add rec model for tipc

deploy/python/predict_rec.py

@@ -35,6 +35,24 @@ class RecPredictor(Predictor):
         self.preprocess_ops = create_operators(config["RecPreProcess"][
             "transform_ops"])
         self.postprocess = build_postprocess(config["RecPostProcess"])
+        self.benchmark = config["Global"].get("benchmark", False)
+
+        import auto_log
+        pid = os.getpid()
+        self.auto_logger = auto_log.AutoLogger(
+            model_name=config["Global"].get("model_name", "rec"),
+            model_precision='fp16' if config["Global"]["use_fp16"] else 'fp32',
+            batch_size=config["Global"].get("batch_size", 1),
+            data_shape=[3, 224, 224],
+            save_path=config["Global"].get("save_log_path", "./auto_log.log"),
+            inference_config=self.config,
+            pids=pid,
+            process_name=None,
+            gpu_ids=None,
+            time_keys=[
+                'preprocess_time', 'inference_time', 'postprocess_time'
+            ],
+            warmup=2)
 
     def predict(self, images, feature_normalize=True):
         input_names = self.paddle_predictor.get_input_names()
@@ -44,16 +62,22 @@ class RecPredictor(Predictor):
         output_tensor = self.paddle_predictor.get_output_handle(output_names[
             0])
 
+        if self.benchmark:
+            self.auto_logger.times.start()
         if not isinstance(images, (list, )):
             images = [images]
         for idx in range(len(images)):
             for ops in self.preprocess_ops:
                 images[idx] = ops(images[idx])
         image = np.array(images)
+        if self.benchmark:
+            self.auto_logger.times.stamp()
 
         input_tensor.copy_from_cpu(image)
         self.paddle_predictor.run()
         batch_output = output_tensor.copy_to_cpu()
+        if self.benchmark:
+            self.auto_logger.times.stamp()
 
         if feature_normalize:
             feas_norm = np.sqrt(
@@ -62,6 +86,9 @@ class RecPredictor(Predictor):
 
         if self.postprocess is not None:
             batch_output = self.postprocess(batch_output)
+
+        if self.benchmark:
+            self.auto_logger.times.end(stamp=True)
         return batch_output
 
 
@@ -85,16 +112,19 @@ def main(config):
             batch_names.append(img_name)
             cnt += 1
 
-        if cnt % config["Global"]["batch_size"] == 0 or (idx + 1) == len(image_list):
-            if len(batch_imgs) == 0: 
+        if cnt % config["Global"]["batch_size"] == 0 or (idx + 1
+                                                         ) == len(image_list):
+            if len(batch_imgs) == 0:
                 continue
-                
+
             batch_results = rec_predictor.predict(batch_imgs)
             for number, result_dict in enumerate(batch_results):
                 filename = batch_names[number]
                 print("{}:\t{}".format(filename, result_dict))
             batch_imgs = []
             batch_names = []
+    if rec_predictor.benchmark:
+        rec_predictor.auto_logger.report()
 
     return
 

test_tipc/config/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5_train_infer_python.txt

+===========================train_params===========================
+model_name:GeneralRecognition_PPLCNet_x2_5
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.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:tools/eval.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/general_PPLCNet_x2_5_pretrained_v1.0.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_rec.py -c configs/inference_rec.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.rec_inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/Aliproduct/demo_test/
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/prepare.sh

@@ -37,6 +37,22 @@ model_name=$(func_parser_value "${lines[1]}")
 model_url_value=$(func_parser_value "${lines[35]}")
 model_url_key=$(func_parser_key "${lines[35]}")
 
+if [[ $FILENAME == *GeneralRecognition* ]];then
+   cd dataset
+   rm -rf Aliproduct
+   rm -rf train_reg_all_data.txt
+   rm -rf demo_train
+   wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/data/whole_chain/tipc_shitu_demo_data.tar
+   tar -xf tipc_shitu_demo_data.tar
+   ln -s tipc_shitu_demo_data Aliproduct
+   ln -s tipc_shitu_demo_data/demo_train.txt train_reg_all_data.txt
+   ln -s tipc_shitu_demo_data/demo_train demo_train
+   cd tipc_shitu_demo_data
+   ln -s demo_test.txt val_list.txt
+   cd ../../
+   exit 0
+fi
+
 if [ ${MODE} = "lite_train_lite_infer" ] || [ ${MODE} = "lite_train_whole_infer" ];then
     # pretrain lite train data
     cd dataset

test_tipc/test_train_inference_python.sh

@@ -291,8 +291,12 @@ else
 		export FLAGS_cudnn_deterministic=True
                 eval $cmd
                 status_check $? "${cmd}" "${status_log}"
-
-                set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${$model_name}/${train_model_name}")
+		
+		if [[ $FILENAME == *GeneralRecognition* ]]; then
+		    set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/RecModel/${train_model_name}")
+		else
+                    set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${model_name}/${train_model_name}")
+		fi
                 # save norm trained models to set pretrain for pact training and fpgm training 
                 if [ ${trainer} = ${trainer_norm} ]; then
                     load_norm_train_model=${set_eval_pretrain}
@@ -308,7 +312,11 @@ else
                 if [ ${run_export} != "null" ]; then 
                     # run export model
                     save_infer_path="${save_log}"
-                    set_export_weight=$(func_set_params "${export_weight}" "${save_log}/${model_name}/${train_model_name}")
+		    if [[ $FILENAME == *GeneralRecognition* ]]; then
+		        set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/RecModel/${train_model_name}")
+		    else
+		        set_export_weight=$(func_set_params "${export_weight}" "${save_log}/${model_name}/${train_model_name}")
+		    fi
                     set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_path}")
                     export_cmd="${python} ${run_export} ${set_export_weight} ${set_save_infer_key}"
                     eval $export_cmd