support cpu+mkldnn+quant_model infer

tests/params.txt

-===========================train_params=========================== 
-model_name:ocr_det
-python:python3.7
-gpu_list:0|0,1
-Global.auto_cast:null
-Global.epoch_num:2
-Global.save_model_dir:./output/
-Train.loader.batch_size_per_card:2
-Global.use_gpu:
-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|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
-fpgm_train:null
-distill_train:null
-null:null
-null:null
-##
-===========================eval_params=========================== 
-eval:null
-null:null
-##
-===========================infer_params===========================
-Global.save_inference_dir:./output/
-Global.pretrained_model:
-norm_export:tools/export_model.py -c configs/det/det_mv3_db.yml -o 
-quant_export:deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o 
-fpgm_export:deploy/slim/prune/export_prune_model.py
-distill_export:null
-null:null
-null:null
-##
-inference:tools/infer/predict_det.py
---use_gpu:True|False
---enable_mkldnn:True|False
---cpu_threads:1|6
---rec_batch_num:1
---use_tensorrt:True|False
---precision:fp32|fp16|int8
---det_model_dir:./inference/ch_ppocr_mobile_v2.0_det_infer/
---image_dir:./inference/ch_det_data_50/all-sum-510/
---save_log_path:null
---benchmark:True
-null:null

tests/test.sh

@@ -132,11 +132,11 @@ function func_inference(){
     _flag_quant=$6
     # inference 
     for use_gpu in ${use_gpu_list[*]}; do
-        if [ ${use_gpu} = "False" ] && [ ${_flag_quant} = "True" ]; then
-            continue
-        fi
         if [ ${use_gpu} = "False" ]; 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"