HydrogenSulfate
GitHub
commit
f3f0605c7e
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@ -48,6 +48,12 @@ def quantize_model(config, model, mode="train"):
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QUANT_CONFIG["activation_preprocess_type"] = "PACT"
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if mode in ["infer", "export"]:
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QUANT_CONFIG['activation_preprocess_type'] = None
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# for rep nets, convert to reparameterized model first
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for layer in model.sublayers():
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if hasattr(layer, "rep"):
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layer.rep()
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model.quanter = QAT(config=QUANT_CONFIG)
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model.quanter.quantize(model)
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logger.info("QAT model summary:")
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@ -466,7 +466,7 @@ class Engine(object):
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# for rep nets
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for layer in self.model.sublayers():
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if hasattr(layer, "rep"):
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if hasattr(layer, "rep") and not getattr(layer, "is_repped"):
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layer.rep()
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save_path = os.path.join(self.config["Global"]["save_inference_dir"],
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@ -107,6 +107,7 @@ bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/MobileNetV3/Mo
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各功能测试中涉及混合精度、裁剪、量化等训练相关，及mkldnn、Tensorrt等多种预测相关参数配置，请点击下方相应链接了解更多细节和使用教程：
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- [test_train_inference_python 使用](docs/test_train_inference_python.md)：测试基于Python的模型训练、评估、推理等基本功能，包括裁剪、量化、蒸馏。
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- [test_train_pact_inference_python 使用](docs/test_train_pact_inference_python.md)：测试基于Python的模型PACT在线量化等基本功能。
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- [test_inference_cpp 使用](docs/test_inference_cpp.md) ：测试基于C++的模型推理。
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- [test_serving 使用](docs/test_serving.md) ：测试基于Paddle Serving的服务化部署功能。
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- [test_lite_arm_cpu_cpp 使用](docs/test_lite_arm_cpu_cpp.md): 测试基于Paddle-Lite的ARM CPU端c++预测部署功能.
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@ -0,0 +1,54 @@
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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
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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=100
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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:pact_train
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norm_train:null
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pact_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 -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.004 -o Global.pretrained_model="pretrained_model/general_PPLCNet_x2_5_pretrained_v1.0"
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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 -o Slim.quant.name=pact
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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:null
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quant_export:tools/export_model.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml -o Slim.quant.name=pact
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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:False
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-o Global.cpu_num_threads:1
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-o Global.batch_size:1
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-o Global.use_tensorrt:False
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-o Global.use_fp16: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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===========================infer_benchmark_params==========================
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random_infer_input:[{float32,[3,224,224]}]
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@ -0,0 +1,18 @@
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===========================cpp_infer_params===========================
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model_name:MobileNetV3_large_x1_0_PACT
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cpp_infer_type:cls
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cls_inference_model_dir:./MobileNetV3_large_x1_0_pact_infer/
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det_inference_model_dir:
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cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/MobileNetV3_large_x1_0_pact_infer.tar
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det_inference_url:
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infer_quant:False
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inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
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use_gpu:True|False
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enable_mkldnn:False
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cpu_threads:1
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batch_size:1
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use_tensorrt:False
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precision:fp32
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image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
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benchmark:False
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generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
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@ -0,0 +1,14 @@
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===========================serving_params===========================
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model_name:MobileNetV3_large_x1_0_PACT
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python:python3.7
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inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/MobileNetV3_large_x1_0_pact_infer.tar
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trans_model:-m paddle_serving_client.convert
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--dirname:./deploy/paddleserving/MobileNetV3_large_x1_0_pact_infer/
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--model_filename:inference.pdmodel
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--params_filename:inference.pdiparams
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--serving_server:./deploy/paddleserving/MobileNetV3_large_x1_0_pact_serving/
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--serving_client:./deploy/paddleserving/MobileNetV3_large_x1_0_pact_client/
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serving_dir:./deploy/paddleserving
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web_service:null
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--use_gpu:0|null
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pipline:test_cpp_serving_client.py
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@ -0,0 +1,14 @@
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===========================serving_params===========================
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model_name:MobileNetV3_large_x1_0_PACT
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python:python3.7
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inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/MobileNetV3_large_x1_0_pact_infer.tar
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trans_model:-m paddle_serving_client.convert
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--dirname:./deploy/paddleserving/MobileNetV3_large_x1_0_pact_infer/
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--model_filename:inference.pdmodel
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--params_filename:inference.pdiparams
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--serving_server:./deploy/paddleserving/MobileNetV3_large_x1_0_pact_serving/
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--serving_client:./deploy/paddleserving/MobileNetV3_large_x1_0_pact_client/
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serving_dir:./deploy/paddleserving
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web_service:classification_web_service.py
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--use_gpu:0|null
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pipline:pipeline_http_client.py
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@ -0,0 +1,60 @@
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===========================train_params===========================
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model_name:MobileNetV3_large_x1_0
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python:python3.7
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gpu_list:0
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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:pact_train
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norm_train:null
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pact_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 -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.01 -o Global.pretrained_model="pretrained_model/MobileNetV3_large_x1_0_pretrained"
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fpgm_train:null
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distill_train:null
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to_static_train:-o Global.to_static=True
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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/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml -o Slim.quant.name=pact
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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:null
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quant_export:tools/export_model.py -c ppcls/configs/slim/MobileNetV3_large_x1_0_quantization.yaml -o Slim.quant.name=pact
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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/legendary_models/MobileNetV3_large_x1_0_pretrained.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_cls.py -c configs/inference_cls.yaml
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-o Global.use_gpu:True|False
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-o Global.enable_mkldnn:False
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-o Global.cpu_num_threads:1
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-o Global.batch_size:1
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-o Global.use_tensorrt:False
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-o Global.use_fp16:False
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-o Global.inference_model_dir:../inference
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-o Global.infer_imgs:../dataset/ILSVRC2012/val
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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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===========================train_benchmark_params==========================
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batch_size:256|640
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fp_items:fp32
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epoch:1
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--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
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flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096
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===========================infer_benchmark_params==========================
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random_infer_input:[{float32,[3,224,224]}]
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===========================cpp_infer_params===========================
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model_name:GeneralRecognition_PPLCNet_x2_5_PACT
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cpp_infer_type:cls
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cls_inference_model_dir:./general_PPLCNet_x2_5_lite_v1.0_pact_infer/
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det_inference_model_dir:
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cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/general_PPLCNet_x2_5_lite_v1.0_pact_infer.tar
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det_inference_url:
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infer_quant:False
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inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
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use_gpu:True|False
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enable_mkldnn:False
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cpu_threads:1
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batch_size:1
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use_tensorrt:False
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precision:fp32
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image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
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benchmark:False
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generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
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@ -0,0 +1,14 @@
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===========================serving_params===========================
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model_name:GeneralRecognition_PPLCNet_x2_5_PACT
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python:python3.7
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inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/general_PPLCNet_x2_5_lite_v1.0_pact_infer.tar
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trans_model:-m paddle_serving_client.convert
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--dirname:./deploy/paddleserving/general_PPLCNet_x2_5_lite_v1.0_pact_infer/
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--model_filename:inference.pdmodel
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--params_filename:inference.pdiparams
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--serving_server:./deploy/paddleserving/GeneralRecognition_PPLCNet_x2_5_pact_serving/
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--serving_client:./deploy/paddleserving/GeneralRecognition_PPLCNet_x2_5_pact_client/
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serving_dir:./deploy/paddleserving
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web_service:null
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--use_gpu:0|null
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pipline:test_cpp_serving_client.py
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@ -0,0 +1,14 @@
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===========================serving_params===========================
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model_name:GeneralRecognition_PPLCNet_x2_5_PACT
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python:python3.7
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inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/general_PPLCNet_x2_5_lite_v1.0_pact_infer.tar
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trans_model:-m paddle_serving_client.convert
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--dirname:./deploy/paddleserving/general_PPLCNet_x2_5_lite_v1.0_pact_infer/
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--model_filename:inference.pdmodel
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--params_filename:inference.pdiparams
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--serving_server:./deploy/paddleserving/GeneralRecognition_PPLCNet_x2_5_pact_serving/
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--serving_client:./deploy/paddleserving/GeneralRecognition_PPLCNet_x2_5_pact_client/
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serving_dir:./deploy/paddleserving
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web_service:classification_web_service.py
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--use_gpu:0|null
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pipline:pipeline_http_client.py
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@ -0,0 +1,18 @@
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===========================cpp_infer_params===========================
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model_name:PPHGNet_small_PACT
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cpp_infer_type:cls
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cls_inference_model_dir:./PPHGNet_small_pact_infer/
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det_inference_model_dir:
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cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/PPHGNet_small_pact_infer.tar
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det_inference_url:
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infer_quant:False
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inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
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use_gpu:True|False
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enable_mkldnn:False
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cpu_threads:1
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batch_size:1
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use_tensorrt:False
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precision:fp32
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image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
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benchmark:False
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generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
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===========================serving_params===========================
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model_name:PPHGNet_small_PACT
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python:python3.7
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inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/PPHGNet_small_pact_infer.tar
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trans_model:-m paddle_serving_client.convert
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--dirname:./deploy/paddleserving/PPHGNet_small_pact_infer/
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--model_filename:inference.pdmodel
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--params_filename:inference.pdiparams
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--serving_server:./deploy/paddleserving/PPHGNet_small_pact_serving/
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--serving_client:./deploy/paddleserving/PPHGNet_small_pact_client/
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serving_dir:./deploy/paddleserving
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web_service:null
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--use_gpu:0|null
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pipline:test_cpp_serving_client.py
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@ -0,0 +1,14 @@
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===========================serving_params===========================
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model_name:PPHGNet_small_PACT
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python:python3.7
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inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/PPHGNet_small_pact_infer.tar
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trans_model:-m paddle_serving_client.convert
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--dirname:./deploy/paddleserving/PPHGNet_small_pact_infer/
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--model_filename:inference.pdmodel
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--params_filename:inference.pdiparams
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--serving_server:./deploy/paddleserving/PPHGNet_small_pact_serving/
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--serving_client:./deploy/paddleserving/PPHGNet_small_pact_client/
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serving_dir:./deploy/paddleserving
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web_service:classification_web_service.py
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--use_gpu:0|null
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pipline:pipeline_http_client.py
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@ -0,0 +1,53 @@
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===========================train_params===========================
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model_name:PPHGNet_small
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python:python3.7
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gpu_list:0
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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:pact_train
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norm_train:null
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pact_train:tools/train.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_small.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 -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.01 -o Global.pretrained_model="pretrained_model/PPHGNet_small_pretrained" -o AMP=None
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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/ImageNet/PPHGNet/PPHGNet_small.yaml -o Slim.quant.name=pact
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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:null
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quant_export:tools/export_model.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_small.yaml -o Slim.quant.name=pact
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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/legendary_models/PPHGNet_small_pretrained.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_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=236
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPHGNet_tiny
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-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:pact_train
|
||||
norm_train:null
|
||||
pact_train:tools/train.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_tiny.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 -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.01 -o Global.pretrained_model="pretrained_model/PPHGNet_tiny_pretrained" -o AMP=None
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_tiny.yaml -o Slim.quant.name=pact
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:null
|
||||
quant_export:tools/export_model.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_tiny.yaml -o Slim.quant.name=pact
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_tiny_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=236
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPHGNet_tiny
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-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/ImageNet/PPHGNet/PPHGNet_tiny.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/ImageNet/PPHGNet/PPHGNet_tiny.yaml
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_tiny.yaml
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_tiny.yaml -o Global.save_inference_dir=./PPHGNet_tiny_infer
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_tiny_infer.tar
|
||||
infer_model:./PPHGNet_tiny_infer/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=236
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x0_25
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-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:pact_train
|
||||
norm_train:null
|
||||
pact_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.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 -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.08 -o Global.pretrained_model="pretrained_model/PPLCNet_x0_25_pretrained"
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml -o Slim.quant.name=pact
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:null
|
||||
quant_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml -o Slim.quant.name=pact
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_25_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x0_25
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-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/ImageNet/PPLCNet/PPLCNet_x0_25.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/ImageNet/PPLCNet/PPLCNet_x0_25.yaml
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml -o Global.save_inference_dir=./PPLCNet_x0_25_infer
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_25_infer.tar
|
||||
infer_model:./PPLCNet_x0_25_infer/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x0_35
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-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:pact_train
|
||||
norm_train:null
|
||||
pact_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.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 -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.08 -o Global.pretrained_model="pretrained_model/PPLCNet_x0_35_pretrained"
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml -o Slim.quant.name=pact
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:null
|
||||
quant_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml -o Slim.quant.name=pact
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_35_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x0_35
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-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/ImageNet/PPLCNet/PPLCNet_x0_35.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/ImageNet/PPLCNet/PPLCNet_x0_35.yaml
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml -o Global.save_inference_dir=./PPLCNet_x0_35_infer
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_35_infer.tar
|
||||
infer_model:./PPLCNet_x0_35_infer/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x0_5
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-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:pact_train
|
||||
norm_train:null
|
||||
pact_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_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 -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.08 -o Global.pretrained_model="pretrained_model/PPLCNet_x0_5_pretrained"
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml -o Slim.quant.name=pact
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:null
|
||||
quant_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml -o Slim.quant.name=pact
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_5_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x0_5
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-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/ImageNet/PPLCNet/PPLCNet_x0_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/ImageNet/PPLCNet/PPLCNet_x0_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/ImageNet/PPLCNet/PPLCNet_x0_5.yaml
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml -o Global.save_inference_dir=./PPLCNet_x0_5_infer
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_5_infer.tar
|
||||
infer_model:./PPLCNet_x0_5_infer/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x0_75
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-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:pact_train
|
||||
norm_train:null
|
||||
pact_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.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 -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.08 -o Global.pretrained_model="pretrained_model/PPLCNet_x0_75_pretrained"
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml -o Slim.quant.name=pact
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:null
|
||||
quant_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml -o Slim.quant.name=pact
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_75_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x0_75
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-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/ImageNet/PPLCNet/PPLCNet_x0_75.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/ImageNet/PPLCNet/PPLCNet_x0_75.yaml
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml -o Global.save_inference_dir=./PPLCNet_x0_75_infer
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_75_infer.tar
|
||||
infer_model:./PPLCNet_x0_75_infer/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
===========================cpp_infer_params===========================
|
||||
model_name:PPLCNet_x1_0_PACT
|
||||
cpp_infer_type:cls
|
||||
cls_inference_model_dir:./PPLCNet_x1_0_pact_infer/
|
||||
det_inference_model_dir:
|
||||
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/PPLCNet_x1_0_pact_infer.tar
|
||||
det_inference_url:
|
||||
infer_quant:False
|
||||
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
|
||||
use_gpu:True|False
|
||||
enable_mkldnn:False
|
||||
cpu_threads:1
|
||||
batch_size:1
|
||||
use_tensorrt:False
|
||||
precision:fp32
|
||||
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
|
||||
benchmark:False
|
||||
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
===========================serving_params===========================
|
||||
model_name:PPLCNet_x1_0_PACT
|
||||
python:python3.7
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/PPLCNet_x1_0_pact_infer.tar
|
||||
trans_model:-m paddle_serving_client.convert
|
||||
--dirname:./deploy/paddleserving/PPLCNet_x1_0_pact_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--serving_server:./deploy/paddleserving/PPLCNet_x1_0_pact_serving/
|
||||
--serving_client:./deploy/paddleserving/PPLCNet_x1_0_pact_client/
|
||||
serving_dir:./deploy/paddleserving
|
||||
web_service:null
|
||||
--use_gpu:0|null
|
||||
pipline:test_cpp_serving_client.py
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
===========================serving_params===========================
|
||||
model_name:PPLCNet_x1_0_PACT
|
||||
python:python3.7
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/PPLCNet_x1_0_pact_infer.tar
|
||||
trans_model:-m paddle_serving_client.convert
|
||||
--dirname:./deploy/paddleserving/PPLCNet_x1_0_pact_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--serving_server:./deploy/paddleserving/PPLCNet_x1_0_pact_serving/
|
||||
--serving_client:./deploy/paddleserving/PPLCNet_x1_0_pact_client/
|
||||
serving_dir:./deploy/paddleserving
|
||||
web_service:classification_web_service.py
|
||||
--use_gpu:0|null
|
||||
pipline:pipeline_http_client.py
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x1_0
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-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:pact_train
|
||||
norm_train:null
|
||||
pact_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_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 -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.08 -o Global.pretrained_model="pretrained_model/PPLCNet_x1_0_pretrained"
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml -o Slim.quant.name=pact
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:null
|
||||
quant_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml -o Slim.quant.name=pact
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_0_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x1_5
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-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:pact_train
|
||||
norm_train:null
|
||||
pact_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_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 -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.08 -o Global.pretrained_model="pretrained_model/PPLCNet_x1_5_pretrained"
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml -o Slim.quant.name=pact
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:null
|
||||
quant_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml -o Slim.quant.name=pact
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_5_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x1_5
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-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/ImageNet/PPLCNet/PPLCNet_x1_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/ImageNet/PPLCNet/PPLCNet_x1_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/ImageNet/PPLCNet/PPLCNet_x1_5.yaml
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml -o Global.save_inference_dir=./PPLCNet_x1_5_infer
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x1_5_infer.tar
|
||||
infer_model:./PPLCNet_x1_5_infer/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x2_0
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-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:pact_train
|
||||
norm_train:null
|
||||
pact_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_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 -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.08 -o Global.pretrained_model="pretrained_model/PPLCNet_x2_0_pretrained"
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml -o Slim.quant.name=pact
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:null
|
||||
quant_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml -o Slim.quant.name=pact
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_0_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x2_0
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-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/ImageNet/PPLCNet/PPLCNet_x2_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
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml -o Global.save_inference_dir=./PPLCNet_x2_0_infer
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x2_0_infer.tar
|
||||
infer_model:./PPLCNet_x2_0_infer/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x2_5
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-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:pact_train
|
||||
norm_train:null
|
||||
pact_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/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 -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.08 -o Global.pretrained_model="pretrained_model/PPLCNet_x2_5_pretrained"
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml -o Slim.quant.name=pact
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:null
|
||||
quant_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml -o Slim.quant.name=pact
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_5_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x2_5
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-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/ImageNet/PPLCNet/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/ImageNet/PPLCNet/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/ImageNet/PPLCNet/PPLCNet_x2_5.yaml
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml -o Global.save_inference_dir=./PPLCNet_x2_5_infer
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x2_5_infer.tar
|
||||
infer_model:./PPLCNet_x2_5_infer/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
===========================cpp_infer_params===========================
|
||||
model_name:PPLCNetV2_base_PACT
|
||||
cpp_infer_type:cls
|
||||
cls_inference_model_dir:./PPLCNetV2_base_pact_infer/
|
||||
det_inference_model_dir:
|
||||
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/PPLCNetV2_base_pact_infer.tar
|
||||
det_inference_url:
|
||||
infer_quant:False
|
||||
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
|
||||
use_gpu:True|False
|
||||
enable_mkldnn:False
|
||||
cpu_threads:1
|
||||
batch_size:1
|
||||
use_tensorrt:False
|
||||
precision:fp32
|
||||
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
|
||||
benchmark:False
|
||||
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
===========================serving_params===========================
|
||||
model_name:PPLCNetV2_base_PACT
|
||||
python:python3.7
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/PPLCNetV2_base_pact_infer.tar
|
||||
trans_model:-m paddle_serving_client.convert
|
||||
--dirname:./deploy/paddleserving/PPLCNetV2_base_pact_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--serving_server:./deploy/paddleserving/PPLCNetV2_base_pact_serving/
|
||||
--serving_client:./deploy/paddleserving/PPLCNetV2_base_pact_client/
|
||||
serving_dir:./deploy/paddleserving
|
||||
web_service:null
|
||||
--use_gpu:0|null
|
||||
pipline:test_cpp_serving_client.py
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
===========================serving_params===========================
|
||||
model_name:PPLCNetV2_base_PACT
|
||||
python:python3.7
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/PPLCNetV2_base_pact_infer.tar
|
||||
trans_model:-m paddle_serving_client.convert
|
||||
--dirname:./deploy/paddleserving/PPLCNetV2_base_pact_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--serving_server:./deploy/paddleserving/PPLCNetV2_base_pact_serving/
|
||||
--serving_client:./deploy/paddleserving/PPLCNetV2_base_pact_client/
|
||||
serving_dir:./deploy/paddleserving
|
||||
web_service:classification_web_service.py
|
||||
--use_gpu:0|null
|
||||
pipline:pipeline_http_client.py
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNetV2_base
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-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.first_bs:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:pact_train
|
||||
norm_train:null
|
||||
pact_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_base.yaml -o Global.seed=1234 -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.08 -o Global.pretrained_model="pretrained_model/PPLCNetV2_base_pretrained"
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_base.yaml -o Slim.quant.name=pact
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:null
|
||||
quant_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_base.yaml -o Slim.quant.name=pact
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNetV2_base_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,60 @@
|
|||
===========================train_params===========================
|
||||
model_name:ResNet50
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-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:pact_train
|
||||
norm_train:null
|
||||
pact_train:tools/train.py -c ppcls/configs/ImageNet/ResNet/ResNet50.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 -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.01 -o Global.pretrained_model="pretrained_model/ResNet50_pretrained"
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
to_static_train:-o Global.to_static=True
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/ResNet/ResNet50.yaml -o Slim.quant.name=pact
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:null
|
||||
quant_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNet/ResNet50.yaml -o Slim.quant.name=pact
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
null:null
|
||||
===========================train_benchmark_params==========================
|
||||
batch_size:128
|
||||
fp_items:fp32
|
||||
epoch:1
|
||||
--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
|
||||
flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,60 @@
|
|||
===========================train_params===========================
|
||||
model_name:ResNet50
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=200
|
||||
-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/ImageNet/ResNet/ResNet50.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
|
||||
to_static_train:-o Global.to_static=True
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/ResNet/ResNet50.yaml
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNet/ResNet50.yaml
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/ResNet/ResNet50.yaml -o Global.save_inference_dir=./ResNet50_infer
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_infer.tar
|
||||
infer_model:./ResNet50_infer/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
null:null
|
||||
===========================train_benchmark_params==========================
|
||||
batch_size:128
|
||||
fp_items:fp32
|
||||
epoch:1
|
||||
--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
|
||||
flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
===========================cpp_infer_params===========================
|
||||
model_name:ResNet50_vd_PACT
|
||||
cpp_infer_type:cls
|
||||
cls_inference_model_dir:./ResNet50_vd_pact_infer/
|
||||
det_inference_model_dir:
|
||||
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/ResNet50_vd_pact_infer.tar
|
||||
det_inference_url:
|
||||
infer_quant:False
|
||||
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
|
||||
use_gpu:True|False
|
||||
enable_mkldnn:False
|
||||
cpu_threads:1
|
||||
batch_size:1
|
||||
use_tensorrt:False
|
||||
precision:fp32
|
||||
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
|
||||
benchmark:False
|
||||
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
===========================serving_params===========================
|
||||
model_name:ResNet50_vd_PACT
|
||||
python:python3.7
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/ResNet50_vd_pact_infer.tar
|
||||
trans_model:-m paddle_serving_client.convert
|
||||
--dirname:./deploy/paddleserving/ResNet50_vd_pact_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--serving_server:./deploy/paddleserving/ResNet50_vd_pact_serving/
|
||||
--serving_client:./deploy/paddleserving/ResNet50_vd_pact_client/
|
||||
serving_dir:./deploy/paddleserving
|
||||
web_service:null
|
||||
--use_gpu:0|null
|
||||
pipline:test_cpp_serving_client.py
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
===========================serving_params===========================
|
||||
model_name:ResNet50_vd_PACT
|
||||
python:python3.7
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/ResNet50_vd_pact_infer.tar
|
||||
trans_model:-m paddle_serving_client.convert
|
||||
--dirname:./deploy/paddleserving/ResNet50_vd_pact_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--serving_server:./deploy/paddleserving/ResNet50_vd_pact_serving/
|
||||
--serving_client:./deploy/paddleserving/ResNet50_vd_pact_client/
|
||||
serving_dir:./deploy/paddleserving
|
||||
web_service:classification_web_service.py
|
||||
--use_gpu:0|null
|
||||
pipline:pipeline_http_client.py
|
||||
|
|
@ -0,0 +1,60 @@
|
|||
===========================train_params===========================
|
||||
model_name:ResNet50_vd
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=200
|
||||
-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:pact_train
|
||||
norm_train:null
|
||||
pact_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 -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.01 -o Global.pretrained_model="pretrained_model/ResNet50_vd_pretrained"
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
to_static_train:-o Global.to_static=True
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml -o Slim.quant.name=pact
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:null
|
||||
quant_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml -o Slim.quant.name=pact
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
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/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
null:null
|
||||
===========================train_benchmark_params==========================
|
||||
batch_size:128
|
||||
fp_items:fp32
|
||||
epoch:1
|
||||
--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
|
||||
flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
===========================cpp_infer_params===========================
|
||||
model_name:SwinTransformer_tiny_patch4_window7_224_PACT
|
||||
cpp_infer_type:cls
|
||||
cls_inference_model_dir:./SwinTransformer_tiny_patch4_window7_224_pact_infer/
|
||||
det_inference_model_dir:
|
||||
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/SwinTransformer_tiny_patch4_window7_224_pact_infer.tar
|
||||
det_inference_url:
|
||||
infer_quant:False
|
||||
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
|
||||
use_gpu:True|False
|
||||
enable_mkldnn:False
|
||||
cpu_threads:1
|
||||
batch_size:1
|
||||
use_tensorrt:False
|
||||
precision:fp32
|
||||
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
|
||||
benchmark:False
|
||||
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
===========================serving_params===========================
|
||||
model_name:SwinTransformer_tiny_patch4_window7_224_PACT
|
||||
python:python3.7
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/SwinTransformer_tiny_patch4_window7_224_pact_infer.tar
|
||||
trans_model:-m paddle_serving_client.convert
|
||||
--dirname:./deploy/paddleserving/SwinTransformer_tiny_patch4_window7_224_pact_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--serving_server:./deploy/paddleserving/SwinTransformer_tiny_patch4_window7_224_pact_serving/
|
||||
--serving_client:./deploy/paddleserving/SwinTransformer_tiny_patch4_window7_224_pact_client/
|
||||
serving_dir:./deploy/paddleserving
|
||||
web_service:null
|
||||
--use_gpu:0|null
|
||||
pipline:test_cpp_serving_client.py
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
===========================serving_params===========================
|
||||
model_name:SwinTransformer_tiny_patch4_window7_224_PACT
|
||||
python:python3.7
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/SwinTransformer_tiny_patch4_window7_224_pact_infer.tar
|
||||
trans_model:-m paddle_serving_client.convert
|
||||
--dirname:./deploy/paddleserving/SwinTransformer_tiny_patch4_window7_224_pact_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--serving_server:./deploy/paddleserving/SwinTransformer_tiny_patch4_window7_224_pact_serving/
|
||||
--serving_client:./deploy/paddleserving/SwinTransformer_tiny_patch4_window7_224_pact_client/
|
||||
serving_dir:./deploy/paddleserving
|
||||
web_service:classification_web_service.py
|
||||
--use_gpu:0|null
|
||||
pipline:pipeline_http_client.py
|
||||
|
|
@ -0,0 +1,60 @@
|
|||
===========================train_params===========================
|
||||
model_name:SwinTransformer_tiny_patch4_window7_224
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-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:pact_train
|
||||
norm_train:null
|
||||
pact_train:tools/train.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_tiny_patch4_window7_224.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 -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.01 -o Global.pretrained_model="pretrained_model/SwinTransformer_tiny_patch4_window7_224_pretrained"
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_tiny_patch4_window7_224.yaml -o Slim.quant.name=pact
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:null
|
||||
quant_export:tools/export_model.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_tiny_patch4_window7_224.yaml -o Slim.quant.name=pact
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_tiny_patch4_window7_224_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
null:null
|
||||
===========================train_benchmark_params==========================
|
||||
batch_size:64|104
|
||||
fp_items:fp32
|
||||
epoch:1
|
||||
--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
|
||||
flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -6,31 +6,37 @@ Linux GPU/CPU C++ 推理功能测试的主程序为`test_inference_cpp.sh`，可
|
|||
|
||||
- 推理相关：
|
||||
|
||||
| 算法名称 | 模型名称 | device_CPU | device_GPU |
|
||||
| :-------------: | :----------------------------------------: | :--------: | :--------: |
|
||||
| MobileNetV3 | MobileNetV3_large_x1_0 | 支持 | 支持 |
|
||||
| MobileNetV3 | MobileNetV3_large_x1_0_KL | 支持 | 支持 |
|
||||
| PP-ShiTu | PPShiTu_general_rec、PPShiTu_mainbody_det | 支持 | 支持 |
|
||||
| PP-ShiTu | GeneralRecognition_PPLCNet_x2_5_KL | 支持 | 支持 |
|
||||
| PPHGNet | PPHGNet_small | 支持 | 支持 |
|
||||
| PPHGNet | PPHGNet_small_KL | 支持 | 支持 |
|
||||
| PPHGNet | PPHGNet_tiny | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x0_25 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x0_35 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x0_5 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x0_75 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x1_0 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x1_0_KL | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x1_5 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x2_0 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x2_5 | 支持 | 支持 |
|
||||
| PPLCNetV2 | PPLCNetV2_base | 支持 | 支持 |
|
||||
| PPLCNetV2 | PPLCNetV2_base_KL | 支持 | 支持 |
|
||||
| ResNet | ResNet50 | 支持 | 支持 |
|
||||
| ResNet | ResNet50_vd | 支持 | 支持 |
|
||||
| ResNet | ResNet50_vd_KL | 支持 | 支持 |
|
||||
| SwinTransformer | SwinTransformer_tiny_patch4_window7_224 | 支持 | 支持 |
|
||||
| SwinTransformer | SwinTransformer_tiny_patch4_window7_224_KL | 支持 | 支持 |
|
||||
| 算法名称 | 模型名称 | device_CPU | device_GPU |
|
||||
| :-------------: | :------------------------------------------: | :--------: | :--------: |
|
||||
| MobileNetV3 | MobileNetV3_large_x1_0 | 支持 | 支持 |
|
||||
| MobileNetV3 | MobileNetV3_large_x1_0_KL | 支持 | 支持 |
|
||||
| MobileNetV3 | MobileNetV3_large_x1_0_PACT | 支持 | 支持 |
|
||||
| PP-ShiTu | PPShiTu_general_rec、PPShiTu_mainbody_det | 支持 | 支持 |
|
||||
| PP-ShiTu | GeneralRecognition_PPLCNet_x2_5_KL | 支持 | 支持 |
|
||||
| PP-ShiTu | GeneralRecognition_PPLCNet_x2_5_PACT | 支持 | 支持 |
|
||||
| PPHGNet | PPHGNet_small | 支持 | 支持 |
|
||||
| PPHGNet | PPHGNet_small_KL | 支持 | 支持 |
|
||||
| PPHGNet | PPHGNet_small_PACT | 支持 | 支持 |
|
||||
| PPHGNet | PPHGNet_tiny | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x0_25 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x0_35 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x0_5 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x0_75 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x1_0 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x1_0_KL | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x1_0_PACT | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x1_5 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x2_0 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x2_5 | 支持 | 支持 |
|
||||
| PPLCNetV2 | PPLCNetV2_base | 支持 | 支持 |
|
||||
| PPLCNetV2 | PPLCNetV2_base_KL | 支持 | 支持 |
|
||||
| ResNet | ResNet50 | 支持 | 支持 |
|
||||
| ResNet | ResNet50_vd | 支持 | 支持 |
|
||||
| ResNet | ResNet50_vd_KL | 支持 | 支持 |
|
||||
| ResNet | ResNet50_vd_PACT | 支持 | 支持 |
|
||||
| SwinTransformer | SwinTransformer_tiny_patch4_window7_224 | 支持 | 支持 |
|
||||
| SwinTransformer | SwinTransformer_tiny_patch4_window7_224_KL | 支持 | 支持 |
|
||||
| SwinTransformer | SwinTransformer_tiny_patch4_window7_224_PACT | 支持 | 支持 |
|
||||
|
||||
## 2. 测试流程(以**ResNet50**为例)
|
||||
|
||||
|
|
|
|||
|
|
@ -7,31 +7,37 @@ Linux GPU/CPU C++ 服务化部署测试的主程序为`test_serving_infer_cpp.sh
|
|||
|
||||
- 推理相关：
|
||||
|
||||
| 算法名称 | 模型名称 | device_CPU | device_GPU |
|
||||
| :-------------: | :----------------------------------------: | :--------: | :--------: |
|
||||
| MobileNetV3 | MobileNetV3_large_x1_0 | 支持 | 支持 |
|
||||
| MobileNetV3 | MobileNetV3_large_x1_0_KL | 支持 | 支持 |
|
||||
| PP-ShiTu | PPShiTu_general_rec、PPShiTu_mainbody_det | 支持 | 支持 |
|
||||
| PP-ShiTu | GeneralRecognition_PPLCNet_x2_5_KL | 支持 | 支持 |
|
||||
| PPHGNet | PPHGNet_small | 支持 | 支持 |
|
||||
| PPHGNet | PPHGNet_small_KL | 支持 | 支持 |
|
||||
| PPHGNet | PPHGNet_tiny | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x0_25 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x0_35 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x0_5 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x0_75 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x1_0 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x1_0_KL | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x1_5 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x2_0 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x2_5 | 支持 | 支持 |
|
||||
| PPLCNetV2 | PPLCNetV2_base | 支持 | 支持 |
|
||||
| PPLCNetV2 | PPLCNetV2_base_KL | 支持 | 支持 |
|
||||
| ResNet | ResNet50 | 支持 | 支持 |
|
||||
| ResNet | ResNet50_vd | 支持 | 支持 |
|
||||
| ResNet | ResNet50_vd_KL | 支持 | 支持 |
|
||||
| SwinTransformer | SwinTransformer_tiny_patch4_window7_224 | 支持 | 支持 |
|
||||
| SwinTransformer | SwinTransformer_tiny_patch4_window7_224_KL | 支持 | 支持 |
|
||||
| 算法名称 | 模型名称 | device_CPU | device_GPU |
|
||||
| :-------------: | :------------------------------------------: | :--------: | :--------: |
|
||||
| MobileNetV3 | MobileNetV3_large_x1_0 | 支持 | 支持 |
|
||||
| MobileNetV3 | MobileNetV3_large_x1_0_KL | 支持 | 支持 |
|
||||
| MobileNetV3 | MobileNetV3_large_x1_0_PACT | 支持 | 支持 |
|
||||
| PP-ShiTu | PPShiTu_general_rec、PPShiTu_mainbody_det | 支持 | 支持 |
|
||||
| PP-ShiTu | GeneralRecognition_PPLCNet_x2_5_KL | 支持 | 支持 |
|
||||
| PP-ShiTu | GeneralRecognition_PPLCNet_x2_5_PACT | 支持 | 支持 |
|
||||
| PPHGNet | PPHGNet_small | 支持 | 支持 |
|
||||
| PPHGNet | PPHGNet_small_KL | 支持 | 支持 |
|
||||
| PPHGNet | PPHGNet_small_PACT | 支持 | 支持 |
|
||||
| PPHGNet | PPHGNet_tiny | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x0_25 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x0_35 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x0_5 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x0_75 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x1_0 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x1_0_KL | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x1_0_PACT | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x1_5 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x2_0 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x2_5 | 支持 | 支持 |
|
||||
| PPLCNetV2 | PPLCNetV2_base | 支持 | 支持 |
|
||||
| PPLCNetV2 | PPLCNetV2_base_KL | 支持 | 支持 |
|
||||
| ResNet | ResNet50 | 支持 | 支持 |
|
||||
| ResNet | ResNet50_vd | 支持 | 支持 |
|
||||
| ResNet | ResNet50_vd_KL | 支持 | 支持 |
|
||||
| ResNet | ResNet50_vd_PACT | 支持 | 支持 |
|
||||
| SwinTransformer | SwinTransformer_tiny_patch4_window7_224 | 支持 | 支持 |
|
||||
| SwinTransformer | SwinTransformer_tiny_patch4_window7_224_KL | 支持 | 支持 |
|
||||
| SwinTransformer | SwinTransformer_tiny_patch4_window7_224_PACT | 支持 | 支持 |
|
||||
|
||||
|
||||
## 2. 测试流程
|
||||
|
|
|
|||
|
|
@ -7,31 +7,37 @@ Linux GPU/CPU PYTHON 服务化部署测试的主程序为`test_serving_infer_pyt
|
|||
|
||||
- 推理相关：
|
||||
|
||||
| 算法名称 | 模型名称 | device_CPU | device_GPU |
|
||||
| :-------------: | :----------------------------------------: | :--------: | :--------: |
|
||||
| MobileNetV3 | MobileNetV3_large_x1_0 | 支持 | 支持 |
|
||||
| MobileNetV3 | MobileNetV3_large_x1_0_KL | 支持 | 支持 |
|
||||
| PP-ShiTu | PPShiTu_general_rec、PPShiTu_mainbody_det | 支持 | 支持 |
|
||||
| PP-ShiTu | GeneralRecognition_PPLCNet_x2_5_KL | 支持 | 支持 |
|
||||
| PPHGNet | PPHGNet_small | 支持 | 支持 |
|
||||
| PPHGNet | PPHGNet_small_KL | 支持 | 支持 |
|
||||
| PPHGNet | PPHGNet_tiny | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x0_25 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x0_35 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x0_5 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x0_75 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x1_0 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x1_0_KL | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x1_5 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x2_0 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x2_5 | 支持 | 支持 |
|
||||
| PPLCNetV2 | PPLCNetV2_base | 支持 | 支持 |
|
||||
| PPLCNetV2 | PPLCNetV2_base_KL | 支持 | 支持 |
|
||||
| ResNet | ResNet50 | 支持 | 支持 |
|
||||
| ResNet | ResNet50_vd | 支持 | 支持 |
|
||||
| ResNet | ResNet50_vd_KL | 支持 | 支持 |
|
||||
| SwinTransformer | SwinTransformer_tiny_patch4_window7_224 | 支持 | 支持 |
|
||||
| SwinTransformer | SwinTransformer_tiny_patch4_window7_224_KL | 支持 | 支持 |
|
||||
| 算法名称 | 模型名称 | device_CPU | device_GPU |
|
||||
| :-------------: | :------------------------------------------: | :--------: | :--------: |
|
||||
| MobileNetV3 | MobileNetV3_large_x1_0 | 支持 | 支持 |
|
||||
| MobileNetV3 | MobileNetV3_large_x1_0_KL | 支持 | 支持 |
|
||||
| MobileNetV3 | MobileNetV3_large_x1_0_PACT | 支持 | 支持 |
|
||||
| PP-ShiTu | PPShiTu_general_rec、PPShiTu_mainbody_det | 支持 | 支持 |
|
||||
| PP-ShiTu | GeneralRecognition_PPLCNet_x2_5_KL | 支持 | 支持 |
|
||||
| PP-ShiTu | GeneralRecognition_PPLCNet_x2_5_PACT | 支持 | 支持 |
|
||||
| PPHGNet | PPHGNet_small | 支持 | 支持 |
|
||||
| PPHGNet | PPHGNet_small_KL | 支持 | 支持 |
|
||||
| PPHGNet | PPHGNet_small_PACT | 支持 | 支持 |
|
||||
| PPHGNet | PPHGNet_tiny | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x0_25 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x0_35 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x0_5 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x0_75 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x1_0 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x1_0_KL | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x1_0_PACT | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x1_5 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x2_0 | 支持 | 支持 |
|
||||
| PPLCNet | PPLCNet_x2_5 | 支持 | 支持 |
|
||||
| PPLCNetV2 | PPLCNetV2_base | 支持 | 支持 |
|
||||
| PPLCNetV2 | PPLCNetV2_base_KL | 支持 | 支持 |
|
||||
| ResNet | ResNet50 | 支持 | 支持 |
|
||||
| ResNet | ResNet50_vd | 支持 | 支持 |
|
||||
| ResNet | ResNet50_vd_KL | 支持 | 支持 |
|
||||
| ResNet | ResNet50_vd_PACT | 支持 | 支持 |
|
||||
| SwinTransformer | SwinTransformer_tiny_patch4_window7_224 | 支持 | 支持 |
|
||||
| SwinTransformer | SwinTransformer_tiny_patch4_window7_224_KL | 支持 | 支持 |
|
||||
| SwinTransformer | SwinTransformer_tiny_patch4_window7_224_PACT | 支持 | 支持 |
|
||||
|
||||
|
||||
## 2. 测试流程
|
||||
|
|
|
|||
|
|
@ -0,0 +1,106 @@
|
|||
# Linux GPU/CPU PACT量化训练推理测试
|
||||
|
||||
Linux GPU/CPU PACT量化训练推理测试的主程序为`test_train_inference_python.sh`，可以测试基于Python的模型PACT在线量化等基本功能。
|
||||
|
||||
## 1. 测试结论汇总
|
||||
|
||||
- 训练相关：
|
||||
|
||||
| 算法名称 | 模型名称 | 单机单卡 |
|
||||
| :-------------: | :-------------------------------------: | :----------: |
|
||||
| MobileNetV3 | MobileNetV3_large_x1_0 | PACT量化训练 |
|
||||
| PP-ShiTu | GeneralRecognition_PPLCNet_x2_5 | PACT量化训练 |
|
||||
| PPHGNet | PPHGNet_small | PACT量化训练 |
|
||||
| PPHGNet | PPHGNet_tiny | PACT量化训练 |
|
||||
| PPLCNet | PPLCNet_x0_25 | PACT量化训练 |
|
||||
| PPLCNet | PPLCNet_x0_35 | PACT量化训练 |
|
||||
| PPLCNet | PPLCNet_x0_5 | PACT量化训练 |
|
||||
| PPLCNet | PPLCNet_x0_75 | PACT量化训练 |
|
||||
| PPLCNet | PPLCNet_x1_0 | PACT量化训练 |
|
||||
| PPLCNet | PPLCNet_x1_5 | PACT量化训练 |
|
||||
| PPLCNet | PPLCNet_x2_0 | PACT量化训练 |
|
||||
| PPLCNet | PPLCNet_x2_5 | PACT量化训练 |
|
||||
| PPLCNetV2 | PPLCNetV2_base | PACT量化训练 |
|
||||
| ResNet | ResNet50 | PACT量化训练 |
|
||||
| ResNet | ResNet50_vd | PACT量化训练 |
|
||||
| SwinTransformer | SwinTransformer_tiny_patch4_window7_224 | PACT量化训练 |
|
||||
|
||||
- 推理相关：
|
||||
|
||||
| 算法名称 | 模型名称 | device_CPU | device_GPU | batchsize |
|
||||
| :-------------: | :-------------------------------------: | :--------: | :--------: | :-------: |
|
||||
| MobileNetV3 | MobileNetV3_large_x1_0 | 支持 | 支持 | 1 |
|
||||
| PP-ShiTu | GeneralRecognition_PPLCNet_x2_5 | 支持 | 支持 | 1 |
|
||||
| PPHGNet | PPHGNet_small | 支持 | 支持 | 1 |
|
||||
| PPHGNet | PPHGNet_tiny | 支持 | 支持 | 1 |
|
||||
| PPLCNet | PPLCNet_x0_25 | 支持 | 支持 | 1 |
|
||||
| PPLCNet | PPLCNet_x0_35 | 支持 | 支持 | 1 |
|
||||
| PPLCNet | PPLCNet_x0_5 | 支持 | 支持 | 1 |
|
||||
| PPLCNet | PPLCNet_x0_75 | 支持 | 支持 | 1 |
|
||||
| PPLCNet | PPLCNet_x1_0 | 支持 | 支持 | 1 |
|
||||
| PPLCNet | PPLCNet_x1_5 | 支持 | 支持 | 1 |
|
||||
| PPLCNet | PPLCNet_x2_0 | 支持 | 支持 | 1 |
|
||||
| PPLCNet | PPLCNet_x2_5 | 支持 | 支持 | 1 |
|
||||
| PPLCNetV2 | PPLCNetV2_base | 支持 | 支持 | 1 |
|
||||
| ResNet | ResNet50 | 支持 | 支持 | 1 |
|
||||
| ResNet | ResNet50_vd | 支持 | 支持 | 1 |
|
||||
| SwinTransformer | SwinTransformer_tiny_patch4_window7_224 | 支持 | 支持 | 1 |
|
||||
|
||||
## 2. 测试流程
|
||||
|
||||
一下测试流程以 MobileNetV3_large_x1_0 模型为例。
|
||||
|
||||
### 2.1 准备环境
|
||||
|
||||
- 安装PaddlePaddle：如果您已经安装了2.2或者以上版本的paddlepaddle，那么无需运行下面的命令安装paddlepaddle。
|
||||
```bash
|
||||
# 需要安装2.2及以上版本的Paddle
|
||||
# 安装GPU版本的Paddle
|
||||
python3.7 -m pip install paddlepaddle-gpu==2.2.0
|
||||
# 安装CPU版本的Paddle
|
||||
python3.7 -m pip install paddlepaddle==2.2.0
|
||||
```
|
||||
|
||||
- 安装PaddleSlim
|
||||
```bash
|
||||
python3.7 -m pip install paddleslim==2.2.0
|
||||
```
|
||||
|
||||
- 安装依赖
|
||||
```bash
|
||||
python3.7 -m pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
|
||||
```
|
||||
|
||||
- 安装AutoLog（规范化日志输出工具）
|
||||
```bash
|
||||
python3.7 -m pip install https://paddleocr.bj.bcebos.com/libs/auto_log-1.2.0-py3-none-any.whl
|
||||
```
|
||||
|
||||
### 2.2 准备数据和模型
|
||||
|
||||
```bash
|
||||
bash test_tipc/prepare.sh test_tipc/config/PPLCNetV2/PPLCNetV2_base_train_pact_infer_python.txt lite_train_lite_infer
|
||||
```
|
||||
|
||||
在线量化的操作流程，可参考[文档](../../deploy/slim/README.md)。
|
||||
|
||||
### 2.3 功能测试
|
||||
|
||||
以`MobileNetV3_large_x1_0`的`Linux GPU/CPU PACT在线量化训练推理测试`为例，命令如下所示。
|
||||
|
||||
```bash
|
||||
bash test_tipc/test_train_inference_python.sh test_tipc/config/PPLCNetV2/PPLCNetV2_base_train_pact_infer_python.txt lite_train_lite_infer
|
||||
```
|
||||
|
||||
输出结果如下，表示命令运行成功。
|
||||
|
||||
```log
|
||||
Run successfully with command - MobileNetV3_large_x1_0 - python3.7 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 -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.01 -o Global.device=gpu -o Global.output_dir=./test_tipc/output/MobileNetV3_large_x1_0/lite_train_lite_infer/pact_train_gpus_0_autocast_null -o Global.epochs=2 -o DataLoader.Train.sampler.batch_size=8 !
|
||||
Run successfully with command - MobileNetV3_large_x1_0 - python3.7 tools/eval.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml -o Slim.quant.name=pact -o Global.pretrained_model=./test_tipc/output/MobileNetV3_large_x1_0/lite_train_lite_infer/pact_train_gpus_0_autocast_null/MobileNetV3_large_x1_0/latest -o Global.device=gpu !
|
||||
Run successfully with command - MobileNetV3_large_x1_0 - python3.7 tools/export_model.py -c ppcls/configs/slim/MobileNetV3_large_x1_0_quantization.yaml -o Slim.quant.name=pact -o Global.pretrained_model=./test_tipc/output/MobileNetV3_large_x1_0/lite_train_lite_infer/pact_train_gpus_0_autocast_null/MobileNetV3_large_x1_0/latest -o Global.save_inference_dir=./test_tipc/output/MobileNetV3_large_x1_0/lite_train_lite_infer/pact_train_gpus_0_autocast_null!
|
||||
Run successfully with command - MobileNetV3_large_x1_0 - python3.7 python/predict_cls.py -c configs/inference_cls.yaml -o Global.use_gpu=True -o Global.use_tensorrt=False -o Global.use_fp16=False -o Global.inference_model_dir=.././test_tipc/output/MobileNetV3_large_x1_0/lite_train_lite_infer/pact_train_gpus_0_autocast_null -o Global.batch_size=1 -o Global.infer_imgs=../dataset/ILSVRC2012/val -o Global.benchmark=True > .././test_tipc/output/MobileNetV3_large_x1_0/lite_train_lite_infer/infer_gpu_usetrt_False_precision_False_batchsize_1.log 2>&1 !
|
||||
Run successfully with command - MobileNetV3_large_x1_0 - python3.7 python/predict_cls.py -c configs/inference_cls.yaml -o Global.use_gpu=False -o Global.enable_mkldnn=False -o Global.cpu_num_threads=1 -o Global.inference_model_dir=.././test_tipc/output/MobileNetV3_large_x1_0/lite_train_lite_infer/pact_train_gpus_0_autocast_null -o Global.batch_size=1 -o Global.infer_imgs=../dataset/ILSVRC2012/val -o Global.benchmark=True > .././test_tipc/output/MobileNetV3_large_x1_0/lite_train_lite_infer/infer_cpu_usemkldnn_False_threads_1_batchsize_1.log 2>&1 !
|
||||
```
|
||||
同时，测试过程中的日志保存在`PaddleClas/test_tipc/output/MobileNetV3_large_x1_0/lite_train_lite_infer`下。
|
||||
|
||||
如果运行失败，也会在终端中输出运行失败的日志信息以及对应的运行命令。可以基于该命令，分析运行失败的原因。
|
||||
|
|
@ -0,0 +1,105 @@
|
|||
# Linux GPU/CPU KL离线量化推理测试
|
||||
|
||||
Linux GPU/CPU KL离线量化推理测试的主程序为`test_ptq_inference_python.sh`，可以测试基于Python的模型KL离线量化推理等基本功能。
|
||||
|
||||
## 1. 测试结论汇总
|
||||
|
||||
- KL离线量化：
|
||||
|
||||
| 算法名称 | 模型名称 | CPU |
|
||||
| :-------------: | :-------------------------------------: | :----------: |
|
||||
| MobileNetV3 | MobileNetV3_large_x1_0 | KL离线量化 |
|
||||
| PP-ShiTu | GeneralRecognition_PPLCNet_x2_5 | KL离线量化 |
|
||||
| PPHGNet | PPHGNet_small | KL离线量化 |
|
||||
| PPHGNet | PPHGNet_tiny | KL离线量化 |
|
||||
| PPLCNet | PPLCNet_x0_25 | KL离线量化 |
|
||||
| PPLCNet | PPLCNet_x0_35 | KL离线量化 |
|
||||
| PPLCNet | PPLCNet_x0_5 | KL离线量化 |
|
||||
| PPLCNet | PPLCNet_x0_75 | KL离线量化 |
|
||||
| PPLCNet | PPLCNet_x1_0 | KL离线量化 |
|
||||
| PPLCNet | PPLCNet_x1_5 | KL离线量化 |
|
||||
| PPLCNet | PPLCNet_x2_0 | KL离线量化 |
|
||||
| PPLCNet | PPLCNet_x2_5 | KL离线量化 |
|
||||
| PPLCNetV2 | PPLCNetV2_base | KL离线量化 |
|
||||
| ResNet | ResNet50 | KL离线量化 |
|
||||
| ResNet | ResNet50_vd | KL离线量化 |
|
||||
| SwinTransformer | SwinTransformer_tiny_patch4_window7_224 | KL离线量化 |
|
||||
|
||||
- 推理相关：
|
||||
|
||||
| 算法名称 | 模型名称 | CPU |
|
||||
| :-------------: | :-------------------------------------: | :----------: |
|
||||
| MobileNetV3 | MobileNetV3_large_x1_0 | KL离线量化 |
|
||||
| PP-ShiTu | GeneralRecognition_PPLCNet_x2_5 | KL离线量化 |
|
||||
| PPHGNet | PPHGNet_small | KL离线量化 |
|
||||
| PPHGNet | PPHGNet_tiny | KL离线量化 |
|
||||
| PPLCNet | PPLCNet_x0_25 | KL离线量化 |
|
||||
| PPLCNet | PPLCNet_x0_35 | KL离线量化 |
|
||||
| PPLCNet | PPLCNet_x0_5 | KL离线量化 |
|
||||
| PPLCNet | PPLCNet_x0_75 | KL离线量化 |
|
||||
| PPLCNet | PPLCNet_x1_0 | KL离线量化 |
|
||||
| PPLCNet | PPLCNet_x1_5 | KL离线量化 |
|
||||
| PPLCNet | PPLCNet_x2_0 | KL离线量化 |
|
||||
| PPLCNet | PPLCNet_x2_5 | KL离线量化 |
|
||||
| PPLCNetV2 | PPLCNetV2_base | KL离线量化 |
|
||||
| ResNet | ResNet50 | KL离线量化 |
|
||||
| ResNet | ResNet50_vd | KL离线量化 |
|
||||
| SwinTransformer | SwinTransformer_tiny_patch4_window7_224 | KL离线量化 |
|
||||
|
||||
|
||||
## 2. 测试流程
|
||||
|
||||
一下测试流程以 MobileNetV3_large_x1_0 模型为例。
|
||||
|
||||
### 2.1 准备环境
|
||||
|
||||
- 安装PaddlePaddle：如果您已经安装了2.2或者以上版本的paddlepaddle，那么无需运行下面的命令安装paddlepaddle。
|
||||
```bash
|
||||
# 需要安装2.2及以上版本的Paddle
|
||||
# 安装GPU版本的Paddle
|
||||
python3.7 -m pip install paddlepaddle-gpu==2.2.0
|
||||
# 安装CPU版本的Paddle
|
||||
python3.7 -m pip install paddlepaddle==2.2.0
|
||||
```
|
||||
|
||||
- 安装PaddleSlim
|
||||
```bash
|
||||
python3.7 -m pip install paddleslim==2.2.0
|
||||
```
|
||||
|
||||
- 安装依赖
|
||||
```bash
|
||||
python3.7 -m pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
|
||||
```
|
||||
|
||||
- 安装AutoLog（规范化日志输出工具）
|
||||
```bash
|
||||
python3.7 -m pip install https://paddleocr.bj.bcebos.com/libs/auto_log-1.2.0-py3-none-any.whl
|
||||
```
|
||||
|
||||
### 2.2 准备数据和模型
|
||||
|
||||
```bash
|
||||
bash test_tipc/prepare.sh test_tipc/config/MobileNetV3/MobileNetV3_large_x1_0_train_ptq_infer_python.txt whole_infer
|
||||
```
|
||||
|
||||
离线量化的操作流程，可参考[文档](../../deploy/slim/README.md)。
|
||||
|
||||
### 2.3 功能测试
|
||||
|
||||
以`MobileNetV3_large_x1_0`的`Linux GPU/CPU KL离线量化训练推理测试`为例，命令如下所示。
|
||||
|
||||
```bash
|
||||
bash test_tipc/test_ptq_inference_python.sh test_tipc/config/MobileNetV3/MobileNetV3_large_x1_0_train_ptq_infer_python.txt whole_infer
|
||||
```
|
||||
|
||||
输出结果如下，表示命令运行成功。
|
||||
|
||||
```log
|
||||
Run successfully with command - MobileNetV3_large_x1_0 - python3.7 deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml -o Global.save_inference_dir=./MobileNetV3_large_x1_0_infer!
|
||||
Run successfully with command - MobileNetV3_large_x1_0 - python3.7 python/predict_cls.py -c configs/inference_cls.yaml -o Global.use_gpu=True -o Global.use_tensorrt=False -o Global.use_fp16=False -o Global.inference_model_dir=.././MobileNetV3_large_x1_0_infer//quant_post_static_model -o Global.batch_size=1 -o Global.infer_imgs=../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg -o Global.benchmark=False > .././test_tipc/output/MobileNetV3_large_x1_0/whole_infer/infer_gpu_usetrt_False_precision_False_batchsize_1.log 2>&1 !
|
||||
Run successfully with command - MobileNetV3_large_x1_0 - python3.7 python/predict_cls.py -c configs/inference_cls.yaml -o Global.use_gpu=False -o Global.enable_mkldnn=False -o Global.cpu_num_threads=1 -o Global.inference_model_dir=.././MobileNetV3_large_x1_0_infer//quant_post_static_model -o Global.batch_size=1 -o Global.infer_imgs=../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg -o Global.benchmark=False > .././test_tipc/output/MobileNetV3_large_x1_0/whole_infer/infer_cpu_usemkldnn_False_threads_1_batchsize_1.log 2>&1 !
|
||||
```
|
||||
同时，测试过程中的日志保存在`PaddleClas/test_tipc/output/MobileNetV3_large_x1_0/whole_infer`下。
|
||||
|
||||
如果运行失败，也会在终端中输出运行失败的日志信息以及对应的运行命令。可以基于该命令，分析运行失败的原因。
|
||||
|
|
@ -2,8 +2,7 @@
|
|||
FILENAME=$1
|
||||
|
||||
# MODE be one of ['lite_train_lite_infer' 'lite_train_whole_infer' 'whole_train_whole_infer',
|
||||
# 'whole_infer', 'klquant_whole_infer',
|
||||
# 'cpp_infer', 'serving_infer', 'lite_infer']
|
||||
# 'whole_infer', 'cpp_infer', 'serving_infer', 'lite_infer']
|
||||
|
||||
MODE=$2
|
||||
|
||||
|
|
@ -107,6 +106,14 @@ if [[ ${MODE} = "cpp_infer" ]]; then
|
|||
model_dir=${tar_name%.*}
|
||||
eval "tar xf ${tar_name}"
|
||||
|
||||
# move '_int8' suffix in pact models
|
||||
if [[ ${tar_name} =~ "pact_infer" ]]; then
|
||||
cd ${cls_inference_model_dir}
|
||||
mv inference_int8.pdiparams inference.pdiparams
|
||||
mv inference_int8.pdmodel inference.pdmodel
|
||||
cd ..
|
||||
fi
|
||||
|
||||
cd dataset
|
||||
rm -rf ILSVRC2012
|
||||
wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/data/whole_chain/whole_chain_infer.tar
|
||||
|
|
@ -176,6 +183,14 @@ if [[ ${MODE} = "lite_train_lite_infer" ]] || [[ ${MODE} = "lite_train_whole_inf
|
|||
mv val.txt val_list.txt
|
||||
cp -r train/* val/
|
||||
cd ../../
|
||||
if [[ ${FILENAME} =~ "pact_infer" ]]; then
|
||||
# download pretrained model for PACT training
|
||||
pretrpretrained_model_url=$(func_parser_value "${lines[35]}")
|
||||
mkdir pretrained_model
|
||||
cd pretrained_model
|
||||
wget -nc ${pretrpretrained_model_url} --no-check-certificate
|
||||
cd ..
|
||||
fi
|
||||
elif [[ ${MODE} = "whole_infer" ]]; then
|
||||
# download data
|
||||
if [[ ${model_name} =~ "GeneralRecognition" ]]; then
|
||||
|
|
@ -225,6 +240,14 @@ elif [[ ${MODE} = "whole_train_whole_infer" ]]; then
|
|||
mv train.txt train_list.txt
|
||||
mv test.txt val_list.txt
|
||||
cd ../../
|
||||
if [[ ${FILENAME} =~ "pact_infer" ]]; then
|
||||
# download pretrained model for PACT training
|
||||
pretrpretrained_model_url=$(func_parser_value "${lines[35]}")
|
||||
mkdir pretrained_model
|
||||
cd pretrained_model
|
||||
wget -nc ${pretrpretrained_model_url} --no-check-certificate
|
||||
cd ..
|
||||
fi
|
||||
fi
|
||||
|
||||
if [[ ${MODE} = "serving_infer" ]]; then
|
||||
|
|
@ -247,7 +270,18 @@ if [[ ${MODE} = "serving_infer" ]]; then
|
|||
cls_inference_model_url=$(func_parser_value "${lines[3]}")
|
||||
cls_tar_name=$(func_get_url_file_name "${cls_inference_model_url}")
|
||||
cd ./deploy/paddleserving
|
||||
wget -nc ${cls_inference_model_url} && tar xf ${cls_tar_name}
|
||||
wget -nc ${cls_inference_model_url}
|
||||
tar xf ${cls_tar_name}
|
||||
|
||||
# move '_int8' suffix in pact models
|
||||
if [[ ${cls_tar_name} =~ "pact_infer" ]]; then
|
||||
cls_inference_model_dir=${cls_tar_name%%.tar}
|
||||
cd ${cls_inference_model_dir}
|
||||
mv inference_int8.pdiparams inference.pdiparams
|
||||
mv inference_int8.pdmodel inference.pdmodel
|
||||
cd ..
|
||||
fi
|
||||
|
||||
cd ../../
|
||||
fi
|
||||
unset http_proxy
|
||||
|
|
|
|||
|
|
@ -0,0 +1,173 @@
|
|||
#!/bin/bash
|
||||
FILENAME=$1
|
||||
source test_tipc/common_func.sh
|
||||
|
||||
# MODE be one of ['whole_infer']
|
||||
MODE=$2
|
||||
|
||||
dataline=$(cat ${FILENAME})
|
||||
|
||||
# parser params
|
||||
IFS=$'\n'
|
||||
lines=(${dataline})
|
||||
|
||||
# The training params
|
||||
model_name=$(func_parser_value "${lines[1]}")
|
||||
python=$(func_parser_value "${lines[2]}")
|
||||
gpu_list=$(func_parser_value "${lines[3]}")
|
||||
train_use_gpu_key=$(func_parser_key "${lines[4]}")
|
||||
train_use_gpu_value=$(func_parser_value "${lines[4]}")
|
||||
autocast_list=$(func_parser_value "${lines[5]}")
|
||||
autocast_key=$(func_parser_key "${lines[5]}")
|
||||
epoch_key=$(func_parser_key "${lines[6]}")
|
||||
epoch_num=$(func_parser_params "${lines[6]}")
|
||||
save_model_key=$(func_parser_key "${lines[7]}")
|
||||
train_batch_key=$(func_parser_key "${lines[8]}")
|
||||
train_batch_value=$(func_parser_value "${lines[8]}")
|
||||
pretrain_model_key=$(func_parser_key "${lines[9]}")
|
||||
pretrain_model_value=$(func_parser_value "${lines[9]}")
|
||||
train_model_name=$(func_parser_value "${lines[10]}")
|
||||
train_infer_img_dir=$(func_parser_value "${lines[11]}")
|
||||
train_param_key1=$(func_parser_key "${lines[12]}")
|
||||
train_param_value1=$(func_parser_value "${lines[12]}")
|
||||
|
||||
trainer_list=$(func_parser_value "${lines[14]}")
|
||||
trainer_norm=$(func_parser_key "${lines[15]}")
|
||||
norm_trainer=$(func_parser_value "${lines[15]}")
|
||||
pact_key=$(func_parser_key "${lines[16]}")
|
||||
pact_trainer=$(func_parser_value "${lines[16]}")
|
||||
fpgm_key=$(func_parser_key "${lines[17]}")
|
||||
fpgm_trainer=$(func_parser_value "${lines[17]}")
|
||||
distill_key=$(func_parser_key "${lines[18]}")
|
||||
distill_trainer=$(func_parser_value "${lines[18]}")
|
||||
to_static_key=$(func_parser_key "${lines[19]}")
|
||||
to_static_trainer=$(func_parser_value "${lines[19]}")
|
||||
trainer_key2=$(func_parser_key "${lines[20]}")
|
||||
trainer_value2=$(func_parser_value "${lines[20]}")
|
||||
|
||||
eval_py=$(func_parser_value "${lines[23]}")
|
||||
eval_key1=$(func_parser_key "${lines[24]}")
|
||||
eval_value1=$(func_parser_value "${lines[24]}")
|
||||
|
||||
save_infer_key=$(func_parser_key "${lines[27]}")
|
||||
export_weight=$(func_parser_key "${lines[28]}")
|
||||
norm_export=$(func_parser_value "${lines[29]}")
|
||||
pact_export=$(func_parser_value "${lines[30]}")
|
||||
fpgm_export=$(func_parser_value "${lines[31]}")
|
||||
distill_export=$(func_parser_value "${lines[32]}")
|
||||
kl_quant_cmd_key=$(func_parser_key "${lines[33]}")
|
||||
kl_quant_cmd_value=$(func_parser_value "${lines[33]}")
|
||||
export_key2=$(func_parser_key "${lines[34]}")
|
||||
export_value2=$(func_parser_value "${lines[34]}")
|
||||
|
||||
# parser inference model
|
||||
infer_model_dir_list=$(func_parser_value "${lines[36]}")
|
||||
infer_export_flag=$(func_parser_value "${lines[37]}")
|
||||
infer_is_quant=$(func_parser_value "${lines[38]}")
|
||||
|
||||
# parser inference
|
||||
inference_py=$(func_parser_value "${lines[39]}")
|
||||
use_gpu_key=$(func_parser_key "${lines[40]}")
|
||||
use_gpu_list=$(func_parser_value "${lines[40]}")
|
||||
use_mkldnn_key=$(func_parser_key "${lines[41]}")
|
||||
use_mkldnn_list=$(func_parser_value "${lines[41]}")
|
||||
cpu_threads_key=$(func_parser_key "${lines[42]}")
|
||||
cpu_threads_list=$(func_parser_value "${lines[42]}")
|
||||
batch_size_key=$(func_parser_key "${lines[43]}")
|
||||
batch_size_list=$(func_parser_value "${lines[43]}")
|
||||
use_trt_key=$(func_parser_key "${lines[44]}")
|
||||
use_trt_list=$(func_parser_value "${lines[44]}")
|
||||
precision_key=$(func_parser_key "${lines[45]}")
|
||||
precision_list=$(func_parser_value "${lines[45]}")
|
||||
infer_model_key=$(func_parser_key "${lines[46]}")
|
||||
image_dir_key=$(func_parser_key "${lines[47]}")
|
||||
infer_img_dir=$(func_parser_value "${lines[47]}")
|
||||
save_log_key=$(func_parser_key "${lines[48]}")
|
||||
benchmark_key=$(func_parser_key "${lines[49]}")
|
||||
benchmark_value=$(func_parser_value "${lines[49]}")
|
||||
infer_key1=$(func_parser_key "${lines[50]}")
|
||||
infer_value1=$(func_parser_value "${lines[50]}")
|
||||
if [ ! $epoch_num ]; then
|
||||
epoch_num=2
|
||||
fi
|
||||
if [[ $MODE = 'benchmark_train' ]]; then
|
||||
epoch_num=1
|
||||
fi
|
||||
|
||||
LOG_PATH="./test_tipc/output/${model_name}/${MODE}"
|
||||
mkdir -p ${LOG_PATH}
|
||||
status_log="${LOG_PATH}/results_python.log"
|
||||
|
||||
function func_inference() {
|
||||
IFS='|'
|
||||
_python=$1
|
||||
_script=$2
|
||||
_model_dir=$3
|
||||
_log_path=$4
|
||||
_img_dir=$5
|
||||
_flag_quant=$6
|
||||
# inference
|
||||
for use_gpu in ${use_gpu_list[*]}; do
|
||||
if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then
|
||||
for use_mkldnn in ${use_mkldnn_list[*]}; do
|
||||
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_${batch_size}.log"
|
||||
set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
|
||||
set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
|
||||
set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
|
||||
set_cpu_threads=$(func_set_params "${cpu_threads_key}" "${threads}")
|
||||
set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
|
||||
set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
|
||||
command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
|
||||
eval $command
|
||||
last_status=${PIPESTATUS[0]}
|
||||
eval "cat ${_save_log_path}"
|
||||
status_check $last_status "${command}" "../${status_log}" "${model_name}"
|
||||
done
|
||||
done
|
||||
done
|
||||
elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then
|
||||
for use_trt in ${use_trt_list[*]}; do
|
||||
for precision in ${precision_list[*]}; do
|
||||
if [ ${precision} = "True" ] && [ ${use_trt} = "False" ]; then
|
||||
continue
|
||||
fi
|
||||
for batch_size in ${batch_size_list[*]}; do
|
||||
_save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
|
||||
set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
|
||||
set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
|
||||
set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
|
||||
set_tensorrt=$(func_set_params "${use_trt_key}" "${use_trt}")
|
||||
set_precision=$(func_set_params "${precision_key}" "${precision}")
|
||||
set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
|
||||
command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} > ${_save_log_path} 2>&1 "
|
||||
eval $command
|
||||
last_status=${PIPESTATUS[0]}
|
||||
eval "cat ${_save_log_path}"
|
||||
status_check $last_status "${command}" "../${status_log}" "${model_name}"
|
||||
done
|
||||
done
|
||||
done
|
||||
else
|
||||
echo "Does not support hardware other than CPU and GPU Currently!"
|
||||
fi
|
||||
done
|
||||
}
|
||||
|
||||
# for kl_quant
|
||||
if [ ${kl_quant_cmd_value} != "null" ] && [ ${kl_quant_cmd_value} != "False" ]; then
|
||||
echo "kl_quant"
|
||||
command="${python} ${kl_quant_cmd_value}"
|
||||
echo ${command}
|
||||
eval $command
|
||||
last_status=${PIPESTATUS[0]}
|
||||
status_check $last_status "${command}" "${status_log}" "${model_name}"
|
||||
cd ${infer_model_dir_list}/quant_post_static_model
|
||||
ln -s __model__ inference.pdmodel
|
||||
ln -s __params__ inference.pdiparams
|
||||
cd ../../deploy
|
||||
is_quant=True
|
||||
func_inference "${python}" "${inference_py}" "../${infer_model_dir_list}/quant_post_static_model" "../${LOG_PATH}" "${infer_img_dir}" ${is_quant}
|
||||
cd ..
|
||||
fi
|
||||
|
|
@ -2,6 +2,7 @@
|
|||
source test_tipc/common_func.sh
|
||||
|
||||
FILENAME=$1
|
||||
MODE=$2
|
||||
dataline=$(awk 'NR==1, NR==19{print}' $FILENAME)
|
||||
|
||||
# parser params
|
||||
|
|
@ -38,7 +39,7 @@ pipeline_py=$(func_parser_value "${lines[13]}")
|
|||
|
||||
|
||||
function func_serving_cls(){
|
||||
LOG_PATH="test_tipc/output/${model_name}"
|
||||
LOG_PATH="test_tipc/output/${model_name}/${MODE}"
|
||||
mkdir -p ${LOG_PATH}
|
||||
LOG_PATH="../../${LOG_PATH}"
|
||||
status_log="${LOG_PATH}/results_serving.log"
|
||||
|
|
@ -153,7 +154,7 @@ function func_serving_cls(){
|
|||
|
||||
|
||||
function func_serving_rec(){
|
||||
LOG_PATH="test_tipc/output/${model_name}"
|
||||
LOG_PATH="test_tipc/output/${model_name}/${MODE}"
|
||||
mkdir -p ${LOG_PATH}
|
||||
LOG_PATH="../../../${LOG_PATH}"
|
||||
status_log="${LOG_PATH}/results_serving.log"
|
||||
|
|
|
|||
|
|
@ -32,6 +32,7 @@ train_param_key1=$(func_parser_key "${lines[12]}")
|
|||
train_param_value1=$(func_parser_value "${lines[12]}")
|
||||
|
||||
trainer_list=$(func_parser_value "${lines[14]}")
|
||||
|
||||
trainer_norm=$(func_parser_key "${lines[15]}")
|
||||
norm_trainer=$(func_parser_value "${lines[15]}")
|
||||
pact_key=$(func_parser_key "${lines[16]}")
|
||||
|
|
@ -276,7 +277,7 @@ else
|
|||
set_eval_pretrain=$(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
|
||||
if [[ ${trainer} = ${trainer_norm} || ${trainer} = ${pact_key} ]]; then
|
||||
load_norm_train_model=${set_eval_pretrain}
|
||||
fi
|
||||
# run eval
|
||||
|
|
|
|||
Loading…
Reference in New Issue