fix ci and output

test/ocr_det_params.txt

 model_name:ocr_det
 python:python3.7
-gpu_list:-1|0|0,1
-Global.auto_cast:False|True
+gpu_list:0
+Global.auto_cast:False
 Global.epoch_num:10
 Global.save_model_dir:./output/
 Global.save_inference_dir:./output/
@@ -9,7 +9,7 @@ Train.loader.batch_size_per_card:
 Global.use_gpu
 Global.pretrained_model
 
-trainer:norm|pact|fpgm
+trainer:norm|pact
 norm_train:tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
 quant_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

test/test.sh

@@ -110,7 +110,7 @@ function func_inference(){
                 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_${batch_size}"
-                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${cpu_threads_key}=${threads} ${model_dir_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir}  ${save_log_key}=${_save_log_path}  --benchmark=True"
+                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${cpu_threads_key}=${threads} ${model_dir_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir}  ${save_log_key}=${_save_log_path}  --benchmark=True "
                         eval $command
                         status_check $? "${command}" "${status_log}"
                     done
@@ -124,7 +124,7 @@ function func_inference(){
                     fi
                     for batch_size in ${batch_size_list[*]}; do
                         _save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}"
-                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_trt_key}=${use_trt} ${precision_key}=${precision} ${model_dir_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir}  ${save_log_key}=${_save_log_path}  --benchmark=True"
+                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_trt_key}=${use_trt} ${precision_key}=${precision} ${model_dir_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir}  ${save_log_key}=${_save_log_path}  --benchmark=True "
                         eval $command
                         status_check $? "${command}" "${status_log}"
                     done

tools/infer/predict_det.py

@@ -106,7 +106,7 @@ class TextDetector(object):
                 model_precision=args.precision,
                 batch_size=1,
                 data_shape="dynamic",
-                save_path="./output/auto_log.lpg",
+                save_path=args.save_log_path,
                 inference_config=self.config,
                 pids=pid,
                 process_name=None,
@@ -174,7 +174,7 @@ class TextDetector(object):
         data = {'image': img}
 
         st = time.time()
-        
+
         if args.benchmark:
             self.autolog.times.start()
 
@@ -262,7 +262,6 @@ if __name__ == "__main__":
                                 "det_res_{}".format(img_name_pure))
         cv2.imwrite(img_path, src_im)
         logger.info("The visualized image saved in {}".format(img_path))
-    
+
     if args.benchmark:
         text_detector.autolog.report()
-