添加RFL文档
zhiminzhang0830
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@ -79,6 +79,7 @@ PaddleOCR将**持续新增**支持OCR领域前沿算法与模型，**欢迎广
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- [x] [VisionLAN](./algorithm_rec_visionlan.md)
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- [x] [SPIN](./algorithm_rec_spin.md)
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- [x] [RobustScanner](./algorithm_rec_robustscanner.md)
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- [x] [RFL](./algorithm_rec_rfl.md)
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参考[DTRB](https://arxiv.org/abs/1904.01906)[3]文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下：
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@ -102,6 +103,7 @@ PaddleOCR将**持续新增**支持OCR领域前沿算法与模型，**欢迎广
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|VisionLAN|Resnet45| 90.30% | rec_r45_visionlan | [训练模型](https://paddleocr.bj.bcebos.com/rec_r45_visionlan_train.tar) |
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|SPIN|ResNet32| 90.00% | rec_r32_gaspin_bilstm_att | coming soon |
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|RobustScanner|ResNet31| 87.77% | rec_r31_robustscanner | coming soon |
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|RFL|ResNetRFL| 88.63% | rec_resnet_rfl_att | [训练模型](https://paddleocr.bj.bcebos.com/contribution/rec_resnet_rfl.tar) |
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<a name="2"></a>
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@ -0,0 +1,161 @@
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# 场景文本识别算法-RFL
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- [1. 算法简介](#1)
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- [2. 环境配置](#2)
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- [3. 模型训练、评估、预测](#3)
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- [3.1 训练](#3-1)
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- [3.2 评估](#3-2)
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- [3.3 预测](#3-3)
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- [4. 推理部署](#4)
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- [4.1 Python推理](#4-1)
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- [4.2 C++推理](#4-2)
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- [4.3 Serving服务化部署](#4-3)
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- [4.4 更多推理部署](#4-4)
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- [5. FAQ](#5)
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<a name="1"></a>
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## 1. 算法简介
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论文信息：
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> [Reciprocal Feature Learning via Explicit and Implicit Tasks in Scene Text Recognition](https://arxiv.org/abs/2105.06229.pdf)
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> Hui Jiang, Yunlu Xu, Zhanzhan Cheng, Shiliang Pu, Yi Niu, Wenqi Ren, Fei Wu, and Wenming Tan
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> ICDAR, 2021
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<a name="model"></a>
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`RFL`使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法复现效果如下：
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|模型|骨干网络|配置文件|Acc|下载链接|
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| --- | --- | --- | --- | --- |
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|RFL-CNT|ResNetRFL|[rec_resnet_rfl_visual.yml](../../configs/rec/rec_resnet_rfl_visual.yml)|93.40%|[训练模型](https://paddleocr.bj.bcebos.com/contribution/rec_resnet_rfl.tar)|
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|RFL-Att|ResNetRFL|[rec_resnet_rfl_att.yml](../../configs/rec/rec_resnet_rfl_att.yml)|88.63%|[训练模型](https://paddleocr.bj.bcebos.com/contribution/rec_resnet_rfl.tar)|
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<a name="2"></a>
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## 2. 环境配置
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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。
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<a name="3"></a>
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## 3. 模型训练、评估、预测
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<a name="3-1"></a>
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### 3.1 模型训练
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请参考[文本识别训练教程](./recognition.md)。PaddleOCR对代码进行了模块化，训练`RFL`识别模型时需要**更换配置文件**为`RFL`的[配置文件](../../configs/rec/rec_resnet_rfl_att.yml)。
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#### 启动训练
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具体地，在完成数据准备后，便可以启动训练，训练命令如下：
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```shell
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#step1:训练CNT分支
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#单卡训练（训练周期长，不建议）
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python3 tools/train.py -c configs/rec/rec_resnet_rfl_visual.yml
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#多卡训练，通过--gpus参数指定卡号
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python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_resnet_rfl_visual.yml
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#step2:联合训练CNT和Att分支,注意将pretrained_model的路径设置为本地路径。
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#单卡训练（训练周期长，不建议）
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python3 tools/train.py -c configs/rec/rec_resnet_rfl_att.yml -o Global.pretrained_model=./output/rec/rec_resnet_rfl_visual/best_accuracy
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#多卡训练，通过--gpus参数指定卡号
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python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_resnet_rfl_att.yml -o Global.pretrained_model=./output/rec/rec_resnet_rfl_visual/best_accuracy
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```
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<a name="3-2"></a>
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### 3.2 评估
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可下载已训练完成的[模型文件](https://paddleocr.bj.bcebos.com/contribution/rec_resnet_rfl.tar)，使用如下命令进行评估：
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```shell
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# 注意将pretrained_model的路径设置为本地路径。
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python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_resnet_rfl_att.yml -o Global.pretrained_model=./output/rec/rec_resnet_rfl_att/best_accuracy
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```
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<a name="3-3"></a>
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### 3.3 预测
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使用如下命令进行单张图片预测：
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```shell
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# 注意将pretrained_model的路径设置为本地路径。
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python3 tools/infer_rec.py -c configs/rec/rec_resnet_rfl_att.yml -o Global.infer_img='./doc/imgs_words_en/word_10.png' Global.pretrained_model=./output/rec/rec_resnet_rfl_att/best_accuracy
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# 预测文件夹下所有图像时，可修改infer_img为文件夹，如 Global.infer_img='./doc/imgs_words_en/'。
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```
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<a name="4"></a>
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## 4. 推理部署
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<a name="4-1"></a>
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### 4.1 Python推理
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首先将训练得到best模型，转换成inference model。这里以训练完成的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/contribution/rec_resnet_rfl.tar) )，可以使用如下命令进行转换：
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```shell
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# 注意将pretrained_model的路径设置为本地路径。
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python3 tools/export_model.py -c configs/rec/rec_resnet_rfl_att.yml -o Global.pretrained_model=./output/rec/rec_resnet_rfl_att/best_accuracy Global.save_inference_dir=./inference/rec_resnet_rfl_att/
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```
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**注意：**
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- 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`是否是所需要的字典文件。
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- 如果您修改了训练时的输入大小，请修改`tools/export_model.py`文件中的对应RFL的`infer_shape`。
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转换成功后，在目录下有三个文件：
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```
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/inference/rec_resnet_rfl_att/
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├── inference.pdiparams # 识别inference模型的参数文件
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├── inference.pdiparams.info # 识别inference模型的参数信息，可忽略
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└── inference.pdmodel # 识别inference模型的program文件
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```
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执行如下命令进行模型推理：
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```shell
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python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words_en/word_10.png' --rec_model_dir='./inference/rec_resnet_rfl_att/' --rec_algorithm='RFL' --rec_image_shape='1,32,100'
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# 预测文件夹下所有图像时，可修改image_dir为文件夹，如 --image_dir='./doc/imgs_words_en/'。
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```
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执行命令后，上面图像的预测结果（识别的文本和得分）会打印到屏幕上，示例如下：
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结果如下：
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```shell
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Predicts of ./doc/imgs_words_en/word_10.png:('pain', 0.9999927282333374)
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```
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**注意**：
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- 训练上述模型采用的图像分辨率是[1，32，100]，需要通过参数`rec_image_shape`设置为您训练时的识别图像形状。
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- 在推理时需要设置参数`rec_char_dict_path`指定字典，如果您修改了字典，请修改该参数为您的字典文件。
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- 如果您修改了预处理方法，需修改`tools/infer/predict_rec.py`中RFL的预处理为您的预处理方法。
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<a name="4-2"></a>
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### 4.2 C++推理部署
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由于C++预处理后处理还未支持RFL，所以暂未支持
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<a name="4-3"></a>
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### 4.3 Serving服务化部署
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暂不支持
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<a name="4-4"></a>
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### 4.4 更多推理部署
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暂不支持
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<a name="5"></a>
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## 5. FAQ
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## 引用
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```bibtex
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@article{2021Reciprocal,
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title = {Reciprocal Feature Learning via Explicit and Implicit Tasks in Scene Text Recognition},
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author = {Jiang, H. and Xu, Y. and Cheng, Z. and Pu, S. and Niu, Y. and Ren, W. and Wu, F. and Tan, W. },
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booktitle = {ICDAR},
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year = {2021},
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url = {https://arxiv.org/abs/2105.06229}
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}
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```
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@ -76,6 +76,7 @@ Supported text recognition algorithms (Click the link to get the tutorial):
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- [x] [VisionLAN](./algorithm_rec_visionlan_en.md)
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- [x] [SPIN](./algorithm_rec_spin_en.md)
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- [x] [RobustScanner](./algorithm_rec_robustscanner_en.md)
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- [x] [RFL](./algorithm_rec_rfl_en.md)
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Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation result of these above text recognition (using MJSynth and SynthText for training, evaluate on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE) is as follow:
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@ -99,7 +100,7 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r
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|VisionLAN|Resnet45| 90.30% | rec_r45_visionlan | [trained model](https://paddleocr.bj.bcebos.com/rec_r45_visionlan_train.tar) |
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|SPIN|ResNet32| 90.00% | rec_r32_gaspin_bilstm_att | coming soon |
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|RobustScanner|ResNet31| 87.77% | rec_r31_robustscanner | coming soon |
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|RFL|ResNetRFL| 88.63% | rec_resnet_rfl_att | [trained model](https://paddleocr.bj.bcebos.com/contribution/rec_resnet_rfl.tar) |
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<a name="2"></a>
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# RFL
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- [1. Introduction](#1)
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- [2. Environment](#2)
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- [3. Model Training / Evaluation / Prediction](#3)
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- [3.1 Training](#3-1)
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- [3.2 Evaluation](#3-2)
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- [3.3 Prediction](#3-3)
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- [4. Inference and Deployment](#4)
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- [4.1 Python Inference](#4-1)
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- [4.2 C++ Inference](#4-2)
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- [4.3 Serving](#4-3)
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- [4.4 More](#4-4)
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- [5. FAQ](#5)
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<a name="1"></a>
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## 1. Introduction
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Paper:
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> [Reciprocal Feature Learning via Explicit and Implicit Tasks in Scene Text Recognition](https://arxiv.org/abs/2105.06229.pdf)
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> Hui Jiang, Yunlu Xu, Zhanzhan Cheng, Shiliang Pu, Yi Niu, Wenqi Ren, Fei Wu, and Wenming Tan
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> ICDAR, 2021
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Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
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|Model|Backbone|config|Acc|Download link|
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| --- | --- | --- | --- | --- |
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|RFL-CNT|ResNetRFL|[rec_resnet_rfl_visual.yml](../../configs/rec/rec_resnet_rfl_visual.yml)|93.40%|[训练模型](https://paddleocr.bj.bcebos.com/contribution/rec_resnet_rfl.tar)|
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|RFL-Att|ResNetRFL|[rec_resnet_rfl_att.yml](../../configs/rec/rec_resnet_rfl_att.yml)|88.63%|[训练模型](https://paddleocr.bj.bcebos.com/contribution/rec_resnet_rfl.tar)|
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<a name="2"></a>
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## 2. Environment
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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.
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<a name="3"></a>
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## 3. Model Training / Evaluation / Prediction
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Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**.
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Training:
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Specifically, after the data preparation is completed, the training can be started. The training command is as follows:
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```
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#step1:train the CNT branch
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#Single GPU training (long training period, not recommended)
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python3 tools/train.py -c configs/rec/rec_resnet_rfl_visual.yml
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#Multi GPU training, specify the gpu number through the --gpus parameter
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python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_resnet_rfl_visual.yml
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#step2:joint training of CNT and Att branches
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#Single GPU training (long training period, not recommended)
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python3 tools/train.py -c configs/rec/rec_resnet_rfl_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
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#Multi GPU training, specify the gpu number through the --gpus parameter
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python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_resnet_rfl_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
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```
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Evaluation:
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```
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# GPU evaluation
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python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_resnet_rfl_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
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```
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Prediction:
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```
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# The configuration file used for prediction must match the training
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python3 tools/infer_rec.py -c configs/rec/rec_resnet_rfl_att.yml -o Global.infer_img='./doc/imgs_words_en/word_10.png' Global.pretrained_model={path/to/weights}/best_accuracy
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```
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<a name="4"></a>
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## 4. Inference and Deployment
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<a name="4-1"></a>
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### 4.1 Python Inference
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First, the model saved during the RFL text recognition training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/contribution/rec_resnet_rfl.tar)) ), you can use the following command to convert:
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```
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python3 tools/export_model.py -c configs/rec/rec_resnet_rfl_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.save_inference_dir=./inference/rec_resnet_rfl_att
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```
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**Note:**
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- If you are training the model on your own dataset and have modified the dictionary file, please pay attention to modify the `character_dict_path` in the configuration file to the modified dictionary file.
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- If you modified the input size during training, please modify the `infer_shape` corresponding to NRTR in the `tools/export_model.py` file.
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After the conversion is successful, there are three files in the directory:
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```
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/inference/rec_resnet_rfl_att/
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├── inference.pdiparams
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├── inference.pdiparams.info
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└── inference.pdmodel
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```
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For RFL text recognition model inference, the following commands can be executed:
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```
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python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words_en/word_10.png' --rec_model_dir='./inference/rec_resnet_rfl_att/' --rec_algorithm='RFL' --rec_image_shape='1,32,100'
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```
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After executing the command, the prediction result (recognized text and score) of the image above is printed to the screen, an example is as follows:
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The result is as follows:
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```shell
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Predicts of ./doc/imgs_words_en/word_10.png:('pain', 0.9999927282333374)
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```
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<a name="4-2"></a>
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### 4.2 C++ Inference
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Not supported
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<a name="4-3"></a>
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### 4.3 Serving
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Not supported
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<a name="4-4"></a>
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### 4.4 More
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Not supported
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<a name="5"></a>
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## 5. FAQ
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## Citation
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```bibtex
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@article{2021Reciprocal,
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title = {Reciprocal Feature Learning via Explicit and Implicit Tasks in Scene Text Recognition},
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author = {Jiang, H. and Xu, Y. and Cheng, Z. and Pu, S. and Niu, Y. and Ren, W. and Wu, F. and Tan, W. },
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booktitle = {ICDAR},
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year = {2021},
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url = {https://arxiv.org/abs/2105.06229}
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}
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```
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