Generally, a more complex model would achive better performance in the task, but it also leads to some redundancy in the model. Model Pruning is a technique that reduces this redundancy by removing the sub-models in the neural network model, so as to reduce model calculation complexity and improve model inference performance.
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@@ -37,25 +37,27 @@ PaddleOCR also provides a series of [models](../../../doc/doc_en/models_list_en.
After the pre-trained model is loaded, sensitivity analysis is performed on each network layer of the model to understand the redundancy of each network layer, and save a sensitivity file which named: sen.pickle. After that, user could load the sensitivity file via the [methods provided by PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/prune/sensitive.py#L221) and determining the pruning ratio of each network layer automatically. For specific details of sensitivity analysis, see：[Sensitivity analysis](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/en/tutorials/image_classification_sensitivity_analysis_tutorial_en.md)
The function would return a dict after loading the sensitivity file. The keys of the dict are name of parameters in each layer. And the value of key is the information about pruning sensitivity of corresponding layer. In example, pruning 10% filter of the layer corresponding to conv10_expand_weights would lead to 0.65% degradation of model performance. The details could be seen at: [Sensitivity analysis](https://github.com/PaddlePaddle/PaddleSlim/blob/release/2.0-alpha/docs/zh_cn/algo/algo.md)
```
The function would return a dict after loading the sensitivity file. The keys of the dict are name of parameters in each layer. And the value of key is the information about pruning sensitivity of corresponding layer. In example, pruning 10% filter of the layer corresponding to conv10_expand_weights would lead to 0.65% degradation of model performance. The details could be seen at: [Sensitivity analysis](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/algo/algo.md#2-%E5%8D%B7%E7%A7%AF%E6%A0%B8%E5%89%AA%E8%A3%81%E5%8E%9F%E7%90%86)
Enter the PaddleOCR root directory，perform sensitivity analysis on the model with the following command：
Windows and Mac users are recommended to use Anaconda to build a Python environment, and Linux users are recommended to use docker to build a Python environment. If you are familiar with the Python environment, you can skip to step 2 to install PaddlePaddle.
Windows and Mac users are recommended to use Anaconda to build a Python environment, and Linux users are recommended to use docker to build a Python environment.
Recommended working environment:
- PaddlePaddle >= 2.0.0 (2.1.2)
- PaddlePaddle >= 2.1.2
- Python 3.7
- CUDA 10.1 / CUDA 10.2
- cuDNN 7.6
> If you already have a Python environment installed, you can skip to [PaddleOCR Quick Start](./quickstart_en.md).
*[1. Python Environment Setup](#1)
+[1.1 Windows](#1.1)
+[1.2 Mac](#1.2)
+[1.3 Linux](#1.3)
*[2. Install PaddlePaddle 2.0](#2)
<aname="1"></a>
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@@ -330,21 +331,3 @@ You can also visit [DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags
# ctrl+P+Q to exit docker, to re-enter docker using the following command:
sudo docker container exec -it ppocr /bin/bash
```
<aname="2"></a>
## 2. Install PaddlePaddle 2.0
- If you have CUDA 9 or CUDA 10 installed on your machine, please run the following command to install
For more software version requirements, please refer to the instructions in [Installation Document](https://www.paddlepaddle.org.cn/install/quick) for operation.
For more software version requirements, please refer to the instructions in [Installation Document](https://www.paddlepaddle.org.cn/install/quick) for operation.
## 1. Install PaddleOCR Whl Package
<aname="12-install-paddleocr-whl-package"></a>
### 1.2 Install PaddleOCR Whl Package
```bash
pip install"paddleocr>=2.0.1"# Recommend to use version 2.0.1+
In this section, you have mastered the use of PaddleOCR whl packages and obtained results.
PaddleOCR is a rich and practical OCR tool library that opens up the whole process of data, model training, compression and inference deployment, so in the [next section](./paddleOCR_overview_en.md) we will first introduce you to the overview of PaddleOCR, and then clone the PaddleOCR project to start the application journey of PaddleOCR.
VQA refers to visual question answering, which mainly asks and answers image content. DOC-VQA is one of the VQA tasks. DOC-VQA mainly asks questions about the text content of text images.
-支持基于多模态方法的语义实体识别 (Semantic Entity Recognition, SER) 以及关系抽取 (Relation Extraction, RE) 任务。基于 SER 任务，可以完成对图像中的文本识别与分类；基于 RE 任务，可以完成对图象中的文本内容的关系提取，如判断问题对(pair)。
-支持SER任务和RE任务的自定义训练。
-支持OCR+SER的端到端系统预测与评估。
-支持OCR+SER+RE的端到端系统预测。
-Integrate [LayoutXLM](https://arxiv.org/pdf/2104.08836.pdf) model and PP-OCR prediction engine.
-Supports Semantic Entity Recognition (SER) and Relation Extraction (RE) tasks based on multimodal methods. Based on the SER task, the text recognition and classification in the image can be completed; based on the RE task, the relationship extraction of the text content in the image can be completed, such as judging the problem pair (pair).
-Supports custom training for SER tasks and RE tasks.
-Supports end-to-end system prediction and evaluation of OCR+SER.
-Supports end-to-end system prediction of OCR+SER+RE.
This project is an open source implementation of [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/pdf/2104.08836.pdf) on Paddle 2.2,
Included fine-tuning code on [XFUND dataset](https://github.com/doc-analysis/XFUND).
Boxes with different colors in the figure represent different categories. For the XFUND dataset, there are 3 categories: `QUESTION`, `ANSWER`, `HEADER`
*深紫色：HEADER
*浅紫色：QUESTION
*军绿色：ANSWER
*Dark purple: HEADER
*Light purple: QUESTION
*Army Green: ANSWER
在OCR检测框的左上方也标出了对应的类别和OCR识别结果。
The corresponding categories and OCR recognition results are also marked on the upper left of the OCR detection frame.
The red box in the figure represents the question, the blue box represents the answer, and the question and the answer are connected by a green line. The corresponding categories and OCR recognition results are also marked on the upper left of the OCR detection frame.
# Note: Code cloud hosting code may not be able to synchronize the update of this github project in real time, there is a delay of 3 to 5 days, please use the recommended method first.
If you want to experience the prediction process directly, you can download the pre-training model provided by us, skip the training process, and just predict directly.
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).
下载并解压该数据集，解压后将数据集放置在当前目录下。
Download and unzip the dataset, and place the dataset in the current directory after unzipping.
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`.
*对`OCR引擎 + SER`预测系统进行端到端评估
* End-to-end evaluation of `OCR engine + SER` prediction system
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`.
## 6. 参考链接
## 6. Reference Links
- LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding, https://arxiv.org/pdf/2104.08836.pdf
The content of this project itself is licensed under the [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/)
The content of this project itself is licensed under the [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/)
