def handle_inputs():
# Set indicator-dictionary for correctly retrieving / checking input options
kwargs = {
'single_task': False,
'only_MNIST': True,
'generative': True,
'compare_code': 'all'
}
# Define input options
parser = options.define_args(
filename="_compare_MNIST",
description=
'Compare performance of various continual learning strategies on different'
' scenarios of splitMNIST.')
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 and process (i.e., set defaults for unselected options) options
args = parser.parse_args()
options.set_defaults(args, **kwargs)
return args
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': True,
'generative': True,
'compare_code': 'all'
}
# Define input options
parser = options.define_args(
filename="_compare_permMNIST100",
description=
'Compare performance of "continual learning strategies" on long instance '
'of standard version (i.e., Domain-IL) of permMNIST (100 tasks).')
parser = options.add_general_options(parser, **kwargs)
parser = options.add_eval_options(parser, **kwargs)
parser = options.add_permutedMNIST_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 and process (i.e., set defaults for unselected options) options
args = parser.parse_args()
args.scenario = "domain"
args.experiment = "permMNIST"
options.set_defaults(args, **kwargs)
return args
def handle_inputs():
# Set indicator-dictionary for correctly retrieving / checking input options
kwargs = {
'single_task': False,
'only_MNIST': True,
'generative': False,
'compare_code': 'hyper'
}
# Define input options
parser = options.define_args(
filename="_compare_MNIST_hyperParams",
description=
'Compare hyperparameters of EWC, online EWC, SI and XdG on different '
'"scenarios" of splitMNIST.')
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_allocation_options(parser, **kwargs)
parser.add_argument('--no-online',
action='store_true',
help="don't do online EWC")
# Parse and process (i.e., set defaults for unselected options) options
args = parser.parse_args()
options.set_defaults(args, **kwargs)
return args
def handle_inputs():
# Set indicator-dictionary for correctly retrieving / checking input options
kwargs = {
'single_task': False,
'only_MNIST': False,
'generative': True,
'compare_code': 'hyper'
}
# Define input options
parser = options.define_args(
filename="_compare_CIFAR100_hyperParams",
description='Compare hyperparameters fo split CIFAR-100.')
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_allocation_options(parser, **kwargs)
parser = options.add_replay_options(parser, **kwargs)
parser = options.add_bir_options(parser, **kwargs)
parser.add_argument(
'--per-bir-comp',
action='store_true',
help="also do gridsearch for individual BI-R components")
# Parse and process (i.e., set defaults for unselected options) options
args = parser.parse_args()
options.set_defaults(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': False, 'only_MNIST': True, 'generative': True, 'compare_code': 'replay'}
# Define input options
parser = options.define_args(filename="_compare_MNIST_replay",
description="Generative replay: effect of quantity & quality.")
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 and process (i.e., set defaults for unselected options) options
args = parser.parse_args()
options.set_defaults(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