diff --git a/tests/test.sh b/tests/test.sh
index 36cf2b6f7828ee44d0464097d66130ed9b026bc9..9caab02aee9610fc6d4e424841985a56ae724f3b 100644
--- a/tests/test.sh
+++ b/tests/test.sh
@@ -138,8 +138,8 @@ infer_img_dir=$(func_parser_value "${lines[44]}")
 save_log_key=$(func_parser_key "${lines[45]}")
 benchmark_key=$(func_parser_key "${lines[46]}")
 benchmark_value=$(func_parser_value "${lines[46]}")
-infer_key2=$(func_parser_key "${lines[47]}")
-infer_value2=$(func_parser_value "${lines[47]}")
+infer_key1=$(func_parser_key "${lines[47]}")
+infer_value1=$(func_parser_value "${lines[47]}")
 
 LOG_PATH="./tests/output"
 mkdir -p ${LOG_PATH}
@@ -169,7 +169,8 @@ function func_inference(){
                         set_batchsize=$(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}")
-                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} > ${_save_log_path} 2>&1 "
+                        set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
+                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
                         eval $command
                         status_check $? "${command}" "${status_log}"
                     done
@@ -285,7 +286,8 @@ for gpu in ${gpu_list[*]}; do
             set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${train_model_name}")
             # run eval 
             if [ ${eval_py} != "null" ]; then
-                eval_cmd="${python} ${eval_py} ${set_eval_pretrain} ${set_use_gpu}" 
+                set_eval_params1=$(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}" "${status_log}"
             fi
@@ -318,3 +320,4 @@ else
     #run inference
     func_inference "${python}" "${inference_py}" "${infer_model}" "${LOG_PATH}" "${infer_img_dir}" "False"
 fi
+
diff --git a/tools/infer/predict_det.py b/tools/infer/predict_det.py
index 568381b1f7e7e825b174bbcfa43af3e55f683135..3de00d83a8f9f55af9b89d5d2cd5c877399c5930 100755
--- a/tools/infer/predict_det.py
+++ b/tools/infer/predict_det.py
@@ -114,7 +114,7 @@ class TextDetector(object):
                 time_keys=[
                     'preprocess_time', 'inference_time', 'postprocess_time'
                 ],
-                warmup=10,
+                warmup=2,
                 logger=logger)
 
     def order_points_clockwise(self, pts):
@@ -237,7 +237,7 @@ if __name__ == "__main__":
 
     if args.warmup:
         img = np.random.uniform(0, 255, [640, 640, 3]).astype(np.uint8)
-        for i in range(10):
+        for i in range(2):
             res = text_detector(img)
 
     if not os.path.exists(draw_img_save):
diff --git a/tools/infer/predict_rec.py b/tools/infer/predict_rec.py
index bc9f713aeafb9977c60fe65bea56fbe2b395efd5..bb4a31706471b9b1745519ac9f390d01b60d5d44 100755
--- a/tools/infer/predict_rec.py
+++ b/tools/infer/predict_rec.py
@@ -73,7 +73,7 @@ class TextRecognizer(object):
                 model_precision=args.precision,
                 batch_size=args.rec_batch_num,
                 data_shape="dynamic",
-                save_path=args.save_log_path,
+                save_path=None,  #args.save_log_path,
                 inference_config=self.config,
                 pids=pid,
                 process_name=None,
@@ -81,7 +81,8 @@ class TextRecognizer(object):
                 time_keys=[
                     'preprocess_time', 'inference_time', 'postprocess_time'
                 ],
-                warmup=10)
+                warmup=2,
+                logger=logger)
 
     def resize_norm_img(self, img, max_wh_ratio):
         imgC, imgH, imgW = self.rec_image_shape
@@ -272,10 +273,10 @@ def main(args):
     valid_image_file_list = []
     img_list = []
 
-    # warmup 10 times
+    # warmup 2 times
     if args.warmup:
         img = np.random.uniform(0, 255, [32, 320, 3]).astype(np.uint8)
-        for i in range(10):
+        for i in range(2):
             res = text_recognizer([img])
 
     for image_file in image_file_list: