35 строки
1.3 KiB
Python
35 строки
1.3 KiB
Python
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from ir_converters.model_to_grapher import*
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from prediction.predictors.predict_by_kernel import*
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from kerneldetection.kernel_detector import*
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import pickle,sys,os
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import argparse
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parser = argparse.ArgumentParser("predict model latency on device")
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parser.add_argument('--hardware', type=str, default='cpu')
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parser.add_argument('--mf', type=str, default='alexnet')
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parser.add_argument('--input_models', type=str, required=True, help='Path to input models. Either json or pb.')
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parser.add_argument( '--save_dir', type=str, default='results', help='Default preserve the original layer names. Readable will assign new kernel names according to types of the layers.')
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parser.add_argument( '--rule_dir', type=str, default='data/fusionrules', help='Default preserve the original layer names. Readable will assign new kernel names according to types of the layers.')
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args=parser.parse_args()
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hardware=args.hardware
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input_models=args.input_models
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for hardware in ['cpu','gpu','gpu1','vpu']:
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print('current hardware',hardware)
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if hardware=='gpu1':
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hw='gpu'
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else:
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hw=hardware
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latency_file="data/model_latency/"+hardware+"/"+args.mf+"-log.csv"
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kernel_types,kernel_with_features=split_model_into_kernels(input_models,hw,args.save_dir)
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rmse,rmspe,error,acc5,acc10=main_kernel_predict(hardware,kernel_with_features,latency_file)
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