Merge branch 'dygraph' of https://github.com/PaddlePaddle/PaddleOCR into new_branch

ppstructure/vqa/README.md

+- [文档视觉问答（DOC-VQA）](#文档视觉问答doc-vqa)
+  - [1. 简介](#1-简介)
+  - [2. 性能](#2-性能)
+  - [3. 效果演示](#3-效果演示)
+    - [3.1 SER](#31-ser)
+    - [3.2 RE](#32-re)
+  - [4. 安装](#4-安装)
+    - [4.1 安装依赖](#41-安装依赖)
+    - [4.2 安装PaddleOCR（包含 PP-OCR 和 VQA）](#42-安装paddleocr包含-pp-ocr-和-vqa)
+  - [5. 使用](#5-使用)
+    - [5.1 数据和预训练模型准备](#51-数据和预训练模型准备)
+    - [5.2 SER](#52-ser)
+    - [5.3 RE](#53-re)
+  - [6. 参考链接](#6-参考链接)
+
+
 # 文档视觉问答（DOC-VQA）
 
+## 1. 简介
+
 VQA指视觉问答，主要针对图像内容进行提问和回答,DOC-VQA是VQA任务中的一种，DOC-VQA主要针对文本图像的文字内容提出问题。
 
 PP-Structure 里的 DOC-VQA算法基于PaddleNLP自然语言处理算法库进行开发。
@@ -16,23 +34,23 @@ PP-Structure 里的 DOC-VQA算法基于PaddleNLP自然语言处理算法库进
 本项目是 [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/pdf/2104.08836.pdf) 在 Paddle 2.2上的开源实现，
 包含了在 [XFUND数据集](https://github.com/doc-analysis/XFUND) 上的微调代码。
 
-## 1 性能
+## 2. 性能
 
 我们在 [XFUN](https://github.com/doc-analysis/XFUND) 的中文数据集上对算法进行了评估，性能如下
 
 | 模型 | 任务 | hmean | 模型下载地址 |
 |:---:|:---:|:---:| :---:|
-| LayoutXLM | RE | 0.7483 | [链接](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) |
 | LayoutXLM | SER | 0.9038 | [链接](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar) |
+| LayoutXLM | RE | 0.7483 | [链接](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) |
+| LayoutLMv2 | SER | 0.8544 | [链接](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh.tar)
+| LayoutLMv2 | RE | 0.6777 | [链接](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutLMv2_xfun_zh.tar) |
 | LayoutLM | SER | 0.7731 | [链接](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh.tar) |
 
-
-
-## 2. 效果演示
+## 3. 效果演示
 
 **注意：** 测试图片来源于XFUN数据集。
 
-### 2.1 SER
+### 3.1 SER
 
 ![](../../doc/vqa/result_ser/zh_val_0_ser.jpg) | ![](../../doc/vqa/result_ser/zh_val_42_ser.jpg)
 ---|---
@@ -45,8 +63,7 @@ PP-Structure 里的 DOC-VQA算法基于PaddleNLP自然语言处理算法库进
 
 在OCR检测框的左上方也标出了对应的类别和OCR识别结果。
 
-
-### 2.2 RE
+### 3.2 RE
 
 ![](../../doc/vqa/result_re/zh_val_21_re.jpg) | ![](../../doc/vqa/result_re/zh_val_40_re.jpg)
 ---|---
@@ -54,10 +71,9 @@ PP-Structure 里的 DOC-VQA算法基于PaddleNLP自然语言处理算法库进
 
 图中红色框表示问题，蓝色框表示答案，问题和答案之间使用绿色线连接。在OCR检测框的左上方也标出了对应的类别和OCR识别结果。
 
+## 4. 安装
 
-## 3. 安装
-
-### 3.1 安装依赖
+### 4.1 安装依赖
 
 - **（1) 安装PaddlePaddle**
 
@@ -73,8 +89,7 @@ python3 -m pip install "paddlepaddle>=2.2" -i https://mirror.baidu.com/pypi/simp
 ```
 更多需求，请参照[安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
 
-
-### 3.2 安装PaddleOCR（包含 PP-OCR 和 VQA ）
+### 4.2 安装PaddleOCR（包含 PP-OCR 和 VQA）
 
 - **（1）pip快速安装PaddleOCR whl包（仅预测）**
 
@@ -99,10 +114,9 @@ git clone https://gitee.com/paddlepaddle/PaddleOCR
 python3 -m pip install -r ppstructure/vqa/requirements.txt
 ```
 
-## 4. 使用
+## 5. 使用
 
-
-### 4.1 数据和预训练模型准备
+### 5.1 数据和预训练模型准备
 
 如果希望直接体验预测过程，可以下载我们提供的预训练模型，跳过训练过程，直接预测即可。
 
@@ -125,7 +139,7 @@ wget https://paddleocr.bj.bcebos.com/dataset/XFUND.tar
 python3 ppstructure/vqa/helper/trans_xfun_data.py --ori_gt_path=path/to/json_path --output_path=path/to/save_path
 ```
 
-### 4.2 SER任务
+### 5.2 SER
 
 启动训练之前，需要修改下面的四个字段
 
@@ -164,7 +178,7 @@ CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/vqa/ser/layoutxlm.yml -o
 使用如下命令即可完成`OCR引擎 + SER`的串联预测
 
 ```shell
-CUDA_VISIBLE_DEVICES=0 python3 tools/infer_vqa_token_ser.py -c configs/vqa/ser/layoutxlm.yml  -o Architecture.Backbone.checkpoints=PP-Layout_v1.0_ser_pretrained/ Global.infer_img=doc/vqa/input/zh_val_42.jpg
+CUDA_VISIBLE_DEVICES=0 python3 tools/infer_vqa_token_ser.py -c configs/vqa/ser/layoutxlm.yml  -o Architecture.Backbone.checkpoints=ser_LayoutXLM_xfun_zh/ Global.infer_img=doc/vqa/input/zh_val_42.jpg
 ```
 
 最终会在`config.Global.save_res_path`字段所配置的目录下保存预测结果可视化图像以及预测结果文本文件，预测结果文本文件名为`infer_results.txt`。
@@ -178,8 +192,7 @@ export CUDA_VISIBLE_DEVICES=0
 python3 helper/eval_with_label_end2end.py --gt_json_path XFUND/zh_val/xfun_normalize_val.json  --pred_json_path output_res/infer_results.txt
 ```
 
-
-### 3.3 RE任务
+### 5.3 RE
 
 * 启动训练
 
@@ -219,13 +232,12 @@ CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/vqa/re/layoutxlm.yml -o
 使用如下命令即可完成`OCR引擎 + SER + RE`的串联预测
 ```shell
 export CUDA_VISIBLE_DEVICES=0
-python3 tools/infer_vqa_token_ser_re.py -c configs/vqa/re/layoutxlm.yml -o Architecture.Backbone.checkpoints=PP-Layout_v1.0_re_pretrained/ Global.infer_img=doc/vqa/input/zh_val_21.jpg -c_ser configs/vqa/ser/layoutxlm.yml -o_ser Architecture.Backbone.checkpoints=PP-Layout_v1.0_ser_pretrained/
+python3 tools/infer_vqa_token_ser_re.py -c configs/vqa/re/layoutxlm.yml -o Architecture.Backbone.checkpoints=re_LayoutXLM_xfun_zh/ Global.infer_img=doc/vqa/input/zh_val_21.jpg -c_ser configs/vqa/ser/layoutxlm.yml -o_ser Architecture.Backbone.checkpoints=ser_LayoutXLM_xfun_zh/
 ```
 
 最终会在`config.Global.save_res_path`字段所配置的目录下保存预测结果可视化图像以及预测结果文本文件，预测结果文本文件名为`infer_results.txt`。
 
-
-## 参考链接
+## 6. 参考链接
 
 - LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding, https://arxiv.org/pdf/2104.08836.pdf
 - microsoft/unilm/layoutxlm, https://github.com/microsoft/unilm/tree/master/layoutxlm

test_tipc/benchmark_train.sh

+#!/bin/bash
+source test_tipc/common_func.sh
+
+# set env
+python=python
+export str_tmp=$(echo `pip list|grep paddlepaddle-gpu|awk -F ' ' '{print $2}'`)
+export frame_version=${str_tmp%%.post*}
+export frame_commit=$(echo `${python} -c "import paddle;print(paddle.version.commit)"`)
+
+# run benchmark sh 
+# Usage:
+# bash run_benchmark_train.sh config.txt params
+# or 
+# bash run_benchmark_train.sh config.txt
+
+function func_parser_params(){
+    strs=$1
+    IFS="="
+    array=(${strs})
+    tmp=${array[1]}
+    echo ${tmp}
+}
+
+function func_sed_params(){
+    filename=$1
+    line=$2
+    param_value=$3
+    params=`sed -n "${line}p" $filename`
+    IFS=":"
+    array=(${params})
+    key=${array[0]}
+    value=${array[1]}
+
+    new_params="${key}:${param_value}"
+    IFS=";"
+    cmd="sed -i '${line}s/.*/${new_params}/' '${filename}'"
+    eval $cmd
+}
+
+function set_gpu_id(){
+    string=$1
+    _str=${string:1:6}
+    IFS="C"
+    arr=(${_str})
+    M=${arr[0]}
+    P=${arr[1]}
+    gn=`expr $P - 1`
+    gpu_num=`expr $gn / $M`
+    seq=`seq -s "," 0 $gpu_num`
+    echo $seq
+}
+
+function get_repo_name(){
+    IFS=";"
+    cur_dir=$(pwd)
+    IFS="/"
+    arr=(${cur_dir})
+    echo ${arr[-1]}
+}
+
+FILENAME=$1
+# copy FILENAME as new
+new_filename="./test_tipc/benchmark_train.txt"
+cmd=`yes|cp $FILENAME $new_filename`
+FILENAME=$new_filename
+# MODE must be one of ['benchmark_train']
+MODE=$2
+PARAMS=$3
+# bash test_tipc/benchmark_train.sh test_tipc/configs/det_mv3_db_v2_0/train_benchmark.txt  benchmark_train dynamic_bs8_null_DP_N1C1
+IFS=$'\n'
+# parser params from train_benchmark.txt
+dataline=`cat $FILENAME`
+# parser params
+IFS=$'\n'
+lines=(${dataline})
+model_name=$(func_parser_value "${lines[1]}")
+
+# 获取benchmark_params所在的行数
+line_num=`grep -n "train_benchmark_params" $FILENAME  | cut -d ":" -f 1`
+# for train log parser
+batch_size=$(func_parser_value "${lines[line_num]}")
+line_num=`expr $line_num + 1`
+fp_items=$(func_parser_value "${lines[line_num]}")
+line_num=`expr $line_num + 1`
+epoch=$(func_parser_value "${lines[line_num]}")
+
+line_num=`expr $line_num + 1`
+profile_option_key=$(func_parser_key "${lines[line_num]}")
+profile_option_params=$(func_parser_value "${lines[line_num]}")
+profile_option="${profile_option_key}:${profile_option_params}"
+
+line_num=`expr $line_num + 1`
+flags_value=$(func_parser_value "${lines[line_num]}")
+# set flags
+IFS=";"
+flags_list=(${flags_value})
+for _flag in ${flags_list[*]}; do
+    cmd="export ${_flag}"
+    eval $cmd
+done
+
+# set log_name
+repo_name=$(get_repo_name )
+SAVE_LOG=${BENCHMARK_LOG_DIR:-$(pwd)}   # */benchmark_log
+mkdir -p "${SAVE_LOG}/benchmark_log/"
+status_log="${SAVE_LOG}/benchmark_log/results.log"
+
+# The number of lines in which train params can be replaced.
+line_python=3
+line_gpuid=4
+line_precision=6
+line_epoch=7
+line_batchsize=9
+line_profile=13
+line_eval_py=24
+line_export_py=30
+
+func_sed_params "$FILENAME" "${line_eval_py}" "null"
+func_sed_params "$FILENAME" "${line_export_py}" "null"
+func_sed_params "$FILENAME" "${line_python}"  "$python"
+
+# if params
+if  [ ! -n "$PARAMS" ] ;then
+    # PARAMS input is not a word.
+    IFS="|"
+    batch_size_list=(${batch_size})
+    fp_items_list=(${fp_items})
+    device_num_list=(N1C4)
+    run_mode="DP"
+else
+    # parser params from input: modeltype_bs${bs_item}_${fp_item}_${run_mode}_${device_num}
+    IFS="_"
+    params_list=(${PARAMS})
+    model_type=${params_list[0]}
+    batch_size=${params_list[1]}
+    batch_size=`echo  ${batch_size} | tr -cd "[0-9]" `
+    precision=${params_list[2]}
+    # run_process_type=${params_list[3]}
+    run_mode=${params_list[3]}
+    device_num=${params_list[4]}
+    IFS=";"
+
+    if [ ${precision} = "null" ];then
+        precision="fp32"
+    fi
+
+    fp_items_list=($precision)
+    batch_size_list=($batch_size)
+    device_num_list=($device_num)
+fi
+
+IFS="|"
+for batch_size in ${batch_size_list[*]}; do 
+    for precision in ${fp_items_list[*]}; do
+        for device_num in ${device_num_list[*]}; do
+            # sed batchsize and precision
+            func_sed_params "$FILENAME" "${line_precision}" "$precision"
+            func_sed_params "$FILENAME" "${line_batchsize}" "$MODE=$batch_size"
+            func_sed_params "$FILENAME" "${line_epoch}" "$MODE=$epoch"
+            gpu_id=$(set_gpu_id $device_num)
+
+            if [ ${#gpu_id} -le 1 ];then
+                run_process_type="SingleP"
+                log_path="$SAVE_LOG/profiling_log"
+                mkdir -p $log_path
+                log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}_${device_num}_profiling"
+                func_sed_params "$FILENAME" "${line_gpuid}" "0"  # sed used gpu_id 
+                # set profile_option params
+                tmp=`sed -i "${line_profile}s/.*/${profile_option}/" "${FILENAME}"`
+
+                # run test_train_inference_python.sh
+                cmd="bash test_tipc/test_train_inference_python.sh ${FILENAME} benchmark_train > ${log_path}/${log_name} 2>&1 "
+                echo $cmd
+                eval $cmd
+                eval "cat ${log_path}/${log_name}"
+
+                # without profile
+                log_path="$SAVE_LOG/train_log"
+                speed_log_path="$SAVE_LOG/index"
+                mkdir -p $log_path
+                mkdir -p $speed_log_path
+                log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}_${device_num}_log"
+                speed_log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}_${device_num}_speed"
+                func_sed_params "$FILENAME" "${line_profile}" "null"  # sed profile_id as null
+                cmd="bash test_tipc/test_train_inference_python.sh ${FILENAME} benchmark_train > ${log_path}/${log_name} 2>&1 "
+                echo $cmd
+                job_bt=`date '+%Y%m%d%H%M%S'`
+                eval $cmd
+                job_et=`date '+%Y%m%d%H%M%S'`
+                export model_run_time=$((${job_et}-${job_bt}))
+                eval "cat ${log_path}/${log_name}"
+
+                # parser log
+                _model_name="${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}"
+                cmd="${python} ${BENCHMARK_ROOT}/scripts/analysis.py --filename ${log_path}/${log_name} \
+                        --speed_log_file '${speed_log_path}/${speed_log_name}' \
+                        --model_name ${_model_name} \
+                        --base_batch_size ${batch_size} \
+                        --run_mode ${run_mode} \
+                        --run_process_type ${run_process_type} \
+                        --fp_item ${precision} \
+                        --keyword ips: \
+                        --skip_steps 2 \
+                        --device_num ${device_num} \
+                        --speed_unit samples/s \
+                        --convergence_key loss: "
+                echo $cmd
+                eval $cmd
+                last_status=${PIPESTATUS[0]}
+                status_check $last_status "${cmd}" "${status_log}"
+            else
+                IFS=";"
+                unset_env=`unset CUDA_VISIBLE_DEVICES`
+                run_process_type="MultiP"
+                log_path="$SAVE_LOG/train_log"
+                speed_log_path="$SAVE_LOG/index"
+                mkdir -p $log_path
+                mkdir -p $speed_log_path
+                log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}_${device_num}_log"
+                speed_log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}_${device_num}_speed"
+                func_sed_params "$FILENAME" "${line_gpuid}" "$gpu_id"  # sed used gpu_id 
+                func_sed_params "$FILENAME" "${line_profile}" "null"  # sed --profile_option as null
+                cmd="bash test_tipc/test_train_inference_python.sh ${FILENAME} benchmark_train > ${log_path}/${log_name} 2>&1 "
+                echo $cmd
+                job_bt=`date '+%Y%m%d%H%M%S'`
+                eval $cmd
+                job_et=`date '+%Y%m%d%H%M%S'`
+                export model_run_time=$((${job_et}-${job_bt}))
+                eval "cat ${log_path}/${log_name}"
+                # parser log
+                _model_name="${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}"
+                
+                cmd="${python} ${BENCHMARK_ROOT}/scripts/analysis.py --filename ${log_path}/${log_name} \
+                        --speed_log_file '${speed_log_path}/${speed_log_name}' \
+                        --model_name ${_model_name} \
+                        --base_batch_size ${batch_size} \
+                        --run_mode ${run_mode} \
+                        --run_process_type ${run_process_type} \
+                        --fp_item ${precision} \
+                        --keyword ips: \
+                        --skip_steps 2 \
+                        --device_num ${device_num} \
+                        --speed_unit images/s \
+                        --convergence_key loss: "
+                echo $cmd
+                eval $cmd
+                last_status=${PIPESTATUS[0]}
+                status_check $last_status "${cmd}" "${status_log}"
+            fi
+        done
+    done
+done

test_tipc/configs/det_mv3_db_v2.0/train_infer_python.txt → test_tipc/configs/det_mv3_db_v2_0/train_infer_python.txt

 ===========================train_params===========================
-model_name:det_mv3_db_v2.0
+model_name:det_mv3_db_v2_0
 python:python3.7
 gpu_list:0|0,1
 Global.use_gpu:True|True
@@ -48,4 +48,10 @@ inference:tools/infer/predict_det.py
 --image_dir:./inference/ch_det_data_50/all-sum-510/
 null:null
 --benchmark:True
-null:null
+null:null
\ No newline at end of file
+===========================train_benchmark_params==========================
+batch_size:8|16
+fp_items:fp32|fp16
+epoch:2
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096
\ No newline at end of file

test_tipc/configs/det_r18_vd_v2_0/train_infer_python.txt

+===========================train_params===========================
+model_name:det_r18_db_v2_0
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=2|whole_train_whole_infer=300
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_lite_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
+norm_train:tools/train.py -c configs/det/det_res18_db_v2.0.yml -o 
+quant_export:null
+fpgm_export:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:null
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.checkpoints:
+norm_export:null
+quant_export:null 
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+##
+train_model:null
+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
+===========================train_benchmark_params==========================
+batch_size:8|16
+fp_items:fp32|fp16
+epoch:2
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile

test_tipc/configs/det_r50_vd_east_v2.0/train_infer_python.txt → test_tipc/configs/det_r50_vd_east_v2_0/train_infer_python.txt

 ===========================train_params===========================
-model_name:det_r50_vd_east_v2.0
+model_name:det_r50_vd_east_v2_0
 python:python3.7
 gpu_list:0
 Global.use_gpu:True|True
@@ -13,7 +13,7 @@ train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
 null:null
 ##
 trainer:norm_train
-norm_train:tools/train.py -c test_tipc/configs/det_r50_vd_east_v2.0/det_r50_vd_east.yml -o 
+norm_train:tools/train.py -c test_tipc/configs/det_r50_vd_east_v2_0/det_r50_vd_east.yml -o 
 pact_train:null
 fpgm_train:null
 distill_train:null
@@ -27,7 +27,7 @@ null:null
 ===========================infer_params===========================
 Global.save_inference_dir:./output/
 Global.checkpoints:
-norm_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_east_v2.0/det_r50_vd_east.yml -o 
+norm_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_east_v2_0/det_r50_vd_east.yml -o 
 quant_export:null 
 fpgm_export:null
 distill_export:null
@@ -35,7 +35,7 @@ export1:null
 export2:null
 ##
 train_model:./inference/det_r50_vd_east_v2.0_train/best_accuracy
-infer_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_east_v2.0/det_r50_vd_east.yml  -o
+infer_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_east_v2_0/det_r50_vd_east.yml  -o
 infer_quant:False
 inference:tools/infer/predict_det.py
 --use_gpu:True|False
@@ -49,3 +49,8 @@ inference:tools/infer/predict_det.py
 --save_log_path:null
 --benchmark:True
 --det_algorithm:EAST
+===========================train_benchmark_params==========================
+batch_size:8
+fp_items:fp32|fp16
+epoch:2
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
\ No newline at end of file

test_tipc/configs/det_r50_vd_pse_v2.0/train_infer_python.txt → test_tipc/configs/det_r50_vd_pse_v2_0/train_infer_python.txt

 ===========================train_params===========================
-model_name:det_r50_vd_pse_v2.0
+model_name:det_r50_vd_pse_v2_0
 python:python3.7
 gpu_list:0
 Global.use_gpu:True|True
@@ -13,7 +13,7 @@ train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
 null:null
 ##
 trainer:norm_train
-norm_train:tools/train.py -c test_tipc/configs/det_r50_vd_pse_v2.0/det_r50_vd_pse.yml -o 
+norm_train:tools/train.py -c test_tipc/configs/det_r50_vd_pse_v2_0/det_r50_vd_pse.yml -o 
 pact_train:null
 fpgm_train:null
 distill_train:null
@@ -27,7 +27,7 @@ null:null
 ===========================infer_params===========================
 Global.save_inference_dir:./output/
 Global.checkpoints:
-norm_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_pse_v2.0/det_r50_vd_pse.yml -o 
+norm_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_pse_v2_0/det_r50_vd_pse.yml -o 
 quant_export:null 
 fpgm_export:null
 distill_export:null
@@ -35,7 +35,7 @@ export1:null
 export2:null
 ##
 train_model:./inference/det_r50_vd_pse_v2.0_train/best_accuracy
-infer_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_pse_v2.0/det_r50_vd_pse.yml  -o
+infer_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_pse_v2_0/det_r50_vd_pse.yml  -o
 infer_quant:False
 inference:tools/infer/predict_det.py
 --use_gpu:True|False
@@ -49,3 +49,8 @@ inference:tools/infer/predict_det.py
 --save_log_path:null
 --benchmark:True
 --det_algorithm:PSE
+===========================train_benchmark_params==========================
+batch_size:8
+fp_items:fp32|fp16
+epoch:2
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile

test_tipc/docs/benchmark_train.md

+
+# TIPC Linux端Benchmark测试文档
+
+该文档为Benchmark测试说明，Benchmark预测功能测试的主程序为`benchmark_train.sh`，用于验证监控模型训练的性能。
+
+# 1. 测试流程
+## 1.1 准备数据和环境安装
+运行`test_tipc/prepare.sh`，完成训练数据准备和安装环境流程。
+
+```shell
+# 运行格式：bash test_tipc/prepare.sh  train_benchmark.txt  mode
+bash test_tipc/prepare.sh test_tipc/configs/det_mv3_db_v2_0/train_benchmark.txt benchmark_train
+```
+
+## 1.2 功能测试
+执行`test_tipc/benchmark_train.sh`，完成模型训练和日志解析
+
+```shell
+# 运行格式：bash test_tipc/benchmark_train.sh train_benchmark.txt mode
+bash test_tipc/benchmark_train.sh test_tipc/configs/det_mv3_db_v2_0/train_infer_python.txt benchmark_train
+
+```
+
+`test_tipc/benchmark_train.sh`支持根据传入的第三个参数实现只运行某一个训练配置，如下：
+```shell
+# 运行格式：bash test_tipc/benchmark_train.sh train_benchmark.txt mode
+bash test_tipc/benchmark_train.sh test_tipc/configs/det_mv3_db_v2_0/train_infer_python.txt benchmark_train  dynamic_bs8_fp32_DP_N1C1
+```
+dynamic_bs8_fp32_DP_N1C1为test_tipc/benchmark_train.sh传入的参数，格式如下：
+`${modeltype}_${batch_size}_${fp_item}_${run_mode}_${device_num}`
+包含的信息有：模型类型、batchsize大小、训练精度如fp32,fp16等、分布式运行模式以及分布式训练使用的机器信息如单机单卡（N1C1）。
+
+
+## 2. 日志输出
+
+运行后将保存模型的训练日志和解析日志，使用 `test_tipc/configs/det_mv3_db_v2_0/train_benchmark.txt` 参数文件的训练日志解析结果是：
+
+```
+{"model_branch": "dygaph", "model_commit": "7c39a1996b19087737c05d883fd346d2f39dbcc0", "model_name": "det_mv3_db_v2_0_bs8_fp32_SingleP_DP", "batch_size": 8, "fp_item": "fp32", "run_process_type": "SingleP", "run_mode": "DP", "convergence_value": "5.413110", "convergence_key": "loss:", "ips": 19.333, "speed_unit": "samples/s", "device_num": "N1C1", "model_run_time": "0", "frame_commit": "8cc09552473b842c651ead3b9848d41827a3dbab", "frame_version": "0.0.0"}
+```
+
+训练日志和日志解析结果保存在benchmark_log目录下，文件组织格式如下：
+```
+train_log/
+├── index
+│   ├── PaddleOCR_det_mv3_db_v2_0_bs8_fp32_SingleP_DP_N1C1_speed
+│   └── PaddleOCR_det_mv3_db_v2_0_bs8_fp32_SingleP_DP_N1C4_speed
+├── profiling_log
+│   └── PaddleOCR_det_mv3_db_v2_0_bs8_fp32_SingleP_DP_N1C1_profiling
+└── train_log
+    ├── PaddleOCR_det_mv3_db_v2_0_bs8_fp32_SingleP_DP_N1C1_log
+    └── PaddleOCR_det_mv3_db_v2_0_bs8_fp32_SingleP_DP_N1C4_log
+```

test_tipc/prepare.sh

@@ -20,6 +20,25 @@ model_name=$(func_parser_value "${lines[1]}")
 
 trainer_list=$(func_parser_value "${lines[14]}")
 
+if [ ${MODE} = "benchmark_train" ];then
+    pip install -r requirements.txt
+    if [[ ${model_name} =~ "det_mv3_db_v2_0" || ${model_name} =~ "det_r50_vd_east_v2_0" || ${model_name} =~ "det_r50_vd_pse_v2_0" || ${model_name} =~ "det_r18_db_v2_0" ]];then
+        rm -rf ./train_data/icdar2015
+        wget -nc -P  ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/MobileNetV3_large_x0_5_pretrained.pdparams  --no-check-certificate
+        wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar --no-check-certificate
+        cd ./train_data/ && tar xf icdar2015.tar && cd ../
+    fi
+    if [[ ${model_name} =~ "det_r50_vd_east_v2_0" || ${model_name} =~ "det_r50_vd_pse_v2_0" ]];then
+        wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/ResNet50_vd_ssld_pretrained.pdparams --no-check-certificate
+        wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar --no-check-certificate
+        cd ./train_data/ && tar xf icdar2015.tar && cd ../
+    fi
+    if [[ ${model_name} =~ "det_r18_db_v2_0" ]];then
+        wget -nc -P ./pretrain_models/  https://paddleocr.bj.bcebos.com/pretrained/ResNet18_vd_pretrained.pdparams  --no-check-certificate
+        wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar --no-check-certificate
+        cd ./train_data/ && tar xf icdar2015.tar && cd ../
+    fi
+fi
 
 if [ ${MODE} = "lite_train_lite_infer" ];then
     # pretrain lite train data
@@ -52,7 +71,7 @@ if [ ${MODE} = "lite_train_lite_infer" ];then
         wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/total_text_lite.tar --no-check-certificate
         cd ./train_data && tar xf total_text_lite.tar && ln -s total_text_lite total_text  && cd ../
     fi
-    if [ ${model_name} == "det_mv3_db_v2.0" ]; then
+    if [ ${model_name} == "det_mv3_db_v2_0" ]; then
         wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar  --no-check-certificate
         cd ./inference/ && tar xf det_mv3_db_v2.0_train.tar && cd ../
     fi
@@ -211,7 +230,7 @@ elif [ ${MODE} = "whole_infer" ];then
         wget -nc -P  ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar --no-check-certificate
         cd ./inference/ && tar xf det_r50_vd_sast_totaltext_v2.0_train.tar && cd ../
     fi
-    if [ ${model_name} == "det_mv3_db_v2.0" ]; then
+    if [ ${model_name} == "det_mv3_db_v2_0" ]; then
         wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar  --no-check-certificate
         cd ./inference/ && tar xf det_mv3_db_v2.0_train.tar && tar xf ch_det_data_50.tar && cd ../
     fi
@@ -223,7 +242,7 @@ elif [ ${MODE} = "whole_infer" ];then
         wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar --no-check-certificate
         cd ./inference/ && tar xf det_mv3_pse_v2.0_train.tar & cd ../
     fi
-    if [ ${model_name} == "det_r50_vd_pse_v2.0" ]; then
+    if [ ${model_name} == "det_r50_vd_pse_v2_0" ]; then
         wget -nc -P ./inference/  https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar  --no-check-certificate
         cd ./inference/ && tar xf det_r50_vd_pse_v2.0_train.tar & cd ../
     fi
@@ -231,7 +250,7 @@ elif [ ${MODE} = "whole_infer" ];then
         wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_east_v2.0_train.tar --no-check-certificate
         cd ./inference/ && tar xf det_mv3_east_v2.0_train.tar & cd ../
     fi
-    if [ ${model_name} == "det_r50_vd_east_v2.0" ]; then
+    if [ ${model_name} == "det_r50_vd_east_v2_0" ]; then
         wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar --no-check-certificate
         cd ./inference/ && tar xf det_r50_vd_east_v2.0_train.tar & cd ../
     fi

test_tipc/readme.md

@@ -28,32 +28,32 @@
 | DB     |ch_ppocr_mobile_v2.0_det_PACT | 检测  | 支持 | 多机多卡 <br> 混合精度 | PACT量化 | Paddle Inference: C++ <br> Paddle Serving: Python, C++ <br> Paddle-Lite: <br> (1) ARM CPU(C++) |
 | DB     |ch_ppocr_mobile_v2.0_det_KL | 检测  | 支持 | 多机多卡 <br> 混合精度 | 离线量化| Paddle Inference: C++ <br> Paddle Serving: Python, C++ <br> Paddle-Lite: <br> (1) ARM CPU(C++) |
 | DB     |ch_ppocr_server_v2.0_det | 检测  | 支持 | 多机多卡 <br> 混合精度 | - | Paddle Inference: C++ <br> Paddle Serving: Python, C++ |
-| DB     |ch_PP-OCRv2_det          | 检测  |
+| DB     |ch_PP-OCRv2_det          | 检测  | 支持 | 多机多卡 <br> 混合精度 | - | Paddle Inference: C++ <br> Paddle Serving: Python, C++ |
 | CRNN   |ch_ppocr_mobile_v2.0_rec | 识别  | 支持 | 多机多卡 <br> 混合精度 | - | Paddle Inference: C++ <br> Paddle Serving: Python, C++ <br> Paddle-Lite: <br> (1) ARM CPU(C++) |
 | CRNN   |ch_ppocr_server_v2.0_rec | 识别  | 支持 | 多机多卡 <br> 混合精度 | - | Paddle Inference: C++ <br> Paddle Serving: Python, C++ |
-| CRNN   |ch_PP-OCRv2_rec          | 识别  |
+| CRNN   |ch_PP-OCRv2_rec          | 识别  | 支持 | 多机多卡 <br> 混合精度 | - | Paddle Inference: C++ <br> Paddle Serving: Python, C++ |
 | PP-OCR |ch_ppocr_mobile_v2.0 | 检测+识别  | 支持 | 多机多卡 <br> 混合精度 | - | Paddle Inference: C++ <br> Paddle Serving: Python, C++ <br> Paddle-Lite: <br> (1) ARM CPU(C++) |
 | PP-OCR |ch_ppocr_server_v2.0 | 检测+识别  | 支持 | 多机多卡 <br> 混合精度 | - | Paddle Inference: C++ <br> Paddle Serving: Python, C++ |
-|PP-OCRv2|ch_PP-OCRv2 | 检测+识别  |
-| DB     |det_mv3_db_v2.0                | 检测  |
-| DB     |det_r50_vd_db_v2.0             | 检测  |
-| EAST   |det_mv3_east_v2.0              | 检测  |
-| EAST   |det_r50_vd_east_v2.0           | 检测  |
-| PSENet |det_mv3_pse_v2.0               | 检测  |
-| PSENet |det_r50_vd_pse_v2.0            | 检测  |
-| SAST   |det_r50_vd_sast_totaltext_v2.0 | 检测  |
-| Rosetta|rec_mv3_none_none_ctc_v2.0     | 识别  |
-| Rosetta|rec_r34_vd_none_none_ctc_v2.0  | 识别  |
-| CRNN   |rec_mv3_none_bilstm_ctc_v2.0   | 识别  |
-| CRNN   |rec_r34_vd_none_bilstm_ctc_v2.0| 识别  |
-| StarNet|rec_mv3_tps_bilstm_ctc_v2.0    | 识别  |
-| StarNet|rec_r34_vd_tps_bilstm_ctc_v2.0 | 识别  |
-| RARE   |rec_mv3_tps_bilstm_att_v2.0    | 识别  |
-| RARE   |rec_r34_vd_tps_bilstm_att_v2.0 | 识别  |
-| SRN    |rec_r50fpn_vd_none_srn         | 识别  |
-| NRTR   |rec_mtb_nrtr                   | 识别  |
-| SAR    |rec_r31_sar                    | 识别  |
-| PGNet  |rec_r34_vd_none_none_ctc_v2.0  | 端到端|
+|PP-OCRv2|ch_PP-OCRv2 | 检测+识别  | 支持 | 多机多卡 <br> 混合精度 | - | Paddle Inference: C++ <br> Paddle Serving: Python, C++ |
+| DB     |det_mv3_db_v2.0                | 检测  | 支持 | 多机多卡 <br> 混合精度 | - | - |
+| DB     |det_r50_vd_db_v2.0             | 检测  | 支持 | 多机多卡 <br> 混合精度 | - | - |
+| EAST   |det_mv3_east_v2.0              | 检测  | 支持 | 多机多卡 <br> 混合精度 | - | - |
+| EAST   |det_r50_vd_east_v2.0           | 检测  | 支持 | 多机多卡 <br> 混合精度 | - | - |
+| PSENet |det_mv3_pse_v2.0               | 检测  | 支持 | 多机多卡 <br> 混合精度 | - | - |
+| PSENet |det_r50_vd_pse_v2.0            | 检测  | 支持 | 多机多卡 <br> 混合精度 | - | - |
+| SAST   |det_r50_vd_sast_totaltext_v2.0 | 检测  | 支持 | 多机多卡 <br> 混合精度 | - | - |
+| Rosetta|rec_mv3_none_none_ctc_v2.0     | 识别  | 支持 | 多机多卡 <br> 混合精度 | - | - |
+| Rosetta|rec_r34_vd_none_none_ctc_v2.0  | 识别  | 支持 | 多机多卡 <br> 混合精度 | - | - |
+| CRNN   |rec_mv3_none_bilstm_ctc_v2.0   | 识别  | 支持 | 多机多卡 <br> 混合精度 | - | - |
+| CRNN   |rec_r34_vd_none_bilstm_ctc_v2.0| 识别  | 支持 | 多机多卡 <br> 混合精度 | - | - |
+| StarNet|rec_mv3_tps_bilstm_ctc_v2.0    | 识别  | 支持 | 多机多卡 <br> 混合精度 | - | - |
+| StarNet|rec_r34_vd_tps_bilstm_ctc_v2.0 | 识别  | 支持 | 多机多卡 <br> 混合精度 | - | - |
+| RARE   |rec_mv3_tps_bilstm_att_v2.0    | 识别  | 支持 | 多机多卡 <br> 混合精度 | - | - |
+| RARE   |rec_r34_vd_tps_bilstm_att_v2.0 | 识别  | 支持 | 多机多卡 <br> 混合精度 | - | - |
+| SRN    |rec_r50fpn_vd_none_srn         | 识别  | 支持 | 多机多卡 <br> 混合精度 | - | - |
+| NRTR   |rec_mtb_nrtr                   | 识别  | 支持 | 多机多卡 <br> 混合精度 | - | - |
+| SAR    |rec_r31_sar                    | 识别  | 支持 | 多机多卡 <br> 混合精度 | - | - |
+| PGNet  |rec_r34_vd_none_none_ctc_v2.0  | 端到端| 支持 | 多机多卡 <br> 混合精度 | - | - |
 
 
 

test_tipc/supplementary/readme.md

@@ -3,7 +3,7 @@
 
 Linux端基础训练预测功能测试的主程序为test_train_python.sh，可以测试基于Python的模型训练、评估等基本功能，包括裁剪、量化、蒸馏训练。
 
-![](./tipc_train.png)
+![](./test_tipc/tipc_train.png)
 
 测试链条如上图所示，主要测试内容有带共享权重，自定义OP的模型的正常训练和slim相关功能训练流程是否正常。
 
@@ -28,23 +28,30 @@ pip3 install -r requirements.txt
 - 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；
 
 ```
-bash test_tipc/test_train_python.sh ./test_tipc/ch_ppocr_mobile_v2.0_det/train_infer_python.txt 'lite_train_lite_infer'
+bash test_tipc/test_train_python.sh ./test_tipc/train_infer_python.txt 'lite_train_lite_infer'
 ```
 
 - 模式2：whole_train_whole_infer，使用全量数据训练，用于快速验证训练到预测的走通流程，验证模型最终训练精度；
 
 ```
-bash test_tipc/test_train_python.sh ./test_tipc/ch_ppocr_mobile_v2.0_det/train_infer_python.txt 'whole_train_whole_infer'
+bash test_tipc/test_train_python.sh ./test_tipc/train_infer_python.txt 'whole_train_whole_infer'
 ```
 
 如果是运行量化裁剪等训练方式，需要使用不同的配置文件。量化训练的测试指令如下：
 ```
-bash test_tipc/test_train_python.sh ./test_tipc/ch_ppocr_mobile_v2.0_det/train_infer_python_PACT.txt 'lite_train_lite_infer'
+bash test_tipc/test_train_python.sh ./test_tipc/train_infer_python_PACT.txt 'lite_train_lite_infer'
 ```
 
 同理，FPGM裁剪的运行方式如下：
 ```
-bash test_tipc/test_train_python.sh ./test_tipc/ch_ppocr_mobile_v2.0_det/train_infer_python_FPGM.txt 'lite_train_lite_infer'
+bash test_tipc/test_train_python.sh ./test_tipc/train_infer_python_FPGM.txt 'lite_train_lite_infer'
+```
+
+多机多卡的运行配置文件分别为 `train_infer_python_fleet.txt`, `train_infer_python_FPGM_fleet.txt` 和 `train_infer_python_PACT_fleet.txt`。
+运行时，需要修改配置文件中的 `gpu_list:xx.xx.xx.xx,yy.yy.yy.yy;0,1`。 将 `xx.xx.xx.xx` 替换为具体的 `ip` 地址，各个`ip`地址之间用`,`分隔。 另外，和单机训练
+不同，启动多机多卡训练需要在多机的每个节点上分别运行命令。以多机多卡量化训练为例，指令如下：
+```
+bash test_tipc/test_train_python.sh ./test_tipc/train_infer_python_PACT_fleet.txt 'lite_train_lite_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'模式运行后，在test_tipc/extra_output文件夹有以下文件：

test_tipc/supplementary/test_tipc/test_train_python.sh

@@ -35,7 +35,6 @@ use_share_conv_key=$(func_parser_key "${lines[13]}")
 use_share_conv_list=$(func_parser_value "${lines[13]}")
 run_train_py=$(func_parser_value "${lines[14]}")
 
-
 LOG_PATH="./test_tipc/extra_output"
 mkdir -p ${LOG_PATH}
 status_log="${LOG_PATH}/results_python.log"
@@ -98,6 +97,8 @@ if [ ${MODE} = "lite_train_lite_infer" ] || [ ${MODE} = "whole_train_whole_infer
                             cmd="${python} ${run_train_py} ${set_use_gpu}  ${set_save_model}  ${set_epoch}  ${set_pretrain} ${set_checkpoints}  ${set_autocast} ${set_batchsize}  ${set_use_custom_op} ${set_model_type} ${set_use_share_conv} ${set_amp_config}"
                         elif [ ${#ips} -le 26 ];then  # train with multi-gpu
                             cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train_py} ${set_use_gpu}  ${set_save_model}  ${set_epoch}  ${set_pretrain} ${set_checkpoints}  ${set_autocast} ${set_batchsize}  ${set_use_custom_op} ${set_model_type} ${set_use_share_conv} ${set_amp_config}"
+                        else
+                            cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train_py} ${set_use_gpu}  ${set_save_model}  ${set_epoch}  ${set_pretrain} ${set_checkpoints}  ${set_autocast} ${set_batchsize}  ${set_use_custom_op} ${set_model_type} ${set_use_share_conv} ${set_amp_config}"
                         fi
 
                         # run train

test_tipc/supplementary/test_tipc/train_infer_python_FPGM.txt

@@ -4,9 +4,9 @@ python:python3.7
 gpu_list:0|0,1
 use_gpu:True|True
 AMP.use_amp:True|False
-epoch:lite_train_lite_infer=20|whole_train_whole_infer=1000
+epoch:lite_train_lite_infer=2|whole_train_whole_infer=1000
 save_model_dir:./output/
-TRAIN.batch_size:lite_train_lite_infer=2|whole_train_whole_infer=4
+TRAIN.batch_size:lite_train_lite_infer=1280|whole_train_whole_infer=1280
 pretrained_model:null
 checkpoints:null
 use_custom_relu:False|True

test_tipc/supplementary/test_tipc/train_infer_python_FPGM_fleet.txt

+===========================train_params===========================
+model_name:ch_PPOCRv2_det
+python:python3.7
+gpu_list:xx.xx.xx.xx,yy.yy.yy.yy;0,1
+use_gpu:True
+AMP.use_amp:True|False
+epoch:lite_train_lite_infer=2|whole_train_whole_infer=1000
+save_model_dir:./output/
+TRAIN.batch_size:lite_train_lite_infer=1280|whole_train_whole_infer=1280
+pretrained_model:null
+checkpoints:null
+use_custom_relu:False|True
+model_type:cls|cls_distill|cls_distill_multiopt
+MODEL.siamese:False|True
+norm_train:train.py -c mv3_large_x0_5.yml -o prune_train=True
+quant_train:False
+prune_train:False

test_tipc/supplementary/test_tipc/train_infer_python_PACT.txt

@@ -4,9 +4,9 @@ python:python3.7
 gpu_list:0|0,1
 use_gpu:True|True
 AMP.use_amp:True|False
-epoch:lite_train_lite_infer=20|whole_train_whole_infer=1000
+epoch:lite_train_lite_infer=2|whole_train_whole_infer=1000
 save_model_dir:./output/
-TRAIN.batch_size:lite_train_lite_infer=2|whole_train_whole_infer=4
+TRAIN.batch_size:lite_train_lite_infer=1280|whole_train_whole_infer=1280
 pretrained_model:null
 checkpoints:null
 use_custom_relu:False|True

test_tipc/supplementary/test_tipc/train_infer_python_PACT_fleet.txt

+===========================train_params===========================
+model_name:ch_PPOCRv2_det
+python:python3.7
+gpu_list:xx.xx.xx.xx,yy.yy.yy.yy;0,1
+use_gpu:True
+AMP.use_amp:True|False
+epoch:lite_train_lite_infer=2|whole_train_whole_infer=1000
+save_model_dir:./output/
+TRAIN.batch_size:lite_train_lite_infer=1280|whole_train_whole_infer=1280
+pretrained_model:null
+checkpoints:null
+use_custom_relu:False|True
+model_type:cls|cls_distill|cls_distill_multiopt
+MODEL.siamese:False|True
+norm_train:train.py -c mv3_large_x0_5.yml -o quant_train=True
+quant_train:False
+prune_train:False

test_tipc/supplementary/test_tipc/train_infer_python_fleet.txt

+===========================train_params===========================
+model_name:ch_PPOCRv2_det
+python:python3.7
+gpu_list:xx.xx.xx.xx,yy.yy.yy.yy;0,1
+use_gpu:True
+AMP.use_amp:True|False
+epoch:lite_train_lite_infer=2|whole_train_whole_infer=1000
+save_model_dir:./output/
+TRAIN.batch_size:lite_train_lite_infer=1280|whole_train_whole_infer=1280
+pretrained_model:null
+checkpoints:null
+use_custom_relu:False|True
+model_type:cls|cls_distill|cls_distill_multiopt
+MODEL.siamese:False|True
+norm_train: train.py -c mv3_large_x0_5.yml -o
+quant_train:False
+prune_train:False

test_tipc/test_train_inference_python.sh

@@ -284,7 +284,6 @@ else
                 set_amp_config=" "
             fi          
             for trainer in ${trainer_list[*]}; do 
-                eval ${env}
                 flag_quant=False
                 if [ ${trainer} = ${pact_key} ]; then
                     run_train=${pact_trainer}
@@ -344,6 +343,7 @@ else
 
                 # run eval 
                 if [ ${eval_py} != "null" ]; then
+                    eval ${env}
                     set_eval_params1=$(func_set_params "${eval_key1}" "${eval_value1}")
                     eval_cmd="${python} ${eval_py} ${set_eval_pretrain} ${set_use_gpu} ${set_eval_params1}" 
                     eval $eval_cmd

tools/eval.py

@@ -28,7 +28,6 @@ from ppocr.modeling.architectures import build_model
 from ppocr.postprocess import build_post_process
 from ppocr.metrics import build_metric
 from ppocr.utils.save_load import load_model
-from ppocr.utils.utility import print_dict
 import tools.program as program