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9877759 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | import json
import argparse
from trainer import train
import json
def string_to_list(args):
"""
Convert a JSON string to a Python list.
Args:
args (str): JSON string representing a list.
Returns:
list: Parsed list from the JSON string.
"""
return json.loads(args)
def main():
"""
Function to set up the training process.
Parses command line arguments, loads configuration from a JSON file, and starts the training.
"""
# Parse command-line arguments
args = setup_parser().parse_args()
param = load_json(args.config)
args = vars(args)
args.update(param)
train(args)
def load_json(settings_path):
"""
Load parameters from a JSON file.
Args:
settings_path (str): Path to the JSON configuration file.
Returns:
dict: Configuration parameters loaded from the JSON file.
"""
with open(settings_path) as data_file:
param = json.load(data_file)
return param
def setup_parser():
parser = argparse.ArgumentParser(description='Reproduce of multiple continual learning algorthms.')
parser.add_argument('--config', type=str, default='./exps/finetune.json', help='Json file of settings.')
parser.add_argument('--dataset', default="gnss", help="Kind of dataset", type=str)
parser.add_argument('--summary_file', type=str, default='run_new',help='path for the tensorboard experiment')
parser.add_argument('--layers', help='delimited list input', type=string_to_list)
parser.add_argument('--type', default="normal", help='Type of memo models (normal or avg)', type=str)
parser.add_argument('--convnet_type', default="memo_resnet32", help="Kind of resnet", type=str)
return parser
if __name__ == '__main__':
main()
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