nni_main_classification_1.py

from comet_ml import experiment
from data_loader.uts_classification_data_loader import UtsClassificationDataLoader
from models.uts_classification_model_2 import UtsClassificationModel
from trainers.uts_classification_trainer import UtsClassificationTrainer
from evaluater.uts_classification_evaluater import UtsClassificationEvaluater
from utils.config import process_config_UtsClassification
from utils.dirs import create_dirs
from utils.utils import get_args
import os
import time
import nni
import logging

LOG = logging.getLogger('main_classification')

from utils.config import get_config_from_json

def process_config_UtsClassification_bayes_optimization(json_file,params):
    config, _ = get_config_from_json(json_file)

    config.model.learning_rate = params['learning_rate']
    config.trainer.batch_size = params['batch_size']
    config.model.type = params['type']
    config.model.nb_filters = params['nb_filters']
    config.model.depth = params['depth']
    config.model.kernel_size = params['kernel_size']

    config.model.params = params

    config.callbacks.tensorboard_log_dir = os.path.join("experiments",time.strftime("%Y-%m-%d/", time.localtime()),
                                                        config.exp.name, config.dataset.name,
                                                        config.model.name, "tensorboard_logs",
                                                        "lr=%s,epoch=%s,batch=%s,type=%s,nb_filters=%s,depth=%s,kernel_size=%s" % (
                                                            config.model.learning_rate, config.trainer.num_epochs,
                                                            config.trainer.batch_size, config.model.type, config.model.nb_filters,
                                                            config.model.depth,config.model.kernel_size)
                                                       )
    config.callbacks.checkpoint_dir = os.path.join("experiments", time.strftime("%Y-%m-%d/", time.localtime()),
                                                   config.exp.name, config.dataset.name,
                                                   config.model.name, "%s-%s-%s-%s-%s-%s-%s" % (
                                                   config.model.learning_rate, config.trainer.num_epochs,
                                                   config.trainer.batch_size,config.model.type, config.model.nb_filters,
                                                            config.model.depth,config.model.kernel_size),
                                                   "checkpoints/")
    config.log_dir = os.path.join("experiments", time.strftime("%Y-%m-%d/", time.localtime()),
                                            config.exp.name, config.dataset.name,
                                            config.model.name, "%s-%s-%s-%s-%s-%s-%s" % (
                                            config.model.learning_rate, config.trainer.num_epochs,
                                            config.trainer.batch_size,config.model.type, config.model.nb_filters,
                                                            config.model.depth,config.model.kernel_size),
                                            "training_logs/")
    config.result_dir = os.path.join("experiments", time.strftime("%Y-%m-%d/", time.localtime()),
                                               config.exp.name, config.dataset.name,
                                               config.model.name, "%s-%s-%s-%s-%s-%s-%s" % (
                                                   config.model.learning_rate, config.trainer.num_epochs,
                                                   config.trainer.batch_size,config.model.type, config.model.nb_filters,
                                                            config.model.depth,config.model.kernel_size),
                                               "result/")
    return config

def generate_default_params():
    '''
    Generate default hyper parameters
    '''
    return {
        'learning_rate': 0.001,
        'batch_size':64,
        'type':'inceptiontime',
        'nb_filters':32,
        'depth':6,
        'kernel_size':41
        # 'learning_rate': 0.001,
        # 'batch_size': 64,
        # 'type' :1,
        # 'convfilt' : 64,
        # 'ksize' : 16,
        # 'depth' : 15,
        # 'drop' : 0.5
    }

def main():

    os.environ['CUDA_VISIBLE_DEVICES'] = '0'
    try:

        RECEIVED_PARAMS = nni.get_next_parameter()

        LOG.debug(RECEIVED_PARAMS)

        PARAMS = generate_default_params()

        PARAMS.update(RECEIVED_PARAMS)

        args = get_args()

        config = process_config_UtsClassification_bayes_optimization(args.config,PARAMS)
        # except:
        #     print("missing or invalid arguments")
        #     exit(0)

        # create the experiments dirs
        create_dirs([config.callbacks.tensorboard_log_dir, config.callbacks.checkpoint_dir,
                     config.log_dir, config.result_dir])

        print('Create the data generator.')
        data_loader = UtsClassificationDataLoader(config)

        print('Create the model.')

        model = UtsClassificationModel(config, data_loader.get_inputshape(), data_loader.get_nbclasses())

        print('Create the trainer')
        trainer = UtsClassificationTrainer(model.model, data_loader.get_train_data(), config)

        print('Start training the model.')
        trainer.train()

        print('Create the evaluater.')
        evaluater = UtsClassificationEvaluater(trainer.best_model, data_loader.get_test_data(), data_loader.get_nbclasses(),
                                               config)

        print('Start evaluating the model.')
        evaluater.evluate()

        nni.report_final_result(evaluater.f1)

        print('done')

    except Exception as e:
        LOG.exception(e)
        raise

if __name__ == '__main__':
    main()