From 2f1892855b47923359bfbadf999ac8dd04fe72b5 Mon Sep 17 00:00:00 2001
From: dongshuilong <dongshuilong@baidu.com>
Date: Wed, 29 Sep 2021 04:26:51 +0000
Subject: [PATCH] kl_quant for whole_chain and add readme

---
 tests/README.md                         | 70 +++++++++++++++++++++++++
 tests/config/MobileNetV3_large_x1_0.txt |  2 +-
 tests/config/ResNet50_vd.txt            |  2 +-
 tests/prepare.sh                        |  1 +
 tests/test.sh                           | 27 ++++++----
 5 files changed, 89 insertions(+), 13 deletions(-)
 create mode 100644 tests/README.md

diff --git a/tests/README.md b/tests/README.md
new file mode 100644
index 00000000..55b3690b
--- /dev/null
+++ b/tests/README.md
@@ -0,0 +1,70 @@
+
+# 从训练到推理部署工具链测试方法介绍
+
+test.sh和config文件夹下的txt文件配合使用，完成Clas模型从训练到预测的流程测试。
+
+# 安装依赖
+- 安装PaddlePaddle >= 2.0
+- 安装PaddleClass依赖
+    ```
+    pip3 install  -r ../requirements.txt
+    ```
+- 安装autolog
+    ```
+    git clone https://github.com/LDOUBLEV/AutoLog
+    cd AutoLog
+    pip3 install -r requirements.txt
+    python3 setup.py bdist_wheel
+    pip3 install ./dist/auto_log-1.0.0-py3-none-any.whl
+    cd ../
+    ```
+
+# 目录介绍
+
+```bash
+tests/
+├── config                        # 测试模型的参数配置文件
+|   |--- *.txt
+└── prepare.sh                    # 完成test.sh运行所需要的数据和模型下载
+└── test.sh                       # 测试主程序
+```
+
+# 使用方法
+
+test.sh包四种运行模式，每种模式的运行数据不同，分别用于测试速度和精度，分别是：
+
+- 模式1：lite_train_infer，使用少量数据训练，用于快速验证训练到预测的走通流程，不验证精度和速度；
+```shell
+bash tests/prepare.sh ./tests/config/ResNet50_vd.txt 'lite_train_infer'
+bash tests/test.sh ./tests/config/ResNet50_vd.txt 'lite_train_infer'
+```  
+
+- 模式2：whole_infer，使用少量数据训练，一定量数据预测，用于验证训练后的模型执行预测，预测速度是否合理；
+```shell
+bash tests/prepare.sh ./tests/config/ResNet50_vd.txt 'whole_infer'
+bash tests/test.sh ./tests/config/ResNet50_vd.txt 'whole_infer'
+```  
+
+- 模式3：infer 不训练，全量数据预测，走通开源模型评估、动转静，检查inference model预测时间和精度;
+```shell
+bash tests/prepare.sh ./tests/config/ResNet50_vd.txt 'infer'
+# 用法1:
+bash tests/test.sh ./tests/config/ResNet50_vd.txt 'infer'
+```  
+
+需注意的是，模型的离线量化需使用`infer`模式进行测试
+
+- 模式4：whole_train_infer , CE： 全量数据训练，全量数据预测，验证模型训练精度，预测精度，预测速度；
+```shell
+bash tests/prepare.sh ./tests/config/ResNet50_vd.txt 'whole_train_infer'
+bash tests/test.sh ./tests/config/ResNet50_vd.txt 'whole_train_infer'
+```  
+
+- 模式5：cpp_infer , CE： 验证inference model的c++预测是否走通；
+```shell
+bash tests/prepare.sh ./tests/config/ResNet50_vd.txt 'cpp_infer'
+bash tests/test.sh ./tests/config/ResNet50_vd.txt 'cpp_infer'
+```  
+
+# 日志输出
+最终在```tests/output```目录下生成.log后缀的日志文件
diff --git a/tests/config/MobileNetV3_large_x1_0.txt b/tests/config/MobileNetV3_large_x1_0.txt
index 0afd2149..f5add2e9 100644
--- a/tests/config/MobileNetV3_large_x1_0.txt
+++ b/tests/config/MobileNetV3_large_x1_0.txt
@@ -35,7 +35,7 @@ export1:null
 export2:null
 inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/whole_chain/MobileNetV3_large_x1_0_inference.tar
 infer_model:../inference/
-infer_export:null
+kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml -o Global.save_inference_dir=./inference
 infer_quant:Fasle
 inference:python/predict_cls.py -c configs/inference_cls.yaml
 -o Global.use_gpu:True|False
diff --git a/tests/config/ResNet50_vd.txt b/tests/config/ResNet50_vd.txt
index 445609bf..aa7f9ac1 100644
--- a/tests/config/ResNet50_vd.txt
+++ b/tests/config/ResNet50_vd.txt
@@ -35,7 +35,7 @@ export1:null
 export2:null
 infer_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/whole_chain/ResNet50_vd_inference.tar
 infer_model:../inference/
-infer_export:null
+kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml -o Global.save_inference_dir=./inference
 infer_quant:Fasle
 inference:python/predict_cls.py -c configs/inference_cls.yaml
 -o Global.use_gpu:True|False
diff --git a/tests/prepare.sh b/tests/prepare.sh
index 35782fd0..6f9701a1 100644
--- a/tests/prepare.sh
+++ b/tests/prepare.sh
@@ -42,6 +42,7 @@ elif [ ${MODE} = "infer" ] || [ ${MODE} = "cpp_infer" ];then
     ln -s whole_chain_infer ILSVRC2012
     cd ILSVRC2012 
     mv val.txt val_list.txt
+    ln -s val_list.txt train_list.txt
     cd ../../
     # download inference model
     eval "wget -nc $inference_model_url"
diff --git a/tests/test.sh b/tests/test.sh
index b573bf48..b3699126 100644
--- a/tests/test.sh
+++ b/tests/test.sh
@@ -299,17 +299,6 @@ if [ ${MODE} = "infer" ]; then
     infer_quant_flag=(${infer_is_quant})
     cd deploy
     for infer_model in ${infer_model_dir_list[*]}; do
-        # run export
-        if [ ${infer_run_exports[Count]} != "null" ];then
-            set_export_weight=$(func_set_params "${export_weight}" "${infer_model}")
-            set_save_infer_key=$(func_set_params "${save_infer_key}" "${infer_model}")
-            export_cmd="${python} ${norm_export} ${set_export_weight} ${set_save_infer_key}"
-            eval $export_cmd
-            status_export=$?
-            if [ ${status_export} = 0 ];then
-                status_check $status_export "${export_cmd}" "../${status_log}"
-            fi
-        fi
         #run inference
         is_quant=${infer_quant_flag[Count]}
         echo "is_quant: ${is_quant}"
@@ -317,6 +306,22 @@ if [ ${MODE} = "infer" ]; then
         Count=$(($Count + 1))
     done
     cd ..
+
+    # for kl_quant
+    echo "kl_quant"
+    if [ ${infer_run_exports} ]; then
+	command="${python} ${infer_run_exports}"
+	eval $command
+	last_status=${PIPESTATUS[0]}
+	status_check $last_status "${command}" "${status_log}"
+	cd inference/quant_post_static_model
+	ln -s __model__ inference.pdmodel
+	ln -s __params__ inference.pdiparams
+	cd ../../deploy
+	is_quant=True
+        func_inference "${python}" "${inference_py}" "${infer_model}/quant_post_static_model" "../${LOG_PATH}" "${infer_img_dir}" ${is_quant}
+	cd ..
+    fi
 elif [ ${MODE} = "cpp_infer" ]; then
     cd deploy
     func_cpp_inference "./cpp/build/clas_system" "../${LOG_PATH}" "${infer_img_dir}"
-- 
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