Merge pull request #2098 from HydrogenSulfate/add_main_KL

Add main model's KL chain

test_tipc/config/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5_train_ptq_infer_python.txt

+===========================train_params===========================
+model_name:GeneralRecognition_PPLCNet_x2_5
+python:python3.7
+gpu_list:0
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=100
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:pact_train
+norm_train:null
+pact_train:tools/train.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:null
+quant_export:tools/export_model.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml
+fpgm_export:null
+distill_export:null
+kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml -o Global.save_inference_dir=./general_PPLCNet_x2_5_lite_v1.0_infer
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_v1.0_infer.tar
+infer_model:./general_PPLCNet_x2_5_lite_v1.0_infer/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_rec.py -c configs/inference_rec.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:False
+-o Global.cpu_num_threads:1
+-o Global.batch_size:1
+-o Global.use_tensorrt:False
+-o Global.use_fp16:False
+-o Global.rec_inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/Aliproduct/demo_test/
+-o Global.save_log_path:null
+-o Global.benchmark:False
+null:null
+null:null
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[3,224,224]}]
\ No newline at end of file

test_tipc/config/MobileNetV3/MobileNetV3_large_x1_0_train_ptq_infer_python.txt

+===========================train_params===========================
+model_name:MobileNetV3_large_x1_0
+python:python3.7
+gpu_list:0
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
+pact_train:tools/train.py -c ppcls/configs/slim/MobileNetV3_large_x1_0_quantization.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
+fpgm_train:tools/train.py -c ppcls/configs/slim/MobileNetV3_large_x1_0_prune.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
+distill_train:null
+to_static_train:-o Global.to_static=True
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml
+quant_export:tools/export_model.py -c ppcls/configs/slim/MobileNetV3_large_x1_0_quantization.yaml
+fpgm_export:tools/export_model.py -c ppcls/configs/slim/MobileNetV3_large_x1_0_prune.yaml
+distill_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=./MobileNetV3_large_x1_0_infer
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x1_0_infer.tar
+infer_model:./MobileNetV3_large_x1_0_infer/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:False
+-o Global.cpu_num_threads:1
+-o Global.batch_size:1
+-o Global.use_tensorrt:False
+-o Global.use_fp16:False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+-o Global.save_log_path:null
+-o Global.benchmark:False
+null:null
+null:null
+===========================train_benchmark_params==========================
+batch_size:256|640
+fp_items:fp32
+epoch:1
+--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
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[3,224,224]}]
\ No newline at end of file

test_tipc/config/PPHGNet/PPHGNet_small_train_ptq_infer_python.txt

+===========================train_params===========================
+model_name:PPHGNet_small
+python:python3.7
+gpu_list:0
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_small.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_small.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_small.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_small.yaml -o Global.save_inference_dir=./PPHGNet_small_infer
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_small_infer.tar
+infer_model:./PPHGNet_small_infer/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=236
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:False
+-o Global.cpu_num_threads:1
+-o Global.batch_size:1
+-o Global.use_tensorrt:False
+-o Global.use_fp16:False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+-o Global.save_log_path:null
+-o Global.benchmark:False
+null:null
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[3,224,224]}]

test_tipc/config/PPLCNet/PPLCNet_x1_0_train_ptq_infer_python.txt

+===========================train_params===========================
+model_name:PPLCNet_x1_0
+python:python3.7
+gpu_list:0
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml -o Global.save_inference_dir=./PPLCNet_x1_0_infer
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x1_0_infer.tar
+infer_model:./PPLCNet_x1_0_infer/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:False
+-o Global.cpu_num_threads:1
+-o Global.batch_size:1
+-o Global.use_tensorrt:False
+-o Global.use_fp16:False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+-o Global.save_log_path:null
+-o Global.benchmark:False
+null:null
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[3,224,224]}]
\ No newline at end of file

test_tipc/config/PPLCNetV2/PPLCNetV2_base_train_ptq_infer_python.txt

+===========================train_params===========================
+model_name:PPLCNetV2_base
+python:python3.7
+gpu_list:0
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.first_bs:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_base.yaml -o Global.seed=1234 -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params===========================
+eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_base.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_base.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_base.yaml -o Global.save_inference_dir=./PPLCNetV2_base_infer
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNetV2_base_infer.tar
+infer_model:./PPLCNetV2_base_infer/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:False
+-o Global.cpu_num_threads:1
+-o Global.batch_size:1
+-o Global.use_tensorrt:False
+-o Global.use_fp16:False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+-o Global.save_log_path:null
+-o Global.benchmark:False
+null:null
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[3,224,224]}]

test_tipc/config/ResNet/ResNet50_vd_train_ptq_infer_python.txt

+===========================train_params===========================
+model_name:ResNet50_vd
+python:python3.7
+gpu_list:0
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=200
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
+pact_train:null
+fpgm_train:null
+distill_train:null
+to_static_train:-o Global.to_static=True
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml -o Global.save_inference_dir=./ResNet50_vd_infer
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vd_infer.tar
+infer_model:./ResNet50_vd_infer/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:False
+-o Global.cpu_num_threads:1
+-o Global.batch_size:1
+-o Global.use_tensorrt:False
+-o Global.use_fp16:False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+-o Global.save_log_path:null
+-o Global.benchmark:False
+null:null
+null:null
+===========================train_benchmark_params==========================
+batch_size:128
+fp_items:fp32
+epoch:1
+--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
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[3,224,224]}]
\ No newline at end of file

test_tipc/config/SwinTransformer/SwinTransformer_tiny_patch4_window7_224_train_ptq_infer_python.txt

+===========================train_params===========================
+model_name:SwinTransformer_tiny_patch4_window7_224
+python:python3.7
+gpu_list:0
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_tiny_patch4_window7_224.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_tiny_patch4_window7_224.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_tiny_patch4_window7_224.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_tiny_patch4_window7_224.yaml -o Global.save_inference_dir=./SwinTransformer_tiny_patch4_window7_224_infer
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_tiny_patch4_window7_224_infer.tar
+infer_model:./SwinTransformer_tiny_patch4_window7_224_infer/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:False
+-o Global.cpu_num_threads:1
+-o Global.batch_size:1
+-o Global.use_tensorrt:False
+-o Global.use_fp16:False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+-o Global.save_log_path:null
+-o Global.benchmark:False
+null:null
+null:null
+===========================train_benchmark_params==========================
+batch_size:64|104
+fp_items:fp32
+epoch:1
+--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
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[3,224,224]}]
\ No newline at end of file

test_tipc/prepare.sh

@@ -141,7 +141,7 @@ model_name=$(func_parser_value "${lines[1]}")
 model_url_value=$(func_parser_value "${lines[35]}")
 model_url_key=$(func_parser_key "${lines[35]}")
 
-if [[ $FILENAME == *GeneralRecognition* ]]; then
+if [[ $model_name == *ShiTu* ]]; then
     cd dataset
     rm -rf Aliproduct
     rm -rf train_reg_all_data.txt
@@ -176,22 +176,39 @@ if [[ ${MODE} = "lite_train_lite_infer" ]] || [[ ${MODE} = "lite_train_whole_inf
     mv val.txt val_list.txt
     cp -r train/* val/
     cd ../../
-elif [[ ${MODE} = "whole_infer" ]] || [[ ${MODE} = "klquant_whole_infer" ]]; then
+elif [[ ${MODE} = "whole_infer" ]]; then
     # download data
-    cd dataset
-    rm -rf ILSVRC2012
-    wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/data/whole_chain/whole_chain_infer.tar
-    tar xf whole_chain_infer.tar
-    ln -s whole_chain_infer ILSVRC2012
-    cd ILSVRC2012
-    mv val.txt val_list.txt
-    ln -s val_list.txt train_list.txt
-    cd ../../
+    if [[ ${model_name} =~ "GeneralRecognition" ]]; then
+        cd dataset
+        rm -rf Aliproduct
+        rm -rf train_reg_all_data.txt
+        rm -rf demo_train
+        wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/data/whole_chain/tipc_shitu_demo_data.tar --no-check-certificate
+        tar -xf tipc_shitu_demo_data.tar
+        ln -s tipc_shitu_demo_data Aliproduct
+        ln -s tipc_shitu_demo_data/demo_train.txt train_reg_all_data.txt
+        ln -s tipc_shitu_demo_data/demo_train demo_train
+        cd tipc_shitu_demo_data
+        ln -s demo_test.txt val_list.txt
+        cd ../../
+    else
+        cd dataset
+        rm -rf ILSVRC2012
+        wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/data/whole_chain/whole_chain_infer.tar
+        tar xf whole_chain_infer.tar
+        ln -s whole_chain_infer ILSVRC2012
+        cd ILSVRC2012
+        mv val.txt val_list.txt
+        ln -s val_list.txt train_list.txt
+        cd ../../
+    fi
     # download inference or pretrained model
     eval "wget -nc $model_url_value"
-    if [[ $model_url_key == *inference* ]]; then
-        rm -rf inference
-        tar xf "${model_name}_infer.tar"
+    if [[ ${model_url_value} =~ ".tar" ]]; then
+        tar_name=$(func_get_url_file_name "${model_url_value}")
+        echo $tar_name
+        rm -rf {tar_name}
+        tar xf ${tar_name}
     fi
     if [[ $model_name == "SwinTransformer_large_patch4_window7_224" || $model_name == "SwinTransformer_large_patch4_window12_384" ]]; then
         cmd="mv ${model_name}_22kto1k_pretrained.pdparams ${model_name}_pretrained.pdparams"

test_tipc/test_train_inference_python.sh

@@ -88,17 +88,17 @@ benchmark_value=$(func_parser_value "${lines[49]}")
 infer_key1=$(func_parser_key "${lines[50]}")
 infer_value1=$(func_parser_value "${lines[50]}")
 if [ ! $epoch_num ]; then
-  epoch_num=2
+    epoch_num=2
 fi
 if [[ $MODE = 'benchmark_train' ]]; then
-  epoch_num=1
+    epoch_num=1
 fi
 
-LOG_PATH="./test_tipc/output/${model_name}"
+LOG_PATH="./test_tipc/output/${model_name}/${MODE}"
 mkdir -p ${LOG_PATH}
 status_log="${LOG_PATH}/results_python.log"
 
-function func_inference(){
+function func_inference() {
     IFS='|'
     _python=$1
     _script=$2
@@ -110,9 +110,6 @@ function func_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 ${batch_size_list[*]}; do
                         _save_log_path="${_log_path}/infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}.log"
@@ -136,9 +133,6 @@ function func_inference(){
                     if [ ${precision} = "True" ] && [ ${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}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
                         set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
@@ -161,51 +155,23 @@ function func_inference(){
     done
 }
 
-if [[ ${MODE} = "whole_infer" ]] || [[ ${MODE} = "klquant_whole_infer" ]]; then
-   IFS="|"
-   infer_export_flag=(${infer_export_flag})
-   if [ ${infer_export_flag} != "null" ]  && [ ${infer_export_flag} != "False" ]; then
-	rm -rf ${infer_model_dir_list/..\//}
-	export_cmd="${python} ${norm_export} -o Global.pretrained_model=${model_name}_pretrained -o Global.save_inference_dir=${infer_model_dir_list/..\//}"
-	eval $export_cmd
-   fi
-fi
 
 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
-    cd deploy
-    for infer_model in ${infer_model_dir_list[*]}; do
-        #run inference
-        is_quant=${infer_quant_flag[Count]}
-        echo "is_quant: ${is_quant}"
-        func_inference "${python}" "${inference_py}" "${infer_model}" "../${LOG_PATH}" "${infer_img_dir}" ${is_quant}
-        Count=$(($Count + 1))
-    done
-    cd ..
-
-elif [[ ${MODE} = "klquant_whole_infer" ]]; then
     # for kl_quant
     if [ ${kl_quant_cmd_value} != "null" ] && [ ${kl_quant_cmd_value} != "False" ]; then
-	echo "kl_quant"
-	command="${python} ${kl_quant_cmd_value}"
-	eval $command
-	last_status=${PIPESTATUS[0]}
-	status_check $last_status "${command}" "${status_log}" "${model_name}"
-	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}" "${infer_model_dir_list}/quant_post_static_model" "../${LOG_PATH}" "${infer_img_dir}" ${is_quant}
-	cd ..
+        echo "kl_quant"
+        command="${python} ${kl_quant_cmd_value}"
+        echo ${command}
+        eval $command
+        last_status=${PIPESTATUS[0]}
+        status_check $last_status "${command}" "${status_log}" "${model_name}"
+        cd ${infer_model_dir_list}/quant_post_static_model
+        ln -s __model__ inference.pdmodel
+        ln -s __params__ inference.pdiparams
+        cd ../../deploy
+        is_quant=True
+        func_inference "${python}" "${inference_py}" "../${infer_model_dir_list}/quant_post_static_model" "../${LOG_PATH}" "${infer_img_dir}" ${is_quant}
+        cd ..
     fi
 else
     IFS="|"
@@ -215,12 +181,12 @@ else
         train_use_gpu=${USE_GPU_KEY[Count]}
         Count=$(($Count + 1))
         ips=""
-        if [ ${gpu} = "-1" ];then
+        if [ ${gpu} = "-1" ]; then
             env=""
-        elif [ ${#gpu} -le 1 ];then
+        elif [ ${#gpu} -le 1 ]; then
             env="export CUDA_VISIBLE_DEVICES=${gpu}"
             eval ${env}
-        elif [ ${#gpu} -le 15 ];then
+        elif [ ${#gpu} -le 15 ]; then
             IFS=","
             array=(${gpu})
             env="export CUDA_VISIBLE_DEVICES=${array[0]}"
@@ -270,7 +236,7 @@ else
                 set_batchsize=$(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_value}")
-                if [ ${#ips} -le 15 ];then
+                if [ ${#ips} -le 15 ]; then
                     # if length of ips >= 15, then it is seen as multi-machine
                     # 15 is the min length of ips info for multi-machine: 0.0.0.0,0.0.0.0
                     save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
@@ -289,26 +255,26 @@ else
                 # fi
 
                 set_save_model=$(func_set_params "${save_model_key}" "${save_log}")
-                if [ ${#gpu} -le 2 ];then  # train with cpu or single gpu
+                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} "
-                elif [ ${#ips} -le 15 ];then  # train with multi-gpu
+                elif [ ${#ips} -le 15 ]; then # train with multi-gpu
                     cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1}"
-                else     # train with multi-machine
+                else # train with multi-machine
                     cmd="${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}"
                 fi
                 # run train
-		eval "unset CUDA_VISIBLE_DEVICES"
-		# export FLAGS_cudnn_deterministic=True
-		sleep 5
+                eval "unset CUDA_VISIBLE_DEVICES"
+                # export FLAGS_cudnn_deterministic=True
+                sleep 5
                 eval $cmd
                 status_check $? "${cmd}" "${status_log}" "${model_name}"
                 sleep 5
 
-		if [[ $FILENAME == *GeneralRecognition* ]]; then
-		    set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/RecModel/${train_model_name}")
-		else
+                if [[ $FILENAME == *GeneralRecognition* ]]; then
+                    set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/RecModel/${train_model_name}")
+                else
                     set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${model_name}/${train_model_name}")
-		fi
+                fi
                 # 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}
@@ -325,11 +291,11 @@ else
                 if [ ${run_export} != "null" ]; then
                     # run export model
                     save_infer_path="${save_log}"
-		    if [[ $FILENAME == *GeneralRecognition* ]]; then
-		        set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/RecModel/${train_model_name}")
-		    else
-		        set_export_weight=$(func_set_params "${export_weight}" "${save_log}/${model_name}/${train_model_name}")
-		    fi
+                    if [[ $FILENAME == *GeneralRecognition* ]]; then
+                        set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/RecModel/${train_model_name}")
+                    else
+                        set_export_weight=$(func_set_params "${export_weight}" "${save_log}/${model_name}/${train_model_name}")
+                    fi
                     set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_path}")
                     export_cmd="${python} ${run_export} ${set_export_weight} ${set_save_infer_key}"
                     eval $export_cmd
@@ -338,12 +304,12 @@ else
                     #run inference
                     eval $env
                     save_infer_path="${save_log}"
-		    cd deploy
+                    cd deploy
                     func_inference "${python}" "${inference_py}" "../${save_infer_path}" "../${LOG_PATH}" "${infer_img_dir}" "${flag_quant}"
-		    cd ..
+                    cd ..
                 fi
                 eval "unset CUDA_VISIBLE_DEVICES"
-            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
+            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