Merge branch 'dygraph' into dygraph

doc/doc_ch/knowledge_distillation.md

@@ -527,7 +527,6 @@ PostProcess:
 
 关于`DistillationDBPostProcess`更加具体的实现可以参考: [db_postprocess.py](../../ppocr/postprocess/db_postprocess.py#L195)
 
-
 <a name="224"></a>
 #### 2.2.4 蒸馏指标计算
 

doc/doc_ch/models_and_config.md

-
-# PP-OCR模型与配置文件
-PP-OCR模型与配置文件一章主要补充一些OCR模型的基本概念、配置文件的内容与作用以便对模型后续的参数调整和训练中拥有更好的体验。
-
-本章包含三个部分，首先在[PP-OCR模型下载](./models_list.md)中解释PP-OCR模型的类型概念，并提供所有模型的下载链接。然后在[配置文件内容与生成](./config.md)中详细说明调整PP-OCR模型所需的参数。最后的[模型库快速使用](./inference_ppocr.md)是对第一节PP-OCR模型库使用方法的介绍，可以通过Python推理引擎快速利用丰富的模型库模型获得测试结果。
-
-------
-
-下面我们首先了解一些OCR相关的基本概念：
-
-- [1. OCR 简要介绍](#1-ocr-----)
-  * [1.1 OCR 检测模型基本概念](#11-ocr---------)
-  * [1.2 OCR 识别模型基本概念](#12-ocr---------)
-  * [1.3 PP-OCR模型](#13-pp-ocr--)
-
-<a name="1-ocr-----"></a>
-## 1. OCR 简要介绍
-本节简要介绍OCR检测模型、识别模型的基本概念，并介绍PaddleOCR的PP-OCR模型。
-
-OCR（Optical Character Recognition，光学字符识别）目前是文字识别的统称，已不限于文档或书本文字识别，更包括识别自然场景下的文字，又可以称为STR（Scene Text Recognition）。
-
-OCR文字识别一般包括两个部分，文本检测和文本识别；文本检测首先利用检测算法检测到图像中的文本行；然后检测到的文本行用识别算法去识别到具体文字。
-
-<a name="11-ocr---------"></a>
-### 1.1 OCR 检测模型基本概念
-
-文本检测就是要定位图像中的文字区域，然后通常以边界框的形式将单词或文本行标记出来。传统的文字检测算法多是通过手工提取特征的方式，特点是速度快，简单场景效果好，但是面对自然场景，效果会大打折扣。当前多是采用深度学习方法来做。
-
-基于深度学习的文本检测算法可以大致分为以下几类：
-1. 基于目标检测的方法；一般是预测得到文本框后，通过NMS筛选得到最终文本框，多是四点文本框，对弯曲文本场景效果不理想。典型算法为EAST、Text Box等方法。
-2. 基于分割的方法；将文本行当成分割目标，然后通过分割结果构建外接文本框，可以处理弯曲文本，对于文本交叉场景问题效果不理想。典型算法为DB、PSENet等方法。
-3. 混合目标检测和分割的方法；
-
-<a name="12-ocr---------"></a>
-### 1.2 OCR 识别模型基本概念
-
-OCR识别算法的输入数据一般是文本行，背景信息不多，文字占据主要部分，识别算法目前可以分为两类算法：
-1. 基于CTC的方法；即识别算法的文字预测模块是基于CTC的，常用的算法组合为CNN+RNN+CTC。目前也有一些算法尝试在网络中加入transformer模块等等。
-2. 基于Attention的方法；即识别算法的文字预测模块是基于Attention的，常用算法组合是CNN+RNN+Attention。
-
-<a name="13-pp-ocr--"></a>
-### 1.3 PP-OCR模型
-
-PaddleOCR 中集成了很多OCR算法，文本检测算法有DB、EAST、SAST等等，文本识别算法有CRNN、RARE、StarNet、Rosetta、SRN等算法。
-
-其中PaddleOCR针对中英文自然场景通用OCR，推出了PP-OCR系列模型，PP-OCR模型由DB+CRNN算法组成，利用海量中文数据训练加上模型调优方法，在中文场景上具备较高的文本检测识别能力。并且PaddleOCR推出了高精度超轻量PP-OCRv2模型，检测模型仅3M，识别模型仅8.5M，利用[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim)的模型量化方法，可以在保持精度不降低的情况下，将检测模型压缩到0.8M，识别压缩到3M，更加适用于移动端部署场景。
-

doc/doc_ch/training.md

@@ -143,8 +143,10 @@ PaddleOCR主要聚焦通用OCR，如果有垂类需求，您可以用PaddleOCR+
 
 具体的训练教程可点击下方链接跳转： 
 
-\- [文本检测模型训练](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/detection.md) 
+- [文本检测模型训练](./detection.md) 
 
-\- [文本识别模型训练](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/recognition.md)  
+- [文本识别模型训练](./recognition.md)  
+
+- [文本方向分类器训练](./angle_class.md)  
+- [知识蒸馏](./knowledge_distillation.md)
 
-\- [文本方向分类器训练](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/angle_class.md)  

doc/doc_en/detection_en.md

@@ -67,11 +67,11 @@ And the responding download link of backbone pretrain weights can be found in (h
 ```shell
 cd PaddleOCR/
 # Download the pre-trained model of MobileNetV3
-wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
+wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/MobileNetV3_large_x0_5_pretrained.pdparams
 # or, download the pre-trained model of ResNet18_vd
-wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet18_vd_pretrained.pdparams
+wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/ResNet18_vd_pretrained.pdparams
 # or, download the pre-trained model of ResNet50_vd
-wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams
+wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/ResNet50_vd_ssld_pretrained.pdparams
 
 ```
 

doc/doc_en/models_and_config_en.md

-# PP-OCR Model and Configuration
-The chapter on PP-OCR model and configuration file mainly adds some basic concepts of OCR model and the content and role of configuration file to have a better experience in the subsequent parameter adjustment and training of the model.
-
-This chapter contains three parts. Firstly, [PP-OCR Model Download](. /models_list_en.md) explains the concept of PP-OCR model types and provides links to download all models. Then in [Yml Configuration](. /config_en.md) details the parameters needed to fine-tune the PP-OCR models. The final [Python Inference for PP-OCR Model Library](. /inference_ppocr_en.md) is an introduction to the use of the PP-OCR model library in the first section, which can quickly utilize the rich model library models to obtain test results through the Python inference engine.
-
-------
-
-Let's first understand some basic concepts.
-
-- [INTRODUCTION ABOUT OCR](#introduction-about-ocr)
-  * [BASIC CONCEPTS OF OCR DETECTION MODEL](#basic-concepts-of-ocr-detection-model)
-  * [Basic concepts of OCR recognition model](#basic-concepts-of-ocr-recognition-model)
-  * [PP-OCR model](#pp-ocr-model)
-  * [And a table of contents](#and-a-table-of-contents)
-  * [On the right](#on-the-right)
-
-
-## 1. INTRODUCTION ABOUT OCR
-
-This section briefly introduces the basic concepts of OCR detection model and recognition model, and introduces PaddleOCR's PP-OCR model.
-
-OCR (Optical Character Recognition, Optical Character Recognition) is currently the general term for text recognition. It is not limited to document or book text recognition, but also includes recognizing text in natural scenes. It can also be called STR (Scene Text Recognition).
-
-OCR text recognition generally includes two parts, text detection and text recognition. The text detection module first uses detection algorithms to detect text lines in the image. And then the recognition algorithm to identify the specific text in the text line.
-
-
-### 1.1 BASIC CONCEPTS OF OCR DETECTION MODEL
-
-Text detection can locate the text area in the image, and then usually mark the word or text line in the form of a bounding box. Traditional text detection algorithms mostly extract features manually, which are characterized by fast speed and good effect in simple scenes, but the effect will be greatly reduced when faced with natural scenes. Currently, deep learning methods are mostly used.
-
-Text detection algorithms based on deep learning can be roughly divided into the following categories:
-1. Method based on target detection. Generally, after the text box is predicted, the final text box is filtered through NMS, which is mostly four-point text box, which is not ideal for curved text scenes. Typical algorithms are methods such as EAST and Text Box.
-2. Method based on text segmentation. The text line is regarded as the segmentation target, and then the external text box is constructed through the segmentation result, which can handle curved text, and the effect is not ideal for the text cross scene problem. Typical algorithms are DB, PSENet and other methods.
-3. Hybrid target detection and segmentation method.
-
-
-### 1.2 Basic concepts of OCR recognition model
-
-The input of the OCR recognition algorithm is generally text lines images which has less background information, and the text information occupies the main part. The recognition algorithm can be divided into two types of algorithms:
-1. CTC-based method. The text prediction module of the recognition algorithm is based on CTC, and the commonly used algorithm combination is CNN+RNN+CTC. There are also some algorithms that try to add transformer modules to the network and so on.
-2. Attention-based method. The text prediction module of the recognition algorithm is based on Attention, and the commonly used algorithm combination is CNN+RNN+Attention.
-
-
-### 1.3 PP-OCR model
-
-PaddleOCR integrates many OCR algorithms, text detection algorithms include DB, EAST, SAST, etc., text recognition algorithms include CRNN, RARE, StarNet, Rosetta, SRN and other algorithms.
-
-Among them, PaddleOCR has released the PP-OCR series model for the general OCR in Chinese and English natural scenes. The PP-OCR model is composed of the DB+CRNN algorithm. It uses massive Chinese data training and model tuning methods to have high text detection and recognition capabilities in Chinese scenes. And PaddleOCR has launched a high-precision and ultra-lightweight PP-OCRv2 model. The detection model is only 3M, and the recognition model is only 8.5M. Using [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim)'s model quantification method, the detection model can be compressed to 0.8M without reducing the accuracy. The recognition is compressed to 3M, which is more suitable for mobile deployment scenarios.

ppocr/data/imaug/label_ops.py

@@ -344,7 +344,7 @@ class KieLabelEncode(object):
         max_num = 300
         temp_bboxes = np.zeros([max_num, 4])
         h, _ = bboxes.shape
-        temp_bboxes[:h, :h] = bboxes
+        temp_bboxes[:h, :] = bboxes
 
         temp_relations = np.zeros([max_num, max_num, 5])
         temp_relations[:h, :h, :] = relations

ppocr/data/imaug/operators.py

@@ -23,7 +23,6 @@ import sys
 import six
 import cv2
 import numpy as np
-import fasttext
 
 
 class DecodeImage(object):
@@ -136,6 +135,7 @@ class ToCHWImage(object):
 
 class Fasttext(object):
     def __init__(self, path="None", **kwargs):
+        import fasttext
         self.fast_model = fasttext.load_model(path)
 
     def __call__(self, data):

ppocr/utils/save_load.py

@@ -111,13 +111,16 @@ def load_pretrained_params(model, path):
     params = paddle.load(path + '.pdparams')
     state_dict = model.state_dict()
     new_state_dict = {}
-    for k1, k2 in zip(state_dict.keys(), params.keys()):
-        if list(state_dict[k1].shape) == list(params[k2].shape):
-            new_state_dict[k1] = params[k2]
+    for k1 in params.keys():
+        if k1 not in state_dict.keys():
+            logger.warning("The pretrained params {} not in model".format(k1))
+        else:
+            if list(state_dict[k1].shape) == list(params[k1].shape):
+                new_state_dict[k1] = params[k1]
             else:
                 logger.warning(
                     "The shape of model params {} {} not matched with loaded params {} {} !".
-                format(k1, state_dict[k1].shape, k2, params[k2].shape))
+                    format(k1, state_dict[k1].shape, k1, params[k1].shape))
     model.set_state_dict(new_state_dict)
     logger.info("load pretrain successful from {}".format(path))
     return model

ppstructure/vqa/README.md

@@ -96,10 +96,7 @@ git clone https://gitee.com/paddlepaddle/PaddleOCR
 - **（3）安装PaddleNLP**
 
 ```bash
-# 需要使用PaddleNLP最新的代码版本进行安装
-git clone https://github.com/PaddlePaddle/PaddleNLP -b develop
-cd PaddleNLP
-pip3 install -e .
+pip3 install "paddlenlp>=2.2.1"
 ```
 
 

requirements.txt

@@ -12,5 +12,4 @@ cython
 lxml
 premailer
 openpyxl
-fasttext==0.9.1
-
+paddlenlp>=2.2.1

test_tipc/configs/ch_PP-OCRv2_det_KL/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt

@@ -3,7 +3,7 @@ model_name:PPOCRv2_ocr_det_kl
 python:python3.7
 Global.pretrained_model:null
 Global.save_inference_dir:null
-infer_model:./inference/ch_PP-OCRv2_det_infer/
+infer_model:./inference/ch_PP-OCRv2_det_infer
 infer_export:deploy/slim/quantization/quant_kl.py -c configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml -o
 infer_quant:True
 inference:tools/infer/predict_det.py

test_tipc/configs/ch_ppocr_mobile_v2.0_rec_KL/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt

@@ -13,7 +13,7 @@ inference:tools/infer/predict_rec.py
 --rec_batch_num:1
 --use_tensorrt:False|True
 --precision:int8
---det_model_dir:
+--rec_model_dir:
 --image_dir:./inference/rec_inference
 null:null
 --benchmark:True

test_tipc/configs/det_r50_vd_sast_icdar15_v2.0/train_infer_python.txt

@@ -43,7 +43,7 @@ inference:tools/infer/predict_det.py
 --cpu_threads:1|6
 --rec_batch_num:1
 --use_tensorrt:False
---precision:fp32|fp16|int8
+--precision:fp32|int8
 --det_model_dir:
 --image_dir:./inference/ch_det_data_50/all-sum-510/
 null:null