def handle_inputs(): # Set indicator-dictionary for correctly retrieving / checking input options kwargs = { 'single_task': False, 'only_MNIST': False, 'generative': True, 'compare_code': 'none' } # Define input options parser = options.define_args( filename="main_cl", description='Compare & combine continual learning approaches.') parser = options.add_general_options(parser, **kwargs) parser = options.add_eval_options(parser, **kwargs) parser = options.add_task_options(parser, **kwargs) parser = options.add_model_options(parser, **kwargs) parser = options.add_train_options(parser, **kwargs) parser = options.add_replay_options(parser, **kwargs) parser = options.add_bir_options(parser, **kwargs) parser = options.add_allocation_options(parser, **kwargs) # Parse, process (i.e., set defaults for unselected options) and check chosen options args = parser.parse_args() options.set_defaults(args, **kwargs) options.check_for_errors(args, **kwargs) return args
def handle_inputs(): # Set indicator-dictionary for correctly retrieving / checking input options kwargs = {'single_task': False, 'only_MNIST': False} # Define input options parser = options.define_args( filename="compare_both", description='Compare CL approaches in terms of transfer efficiency.') parser = options.add_general_options(parser, **kwargs) parser = options.add_eval_options(parser, **kwargs) parser = options.add_task_options(parser, **kwargs) parser = options.add_model_options(parser, **kwargs) parser = options.add_train_options(parser, **kwargs) parser = options.add_replay_options(parser, **kwargs) parser = options.add_allocation_options(parser, **kwargs) # Parse, process (i.e., set defaults for unselected options) and check chosen options parser.add_argument('--n-seeds', type=int, default=1) parser.add_argument('--o-lambda', metavar="LAMBDA", type=float, help="--> Online EWC: regularisation strength") parser.add_argument('--c-500', metavar="C", type=float, help="--> SI: reg strength with 500 training samples") parser.add_argument('--lambda-500', metavar="LAMBDA", type=float, help="--> EWC: reg strength with 500 training samples") parser.add_argument( '--o-lambda-500', metavar="LAMBDA", type=float, help="--> Online EWC: reg strength with 500 training samples") parser.add_argument( '--shift', metavar="LAMBDA", type=int, help="-->shift: The number of shift to perform on test-train set") parser.add_argument( '--slot', metavar="LAMBDA", type=int, help="--> slot: The number of slot to perform the training") args = parser.parse_args() options.set_defaults(args, **kwargs) options.check_for_errors(args, **kwargs) return args
def handle_inputs(): # Set indicator-dictionary for correctly retrieving / checking input options kwargs = {'single_task': True, 'only_MNIST': False, 'generative': False, 'compare_code': 'none', 'train_options': 'all'} # Define input options parser = options.define_args(filename="main_pretrain", description='Train classifier for pretraining conv-layers.') parser = options.add_general_options(parser, **kwargs) parser = options.add_eval_options(parser, **kwargs) parser = options.add_task_options(parser, **kwargs) parser = options.add_model_options(parser, **kwargs) parser = options.add_train_options(parser, **kwargs) # Parse, process (i.e., set defaults for unselected options) and check chosen options args = parser.parse_args() options.set_defaults(args, **kwargs) options.check_for_errors(args, **kwargs) return args
def handle_inputs(): # Set indicator-dictionary for correctly retrieving / checking input options kwargs = {'single_task': False, 'only_MNIST': False} # Define input options parser = options.define_args( filename="main_cl", description='Select hyperparameters for EWC, online EWC and SI.') parser = options.add_general_options(parser, **kwargs) parser = options.add_eval_options(parser, **kwargs) parser = options.add_task_options(parser, **kwargs) parser = options.add_model_options(parser, **kwargs) parser = options.add_train_options(parser, **kwargs) parser = options.add_replay_options(parser, **kwargs) parser = options.add_allocation_options(parser, **kwargs) # Parse, process (i.e., set defaults for unselected options) and check chosen options args = parser.parse_args() options.set_defaults(args, **kwargs) options.check_for_errors(args, **kwargs) return args
def handle_inputs(): # Set indicator-dictionary for correctly retrieving / checking input options kwargs = {'single_task': False, 'only_MNIST': False} # Define input options parser = options.define_args( filename="main_cl", description='Compare & combine continual learning approaches.') parser = options.add_general_options(parser, **kwargs) parser = options.add_eval_options(parser, **kwargs) parser = options.add_task_options(parser, **kwargs) parser = options.add_model_options(parser, **kwargs) parser = options.add_train_options(parser, **kwargs) parser = options.add_replay_options(parser, **kwargs) parser = options.add_allocation_options(parser, **kwargs) # Parse, process (i.e., set defaults for unselected options) and check chosen options parser.add_argument('--n-seeds', type=int, default=1) parser.add_argument('--o-lambda', type=float, help="--> Online EWC: regularisation strength") args = parser.parse_args() options.set_defaults(args, **kwargs) options.check_for_errors(args, **kwargs) return args