remove ResNet59_vd and MobileNetV3_large_x1_0 configs' redundant pathes in...

remove ResNet59_vd and MobileNetV3_large_x1_0 configs' redundant pathes in config file, fix test_inference_cpp.sh

test_tipc/config/MobileNetV3/MobileNetV3_large_x1_0_train_infer_python.txt

@@ -12,7 +12,7 @@ train_model_name:latest
 train_infer_img_dir:./dataset/ILSVRC2012/val
 null:null
 ##
-trainer:norm_train|pact_train|fpgm_train
+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:null
 fpgm_train:null

test_tipc/config/ResNet/ResNet50_vd_train_infer_python.txt

@@ -12,15 +12,15 @@ train_model_name:latest
 train_infer_img_dir:./dataset/ILSVRC2012/val
 null:null
 ##
-trainer:norm_train|pact_train|fpgm_train
+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:tools/train.py -c ppcls/configs/slim/ResNet50_vd_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/ResNet50_vd_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
+pact_train:null
+fpgm_train:null
 distill_train:null
 null:null
 null:null
 ##
-===========================eval_params=========================== 
+===========================eval_params===========================
 eval:tools/eval.py -c ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml
 null:null
 ##
@@ -28,10 +28,10 @@ null:null
 -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:tools/export_model.py -c ppcls/configs/slim/ResNet50_vd_quantization.yaml
-fpgm_export:tools/export_model.py -c ppcls/configs/slim/ResNet50_vd_prune.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=./inference
+kl_quant:null
 export2:null
 pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_pretrained.pdparams
 infer_model:../inference/

test_tipc/test_inference_cpp.sh

@@ -64,9 +64,9 @@ function func_shitu_cpp_inference(){
                             precison="int8"
                         fi
                         _save_log_path="${_log_path}/cpp_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_precision_${precision}_batchsize_${batch_size}.log"
-                        eval $transform_index_cmd
                         command="${generate_yaml_cmd} --type shitu --batch_size ${batch_size} --mkldnn ${use_mkldnn} --gpu ${use_gpu} --cpu_thread ${threads} --tensorrt False --precision ${precision} --data_dir ${_img_dir} --benchmark True --cls_model_dir ${cpp_infer_model_dir} --det_model_dir ${cpp_det_infer_model_dir} --gpu_id ${GPUID}"
                         eval $command
+                        eval $transform_index_cmd
                         command="${_script} > ${_save_log_path} 2>&1"
                         eval $command
                         last_status=${PIPESTATUS[0]}
@@ -88,9 +88,9 @@ function func_shitu_cpp_inference(){
                     fi
                     for batch_size in ${cpp_batch_size_list[*]}; do
                         _save_log_path="${_log_path}/cpp_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
-                        eval $transform_index_cmd
                         command="${generate_yaml_cmd} --type shitu --batch_size ${batch_size} --mkldnn False --gpu ${use_gpu} --cpu_thread 1 --tensorrt ${use_trt} --precision ${precision} --data_dir ${_img_dir} --benchmark True --cls_model_dir ${cpp_infer_model_dir} --det_model_dir ${cpp_det_infer_model_dir} --gpu_id ${GPUID}"
                         eval $command
+                        eval $transform_index_cmd
                         command="${_script} > ${_save_log_path} 2>&1"
                         eval $command
                         last_status=${PIPESTATUS[0]}