Merge remote-tracking branch 'origin/dygraph' into dygraph

test_tipc/configs/ppocr_rec_mobile/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt

+===========================paddle2onnx_params===========================
+model_name:ocr_rec_mobile
+python:python3.7
+2onnx: paddle2onnx
+--model_dir:./inference/ch_ppocr_mobile_v2.0_rec_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--save_file:./inference/rec_mobile_onnx/model.onnx
+--opset_version:10
+--enable_onnx_checker:True
+inference:tools/infer/predict_rec.py
+--use_gpu:True|False
+--rec_model_dir:
+--image_dir:./inference/rec_inference
\ No newline at end of file

test_tipc/configs/ppocr_rec_mobile/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:ocr_rec_mobile
+python:python3.7|cpp
+trans_model:-m paddle_serving_client.convert
+--dirname:./inference/ch_ppocr_mobile_v2.0_rec_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--serving_server:./deploy/pdserving/ppocr_rec_mobile_2.0_serving/
+--serving_client:./deploy/pdserving/ppocr_rec_mobile_2.0_client/
+serving_dir:./deploy/pdserving
+web_service:web_service_rec.py --config=config.yml --opt op.rec.concurrency=1
+op.rec.local_service_conf.devices:null|0
+op.rec.local_service_conf.use_mkldnn:True|False
+op.rec.local_service_conf.thread_num:1|6
+op.rec.local_service_conf.use_trt:False|True
+op.rec.local_service_conf.precision:fp32|fp16|int8
+pipline:pipeline_rpc_client.py|pipeline_http_client.py
+--image_dir:../../doc/imgs_words_en
\ No newline at end of file

test_tipc/configs/ppocr_rec_mobile_params.txt

-===========================train_params===========================
-model_name:ocr_rec
-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=128|whole_train_infer=128
-Global.pretrained_model:null
-train_model_name:latest
-train_infer_img_dir:./inference/rec_inference
-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|6
---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
-===========================cpp_infer_params===========================
-use_opencv:True
-infer_model:./inference/ch_ppocr_mobile_v2.0_rec_infer/
-infer_quant:False
-inference:./deploy/cpp_infer/build/ppocr rec
---use_gpu:True|False
---enable_mkldnn:True|False
---cpu_threads:1|6
---rec_batch_num:1
---use_tensorrt:False|True
---precision:fp32|fp16
---rec_model_dir:
---image_dir:./inference/rec_inference/
-null:null
---benchmark:True
-===========================serving_params===========================
-model_name:ocr_rec
-python:python3.7
-trans_model:-m paddle_serving_client.convert
---dirname:./inference/ch_ppocr_mobile_v2.0_rec_infer/
---model_filename:inference.pdmodel
---params_filename:inference.pdiparams
---serving_server:./deploy/pdserving/ppocr_rec_mobile_2.0_serving/
---serving_client:./deploy/pdserving/ppocr_rec_mobile_2.0_client/
-serving_dir:./deploy/pdserving
-web_service:web_service_rec.py --config=config.yml --opt op.rec.concurrency=1
-op.rec.local_service_conf.devices:null|0
-op.rec.local_service_conf.use_mkldnn:True|False
-op.rec.local_service_conf.thread_num:1|6
-op.rec.local_service_conf.use_trt:False|True
-op.rec.local_service_conf.precision:fp32|fp16|int8
-pipline:pipeline_http_client.py|pipeline_rpc_client.py
---image_dir=../../doc/imgs_words_en

test_tipc/configs/ppocr_rec_server/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt

+===========================paddle2onnx_params===========================
+model_name:ocr_rec_server
+python:python3.7
+2onnx: paddle2onnx
+--model_dir:./inference/ch_ppocr_server_v2.0_rec_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--save_file:./inference/rec_server_onnx/model.onnx
+--opset_version:10
+--enable_onnx_checker:True
+inference:tools/infer/predict_rec.py
+--use_gpu:True|False
+--rec_model_dir:
+--image_dir:./inference/rec_inference
\ No newline at end of file

test_tipc/configs/ppocr_rec_server/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:ocr_rec_server
+python:python3.7
+trans_model:-m paddle_serving_client.convert
+--dirname:./inference/ch_ppocr_server_v2.0_rec_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--serving_server:./deploy/pdserving/ppocr_rec_server_2.0_serving/
+--serving_client:./deploy/pdserving/ppocr_rec_server_2.0_client/
+serving_dir:./deploy/pdserving
+web_service:web_service_rec.py --config=config.yml --opt op.rec.concurrency=1
+op.rec.local_service_conf.devices:null|0
+op.rec.local_service_conf.use_mkldnn:True|False
+op.rec.local_service_conf.thread_num:1|6
+op.rec.local_service_conf.use_trt:False|True
+op.rec.local_service_conf.precision:fp32|fp16|int8
+pipline:pipeline_rpc_client.py|pipeline_http_client.py
+--image_dir:../../doc/imgs_words_en
\ No newline at end of file

test_tipc/configs/ppocr_rec_server_params.txt

-===========================train_params===========================
-model_name:ocr_server_rec
-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=128|whole_train_infer=128
-Global.pretrained_model:null
-train_model_name:latest
-train_infer_img_dir:./inference/rec_inference
-null:null
-##
-trainer:norm_train|pact_train
-norm_train:tools/train.py -c tests/configs/rec_icdar15_r34_train.yml -o
-pact_train:deploy/slim/quantization/quant.py -c tests/configs/rec_icdar15_r34_train.yml -o
-fpgm_train:null
-distill_train:null
-null:null
-null:null
-##
-===========================eval_params===========================
-eval:tools/eval.py -c tests/configs/rec_icdar15_r34_train.yml -o
-null:null
-##
-===========================infer_params===========================
-Global.save_inference_dir:./output/
-Global.pretrained_model:
-norm_export:tools/export_model.py -c tests/configs/rec_icdar15_r34_train.yml -o
-quant_export:deploy/slim/quantization/export_model.py -c tests/configs/rec_icdar15_r34_train.yml -o
-fpgm_export:null
-distill_export:null
-export1:null
-export2:null
-##
-infer_model:./inference/ch_ppocr_server_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|6
---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
-===========================cpp_infer_params===========================
-use_opencv:True
-infer_model:./inference/ch_ppocr_server_v2.0_rec_infer/
-infer_quant:False
-inference:./deploy/cpp_infer/build/ppocr rec
---use_gpu:True|False
---enable_mkldnn:True|False
---cpu_threads:1|6
---rec_batch_num:1
---use_tensorrt:False|True
---precision:fp32|fp16
---rec_model_dir:
---image_dir:./inference/rec_inference/
-null:null
---benchmark:True
-===========================serving_params===========================
-model_name:ocr_server_rec
-python:python3.7
-trans_model:-m paddle_serving_client.convert
---dirname:./inference/ch_ppocr_server_v2.0_rec_infer/
---model_filename:inference.pdmodel
---params_filename:inference.pdiparams
---serving_server:./deploy/pdserving/ppocr_rec_mobile_2.0_serving/
---serving_client:./deploy/pdserving/ppocr_rec_mobile_2.0_client/
-serving_dir:./deploy/pdserving
-web_service:web_service_rec.py --config=config.yml --opt op.rec.concurrency=1
-op.rec.local_service_conf.devices:null|0
-op.rec.local_service_conf.use_mkldnn:True|False
-op.rec.local_service_conf.thread_num:1|6
-op.rec.local_service_conf.use_trt:False|True
-op.rec.local_service_conf.precision:fp32|fp16|int8
-pipline:pipeline_http_client.py|pipeline_rpc_client.py
---image_dir=../../doc/imgs_words_en

test_tipc/configs/ppocr_sys_server_params.txt

-===========================train_params===========================
-model_name:ocr_system_server
-python:python3.7
-gpu_list:null
-Global.use_gpu:null
-Global.auto_cast:null
-Global.epoch_num:null
-Global.save_model_dir:./output/
-Train.loader.batch_size_per_card:null
-Global.pretrained_model:null
-train_model_name:null
-train_infer_img_dir:null
-null:null
-##
-trainer:
-norm_train:null
-pact_train:null
-fpgm_train:null
-distill_train:null
-null:null
-null:null
-##
-===========================eval_params===========================
-eval:null
-null:null
-##
-===========================infer_params===========================
-Global.save_inference_dir:./output/
-Global.pretrained_model:
-norm_export:null
-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_system.py
---use_gpu:True
---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
---rec_model_dir:./inference/ch_ppocr_server_v2.0_rec_infer/
-===========================cpp_infer_params===========================
-use_opencv:True
-infer_model:./inference/ch_ppocr_server_v2.0_det_infer/
-infer_quant:False
-inference:./deploy/cpp_infer/build/ppocr system
---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/
---rec_model_dir:./inference/ch_ppocr_server_v2.0_rec_infer/
---benchmark:True
\ No newline at end of file

test_tipc/docs/install.md

 ## 1. 环境准备
 
-本教程适用于PTDN目录下基础功能测试的运行环境搭建。
+本教程适用于test_tipc目录下基础功能测试的运行环境搭建。
 
 推荐环境：
 - CUDA 10.1/10.2

test_tipc/docs/test_inference_cpp.md

@@ -20,12 +20,12 @@ C++预测功能测试的主程序为`test_inference_cpp.sh`，可以测试基于
 先运行`prepare.sh`准备数据和模型，然后运行`test_inference_cpp.sh`进行测试，最终在```test_tipc/output```目录下生成`cpp_infer_*.log`后缀的日志文件。
 
 ```shell
-bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt "cpp_infer"
+bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt "cpp_infer"
 
 # 用法1:
-bash test_tipc/test_inference_cpp.sh ./test_tipc/configs/ppocr_det_mobile_params.txt
+bash test_tipc/test_inference_cpp.sh test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt
 # 用法2: 指定GPU卡预测，第三个传入参数为GPU卡号
-bash test_tipc/test_inference_cpp.sh ./test_tipc/configs/ppocr_det_mobile_params.txt '1'
+bash test_tipc/test_inference_cpp.sh test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt '1'
 ```  
 
 运行预测指令后，在`test_tipc/output`文件夹下自动会保存运行日志，包括以下文件：

test_tipc/docs/test_lite.md → test_tipc/docs/test_lite_arm_cpu_cpp.md

-# Lite预测功能测试
+# Lite\_arm\_cpu\_cpp预测功能测试
 
-Lite预测功能测试的主程序为`test_lite.sh`，可以测试基于Lite预测库的模型推理功能。
+Lite\_arm\_cpu\_cpp预测功能测试的主程序为`test_lite_arm_cpu_cpp.sh`，可以在ARM CPU上基于Lite预测库测试模型的C++推理功能。
 
 ## 1. 测试结论汇总
 
 目前Lite端的样本间支持以方式的组合：
 
 **字段说明：**
-- 输入设置：包括C++预测、python预测、java预测
-- 模型类型：包括正常模型（FP32）和量化模型（FP16）
+- 模型类型：包括正常模型（FP32）和量化模型（INT8）
 - batch-size：包括1和4
+- threads：包括1和4
 - predictor数量：包括多predictor预测和单predictor预测
-- 功耗模式：包括高性能模式（LITE_POWER_HIGH）和省电模式（LITE_POWER_LOW）
-- 预测库来源：包括下载方式和编译方式，其中编译方式分为以下目标硬件：(1)ARM CPU;(2)Linux XPU;(3)OpenCL GPU;(4)Metal GPU
+- 预测库来源：包括下载方式和编译方式
 
-| 模型类型 | batch-size | predictor数量 | 功耗模式 | 预测库来源 | 支持语言 |
-|  :----:   |  :----:   |  :----:  |  :----:  |  :----:  |  :----:  |
-| 正常模型/量化模型 | 1 | 1 | 高性能模式/省电模式 | 下载方式 | C++预测 |
+| 模型类型 | batch-size | threads | predictor数量 | 预测库来源 |
+|  :----:   |  :----:  | :----:  |  :----:  |  :----:  |
+| 正常模型/量化模型 | 1 | 1/4 |  1 | 下载方式 |
 
 
 ## 2. 测试流程
@@ -24,15 +23,15 @@ Lite预测功能测试的主程序为`test_lite.sh`，可以测试基于Lite预
 
 ### 2.1 功能测试
 
-先运行`prepare.sh`准备数据和模型，模型和数据会打包到test_lite.tar中，将test_lite.tar上传到手机上，解压后进`入test_lite`目录中，然后运行`test_lite.sh`进行测试，最终在`test_lite/output`目录下生成`lite_*.log`后缀的日志文件。
+先运行`prepare_lite.sh`，运行后会在当前路径下生成`test_lite.tar`，其中包含了测试数据、测试模型和用于预测的可执行文件。将`test_lite.tar`上传到被测试的手机上，在手机的终端解压该文件，进入`test_lite`目录中，然后运行`test_lite_arm_cpu_cpp.sh`进行测试，最终在`test_lite/output`目录下生成`lite_*.log`后缀的日志文件。
 
 ```shell
 
 # 数据和模型准备
-bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt "lite_infer"
+bash test_tipc/prepare_lite.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt
 
 # 手机端测试:
-bash test_lite.sh ppocr_det_mobile_params.txt
+bash test_lite_arm_cpu_cpp.sh model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt
 
 ```  
 
@@ -44,7 +43,7 @@ bash test_lite.sh ppocr_det_mobile_params.txt
 运行成功时会输出：
 
 ```
-Run successfully with command - ./ocr_db_crnn det ./models/ch_ppocr_mobile_v2.0_det_slim_opt.nb INT8 4 1 LITE_POWER_LOW ./test_data/icdar2015_lite/text_localization/ch4_test_images/img_233.jpg ./config.txt True > ./output/lite_ch_ppocr_mobile_v2.0_det_slim_opt.nb_precision_INT8_batchsize_1_threads_4_powermode_LITE_POWER_LOW_singleimg_True.log 2>&1!
+Run successfully with command - ./ocr_db_crnn det ch_PP-OCRv2_det_infer_opt.nb ARM_CPU FP32 1 1  ./test_data/icdar2015_lite/text_localization/ch4_test_images/ ./config.txt True > ./output/lite_ch_PP-OCRv2_det_infer_opt.nb_runtime_device_ARM_CPU_precision_FP32_batchsize_1_threads_1.log 2>&1!
 Run successfully with command xxx
 ...
 ```
@@ -52,7 +51,7 @@ Run successfully with command xxx
 运行失败时会输出：
 
 ```
-Run failed with command - ./ocr_db_crnn det ./models/ch_ppocr_mobile_v2.0_det_slim_opt.nb INT8 4 1 LITE_POWER_LOW ./test_data/icdar2015_lite/text_localization/ch4_test_images/img_233.jpg ./config.txt True > ./output/lite_ch_ppocr_mobile_v2.0_det_slim_opt.nb_precision_INT8_batchsize_1_threads_4_powermode_LITE_POWER_LOW_singleimg_True.log 2>&1!
+Run failed with command - ./ocr_db_crnn det ch_PP-OCRv2_det_infer_opt.nb ARM_CPU FP32 1 1  ./test_data/icdar2015_lite/text_localization/ch4_test_images/ ./config.txt True > ./output/lite_ch_PP-OCRv2_det_infer_opt.nb_runtime_device_ARM_CPU_precision_FP32_batchsize_1_threads_1.log 2>&1!
 Run failed with command xxx
 ...
 ```

test_tipc/docs/test_paddle2onnx.md

@@ -18,10 +18,10 @@ PaddleServing预测功能测试的主程序为`test_paddle2onnx.sh`，可以测
 先运行`prepare.sh`准备数据和模型，然后运行`test_paddle2onnx.sh`进行测试，最终在```test_tipc/output```目录下生成`paddle2onnx_infer_*.log`后缀的日志文件。
 
 ```shell
-bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt "paddle2onnx_infer"
+bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt "paddle2onnx_infer"
 
 # 用法:
-bash test_tipc/test_paddle2onnx.sh ./test_tipc/configs/ppocr_det_mobile_params.txt
+bash test_tipc/test_paddle2onnx.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt
 ```  
 
 #### 运行结果

test_tipc/docs/test_serving.md

@@ -20,10 +20,10 @@ PaddleServing预测功能测试的主程序为`test_serving.sh`，可以测试
 先运行`prepare.sh`准备数据和模型，然后运行`test_serving.sh`进行测试，最终在```test_tipc/output```目录下生成`serving_infer_*.log`后缀的日志文件。
 
 ```shell
-bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt "serving_infer"
+bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt "serving_infer"
 
 # 用法:
-bash test_tipc/test_serving.sh ./test_tipc/configs/ppocr_det_mobile_params.txt
+bash test_tipc/test_serving.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt
 ```  
 
 #### 运行结果

test_tipc/docs/test_train_inference_python.md

@@ -64,35 +64,35 @@ Linux端基础训练预测功能测试的主程序为`test_train_inference_pytho
 
 - 模式1：lite_train_lite_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；
 ```shell
-bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'lite_train_lite_infer'
-bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'lite_train_lite_infer'
+bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile/train_infer_python.txt 'lite_train_lite_infer'
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ppocr_det_mobile/train_infer_python.txt 'lite_train_lite_infer'
 ```  
 
 - 模式2：lite_train_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；
 ```shell
-bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'lite_train_whole_infer'
-bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'lite_train_whole_infer'
+bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile/train_infer_python.txt  'lite_train_whole_infer'
+bash test_tipc/test_train_inference_python.sh ../test_tipc/configs/ppocr_det_mobile/train_infer_python.txt 'lite_train_whole_infer'
 ```  
 
 - 模式3：whole_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;
 ```shell
-bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'whole_infer'
+bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile/train_infer_python.txt 'whole_infer'
 # 用法1:
-bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'whole_infer'
+bash test_tipc/test_train_inference_python.sh ../test_tipc/configs/ppocr_det_mobile/train_infer_python.txt 'whole_infer'
 # 用法2: 指定GPU卡预测，第三个传入参数为GPU卡号
-bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'whole_infer' '1'
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ppocr_det_mobile/train_infer_python.txt 'whole_infer' '1'
 ```  
 
 - 模式4：whole_train_whole_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；
 ```shell
-bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'whole_train_whole_infer'
-bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'whole_train_whole_infer'
+bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile/train_infer_python.txt 'whole_train_whole_infer'
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ppocr_det_mobile/train_infer_python.txt 'whole_train_whole_infer'
 ```  
 
 - 模式5：klquant_whole_infer，测试离线量化；
 ```shell
-bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'klquant_whole_infer'
-bash test_tipc/test_train_inference_python.sh test_tipc/configs/ppocr_det_mobile_params.txt  'klquant_whole_infer'
+bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile/train_infer_python.txt  'klquant_whole_infer'
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ppocr_det_mobile/train_infer_python.txt  'klquant_whole_infer'
 ```
 
 运行相应指令后，在`test_tipc/output`文件夹下自动会保存运行日志。如'lite_train_lite_infer'模式下，会运行训练+inference的链条，因此，在`test_tipc/output`文件夹有以下文件：

test_tipc/prepare.sh

 #!/bin/bash
+source test_tipc/common_func.sh
+
 FILENAME=$1
 
 # MODE be one of ['lite_train_lite_infer' 'lite_train_whole_infer' 'whole_train_whole_infer',  
 #                 'whole_infer', 'klquant_whole_infer',
-#                 'cpp_infer', 'serving_infer',  'lite_infer']
+#                 'cpp_infer', 'serving_infer']
 
 MODE=$2
 
@@ -12,30 +14,12 @@ 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_lite_infer' 'lite_train_whole_infer' 'whole_train_whole_infer',  
-#                 'whole_infer', 'klquant_whole_infer',
-#                 'cpp_infer', 'serving_infer',  'lite_infer']
-MODE=$2
 
 if [ ${MODE} = "lite_train_lite_infer" ];then
     # pretrain lite train data
@@ -87,7 +71,8 @@ elif [ ${MODE} = "whole_infer" ];then
         rm -rf ./train_data/icdar2015
         wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar --no-check-certificate
         wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar --no-check-certificate
-        cd ./inference && tar xf ${eval_model_name}.tar && tar xf ch_det_data_50.tar && cd ../
+        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar --no-check-certificate
+        cd ./inference && tar xf ${eval_model_name}.tar && tar xf ch_det_data_50.tar && tar xf ch_ppocr_mobile_v2.0_det_infer.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_train.tar --no-check-certificate
         wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar  --no-check-certificate
@@ -168,40 +153,6 @@ if [ ${MODE} = "serving_infer" ];then
     cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_server_v2.0_rec_infer.tar && tar xf ch_ppocr_server_v2.0_det_infer.tar && cd ../
 fi
 
-
-if [ ${MODE} = "lite_infer" ];then    
-    # prepare lite nb model and test data
-    current_dir=${PWD}
-    wget -nc  -P ./models https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_opt.nb
-    wget -nc  -P ./models https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_slim_opt.nb
-    wget -nc  -P ./test_data https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar
-    cd ./test_data && tar -xf icdar2015_lite.tar && rm icdar2015_lite.tar && cd ../
-    # prepare lite env
-    export http_proxy=http://172.19.57.45:3128
-    export https_proxy=http://172.19.57.45:3128
-    paddlelite_url=https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv.tar.gz
-    paddlelite_zipfile=$(echo $paddlelite_url | awk -F "/" '{print $NF}')
-    paddlelite_file=${paddlelite_zipfile:0:66}
-    wget ${paddlelite_url}
-    tar -xf ${paddlelite_zipfile}
-    mkdir -p  ${paddlelite_file}/demo/cxx/ocr/test_lite
-    mv models test_data ${paddlelite_file}/demo/cxx/ocr/test_lite
-    cp ppocr/utils/ppocr_keys_v1.txt deploy/lite/config.txt ${paddlelite_file}/demo/cxx/ocr/test_lite
-    cp ./deploy/lite/* ${paddlelite_file}/demo/cxx/ocr/
-    cp ${paddlelite_file}/cxx/lib/libpaddle_light_api_shared.so ${paddlelite_file}/demo/cxx/ocr/test_lite
-    cp test_tipc/configs/ppocr_det_mobile_params.txt test_tipc/test_lite.sh test_tipc/common_func.sh ${paddlelite_file}/demo/cxx/ocr/test_lite
-    cd ${paddlelite_file}/demo/cxx/ocr/
-    git clone https://github.com/LDOUBLEV/AutoLog.git
-    unset http_proxy
-    unset https_proxy
-    make -j
-    sleep 1
-    make -j
-    cp ocr_db_crnn test_lite && cp test_lite/libpaddle_light_api_shared.so test_lite/libc++_shared.so
-    tar -cf test_lite.tar ./test_lite && cp test_lite.tar ${current_dir} && cd ${current_dir}
-fi
-
-
 if [ ${MODE} = "paddle2onnx_infer" ];then
     # prepare serving env
     python_name=$(func_parser_value "${lines[2]}")

test_tipc/prepare_lite.sh

+#!/bin/bash
+source ./test_tipc/common_func.sh
+FILENAME=$1
+dataline=$(cat ${FILENAME})
+# parser params
+IFS=$'\n'
+lines=(${dataline})
+IFS=$'\n'
+lite_model_list=$(func_parser_value "${lines[2]}")
+
+# prepare lite .nb model
+pip install paddlelite==2.9
+current_dir=${PWD}
+IFS="|"
+model_path=./inference_models
+for model in ${lite_model_list[*]}; do
+    inference_model_url=https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/${model}.tar
+    inference_model=${inference_model_url##*/}
+    wget -nc  -P ${model_path} ${inference_model_url}
+    cd ${model_path} && tar -xf ${inference_model} && cd ../
+    model_dir=${model_path}/${inference_model%.*}
+    model_file=${model_dir}/inference.pdmodel
+    param_file=${model_dir}/inference.pdiparams
+    paddle_lite_opt --model_dir=${model_dir} --model_file=${model_file} --param_file=${param_file} --valid_targets=arm --optimize_out=${model_dir}_opt
+done
+
+# prepare test data
+data_url=https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar
+model_path=./inference_models
+inference_model=${inference_model_url##*/}
+data_file=${data_url##*/}
+wget -nc  -P ./inference_models ${inference_model_url}
+wget -nc  -P ./test_data ${data_url}
+cd ./inference_models && tar -xf ${inference_model} && cd ../
+cd ./test_data && tar -xf ${data_file} && rm ${data_file} && cd ../
+
+# prepare lite env
+paddlelite_url=https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv.tar.gz
+paddlelite_zipfile=$(echo $paddlelite_url | awk -F "/" '{print $NF}')
+paddlelite_file=${paddlelite_zipfile:0:66}
+wget ${paddlelite_url} && tar -xf ${paddlelite_zipfile}
+mkdir -p  ${paddlelite_file}/demo/cxx/ocr/test_lite
+cp -r ${model_path}/*_opt.nb test_data ${paddlelite_file}/demo/cxx/ocr/test_lite
+cp ppocr/utils/ppocr_keys_v1.txt deploy/lite/config.txt ${paddlelite_file}/demo/cxx/ocr/test_lite
+cp -r ./deploy/lite/* ${paddlelite_file}/demo/cxx/ocr/
+cp ${paddlelite_file}/cxx/lib/libpaddle_light_api_shared.so ${paddlelite_file}/demo/cxx/ocr/test_lite
+cp ${FILENAME} test_tipc/test_lite_arm_cpu_cpp.sh test_tipc/common_func.sh ${paddlelite_file}/demo/cxx/ocr/test_lite
+cd ${paddlelite_file}/demo/cxx/ocr/
+git clone https://github.com/cuicheng01/AutoLog.git
+make -j
+sleep 1
+make -j
+cp ocr_db_crnn test_lite && cp test_lite/libpaddle_light_api_shared.so test_lite/libc++_shared.so
+tar -cf test_lite.tar ./test_lite && cp test_lite.tar ${current_dir} && cd ${current_dir}
+rm -rf ${paddlelite_file}* && rm -rf ${model_path}

test_tipc/readme.md

@@ -60,16 +60,20 @@
 ```shell
 test_tipc/
 ├── configs/  # 配置文件目录
-	├── det_mv3_db.yml               # 测试mobile版ppocr检测模型训练的yml文件
-	├── det_r50_vd_db.yml            # 测试server版ppocr检测模型训练的yml文件
-	├── rec_icdar15_r34_train.yml    # 测试server版ppocr识别模型训练的yml文件
-	├── ppocr_sys_mobile_params.txt     # 测试mobile版ppocr检测+识别模型串联的参数配置文件
-	├── ppocr_det_mobile_params.txt     # 测试mobile版ppocr检测模型的参数配置文件
-	├── ppocr_rec_mobile_params.txt     # 测试mobile版ppocr识别模型的参数配置文件
-	├── ppocr_sys_server_params.txt     # 测试server版ppocr检测+识别模型串联的参数配置文件
-	├── ppocr_det_server_params.txt     # 测试server版ppocr检测模型的参数配置文件
-	├── ppocr_rec_server_params.txt     # 测试server版ppocr识别模型的参数配置文件
-	├── ...  
+    ├── ppocr_det_mobile             # ppocr_det_mobile模型的测试配置文件目录
+        ├── det_mv3_db.yml                  # 测试mobile版ppocr检测模型训练的yml文件
+        ├── train_infer_python.txt.txt      # 测试Linux上python训练预测（基础训练预测）的配置文件
+        ├── model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt     # 测试Linux上c++预测的配置文件
+        ├── model_linux_gpu_normal_normal_infer_python_jetson.txt         # 测试Jetson上python预测的配置文件
+        ├── train_linux_gpu_fleet_amp_infer_python_linux_gpu_cpu.txt      # 测试Linux上多机多卡、混合精度训练和python预测的配置文件
+        ├── ...  
+    ├── ppocr_det_server               # ppocr_det_server模型的测试配置文件目录
+        ├── ...  
+    ├── ppocr_rec_mobile               # ppocr_rec_mobile模型的测试配置文件目录
+        ├── ...  
+    ├── ppocr_rec_server               # ppocr_rec_server模型的测试配置文件目录
+        ├── ...  
+    ├── ...  
 ├── results/   # 预先保存的预测结果，用于和实际预测结果进行精读比对
 	├── python_ppocr_det_mobile_results_fp32.txt           # 预存的mobile版ppocr检测模型python预测fp32精度的结果
 	├── python_ppocr_det_mobile_results_fp16.txt           # 预存的mobile版ppocr检测模型python预测fp16精度的结果
@@ -80,11 +84,22 @@ test_tipc/
 ├── test_train_inference_python.sh    # 测试python训练预测的主程序
 ├── test_inference_cpp.sh             # 测试c++预测的主程序
 ├── test_serving.sh                   # 测试serving部署预测的主程序
-├── test_lite.sh                      # 测试lite部署预测的主程序
+├── test_lite_arm_cpu_cpp.sh          # 测试lite在arm_cpu上部署的C++预测的主程序
 ├── compare_results.py                # 用于对比log中的预测结果与results中的预存结果精度误差是否在限定范围内
 └── readme.md                         # 使用文档
 ```
 
+### 配置文件命名规范
+在`configs`目录下，按模型名称划分为子目录，子目录中存放所有该模型测试需要用到的配置文件，配置文件的命名遵循如下规范：
+
+1. 基础训练预测配置简单命名为：`train_infer_python.txt`，表示**Linux环境下单机、不使用混合精度训练+python预测**，其完整命名对应`train_linux_gpu_fleet_amp_infer_python_linux_gpu_cpu.txt`，由于本配置文件使用频率较高，这里进行了名称简化。
+
+2. 其他带训练配置命名格式为：`train_训练硬件环境(linux_gpu/linux_dcu/…)_是否多机(fleet/normal)_是否混合精度(amp/normal)_预测模式(infer/lite/serving/js)_语言(cpp/python/java)_预测硬件环境(linux_gpu/mac/jetson/opencl_arm_gpu/...).txt`。如，linux gpu下多机多卡+混合精度链条测试对应配置 `train_linux_gpu_fleet_amp_infer_python_linux_gpu_cpu.txt`，linux dcu下基础训练预测对应配置 `train_linux_dcu_normal_normal_infer_python_dcu.txt`。
+
+3. 仅预测的配置（如serving、lite等）命名格式：`model_训练硬件环境(linux_gpu/linux_dcu/…)_是否多机(fleet/normal)_是否混合精度(amp/normal)_(infer/lite/serving/js)_语言(cpp/python/java)_预测硬件环境(linux_gpu/mac/jetson/opencl_arm_gpu/...).txt`，即，与2相比，仅第一个字段从train换为model，测试时模型直接下载获取，这里的“训练硬件环境”表示所测试的模型是在哪种环境下训练得到的。
+
+根据上述命名规范，可以直接从配置文件名看出对应的测试场景和功能。
+
 ### 测试流程
 使用本工具，可以测试不同功能的支持情况，以及预测结果是否对齐，测试流程如下：
 <div align="center">
@@ -99,7 +114,8 @@ test_tipc/
 - `test_train_inference_python.sh`：测试基于Python的模型训练、评估、推理等基本功能，包括裁剪、量化、蒸馏。
 - `test_inference_cpp.sh`：测试基于C++的模型推理。
 - `test_serving.sh`：测试基于Paddle Serving的服务化部署功能。
-- `test_lite.sh`：测试基于Paddle-Lite的端侧预测部署功能。
+- `test_lite_arm_cpu_cpp.sh`：测试基于Paddle-Lite的ARM CPU端c++预测部署功能。
+- `test_paddle2onnx.sh`：测试Paddle2ONNX的模型转化功能，并验证正确性。
 
 <a name="more"></a>
 #### 更多教程
@@ -107,4 +123,5 @@ test_tipc/
 [test_train_inference_python 使用](docs/test_train_inference_python.md)  
 [test_inference_cpp 使用](docs/test_inference_cpp.md)  
 [test_serving 使用](docs/test_serving.md)  
-[test_lite 使用](docs/test_lite.md)  
+[test_lite_arm_cpu_cpp 使用](docs/test_lite_arm_cpu_cpp.md)  
+[test_paddle2onnx 使用](docs/test_paddle2onnx.md)