From cb86f0e0c55177784121046e5c43933836b5c231 Mon Sep 17 00:00:00 2001 From: DanielYang Date: Tue, 7 Sep 2021 20:17:55 +0800 Subject: [PATCH 01/12] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index baddd8b8..f31b833a 100644 --- a/README.md +++ b/README.md @@ -26,7 +26,7 @@ PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools **Recent updates** - PaddleOCR R&D team would like to share the key points of PP-OCRv2, at 20:15 pm on September 8th, [Live Address](https://live.bilibili.com/21689802). -- 2021.9.7 release PaddleOCR v2.3, [PP-OCRv2](#PP-OCRv2) is proposed. The inference speed of PP-OCRv2 is 220% higher than that of PP-OCR server in CPU device. The F-score of PP-OCRv2 is 7% higher than that of PP-OCR mobile. +- 2021.9.7 release PaddleOCR v2.3, [PP-OCRv2](#PP-OCRv2) is proposed. The inference speed of PP-OCRv2 is 220% higher than that of PP-OCR server in CPU device. The F-score of PP-OCRv2 is 7% higher than that of PP-OCR mobile. ([Technical Report](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/PP-OCRv2.pdf)) - 2021.8.3 released PaddleOCR v2.2, add a new structured documents analysis toolkit, i.e., [PP-Structure](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/ppstructure/README.md), support layout analysis and table recognition (One-key to export chart images to Excel files). - 2021.4.8 release end-to-end text recognition algorithm [PGNet](https://www.aaai.org/AAAI21Papers/AAAI-2885.WangP.pdf) which is published in AAAI 2021. Find tutorial [here](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/pgnet_en.md)；release multi language recognition [models](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/multi_languages_en.md), support more than 80 languages recognition; especically, the performance of [English recognition model](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/models_list_en.md#English) is Optimized. From 1cb48b0f52d97251e11d1f09012ad7f57ef91feb Mon Sep 17 00:00:00 2001 From: DanielYang Date: Tue, 7 Sep 2021 20:20:11 +0800 Subject: [PATCH 02/12] Update README_ch.md --- README_ch.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README_ch.md b/README_ch.md index 7e8a8e24..ccea97a6 100755 --- a/README_ch.md +++ b/README_ch.md @@ -25,7 +25,7 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力 **近期更新** - PaddleOCR研发团队对最新发版内容技术深入解读，9月8日晚上20:15，[直播地址](https://live.bilibili.com/21689802)。 -- 2021.9.7 发布PaddleOCR v2.3，发布[PP-OCRv2](#PP-OCRv2)，CPU推理速度相比于PP-OCR server提升220%；效果相比于PP-OCR mobile 提升7%。 +- 2021.9.7 发布PaddleOCR v2.3，发布[PP-OCRv2](#PP-OCRv2)，CPU推理速度相比于PP-OCR server提升220%；效果相比于PP-OCR mobile 提升7%。([技术报告](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/PP-OCRv2.pdf)) - 2021.8.3 发布PaddleOCR v2.2，新增文档结构分析[PP-Structure](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/ppstructure/README_ch.md)工具包，支持版面分析与表格识别（含Excel导出）。 - 2021.6.29 [FAQ](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/FAQ.md)新增5个高频问题，总数248个，每周一都会更新，欢迎大家持续关注。 - 2021.4.8 release 2.1版本，新增AAAI 2021论文[端到端识别算法PGNet](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/pgnet.md)开源，[多语言模型](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/multi_languages.md)支持种类增加到80+。 @@ -142,7 +142,7 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力 [1] PP-OCR是一个实用的超轻量OCR系统。主要由DB文本检测、检测框矫正和CRNN文本识别三部分组成。该系统从骨干网络选择和调整、预测头部的设计、数据增强、学习率变换策略、正则化参数选择、预训练模型使用以及模型自动裁剪量化8个方面，采用19个有效策略，对各个模块的模型进行效果调优和瘦身(如绿框所示)，最终得到整体大小为3.5M的超轻量中英文OCR和2.8M的英文数字OCR。更多细节请参考PP-OCR技术方案 https://arxiv.org/abs/2009.09941 -[2] PP-OCRv2在PP-OCR的基础上，进一步在5个方面重点优化，检测模型采用CML协同互学习知识蒸馏策略和CopyPaste数据增广策略；识别模型采用LCNet轻量级骨干网络、UDML 改进知识蒸馏策略和Enhanced CTC loss损失函数改进（如上图红框所示），进一步在推理速度和预测效果上取得明显提升。更多细节请参考PP-OCR技术方案（arxiv链接生成中）。 +[2] PP-OCRv2在PP-OCR的基础上，进一步在5个方面重点优化，检测模型采用CML协同互学习知识蒸馏策略和CopyPaste数据增广策略；识别模型采用LCNet轻量级骨干网络、UDML 改进知识蒸馏策略和Enhanced CTC loss损失函数改进（如上图红框所示），进一步在推理速度和预测效果上取得明显提升。更多细节请参考PP-OCRv2[技术报告](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/PP-OCRv2.pdf)。 From 9c473f920540317e8fbfdf3f2757cbfad6d2f3d7 Mon Sep 17 00:00:00 2001 From: DanielYang Date: Tue, 7 Sep 2021 20:21:20 +0800 Subject: [PATCH 03/12] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index f31b833a..b8878fae 100644 --- a/README.md +++ b/README.md @@ -146,7 +146,7 @@ For a new language request, please refer to [Guideline for new language_requests [1] PP-OCR is a practical ultra-lightweight OCR system. It is mainly composed of three parts: DB text detection, detection frame correction and CRNN text recognition. The system adopts 19 effective strategies from 8 aspects including backbone network selection and adjustment, prediction head design, data augmentation, learning rate transformation strategy, regularization parameter selection, pre-training model use, and automatic model tailoring and quantization to optimize and slim down the models of each module (as shown in the green box above). The final results are an ultra-lightweight Chinese and English OCR model with an overall size of 3.5M and a 2.8M English digital OCR model. For more details, please refer to the PP-OCR technical article (https://arxiv.org/abs/2009.09941). -[2] On the basis of PP-OCR, PP-OCRv2 is further optimized in five aspects. The detection model adopts CML(Collaborative Mutual Learning) knowledge distillation strategy and CopyPaste data expansion strategy. The recognition model adopts LCNet lightweight backbone network, U-DML knowledge distillation strategy and enhanced CTC loss function improvement (as shown in the red box above), which further improves the inference speed and prediction effect. For more details, please refer to the technical report of PP-OCRv2 (arXiv link is coming soon). +[2] On the basis of PP-OCR, PP-OCRv2 is further optimized in five aspects. The detection model adopts CML(Collaborative Mutual Learning) knowledge distillation strategy and CopyPaste data expansion strategy. The recognition model adopts LCNet lightweight backbone network, U-DML knowledge distillation strategy and enhanced CTC loss function improvement (as shown in the red box above), which further improves the inference speed and prediction effect. For more details, please refer to the [technical report](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/PP-OCRv2.pdf) of PP-OCRv2. From 2dccdf9c232e10f11af6a0dd7a7c1d462c7d6a1c Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Tue, 7 Sep 2021 21:04:00 +0800 Subject: [PATCH 04/12] rm tests/ --- tests/compare_results.py | 133 ------ tests/ocr_det_params.txt | 67 --- tests/ocr_det_server_params.txt | 52 --- tests/ocr_rec_params.txt | 51 --- tests/prepare.sh | 152 ------- tests/readme.md | 58 --- tests/results/det_results_gpu_fp32.txt | 49 --- tests/results/det_results_gpu_trt_fp16.txt | 49 --- tests/test.sh | 476 --------------------- 9 files changed, 1087 deletions(-) delete mode 100644 tests/compare_results.py delete mode 100644 tests/ocr_det_params.txt delete mode 100644 tests/ocr_det_server_params.txt delete mode 100644 tests/ocr_rec_params.txt delete mode 100644 tests/prepare.sh delete mode 100644 tests/readme.md delete mode 100644 tests/results/det_results_gpu_fp32.txt delete mode 100644 tests/results/det_results_gpu_trt_fp16.txt delete mode 100644 tests/test.sh diff --git a/tests/compare_results.py b/tests/compare_results.py deleted file mode 100644 index 1c3fe4ea..00000000 --- a/tests/compare_results.py +++ /dev/null @@ -1,133 +0,0 @@ -import numpy as np -import os -import subprocess -import json -import argparse -import glob - - -def init_args(): - parser = argparse.ArgumentParser() - # params for testing assert allclose - parser.add_argument("--atol", type=float, default=1e-3) - parser.add_argument("--rtol", type=float, default=1e-3) - parser.add_argument("--gt_file", type=str, default="") - parser.add_argument("--log_file", type=str, default="") - parser.add_argument("--precision", type=str, default="fp32") - return parser - - -def parse_args(): - parser = init_args() - return parser.parse_args() - - -def run_shell_command(cmd): - p = subprocess.Popen( - cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True) - out, err = p.communicate() - - if p.returncode == 0: - return out.decode('utf-8') - else: - return None - - -def parser_results_from_log_by_name(log_path, names_list): - if not os.path.exists(log_path): - raise ValueError("The log file {} does not exists!".format(log_path)) - - if names_list is None or len(names_list) < 1: - return [] - - parser_results = {} - for name in names_list: - cmd = "grep {} {}".format(name, log_path) - outs = run_shell_command(cmd) - outs = outs.split("\n")[0] - result = outs.split("{}".format(name))[-1] - result = json.loads(result) - parser_results[name] = result - return parser_results - - -def load_gt_from_file(gt_file): - if not os.path.exists(gt_file): - raise ValueError("The log file {} does not exists!".format(gt_file)) - with open(gt_file, 'r') as f: - data = f.readlines() - f.close() - parser_gt = {} - for line in data: - image_name, result = line.strip("\n").split("\t") - result = json.loads(result) - parser_gt[image_name] = result - return parser_gt - - -def load_gt_from_txts(gt_file): - gt_list = glob.glob(gt_file) - gt_collection = {} - for gt_f in gt_list: - gt_dict = load_gt_from_file(gt_f) - basename = os.path.basename(gt_f) - if "fp32" in basename: - gt_collection["fp32"] = [gt_dict, gt_f] - elif "fp16" in basename: - gt_collection["fp16"] = [gt_dict, gt_f] - elif "int8" in basename: - gt_collection["int8"] = [gt_dict, gt_f] - else: - continue - return gt_collection - - -def collect_predict_from_logs(log_path, key_list): - log_list = glob.glob(log_path) - pred_collection = {} - for log_f in log_list: - pred_dict = parser_results_from_log_by_name(log_f, key_list) - key = os.path.basename(log_f) - pred_collection[key] = pred_dict - - return pred_collection - - -def testing_assert_allclose(dict_x, dict_y, atol=1e-7, rtol=1e-7): - for k in dict_x: - np.testing.assert_allclose( - np.array(dict_x[k]), np.array(dict_y[k]), atol=atol, rtol=rtol) - - -if __name__ == "__main__": - # Usage: - # python3.7 tests/compare_results.py --gt_file=./tests/results/*.txt --log_file=./tests/output/infer_*.log - - args = parse_args() - - gt_collection = load_gt_from_txts(args.gt_file) - key_list = gt_collection["fp32"][0].keys() - - pred_collection = collect_predict_from_logs(args.log_file, key_list) - for filename in pred_collection.keys(): - if "fp32" in filename: - gt_dict, gt_filename = gt_collection["fp32"] - elif "fp16" in filename: - gt_dict, gt_filename = gt_collection["fp16"] - elif "int8" in filename: - gt_dict, gt_filename = gt_collection["int8"] - else: - continue - pred_dict = pred_collection[filename] - - try: - testing_assert_allclose( - gt_dict, pred_dict, atol=args.atol, rtol=args.rtol) - print( - "Assert allclose passed! The results of {} and {} are consistent!". - format(filename, gt_filename)) - except Exception as E: - print(E) - raise ValueError( - "The results of {} and the results of {} are inconsistent!". - format(filename, gt_filename)) diff --git a/tests/ocr_det_params.txt b/tests/ocr_det_params.txt deleted file mode 100644 index 73b12cec..00000000 --- a/tests/ocr_det_params.txt +++ /dev/null @@ -1,67 +0,0 @@ -===========================train_params=========================== -model_name:ocr_det -python:python3.7 -gpu_list:0|0,1 -Global.use_gpu:True|True -Global.auto_cast:null -Global.epoch_num:lite_train_infer=1|whole_train_infer=300 -Global.save_model_dir:./output/ -Train.loader.batch_size_per_card:lite_train_infer=2|whole_train_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|pact_train -norm_train:tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained -pact_train:deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o -fpgm_train:deploy/slim/prune/sensitivity_anal.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/det_mv3_db_v2.0_train/best_accuracy -distill_train:null -null:null -null:null -## -===========================eval_params=========================== -eval:tools/eval.py -c configs/det/det_mv3_db.yml -o -null:null -## -===========================infer_params=========================== -Global.save_inference_dir:./output/ -Global.pretrained_model: -norm_export:tools/export_model.py -c configs/det/det_mv3_db.yml -o -quant_export:deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o -fpgm_export:deploy/slim/prune/export_prune_model.py -c configs/det/det_mv3_db.yml -o -distill_export:null -export1:null -export2:null -## -infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/ -infer_export:null -infer_quant:False -inference:tools/infer/predict_det.py ---use_gpu:True|False ---enable_mkldnn:True|False ---cpu_threads:1|6 ---rec_batch_num:1 ---use_tensorrt:False|True ---precision:fp32|fp16|int8 ---det_model_dir: ---image_dir:./inference/ch_det_data_50/all-sum-510/ ---save_log_path:null ---benchmark:True -null:null -===========================cpp_infer_params=========================== -use_opencv:True -infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/ -infer_quant:False -inference:./deploy/cpp_infer/build/ppocr det ---use_gpu:True|False ---enable_mkldnn:True|False ---cpu_threads:1|6 ---rec_batch_num:1 ---use_tensorrt:False|True ---precision:fp32|fp16 ---det_model_dir: ---image_dir:./inference/ch_det_data_50/all-sum-510/ ---save_log_path:null ---benchmark:True - diff --git a/tests/ocr_det_server_params.txt b/tests/ocr_det_server_params.txt deleted file mode 100644 index 0835cfff..00000000 --- a/tests/ocr_det_server_params.txt +++ /dev/null @@ -1,52 +0,0 @@ -===========================train_params=========================== -model_name:ocr_server_det -python:python3.7 -gpu_list:0|0,1 -Global.use_gpu:True|True -Global.auto_cast:null -Global.epoch_num:lite_train_infer=2|whole_train_infer=300 -Global.save_model_dir:./output/ -Train.loader.batch_size_per_card:lite_train_infer=2|whole_train_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|pact_train -norm_train:tools/train.py -c configs/det/det_r50_vd_db.yml -o Global.pretrained_model="" -pact_train:null -fpgm_train:null -distill_train:null -null:null -null:null -## -===========================eval_params=========================== -eval:tools/eval.py -c configs/det/det_mv3_db.yml -o -null:null -## -===========================infer_params=========================== -Global.save_inference_dir:./output/ -Global.pretrained_model: -norm_export:tools/export_model.py -c configs/det/det_r50_vd_db.yml -o -quant_export:null -fpgm_export:null -distill_export:null -export1:null -export2:null -## -infer_model:./inference/ch_ppocr_server_v2.0_det_infer/ -infer_export:null -infer_quant:False -inference:tools/infer/predict_det.py ---use_gpu:True|False ---enable_mkldnn:True|False ---cpu_threads:1|6 ---rec_batch_num:1 ---use_tensorrt:False|True ---precision:fp32|fp16|int8 ---det_model_dir: ---image_dir:./inference/ch_det_data_50/all-sum-510/ ---save_log_path:null ---benchmark:True -null:null - diff --git a/tests/ocr_rec_params.txt b/tests/ocr_rec_params.txt deleted file mode 100644 index 71d12f90..00000000 --- a/tests/ocr_rec_params.txt +++ /dev/null @@ -1,51 +0,0 @@ -===========================train_params=========================== -model_name:ocr_rec -python:python3.7 -gpu_list:0|2,3 -Global.use_gpu:True|True -Global.auto_cast:null -Global.epoch_num:lite_train_infer=2|whole_train_infer=300 -Global.save_model_dir:./output/ -Train.loader.batch_size_per_card:lite_train_infer=128|whole_train_infer=128 -Global.pretrained_model:null -train_model_name:latest -train_infer_img_dir:./train_data/ic15_data/train -null:null -## -trainer:norm_train|pact_train -norm_train:tools/train.py -c configs/rec/rec_icdar15_train.yml -o -pact_train:deploy/slim/quantization/quant.py -c configs/rec/rec_icdar15_train.yml -o -fpgm_train:null -distill_train:null -null:null -null:null -## -===========================eval_params=========================== -eval:tools/eval.py -c configs/rec/rec_icdar15_train.yml -o -null:null -## -===========================infer_params=========================== -Global.save_inference_dir:./output/ -Global.pretrained_model: -norm_export:tools/export_model.py -c configs/rec/rec_icdar15_train.yml -o -quant_export:deploy/slim/quantization/export_model.py -c configs/rec/rec_icdar15_train.yml -o -fpgm_export:null -distill_export:null -export1:null -export2:null -## -infer_model:./inference/ch_ppocr_mobile_v2.0_rec_infer/ -infer_export:null -infer_quant:False -inference:tools/infer/predict_rec.py ---use_gpu:True|False ---enable_mkldnn:True|False ---cpu_threads:1|6 ---rec_batch_num:1 ---use_tensorrt:True|False ---precision:fp32|fp16|int8 ---rec_model_dir: ---image_dir:./inference/rec_inference ---save_log_path:./test/output/ ---benchmark:True -null:null diff --git a/tests/prepare.sh b/tests/prepare.sh deleted file mode 100644 index 5da74d94..00000000 --- a/tests/prepare.sh +++ /dev/null @@ -1,152 +0,0 @@ -#!/bin/bash -FILENAME=$1 -# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer', 'cpp_infer'] -MODE=$2 - -dataline=$(cat ${FILENAME}) - -# parser params -IFS=$'\n' -lines=(${dataline}) -function func_parser_key(){ - strs=$1 - IFS=":" - array=(${strs}) - tmp=${array[0]} - echo ${tmp} -} -function func_parser_value(){ - strs=$1 - IFS=":" - array=(${strs}) - tmp=${array[1]} - echo ${tmp} -} -IFS=$'\n' -# The training params -model_name=$(func_parser_value "${lines[1]}") - -trainer_list=$(func_parser_value "${lines[14]}") - -# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer'] -MODE=$2 - -if [ ${MODE} = "lite_train_infer" ];then - # pretrain lite train data - wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams - wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar - cd ./pretrain_models/ && tar xf det_mv3_db_v2.0_train.tar && cd ../ - rm -rf ./train_data/icdar2015 - rm -rf ./train_data/ic15_data - wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar - wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar # todo change to bcebos - wget -nc -P ./deploy/slim/prune https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/sen.pickle - - cd ./train_data/ && tar xf icdar2015_lite.tar && tar xf ic15_data.tar - ln -s ./icdar2015_lite ./icdar2015 - cd ../ -elif [ ${MODE} = "whole_train_infer" ];then - wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams - rm -rf ./train_data/icdar2015 - rm -rf ./train_data/ic15_data - wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar - wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar - cd ./train_data/ && tar xf icdar2015.tar && tar xf ic15_data.tar && cd ../ -elif [ ${MODE} = "whole_infer" ];then - wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams - rm -rf ./train_data/icdar2015 - rm -rf ./train_data/ic15_data - wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_infer.tar - wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar - cd ./train_data/ && tar xf icdar2015_infer.tar && tar xf ic15_data.tar - ln -s ./icdar2015_infer ./icdar2015 - cd ../ -elif [ ${MODE} = "infer" ] || [ ${MODE} = "cpp_infer" ];then - if [ ${model_name} = "ocr_det" ]; then - eval_model_name="ch_ppocr_mobile_v2.0_det_infer" - rm -rf ./train_data/icdar2015 - wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar - wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar - cd ./inference && tar xf ${eval_model_name}.tar && tar xf ch_det_data_50.tar && cd ../ - elif [ ${model_name} = "ocr_server_det" ]; then - wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar - wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar - cd ./inference && tar xf ch_ppocr_server_v2.0_det_infer.tar && tar xf ch_det_data_50.tar && cd ../ - else - rm -rf ./train_data/ic15_data - eval_model_name="ch_ppocr_mobile_v2.0_rec_infer" - wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar - wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar - cd ./inference && tar xf ${eval_model_name}.tar && tar xf ic15_data.tar && cd ../ - fi -fi - -if [ ${MODE} = "cpp_infer" ];then - cd deploy/cpp_infer - use_opencv=$(func_parser_value "${lines[52]}") - if [ ${use_opencv} = "True" ]; then - echo "################### build opencv ###################" - rm -rf 3.4.7.tar.gz opencv-3.4.7/ - wget https://github.com/opencv/opencv/archive/3.4.7.tar.gz - tar -xf 3.4.7.tar.gz - - cd opencv-3.4.7/ - install_path=$(pwd)/opencv-3.4.7/opencv3 - - rm -rf build - mkdir build - cd build - - cmake .. \ - -DCMAKE_INSTALL_PREFIX=${install_path} \ - -DCMAKE_BUILD_TYPE=Release \ - -DBUILD_SHARED_LIBS=OFF \ - -DWITH_IPP=OFF \ - -DBUILD_IPP_IW=OFF \ - -DWITH_LAPACK=OFF \ - -DWITH_EIGEN=OFF \ - -DCMAKE_INSTALL_LIBDIR=lib64 \ - -DWITH_ZLIB=ON \ - -DBUILD_ZLIB=ON \ - -DWITH_JPEG=ON \ - -DBUILD_JPEG=ON \ - -DWITH_PNG=ON \ - -DBUILD_PNG=ON \ - -DWITH_TIFF=ON \ - -DBUILD_TIFF=ON - - make -j - make install - cd ../ - echo "################### build opencv finished ###################" - fi - - - echo "################### build PaddleOCR demo ####################" - if [ ${use_opencv} = "True" ]; then - OPENCV_DIR=$(pwd)/opencv-3.4.7/opencv3/ - else - OPENCV_DIR='' - fi - LIB_DIR=$(pwd)/Paddle/build/paddle_inference_install_dir/ - CUDA_LIB_DIR=$(dirname `find /usr -name libcudart.so`) - CUDNN_LIB_DIR=$(dirname `find /usr -name libcudnn.so`) - - BUILD_DIR=build - rm -rf ${BUILD_DIR} - mkdir ${BUILD_DIR} - cd ${BUILD_DIR} - cmake .. \ - -DPADDLE_LIB=${LIB_DIR} \ - -DWITH_MKL=ON \ - -DWITH_GPU=OFF \ - -DWITH_STATIC_LIB=OFF \ - -DWITH_TENSORRT=OFF \ - -DOPENCV_DIR=${OPENCV_DIR} \ - -DCUDNN_LIB=${CUDNN_LIB_DIR} \ - -DCUDA_LIB=${CUDA_LIB_DIR} \ - -DTENSORRT_DIR=${TENSORRT_DIR} \ - - make -j - echo "################### build PaddleOCR demo finished ###################" -fi \ No newline at end of file diff --git a/tests/readme.md b/tests/readme.md deleted file mode 100644 index 1c5e0fae..00000000 --- a/tests/readme.md +++ /dev/null @@ -1,58 +0,0 @@ - -# 介绍 - -test.sh和params.txt文件配合使用，完成OCR轻量检测和识别模型从训练到预测的流程测试。 - -# 安装依赖 -- 安装PaddlePaddle >= 2.0 -- 安装PaddleOCR依赖 - ``` - pip3 install -r ../requirements.txt - ``` -- 安装autolog - ``` - git clone https://github.com/LDOUBLEV/AutoLog - cd AutoLog - pip3 install -r requirements.txt - python3 setup.py bdist_wheel - pip3 install ./dist/auto_log-1.0.0-py3-none-any.whl - cd ../ - ``` - -# 目录介绍 - -```bash -tests/ -├── ocr_det_params.txt # 测试OCR检测模型的参数配置文件 -├── ocr_rec_params.txt # 测试OCR识别模型的参数配置文件 -└── prepare.sh # 完成test.sh运行所需要的数据和模型下载 -└── test.sh # 根据 -``` - -# 使用方法 -test.sh包含四种运行模式，每种模式的运行数据不同，分别用于测试速度和精度，分别是： -- 模式1 lite_train_infer，使用少量数据训练，用于快速验证训练到预测的走通流程，不验证精度和速度； -``` -bash test/prepare.sh ./tests/ocr_det_params.txt 'lite_train_infer' -bash tests/test.sh ./tests/ocr_det_params.txt 'lite_train_infer' -``` -- 模式2 whole_infer，使用少量数据训练，一定量数据预测，用于验证训练后的模型执行预测，预测速度是否合理； -``` -bash tests/prepare.sh ./tests/ocr_det_params.txt 'whole_infer' -bash tests/test.sh ./tests/ocr_det_params.txt 'whole_infer' -``` - -- 模式3 infer 不训练，全量数据预测，走通开源模型评估、动转静，检查inference model预测时间和精度; -``` -bash tests/prepare.sh ./tests/ocr_det_params.txt 'infer' -用法1: -bash tests/test.sh ./tests/ocr_det_params.txt 'infer' -用法2: 指定GPU卡预测，第三个传入参数为GPU卡号 -bash tests/test.sh ./tests/ocr_det_params.txt 'infer' '1' -``` - -模式4: whole_train_infer , CE： 全量数据训练，全量数据预测，验证模型训练精度，预测精度，预测速度 -``` -bash tests/prepare.sh ./tests/ocr_det_params.txt 'whole_train_infer' -bash tests/test.sh ./tests/ocr_det_params.txt 'whole_train_infer' -``` diff --git a/tests/results/det_results_gpu_fp32.txt b/tests/results/det_results_gpu_fp32.txt deleted file mode 100644 index 28af26d0..00000000 --- a/tests/results/det_results_gpu_fp32.txt +++ /dev/null @@ -1,49 +0,0 @@ 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-test_add_4.png [] -test_add_5.png [[[48, 164], [108, 164], [108, 174], [48, 174]], [[52, 121], [169, 121], [169, 134], [52, 134]], [[50, 102], [165, 102], [165, 118], [50, 118]], [[52, 83], [164, 83], [164, 100], [52, 100]], [[51, 68], [166, 68], [166, 84], [51, 84]], [[51, 50], [145, 47], [145, 64], [52, 67]]] -test_add_6.jpg [[[123, 223], [219, 227], [218, 251], [122, 247]], [[172, 172], [186, 186], [172, 200], [158, 186]]] -test_add_7.jpg [[[48, 938], [174, 936], [174, 962], [48, 964]], [[227, 873], [629, 876], [628, 953], [226, 949]], [[56, 745], [638, 745], [638, 790], [56, 790]], [[150, 674], [545, 678], [544, 721], [150, 718]], [[73, 504], [633, 504], [633, 601], [73, 601]], [[59, 270], [655, 279], [652, 441], [56, 432]], [[513, 193], [553, 193], [553, 223], [513, 223]], [[61, 175], [532, 175], [532, 239], [61, 239]], [[533, 178], [642, 178], [642, 236], [533, 236]]] -test_add_8.jpg [[[251, 586], [454, 580], [454, 606], [252, 613]], [[107, 533], [457, 527], [457, 560], [108, 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a/tests/results/det_results_gpu_trt_fp16.txt +++ /dev/null @@ -1,49 +0,0 @@ -00008790.jpg [[[209, 406], [280, 406], [280, 419], [209, 419]], [[60, 398], [105, 398], [105, 411], [60, 411]], [[198, 389], [291, 389], [291, 402], [198, 402]], [[162, 391], [173, 391], [173, 401], [162, 401]], [[35, 380], [133, 380], [133, 393], [35, 393]], [[199, 371], [292, 371], [292, 384], [199, 384]], [[218, 310], [272, 310], [272, 324], [218, 324]], [[162, 305], [172, 305], [172, 314], [162, 314]], [[371, 302], [436, 302], [436, 316], [371, 316]], [[31, 302], [134, 301], [134, 315], [31, 316]], [[223, 292], [269, 292], [269, 306], [223, 306]], [[60, 225], [104, 225], [104, 236], [60, 236]], [[218, 223], [272, 223], [272, 237], [218, 237]], [[162, 219], [173, 219], [173, 227], [162, 227]], [[33, 207], [131, 207], [131, 220], [33, 220]], [[223, 206], [269, 206], [269, 220], [223, 220]], [[74, 146], [383, 146], [383, 159], [74, 159]], [[54, 120], [117, 120], [117, 134], [54, 134]], [[74, 51], [296, 51], 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[[326, 64], [478, 64], [478, 91], [326, 91]], [[173, 64], [306, 60], [306, 89], [174, 93]]] -test_add_4.png [] -test_add_5.png [[[48, 164], [108, 164], [108, 174], [48, 174]], [[52, 121], [169, 121], [169, 134], [52, 134]], [[50, 102], [165, 102], [165, 118], [50, 118]], [[52, 83], [164, 83], [164, 100], [52, 100]], [[51, 68], [166, 68], [166, 84], [51, 84]], [[51, 50], [145, 47], [145, 64], [52, 67]]] -test_add_6.jpg [[[123, 223], [219, 227], [218, 251], [122, 247]], [[172, 172], [186, 186], [172, 200], [158, 186]]] -test_add_7.jpg [[[48, 938], [174, 936], [174, 962], [48, 964]], [[227, 873], [629, 876], [628, 953], [226, 949]], [[56, 745], [638, 745], [638, 790], [56, 790]], [[150, 674], [545, 678], [544, 721], [150, 718]], [[73, 504], [633, 504], [633, 601], [73, 601]], [[59, 270], [655, 279], [652, 441], [56, 432]], [[513, 193], [553, 193], [553, 223], [513, 223]], [[61, 175], [532, 175], [532, 239], [61, 239]], [[533, 178], [642, 178], [642, 236], [533, 236]]] -test_add_8.jpg [[[251, 586], [454, 580], [454, 606], [252, 613]], [[107, 533], [457, 527], [457, 560], [108, 566]], [[336, 494], [384, 494], [384, 507], [336, 507]], [[27, 307], [355, 297], [356, 320], [28, 330]], [[22, 259], [445, 251], [445, 274], [23, 282]], [[78, 209], [445, 205], [445, 225], [78, 229]], [[160, 23], [319, 30], [317, 79], [158, 72]]] -test_add_9.png [[[266, 687], [486, 687], [486, 696], [266, 696]], [[196, 668], [554, 668], [554, 681], [196, 681]], [[154, 596], [597, 596], [597, 606], [154, 606]], [[215, 578], [541, 578], [541, 588], [215, 588]], [[134, 560], [615, 560], [615, 570], [134, 570]], [[85, 543], [665, 543], [665, 553], [85, 553]], [[96, 522], [653, 522], [653, 535], [96, 535]], [[362, 449], [389, 449], [389, 460], [362, 460]], [[238, 376], [513, 376], [513, 389], [238, 389]], [[177, 356], [574, 356], [574, 368], [177, 368]], [[344, 281], [408, 283], [407, 297], [343, 294]], [[257, 205], [493, 205], [493, 219], [257, 219]]] diff --git a/tests/test.sh b/tests/test.sh deleted file mode 100644 index 484d5573..00000000 --- a/tests/test.sh +++ /dev/null @@ -1,476 +0,0 @@ -#!/bin/bash -FILENAME=$1 -# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer', 'cpp_infer'] -MODE=$2 - -dataline=$(cat ${FILENAME}) - -# parser params -IFS=$'\n' -lines=(${dataline}) - -function func_parser_key(){ - strs=$1 - IFS=":" - array=(${strs}) - tmp=${array[0]} - echo ${tmp} -} -function func_parser_value(){ - strs=$1 - IFS=":" - array=(${strs}) - tmp=${array[1]} - echo ${tmp} -} -function func_set_params(){ - key=$1 - value=$2 - if [ ${key} = "null" ];then - echo " " - elif [[ ${value} = "null" ]] || [[ ${value} = " " ]] || [ ${#value} -le 0 ];then - echo " " - else - echo "${key}=${value}" - fi -} -function func_parser_params(){ - strs=$1 - IFS=":" - array=(${strs}) - key=${array[0]} - tmp=${array[1]} - IFS="|" - res="" - for _params in ${tmp[*]}; do - IFS="=" - array=(${_params}) - mode=${array[0]} - value=${array[1]} - if [[ ${mode} = ${MODE} ]]; then - IFS="|" - #echo $(func_set_params "${mode}" "${value}") - echo $value - break - fi - IFS="|" - done - echo ${res} -} -function status_check(){ - last_status=$1 # the exit code - run_command=$2 - run_log=$3 - if [ $last_status -eq 0 ]; then - echo -e "\033[33m Run successfully with command - ${run_command}! \033[0m" | tee -a ${run_log} - else - echo -e "\033[33m Run failed with command - ${run_command}! \033[0m" | tee -a ${run_log} - fi -} - -IFS=$'\n' -# The training params -model_name=$(func_parser_value "${lines[1]}") -python=$(func_parser_value "${lines[2]}") -gpu_list=$(func_parser_value "${lines[3]}") -train_use_gpu_key=$(func_parser_key "${lines[4]}") -train_use_gpu_value=$(func_parser_value "${lines[4]}") -autocast_list=$(func_parser_value "${lines[5]}") -autocast_key=$(func_parser_key "${lines[5]}") -epoch_key=$(func_parser_key "${lines[6]}") -epoch_num=$(func_parser_params "${lines[6]}") -save_model_key=$(func_parser_key "${lines[7]}") -train_batch_key=$(func_parser_key "${lines[8]}") -train_batch_value=$(func_parser_params "${lines[8]}") -pretrain_model_key=$(func_parser_key "${lines[9]}") -pretrain_model_value=$(func_parser_value "${lines[9]}") -train_model_name=$(func_parser_value "${lines[10]}") -train_infer_img_dir=$(func_parser_value "${lines[11]}") -train_param_key1=$(func_parser_key "${lines[12]}") -train_param_value1=$(func_parser_value "${lines[12]}") - -trainer_list=$(func_parser_value "${lines[14]}") -trainer_norm=$(func_parser_key "${lines[15]}") -norm_trainer=$(func_parser_value "${lines[15]}") -pact_key=$(func_parser_key "${lines[16]}") -pact_trainer=$(func_parser_value "${lines[16]}") -fpgm_key=$(func_parser_key "${lines[17]}") -fpgm_trainer=$(func_parser_value "${lines[17]}") -distill_key=$(func_parser_key "${lines[18]}") -distill_trainer=$(func_parser_value "${lines[18]}") -trainer_key1=$(func_parser_key "${lines[19]}") -trainer_value1=$(func_parser_value "${lines[19]}") -trainer_key2=$(func_parser_key "${lines[20]}") -trainer_value2=$(func_parser_value "${lines[20]}") - -eval_py=$(func_parser_value "${lines[23]}") -eval_key1=$(func_parser_key "${lines[24]}") -eval_value1=$(func_parser_value "${lines[24]}") - -save_infer_key=$(func_parser_key "${lines[27]}") -export_weight=$(func_parser_key "${lines[28]}") -norm_export=$(func_parser_value "${lines[29]}") -pact_export=$(func_parser_value "${lines[30]}") -fpgm_export=$(func_parser_value "${lines[31]}") -distill_export=$(func_parser_value "${lines[32]}") -export_key1=$(func_parser_key "${lines[33]}") -export_value1=$(func_parser_value "${lines[33]}") -export_key2=$(func_parser_key "${lines[34]}") -export_value2=$(func_parser_value "${lines[34]}") - -# parser inference model -infer_model_dir_list=$(func_parser_value "${lines[36]}") -infer_export_list=$(func_parser_value "${lines[37]}") -infer_is_quant=$(func_parser_value "${lines[38]}") -# parser inference -inference_py=$(func_parser_value "${lines[39]}") -use_gpu_key=$(func_parser_key "${lines[40]}") -use_gpu_list=$(func_parser_value "${lines[40]}") -use_mkldnn_key=$(func_parser_key "${lines[41]}") -use_mkldnn_list=$(func_parser_value "${lines[41]}") -cpu_threads_key=$(func_parser_key "${lines[42]}") -cpu_threads_list=$(func_parser_value "${lines[42]}") -batch_size_key=$(func_parser_key "${lines[43]}") -batch_size_list=$(func_parser_value "${lines[43]}") -use_trt_key=$(func_parser_key "${lines[44]}") -use_trt_list=$(func_parser_value "${lines[44]}") -precision_key=$(func_parser_key "${lines[45]}") -precision_list=$(func_parser_value "${lines[45]}") -infer_model_key=$(func_parser_key "${lines[46]}") -image_dir_key=$(func_parser_key "${lines[47]}") -infer_img_dir=$(func_parser_value "${lines[47]}") -save_log_key=$(func_parser_key "${lines[48]}") -benchmark_key=$(func_parser_key "${lines[49]}") -benchmark_value=$(func_parser_value "${lines[49]}") -infer_key1=$(func_parser_key "${lines[50]}") -infer_value1=$(func_parser_value "${lines[50]}") - -if [ ${MODE} = "cpp_infer" ]; then - # parser cpp inference model - cpp_infer_model_dir_list=$(func_parser_value "${lines[53]}") - cpp_infer_is_quant=$(func_parser_value "${lines[54]}") - # parser cpp inference - inference_cmd=$(func_parser_value "${lines[55]}") - cpp_use_gpu_key=$(func_parser_key "${lines[56]}") - cpp_use_gpu_list=$(func_parser_value "${lines[56]}") - cpp_use_mkldnn_key=$(func_parser_key "${lines[57]}") - cpp_use_mkldnn_list=$(func_parser_value "${lines[57]}") - cpp_cpu_threads_key=$(func_parser_key "${lines[58]}") - cpp_cpu_threads_list=$(func_parser_value "${lines[58]}") - cpp_batch_size_key=$(func_parser_key "${lines[59]}") - cpp_batch_size_list=$(func_parser_value "${lines[59]}") - cpp_use_trt_key=$(func_parser_key "${lines[60]}") - cpp_use_trt_list=$(func_parser_value "${lines[60]}") - cpp_precision_key=$(func_parser_key "${lines[61]}") - cpp_precision_list=$(func_parser_value "${lines[61]}") - cpp_infer_model_key=$(func_parser_key "${lines[62]}") - cpp_image_dir_key=$(func_parser_key "${lines[63]}") - cpp_infer_img_dir=$(func_parser_value "${lines[63]}") - cpp_save_log_key=$(func_parser_key "${lines[64]}") - cpp_benchmark_key=$(func_parser_key "${lines[65]}") - cpp_benchmark_value=$(func_parser_value "${lines[65]}") -fi - - -LOG_PATH="./tests/output" -mkdir -p ${LOG_PATH} -status_log="${LOG_PATH}/results.log" - - -function func_inference(){ - IFS='|' - _python=$1 - _script=$2 - _model_dir=$3 - _log_path=$4 - _img_dir=$5 - _flag_quant=$6 - # inference - for use_gpu in ${use_gpu_list[*]}; do - if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then - for use_mkldnn in ${use_mkldnn_list[*]}; do - if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then - continue - fi - for threads in ${cpu_threads_list[*]}; do - for batch_size in ${batch_size_list[*]}; do - _save_log_path="${_log_path}/infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}.log" - set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}") - set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}") - set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}") - set_cpu_threads=$(func_set_params "${cpu_threads_key}" "${threads}") - set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}") - set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}") - command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 " - eval $command - last_status=${PIPESTATUS[0]} - eval "cat ${_save_log_path}" - status_check $last_status "${command}" "${status_log}" - done - done - done - elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then - for use_trt in ${use_trt_list[*]}; do - for precision in ${precision_list[*]}; do - if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then - continue - fi - if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then - continue - fi - if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [ ${_flag_quant} = "True" ]; then - continue - fi - for batch_size in ${batch_size_list[*]}; do - _save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log" - set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}") - set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}") - set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}") - set_tensorrt=$(func_set_params "${use_trt_key}" "${use_trt}") - set_precision=$(func_set_params "${precision_key}" "${precision}") - set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}") - set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}") - command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 " - eval $command - last_status=${PIPESTATUS[0]} - eval "cat ${_save_log_path}" - status_check $last_status "${command}" "${status_log}" - - done - done - done - else - echo "Does not support hardware other than CPU and GPU Currently!" - fi - done -} - -function func_cpp_inference(){ - IFS='|' - _script=$1 - _model_dir=$2 - _log_path=$3 - _img_dir=$4 - _flag_quant=$5 - # inference - for use_gpu in ${cpp_use_gpu_list[*]}; do - if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then - for use_mkldnn in ${cpp_use_mkldnn_list[*]}; do - if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then - continue - fi - for threads in ${cpp_cpu_threads_list[*]}; do - for batch_size in ${cpp_batch_size_list[*]}; do - _save_log_path="${_log_path}/cpp_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}.log" - set_infer_data=$(func_set_params "${cpp_image_dir_key}" "${_img_dir}") - set_benchmark=$(func_set_params "${cpp_benchmark_key}" "${cpp_benchmark_value}") - set_batchsize=$(func_set_params "${cpp_batch_size_key}" "${batch_size}") - set_cpu_threads=$(func_set_params "${cpp_cpu_threads_key}" "${threads}") - set_model_dir=$(func_set_params "${cpp_infer_model_key}" "${_model_dir}") - command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${cpp_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} > ${_save_log_path} 2>&1 " - eval $command - last_status=${PIPESTATUS[0]} - eval "cat ${_save_log_path}" - status_check $last_status "${command}" "${status_log}" - done - done - done - elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then - for use_trt in ${cpp_use_trt_list[*]}; do - for precision in ${cpp_precision_list[*]}; do - if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then - continue - fi - if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then - continue - fi - if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [ ${_flag_quant} = "True" ]; then - continue - fi - for batch_size in ${cpp_batch_size_list[*]}; do - _save_log_path="${_log_path}/cpp_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log" - set_infer_data=$(func_set_params "${cpp_image_dir_key}" "${_img_dir}") - set_benchmark=$(func_set_params "${cpp_benchmark_key}" "${cpp_benchmark_value}") - set_batchsize=$(func_set_params "${cpp_batch_size_key}" "${batch_size}") - set_tensorrt=$(func_set_params "${cpp_use_trt_key}" "${use_trt}") - set_precision=$(func_set_params "${cpp_precision_key}" "${precision}") - set_model_dir=$(func_set_params "${cpp_infer_model_key}" "${_model_dir}") - command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} > ${_save_log_path} 2>&1 " - eval $command - last_status=${PIPESTATUS[0]} - eval "cat ${_save_log_path}" - status_check $last_status "${command}" "${status_log}" - - done - done - done - else - echo "Does not support hardware other than CPU and GPU Currently!" - fi - done -} - -if [ ${MODE} = "infer" ]; then - GPUID=$3 - if [ ${#GPUID} -le 0 ];then - env=" " - else - env="export CUDA_VISIBLE_DEVICES=${GPUID}" - fi - # set CUDA_VISIBLE_DEVICES - eval $env - export Count=0 - IFS="|" - infer_run_exports=(${infer_export_list}) - infer_quant_flag=(${infer_is_quant}) - for infer_model in ${infer_model_dir_list[*]}; do - # run export - if [ ${infer_run_exports[Count]} != "null" ];then - save_infer_dir=$(dirname $infer_model) - set_export_weight=$(func_set_params "${export_weight}" "${infer_model}") - set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_dir}") - export_cmd="${python} ${norm_export} ${set_export_weight} ${set_save_infer_key}" - eval $export_cmd - status_export=$? - if [ ${status_export} = 0 ];then - status_check $status_export "${export_cmd}" "${status_log}" - fi - else - save_infer_dir=${infer_model} - fi - #run inference - is_quant=${infer_quant_flag[Count]} - func_inference "${python}" "${inference_py}" "${save_infer_dir}" "${LOG_PATH}" "${infer_img_dir}" ${is_quant} - Count=$(($Count + 1)) - done - -elif [ ${MODE} = "cpp_infer" ]; then - GPUID=$3 - if [ ${#GPUID} -le 0 ];then - env=" " - else - env="export CUDA_VISIBLE_DEVICES=${GPUID}" - fi - # set CUDA_VISIBLE_DEVICES - eval $env - export Count=0 - IFS="|" - infer_quant_flag=(${cpp_infer_is_quant}) - for infer_model in ${cpp_infer_model_dir_list[*]}; do - #run inference - is_quant=${infer_quant_flag[Count]} - func_cpp_inference "${inference_cmd}" "${infer_model}" "${LOG_PATH}" "${cpp_infer_img_dir}" ${is_quant} - Count=$(($Count + 1)) - done - -else - IFS="|" - export Count=0 - USE_GPU_KEY=(${train_use_gpu_value}) - for gpu in ${gpu_list[*]}; do - use_gpu=${USE_GPU_KEY[Count]} - Count=$(($Count + 1)) - if [ ${gpu} = "-1" ];then - env="" - elif [ ${#gpu} -le 1 ];then - env="export CUDA_VISIBLE_DEVICES=${gpu}" - eval ${env} - elif [ ${#gpu} -le 15 ];then - IFS="," - array=(${gpu}) - env="export CUDA_VISIBLE_DEVICES=${array[0]}" - IFS="|" - else - IFS=";" - array=(${gpu}) - ips=${array[0]} - gpu=${array[1]} - IFS="|" - env=" " - fi - for autocast in ${autocast_list[*]}; do - for trainer in ${trainer_list[*]}; do - flag_quant=False - if [ ${trainer} = ${pact_key} ]; then - run_train=${pact_trainer} - run_export=${pact_export} - flag_quant=True - elif [ ${trainer} = "${fpgm_key}" ]; then - run_train=${fpgm_trainer} - run_export=${fpgm_export} - elif [ ${trainer} = "${distill_key}" ]; then - run_train=${distill_trainer} - run_export=${distill_export} - elif [ ${trainer} = ${trainer_key1} ]; then - run_train=${trainer_value1} - run_export=${export_value1} - elif [[ ${trainer} = ${trainer_key2} ]]; then - run_train=${trainer_value2} - run_export=${export_value2} - else - run_train=${norm_trainer} - run_export=${norm_export} - fi - - 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}" "${train_batch_value}") - set_train_params1=$(func_set_params "${train_param_key1}" "${train_param_value1}") - set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${use_gpu}") - save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}" - - # load pretrain from norm training if current trainer is pact or fpgm trainer - if [ ${trainer} = ${pact_key} ] || [ ${trainer} = ${fpgm_key} ]; then - set_pretrain="${load_norm_train_model}" - fi - - 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} " - elif [ ${#gpu} -le 15 ];then # train with multi-gpu - cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1}" - else # train with multi-machine - cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} ${set_train_params1}" - fi - # run train - eval "unset CUDA_VISIBLE_DEVICES" - eval $cmd - status_check $? "${cmd}" "${status_log}" - - set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${train_model_name}") - # save norm trained models to set pretrain for pact training and fpgm training - if [ ${trainer} = ${trainer_norm} ]; then - load_norm_train_model=${set_eval_pretrain} - fi - # run eval - if [ ${eval_py} != "null" ]; then - set_eval_params1=$(func_set_params "${eval_key1}" "${eval_value1}") - eval_cmd="${python} ${eval_py} ${set_eval_pretrain} ${set_use_gpu} ${set_eval_params1}" - eval $eval_cmd - status_check $? "${eval_cmd}" "${status_log}" - fi - # run export model - if [ ${run_export} != "null" ]; then - # run export model - save_infer_path="${save_log}" - set_export_weight=$(func_set_params "${export_weight}" "${save_log}/${train_model_name}") - set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_path}") - export_cmd="${python} ${run_export} ${set_export_weight} ${set_save_infer_key}" - eval $export_cmd - status_check $? "${export_cmd}" "${status_log}" - - #run inference - eval $env - save_infer_path="${save_log}" - func_inference "${python}" "${inference_py}" "${save_infer_path}" "${LOG_PATH}" "${train_infer_img_dir}" "${flag_quant}" - eval "unset CUDA_VISIBLE_DEVICES" - fi - done # done with: for trainer in ${trainer_list[*]}; do - done # done with: for autocast in ${autocast_list[*]}; do - done # done with: for gpu in ${gpu_list[*]}; do -fi # end if [ ${MODE} = "infer" ]; then From 7f555d23109e0916e3b590273021db0c63ef8972 Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Wed, 8 Sep 2021 10:12:41 +0800 Subject: [PATCH 05/12] fix inferecne ppocr --- doc/doc_ch/inference_ppocr.md | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/doc/doc_ch/inference_ppocr.md b/doc/doc_ch/inference_ppocr.md index 493a4c98..689949d6 100644 --- a/doc/doc_ch/inference_ppocr.md +++ b/doc/doc_ch/inference_ppocr.md @@ -21,7 +21,9 @@ ``` # 下载超轻量中文检测模型： -wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tartar xf ch_ppocr_mobile_v2.0_det_infer.tarpython3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_ppocr_mobile_v2.0_det_infer/" +wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar +tar xf ch_PP-OCRv2_det_infer.tar +python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv2_det_infer.tar/" ``` 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： @@ -39,13 +41,13 @@ wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_i 如果输入图片的分辨率比较大，而且想使用更大的分辨率预测，可以设置det_limit_side_len 为想要的值，比如1216： ``` -python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1216 +python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --det_limit_type=max --det_limit_side_len=1216 ``` 如果想使用CPU进行预测，执行命令如下 ``` -python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False +python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --use_gpu=False ``` @@ -62,9 +64,9 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_di ``` # 下载超轻量中文识别模型： -wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar -tar xf ch_ppocr_mobile_v2.0_rec_infer.tar -python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="ch_ppocr_mobile_v2.0_rec_infer" +wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar +tar xf ch_PP-OCRv2_rec_infer.tar +python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="./ch_PP-OCRv2_rec_infer/" ``` ![](/Users/zhulingfeng01/OCR/PaddleOCR/doc/imgs_words/ch/word_4.jpg) @@ -79,10 +81,9 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:('实力活力', 0.98458153) ### 2.2 多语言模型的推理 -如果您需要预测的是其他语言模型，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： - +如果您需要预测的是其他语言模型，可以在[此链接](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/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： ``` +wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf" ``` @@ -123,14 +124,13 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999982] ```shell # 使用方向分类器 -python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=true +python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=true # 不使用方向分类器 -python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false +python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=false # 使用多进程 -python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false --use_mp=True --total_process_num=6 +python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=false --use_mp=True --total_process_num=6 ``` 执行命令后，识别结果图像如下： ![](/Users/zhulingfeng01/OCR/PaddleOCR/doc/imgs_results/system_res_00018069.jpg) - From 3e9830d9b7a4a22c66bc874c05666ac42f729d71 Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Wed, 8 Sep 2021 10:12:55 +0800 Subject: [PATCH 06/12] fix inferecne ppocr en --- doc/doc_en/inference_ppocr_en.md | 31 +++++++++++++++++-------------- 1 file changed, 17 insertions(+), 14 deletions(-) diff --git a/doc/doc_en/inference_ppocr_en.md b/doc/doc_en/inference_ppocr_en.md index fa3b1c88..47d070e7 100755 --- a/doc/doc_en/inference_ppocr_en.md +++ b/doc/doc_en/inference_ppocr_en.md @@ -9,7 +9,7 @@ This article introduces the use of the Python inference engine for the PP-OCR mo - [Text Recognition Model Inference](#RECOGNITION_MODEL_INFERENCE) - [1. Lightweight Chinese Recognition Model Inference](#LIGHTWEIGHT_RECOGNITION) - [2. Multilingaul Model Inference](#MULTILINGUAL_MODEL_INFERENCE) - + - [Angle Classification Model Inference](#ANGLE_CLASS_MODEL_INFERENCE) - [Text Detection Angle Classification and Recognition Inference Concatenation](#CONCATENATION) @@ -22,10 +22,10 @@ The default configuration is based on the inference setting of the DB text detec ``` # download DB text detection inference model -wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar -tar xf ch_ppocr_mobile_v2.0_det_infer.tar -# predict -python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" +wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar +tar xf ch_PP-OCRv2_det_infer.tar +# run inference +python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv2_det_infer.tar/" ``` The visual text detection results are saved to the ./inference_results folder by default, and the name of the result file is prefixed with'det_res'. Examples of results are as follows: @@ -42,12 +42,12 @@ Set as `limit_type='min', det_limit_side_len=960`, it means that the shortest si If the resolution of the input picture is relatively large and you want to use a larger resolution prediction, you can set det_limit_side_len to the desired value, such as 1216: ``` -python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1216 +python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --det_limit_type=max --det_limit_side_len=1216 ``` If you want to use the CPU for prediction, execute the command as follows ``` -python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False +python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --use_gpu=False ``` @@ -62,9 +62,10 @@ For lightweight Chinese recognition model inference, you can execute the followi ``` # download CRNN text recognition inference model -wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar -tar xf ch_ppocr_mobile_v2.0_rec_infer.tar -python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_10.png" --rec_model_dir="ch_ppocr_mobile_v2.0_rec_infer" +wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar +tar xf ch_PP-OCRv2_rec_infer.tar +# run inference +python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="./ch_PP-OCRv2_rec_infer/" ``` ![](../imgs_words_en/word_10.png) @@ -78,10 +79,12 @@ Predicts of ./doc/imgs_words_en/word_10.png:('PAIN', 0.9897658) ### 2. Multilingaul Model Inference -If you need to predict other language models, 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, +If you need to predict [other language models](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/models_list.md#%E5%A4%9A%E8%AF%AD%E8%A8%80%E8%AF%86%E5%88%AB%E6%A8%A1%E5%9E%8B), 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: ``` +wget wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar + python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf" ``` ![](../imgs_words/korean/1.jpg) @@ -120,13 +123,13 @@ When performing prediction, you need to specify the path of a single image or a ```shell # use direction classifier -python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=true +python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=true # not use use direction classifier -python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" +python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=false # use multi-process -python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false --use_mp=True --total_process_num=6 +python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=false --use_mp=True --total_process_num=6 ``` From e79667ac885eb93444cf35c691c4d249d52f0f50 Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Wed, 8 Sep 2021 10:24:30 +0800 Subject: [PATCH 07/12] support dynamic batch shape for trt prediction --- tools/infer/utility.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tools/infer/utility.py b/tools/infer/utility.py index 7f60773c..466f824c 100755 --- a/tools/infer/utility.py +++ b/tools/infer/utility.py @@ -236,11 +236,11 @@ def create_predictor(args, mode, logger): max_input_shape.update(max_pact_shape) opt_input_shape.update(opt_pact_shape) elif mode == "rec": - min_input_shape = {"x": [args.rec_batch_num, 3, 32, 10]} + min_input_shape = {"x": [1, 3, 32, 10]} max_input_shape = {"x": [args.rec_batch_num, 3, 32, 2000]} opt_input_shape = {"x": [args.rec_batch_num, 3, 32, 320]} elif mode == "cls": - min_input_shape = {"x": [args.rec_batch_num, 3, 48, 10]} + min_input_shape = {"x": [1, 3, 48, 10]} max_input_shape = {"x": [args.rec_batch_num, 3, 48, 2000]} opt_input_shape = {"x": [args.rec_batch_num, 3, 48, 320]} else: From b451946f6550138617687b63e01ac1530b029ab6 Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Wed, 8 Sep 2021 10:37:49 +0800 Subject: [PATCH 08/12] fix doc link --- doc/doc_ch/inference_ppocr.md | 2 +- doc/doc_en/inference_ppocr_en.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/doc_ch/inference_ppocr.md b/doc/doc_ch/inference_ppocr.md index 4c98fc72..daa9b8d4 100644 --- a/doc/doc_ch/inference_ppocr.md +++ b/doc/doc_ch/inference_ppocr.md @@ -82,7 +82,7 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:('实力活力', 0.98458153) ### 2.2 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](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/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： +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： ``` wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf" diff --git a/doc/doc_en/inference_ppocr_en.md b/doc/doc_en/inference_ppocr_en.md index 47d070e7..1cef2cee 100755 --- a/doc/doc_en/inference_ppocr_en.md +++ b/doc/doc_en/inference_ppocr_en.md @@ -79,7 +79,7 @@ Predicts of ./doc/imgs_words_en/word_10.png:('PAIN', 0.9897658) ### 2. Multilingaul Model Inference -If you need to predict [other language models](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/models_list.md#%E5%A4%9A%E8%AF%AD%E8%A8%80%E8%AF%86%E5%88%AB%E6%A8%A1%E5%9E%8B), 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, +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: ``` From a419875943ebc16f9be9d58f4222cb148c93ec90 Mon Sep 17 00:00:00 2001 From: Double_V Date: Wed, 8 Sep 2021 10:52:47 +0800 Subject: [PATCH 09/12] fix cpp img read --- deploy/cpp_infer/src/main.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deploy/cpp_infer/src/main.cpp b/deploy/cpp_infer/src/main.cpp index 6e8ded7f..6d2bdef2 100644 --- a/deploy/cpp_infer/src/main.cpp +++ b/deploy/cpp_infer/src/main.cpp @@ -179,7 +179,7 @@ int main_system(std::vector cv_all_img_names) { for (int i = 0; i < cv_all_img_names.size(); ++i) { LOG(INFO) << "The predict img: " << cv_all_img_names[i]; - cv::Mat srcimg = cv::imread(FLAGS_image_dir, cv::IMREAD_COLOR); + cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR); if (!srcimg.data) { std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << endl; exit(1); From 7fe2c48fc9d0c25e1ed927581fe28621f66a2d5c Mon Sep 17 00:00:00 2001 From: DanielYang Date: Wed, 8 Sep 2021 12:04:01 +0800 Subject: [PATCH 10/12] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 8476d0cf..4e066861 100644 --- a/README.md +++ b/README.md @@ -26,7 +26,7 @@ PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools **Recent updates** - PaddleOCR R&D team would like to share the key points of PP-OCRv2, at 20:15 pm on September 8th, [Live Address](https://live.bilibili.com/21689802). -- 2021.9.7 release PaddleOCR v2.3, [PP-OCRv2](#PP-OCRv2) is proposed. The inference speed of PP-OCRv2 is 220% higher than that of PP-OCR server in CPU device. The F-score of PP-OCRv2 is 7% higher than that of PP-OCR mobile. ([Technical Report](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/PP-OCRv2.pdf)) +- 2021.9.7 release PaddleOCR v2.3, [PP-OCRv2](#PP-OCRv2) is proposed. The inference speed of PP-OCRv2 is 220% higher than that of PP-OCR server in CPU device. The F-score of PP-OCRv2 is 7% higher than that of PP-OCR mobile. ([arxiv paper](https://arxiv.org/abs/2109.03144)) - 2021.8.3 released PaddleOCR v2.2, add a new structured documents analysis toolkit, i.e., [PP-Structure](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/ppstructure/README.md), support layout analysis and table recognition (One-key to export chart images to Excel files). - 2021.4.8 release end-to-end text recognition algorithm [PGNet](https://www.aaai.org/AAAI21Papers/AAAI-2885.WangP.pdf) which is published in AAAI 2021. Find tutorial [here](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/pgnet_en.md)；release multi language recognition [models](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/multi_languages_en.md), support more than 80 languages recognition; especically, the performance of [English recognition model](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/models_list_en.md#English) is Optimized. @@ -146,7 +146,7 @@ For a new language request, please refer to [Guideline for new language_requests [1] PP-OCR is a practical ultra-lightweight OCR system. It is mainly composed of three parts: DB text detection, detection frame correction and CRNN text recognition. The system adopts 19 effective strategies from 8 aspects including backbone network selection and adjustment, prediction head design, data augmentation, learning rate transformation strategy, regularization parameter selection, pre-training model use, and automatic model tailoring and quantization to optimize and slim down the models of each module (as shown in the green box above). The final results are an ultra-lightweight Chinese and English OCR model with an overall size of 3.5M and a 2.8M English digital OCR model. For more details, please refer to the PP-OCR technical article (https://arxiv.org/abs/2009.09941). -[2] On the basis of PP-OCR, PP-OCRv2 is further optimized in five aspects. The detection model adopts CML(Collaborative Mutual Learning) knowledge distillation strategy and CopyPaste data expansion strategy. The recognition model adopts LCNet lightweight backbone network, U-DML knowledge distillation strategy and enhanced CTC loss function improvement (as shown in the red box above), which further improves the inference speed and prediction effect. For more details, please refer to the [technical report](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/PP-OCRv2.pdf) of PP-OCRv2. +[2] On the basis of PP-OCR, PP-OCRv2 is further optimized in five aspects. The detection model adopts CML(Collaborative Mutual Learning) knowledge distillation strategy and CopyPaste data expansion strategy. The recognition model adopts LCNet lightweight backbone network, U-DML knowledge distillation strategy and enhanced CTC loss function improvement (as shown in the red box above), which further improves the inference speed and prediction effect. For more details, please refer to the [technical report](https://arxiv.org/abs/2109.03144) of PP-OCRv2. From ab08c08e1d6219306556f03d87b296870821be51 Mon Sep 17 00:00:00 2001 From: DanielYang Date: Wed, 8 Sep 2021 12:04:43 +0800 Subject: [PATCH 11/12] Update README_ch.md --- README_ch.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README_ch.md b/README_ch.md index 12c2a380..5a4bb02b 100755 --- a/README_ch.md +++ b/README_ch.md @@ -25,7 +25,7 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力 **近期更新** - PaddleOCR研发团队对最新发版内容技术深入解读，9月8日晚上20:15，[直播地址](https://live.bilibili.com/21689802)。 -- 2021.9.7 发布PaddleOCR v2.3，发布[PP-OCRv2](#PP-OCRv2)，CPU推理速度相比于PP-OCR server提升220%；效果相比于PP-OCR mobile 提升7%。([技术报告](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/PP-OCRv2.pdf)) +- 2021.9.7 发布PaddleOCR v2.3，发布[PP-OCRv2](#PP-OCRv2)，CPU推理速度相比于PP-OCR server提升220%；效果相比于PP-OCR mobile 提升7%。([arxiv论文](https://arxiv.org/abs/2109.03144)) - 2021.8.3 发布PaddleOCR v2.2，新增文档结构分析[PP-Structure](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/ppstructure/README_ch.md)工具包，支持版面分析与表格识别（含Excel导出）。 - 2021.6.29 [FAQ](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/FAQ.md)新增5个高频问题，总数248个，每周一都会更新，欢迎大家持续关注。 - 2021.4.8 release 2.1版本，新增AAAI 2021论文[端到端识别算法PGNet](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/pgnet.md)开源，[多语言模型](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/multi_languages.md)支持种类增加到80+。 @@ -142,7 +142,7 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力 [1] PP-OCR是一个实用的超轻量OCR系统。主要由DB文本检测、检测框矫正和CRNN文本识别三部分组成。该系统从骨干网络选择和调整、预测头部的设计、数据增强、学习率变换策略、正则化参数选择、预训练模型使用以及模型自动裁剪量化8个方面，采用19个有效策略，对各个模块的模型进行效果调优和瘦身(如绿框所示)，最终得到整体大小为3.5M的超轻量中英文OCR和2.8M的英文数字OCR。更多细节请参考PP-OCR技术方案 https://arxiv.org/abs/2009.09941 -[2] PP-OCRv2在PP-OCR的基础上，进一步在5个方面重点优化，检测模型采用CML协同互学习知识蒸馏策略和CopyPaste数据增广策略；识别模型采用LCNet轻量级骨干网络、UDML 改进知识蒸馏策略和Enhanced CTC loss损失函数改进（如上图红框所示），进一步在推理速度和预测效果上取得明显提升。更多细节请参考PP-OCRv2[技术报告](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/PP-OCRv2.pdf)。 +[2] PP-OCRv2在PP-OCR的基础上，进一步在5个方面重点优化，检测模型采用CML协同互学习知识蒸馏策略和CopyPaste数据增广策略；识别模型采用LCNet轻量级骨干网络、UDML 改进知识蒸馏策略和Enhanced CTC loss损失函数改进（如上图红框所示），进一步在推理速度和预测效果上取得明显提升。更多细节请参考PP-OCRv2[技术报告](https://arxiv.org/abs/2109.03144)。 From 3627b8aa7d1c839172ac9505c31793cd57310921 Mon Sep 17 00:00:00 2001 From: tink2123 Date: Wed, 8 Sep 2021 11:33:06 +0800 Subject: [PATCH 12/12] update benchmark doc --- doc/doc_ch/benchmark.md | 39 +++++++++++++------------------------- doc/doc_en/benchmark_en.md | 35 ++++++++++------------------------ 2 files changed, 23 insertions(+), 51 deletions(-) diff --git a/doc/doc_ch/benchmark.md b/doc/doc_ch/benchmark.md index 520a2fce..7ab82957 100644 --- a/doc/doc_ch/benchmark.md +++ b/doc/doc_ch/benchmark.md @@ -12,40 +12,27 @@ ## 评估指标 说明： -- v1.0是未添加优化策略的DB+CRNN模型，v1.1是添加多种优化策略和方向分类器的PP-OCR模型。slim_v1.1是使用裁剪或量化的模型。 + - 检测输入图像的的长边尺寸是960。 -- 评估耗时阶段为图像输入到结果输出的完整阶段，包括了图像的预处理和后处理。 +- 评估耗时阶段为图像预测耗时，不包括图像的预处理和后处理。 - `Intel至强6148`为服务器端CPU型号，测试中使用Intel MKL-DNN 加速。 - `骁龙855`为移动端处理平台型号。 -不同预测模型大小和整体识别精度对比 +预测模型大小和整体识别精度对比 | 模型名称 | 整体模型
大小\(M\) | 检测模型
大小\(M\) | 方向分类器
模型大小\(M\) | 识别模型
大小\(M\) | 整体识别
F\-score | |:-:|:-:|:-:|:-:|:-:|:-:| -| ch\_ppocr\_mobile\_v1\.1 | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.5193 | -| ch\_ppocr\_server\_v1\.1 | 155\.1 | 47\.2 | 0\.9 | 107 | 0\.5414 | -| ch\_ppocr\_mobile\_v1\.0 | 8\.6 | 4\.1 | \- | 4\.5 | 0\.393 | -| ch\_ppocr\_server\_v1\.0 | 203\.8 | 98\.5 | \- | 105\.3 | 0\.4436 | +| PP-OCRv2 | 11\.6 | 3\.0 | 0\.9 | 8\.6 | 0\.5224 | +| PP-OCR mobile | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.503 | +| PP-OCR server | 155\.1 | 47\.2 | 0\.9 | 107 | 0\.570 | -不同预测模型在T4 GPU上预测速度对比，单位ms -| 模型名称 | 整体 | 检测 | 方向分类器 | 识别 | -|:-:|:-:|:-:|:-:|:-:| -| ch\_ppocr\_mobile\_v1\.1 | 137 | 35 | 24 | 78 | -| ch\_ppocr\_server\_v1\.1 | 204 | 39 | 25 | 140 | -| ch\_ppocr\_mobile\_v1\.0 | 117 | 41 | \- | 76 | -| ch\_ppocr\_server\_v1\.0 | 199 | 52 | \- | 147 | +预测模型在CPU和GPU上的速度对比，单位ms -不同预测模型在CPU上预测速度对比，单位ms +| 模型名称 | CPU | T4 GPU | +|:-:|:-:|:-:| +| PP-OCRv2 | 330 | 111 | +| PP-OCR mobile | 356 | 11 6| +| PP-OCR server | 1056 | 200 | -| 模型名称 | 整体 | 检测 | 方向分类器 | 识别 | -|:-:|:-:|:-:|:-:|:-:| -| ch\_ppocr\_mobile\_v1\.1 | 421 | 164 | 51 | 206 | -| ch\_ppocr\_mobile\_v1\.0 | 398 | 219 | \- | 179 | - -裁剪量化模型和原始模型模型大小，整体识别精度和在SD 855上预测速度对比 - -| 模型名称 | 整体模型
大小\(M\) | 检测模型
大小\(M\) | 方向分类器
模型大小\(M\) | 识别模型
大小\(M\) | 整体识别
F\-score | SD 855
\(ms\) | -|:-:|:-:|:-:|:-:|:-:|:-:|:-:| -| ch\_ppocr\_mobile\_v1\.1 | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.5193 | 306 | -| ch\_ppocr\_mobile\_slim\_v1\.1 | 3\.5 | 1\.4 | 0\.5 | 1\.6 | 0\.521 | 268 | +更多 PP-OCR 系列模型的预测指标可以参考[PP-OCR Benchamrk](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/benchmark.md) diff --git a/doc/doc_en/benchmark_en.md b/doc/doc_en/benchmark_en.md index 91b01594..0d3ffaec 100755 --- a/doc/doc_en/benchmark_en.md +++ b/doc/doc_en/benchmark_en.md @@ -13,7 +13,6 @@ We collected 300 images for different real application scenarios to evaluate the ## MEASUREMENT Explanation: -- v1.0 indicates DB+CRNN models without the strategies. v1.1 indicates the PP-OCR models with the strategies and the direction classify. slim_v1.1 indicates the PP-OCR models with prunner or quantization. - The long size of the input for the text detector is 960. @@ -27,30 +26,16 @@ Compares the model size and F-score: | Model Name | Model Size
of the
Whole System\(M\) | Model Size
of the Text
Detector\(M\) | Model Size
of the Direction
Classifier\(M\) | Model Size
of the Text
Recognizer \(M\) | F\-score | |:-:|:-:|:-:|:-:|:-:|:-:| -| ch\_ppocr\_mobile\_v1\.1 | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.5193 | -| ch\_ppocr\_server\_v1\.1 | 155\.1 | 47\.2 | 0\.9 | 107 | 0\.5414 | -| ch\_ppocr\_mobile\_v1\.0 | 8\.6 | 4\.1 | \- | 4\.5 | 0\.393 | -| ch\_ppocr\_server\_v1\.0 | 203\.8 | 98\.5 | \- | 105\.3 | 0\.4436 | +| PP-OCRv2 | 11\.6 | 3\.0 | 0\.9 | 8\.6 | 0\.5224 | +| PP-OCR mobile | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.503 | +| PP-OCR server | 155\.1 | 47\.2 | 0\.9 | 107 | 0\.570 | -Compares the time-consuming on T4 GPU (ms): +Compares the time-consuming on CPU and T4 GPU (ms): -| Model Name | Overall | Text Detector | Direction Classifier | Text Recognizer | -|:-:|:-:|:-:|:-:|:-:| -| ch\_ppocr\_mobile\_v1\.1 | 137 | 35 | 24 | 78 | -| ch\_ppocr\_server\_v1\.1 | 204 | 39 | 25 | 140 | -| ch\_ppocr\_mobile\_v1\.0 | 117 | 41 | \- | 76 | -| ch\_ppocr\_server\_v1\.0 | 199 | 52 | \- | 147 | +| Model Name | CPU | T4 GPU | +|:-:|:-:|:-:| +| PP-OCRv2 | 330 | 111 | +| PP-OCR mobile | 356 | 116| +| PP-OCR server | 1056 | 200 | -Compares the time-consuming on CPU (ms): - -| Model Name | Overall | Text Detector | Direction Classifier | Text Recognizer | -|:-:|:-:|:-:|:-:|:-:| -| ch\_ppocr\_mobile\_v1\.1 | 421 | 164 | 51 | 206 | -| ch\_ppocr\_mobile\_v1\.0 | 398 | 219 | \- | 179 | - -Compares the model size, F-score, the time-consuming on SD 855 of between the slim models and the original models: - -| Model Name | Model Size
of the
Whole System\(M\) | Model Size
of the Text
Detector\(M\) | Model Size
of the Direction
Classifier\(M\) | Model Size
of the Text
Recognizer \(M\) | F\-score | SD 855
\(ms\) | -|:-:|:-:|:-:|:-:|:-:|:-:|:-:| -| ch\_ppocr\_mobile\_v1\.1 | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.5193 | 306 | -| ch\_ppocr\_mobile\_slim\_v1\.1 | 3\.5 | 1\.4 | 0\.5 | 1\.6 | 0\.521 | 268 | +More indicators of PP-OCR series models can be referred to [PP-OCR Benchamrk](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_en/benchmark_en.md)