飞桨常规赛：中文场景文字识别- 12月第8名方案

该内容围绕中文场景文字识别常规赛展开，介绍了比赛任务是用飞桨框架预测图像文字行内容。涵盖数据集情况，利用PaddleOCR的配置、训练、评估、预测等流程，包括模型选择、参数设置、预训练模型使用，以及结果提交相关的模型导出等内容。

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一、常规赛：中文场景文字识别

比赛地址：https://aistudio.baidu.com/aistudio/competition/detail/20/0/datasets

1.比赛简介

中文场景文字识别技术在人们的日常生活中受到广泛关注，具有丰富的应用场景，如：拍照翻译、图像检索、场景理解等。然而，中文场景中的文字面临着包括光照变化、低分辨率、字体以及排布多样性、中文字符种类多等复杂情况。如何解决上述问题成为一项极具挑战性的任务。

中文场景文字识别常规赛全新升级，提供轻量级中文场景文字识别数据，要求选手使用飞桨框架，对图像区域中的文字行进行预测，并返回文字行的内容。

2.数据集描述

本次赛题数据集共包括6万张图片，其中5万张图片作为训练集，1万张作为测试集。数据集采自中国街景，并由街景图片中的文字行区域（例如店铺标牌、地标等等）截取出来而形成。

具体数据介绍

数据集中所有图像都经过一些预处理，如下图所示：

(a) 标注：久斯台球会所

(b) 标注：上海创科泵业制造有限公司

标注文件

平台提供的标注文件为.csv文件格式，文件中的四列分别为图片的宽、高、文件名和文字标注。样例如下：

0.jpg文本0—————-1.jpg文本0

二、环境设置

PaddleOCR https://github.com/paddlepaddle/PaddleOCR 是一款全宇宙最强的用的OCR工具库，开箱即用，速度杠杠的。

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# 从gitee上下载PaddleOCR代码，也可以从GitHub链接下载!git clone https://gitee.com/paddlepaddle/PaddleOCR.git --depth=1# 升级pip!pip install -U pip # 安装依赖%cd ~/PaddleOCR%pip install -r requirements.txt

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%cd ~/PaddleOCR/!tree   -L 1

/home/aistudio/PaddleOCR.├── benchmark├── configs├── deploy├── doc├── __init__.py├── LICENSE├── MANIFEST.in├── paddleocr.py├── ppocr├── PPOCRLabel├── ppstructure├── README_ch.md├── README.md├── requirements.txt├── setup.py├── StyleText├── test_tipc├── tools└── train.sh10 directories, 9 files

三、数据准备

据悉train数据集共10万张，解压，并划分出10000张作为测试集。

1.数据下载解压

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#  解压缩数据集%cd ~!unzip -qa data/data62842/train_images.zip -d data/data62842/!unzip -qa data/data62843/test_images.zip -d data/data62843/

/home/aistudio

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# 使用命令查看训练数据文件夹下数据量是否是5万张!cd ~/data/data62842/train_images  &&  ls -l | grep "^-" | wc -l

50000

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# 使用命令查看test数据文件夹下数据量是否是1万张!cd ~/data/data62843/test_images  &&  ls -l | grep "^-" | wc -l

10000

2. 数据集划分

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# 读取数据列表文件import pandas as pd%cd ~data_label=pd.read_csv('data/data62842/train_label.csv', encoding='gb2312')data_label.head()

/home/aistudio

    name    value0  0.jpg       拉拉1  1.jpg       ６号2  2.jpg       胖胖3  3.jpg  前门大栅栏总店4  4.jpg   你来就是旺季

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# 对数据列表文件进行划分%cd ~/data/data62842/print(data_label.shape)train=data_label[:45000]val=data_label[45000:]train.to_csv('train.txt',sep='t',header=None,index=None)val.to_csv('val.txt',sep='t',header=None,index=None)

/home/aistudio/data/data62842(50000, 2)

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# 查看数量print(train.shape)print(val.shape)

(45000, 2)(5000, 2)

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!head val.txt

45000.jpg责任单位：北京市环清环卫设施维修45001.jpg眼镜45002.jpg光临45003.jpg主治45004.jpg菜饭骨头汤45005.jpg理45006.jpg要多者提前预定45007.jpg干洗湿洗45008.jpg画布咖啡45009.jpg电焊、气割、专业自卸车

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!head train.txt

0.jpg拉拉1.jpg６号2.jpg胖胖3.jpg前门大栅栏总店4.jpg你来就是旺季5.jpg毛衣厂家直销6.jpg137619162187.jpg福鼎白茶8.jpg妍心美容9.jpg童车童床

四、配置训练参数

以PaddleOCR/configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml为基准进行配置

1.配置模型网络

使用CRNN算法，backbone是MobileNetV3，损失函数是CTCLoss

Architecture:  model_type: rec  algorithm: CRNN  Transform:  Backbone:    name: MobileNetV3    scale: 0.5    model_name: small    small_stride: [1, 2, 2, 2]  Neck:    name: SequenceEncoder    encoder_type: rnn    hidden_size: 48  Head:    name: CTCHead    fc_decay: 0.00001

2.配置数据

对Train.data_dir, Train.label_file_list, Eval.data_dir, Eval.label_file_list进行配置

Train:  dataset:    name: SimpleDataSet    data_dir: /home/aistudio/data/data62842/train_images    label_file_list: ["/home/aistudio/data/data62842/train.txt"]......Eval:  dataset:    name: SimpleDataSet    data_dir: /home/aistudio/data/data62842/train_images    label_file_list: ["/home/aistudio/data/data62842/val.txt"]

3. 显卡、评估设置

use_gpu、cal_metric_during_train分别是GPU、评估开关

Global:  use_gpu: false             # true 使用GPU  .....  cal_metric_during_train: False   # true 打开评估

4. 多线程任务

Train.loader.num_workers:4Eval.loader.num_workers: 4

5.完整配置

Global:  use_gpu: true  epoch_num: 500  log_smooth_window: 20  print_batch_step: 10  save_model_dir: ./output/rec_chinese_lite_v2.0  save_epoch_step: 3  # evaluation is run every 5000 iterations after the 4000th iteration  eval_batch_step: [0, 2000]  cal_metric_during_train: True  pretrained_model: ./ch_ppocr_mobile_v2.0_rec_pre/best_accuracy  checkpoints:   save_inference_dir:  use_visualdl: True  infer_img: doc/imgs_words/ch/word_1.jpg  # for data or label process  character_dict_path: ppocr/utils/ppocr_keys_v1.txt  max_text_length: 25  infer_mode: False  use_space_char: True  save_res_path: ./output/rec/predicts_chinese_lite_v2.0.txtOptimizer:  name: Adam  beta1: 0.9  beta2: 0.999  lr:    name: Cosine    learning_rate: 0.001    warmup_epoch: 5  regularizer:    name: 'L2'    factor: 0.00001Architecture:  model_type: rec  algorithm: CRNN  Transform:  Backbone:    name: MobileNetV3    scale: 0.5    model_name: small    small_stride: [1, 2, 2, 2]  Neck:    name: SequenceEncoder    encoder_type: rnn    hidden_size: 48  Head:    name: CTCHead    fc_decay: 0.00001Loss:  name: CTCLossPostProcess:  name: CTCLabelDecodeMetric:  name: RecMetric  main_indicator: accTrain:  dataset:    name: SimpleDataSet    data_dir: /home/aistudio/data/data62842/train_images    label_file_list: ["/home/aistudio/data/data62842/train.txt"]    transforms:      - DecodeImage: # load image          img_mode: BGR          channel_first: False      - RecAug:       - CTCLabelEncode: # Class handling label      - RecResizeImg:          image_shape: [3, 32, 320]      - KeepKeys:          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order  loader:    shuffle: True    batch_size_per_card: 256    drop_last: True    num_workers: 8Eval:  dataset:    name: SimpleDataSet    data_dir: /home/aistudio/data/data62842/train_images    label_file_list: ["/home/aistudio/data/data62842/val.txt"]    transforms:      - DecodeImage: # load image          img_mode: BGR          channel_first: False      - CTCLabelEncode: # Class handling label      - RecResizeImg:          image_shape: [3, 32, 320]      - KeepKeys:          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order  loader:    shuffle: False    drop_last: False    batch_size_per_card: 256    num_workers: 8

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# 覆盖配置!cp -f  ~/rec_chinese_lite_train_v2.0.yml ~/PaddleOCR/configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml

cp: cannot stat '/home/aistudio/PaddleOCR/configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml': No such file or directory

6.使用预训练模型

据悉使用预训练模型，训练速度更快！！！

PaddleOCR提供的可下载模型包括推理模型、训练模型、预训练模型、slim模型，模型区别说明如下：

推理模型inference.pdmodel、inference.pdiparams用于预测引擎推理，详情训练模型、预训练模型*.pdparams、*.pdopt、*.states训练过程中保存的模型的参数、优化器状态和训练中间信息，多用于模型指标评估和恢复训练slim模型*.nb经过飞桨模型压缩工具PaddleSlim压缩后的模型，适用于移动端/IoT端等端侧部署场景（需使用飞桨Paddle Lite部署）。

各个模型的关系如下面的示意图所示。

文本检测模型

ch_ppocr_mobile_slim_v2.0_detslim裁剪版超轻量模型，支持中英文、多语种文本检测ch_det_mv3_db_v2.0.yml2.6M推理模型ch_ppocr_mobile_v2.0_det原始超轻量模型，支持中英文、多语种文本检测ch_det_mv3_db_v2.0.yml3M推理模型 / 训练模型ch_ppocr_server_v2.0_det通用模型，支持中英文、多语种文本检测，比超轻量模型更大，但效果更好ch_det_res18_db_v2.0.yml47M推理模型 / 训练模型

文本识别模型

中文识别模型

ch_ppocr_mobile_slim_v2.0_recslim裁剪量化版超轻量模型，支持中英文、数字识别rec_chinese_lite_train_v2.0.yml6M推理模型 / 训练模型ch_ppocr_mobile_v2.0_rec原始超轻量模型，支持中英文、数字识别rec_chinese_lite_train_v2.0.yml5.2M推理模型 / 训练模型 / 预训练模型ch_ppocr_server_v2.0_rec通用模型，支持中英文、数字识别rec_chinese_common_train_v2.0.yml94.8M推理模型 / 训练模型 / 预训练模型

说明： 训练模型是基于预训练模型在真实数据与竖排合成文本数据上finetune得到的模型，在真实应用场景中有着更好的表现，预训练模型则是直接基于全量真实数据与合成数据训练得到，更适合用于在自己的数据集上finetune。

英文识别模型

en_number_mobile_slim_v2.0_recslim裁剪量化版超轻量模型，支持英文、数字识别rec_en_number_lite_train.yml2.7M推理模型 / 训练模型en_number_mobile_v2.0_rec原始超轻量模型，支持英文、数字识别rec_en_number_lite_train.yml2.6M推理模型 / 训练模型

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%cd ~/PaddleOCR/# mobile模型# !wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar# !tar -xf ch_ppocr_mobile_v2.0_rec_pre.tar# server模型!wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar!tar -xf ch_ppocr_server_v2.0_rec_pre.tar

/home/aistudio/PaddleOCR--2021-12-31 12:58:03--  https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tarResolving paddleocr.bj.bcebos.com (paddleocr.bj.bcebos.com)... 182.61.200.195, 182.61.200.229, 2409:8c04:1001:1002:0:ff:b001:368aConnecting to paddleocr.bj.bcebos.com (paddleocr.bj.bcebos.com)|182.61.200.195|:443... connected.HTTP request sent, awaiting response... 200 OKLength: 490184704 (467M) [application/x-tar]Saving to: ‘ch_ppocr_server_v2.0_rec_pre.tar’ch_ppocr_server_v2. 100%[===================>] 467.48M  56.0MB/s    in 13s     2021-12-31 12:58:17 (34.9 MB/s) - ‘ch_ppocr_server_v2.0_rec_pre.tar’ saved [490184704/490184704]

五、训练

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%cd ~/PaddleOCR/# mobile模型# !python tools/train.py -c ./configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.checkpoints=./output/rec_chinese_lite_v2.0/latest# server模型!python tools/train.py -c ./configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml

1.选择合适的batch size

2.训练日志

[2021/12/30 23:26:54] root INFO: epoch: [68/500], iter: 9930, lr: 0.000962, loss: 5.635038, acc: 0.521482, norm_edit_dis: 0.745346, reader_cost: 0.01405 s, batch_cost: 0.26990 s, samples: 2560, ips: 948.50786[2021/12/30 23:27:11] root INFO: epoch: [68/500], iter: 9940, lr: 0.000962, loss: 5.653114, acc: 0.509764, norm_edit_dis: 0.740487, reader_cost: 0.01402 s, batch_cost: 0.26862 s, samples: 2560, ips: 953.03473[2021/12/30 23:27:26] root INFO: epoch: [68/500], iter: 9950, lr: 0.000962, loss: 5.411234, acc: 0.515623, norm_edit_dis: 0.748549, reader_cost: 0.00091 s, batch_cost: 0.26371 s, samples: 2560, ips: 970.76457[2021/12/30 23:27:40] root INFO: epoch: [68/500], iter: 9960, lr: 0.000962, loss: 5.588465, acc: 0.525389, norm_edit_dis: 0.755345, reader_cost: 0.00684 s, batch_cost: 0.25901 s, samples: 2560, ips: 988.38445[2021/12/30 23:27:48] root INFO: epoch: [68/500], iter: 9970, lr: 0.000961, loss: 5.789876, acc: 0.513670, norm_edit_dis: 0.740609, reader_cost: 0.00095 s, batch_cost: 0.15022 s, samples: 2560, ips: 1704.17763[2021/12/30 23:27:51] root INFO: epoch: [68/500], iter: 9974, lr: 0.000961, loss: 5.787237, acc: 0.511717, norm_edit_dis: 0.747102, reader_cost: 0.00018 s, batch_cost: 0.05935 s, samples: 1024, ips: 1725.41448[2021/12/30 23:27:51] root INFO: save model in ./output/rec_chinese_lite_v2.0/latest[2021/12/30 23:27:51] root INFO: Initialize indexs of datasets:['/home/aistudio/data/data62842/train.txt'][2021/12/30 23:28:21] root INFO: epoch: [69/500], iter: 9980, lr: 0.000961, loss: 5.801509, acc: 0.517576, norm_edit_dis: 0.749756, reader_cost: 1.10431 s, batch_cost: 1.37585 s, samples: 1536, ips: 111.64048[2021/12/30 23:28:40] root INFO: epoch: [69/500], iter: 9990, lr: 0.000961, loss: 5.548770, acc: 0.533201, norm_edit_dis: 0.762078, reader_cost: 0.00839 s, batch_cost: 0.32035 s, samples: 2560, ips: 799.11578[2021/12/30 23:28:56] root INFO: epoch: [69/500], iter: 10000, lr: 0.000961, loss: 5.449094, acc: 0.537107, norm_edit_dis: 0.762517, reader_cost: 0.00507 s, batch_cost: 0.25845 s, samples: 2560, ips: 990.51517eval model:: 100%|██████████████████████████████| 20/20 [00:15<00:00,  1.98it/s][2021/12/30 23:29:12] root INFO: cur metric, acc: 0.4641999071600186, norm_edit_dis: 0.6980459628854201, fps: 4204.853978632389[2021/12/30 23:29:12] root INFO: best metric, acc: 0.48179990364001923, start_epoch: 12, norm_edit_dis: 0.7096561279006699, fps: 4618.199275059127, best_epoch: 46

3. visualdl可视化

本地安装visualdl pip install visualdl下载日志至本地启动visualdl可视化  visualdl –logdir ./打开浏览器查看  http://localhost:8040/

六、模型评估

In [10]

# GPU 评估， Global.checkpoints 为待测权重%cd ~/PaddleOCR/# mobile模型# !python  -m paddle.distributed.launch tools/eval.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml #     -o Global.checkpoints=./output/rec_chinese_lite_v2.0/latest# server模型!python  -m paddle.distributed.launch tools/eval.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml     -o Global.checkpoints=./output/rec_chinese_common_v2.0/best_accuracy.pdparams

/home/aistudio/PaddleOCR-----------  Configuration Arguments -----------backend: autoelastic_server: Noneforce: Falsegpus: Noneheter_devices: heter_worker_num: Noneheter_workers: host: Nonehttp_port: Noneips: 127.0.0.1job_id: Nonelog_dir: lognp: Nonenproc_per_node: Nonerun_mode: Nonescale: 0server_num: Noneservers: training_script: tools/eval.pytraining_script_args: ['-c', 'configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml', '-o', 'Global.checkpoints=./output/rec_chinese_common_v2.0/best_accuracy.pdparams']worker_num: Noneworkers: ------------------------------------------------WARNING 2021-12-31 18:51:19,722 launch.py:423] Not found distinct arguments and compiled with cuda or xpu. Default use collective modelaunch train in GPU mode!INFO 2021-12-31 18:51:19,725 launch_utils.py:528] Local start 1 processes. First process distributed environment info (Only For Debug):     +=======================================================================================+    |                        Distributed Envs                      Value                    |    +---------------------------------------------------------------------------------------+    |                       PADDLE_TRAINER_ID                        0                      |    |                 PADDLE_CURRENT_ENDPOINT                 127.0.0.1:46063               |    |                     PADDLE_TRAINERS_NUM                        1                      |    |                PADDLE_TRAINER_ENDPOINTS                 127.0.0.1:46063               |    |                     PADDLE_RANK_IN_NODE                        0                      |    |                 PADDLE_LOCAL_DEVICE_IDS                        0                      |    |                 PADDLE_WORLD_DEVICE_IDS                        0                      |    |                     FLAGS_selected_gpus                        0                      |    |             FLAGS_selected_accelerators                        0                      |    +=======================================================================================+INFO 2021-12-31 18:51:19,725 launch_utils.py:532] details abouts PADDLE_TRAINER_ENDPOINTS can be found in log/endpoints.log, and detail running logs maybe found in log/workerlog.0launch proc_id:16263 idx:0[2021/12/31 18:51:21] root INFO: Architecture : [2021/12/31 18:51:21] root INFO:     Backbone : [2021/12/31 18:51:21] root INFO:         layers : 34[2021/12/31 18:51:21] root INFO:         name : ResNet[2021/12/31 18:51:21] root INFO:     Head : [2021/12/31 18:51:21] root INFO:         fc_decay : 4e-05[2021/12/31 18:51:21] root INFO:         name : CTCHead[2021/12/31 18:51:21] root INFO:     Neck : [2021/12/31 18:51:21] root INFO:         encoder_type : rnn[2021/12/31 18:51:21] root INFO:         hidden_size : 256[2021/12/31 18:51:21] root INFO:         name : SequenceEncoder[2021/12/31 18:51:21] root INFO:     Transform : None[2021/12/31 18:51:21] root INFO:     algorithm : CRNN[2021/12/31 18:51:21] root INFO:     model_type : rec[2021/12/31 18:51:21] root INFO: Eval : [2021/12/31 18:51:21] root INFO:     dataset : [2021/12/31 18:51:21] root INFO:         data_dir : /home/aistudio/data/data62842/train_images[2021/12/31 18:51:21] root INFO:         label_file_list : ['/home/aistudio/data/data62842/val.txt'][2021/12/31 18:51:21] root INFO:         name : SimpleDataSet[2021/12/31 18:51:21] root INFO:         transforms : [2021/12/31 18:51:21] root INFO:             DecodeImage : [2021/12/31 18:51:21] root INFO:                 channel_first : False[2021/12/31 18:51:21] root INFO:                 img_mode : BGR[2021/12/31 18:51:21] root INFO:             CTCLabelEncode : None[2021/12/31 18:51:21] root INFO:             RecResizeImg : [2021/12/31 18:51:21] root INFO:                 image_shape : [3, 32, 320][2021/12/31 18:51:21] root INFO:             KeepKeys : [2021/12/31 18:51:21] root INFO:                 keep_keys : ['image', 'label', 'length'][2021/12/31 18:51:21] root INFO:     loader : [2021/12/31 18:51:21] root INFO:         batch_size_per_card : 256[2021/12/31 18:51:21] root INFO:         drop_last : False[2021/12/31 18:51:21] root INFO:         num_workers : 8[2021/12/31 18:51:21] root INFO:         shuffle : False[2021/12/31 18:51:21] root INFO: Global : [2021/12/31 18:51:21] root INFO:     cal_metric_during_train : True[2021/12/31 18:51:21] root INFO:     character_dict_path : ppocr/utils/ppocr_keys_v1.txt[2021/12/31 18:51:21] root INFO:     checkpoints : ./output/rec_chinese_common_v2.0/best_accuracy.pdparams[2021/12/31 18:51:21] root INFO:     debug : False[2021/12/31 18:51:21] root INFO:     distributed : False[2021/12/31 18:51:21] root INFO:     epoch_num : 500[2021/12/31 18:51:21] root INFO:     eval_batch_step : [0, 2000][2021/12/31 18:51:21] root INFO:     infer_img : doc/imgs_words/ch/word_1.jpg[2021/12/31 18:51:21] root INFO:     infer_mode : False[2021/12/31 18:51:21] root INFO:     log_smooth_window : 20[2021/12/31 18:51:21] root INFO:     max_text_length : 25[2021/12/31 18:51:21] root INFO:     pretrained_model : ./ch_ppocr_server_v2.0_rec_pre/best_accuracy[2021/12/31 18:51:21] root INFO:     print_batch_step : 10[2021/12/31 18:51:21] root INFO:     save_epoch_step : 3[2021/12/31 18:51:21] root INFO:     save_inference_dir : None[2021/12/31 18:51:21] root INFO:     save_model_dir : ./output/rec_chinese_common_v2.0[2021/12/31 18:51:21] root INFO:     save_res_path : ./output/rec/predicts_chinese_common_v2.0.txt[2021/12/31 18:51:21] root INFO:     use_gpu : True[2021/12/31 18:51:21] root INFO:     use_space_char : True[2021/12/31 18:51:21] root INFO:     use_visualdl : False[2021/12/31 18:51:21] root INFO: Loss : [2021/12/31 18:51:21] root INFO:     name : CTCLoss[2021/12/31 18:51:21] root INFO: Metric : [2021/12/31 18:51:21] root INFO:     main_indicator : acc[2021/12/31 18:51:21] root INFO:     name : RecMetric[2021/12/31 18:51:21] root INFO: Optimizer : [2021/12/31 18:51:21] root INFO:     beta1 : 0.9[2021/12/31 18:51:21] root INFO:     beta2 : 0.999[2021/12/31 18:51:21] root INFO:     lr : [2021/12/31 18:51:21] root INFO:         learning_rate : 0.001[2021/12/31 18:51:21] root INFO:         name : Cosine[2021/12/31 18:51:21] root INFO:         warmup_epoch : 5[2021/12/31 18:51:21] root INFO:     name : Adam[2021/12/31 18:51:21] root INFO:     regularizer : [2021/12/31 18:51:21] root INFO:         factor : 4e-05[2021/12/31 18:51:21] root INFO:         name : L2[2021/12/31 18:51:21] root INFO: PostProcess : [2021/12/31 18:51:21] root INFO:     name : CTCLabelDecode[2021/12/31 18:51:21] root INFO: Train : [2021/12/31 18:51:21] root INFO:     dataset : [2021/12/31 18:51:21] root INFO:         data_dir : /home/aistudio/data/data62842/train_images[2021/12/31 18:51:21] root INFO:         label_file_list : ['/home/aistudio/data/data62842/train.txt'][2021/12/31 18:51:21] root INFO:         name : SimpleDataSet[2021/12/31 18:51:21] root INFO:         transforms : [2021/12/31 18:51:21] root INFO:             DecodeImage : [2021/12/31 18:51:21] root INFO:                 channel_first : False[2021/12/31 18:51:21] root INFO:                 img_mode : BGR[2021/12/31 18:51:21] root INFO:             RecAug : None[2021/12/31 18:51:21] root INFO:             CTCLabelEncode : None[2021/12/31 18:51:21] root INFO:             RecResizeImg : [2021/12/31 18:51:21] root INFO:                 image_shape : [3, 32, 320][2021/12/31 18:51:21] root INFO:             KeepKeys : [2021/12/31 18:51:21] root INFO:                 keep_keys : ['image', 'label', 'length'][2021/12/31 18:51:21] root INFO:     loader : [2021/12/31 18:51:21] root INFO:         batch_size_per_card : 256[2021/12/31 18:51:21] root INFO:         drop_last : True[2021/12/31 18:51:21] root INFO:         num_workers : 8[2021/12/31 18:51:21] root INFO:         shuffle : True[2021/12/31 18:51:21] root INFO: profiler_options : None[2021/12/31 18:51:21] root INFO: train with paddle 2.2.1 and device CUDAPlace(0)[2021/12/31 18:51:21] root INFO: Initialize indexs of datasets:['/home/aistudio/data/data62842/val.txt']W1231 18:51:21.482865 16263 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1W1231 18:51:21.487445 16263 device_context.cc:465] device: 0, cuDNN Version: 7.6.[2021/12/31 18:51:26] root INFO: resume from ./output/rec_chinese_common_v2.0/best_accuracy[2021/12/31 18:51:26] root INFO: metric in ckpt ***************[2021/12/31 18:51:26] root INFO: acc:0.6035998792800241[2021/12/31 18:51:26] root INFO: norm_edit_dis:0.8053270782756357[2021/12/31 18:51:26] root INFO: fps:438.9587163608945[2021/12/31 18:51:26] root INFO: best_epoch:23[2021/12/31 18:51:26] root INFO: start_epoch:24eval model::   0%|          | 0/20 [00:00<?, ?it/s]eval model::   5%|▌         | 1/20 [00:02<00:54,  2.88s/it]eval model::  10%|█         | 2/20 [00:04<00:43,  2.42s/it]eval model::  15%|█▌        | 3/20 [00:05<00:35,  2.10s/it]eval model::  20%|██        | 4/20 [00:06<00:29,  1.87s/it]eval model::  25%|██▌       | 5/20 [00:08<00:25,  1.71s/it]eval model::  30%|███       | 6/20 [00:09<00:22,  1.60s/it]eval model::  35%|███▌      | 7/20 [00:10<00:19,  1.53s/it]eval model::  40%|████      | 8/20 [00:12<00:17,  1.48s/it]eval model::  45%|████▌     | 9/20 [00:13<00:15,  1.44s/it]eval model::  50%|█████     | 10/20 [00:15<00:14,  1.42s/it]eval model::  55%|█████▌    | 11/20 [00:16<00:12,  1.40s/it]eval model::  60%|██████    | 12/20 [00:17<00:11,  1.39s/it]eval model::  65%|██████▌   | 13/20 [00:19<00:09,  1.38s/it]eval model::  70%|███████   | 14/20 [00:20<00:08,  1.38s/it]eval model::  75%|███████▌  | 15/20 [00:21<00:06,  1.38s/it]eval model::  80%|████████  | 16/20 [00:23<00:05,  1.38s/it]eval model::  85%|████████▌ | 17/20 [00:24<00:04,  1.38s/it]eval model::  90%|█████████ | 18/20 [00:25<00:02,  1.37s/it]eval model::  95%|█████████▌| 19/20 [00:27<00:01,  1.37s/it]eval model:: 100%|██████████| 20/20 [00:28<00:00,  1.17s/it][2021/12/31 18:51:54] root INFO: metric eval ***************[2021/12/31 18:51:54] root INFO: acc:0.6035998792800241[2021/12/31 18:51:54] root INFO: norm_edit_dis:0.8053270782756357[2021/12/31 18:51:54] root INFO: fps:439.3796693669832INFO 2021-12-31 18:51:55,788 launch.py:311] Local processes completed.

七、结果预测

预测脚本使用预测训练好的模型，并将结果保存成txt格式，可以直接送到比赛提交入口测评，文件默认保存在output/rec/predicts_chinese_lite_v2.0.txt

1.提交内容与格式

本次比赛要求参赛选手必须提交使用深度学习平台飞桨（PaddlePaddle）训练的模型。参赛者要求以.txt 文本格式提交结果，其中每一行是图片名称和文字预测的结果，中间以 “t” 作为分割符，示例如下：

0.jpg文本0

2. infer_rec.py修改

 with open(save_res_path, "w") as fout: #添加列头 fout.write('new_name' + "t" + 'value' +'n')        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)            if config['Architecture']['algorithm'] == "SRN":                encoder_word_pos_list = np.expand_dims(batch[1], axis=0)                gsrm_word_pos_list = np.expand_dims(batch[2], axis=0)                gsrm_slf_attn_bias1_list = np.expand_dims(batch[3], axis=0)                gsrm_slf_attn_bias2_list = np.expand_dims(batch[4], axis=0)                others = [                    paddle.to_tensor(encoder_word_pos_list),                    paddle.to_tensor(gsrm_word_pos_list),                    paddle.to_tensor(gsrm_slf_attn_bias1_list),                    paddle.to_tensor(gsrm_slf_attn_bias2_list)                ]            if config['Architecture']['algorithm'] == "SAR":                valid_ratio = np.expand_dims(batch[-1], axis=0)                img_metas = [paddle.to_tensor(valid_ratio)]            images = np.expand_dims(batch[0], axis=0)            images = paddle.to_tensor(images)            if config['Architecture']['algorithm'] == "SRN":                preds = model(images, others)            elif config['Architecture']['algorithm'] == "SAR":                preds = model(images, img_metas)            else:                preds = model(images)            post_result = post_process_class(preds)            info = None            if isinstance(post_result, dict):                rec_info = dict()                for key in post_result:                    if len(post_result[key][0]) >= 2:                        rec_info[key] = {                            "label": post_result[key][0][0],                            "score": float(post_result[key][0][1]),                        }                info = json.dumps(rec_info)            else:                if len(post_result[0]) >= 2:                    info = post_result[0][0] + "t" + str(post_result[0][1])            if info is not None:                logger.info("t result: {}".format(info))                # fout.write(file + "t" + info)                # 格式化输出                fout.write(file + "t" + post_result[0][0] +'n')    logger.info("success!")

In [11]

%cd ~/PaddleOCR/# mobile模型# !python tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml #     -o Global.infer_img="/home/aistudio/data/data62843/test_images" #     Global.pretrained_model="./output/rec_chinese_lite_v2.0/best_accuracy"# server模型!python tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml     -o Global.infer_img="/home/aistudio/data/data62843/test_images"     Global.checkpoints=./output/rec_chinese_common_v2.0/best_accuracy

预测日志

[2021/12/30 23:53:50] root INFO:  result: 萧记果点0.66611135[2021/12/30 23:53:50] root INFO: infer_img: /home/aistudio/data/data62843/test_images/9995.jpg[2021/12/30 23:53:50] root INFO:  result: 福0.1693737[2021/12/30 23:53:50] root INFO: infer_img: /home/aistudio/data/data62843/test_images/9996.jpg[2021/12/30 23:53:50] root INFO:  result: 2790.97771764[2021/12/30 23:53:50] root INFO: infer_img: /home/aistudio/data/data62843/test_images/9997.jpg[2021/12/30 23:53:50] root INFO:  result: 公牛装饰开关0.9916236[2021/12/30 23:53:50] root INFO: infer_img: /home/aistudio/data/data62843/test_images/9998.jpg[2021/12/30 23:53:50] root INFO:  result: 专酒0.118371546[2021/12/30 23:53:50] root INFO: infer_img: /home/aistudio/data/data62843/test_images/9999.jpg[2021/12/30 23:53:50] root INFO:  result: 东之家0.871051[2021/12/30 23:53:50] root INFO: success!......

八、基于预测引擎的预测

1.模型大小限制

约束性条件1：模型总大小不超过10MB（以.pdmodel和.pdiparams文件非压缩状态磁盘占用空间之和为准）；

2.解决办法

训练过程中保存的模型是checkpoints模型，保存的只有模型的参数，多用于恢复训练等。实际上，此处的约束条件限制的是inference 模型的大小。inference 模型一般是模型训练，把模型结构和模型参数保存在文件中的固化模型，多用于预测部署场景。与checkpoints模型相比，inference 模型会额外保存模型的结构信息，在预测部署、加速推理上性能优越，灵活方便，适合于实际系统集成，模型大小也会小一些。

In [ ]

# 静态模型导出%cd ~/PaddleOCR/# mobile模型# !python tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model=./output/rec_chinese_lite_v2.0/best_accuracy.pdparams  Global.save_inference_dir=./inference/rec_inference/# server模型!python tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_common_v2.0.yml -o Global.pretrained_model=./output/rec_chinese_common_train_v2.0/best_accuracy.pdparams  Global.save_inference_dir=./inference/rec_inference/

/home/aistudio/PaddleOCRW1230 23:54:48.747483 13346 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.0, Runtime API Version: 10.1W1230 23:54:48.752360 13346 device_context.cc:465] device: 0, cuDNN Version: 7.6.[2021/12/30 23:54:52] root INFO: load pretrain successful from ./output/rec_chinese_lite_v2.0/best_accuracy[2021/12/30 23:54:54] root INFO: inference model is saved to ./inference/rec_inference/inference

In [ ]

%cd ~/PaddleOCR/!du -sh ./inference/rec_inference/

/home/aistudio/PaddleOCR5.2M./inference/rec_inference/

可以看到，当前训练使用的CRNN算法导出inference后，仅有5.2M。导出的inference模型也可以用来预测，预测逻辑如下代码所示。In [ ]

# 使用导出静态模型预测%cd ~/PaddleOCR/!python3.7 tools/infer/predict_rec.py  --rec_model_dir=./inference/rec_inference/  --image_dir="/home/aistudio/data/A榜测试数据集/TestAImages"

预测日志

[2021/12/30 13:20:08] root INFO: Predicts of /home/aistudio/data/A榜测试数据集/TestAImages/TestA_000001.jpg:('MJ', 0.2357887)[2021/12/30 13:20:08] root INFO: Predicts of /home/aistudio/data/A榜测试数据集/TestAImages/TestA_000002.jpg:('中门', 0.7167614)[2021/12/30 13:20:08] root INFO: Predicts of /home/aistudio/data/A榜测试数据集/TestAImages/TestA_000003.jpg:('黄焖鸡米饭', 0.7325407)[2021/12/30 13:20:08] root INFO: Predicts of /home/aistudio/data/A榜测试数据集/TestAImages/TestA_000004.jpg:('加行', 0.06699998)[2021/12/30 13:20:08] root INFO: Predicts of /home/aistudio/data/A榜测试数据集/TestAImages/TestA_000005.jpg:('学商烤面航', 0.40579563)[2021/12/30 13:20:08] root INFO: Predicts of /home/aistudio/data/A榜测试数据集/TestAImages/TestA_000006.jpg:('绿村装机 滋光彩机 CP口出国', 0.38243735)[2021/12/30 13:20:08] root INFO: Predicts of /home/aistudio/data/A榜测试数据集/TestAImages/TestA_000007.jpg:('有酸锁 四好吃', 0.38957664)[2021/12/30 13:20:08] root INFO: Predicts of /home/aistudio/data/A榜测试数据集/TestAImages/TestA_000008.jpg:('婚汽中海', 0.36037388)[2021/12/30 13:20:08] root INFO: Predicts of /home/aistudio/data/A榜测试数据集/TestAImages/TestA_000009.jpg:('L', 0.25453746)[2021/12/30 13:20:08] root INFO: Predicts of /home/aistudio/data/A榜测试数据集/TestAImages/TestA_000010.jpg:('清女装', 0.79736567)[2021/12/30 13:20:08] root INFO: Predicts of /home/aistudio/data/A榜测试数据集/TestAImages/TestA_000011.jpg:('幼小数学视食', 0.50577885)......

九、提交

预测结果保存到配置文件指定的 output/rec/predicts_chinese_lite_v2.0.txt文件，可直接提交即可。

In [12]

%cd ~!head PaddleOCR/output/rec/predicts_chinese_common_v2.0.txt

/home/aistudionew_namevalue0.jpg邦佳洗衣1.jpg不锈钢配件大全10.jpg诊疗科目：中医科100.jpg2101000.jpg电线电缆等1001.jpg201002.jpg进口滤纸　专业制造1003.jpg15065401004.jpgｉＷｏＷ

1.mobile模型

2.server模型

大家可以再处理处理，优化优化，多跑几轮。

以上就是飞桨常规赛：中文场景文字识别- 12月第8名方案的详细内容，更多请关注创想鸟其它相关文章！