add rec model for tipc
dongshuilong
parent
475a60a37c
commit
a3465a6591
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@ -35,6 +35,24 @@ class RecPredictor(Predictor):
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self.preprocess_ops = create_operators(config["RecPreProcess"][
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"transform_ops"])
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self.postprocess = build_postprocess(config["RecPostProcess"])
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self.benchmark = config["Global"].get("benchmark", False)
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import auto_log
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pid = os.getpid()
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self.auto_logger = auto_log.AutoLogger(
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model_name=config["Global"].get("model_name", "rec"),
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model_precision='fp16' if config["Global"]["use_fp16"] else 'fp32',
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batch_size=config["Global"].get("batch_size", 1),
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data_shape=[3, 224, 224],
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save_path=config["Global"].get("save_log_path", "./auto_log.log"),
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inference_config=self.config,
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pids=pid,
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process_name=None,
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gpu_ids=None,
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time_keys=[
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'preprocess_time', 'inference_time', 'postprocess_time'
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],
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warmup=2)
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def predict(self, images, feature_normalize=True):
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input_names = self.paddle_predictor.get_input_names()
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@ -44,16 +62,22 @@ class RecPredictor(Predictor):
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output_tensor = self.paddle_predictor.get_output_handle(output_names[
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0])
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if self.benchmark:
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self.auto_logger.times.start()
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if not isinstance(images, (list, )):
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images = [images]
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for idx in range(len(images)):
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for ops in self.preprocess_ops:
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images[idx] = ops(images[idx])
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image = np.array(images)
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if self.benchmark:
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self.auto_logger.times.stamp()
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input_tensor.copy_from_cpu(image)
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self.paddle_predictor.run()
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batch_output = output_tensor.copy_to_cpu()
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if self.benchmark:
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self.auto_logger.times.stamp()
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if feature_normalize:
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feas_norm = np.sqrt(
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@ -62,6 +86,9 @@ class RecPredictor(Predictor):
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if self.postprocess is not None:
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batch_output = self.postprocess(batch_output)
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if self.benchmark:
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self.auto_logger.times.end(stamp=True)
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return batch_output
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@ -85,16 +112,19 @@ def main(config):
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batch_names.append(img_name)
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cnt += 1
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if cnt % config["Global"]["batch_size"] == 0 or (idx + 1) == len(image_list):
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if len(batch_imgs) == 0:
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if cnt % config["Global"]["batch_size"] == 0 or (idx + 1
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) == len(image_list):
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if len(batch_imgs) == 0:
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continue
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batch_results = rec_predictor.predict(batch_imgs)
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for number, result_dict in enumerate(batch_results):
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filename = batch_names[number]
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print("{}:\t{}".format(filename, result_dict))
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batch_imgs = []
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batch_names = []
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if rec_predictor.benchmark:
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rec_predictor.auto_logger.report()
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return
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@ -0,0 +1,52 @@
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===========================train_params===========================
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model_name:GeneralRecognition_PPLCNet_x2_5
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python:python3.7
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gpu_list:0|0,1
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-o Global.device:gpu
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-o Global.auto_cast:null
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-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
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-o Global.output_dir:./output/
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-o DataLoader.Train.sampler.batch_size:8
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-o Global.pretrained_model:null
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train_model_name:latest
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train_infer_img_dir:./dataset/ILSVRC2012/val
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null:null
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##
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trainer:norm_train
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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
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pact_train:null
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fpgm_train:null
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distill_train:null
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null:null
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null:null
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##
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===========================eval_params===========================
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eval:tools/eval.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml
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null:null
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##
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===========================infer_params==========================
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-o Global.save_inference_dir:./inference
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-o Global.pretrained_model:
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norm_export:tools/export_model.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml
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quant_export:null
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fpgm_export:null
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distill_export:null
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kl_quant:null
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export2:null
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pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/general_PPLCNet_x2_5_pretrained_v1.0.pdparams
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infer_model:../inference/
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infer_export:True
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infer_quant:Fasle
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inference:python/predict_rec.py -c configs/inference_rec.yaml
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-o Global.use_gpu:True|False
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-o Global.enable_mkldnn:True|False
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-o Global.cpu_num_threads:1|6
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-o Global.batch_size:1|16
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-o Global.use_tensorrt:True|False
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-o Global.use_fp16:True|False
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-o Global.rec_inference_model_dir:../inference
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-o Global.infer_imgs:../dataset/Aliproduct/demo_test/
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-o Global.save_log_path:null
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-o Global.benchmark:True
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null:null
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null:null
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@ -37,6 +37,22 @@ model_name=$(func_parser_value "${lines[1]}")
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model_url_value=$(func_parser_value "${lines[35]}")
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model_url_key=$(func_parser_key "${lines[35]}")
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if [[ $FILENAME == *GeneralRecognition* ]];then
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cd dataset
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rm -rf Aliproduct
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rm -rf train_reg_all_data.txt
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rm -rf demo_train
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wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/data/whole_chain/tipc_shitu_demo_data.tar
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tar -xf tipc_shitu_demo_data.tar
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ln -s tipc_shitu_demo_data Aliproduct
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ln -s tipc_shitu_demo_data/demo_train.txt train_reg_all_data.txt
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ln -s tipc_shitu_demo_data/demo_train demo_train
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cd tipc_shitu_demo_data
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ln -s demo_test.txt val_list.txt
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cd ../../
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exit 0
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fi
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if [ ${MODE} = "lite_train_lite_infer" ] || [ ${MODE} = "lite_train_whole_infer" ];then
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# pretrain lite train data
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cd dataset
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@ -291,8 +291,12 @@ else
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export FLAGS_cudnn_deterministic=True
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eval $cmd
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status_check $? "${cmd}" "${status_log}"
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set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${$model_name}/${train_model_name}")
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if [[ $FILENAME == *GeneralRecognition* ]]; then
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set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/RecModel/${train_model_name}")
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else
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set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${model_name}/${train_model_name}")
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fi
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# save norm trained models to set pretrain for pact training and fpgm training
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if [ ${trainer} = ${trainer_norm} ]; then
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load_norm_train_model=${set_eval_pretrain}
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@ -308,7 +312,11 @@ else
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if [ ${run_export} != "null" ]; then
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# run export model
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save_infer_path="${save_log}"
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set_export_weight=$(func_set_params "${export_weight}" "${save_log}/${model_name}/${train_model_name}")
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if [[ $FILENAME == *GeneralRecognition* ]]; then
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set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/RecModel/${train_model_name}")
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else
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set_export_weight=$(func_set_params "${export_weight}" "${save_log}/${model_name}/${train_model_name}")
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fi
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set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_path}")
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export_cmd="${python} ${run_export} ${set_export_weight} ${set_save_infer_key}"
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eval $export_cmd
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