
+ if isinstance(preds, tuple):
+ preds = preds[0]
if isinstance(preds, paddle.Tensor):
preds = preds.numpy()
if batch is not None:
- return self._metric(preds, batch[1])
+ return self._metric(preds, batch[5])
else:
return self._infer(preds, **kwargs)
@@ -63,11 +65,11 @@ class VQASerTokenLayoutLMPostProcess(object):
j]])
return decode_out_list, label_decode_out_list
- def _infer(self, preds, attention_masks, segment_offset_ids, ocr_infos):
+ def _infer(self, preds, segment_offset_ids, ocr_infos):
results = []
- for pred, attention_mask, segment_offset_id, ocr_info in zip(
- preds, attention_masks, segment_offset_ids, ocr_infos):
+ for pred, segment_offset_id, ocr_info in zip(preds, segment_offset_ids,
+ ocr_infos):
pred = np.argmax(pred, axis=1)
pred = [self.id2label_map[idx] for idx in pred]
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
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-l
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-
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-.
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-
-?
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-Z
-X
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-—
-β
-'
-†
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-"
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-$
-→
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-
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-€
-∧
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-‰
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-♦
-{
-}
-̀
-∑
-∫
-ø
-κ
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-¥
-※
-`
-ω
-Σ
-➔
-‖
-Β
-̸
-
-─
-●
-⩾
-Χ
-Α
-⋅
-◆
-★
-■
-ψ
-ǂ
-□
-ζ
-!
-Γ
-↔
-θ
-⁄
-〈
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-υ
-τ
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-
-✗
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-
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-
-
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-̆
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-▼
-
-ι
-ν
-║
-
-
-◦
-
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-⊕
-⇒
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-̨
-Ι
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-⋯
-А
-⋮
@@ -303,2457 +25,4 @@ $
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-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/utility.py b/ppocr/utils/utility.py
index 4a25ff8b2fa182faaf4f4ce8909c9ec2e9b55ccc..b881fcab20bc5ca076a0002bd72349768c7d881a 100755
--- a/ppocr/utils/utility.py
+++ b/ppocr/utils/utility.py
@@ -91,18 +91,19 @@ def check_and_read_gif(img_path):
def load_vqa_bio_label_maps(label_map_path):
with open(label_map_path, "r", encoding='utf-8') as fin:
lines = fin.readlines()
- lines = [line.strip() for line in lines]
- if "O" not in lines:
- lines.insert(0, "O")
- labels = []
- for line in lines:
- if line == "O":
- labels.append("O")
- else:
- labels.append("B-" + line)
- labels.append("I-" + line)
- label2id_map = {label: idx for idx, label in enumerate(labels)}
- id2label_map = {idx: label for idx, label in enumerate(labels)}
+ old_lines = [line.strip() for line in lines]
+ lines = ["O"]
+ for line in old_lines:
+ # "O" has already been in lines
+ if line.upper() in ["OTHER", "OTHERS", "IGNORE"]:
+ continue
+ lines.append(line)
+ labels = ["O"]
+ for line in lines[1:]:
+ labels.append("B-" + line)
+ labels.append("I-" + line)
+ label2id_map = {label.upper(): idx for idx, label in enumerate(labels)}
+ id2label_map = {idx: label.upper() for idx, label in enumerate(labels)}
return label2id_map, id2label_map
diff --git a/ppocr/utils/visual.py b/ppocr/utils/visual.py
index 7a8c1674a74f89299de59f7cd120b4577a7499d8..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
@@ -19,7 +20,7 @@ from PIL import Image, ImageDraw, ImageFont
def draw_ser_results(image,
ocr_results,
font_path="doc/fonts/simfang.ttf",
- font_size=18):
+ font_size=14):
np.random.seed(2021)
color = (np.random.permutation(range(255)),
np.random.permutation(range(255)),
@@ -40,9 +41,15 @@ def draw_ser_results(image,
if ocr_info["pred_id"] not in color_map:
continue
color = color_map[ocr_info["pred_id"]]
- text = "{}: {}".format(ocr_info["pred"], ocr_info["text"])
+ text = "{}: {}".format(ocr_info["pred"], ocr_info["transcription"])
- draw_box_txt(ocr_info["bbox"], text, draw, font, font_size, color)
+ if "bbox" in ocr_info:
+ # draw with ocr engine
+ bbox = ocr_info["bbox"]
+ else:
+ # draw with ocr groundtruth
+ bbox = trans_poly_to_bbox(ocr_info["points"])
+ draw_box_txt(bbox, text, draw, font, font_size, color)
img_new = Image.blend(image, img_new, 0.5)
return np.array(img_new)
@@ -62,6 +69,14 @@ def draw_box_txt(bbox, text, draw, font, font_size, color):
draw.text((bbox[0][0] + 1, start_y), text, fill=(255, 255, 255), font=font)
+def trans_poly_to_bbox(poly):
+ x1 = np.min([p[0] for p in poly])
+ x2 = np.max([p[0] for p in poly])
+ y1 = np.min([p[1] for p in poly])
+ y2 = np.max([p[1] for p in poly])
+ return [x1, y1, x2, y2]
+
+
def draw_re_results(image,
result,
font_path="doc/fonts/simfang.ttf",
@@ -80,10 +95,10 @@ def draw_re_results(image,
color_line = (0, 255, 0)
for ocr_info_head, ocr_info_tail in result:
- draw_box_txt(ocr_info_head["bbox"], ocr_info_head["text"], draw, font,
- font_size, color_head)
- draw_box_txt(ocr_info_tail["bbox"], ocr_info_tail["text"], draw, font,
- font_size, color_tail)
+ draw_box_txt(ocr_info_head["bbox"], ocr_info_head["transcription"],
+ draw, font, font_size, color_head)
+ draw_box_txt(ocr_info_tail["bbox"], ocr_info_tail["transcription"],
+ draw, font, font_size, color_tail)
center_head = (
(ocr_info_head['bbox'][0] + ocr_info_head['bbox'][2]) // 2,
@@ -96,3 +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/kie.md b/ppstructure/docs/kie.md
index 35498b33478d1010fd2548dfcb8586b4710723a1..315dd9f7bafa6b6160489eab330e8d278b2d119d 100644
--- a/ppstructure/docs/kie.md
+++ b/ppstructure/docs/kie.md
@@ -16,7 +16,7 @@ SDMGR是一个关键信息提取算法,将每个检测到的文本区域分类
训练和测试的数据采用wildreceipt数据集,通过如下指令下载数据集:
```
-wget https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/wildreceipt.tar && tar xf wildreceipt.tar
+wget https://paddleocr.bj.bcebos.com/ppstructure/dataset/wildreceipt.tar && tar xf wildreceipt.tar
```
执行预测:
diff --git a/ppstructure/docs/kie_en.md b/ppstructure/docs/kie_en.md
index 1fe38b0b399e9290526dafa5409673dc87026db7..7b3752223dd765e780d56d146c90bd0f892aac7b 100644
--- a/ppstructure/docs/kie_en.md
+++ b/ppstructure/docs/kie_en.md
@@ -15,7 +15,7 @@ This section provides a tutorial example on how to quickly use, train, and evalu
[Wildreceipt dataset](https://paperswithcode.com/dataset/wildreceipt) is used for this tutorial. It contains 1765 photos, with 25 classes, and 50000 text boxes, which can be downloaded by wget:
```shell
-wget https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/wildreceipt.tar && tar xf wildreceipt.tar
+wget https://paddleocr.bj.bcebos.com/ppstructure/dataset/wildreceipt.tar && tar xf wildreceipt.tar
```
Download the pretrained model and predict the result:
diff --git a/ppstructure/docs/models_list.md b/ppstructure/docs/models_list.md
index c7dab999ff6e370c56c5495e22e91f117b3d1275..42d44009dad1ba1b07bb410c199993c6f79f3d5d 100644
--- a/ppstructure/docs/models_list.md
+++ b/ppstructure/docs/models_list.md
@@ -1,11 +1,11 @@
# PP-Structure 系列模型列表
-- [1. 版面分析模型](#1)
-- [2. OCR和表格识别模型](#2)
- - [2.1 OCR](#21)
- - [2.2 表格识别模型](#22)
-- [3. VQA模型](#3)
-- [4. KIE模型](#4)
+- [1. 版面分析模型](#1-版面分析模型)
+- [2. OCR和表格识别模型](#2-ocr和表格识别模型)
+ - [2.1 OCR](#21-ocr)
+ - [2.2 表格识别模型](#22-表格识别模型)
+- [3. VQA模型](#3-vqa模型)
+- [4. KIE模型](#4-kie模型)
@@ -35,18 +35,18 @@
|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
|模型名称|模型简介|推理模型大小|下载地址|
| --- | --- | --- | --- |
-|ser_LayoutXLM_xfun_zh|基于LayoutXLM在xfun中文数据集上训练的SER模型|1.4G|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) |
-|re_LayoutXLM_xfun_zh|基于LayoutXLM在xfun中文数据集上训练的RE模型|1.4G|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar) |
-|ser_LayoutLMv2_xfun_zh|基于LayoutLMv2在xfun中文数据集上训练的SER模型|778M|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh.tar) |
+|ser_LayoutXLM_xfun_zh|基于LayoutXLM在xfun中文数据集上训练的SER模型|1.4G|[推理模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar) |
+|re_LayoutXLM_xfun_zh|基于LayoutXLM在xfun中文数据集上训练的RE模型|1.4G|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) |
+|ser_LayoutLMv2_xfun_zh|基于LayoutLMv2在xfun中文数据集上训练的SER模型|778M|[推理模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh.tar) |
|re_LayoutLMv2_xfun_zh|基于LayoutLMv2在xfun中文数据集上训练的RE模型|765M|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutLMv2_xfun_zh.tar) |
-|ser_LayoutLM_xfun_zh|基于LayoutLM在xfun中文数据集上训练的SER模型|430M|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh.tar) |
+|ser_LayoutLM_xfun_zh|基于LayoutLM在xfun中文数据集上训练的SER模型|430M|[推理模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh.tar) |
## 4. KIE模型
diff --git a/ppstructure/docs/models_list_en.md b/ppstructure/docs/models_list_en.md
index b92c10c241df72c85649b64f915b4266cd3fe410..e133a0bb2a9b017207b5e92ea444aba4633a7457 100644
--- a/ppstructure/docs/models_list_en.md
+++ b/ppstructure/docs/models_list_en.md
@@ -1,11 +1,11 @@
# PP-Structure Model list
-- [1. Layout Analysis](#1)
-- [2. OCR and Table Recognition](#2)
- - [2.1 OCR](#21)
- - [2.2 Table Recognition](#22)
-- [3. VQA](#3)
-- [4. KIE](#4)
+- [1. Layout Analysis](#1-layout-analysis)
+- [2. OCR and Table Recognition](#2-ocr-and-table-recognition)
+ - [2.1 OCR](#21-ocr)
+ - [2.2 Table Recognition](#22-table-recognition)
+- [3. VQA](#3-vqa)
+- [4. KIE](#4-kie)
@@ -42,11 +42,11 @@ If you need to use other OCR models, you can download the model in [PP-OCR model
|model| description |inference model size|download|
| --- |----------------------------------------------------------------| --- | --- |
-|ser_LayoutXLM_xfun_zh| SER model trained on xfun Chinese dataset based on LayoutXLM |1.4G|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) |
-|re_LayoutXLM_xfun_zh| Re model trained on xfun Chinese dataset based on LayoutXLM |1.4G|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar) |
-|ser_LayoutLMv2_xfun_zh| SER model trained on xfun Chinese dataset based on LayoutXLMv2 |778M|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh.tar) |
+|ser_LayoutXLM_xfun_zh| SER model trained on xfun Chinese dataset based on LayoutXLM |1.4G|[inference model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar) |
+|re_LayoutXLM_xfun_zh| Re model trained on xfun Chinese dataset based on LayoutXLM |1.4G|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) |
+|ser_LayoutLMv2_xfun_zh| SER model trained on xfun Chinese dataset based on LayoutXLMv2 |778M|[inference model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh.tar) |
|re_LayoutLMv2_xfun_zh| Re model trained on xfun Chinese dataset based on LayoutXLMv2 |765M|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutLMv2_xfun_zh.tar) |
-|ser_LayoutLM_xfun_zh| SER model trained on xfun Chinese dataset based on LayoutLM |430M|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh.tar) |
+|ser_LayoutLM_xfun_zh| SER model trained on xfun Chinese dataset based on LayoutLM |430M|[inference model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh.tar) |
## 4. KIE
diff --git a/ppstructure/table/predict_structure.py b/ppstructure/table/predict_structure.py
index 0179c614ae4864677576f6073f291282fb772988..7a7d3169d567493b4707b63c75cec07485cf5acb 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_res, elapse = table_structurer(img)
+ structure_str_list, bbox_list = structure_res
+ 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 1ad902e7e6be95a6901e3774420fad337f594861..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",
@@ -40,6 +41,13 @@ def init_args():
type=ast.literal_eval,
default=None,
help='label map according to ppstructure/layout/README_ch.md')
+ # params for vqa
+ parser.add_argument("--vqa_algorithm", type=str, default='LayoutXLM')
+ parser.add_argument("--ser_model_dir", type=str)
+ parser.add_argument(
+ "--ser_dict_path",
+ type=str,
+ default="../train_data/XFUND/class_list_xfun.txt")
# params for inference
parser.add_argument(
"--mode",
@@ -65,7 +73,7 @@ def init_args():
"--recovery",
type=bool,
default=False,
- help='Whether to enable layout of recovery')
+ help='Whether to enable layout of recovery')
return parser
diff --git a/ppstructure/vqa/README.md b/ppstructure/vqa/README.md
index e3a10671ddb6494eb15073e7ac007aa1e8e6a32a..05635265b5e5eff18429e2d595fc4195381299f5 100644
--- a/ppstructure/vqa/README.md
+++ b/ppstructure/vqa/README.md
@@ -1,19 +1,15 @@
English | [简体中文](README_ch.md)
-- [Document Visual Question Answering (Doc-VQA)](#Document-Visual-Question-Answering)
- - [1. Introduction](#1-Introduction)
- - [2. Performance](#2-performance)
- - [3. Effect demo](#3-Effect-demo)
- - [3.1 SER](#31-ser)
- - [3.2 RE](#32-re)
- - [4. Install](#4-Install)
- - [4.1 Installation dependencies](#41-Install-dependencies)
- - [4.2 Install PaddleOCR](#42-Install-PaddleOCR)
- - [5. Usage](#5-Usage)
- - [5.1 Data and Model Preparation](#51-Data-and-Model-Preparation)
- - [5.2 SER](#52-ser)
- - [5.3 RE](#53-re)
- - [6. Reference](#6-Reference-Links)
+- [1 Introduction](#1-introduction)
+- [2. Performance](#2-performance)
+- [3. Effect demo](#3-effect-demo)
+ - [3.1 SER](#31-ser)
+ - [3.2 RE](#32-re)
+- [4. Install](#4-install)
+ - [4.1 Install dependencies](#41-install-dependencies)
+ - [5.3 RE](#53-re)
+- [6. Reference Links](#6-reference-links)
+- [License](#license)
# Document Visual Question Answering
@@ -125,13 +121,13 @@ If you want to experience the prediction process directly, you can download the
* Download the processed dataset
-The download address of the processed XFUND Chinese dataset: [https://paddleocr.bj.bcebos.com/dataset/XFUND.tar](https://paddleocr.bj.bcebos.com/dataset/XFUND.tar).
+The download address of the processed XFUND Chinese dataset: [link](https://paddleocr.bj.bcebos.com/ppstructure/dataset/XFUND.tar).
Download and unzip the dataset, and place the dataset in the current directory after unzipping.
```shell
-wget https://paddleocr.bj.bcebos.com/dataset/XFUND.tar
+wget https://paddleocr.bj.bcebos.com/ppstructure/dataset/XFUND.tar
````
* Convert the dataset
@@ -187,17 +183,17 @@ CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/vqa/ser/layoutxlm.yml -o
````
Finally, `precision`, `recall`, `hmean` and other indicators will be printed
-* Use `OCR engine + SER` tandem prediction
+* `OCR + SER` tandem prediction based on training engine
-Use the following command to complete the series prediction of `OCR engine + SER`, taking the pretrained SER model as an example:
+Use the following command to complete the series prediction of `OCR engine + SER`, taking the SER model based on LayoutXLM as an example::
```shell
-CUDA_VISIBLE_DEVICES=0 python3 tools/infer_vqa_token_ser.py -c configs/vqa/ser/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/Global.infer_img=doc/vqa/input/zh_val_42.jpg
+python3.7 tools/export_model.py -c configs/vqa/ser/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/ Global.save_inference_dir=output/ser/infer
````
Finally, the prediction result visualization image and the prediction result text file will be saved in the directory configured by the `config.Global.save_res_path` field. The prediction result text file is named `infer_results.txt`.
-* End-to-end evaluation of `OCR engine + SER` prediction system
+* End-to-end evaluation of `OCR + SER` prediction system
First use the `tools/infer_vqa_token_ser.py` script to complete the prediction of the dataset, then use the following command to evaluate.
@@ -205,6 +201,24 @@ First use the `tools/infer_vqa_token_ser.py` script to complete the prediction o
export CUDA_VISIBLE_DEVICES=0
python3 tools/eval_with_label_end2end.py --gt_json_path XFUND/zh_val/xfun_normalize_val.json --pred_json_path output_res/infer_results.txt
````
+* export model
+
+Use the following command to complete the model export of the SER model, taking the SER model based on LayoutXLM as an example:
+
+```shell
+python3.7 tools/export_model.py -c configs/vqa/ser/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/ Global.save_inference_dir=output/ser/infer
+```
+The converted model will be stored in the directory specified by the `Global.save_inference_dir` field.
+
+* `OCR + SER` tandem prediction based on prediction engine
+
+Use the following command to complete the tandem prediction of `OCR + SER` based on the prediction engine, taking the SER model based on LayoutXLM as an example:
+
+```shell
+cd ppstructure
+CUDA_VISIBLE_DEVICES=0 python3.7 vqa/predict_vqa_token_ser.py --vqa_algorithm=LayoutXLM --ser_model_dir=../output/ser/infer --ser_dict_path=../train_data/XFUND/class_list_xfun.txt --image_dir=docs/vqa/input/zh_val_42.jpg --output=output
+```
+After the prediction is successful, the visualization images and results will be saved in the directory specified by the `output` field
### 5.3 RE
@@ -247,11 +261,19 @@ Finally, `precision`, `recall`, `hmean` and other indicators will be printed
Use the following command to complete the series prediction of `OCR engine + SER + RE`, taking the pretrained SER and RE models as an example:
```shell
export CUDA_VISIBLE_DEVICES=0
-python3 tools/infer_vqa_token_ser_re.py -c configs/vqa/re/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/re_LayoutXLM_xfun_zh/Global.infer_img=doc/vqa/input/zh_val_21.jpg -c_ser configs/vqa/ser/layoutxlm. yml -o_ser Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/
+python3 tools/infer_vqa_token_ser_re.py -c configs/vqa/re/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/re_LayoutXLM_xfun_zh/Global.infer_img=ppstructure/docs/vqa/input/zh_val_21.jpg -c_ser configs/vqa/ser/layoutxlm. yml -o_ser Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/
````
Finally, the prediction result visualization image and the prediction result text file will be saved in the directory configured by the `config.Global.save_res_path` field. The prediction result text file is named `infer_results.txt`.
+* export model
+
+cooming soon
+
+* `OCR + SER + RE` tandem prediction based on prediction engine
+
+cooming soon
+
## 6. Reference Links
- LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding, https://arxiv.org/pdf/2104.08836.pdf
diff --git a/ppstructure/vqa/README_ch.md b/ppstructure/vqa/README_ch.md
index b677dc07bce6c1a752d753b6a1c538b4d3f99271..b421a82d3a1cbe39f5c740bea486ec26593ab20f 100644
--- a/ppstructure/vqa/README_ch.md
+++ b/ppstructure/vqa/README_ch.md
@@ -1,19 +1,19 @@
[English](README.md) | 简体中文
-- [文档视觉问答(DOC-VQA)](#文档视觉问答doc-vqa)
- - [1. 简介](#1-简介)
- - [2. 性能](#2-性能)
- - [3. 效果演示](#3-效果演示)
- - [3.1 SER](#31-ser)
- - [3.2 RE](#32-re)
- - [4. 安装](#4-安装)
- - [4.1 安装依赖](#41-安装依赖)
- - [4.2 安装PaddleOCR(包含 PP-OCR 和 VQA)](#42-安装paddleocr包含-pp-ocr-和-vqa)
- - [5. 使用](#5-使用)
- - [5.1 数据和预训练模型准备](#51-数据和预训练模型准备)
- - [5.2 SER](#52-ser)
- - [5.3 RE](#53-re)
- - [6. 参考链接](#6-参考链接)
+- [1. 简介](#1-简介)
+- [2. 性能](#2-性能)
+- [3. 效果演示](#3-效果演示)
+ - [3.1 SER](#31-ser)
+ - [3.2 RE](#32-re)
+- [4. 安装](#4-安装)
+ - [4.1 安装依赖](#41-安装依赖)
+ - [4.2 安装PaddleOCR(包含 PP-OCR 和 VQA)](#42-安装paddleocr包含-pp-ocr-和-vqa)
+- [5. 使用](#5-使用)
+ - [5.1 数据和预训练模型准备](#51-数据和预训练模型准备)
+ - [5.2 SER](#52-ser)
+ - [5.3 RE](#53-re)
+- [6. 参考链接](#6-参考链接)
+- [License](#license)
# 文档视觉问答(DOC-VQA)
@@ -122,13 +122,13 @@ python3 -m pip install -r ppstructure/vqa/requirements.txt
* 下载处理好的数据集
-处理好的XFUND中文数据集下载地址:[https://paddleocr.bj.bcebos.com/dataset/XFUND.tar](https://paddleocr.bj.bcebos.com/dataset/XFUND.tar)。
+处理好的XFUND中文数据集下载地址:[链接](https://paddleocr.bj.bcebos.com/ppstructure/dataset/XFUND.tar)。
下载并解压该数据集,解压后将数据集放置在当前目录下。
```shell
-wget https://paddleocr.bj.bcebos.com/dataset/XFUND.tar
+wget https://paddleocr.bj.bcebos.com/ppstructure/dataset/XFUND.tar
```
* 转换数据集
@@ -183,16 +183,16 @@ CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/vqa/ser/layoutxlm.yml -o
```
最终会打印出`precision`, `recall`, `hmean`等指标
-* 使用`OCR引擎 + SER`串联预测
+* 基于训练引擎的`OCR + SER`串联预测
-使用如下命令即可完成`OCR引擎 + SER`的串联预测, 以SER预训练模型为例:
+使用如下命令即可完成基于训练引擎的`OCR + SER`的串联预测, 以基于LayoutXLM的SER模型为例:
```shell
CUDA_VISIBLE_DEVICES=0 python3 tools/infer_vqa_token_ser.py -c configs/vqa/ser/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/ Global.infer_img=doc/vqa/input/zh_val_42.jpg
```
最终会在`config.Global.save_res_path`字段所配置的目录下保存预测结果可视化图像以及预测结果文本文件,预测结果文本文件名为`infer_results.txt`。
-* 对`OCR引擎 + SER`预测系统进行端到端评估
+* 对`OCR + SER`预测系统进行端到端评估
首先使用 `tools/infer_vqa_token_ser.py` 脚本完成数据集的预测,然后使用下面的命令进行评估。
@@ -200,6 +200,24 @@ CUDA_VISIBLE_DEVICES=0 python3 tools/infer_vqa_token_ser.py -c configs/vqa/ser/l
export CUDA_VISIBLE_DEVICES=0
python3 tools/eval_with_label_end2end.py --gt_json_path XFUND/zh_val/xfun_normalize_val.json --pred_json_path output_res/infer_results.txt
```
+* 模型导出
+
+使用如下命令即可完成SER模型的模型导出, 以基于LayoutXLM的SER模型为例:
+
+```shell
+python3.7 tools/export_model.py -c configs/vqa/ser/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/ Global.save_inference_dir=output/ser/infer
+```
+转换后的模型会存放在`Global.save_inference_dir`字段指定的目录下。
+
+* 基于预测引擎的`OCR + SER`串联预测
+
+使用如下命令即可完成基于预测引擎的`OCR + SER`的串联预测, 以基于LayoutXLM的SER模型为例:
+
+```shell
+cd ppstructure
+CUDA_VISIBLE_DEVICES=0 python3.7 vqa/predict_vqa_token_ser.py --vqa_algorithm=LayoutXLM --ser_model_dir=../output/ser/infer --ser_dict_path=../train_data/XFUND/class_list_xfun.txt --image_dir=docs/vqa/input/zh_val_42.jpg --output=output
+```
+预测成功后,可视化图片和结果会保存在`output`字段指定的目录下
### 5.3 RE
@@ -236,16 +254,24 @@ CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/vqa/re/layoutxlm.yml -o
```
最终会打印出`precision`, `recall`, `hmean`等指标
-* 使用`OCR引擎 + SER + RE`串联预测
+* 基于训练引擎的`OCR + SER + RE`串联预测
-使用如下命令即可完成`OCR引擎 + SER + RE`的串联预测, 以预训练SER和RE模型为例:
+使用如下命令即可完成基于训练引擎的`OCR + SER + RE`串联预测, 以基于LayoutXLMSER和RE模型为例:
```shell
export CUDA_VISIBLE_DEVICES=0
-python3 tools/infer_vqa_token_ser_re.py -c configs/vqa/re/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/re_LayoutXLM_xfun_zh/ Global.infer_img=doc/vqa/input/zh_val_21.jpg -c_ser configs/vqa/ser/layoutxlm.yml -o_ser Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/
+python3 tools/infer_vqa_token_ser_re.py -c configs/vqa/re/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/re_LayoutXLM_xfun_zh/ Global.infer_img=ppstructure/docs/vqa/input/zh_val_21.jpg -c_ser configs/vqa/ser/layoutxlm.yml -o_ser Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/
```
最终会在`config.Global.save_res_path`字段所配置的目录下保存预测结果可视化图像以及预测结果文本文件,预测结果文本文件名为`infer_results.txt`。
+* 模型导出
+
+cooming soon
+
+* 基于预测引擎的`OCR + SER + RE`串联预测
+
+cooming soon
+
## 6. 参考链接
- LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding, https://arxiv.org/pdf/2104.08836.pdf
diff --git a/ppstructure/vqa/labels/labels_ser.txt b/ppstructure/vqa/labels/labels_ser.txt
deleted file mode 100644
index 508e48112412f62538baf0c78bcf99ec8945196e..0000000000000000000000000000000000000000
--- a/ppstructure/vqa/labels/labels_ser.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-QUESTION
-ANSWER
-HEADER
diff --git a/ppstructure/vqa/predict_vqa_token_ser.py b/ppstructure/vqa/predict_vqa_token_ser.py
new file mode 100644
index 0000000000000000000000000000000000000000..de0bbfe72d80d9a16de8b09657a98dc5285bb348
--- /dev/null
+++ b/ppstructure/vqa/predict_vqa_token_ser.py
@@ -0,0 +1,169 @@
+# 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.
+import os
+import sys
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../..')))
+
+os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
+
+import cv2
+import json
+import numpy as np
+import time
+
+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.visual import draw_ser_results
+from ppocr.utils.utility import get_image_file_list, check_and_read_gif
+from ppstructure.utility import parse_args
+
+from paddleocr import PaddleOCR
+
+logger = get_logger()
+
+
+class SerPredictor(object):
+ def __init__(self, args):
+ self.ocr_engine = PaddleOCR(use_angle_cls=False, show_log=False)
+
+ pre_process_list = [{
+ 'VQATokenLabelEncode': {
+ 'algorithm': args.vqa_algorithm,
+ 'class_path': args.ser_dict_path,
+ 'contains_re': False,
+ 'ocr_engine': self.ocr_engine
+ }
+ }, {
+ 'VQATokenPad': {
+ 'max_seq_len': 512,
+ 'return_attention_mask': True
+ }
+ }, {
+ 'VQASerTokenChunk': {
+ 'max_seq_len': 512,
+ 'return_attention_mask': True
+ }
+ }, {
+ 'Resize': {
+ 'size': [224, 224]
+ }
+ }, {
+ 'NormalizeImage': {
+ 'std': [58.395, 57.12, 57.375],
+ 'mean': [123.675, 116.28, 103.53],
+ 'scale': '1',
+ 'order': 'hwc'
+ }
+ }, {
+ 'ToCHWImage': None
+ }, {
+ 'KeepKeys': {
+ 'keep_keys': [
+ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids',
+ 'image', 'labels', 'segment_offset_id', 'ocr_info',
+ 'entities'
+ ]
+ }
+ }]
+ postprocess_params = {
+ 'name': 'VQASerTokenLayoutLMPostProcess',
+ "class_path": args.ser_dict_path,
+ }
+
+ self.preprocess_op = create_operators(pre_process_list,
+ {'infer_mode': True})
+ self.postprocess_op = build_post_process(postprocess_params)
+ self.predictor, self.input_tensor, self.output_tensors, self.config = \
+ utility.create_predictor(args, 'ser', logger)
+
+ def __call__(self, img):
+ ori_im = img.copy()
+ data = {'image': img}
+ data = transform(data, self.preprocess_op)
+ img = data[0]
+ if img is None:
+ return None, 0
+ img = np.expand_dims(img, axis=0)
+ img = img.copy()
+ starttime = time.time()
+
+ for idx in range(len(self.input_tensor)):
+ expand_input = np.expand_dims(data[idx], axis=0)
+ self.input_tensor[idx].copy_from_cpu(expand_input)
+
+ self.predictor.run()
+
+ outputs = []
+ for output_tensor in self.output_tensors:
+ output = output_tensor.copy_to_cpu()
+ outputs.append(output)
+ preds = outputs[0]
+
+ post_result = self.postprocess_op(
+ preds, segment_offset_ids=[data[6]], ocr_infos=[data[7]])
+ elapse = time.time() - starttime
+ return post_result, elapse
+
+
+def main(args):
+ image_file_list = get_image_file_list(args.image_dir)
+ ser_predictor = SerPredictor(args)
+ count = 0
+ total_time = 0
+
+ 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)
+ img = img[:, :, ::-1]
+ if img is None:
+ logger.info("error in loading image:{}".format(image_file))
+ continue
+ ser_res, elapse = ser_predictor(img)
+ ser_res = ser_res[0]
+
+ res_str = '{}\t{}\n'.format(
+ image_file,
+ json.dumps(
+ {
+ "ocr_info": ser_res,
+ }, ensure_ascii=False))
+ f_w.write(res_str)
+
+ img_res = draw_ser_results(
+ image_file,
+ ser_res,
+ font_path="../doc/fonts/simfang.ttf", )
+
+ img_save_path = os.path.join(args.output,
+ os.path.basename(image_file))
+ cv2.imwrite(img_save_path, img_res)
+ 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__":
+ main(parse_args())
diff --git a/ppstructure/vqa/requirements.txt b/ppstructure/vqa/requirements.txt
index 0042ec0baedcc3e7bbecb922d10b93c95219219d..fcd882274c4402ba2a1d34f20ee6e2befa157121 100644
--- a/ppstructure/vqa/requirements.txt
+++ b/ppstructure/vqa/requirements.txt
@@ -1,4 +1,7 @@
sentencepiece
yacs
seqeval
-paddlenlp>=2.2.1
\ No newline at end of file
+paddlenlp>=2.2.1
+pypandoc
+attrdict
+python_docx
\ No newline at end of file
diff --git a/ppstructure/vqa/tools/trans_funsd_label.py b/ppstructure/vqa/tools/trans_funsd_label.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef7d1db010a925b37d285befe77aa202db2141d9
--- /dev/null
+++ b/ppstructure/vqa/tools/trans_funsd_label.py
@@ -0,0 +1,151 @@
+# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import os
+import sys
+import cv2
+import numpy as np
+from copy import deepcopy
+
+
+def trans_poly_to_bbox(poly):
+ x1 = np.min([p[0] for p in poly])
+ x2 = np.max([p[0] for p in poly])
+ y1 = np.min([p[1] for p in poly])
+ y2 = np.max([p[1] for p in poly])
+ return [x1, y1, x2, y2]
+
+
+def get_outer_poly(bbox_list):
+ x1 = min([bbox[0] for bbox in bbox_list])
+ y1 = min([bbox[1] for bbox in bbox_list])
+ x2 = max([bbox[2] for bbox in bbox_list])
+ y2 = max([bbox[3] for bbox in bbox_list])
+ return [[x1, y1], [x2, y1], [x2, y2], [x1, y2]]
+
+
+def load_funsd_label(image_dir, anno_dir):
+ imgs = os.listdir(image_dir)
+ annos = os.listdir(anno_dir)
+
+ imgs = [img.replace(".png", "") for img in imgs]
+ annos = [anno.replace(".json", "") for anno in annos]
+
+ fn_info_map = dict()
+ for anno_fn in annos:
+ res = []
+ with open(os.path.join(anno_dir, anno_fn + ".json"), "r") as fin:
+ infos = json.load(fin)
+ infos = infos["form"]
+ old_id2new_id_map = dict()
+ global_new_id = 0
+ for info in infos:
+ if info["text"] is None:
+ continue
+ words = info["words"]
+ if len(words) <= 0:
+ continue
+ word_idx = 1
+ curr_bboxes = [words[0]["box"]]
+ curr_texts = [words[0]["text"]]
+ while word_idx < len(words):
+ # switch to a new link
+ if words[word_idx]["box"][0] + 10 <= words[word_idx - 1][
+ "box"][2]:
+ if len("".join(curr_texts[0])) > 0:
+ res.append({
+ "transcription": " ".join(curr_texts),
+ "label": info["label"],
+ "points": get_outer_poly(curr_bboxes),
+ "linking": info["linking"],
+ "id": global_new_id,
+ })
+ if info["id"] not in old_id2new_id_map:
+ old_id2new_id_map[info["id"]] = []
+ old_id2new_id_map[info["id"]].append(global_new_id)
+ global_new_id += 1
+ curr_bboxes = [words[word_idx]["box"]]
+ curr_texts = [words[word_idx]["text"]]
+ else:
+ curr_bboxes.append(words[word_idx]["box"])
+ curr_texts.append(words[word_idx]["text"])
+ word_idx += 1
+ if len("".join(curr_texts[0])) > 0:
+ res.append({
+ "transcription": " ".join(curr_texts),
+ "label": info["label"],
+ "points": get_outer_poly(curr_bboxes),
+ "linking": info["linking"],
+ "id": global_new_id,
+ })
+ if info["id"] not in old_id2new_id_map:
+ old_id2new_id_map[info["id"]] = []
+ old_id2new_id_map[info["id"]].append(global_new_id)
+ global_new_id += 1
+ res = sorted(
+ res, key=lambda r: (r["points"][0][1], r["points"][0][0]))
+ for i in range(len(res) - 1):
+ for j in range(i, 0, -1):
+ if abs(res[j + 1]["points"][0][1] - res[j]["points"][0][1]) < 20 and \
+ (res[j + 1]["points"][0][0] < res[j]["points"][0][0]):
+ tmp = deepcopy(res[j])
+ res[j] = deepcopy(res[j + 1])
+ res[j + 1] = deepcopy(tmp)
+ else:
+ break
+ # re-generate unique ids
+ for idx, r in enumerate(res):
+ new_links = []
+ for link in r["linking"]:
+ # illegal links will be removed
+ if link[0] not in old_id2new_id_map or link[
+ 1] not in old_id2new_id_map:
+ continue
+ for src in old_id2new_id_map[link[0]]:
+ for dst in old_id2new_id_map[link[1]]:
+ new_links.append([src, dst])
+ res[idx]["linking"] = deepcopy(new_links)
+
+ fn_info_map[anno_fn] = res
+
+ return fn_info_map
+
+
+def main():
+ test_image_dir = "train_data/FUNSD/testing_data/images/"
+ test_anno_dir = "train_data/FUNSD/testing_data/annotations/"
+ test_output_dir = "train_data/FUNSD/test.json"
+
+ fn_info_map = load_funsd_label(test_image_dir, test_anno_dir)
+ with open(test_output_dir, "w") as fout:
+ for fn in fn_info_map:
+ fout.write(fn + ".png" + "\t" + json.dumps(
+ fn_info_map[fn], ensure_ascii=False) + "\n")
+
+ train_image_dir = "train_data/FUNSD/training_data/images/"
+ train_anno_dir = "train_data/FUNSD/training_data/annotations/"
+ train_output_dir = "train_data/FUNSD/train.json"
+
+ fn_info_map = load_funsd_label(train_image_dir, train_anno_dir)
+ with open(train_output_dir, "w") as fout:
+ for fn in fn_info_map:
+ fout.write(fn + ".png" + "\t" + json.dumps(
+ fn_info_map[fn], ensure_ascii=False) + "\n")
+ print("====ok====")
+ return
+
+
+if __name__ == "__main__":
+ main()
diff --git a/ppstructure/vqa/tools/trans_xfun_data.py b/ppstructure/vqa/tools/trans_xfun_data.py
index 93ec98163c6cec96ec93399c1d41524200ddc499..11d221bea40367f091b3e09dde42e87f2217a617 100644
--- a/ppstructure/vqa/tools/trans_xfun_data.py
+++ b/ppstructure/vqa/tools/trans_xfun_data.py
@@ -21,26 +21,22 @@ def transfer_xfun_data(json_path=None, output_file=None):
json_info = json.loads(lines[0])
documents = json_info["documents"]
- label_info = {}
with open(output_file, "w", encoding='utf-8') as fout:
for idx, document in enumerate(documents):
+ label_info = []
img_info = document["img"]
document = document["document"]
image_path = img_info["fname"]
- label_info["height"] = img_info["height"]
- label_info["width"] = img_info["width"]
-
- label_info["ocr_info"] = []
-
for doc in document:
- label_info["ocr_info"].append({
- "text": doc["text"],
+ x1, y1, x2, y2 = doc["box"]
+ points = [[x1, y1], [x2, y1], [x2, y2], [x1, y2]]
+ label_info.append({
+ "transcription": doc["text"],
"label": doc["label"],
- "bbox": doc["box"],
+ "points": points,
"id": doc["id"],
- "linking": doc["linking"],
- "words": doc["words"]
+ "linking": doc["linking"]
})
fout.write(image_path + "\t" + json.dumps(
diff --git a/test_tipc/benchmark_train.sh b/test_tipc/benchmark_train.sh
index e3e4d627fa27f3a34ae0ae47a8613d6ec0a0f60e..c74054ed557f4d42c7db452fe41af6839f8ea6b7 100644
--- a/test_tipc/benchmark_train.sh
+++ b/test_tipc/benchmark_train.sh
@@ -139,8 +139,8 @@ else
device_num=${params_list[4]}
IFS=";"
- if [ ${precision} = "null" ];then
- precision="fp32"
+ if [ ${precision} = "fp16" ];then
+ precision="amp"
fi
fp_items_list=($precision)
@@ -150,10 +150,16 @@ fi
IFS="|"
for batch_size in ${batch_size_list[*]}; do
- for precision in ${fp_items_list[*]}; do
+ for train_precision in ${fp_items_list[*]}; do
for device_num in ${device_num_list[*]}; do
# sed batchsize and precision
- func_sed_params "$FILENAME" "${line_precision}" "$precision"
+ if [ ${train_precision} = "amp" ];then
+ precision="fp16"
+ else
+ precision="fp32"
+ fi
+
+ func_sed_params "$FILENAME" "${line_precision}" "$train_precision"
func_sed_params "$FILENAME" "${line_batchsize}" "$MODE=$batch_size"
func_sed_params "$FILENAME" "${line_epoch}" "$MODE=$epoch"
gpu_id=$(set_gpu_id $device_num)
diff --git a/test_tipc/configs/det_mv3_db_v2_0/train_infer_python.txt b/test_tipc/configs/det_mv3_db_v2_0/train_infer_python.txt
index ab3aa59b601db58b48cf18de79f77710611e2596..2c8aa953449c4b97790842bb90256280b8b20d9a 100644
--- a/test_tipc/configs/det_mv3_db_v2_0/train_infer_python.txt
+++ b/test_tipc/configs/det_mv3_db_v2_0/train_infer_python.txt
@@ -54,6 +54,6 @@ random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}]
===========================train_benchmark_params==========================
batch_size:8|16
fp_items:fp32|fp16
-epoch:2
+epoch:15
--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096
diff --git a/test_tipc/configs/det_r18_vd_db_v2_0/train_infer_python.txt b/test_tipc/configs/det_r18_vd_db_v2_0/train_infer_python.txt
index 33e4dbf2337f3799328516119a213bc0f14af9fe..df88c0e5434511fb48deac699e8f67fc535765d3 100644
--- a/test_tipc/configs/det_r18_vd_db_v2_0/train_infer_python.txt
+++ b/test_tipc/configs/det_r18_vd_db_v2_0/train_infer_python.txt
@@ -54,5 +54,5 @@ random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}]
===========================train_benchmark_params==========================
batch_size:8|16
fp_items:fp32|fp16
-epoch:2
+epoch:15
--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
diff --git a/test_tipc/configs/det_r50_db_plusplus/train_infer_python.txt b/test_tipc/configs/det_r50_db_plusplus/train_infer_python.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bcfdef63942bfdebe041d0a8fbe5e00833b96c46
--- /dev/null
+++ b/test_tipc/configs/det_r50_db_plusplus/train_infer_python.txt
@@ -0,0 +1,59 @@
+===========================train_params===========================
+model_name:det_r50_db_plusplus
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c configs/det/det_r50_db++_icdar15.yml -o Global.pretrained_model=./pretrain_models/ResNet50_dcn_asf_synthtext_pretrained
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params===========================
+eval:null
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.checkpoints:
+norm_export:tools/export_model.py -c configs/det/det_r50_db++_icdar15.yml -o
+quant_export:null
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+inference_dir:null
+train_model:./inference/det_r50_db++_train/best_accuracy
+infer_export:tools/export_model.py -c configs/det/det_r50_db++_icdar15.yml -o
+infer_quant:False
+inference:tools/infer/predict_det.py --det_algorithm="DB++"
+--use_gpu:True|False
+--enable_mkldnn:False
+--cpu_threads:6
+--rec_batch_num:1
+--use_tensorrt:False
+--precision:fp32
+--det_model_dir:
+--image_dir:./inference/ch_det_data_50/all-sum-510/
+null:null
+--benchmark:True
+null:null
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}]
+===========================train_benchmark_params==========================
+batch_size:8|16
+fp_items:fp32|fp16
+epoch:2
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096
diff --git a/test_tipc/configs/det_r50_vd_east_v2_0/train_infer_python.txt b/test_tipc/configs/det_r50_vd_east_v2_0/train_infer_python.txt
index 8477a4fa74f7a0617104aa83617fc6f61b8234b3..24e4d760c37828c213741b9ff127d55df2f9335a 100644
--- a/test_tipc/configs/det_r50_vd_east_v2_0/train_infer_python.txt
+++ b/test_tipc/configs/det_r50_vd_east_v2_0/train_infer_python.txt
@@ -1,13 +1,13 @@
===========================train_params===========================
model_name:det_r50_vd_east_v2_0
python:python3.7
-gpu_list:0
+gpu_list:0|0,1
Global.use_gpu:True|True
Global.auto_cast:fp32
Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=500
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
-Global.pretrained_model:null
+Global.pretrained_model:./pretrain_models/det_r50_vd_east_v2.0_train/best_accuracy
train_model_name:latest
train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
null:null
diff --git a/test_tipc/configs/det_r50_vd_pse_v2_0/train_infer_python.txt b/test_tipc/configs/det_r50_vd_pse_v2_0/train_infer_python.txt
index 62da89fe1c8e3a7c2b7586eae6b2589f94237a2e..53511e6ae21003cb9df6a92d3931577fbbef5b18 100644
--- a/test_tipc/configs/det_r50_vd_pse_v2_0/train_infer_python.txt
+++ b/test_tipc/configs/det_r50_vd_pse_v2_0/train_infer_python.txt
@@ -54,5 +54,5 @@ random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}]
===========================train_benchmark_params==========================
batch_size:8
fp_items:fp32|fp16
-epoch:2
+epoch:10
--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
diff --git a/test_tipc/configs/en_table_structure/table_mv3.yml b/test_tipc/configs/en_table_structure/table_mv3.yml
index adf326bd02aeff4683c8f37a704125b4e426efa9..281038b968a5bf829483882117d779ec7de1976d 100755
--- a/test_tipc/configs/en_table_structure/table_mv3.yml
+++ b/test_tipc/configs/en_table_structure/table_mv3.yml
@@ -9,16 +9,15 @@ Global:
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/pubtabnet/train
- label_file_path: ./train_data/pubtabnet/train.jsonl
+ label_file_list: [./train_data/pubtabnet/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
@@ -93,23 +95,28 @@ Eval:
dataset:
name: PubTabDataSet
data_dir: ./train_data/pubtabnet/test/
- label_file_path: ./train_data/pubtabnet/test.jsonl
+ label_file_list: [./train_data/pubtabnet/test.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/test_tipc/configs/en_table_structure/train_pact_infer_python.txt b/test_tipc/configs/en_table_structure/train_pact_infer_python.txt
index f62e8b68bc6c1af06a65a8dfb438d5d63576e123..9890b906a1d3b1127352af567dca0d7186f94694 100644
--- a/test_tipc/configs/en_table_structure/train_pact_infer_python.txt
+++ b/test_tipc/configs/en_table_structure/train_pact_infer_python.txt
@@ -6,7 +6,7 @@ Global.use_gpu:True|True
Global.auto_cast:fp32
Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=50
Global.save_model_dir:./output/
-Train.loader.batch_size_per_card:lite_train_lite_infer=16|whole_train_whole_infer=128
+Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=2
Global.pretrained_model:./pretrain_models/en_ppocr_mobile_v2.0_table_structure_train/best_accuracy
train_model_name:latest
train_infer_img_dir:./ppstructure/docs/table/table.jpg
diff --git a/test_tipc/configs/table_master/table_master.yml b/test_tipc/configs/table_master/table_master.yml
new file mode 100644
index 0000000000000000000000000000000000000000..c519b5b8f464d8843888155387b74a8416821f2f
--- /dev/null
+++ b/test_tipc/configs/table_master/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/pubtabnet/train
+ label_file_list: [./train_data/pubtabnet/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: ./train_data/pubtabnet/test/
+ label_file_list: [./train_data/pubtabnet/test.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/test_tipc/configs/table_master/train_infer_python.txt b/test_tipc/configs/table_master/train_infer_python.txt
new file mode 100644
index 0000000000000000000000000000000000000000..56b8e636026939ae8cd700308690010e1300d8f6
--- /dev/null
+++ b/test_tipc/configs/table_master/train_infer_python.txt
@@ -0,0 +1,53 @@
+===========================train_params===========================
+model_name:table_master
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:fp32
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=17
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
+Global.pretrained_model:./pretrain_models/table_structure_tablemaster_train/best_accuracy
+train_model_name:latest
+train_infer_img_dir:./ppstructure/docs/table/table.jpg
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c test_tipc/configs/table_master/table_master.yml -o Global.print_batch_step=10
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params===========================
+eval:null
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.checkpoints:
+norm_export:tools/export_model.py -c test_tipc/configs/table_master/table_master.yml -o
+quant_export:
+fpgm_export:
+distill_export:null
+export1:null
+export2:null
+##
+infer_model:null
+infer_export:null
+infer_quant:False
+inference:ppstructure/table/predict_structure.py --table_char_dict_path=./ppocr/utils/dict/table_master_structure_dict.txt --image_dir=./ppstructure/docs/table/table.jpg --output ./output/table --table_algorithm=TableMaster --table_max_len=480
+--use_gpu:True|False
+--enable_mkldnn:False
+--cpu_threads:6
+--rec_batch_num:1
+--use_tensorrt:False
+--precision:fp32
+--table_model_dir:
+--image_dir:./ppstructure/docs/table/table.jpg
+null:null
+--benchmark:False
+null:null
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[3,480,480]}]
diff --git a/test_tipc/prepare.sh b/test_tipc/prepare.sh
index 2c9bd2901b52ff7d4b6af483a9aa201aef339099..ec6dece42a0126e6d05405b3262c1c1d24f0a376 100644
--- a/test_tipc/prepare.sh
+++ b/test_tipc/prepare.sh
@@ -22,13 +22,19 @@ trainer_list=$(func_parser_value "${lines[14]}")
if [ ${MODE} = "benchmark_train" ];then
pip install -r requirements.txt
- if [[ ${model_name} =~ "det_mv3_db_v2_0" || ${model_name} =~ "det_r50_vd_east_v2_0" || ${model_name} =~ "det_r50_vd_pse_v2_0" || ${model_name} =~ "det_r18_db_v2_0" ]];then
+ if [[ ${model_name} =~ "det_mv3_db_v2_0" || ${model_name} =~ "det_r50_vd_pse_v2_0" || ${model_name} =~ "det_r18_db_v2_0" ]];then
rm -rf ./train_data/icdar2015
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/MobileNetV3_large_x0_5_pretrained.pdparams --no-check-certificate
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar --no-check-certificate
cd ./train_data/ && tar xf icdar2015.tar && cd ../
fi
- if [[ ${model_name} =~ "det_r50_vd_east_v2_0" || ${model_name} =~ "det_r50_vd_pse_v2_0" ]];then
+ if [[ ${model_name} =~ "det_r50_vd_east_v2_0" ]]; then
+ wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar --no-check-certificate
+ cd ./pretrain_models/ && tar xf det_r50_vd_east_v2.0_train.tar && cd ../
+ wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar --no-check-certificate
+ cd ./train_data/ && tar xf icdar2015.tar && cd ../
+ fi
+ if [[ ${model_name} =~ "det_r50_vd_pse_v2_0" ]];then
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/ResNet50_vd_ssld_pretrained.pdparams --no-check-certificate
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar --no-check-certificate
cd ./train_data/ && tar xf icdar2015.tar && cd ../
@@ -52,13 +58,20 @@ if [ ${MODE} = "lite_train_lite_infer" ];then
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_distill_train.tar --no-check-certificate
cd ./pretrain_models/ && tar xf ch_PP-OCRv3_det_distill_train.tar && cd ../
fi
- if [ ${model_name} == "en_table_structure" ];then
+ if [ ${model_name} == "en_table_structure" ] || [ ${model_name} == "en_table_structure_PACT" ];then
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_structure_train.tar --no-check-certificate
cd ./pretrain_models/ && tar xf en_ppocr_mobile_v2.0_table_structure_train.tar && cd ../
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar --no-check-certificate
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar --no-check-certificate
cd ./inference/ && tar xf en_ppocr_mobile_v2.0_table_det_infer.tar && tar xf en_ppocr_mobile_v2.0_table_rec_infer.tar && cd ../
fi
+ if [[ ${model_name} =~ "det_r50_db_plusplus" ]];then
+ wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/ResNet50_dcn_asf_synthtext_pretrained.pdparams --no-check-certificate
+ fi
+ if [ ${model_name} == "table_master" ];then
+ wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar --no-check-certificate
+ cd ./pretrain_models/ && tar xf table_structure_tablemaster_train.tar && cd ../
+ fi
cd ./pretrain_models/ && tar xf det_mv3_db_v2.0_train.tar && cd ../
rm -rf ./train_data/icdar2015
rm -rf ./train_data/ic15_data
@@ -120,6 +133,10 @@ if [ ${MODE} = "lite_train_lite_infer" ];then
wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_east_v2.0_train.tar --no-check-certificate
cd ./pretrain_models/ && tar xf det_mv3_east_v2.0_train.tar && cd ../
fi
+ if [ ${model_name} == "det_r50_vd_east_v2_0" ]; then
+ wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar --no-check-certificate
+ cd ./pretrain_models/ && tar xf det_r50_vd_east_v2.0_train.tar && cd ../
+ fi
elif [ ${MODE} = "whole_train_whole_infer" ];then
wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams --no-check-certificate
diff --git a/test_tipc/readme.md b/test_tipc/readme.md
index effb2f168b6cc91012bef3de120de9e98a21dbda..1c637d76f99fffdfdc5a053fa0c5b9336fe4b731 100644
--- a/test_tipc/readme.md
+++ b/test_tipc/readme.md
@@ -54,6 +54,7 @@
| NRTR |rec_mtb_nrtr | 识别 | 支持 | 多机多卡 混合精度 | - | - |
| SAR |rec_r31_sar | 识别 | 支持 | 多机多卡 混合精度 | - | - |
| PGNet |rec_r34_vd_none_none_ctc_v2.0 | 端到端| 支持 | 多机多卡 混合精度 | - | - |
+| TableMaster |table_structure_tablemaster_train | 表格识别| 支持 | 多机多卡 混合精度 | - | - |
diff --git a/test_tipc/test_ptq_inference_python.sh b/test_tipc/test_ptq_inference_python.sh
index c1aa3daa6c0c647d7a9f9980e7698a85ee09ca78..e2939fd5e638ad0f6b4c44422a6fec6459903d1c 100644
--- a/test_tipc/test_ptq_inference_python.sh
+++ b/test_tipc/test_ptq_inference_python.sh
@@ -139,7 +139,7 @@ if [ ${MODE} = "whole_infer" ]; then
save_infer_dir="${infer_model}_klquant"
set_export_weight=$(func_set_params "${export_weight}" "${infer_model}")
set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_dir}")
- export_log_path="${LOG_PATH}/_export_${Count}.log"
+ export_log_path="${LOG_PATH}/${MODE}_export_${Count}.log"
export_cmd="${python} ${infer_run_exports[Count]} ${set_export_weight} ${set_save_infer_key} > ${export_log_path} 2>&1 "
echo ${infer_run_exports[Count]}
echo $export_cmd
diff --git a/test_tipc/test_serving_infer_cpp.sh b/test_tipc/test_serving_infer_cpp.sh
index 4088c66f576187215e5945793868d13fc74005af..0be6a45adf3105f088a96336dddfbe9ac612f19b 100644
--- a/test_tipc/test_serving_infer_cpp.sh
+++ b/test_tipc/test_serving_infer_cpp.sh
@@ -87,11 +87,12 @@ function func_serving(){
set_image_dir=$(func_set_params "${image_dir_key}" "${image_dir_value}")
python_list=(${python_list})
cd ${serving_dir_value}
+
# cpp serving
for gpu_id in ${gpu_value[*]}; do
if [ ${gpu_id} = "null" ]; then
server_log_path="${LOG_PATH}/cpp_server_cpu.log"
- web_service_cpp_cmd="${python_list[0]} ${web_service_py} --model ${det_server_value} ${rec_server_value} ${op_key} ${op_value} ${port_key} ${port_value} > ${server_log_path} 2>&1 "
+ web_service_cpp_cmd="nohup ${python_list[0]} ${web_service_py} --model ${det_server_value} ${rec_server_value} ${op_key} ${op_value} ${port_key} ${port_value} > ${server_log_path} 2>&1 &"
eval $web_service_cpp_cmd
last_status=${PIPESTATUS[0]}
status_check $last_status "${web_service_cpp_cmd}" "${status_log}" "${model_name}"
@@ -105,7 +106,7 @@ function func_serving(){
ps ux | grep -i ${port_value} | awk '{print $2}' | xargs kill -s 9
else
server_log_path="${LOG_PATH}/cpp_server_gpu.log"
- web_service_cpp_cmd="${python_list[0]} ${web_service_py} --model ${det_server_value} ${rec_server_value} ${op_key} ${op_value} ${port_key} ${port_value} ${gpu_key} ${gpu_id} > ${server_log_path} 2>&1 "
+ web_service_cpp_cmd="nohup ${python_list[0]} ${web_service_py} --model ${det_server_value} ${rec_server_value} ${op_key} ${op_value} ${port_key} ${port_value} ${gpu_key} ${gpu_id} > ${server_log_path} 2>&1 &"
eval $web_service_cpp_cmd
sleep 5s
_save_log_path="${LOG_PATH}/cpp_client_gpu.log"
diff --git a/test_tipc/test_serving_infer_python.sh b/test_tipc/test_serving_infer_python.sh
index 57dab6aeb5dab31605a56abd072cb1568368a66c..4ccccc06e23ce086e7dac1f3446aae9130605444 100644
--- a/test_tipc/test_serving_infer_python.sh
+++ b/test_tipc/test_serving_infer_python.sh
@@ -112,7 +112,7 @@ function func_serving(){
cd ${serving_dir_value}
python=${python_list[0]}
-
+
# python serving
for use_gpu in ${web_use_gpu_list[*]}; do
if [ ${use_gpu} = "null" ]; then
@@ -123,19 +123,19 @@ function func_serving(){
if [ ${model_name} = "ch_PP-OCRv2" ] || [ ${model_name} = "ch_PP-OCRv3" ] || [ ${model_name} = "ch_ppocr_mobile_v2.0" ] || [ ${model_name} = "ch_ppocr_server_v2.0" ]; then
set_det_model_config=$(func_set_params "${det_server_key}" "${det_server_value}")
set_rec_model_config=$(func_set_params "${rec_server_key}" "${rec_server_value}")
- web_service_cmd="${python} ${web_service_py} ${web_use_gpu_key}="" ${web_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_det_model_config} ${set_rec_model_config} > ${server_log_path} 2>&1 "
+ web_service_cmd="nohup ${python} ${web_service_py} ${web_use_gpu_key}="" ${web_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_det_model_config} ${set_rec_model_config} > ${server_log_path} 2>&1 &"
eval $web_service_cmd
last_status=${PIPESTATUS[0]}
status_check $last_status "${web_service_cmd}" "${status_log}" "${model_name}"
elif [[ ${model_name} =~ "det" ]]; then
set_det_model_config=$(func_set_params "${det_server_key}" "${det_server_value}")
- web_service_cmd="${python} ${web_service_py} ${web_use_gpu_key}="" ${web_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_det_model_config} > ${server_log_path} 2>&1 "
+ web_service_cmd="nohup ${python} ${web_service_py} ${web_use_gpu_key}="" ${web_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_det_model_config} > ${server_log_path} 2>&1 &"
eval $web_service_cmd
last_status=${PIPESTATUS[0]}
status_check $last_status "${web_service_cmd}" "${status_log}" "${model_name}"
elif [[ ${model_name} =~ "rec" ]]; then
set_rec_model_config=$(func_set_params "${rec_server_key}" "${rec_server_value}")
- web_service_cmd="${python} ${web_service_py} ${web_use_gpu_key}="" ${web_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_rec_model_config} > ${server_log_path} 2>&1 "
+ web_service_cmd="nohup ${python} ${web_service_py} ${web_use_gpu_key}="" ${web_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_rec_model_config} > ${server_log_path} 2>&1 &"
eval $web_service_cmd
last_status=${PIPESTATUS[0]}
status_check $last_status "${web_service_cmd}" "${status_log}" "${model_name}"
@@ -174,19 +174,19 @@ function func_serving(){
if [ ${model_name} = "ch_PP-OCRv2" ] || [ ${model_name} = "ch_PP-OCRv3" ] || [ ${model_name} = "ch_ppocr_mobile_v2.0" ] || [ ${model_name} = "ch_ppocr_server_v2.0" ]; then
set_det_model_config=$(func_set_params "${det_server_key}" "${det_server_value}")
set_rec_model_config=$(func_set_params "${rec_server_key}" "${rec_server_value}")
- web_service_cmd="${python} ${web_service_py} ${set_tensorrt} ${set_precision} ${set_det_model_config} ${set_rec_model_config} > ${server_log_path} 2>&1 "
+ web_service_cmd="nohup ${python} ${web_service_py} ${set_tensorrt} ${set_precision} ${set_det_model_config} ${set_rec_model_config} > ${server_log_path} 2>&1 &"
eval $web_service_cmd
last_status=${PIPESTATUS[0]}
status_check $last_status "${web_service_cmd}" "${status_log}" "${model_name}"
elif [[ ${model_name} =~ "det" ]]; then
set_det_model_config=$(func_set_params "${det_server_key}" "${det_server_value}")
- web_service_cmd="${python} ${web_service_py} ${set_tensorrt} ${set_precision} ${set_det_model_config} > ${server_log_path} 2>&1 "
+ web_service_cmd="nohup ${python} ${web_service_py} ${set_tensorrt} ${set_precision} ${set_det_model_config} > ${server_log_path} 2>&1 &"
eval $web_service_cmd
last_status=${PIPESTATUS[0]}
status_check $last_status "${web_service_cmd}" "${status_log}" "${model_name}"
elif [[ ${model_name} =~ "rec" ]]; then
set_rec_model_config=$(func_set_params "${rec_server_key}" "${rec_server_value}")
- web_service_cmd="${python} ${web_service_py} ${set_tensorrt} ${set_precision} ${set_rec_model_config} > ${server_log_path} 2>&1 "
+ web_service_cmd="nohup ${python} ${web_service_py} ${set_tensorrt} ${set_precision} ${set_rec_model_config} > ${server_log_path} 2>&1 &"
eval $web_service_cmd
last_status=${PIPESTATUS[0]}
status_check $last_status "${web_service_cmd}" "${status_log}" "${model_name}"
diff --git a/test_tipc/test_train_inference_python.sh b/test_tipc/test_train_inference_python.sh
index fa68cb2632ee69fe361f99093e7a2352006ed283..402f636b1b92fa75380142803c6b513a897a89e4 100644
--- a/test_tipc/test_train_inference_python.sh
+++ b/test_tipc/test_train_inference_python.sh
@@ -193,7 +193,7 @@ if [ ${MODE} = "whole_infer" ]; then
save_infer_dir="${infer_model}"
set_export_weight=$(func_set_params "${export_weight}" "${infer_model}")
set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_dir}")
- export_log_path="${LOG_PATH}/_export_${Count}.log"
+ export_log_path="${LOG_PATH}_export_${Count}.log"
export_cmd="${python} ${infer_run_exports[Count]} ${set_export_weight} ${set_save_infer_key} > ${export_log_path} 2>&1 "
echo ${infer_run_exports[Count]}
echo $export_cmd
@@ -265,7 +265,7 @@ else
if [ ${run_train} = "null" ]; then
continue
fi
- set_autocast=$(func_set_params "${autocast_key}" "${autocast}")
+
set_epoch=$(func_set_params "${epoch_key}" "${epoch_num}")
set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_value}")
set_batchsize=$(func_set_params "${train_batch_key}" "${train_batch_value}")
@@ -287,14 +287,15 @@ else
set_save_model=$(func_set_params "${save_model_key}" "${save_log}")
if [ ${#gpu} -le 2 ];then # train with cpu or single gpu
- cmd="${python} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config} "
+ cmd="${python} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_batchsize} ${set_train_params1} ${set_amp_config} "
elif [ ${#ips} -le 15 ];then # train with multi-gpu
- cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}"
+ cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_batchsize} ${set_train_params1} ${set_amp_config}"
else # train with multi-machine
- cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}"
+ cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_batchsize} ${set_train_params1} ${set_amp_config}"
fi
# run train
eval $cmd
+ eval "cat ${save_log}/train.log >> ${save_log}.log"
status_check $? "${cmd}" "${status_log}" "${model_name}"
set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${train_model_name}")
diff --git a/tools/export_model.py b/tools/export_model.py
index b10d41d5b288258ad895cefa7d8cc243eff10546..afecbff8cbb834a5aa5ef3ea1448cf04fbd8c3bb 100755
--- a/tools/export_model.py
+++ b/tools/export_model.py
@@ -97,6 +97,22 @@ def export_single_model(model,
shape=[None, 1, 32, 100], dtype="float32"),
]
model = to_static(model, input_spec=other_shape)
+ elif arch_config["algorithm"] in ["LayoutLM", "LayoutLMv2", "LayoutXLM"]:
+ input_spec = [
+ paddle.static.InputSpec(
+ shape=[None, 512], dtype="int64"), # input_ids
+ paddle.static.InputSpec(
+ shape=[None, 512, 4], dtype="int64"), # bbox
+ paddle.static.InputSpec(
+ shape=[None, 512], dtype="int64"), # attention_mask
+ paddle.static.InputSpec(
+ shape=[None, 512], dtype="int64"), # token_type_ids
+ paddle.static.InputSpec(
+ shape=[None, 3, 224, 224], dtype="int64"), # image
+ ]
+ if arch_config["algorithm"] == "LayoutLM":
+ input_spec.pop(4)
+ model = to_static(model, input_spec=[input_spec])
else:
infer_shape = [3, -1, -1]
if arch_config["model_type"] == "rec":
@@ -110,6 +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=[
@@ -172,7 +190,7 @@ def main():
config["Architecture"]["Head"]["out_channels"] = char_num
model = build_model(config["Architecture"])
- load_model(config, model)
+ load_model(config, model, model_type=config['Architecture']["model_type"])
model.eval()
save_path = config["Global"]["save_inference_dir"]
diff --git a/tools/infer/predict_det.py b/tools/infer/predict_det.py
index 7b6bebf1fbced2de5bb0e4e75840fb8dd7beb374..394a48948b1f284bd405532769b76eeb298668bd 100755
--- a/tools/infer/predict_det.py
+++ b/tools/infer/predict_det.py
@@ -67,6 +67,23 @@ class TextDetector(object):
postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
postprocess_params["use_dilation"] = args.use_dilation
postprocess_params["score_mode"] = args.det_db_score_mode
+ elif self.det_algorithm == "DB++":
+ postprocess_params['name'] = 'DBPostProcess'
+ postprocess_params["thresh"] = args.det_db_thresh
+ postprocess_params["box_thresh"] = args.det_db_box_thresh
+ postprocess_params["max_candidates"] = 1000
+ postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
+ postprocess_params["use_dilation"] = args.use_dilation
+ postprocess_params["score_mode"] = args.det_db_score_mode
+ pre_process_list[1] = {
+ 'NormalizeImage': {
+ 'std': [1.0, 1.0, 1.0],
+ 'mean':
+ [0.48109378172549, 0.45752457890196, 0.40787054090196],
+ 'scale': '1./255.',
+ 'order': 'hwc'
+ }
+ }
elif self.det_algorithm == "EAST":
postprocess_params['name'] = 'EASTPostProcess'
postprocess_params["score_thresh"] = args.det_east_score_thresh
@@ -231,7 +248,7 @@ class TextDetector(object):
preds['f_score'] = outputs[1]
preds['f_tco'] = outputs[2]
preds['f_tvo'] = outputs[3]
- elif self.det_algorithm in ['DB', 'PSE']:
+ elif self.det_algorithm in ['DB', 'PSE', 'DB++']:
preds['maps'] = outputs[0]
elif self.det_algorithm == 'FCE':
for i, output in enumerate(outputs):
diff --git a/tools/infer/utility.py b/tools/infer/utility.py
index 366212f228eec33f11c825bfaf1e360258af9b2e..7eb77dec74bf283936e1143edcb5b5dfc28365bd 100644
--- a/tools/infer/utility.py
+++ b/tools/infer/utility.py
@@ -153,6 +153,8 @@ def create_predictor(args, mode, logger):
model_dir = args.rec_model_dir
elif mode == 'table':
model_dir = args.table_model_dir
+ elif mode == 'ser':
+ model_dir = args.ser_model_dir
else:
model_dir = args.e2e_model_dir
@@ -316,8 +318,13 @@ def create_predictor(args, mode, logger):
# create predictor
predictor = inference.create_predictor(config)
input_names = predictor.get_input_names()
- for name in input_names:
- input_tensor = predictor.get_input_handle(name)
+ if mode in ['ser', 're']:
+ input_tensor = []
+ for name in input_names:
+ input_tensor.append(predictor.get_input_handle(name))
+ else:
+ for name in input_names:
+ input_tensor = predictor.get_input_handle(name)
output_tensors = get_output_tensors(args, mode, predictor)
return predictor, input_tensor, output_tensors, config
diff --git a/tools/infer_det.py b/tools/infer_det.py
index 1acecedf3e42fe67a93644a7f06c07c8b6bea2e3..f253e8f2876a5942538f18e93dfdada4391875b2 100755
--- a/tools/infer_det.py
+++ b/tools/infer_det.py
@@ -44,7 +44,7 @@ def draw_det_res(dt_boxes, config, img, img_name, save_path):
import cv2
src_im = img
for box in dt_boxes:
- box = box.astype(np.int32).reshape((-1, 1, 2))
+ box = np.array(box).astype(np.int32).reshape((-1, 1, 2))
cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
if not os.path.exists(save_path):
os.makedirs(save_path)
@@ -106,7 +106,7 @@ def main():
dt_boxes_list = []
for box in boxes:
tmp_json = {"transcription": ""}
- tmp_json['points'] = box.tolist()
+ tmp_json['points'] = np.array(box).tolist()
dt_boxes_list.append(tmp_json)
det_box_json[k] = dt_boxes_list
save_det_path = os.path.dirname(config['Global'][
@@ -118,7 +118,7 @@ def main():
# write result
for box in boxes:
tmp_json = {"transcription": ""}
- tmp_json['points'] = box.tolist()
+ tmp_json['points'] = np.array(box).tolist()
dt_boxes_json.append(tmp_json)
save_det_path = os.path.dirname(config['Global'][
'save_res_path']) + "/det_results/"
diff --git a/tools/infer_kie.py b/tools/infer_kie.py
index 0cb0b8702cbd7ea74a7b7fcff69122731578a1bd..346e2e0aeeee695ab49577b6b13dcc058150df1a 100755
--- a/tools/infer_kie.py
+++ b/tools/infer_kie.py
@@ -39,13 +39,12 @@ import time
def read_class_list(filepath):
- dict = {}
+ ret = {}
with open(filepath, "r") as f:
lines = f.readlines()
- for line in lines:
- key, value = line.split(" ")
- dict[key] = value.rstrip()
- return dict
+ for idx, line in enumerate(lines):
+ ret[idx] = line.strip("\n")
+ return ret
def draw_kie_result(batch, node, idx_to_cls, count):
@@ -71,7 +70,7 @@ def draw_kie_result(batch, node, idx_to_cls, count):
x_min = int(min([point[0] for point in new_box]))
y_min = int(min([point[1] for point in new_box]))
- pred_label = str(node_pred_label[i])
+ pred_label = node_pred_label[i]
if pred_label in idx_to_cls:
pred_label = idx_to_cls[pred_label]
pred_score = '{:.2f}'.format(node_pred_score[i])
@@ -109,8 +108,7 @@ def main():
save_res_path = config['Global']['save_res_path']
class_path = config['Global']['class_path']
idx_to_cls = read_class_list(class_path)
- if not os.path.exists(os.path.dirname(save_res_path)):
- os.makedirs(os.path.dirname(save_res_path))
+ os.makedirs(os.path.dirname(save_res_path), exist_ok=True)
model.eval()
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/infer_vqa_token_ser.py b/tools/infer_vqa_token_ser.py
index 83ed72b392e627c161903c3945f57be0abfabc2b..0173a554cace31e20ab47dbe36d132a4dbb2127b 100755
--- a/tools/infer_vqa_token_ser.py
+++ b/tools/infer_vqa_token_ser.py
@@ -44,6 +44,7 @@ def to_tensor(data):
from collections import defaultdict
data_dict = defaultdict(list)
to_tensor_idxs = []
+
for idx, v in enumerate(data):
if isinstance(v, (np.ndarray, paddle.Tensor, numbers.Number)):
if idx not in to_tensor_idxs:
@@ -57,6 +58,7 @@ def to_tensor(data):
class SerPredictor(object):
def __init__(self, config):
global_config = config['Global']
+ self.algorithm = config['Architecture']["algorithm"]
# build post process
self.post_process_class = build_post_process(config['PostProcess'],
@@ -70,7 +72,10 @@ class SerPredictor(object):
from paddleocr import PaddleOCR
- self.ocr_engine = PaddleOCR(use_angle_cls=False, show_log=False)
+ self.ocr_engine = PaddleOCR(
+ use_angle_cls=False,
+ show_log=False,
+ use_gpu=global_config['use_gpu'])
# create data ops
transforms = []
@@ -80,29 +85,30 @@ class SerPredictor(object):
op[op_name]['ocr_engine'] = self.ocr_engine
elif op_name == 'KeepKeys':
op[op_name]['keep_keys'] = [
- 'input_ids', 'labels', 'bbox', 'image', 'attention_mask',
- 'token_type_ids', 'segment_offset_id', 'ocr_info',
+ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids',
+ 'image', 'labels', 'segment_offset_id', 'ocr_info',
'entities'
]
transforms.append(op)
- global_config['infer_mode'] = True
+ if config["Global"].get("infer_mode", None) is None:
+ global_config['infer_mode'] = True
self.ops = create_operators(config['Eval']['dataset']['transforms'],
global_config)
self.model.eval()
- def __call__(self, img_path):
- with open(img_path, 'rb') as f:
+ def __call__(self, data):
+ with open(data["img_path"], 'rb') as f:
img = f.read()
- data = {'image': img}
+ data["image"] = img
batch = transform(data, self.ops)
batch = to_tensor(batch)
preds = self.model(batch)
+ if self.algorithm in ['LayoutLMv2', 'LayoutXLM']:
+ preds = preds[0]
+
post_result = self.post_process_class(
- preds,
- attention_masks=batch[4],
- segment_offset_ids=batch[6],
- ocr_infos=batch[7])
+ preds, segment_offset_ids=batch[6], ocr_infos=batch[7])
return post_result, batch
@@ -112,20 +118,33 @@ if __name__ == '__main__':
ser_engine = SerPredictor(config)
- infer_imgs = get_image_file_list(config['Global']['infer_img'])
+ if config["Global"].get("infer_mode", None) is False:
+ data_dir = config['Eval']['dataset']['data_dir']
+ with open(config['Global']['infer_img'], "rb") as f:
+ infer_imgs = f.readlines()
+ else:
+ infer_imgs = get_image_file_list(config['Global']['infer_img'])
+
with open(
os.path.join(config['Global']['save_res_path'],
"infer_results.txt"),
"w",
encoding='utf-8') as fout:
- for idx, img_path in enumerate(infer_imgs):
+ for idx, info in enumerate(infer_imgs):
+ if config["Global"].get("infer_mode", None) is False:
+ data_line = info.decode('utf-8')
+ substr = data_line.strip("\n").split("\t")
+ img_path = os.path.join(data_dir, substr[0])
+ data = {'img_path': img_path, 'label': substr[1]}
+ else:
+ img_path = info
+ data = {'img_path': img_path}
+
save_img_path = os.path.join(
config['Global']['save_res_path'],
os.path.splitext(os.path.basename(img_path))[0] + "_ser.jpg")
- logger.info("process: [{}/{}], save result to {}".format(
- idx, len(infer_imgs), save_img_path))
- result, _ = ser_engine(img_path)
+ result, _ = ser_engine(data)
result = result[0]
fout.write(img_path + "\t" + json.dumps(
{
@@ -133,3 +152,6 @@ if __name__ == '__main__':
}, ensure_ascii=False) + "\n")
img_res = draw_ser_results(img_path, result)
cv2.imwrite(save_img_path, img_res)
+
+ logger.info("process: [{}/{}], save result to {}".format(
+ idx, len(infer_imgs), save_img_path))
diff --git a/tools/infer_vqa_token_ser_re.py b/tools/infer_vqa_token_ser_re.py
index 6210f7f3c24227c9d366b08ce93ccfe4df849ce1..20ab1fe176c3be75f7a7b01a8d77df6419c58c75 100755
--- a/tools/infer_vqa_token_ser_re.py
+++ b/tools/infer_vqa_token_ser_re.py
@@ -38,7 +38,7 @@ from ppocr.utils.save_load import load_model
from ppocr.utils.visual import draw_re_results
from ppocr.utils.logging import get_logger
from ppocr.utils.utility import get_image_file_list, load_vqa_bio_label_maps, print_dict
-from tools.program import ArgsParser, load_config, merge_config, check_gpu
+from tools.program import ArgsParser, load_config, merge_config
from tools.infer_vqa_token_ser import SerPredictor
@@ -107,7 +107,7 @@ def make_input(ser_inputs, ser_results):
# remove ocr_info segment_offset_id and label in ser input
ser_inputs.pop(7)
ser_inputs.pop(6)
- ser_inputs.pop(1)
+ ser_inputs.pop(5)
return ser_inputs, entity_idx_dict_batch
@@ -131,9 +131,7 @@ class SerRePredictor(object):
self.model.eval()
def __call__(self, img_path):
- ser_results, ser_inputs = self.ser_engine(img_path)
- paddle.save(ser_inputs, 'ser_inputs.npy')
- paddle.save(ser_results, 'ser_results.npy')
+ ser_results, ser_inputs = self.ser_engine({'img_path': img_path})
re_input, entity_idx_dict_batch = make_input(ser_inputs, ser_results)
preds = self.model(re_input)
post_result = self.post_process_class(
@@ -155,7 +153,6 @@ def preprocess():
# check if set use_gpu=True in paddlepaddle cpu version
use_gpu = config['Global']['use_gpu']
- check_gpu(use_gpu)
device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'
device = paddle.set_device(device)
@@ -185,9 +182,7 @@ if __name__ == '__main__':
for idx, img_path in enumerate(infer_imgs):
save_img_path = os.path.join(
config['Global']['save_res_path'],
- os.path.splitext(os.path.basename(img_path))[0] + "_ser.jpg")
- logger.info("process: [{}/{}], save result to {}".format(
- idx, len(infer_imgs), save_img_path))
+ os.path.splitext(os.path.basename(img_path))[0] + "_ser_re.jpg")
result = ser_re_engine(img_path)
result = result[0]
@@ -197,3 +192,6 @@ if __name__ == '__main__':
}, ensure_ascii=False) + "\n")
img_res = draw_re_results(img_path, result)
cv2.imwrite(save_img_path, img_res)
+
+ logger.info("process: [{}/{}], save result to {}".format(
+ idx, len(infer_imgs), save_img_path))
diff --git a/tools/program.py b/tools/program.py
index aa3ba82c44d6afba725a8059dc7f8cae41947b3d..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:
@@ -576,8 +578,8 @@ def preprocess(is_train=False):
assert alg in [
'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR', 'PSE',
- 'SEED', 'SDMGR', 'LayoutXLM', 'LayoutLM', 'PREN', 'FCE', 'SVTR',
- 'ViTSTR', 'ABINet'
+ 'SEED', 'SDMGR', 'LayoutXLM', 'LayoutLM', 'LayoutLMv2', 'PREN', 'FCE',
+ 'SVTR', 'ViTSTR', 'ABINet', 'DB++', 'TableMaster'
]
if use_xpu:
| |