fix nonfinte

test_tipc/configs/ch_PP-OCRv2_det_PACT/train_infer_python.txt

@@ -26,7 +26,7 @@ null:null
 ##
 ===========================infer_params===========================
 Global.save_inference_dir:./output/
-Global.pretrained_model:
+Global.checkpoints:
 norm_export:null
 quant_export:deploy/slim/quantization/export_model.py -c configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml -o 
 fpgm_export: 

test_tipc/configs/ch_ppocr_mobile_v2.0_det_PACT/train_infer_python.txt

@@ -4,7 +4,7 @@ python:python3.7
 gpu_list:0|0,1
 Global.use_gpu:True|True
 Global.auto_cast:null
-Global.epoch_num:lite_train_lite_infer=5|whole_train_whole_infer=300
+Global.epoch_num:lite_train_lite_infer=20|whole_train_whole_infer=300
 Global.save_model_dir:./output/
 Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
 Global.pretrained_model:null
@@ -26,7 +26,7 @@ null:null
 ##
 ===========================infer_params===========================
 Global.save_inference_dir:./output/
-Global.pretrained_model:
+Global.checkpoints:
 norm_export:null
 quant_export:deploy/slim/quantization/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o 
 fpgm_export:null

test_tipc/test_train_inference_python.sh

@@ -322,10 +322,6 @@ else
                     save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}"
                 fi
 
-                # load pretrain from norm training if current trainer is pact or fpgm trainer
-                if ([ ${trainer} = ${pact_key} ] || [ ${trainer} = ${fpgm_key} ]) && [ ${nodes} -le 1 ]; then
-                    set_pretrain="${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
@@ -341,10 +337,7 @@ else
                 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} ] && [ ${nodes} -le 1 ]; then
-                    load_norm_train_model=${set_eval_pretrain}
-                fi
+
                 # run eval 
                 if [ ${eval_py} != "null" ]; then
                     set_eval_params1=$(func_set_params "${eval_key1}" "${eval_value1}")