fix comments

tests/ocr_det_params.txt

@@ -14,7 +14,7 @@ null:null
 ##
 trainer:norm_train|pact_train
 norm_train:tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
-pact_train:deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/det_mv3_db_v2.0_train/best_accuracy
+pact_train:deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o
 fpgm_train:null
 distill_train:null
 null:null

tests/prepare.sh

@@ -25,34 +25,27 @@ function func_parser_value(){
 IFS=$'\n'
 # The training params
 model_name=$(func_parser_value "${lines[1]}")
-train_model_list=$(func_parser_value "${lines[1]}")
 
 trainer_list=$(func_parser_value "${lines[14]}")
 
-
 # MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer']
 MODE=$2
-# prepare pretrained weights and dataset 
-wget -nc -P  ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
-wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar
-cd pretrain_models && tar xf det_mv3_db_v2.0_train.tar && cd ../
 
 if [ ${MODE} = "lite_train_infer" ];then
     # pretrain lite train data
+    wget -nc -P  ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
     rm -rf ./train_data/icdar2015
     wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar
     cd ./train_data/ && tar xf icdar2015_lite.tar
     ln -s ./icdar2015_lite ./icdar2015
     cd ../
-    epoch=10
-    eval_batch_step=10
 elif [ ${MODE} = "whole_train_infer" ];then
+    wget -nc -P  ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
     rm -rf ./train_data/icdar2015
     wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar
     cd ./train_data/ && tar xf icdar2015.tar && cd ../
-    epoch=500
-    eval_batch_step=200
 elif [ ${MODE} = "whole_infer" ];then
+    wget -nc -P  ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
     rm -rf ./train_data/icdar2015
     wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_infer.tar
     cd ./train_data/ && tar xf icdar2015_infer.tar

tests/test.sh

@@ -182,10 +182,7 @@ function func_inference(){
                     if [ ${use_trt} = "False" ] && [ ${precision} != "fp32" ]; then
                         continue
                     fi
-                    if [ ${use_trt} = "False" ] && [ ${_flag_quant} = "True" ]; then
-                        continue
-                    fi
-                    if [ ${precision} != "int8" ] && [ ${_flag_quant} = "True" ]; then
+                    if [ ${use_trt} = "False" || ${precision} != "int8"]  && [ ${_flag_quant} = "True" ]; then
                         continue
                     fi
                     for batch_size in ${batch_size_list[*]}; do
@@ -208,12 +205,22 @@ function func_inference(){
     done
 }
 
-if [ ${MODE} != "infer" ]; then
+if [ ${MODE} = "infer" ]; then
+    GPUID=$3
+    if [ ${#GPUID} -le 0 ];then
+        env=" "
+    else
+        env="export CUDA_VISIBLE_DEVICES=${GPUID}"
+    fi
+    echo $env
+    #run inference
+    func_inference "${python}" "${inference_py}" "${infer_model}" "${LOG_PATH}" "${infer_img_dir}" "False"
 
-IFS="|"
-export Count=0
-USE_GPU_KEY=(${train_use_gpu_value})
-for gpu in ${gpu_list[*]}; do
+else
+    IFS="|"
+    export Count=0
+    USE_GPU_KEY=(${train_use_gpu_value})
+    for gpu in ${gpu_list[*]}; do
         use_gpu=${USE_GPU_KEY[Count]}
         Count=$(($Count + 1))
         if [ ${gpu} = "-1" ];then
@@ -270,6 +277,11 @@ for gpu in ${gpu_list[*]}; do
                 set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${use_gpu}")
                 save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
                 
+                # load pretrain from norm training if current trainer is pact or fpgm trainer
+                if [ ${trainer} = ${pact_key} ] || [ ${trainer} = ${fpgm_key} ]; then
+                    set_pretrain="${load_norm_train_model}"
+                fi
+
                 set_save_model=$(func_set_params "${save_model_key}" "${save_log}")
                 if [ ${#gpu} -le 2 ];then  # train with cpu or single gpu
                     cmd="${python} ${run_train} ${set_use_gpu}  ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} "
@@ -284,6 +296,10 @@ for gpu in ${gpu_list[*]}; do
                 status_check $? "${cmd}" "${status_log}"
 
                 set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${train_model_name}")
+                # save norm trained models to set pretrain for pact training and fpgm training 
+                if [ ${trainer} = ${trainer_norm} ]; then
+                    load_norm_train_model=${set_eval_pretrain}
+                fi
                 # run eval 
                 if [ ${eval_py} != "null" ]; then
                     set_eval_params1=$(func_set_params "${eval_key1}" "${eval_value1}")
@@ -291,7 +307,7 @@ for gpu in ${gpu_list[*]}; do
                     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}"
@@ -305,19 +321,8 @@ for gpu in ${gpu_list[*]}; do
                     func_inference "${python}" "${inference_py}" "${save_infer_path}" "${LOG_PATH}" "${train_infer_img_dir}" "${flag_quant}"
                     eval "unset CUDA_VISIBLE_DEVICES"
                 fi
-        done
-    done
-done
-
-else
-    GPUID=$3
-    if [ ${#GPUID} -le 0 ];then
-        env=" "
-    else
-        env="export CUDA_VISIBLE_DEVICES=${GPUID}"
-    fi
-    echo $env
-    #run inference
-    func_inference "${python}" "${inference_py}" "${infer_model}" "${LOG_PATH}" "${infer_img_dir}" "False"
-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