diff --git a/test_tipc/common_func.sh b/test_tipc/common_func.sh
new file mode 100644
index 0000000000000000000000000000000000000000..3f0fa66b77ff50b23b1e83dea506580f549f8ecf
--- /dev/null
+++ b/test_tipc/common_func.sh
@@ -0,0 +1,65 @@
+#!/bin/bash
+
+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}x = "null"x ];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
+}
+
diff --git a/test_tipc/configs/basicvsr/train_infer_python.txt b/test_tipc/configs/basicvsr/train_infer_python.txt
new file mode 100644
index 0000000000000000000000000000000000000000..da70c25262afffe15b9831bdc9998b1b1c50279b
--- /dev/null
+++ b/test_tipc/configs/basicvsr/train_infer_python.txt
@@ -0,0 +1,51 @@
+===========================train_params===========================
+model_name:basicvsr
+python:python3.7
+gpu_list:0
+##
+auto_cast:null
+total_iters:lite_train_lite_infer=5|whole_train_whole_infer=200
+output_dir:./output/
+dataset.train.batch_size:lite_train_lite_infer=1|whole_train_whole_infer=1
+pretrained_model:null
+train_model_name:basicvsr_reds*/*checkpoint.pdparams
+train_infer_img_dir:./data/basicvsr_reds/test
+null:null
+##
+trainer:norm_train
+norm_train:tools/main.py -c configs/basicvsr_reds.yaml -o dataset.train.dataset.num_clips=2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:null
+null:null
+##
+===========================infer_params===========================
+--output_dir:./output/
+load:null
+norm_export:tools/export_model.py -c configs/basicvsr_reds.yaml  --inputs_size="1,6,3,180,320" --load 
+quant_export:null 
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+inference_dir:basicvsrmodel_generator
+train_model:./inference/basicvsr/basicvsrmodel_generator
+infer_export:null
+infer_quant:False
+inference:tools/inference.py --model_type basicvsr -c configs/basicvsr_reds.yaml  -o dataset.test.num_clips=2 dataset.test.number_frames=6
+--device:gpu
+null:null
+null:null
+null:null
+null:null
+null:null
+--model_path:
+null:null
+null:null
+--benchmark:True
+null:null
\ No newline at end of file
diff --git a/test_tipc/configs/cyclegan/train_infer_python.txt b/test_tipc/configs/cyclegan/train_infer_python.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7eeb244c54584045a49de63c319d24814e63a64f
--- /dev/null
+++ b/test_tipc/configs/cyclegan/train_infer_python.txt
@@ -0,0 +1,51 @@
+===========================train_params===========================
+model_name:cyclegan
+python:python3.7
+gpu_list:0|0,1
+##
+auto_cast:null
+epochs:lite_train_lite_infer=5|whole_train_whole_infer=200
+output_dir:./output/
+dataset.train.batch_size:lite_train_lite_infer=1|whole_train_whole_infer=1
+pretrained_model:null
+train_model_name:cyclegan_horse2zebra*/*checkpoint.pdparams
+train_infer_img_dir:./data/horse2zebra/test
+null:null
+##
+trainer:norm_train
+norm_train:tools/main.py -c configs/cyclegan_horse2zebra.yaml -o
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:null
+null:null
+##
+===========================infer_params===========================
+--output_dir:./output/
+load:null
+norm_export:tools/export_model.py -c configs/cyclegan_horse2zebra.yaml  --inputs_size="-1,3,-1,-1;-1,3,-1,-1" --load 
+quant_export:null 
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+inference_dir:cycleganmodel_netG_A
+train_model:./inference/cyclegan_horse2zebra/cycleganmodel_netG_A
+infer_export:null
+infer_quant:False
+inference:tools/inference.py --model_type cyclegan -c configs/cyclegan_horse2zebra.yaml
+--device:gpu
+null:null
+null:null
+null:null
+null:null
+null:null
+--model_path:
+null:null
+null:null
+--benchmark:True
+null:null
\ No newline at end of file
diff --git a/test_tipc/configs/fom/train_infer_python.txt b/test_tipc/configs/fom/train_infer_python.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d2303556a722ebb31788749b6e3bc2d1fcedeebd
--- /dev/null
+++ b/test_tipc/configs/fom/train_infer_python.txt
@@ -0,0 +1,51 @@
+===========================train_params===========================
+model_name:fom
+python:python3.7
+gpu_list:0
+##
+auto_cast:null
+epochs:lite_train_lite_infer=10|whole_train_whole_infer=100
+output_dir:./output/
+dataset.train.batch_size:lite_train_lite_infer=8|whole_train_whole_infer=8
+pretrained_model:null
+train_model_name:firstorder_vox_256*/*checkpoint.pdparams
+train_infer_img_dir:./data/firstorder_vox_256/test
+null:null
+##
+trainer:norm_train
+norm_train:tools/main.py -c configs/firstorder_vox_256.yaml -o 
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:null
+null:null
+##
+===========================infer_params===========================
+--output_dir:./output/
+load:null
+norm_export:tools/export_model.py -c configs/firstorder_vox_256.yaml --inputs_size="1,3,256,256;1,3,256,256;1,10,2;1,10,2,2" --load 
+quant_export:null 
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+inference_dir:fom_dy2st
+train_model:./inference/fom_dy2st/
+infer_export:null
+infer_quant:False
+inference:tools/fom_infer.py --driving_path data/first_order/Voxceleb/test --output_path infer_output/fom
+--device:gpu
+null:null
+null:null
+null:null
+null:null
+null:null
+--model_path:
+null:null
+null:null
+--benchmark:True
+null:null
\ No newline at end of file
diff --git a/test_tipc/configs/pix2pix/train_infer_python.txt b/test_tipc/configs/pix2pix/train_infer_python.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ed5cf514b08963d176597f740ec1d216989a5eda
--- /dev/null
+++ b/test_tipc/configs/pix2pix/train_infer_python.txt
@@ -0,0 +1,51 @@
+===========================train_params===========================
+model_name:pix2pix
+python:python3.7
+gpu_list:0|0,1
+##
+auto_cast:null
+epochs:lite_train_lite_infer=5|whole_train_whole_infer=200
+output_dir:./output/
+dataset.train.batch_size:lite_train_lite_infer=1|whole_train_whole_infer=1
+pretrained_model:null
+train_model_name:pix2pix_facades*/*checkpoint.pdparams
+train_infer_img_dir:./data/facades/test
+null:null
+##
+trainer:norm_train
+norm_train:tools/main.py -c configs/pix2pix_facades.yaml -o
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:null
+null:null
+##
+===========================infer_params===========================
+--output_dir:./output/
+load:null
+norm_export:tools/export_model.py -c configs/pix2pix_facades.yaml  --inputs_size="-1,3,-1,-1" --load 
+quant_export:null 
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+inference_dir:pix2pixmodel_netG
+train_model:./inference/pix2pix_facade/pix2pixmodel_netG
+infer_export:null
+infer_quant:False
+inference:tools/inference.py --model_type pix2pix -c configs/pix2pix_facades.yaml
+--device:cpu
+null:null
+null:null
+null:null
+null:null
+null:null
+--model_path:
+null:null
+null:null
+--benchmark:True
+null:null
\ No newline at end of file
diff --git a/test_tipc/configs/stylegan2/train_infer_python.txt b/test_tipc/configs/stylegan2/train_infer_python.txt
new file mode 100644
index 0000000000000000000000000000000000000000..99cfccf7db2e148e42b708543e4179544a5a7058
--- /dev/null
+++ b/test_tipc/configs/stylegan2/train_infer_python.txt
@@ -0,0 +1,51 @@
+===========================train_params===========================
+model_name:stylegan2
+python:python3.7
+gpu_list:0
+##
+auto_cast:null
+total_iters::lite_train_lite_infer=10|whole_train_whole_infer=800
+output_dir:./output/
+dataset.train.batch_size:lite_train_lite_infer=3|whole_train_whole_infer=3
+pretrained_model:null
+train_model_name:stylegan_v2_256_ffhq*/*checkpoint.pdparams
+train_infer_img_dir:null
+null:null
+##
+trainer:norm_train
+norm_train:tools/main.py -c configs/stylegan_v2_256_ffhq.yaml -o
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:null
+null:null
+##
+===========================infer_params===========================
+--output_dir:./output/
+load:null
+norm_export:tools/export_model.py -c configs/stylegan_v2_256_ffhq.yaml  --inputs_size="1,1,512;1,1" --load 
+quant_export:null 
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+inference_dir:stylegan2model_gen
+train_model:./inference/stylegan2/stylegan2model_gen
+infer_export:null
+infer_quant:False
+inference:tools/inference.py --model_type stylegan2 -c configs/stylegan_v2_256_ffhq.yaml
+--device:gpu
+null:null
+null:null
+null:null
+null:null
+null:null
+--model_path:
+null:null
+null:null
+--benchmark:True
+null:null
\ No newline at end of file
diff --git a/test_tipc/docs/test_train_inference_python.md b/test_tipc/docs/test_train_inference_python.md
new file mode 100644
index 0000000000000000000000000000000000000000..daca3c3476eb92a61f01b263ec959e8a7d3e626d
--- /dev/null
+++ b/test_tipc/docs/test_train_inference_python.md
@@ -0,0 +1,127 @@
+# Linux端基础训练预测功能测试
+
+Linux端基础训练预测功能测试的主程序为`test_train_inference_python.sh`，可以测试基于Python的模型训练、评估、推理等基本功能。
+
+
+## 1. 测试结论汇总
+
+- 训练相关：
+
+| 算法论文 | 模型名称 | 模型类型 | 基础<br>训练预测 | 更多<br>训练方式 | 模型压缩 |  其他预测部署  |
+| :--- | :--- |  :----:  | :--------: |  :----  |   :----  |   :----  |
+| Pix2Pix |Pix2Pix | 生成  | 支持 | 多机多卡  | | |
+| CycleGAN |CycleGAN | 生成  | 支持 | 多机多卡  | | |
+| StyleGAN2 |StyleGAN2 | 生成  | 支持 | 多机多卡  | | |
+| FOMM |FOMM | 生成  | 支持 | 多机多卡  | | |
+| BasicVSR |BasicVSR | 超分  | 支持 | 多机多卡  | | |
+|PP-MSVSR|PP-MSVSR | 超分|
+
+- 预测相关：预测功能汇总如下，
+
+| 模型类型 |device | batchsize | tensorrt | mkldnn | cpu多线程 |
+|  ----   |  ---- |   ----   |  :----:  |   :----:   |  :----:  |
+| 正常模型 | GPU | 1/6 | fp32 | - | - |
+
+
+
+## 2. 测试流程
+
+运行环境配置请参考[文档](../../docs/zh_CN/install.md)的内容配置运行环境。
+
+### 2.1 安装依赖
+- 安装PaddlePaddle >= 2.1
+- 安装PaddleGAN依赖
+    ```
+    pip install -v -e .
+    ```
+- 安装autolog（规范化日志输出工具）
+    ```
+    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 ../
+    ```
+
+
+### 2.2 功能测试
+先运行`prepare.sh`准备数据和模型，然后运行`test_train_inference_python.sh`进行测试，最终在```test_tipc/output```目录下生成`python_infer_*.log`格式的日志文件。
+
+
+`test_train_inference_python.sh`包含5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_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/basicvsr/train_infer_python.txt 'lite_train_lite_infer'
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/basicvsr/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/basicvsr/train_infer_python.txt 'lite_train_whole_infer'
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/basicvsr/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/basicvsr/train_infer_python.txt 'whole_infer'
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/basicvsr/train_infer_python.txt 'whole_infer'
+```  
+
+- 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/basicvsr/train_infer_python.txt 'whole_train_whole_infer'
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/basicvsr/train_infer_python.txt 'whole_train_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/output/
+|- results_python.log    # 运行指令状态的日志
+|- norm_train_gpus_0_autocast_null/  # GPU 0号卡上正常训练的训练日志和模型保存文件夹
+......
+```
+
+其中`results_python.log`中包含了每条指令的运行状态，如果运行成功会输出：
+```
+Run successfully with command - python3.7 tools/main.py -c configs/basicvsr_reds.yaml -o dataset.train.dataset.num_clips=2    output_dir=./test_tipc/output/norm_train_gpus_0_autocast_null total_iters=5     dataset.train.batch_size=1     !
+-=Run successfully with command - python3.7 tools/export_model.py -c configs/basicvsr_reds.yaml  --inputs_size="1,6,3,180,320" --load   ./test_tipc/output/norm_train_gpus_0_autocast_null/basicvsr_reds-2021-11-22-07-18/iter_1_checkpoint.pdparams --output_dir ./test_tipc/output/norm_train_gpus_0_autocast_null!
+......
+```
+如果运行失败，会输出：
+```
+Run failed with command - python3.7 tools/main.py -c configs/basicvsr_reds.yaml -o dataset.train.dataset.num_clips=2    output_dir=./test_tipc/output/norm_train_gpus_0_autocast_null total_iters=5     dataset.train.batch_size=1     !   !
+Run failed with command - python3.7 tools/export_model.py -c configs/basicvsr_reds.yaml  --inputs_size="1,6,3,180,320" --load   ./test_tipc/output/norm_train_gpus_0_autocast_null/basicvsr_reds-2021-11-22-07-18/iter_1_checkpoint.pdparams --output_dir ./test_tipc/output/norm_train_gpus_0_autocast_null!
+......
+```
+可以很方便的根据`results_python.log`中的内容判定哪一个指令运行错误。
+
+
+### 2.3 精度测试
+
+使用compare_results.py脚本比较模型预测的结果是否符合预期，主要步骤包括：
+- 提取日志中的预测坐标；
+- 从本地文件中提取保存好的坐标结果；
+- 比较上述两个结果是否符合精度预期，误差大于设置阈值时会报错。
+
+#### 使用方式
+运行命令：
+```shell
+python3.7 test_tipc/compare_results.py --gt_file=./test_tipc/results/python_*.txt  --log_file=./test_tipc/output/python_*.log --atol=1e-3 --rtol=1e-3
+```
+
+参数介绍：  
+- gt_file： 指向事先保存好的预测结果路径，支持*.txt 结尾，会自动索引*.txt格式的文件，文件默认保存在test_tipc/result/ 文件夹下
+- log_file: 指向运行test_tipc/test_train_inference_python.sh 脚本的infer模式保存的预测日志，预测日志中打印的有预测结果，
+- atol: 设置的绝对误差
+- rtol: 设置的相对误差
+
+#### 运行结果
+
+正常运行效果如下图：
+<img src="compare_right.png" width="1000">
+
+出现不一致结果时的运行输出：
+<img src="compare_wrong.png" width="1000">
+
diff --git a/test_tipc/prepare.sh b/test_tipc/prepare.sh
new file mode 100644
index 0000000000000000000000000000000000000000..b967a47e61019b122948d2b670effee8a02dc05e
--- /dev/null
+++ b/test_tipc/prepare.sh
@@ -0,0 +1,128 @@
+#!/bin/bash
+FILENAME=$1
+
+# MODE be one of ['lite_train_lite_infer' 'lite_train_whole_infer' 'whole_train_whole_infer',  
+#                 'whole_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_lite_infer' 'lite_train_whole_infer' 'whole_train_whole_infer',  
+#                 'whole_infer']
+MODE=$2
+
+if [ ${MODE} = "lite_train_lite_infer" ];then
+    if [ ${model_name} == "pix2pix" ]; then
+        rm -rf ./data/facades*
+        rm -rf ./data/pix2pix*
+        wget -nc -P ./data/ https://paddlegan.bj.bcebos.com/datasets/pix2pix_facade_lite.tar --no-check-certificate
+        cd ./data/ && tar xf pix2pix_facade_lite.tar && cd ../
+    elif [ ${model_name} == "cyclegan" ]; then
+        rm -rf ./data/horse2zebra*
+        wget -nc -P ./data/ https://paddlegan.bj.bcebos.com/datasets/cyclegan_horse2zebra_lite.tar --no-check-certificate
+        cd ./data/ && tar xf cyclegan_horse2zebra_lite.tar && cd ../
+    elif [ ${model_name} == "stylegan2" ]; then
+        rm -rf ./data/ffhq*
+        wget -nc -P ./data/ https://paddlegan.bj.bcebos.com/datasets/ffhq.tar --no-check-certificate
+        cd ./data/ && tar xf ffhq.tar && cd ../
+    elif [ ${model_name} == "fom" ]; then
+        rm -rf ./data/first_order*
+        wget -nc -P ./data/ https://paddlegan.bj.bcebos.com/datasets/fom_lite.tar  --no-check-certificate --no-check-certificate
+        cd ./data/ && tar xf fom_lite.tar && cd ../
+    elif [ ${model_name} == "basicvsr" ]; then
+        rm -rf ./data/REDS*
+        wget -nc -P ./data/ https://paddlegan.bj.bcebos.com/datasets/basicvsr_lite.tar --no-check-certificate
+        cd ./data/ && tar xf basicvsr_lite.tar && cd ../
+    fi
+    
+elif [ ${MODE} = "whole_train_whole_infer" ];then
+    if [ ${model_name} == "pix2pix" ]; then
+        rm -rf ./data/facades*
+        wget -nc -P ./data/ http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/facades.tar.gz --no-check-certificate
+        cd ./data/ && tar -xzf facades.tar.gz && cd ../
+    elif [ ${model_name} == "cyclegan" ]; then
+        rm -rf ./data/horse2zebra*
+        wget -nc -P ./data/ https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/horse2zebra.zip --no-check-certificate
+        cd ./data/ && unzip horse2zebra.zip && cd ../
+    fi
+elif [ ${MODE} = "lite_train_whole_infer" ];then
+    if [ ${model_name} == "pix2pix" ]; then
+        rm -rf ./data/facades*
+        wget -nc -P ./data/ https://paddlegan.bj.bcebos.com/datasets/pix2pix_facade_lite.tar --no-check-certificate
+        cd ./data/ && tar xf pix2pix_facade_lite.tar && cd ../
+    elif [ ${model_name} == "cyclegan" ]; then
+        rm -rf ./data/horse2zebra*
+        wget -nc -P ./data/ https://paddlegan.bj.bcebos.com/datasets/cyclegan_horse2zebra_lite.tar  --no-check-certificate --no-check-certificate
+        cd ./data/ && tar xf cyclegan_horse2zebra_lite.tar && cd ../
+    elif [ ${model_name} == "fom" ]; then
+        rm -rf ./data/first_order*
+        wget -nc -P ./data/ https://paddlegan.bj.bcebos.com/datasets/fom_lite.tar  --no-check-certificate --no-check-certificate
+        cd ./data/ && tar xf fom_lite.tar && cd ../
+    elif [ ${model_name} == "stylegan2" ]; then
+        rm -rf ./data/ffhq*
+        wget -nc -P ./data/ https://paddlegan.bj.bcebos.com/datasets/ffhq.tar --no-check-certificate
+        cd ./data/ && tar xf ffhq.tar && cd ../
+    elif [ ${model_name} == "basicvsr" ]; then
+        rm -rf ./data/REDS*
+        wget -nc -P ./data/ https://paddlegan.bj.bcebos.com/datasets/basicvsr_lite.tar --no-check-certificate
+        cd ./data/ && tar xf basicvsr_lite.tar && cd ../
+    fi
+elif [ ${MODE} = "whole_infer" ];then
+    if [ ${model_name} = "pix2pix" ]; then
+        rm -rf ./data/facades*
+        wget -nc  -P ./inference https://paddlegan.bj.bcebos.com/static_model/pix2pix_facade.tar --no-check-certificate
+        wget -nc  -P ./data https://paddlegan.bj.bcebos.com/datasets/facades_test.tar --no-check-certificate
+        cd ./data && tar xf facades_test.tar && mv facades_test facades && cd ../
+        cd ./inference && tar xf pix2pix_facade.tar && cd ../
+    elif [ ${model_name} = "cyclegan" ]; then
+        rm -rf ./data/horse2zebra*
+        wget -nc  -P ./inference https://paddlegan.bj.bcebos.com/static_model/cyclegan_horse2zebra.tar  --no-check-certificate
+        wget -nc  -P ./data https://paddlegan.bj.bcebos.com/datasets/cyclegan_horse2zebra_test.tar  --no-check-certificate
+        cd ./data && tar xf cyclegan_horse2zebra_test.tar && mv cyclegan_test horse2zebra && cd ../
+        cd ./inference && tar xf cyclegan_horse2zebra.tar && cd ../
+    elif [ ${model_name} == "fom" ]; then
+        rm -rf ./data/first_order*
+        wget -nc -P ./data/ https://paddlegan.bj.bcebos.com/datasets/fom_lite_test.tar  --no-check-certificate
+        wget -nc  -P ./inference https://paddlegan.bj.bcebos.com/static_model/fom_dy2st.tar --no-check-certificate
+        cd ./data/ && tar xf fom_lite_test.tar && cd ../
+        cd ./inference && tar xf fom_dy2st.tar && cd ../
+    elif [ ${model_name} == "stylegan2" ]; then
+        rm -rf ./data/ffhq*
+        wget -nc -P ./data/ https://paddlegan.bj.bcebos.com/datasets/ffhq.tar --no-check-certificate
+        wget -nc  -P ./inference https://paddlegan.bj.bcebos.com/static_model/stylegan2_1024.tar --no-check-certificate
+        cd ./inference && tar xf stylegan2_1024.tar && cd ../
+        cd ./data/ && tar xf ffhq.tar && cd ../
+    elif [ ${model_name} == "basicvsr" ]; then
+        rm -rf ./data/basic*
+        rm -rf ./inference/basic*
+        wget -nc -P ./data/ https://paddlegan.bj.bcebos.com/datasets/basicvsr_lite_test.tar --no-check-certificate
+        wget -nc  -P ./inference https://paddlegan.bj.bcebos.com/static_model/basicvsr.tar --no-check-certificate
+        cd ./inference && tar xf basicvsr.tar && cd ../
+        cd ./data/ && tar xf basicvsr_lite_test.tar && cd ../
+    fi
+    
+fi
diff --git a/test_tipc/readme.md b/test_tipc/readme.md
new file mode 100644
index 0000000000000000000000000000000000000000..d4df724fc66d144f450a4d3fe77e22f82c0b1851
--- /dev/null
+++ b/test_tipc/readme.md
@@ -0,0 +1,74 @@
+
+# 飞桨训推一体认证
+
+## 1. 简介
+
+飞桨除了基本的模型训练和预测，还提供了支持多端多平台的高性能推理部署工具。本文档提供了PaddleGAN中所有模型的飞桨训推一体认证 (Training and Inference Pipeline Certification(TIPC)) 信息和测试工具，方便用户查阅每种模型的训练推理部署打通情况，并可以进行一键测试。
+
+## 2. 汇总信息
+
+打通情况汇总如下，已填写的部分表示可以使用本工具进行一键测试，未填写的表示正在支持中。
+
+**字段说明：**
+- 基础训练预测：包括模型训练、Paddle Inference Python预测。
+- 更多训练方式：包括多机多卡、混合精度。
+- 模型压缩：包括裁剪、离线/在线量化、蒸馏。
+- 其他预测部署：包括Paddle Inference C++预测、Paddle Serving部署、Paddle-Lite部署等。
+
+更详细的mkldnn、Tensorrt等预测加速相关功能的支持情况可以查看各测试工具的[更多教程](#more)。
+
+| 算法论文 | 模型名称 | 模型类型 | 基础<br>训练预测 | 更多<br>训练方式 | 模型压缩 |  其他预测部署  |
+| :--- | :--- |  :----:  | :--------: |  :----  |   :----  |   :----  |
+| Pix2Pix |Pix2Pix | 生成  | 支持 | 多机多卡  | | |
+| CycleGAN |CycleGAN | 生成  | 支持 | 多机多卡  | | |
+| StyleGAN2 |StyleGAN2 | 生成  | 支持 | 多机多卡  | | |
+| FOMM |FOMM | 生成  | 支持 | 多机多卡  | | |
+| BasicVSR |BasicVSR | 超分  | 支持 | 多机多卡  | | |
+|PP-MSVSR|PP-MSVSR | 超分|
+
+
+
+
+## 3. 一键测试工具使用
+### 目录介绍
+
+```shell
+test_tipc/
+├── configs/  # 配置文件目录
+	├── basicvsr_reds.yaml             # 测试basicvsr模型训练的yaml文件
+	├── cyclegan_horse2zebra.yaml      # 测试cyclegan模型训练的yaml文件
+	├── firstorder_vox_256.yaml        # 测试fomm模型训练的yaml文件
+	├── pix2pix_facedes.yaml           # 测试pix2pix模型训练的yaml文件
+	├── stylegan_v2_256_ffhq.yaml      # 测试stylegan模型训练的yaml文件
+
+	├── ...  
+├── 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_basicvsr_results_fp32.txt           # 预存的basicvsr模型python预测fp32精度的结果
+	├── python_cyclegan_results_fp32.txt           # 预存的cyclegan模型python预测fp32精度的结果
+	├── python_pix2pix_results_fp32.txt            # 预存的pix2pix模型python预测的fp32精度的结果
+	├── python_stylegan_results_fp32.txt           # 预存的stylegan模型python预测的fp32精度的结果
+	├── ...
+├── prepare.sh                        # 完成test_*.sh运行所需要的数据和模型下载
+├── test_train_inference_python.sh    # 测试python训练预测的主程序
+├── 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
+```
+
+### 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">
+    <img src="docs/test.png" width="800">
+</div>
+
+1. 运行prepare.sh准备测试所需数据和模型；
+2. 运行要测试的功能对应的测试脚本`test_*.sh`，产出log，由log可以看到不同配置是否运行成功；
+3. 用`compare_results.py`对比log中的预测结果和预存在results目录下的结果，判断预测精度是否符合预期（在误差范围内）。
+
+其中，有4个测试主程序，功能如下：
+- `test_train_inference_python.sh`：测试基于Python的模型训练、评估、推理等基本功能。
+
+
+<a name="more"></a>
+#### 更多教程
+各功能测试中涉及混合精度、裁剪、量化等训练相关，及mkldnn、Tensorrt等多种预测相关参数配置，请点击下方相应链接了解更多细节和使用教程：  
+[test_train_inference_python 使用](docs/test_train_inference_python.md)  
diff --git a/test_tipc/test_train_inference_python.sh b/test_tipc/test_train_inference_python.sh
new file mode 100644
index 0000000000000000000000000000000000000000..3ab490cf4b4860f3a3d92a2abaffd5b14949d59c
--- /dev/null
+++ b/test_tipc/test_train_inference_python.sh
@@ -0,0 +1,297 @@
+#!/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']
+MODE=$2
+
+dataline=$(awk 'NR==1, NR==51{print}'  $FILENAME)
+
+# parser params
+IFS=$'\n'
+lines=(${dataline})
+# The training params
+model_name=$(func_parser_value "${lines[1]}")
+python=$(func_parser_value "${lines[2]}")
+gpu_list=$(func_parser_value "${lines[3]}")
+
+autocast_list=$(func_parser_value "${lines[5]}")
+epoch_key=$(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 "${lines[15]}")
+
+trainer_key1=$(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_value "${lines[28]}")
+norm_export=$(func_parser_value "${lines[29]}")
+
+inference_dir=$(func_parser_value "${lines[35]}")
+
+# parser inference model 
+infer_model_dir_list=$(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 "${lines[48]}")
+infer_key1=$(func_parser_key "${lines[50]}")
+infer_value1=$(func_parser_value "${lines[50]}")
+
+LOG_PATH="./test_tipc/output"
+mkdir -p ${LOG_PATH}
+status_log="${LOG_PATH}/results_python.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
+                        for precision in ${precision_list[*]}; do
+                            if [ ${use_mkldnn} = "False" ] && [ ${precision} = "fp16" ]; then
+                                continue
+                            fi # skip when enable fp16 but disable mkldnn
+                            if [ ${_flag_quant} = "True" ] && [ ${precision} != "int8" ]; then
+                                continue
+                            fi # skip when quant model inference but precision is not int8
+                            set_precision=$(func_set_params "${precision_key}" "${precision}")
+                            
+                            _save_log_path="${_log_path}/python_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_precision_${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_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} ${set_model_dir} > ${_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
+            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}/python_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
+}
+
+if [ ${MODE} = "whole_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="${save_infer_key} ${save_infer_dir}"
+            export_cmd="${python} ${infer_run_exports[Count]} ${set_export_weight} ${set_save_infer_key}"
+            echo ${infer_run_exports[Count]} 
+            echo  $export_cmd
+            eval $export_cmd
+            status_export=$?
+            status_check $status_export "${export_cmd}" "${status_log}"
+        else
+            save_infer_dir=${infer_model}
+        fi
+        #run inference
+        func_inference "${python}" "${inference_py}" "${save_infer_dir}" "${LOG_PATH}" "${infer_img_dir}"
+        Count=$(($Count + 1))
+    done
+else
+    IFS="|"
+    export Count=0
+    USE_GPU_KEY=(${train_use_gpu_value})
+    for gpu in ${gpu_list[*]}; do
+        train_use_gpu=${USE_GPU_KEY[Count]}
+        Count=$(($Count + 1))
+        ips=""
+        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=${gpu}"
+            IFS="|"
+        else
+            IFS=";"
+            array=(${gpu})
+            ips=${array[0]}
+            gpu=${array[1]}
+            IFS="|"
+            env=" "
+        fi
+        for autocast in ${autocast_list[*]}; do 
+            if [ ${autocast} = "amp" ]; then
+                set_amp_config="Global.use_amp=True Global.scale_loss=1024.0 Global.use_dynamic_loss_scaling=True"
+            else
+                set_amp_config=" "
+            fi          
+            for trainer in ${trainer_list[*]}; do 
+                flag_quant=False
+                run_train=${norm_trainer}
+                run_export=${norm_export}
+                
+                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}" "${train_use_gpu}")
+                if [ ${#ips} -le 26 ];then
+                    save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
+                    nodes=1
+                else
+                    IFS=","
+                    ips_array=(${ips})
+                    IFS="|"
+                    nodes=${#ips_array[@]}
+                    save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}"
+                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} ${set_amp_config} "
+                elif [ ${#ips} -le 26 ];then  # train with multi-gpu
+                    cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}"
+                else     # train with multi-machine
+                    cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}"
+                fi
+                # run train
+                eval "unset CUDA_VISIBLE_DEVICES"
+                eval $cmd
+                echo $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 
+                
+                # 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="${save_log}/${train_model_name}"
+                    set_export_weight_path=$( echo ${set_export_weight})
+                    set_save_infer_key="${save_infer_key} ${save_infer_path}"
+                    export_cmd="${python} ${run_export}  ${set_export_weight_path} ${set_save_infer_key}"
+                    eval "$export_cmd"
+                    status_check $? "${export_cmd}" "${status_log}"
+                    
+                    #run inference
+                    eval $env
+                    save_infer_path="${save_log}"
+                    if [ ${inference_dir} != "null" ] && [ ${inference_dir} != '##' ]; then
+                        infer_model_dir="${save_infer_path}/${inference_dir}"
+                    else
+                        infer_model_dir=${save_infer_path}
+                    fi
+                    func_inference "${python}" "${inference_py}" "${infer_model_dir}" "${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
+