def parse_cmd_arguments(mode='regression_ewc', default=False, argv=None):
"""Parse command-line arguments for EWC experiments.
Args:
mode (str): For what script should the parser assemble the set of
command-line parameters? Options:
- ``'regression_ewc'``
- ``'gmm_ewc'``
- ``'split_mnist_ewc'``
- ``'perm_mnist_ewc'``
- ``'cifar_resnet_ewc'``
default (bool, optional): If ``True``, command-line arguments will be
ignored and only the default values will be parsed.
argv (list, optional): If provided, it will be treated as a list of
command- line argument that is passed to the parser in place of
:code:`sys.argv`.
Returns:
(argparse.Namespace): The Namespace object containing argument names and
values.
"""
if mode == 'regression_ewc':
description = 'Toy regression with tasks trained via EWC'
elif mode == 'gmm_ewc':
description = 'Probabilistic CL on GMM Datasets via EWC'
elif mode == 'split_mnist_ewc':
description = 'Probabilistic CL on Split MNIST via EWC'
elif mode == 'perm_mnist_ewc':
description = 'Probabilistic CL on Permuted MNIST via EWC'
elif mode == 'cifar_resnet_ewc':
description = 'Probabilistic CL on CIFAR-10/100 via EWC on a Resnet'
else:
raise ValueError('Mode "%s" unknown.' % (mode))
parser = argparse.ArgumentParser(description=description)
# If needed, add additional parameters.
if mode == 'regression_ewc':
dout_dir = './out_ewc/run_' + \
datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
cl_argroup = cli.cl_args(parser, show_beta=False,
show_from_scratch=True, show_multi_head=True)
train_agroup = cli.train_args(parser, show_lr=True, dbatch_size=32,
dn_iter=5001, dlr=1e-3, show_use_adam=True, show_use_rmsprop=True,
show_use_adadelta=True, show_use_adagrad=True, show_epochs=True,
show_clip_grad_value=True, show_clip_grad_norm=True)
cli.main_net_args(parser, allowed_nets=['mlp'], dmlp_arch='10,10',
dnet_act='sigmoid', show_no_bias=True, show_batchnorm=True,
show_no_batchnorm=False, show_bn_no_running_stats=True,
show_bn_distill_stats=False, show_bn_no_stats_checkpointing=True,
show_specnorm=False, show_dropout_rate=True, ddropout_rate=-1,
show_net_act=True)
eval_agroup = cli.eval_args(parser, show_val_batch_size=False,
dval_batch_size=10000, dval_iter=250)
rta.data_args(parser)
misc_agroup = cli.miscellaneous_args(parser, big_data=False,
synthetic_data=True, show_plots=True, no_cuda=False,
dout_dir=dout_dir, show_publication_style=True)
ewc_args(parser, dewc_lambda=1., dn_fisher=-1)
pmta.special_train_options(train_agroup, show_soft_targets=False)
rta.mc_args(train_agroup, eval_agroup, show_train_sample_size=False,
dval_sample_size=10)
rta.train_args(train_agroup, show_ll_dist_std=True,
show_local_reparam_trick=False, show_kl_scale=False,
show_radial_bnn=False)
rta.miscellaneous_args(misc_agroup, show_mnet_only=False,
show_use_logvar_enc=False, show_disable_lrt_test=False,
show_mean_only=False)
elif mode == 'gmm_ewc':
dout_dir = './out_gmm_ewc/run_' + \
datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
cl_argroup = cli.cl_args(parser, show_beta=False,
show_from_scratch=True, show_multi_head=False,
show_cl_scenario=True, show_split_head_cl3=True,
show_num_tasks=False, show_num_classes_per_task=False)
train_agroup = cli.train_args(parser, show_lr=True, dbatch_size=32,
dn_iter=2000, dlr=1e-3, show_use_adam=True, show_use_rmsprop=True,
show_use_adadelta=True, show_use_adagrad=True, show_epochs=True,
show_clip_grad_value=True, show_clip_grad_norm=True)
cli.main_net_args(parser, allowed_nets=['mlp'], dmlp_arch='10,10',
dnet_act='sigmoid', show_no_bias=True, show_batchnorm=True,
show_no_batchnorm=False, show_bn_no_running_stats=True,
show_bn_distill_stats=False, show_bn_no_stats_checkpointing=True,
show_specnorm=False, show_dropout_rate=True, ddropout_rate=-1,
show_net_act=True)
eval_agroup = cli.eval_args(parser, show_val_batch_size=True,
dval_batch_size=10000, dval_iter=100)
misc_agroup = cli.miscellaneous_args(parser, big_data=False,
synthetic_data=True, show_plots=True, no_cuda=False,
dout_dir=dout_dir)
ewc_args(parser, dewc_lambda=1., dn_fisher=-1)
pmta.special_train_options(train_agroup, show_soft_targets=False)
cl_args(cl_argroup, show_det_multi_head=True)
pmta.eval_args(eval_agroup)
rta.mc_args(train_agroup, eval_agroup, show_train_sample_size=False,
dval_sample_size=10)
rta.train_args(train_agroup, show_ll_dist_std=False,
show_local_reparam_trick=False, show_kl_scale=False,
show_radial_bnn=False)
rta.miscellaneous_args(misc_agroup, show_mnet_only=False,
show_use_logvar_enc=False, show_disable_lrt_test=False,
show_mean_only=False, show_during_acc_criterion=True)
elif mode == 'split_mnist_ewc':
dout_dir = './out_split_ewc/run_' + \
datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
cl_argroup = cli.cl_args(parser, show_beta=False,
show_from_scratch=True, show_multi_head=False,
show_cl_scenario=True,
show_split_head_cl3=True,
show_num_tasks=True, dnum_tasks=5,
show_num_classes_per_task=True,
dnum_classes_per_task=2)
train_agroup = cli.train_args(parser, show_lr=True, dbatch_size=128,
dn_iter=2000, dlr=1e-3,
show_use_adam=True, show_use_rmsprop=True,
show_use_adadelta=True,
show_use_adagrad=True, show_epochs=True,
show_clip_grad_value=True,
show_clip_grad_norm=True)
cli.main_net_args(parser, allowed_nets=['mlp', 'lenet', 'resnet', 'wrn'],
dmlp_arch='400,400', dlenet_type='mnist_small',
show_no_bias=True, show_batchnorm=True,
show_no_batchnorm=False,
show_bn_no_running_stats=True,
show_bn_distill_stats=False,
show_bn_no_stats_checkpointing=True,
show_specnorm=False, show_dropout_rate=True,
ddropout_rate=-1, show_net_act=True)
eval_agroup = cli.eval_args(parser, show_val_batch_size=True,
dval_batch_size=1000, show_val_set_size=True, dval_set_size=0)
misc_agroup = cli.miscellaneous_args(parser, big_data=False,
synthetic_data=False,
show_plots=False, no_cuda=False,
dout_dir=dout_dir)
ewc_args(parser, dewc_lambda=1., dn_fisher=-1)
pmta.special_train_options(train_agroup, show_soft_targets=False)
cl_args(cl_argroup, show_det_multi_head=True)
pmta.eval_args(eval_agroup)
rta.mc_args(train_agroup, eval_agroup, show_train_sample_size=False,
dval_sample_size=10)
rta.train_args(train_agroup, show_ll_dist_std=False,
show_local_reparam_trick=False, show_kl_scale=False,
show_radial_bnn=False)
rta.miscellaneous_args(misc_agroup, show_mnet_only=False,
show_use_logvar_enc=False,
show_disable_lrt_test=False,
show_mean_only=False,
show_during_acc_criterion=True)
elif mode == 'perm_mnist_ewc':
dout_dir = './out_perm_ewc/run_' + \
datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
cl_argroup = cli.cl_args(parser, show_beta=False,
show_from_scratch=True, show_multi_head=False,
show_cl_scenario=True,
show_split_head_cl3=True,
show_num_tasks=True, dnum_tasks=10,
show_num_classes_per_task=False)
train_agroup = cli.train_args(parser, show_lr=True, dbatch_size=128,
dn_iter=5000, dlr=1e-4,
show_use_adam=True, show_use_rmsprop=True,
show_use_adadelta=True,
show_use_adagrad=True, show_epochs=True,
show_clip_grad_value=True,
show_clip_grad_norm=True)
cli.main_net_args(parser, allowed_nets=['mlp'], dmlp_arch='1000,1000',
show_no_bias=True, show_batchnorm=True,
show_no_batchnorm=False,
show_bn_no_running_stats=True,
show_bn_distill_stats=False,
show_bn_no_stats_checkpointing=True,
show_specnorm=False, show_dropout_rate=True,
ddropout_rate=-1, show_net_act=True)
eval_agroup = cli.eval_args(parser, show_val_batch_size=True,
dval_batch_size=1000, show_val_set_size=True, dval_set_size=0)
misc_agroup = cli.miscellaneous_args(parser, big_data=False,
synthetic_data=True, show_plots=False, no_cuda=False,
dout_dir=dout_dir)
ewc_args(parser, dewc_lambda=1., dn_fisher=-1)
pmta.special_train_options(train_agroup, show_soft_targets=False)
cl_args(cl_argroup, show_det_multi_head=True)
pmta.eval_args(eval_agroup)
rta.mc_args(train_agroup, eval_agroup, show_train_sample_size=False,
dval_sample_size=10)
rta.train_args(train_agroup, show_ll_dist_std=False,
show_local_reparam_trick=False, show_kl_scale=False,
show_radial_bnn=False)
rta.miscellaneous_args(misc_agroup, show_mnet_only=False,
show_use_logvar_enc=False,
show_disable_lrt_test=False,
show_mean_only=False,
show_during_acc_criterion=True)
pmta.perm_args(parser)
elif mode == 'cifar_resnet_ewc':
dout_dir = './out_resnet_ewc/run_' + \
datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
cl_argroup = cli.cl_args(parser, show_beta=False,
show_from_scratch=True, show_multi_head=False,
show_cl_scenario=True,
show_split_head_cl3=True,
show_num_tasks=True, dnum_tasks=6,
show_num_classes_per_task=True,
dnum_classes_per_task=10)
pcta.extra_cl_args(cl_argroup)
train_agroup = cli.train_args(parser, show_lr=True, dbatch_size=32,
dn_iter=2000, dlr=1e-3,
show_use_adam=True, show_use_rmsprop=True,
show_use_adadelta=True,
show_use_adagrad=True, show_epochs=True,
depochs=200, show_clip_grad_value=True,
show_clip_grad_norm=True)
cli.main_net_args(parser,
allowed_nets=['resnet', 'wrn', 'iresnet', 'lenet', 'zenke', 'mlp'],
dmlp_arch='10,10', dlenet_type='cifar', show_no_bias=True,
show_batchnorm=False, show_no_batchnorm=True,
show_bn_no_running_stats=True, show_bn_distill_stats=False,
show_bn_no_stats_checkpointing=True,
show_specnorm=False, show_dropout_rate=True,
ddropout_rate=-1, show_net_act=True)
cli.data_args(parser, show_disable_data_augmentation=True)
eval_agroup = cli.eval_args(parser, show_val_batch_size=True,
dval_batch_size=1000,
show_val_set_size=True, dval_set_size=0)
misc_agroup = cli.miscellaneous_args(parser, big_data=False,
synthetic_data=False,
show_plots=False, no_cuda=False,
dout_dir=dout_dir)
ewc_args(parser, dewc_lambda=1., dn_fisher=-1)
pmta.special_train_options(train_agroup, show_soft_targets=False)
cl_args(cl_argroup, show_det_multi_head=True)
pmta.eval_args(eval_agroup)
rta.mc_args(train_agroup, eval_agroup, show_train_sample_size=False,
dval_sample_size=10)
rta.train_args(train_agroup, show_ll_dist_std=False,
show_local_reparam_trick=False, show_kl_scale=False,
show_radial_bnn=False)
rta.miscellaneous_args(misc_agroup, show_mnet_only=False,
show_use_logvar_enc=False,
show_disable_lrt_test=False,
show_mean_only=False,
show_during_acc_criterion=True)
args = None
if argv is not None:
if default:
warnings.warn('Provided "argv" will be ignored since "default" ' +
'option was turned on.')
args = argv
if default:
args = []
config = parser.parse_args(args=args)
### Check argument values!
cli.check_invalid_argument_usage(config)
rta.check_invalid_args_general(config)
pmta.check_invalid_args_general(config)
check_invalid_args_ewc(config)
if mode == 'regression_ewc':
if config.batchnorm:
# Not properly handled in test and eval function!
raise NotImplementedError()
return config
def parse_cmd_arguments(mode='resnet_cifar', default=False, argv=None):
"""Parse command-line arguments.
Args:
mode (str): For what script should the parser assemble the set of
command-line parameters? Options:
- ``resnet_cifar``
- ``zenke_cifar``
default (bool, optional): If ``True``, command-line arguments will be
ignored and only the default values will be parsed.
argv (list, optional): If provided, it will be treated as a list of
command- line argument that is passed to the parser in place of
:code:`sys.argv`.
Returns:
(argparse.Namespace): The Namespace object containing argument names and
values.
"""
if mode == 'resnet_cifar':
description = 'CIFAR-10/100 CL experiment using a Resnet-32'
elif mode == 'zenke_cifar':
description = 'CIFAR-10/100 CL experiment using the Zenkenet'
else:
raise ValueError('Mode "%s" unknown.' % (mode))
parser = argparse.ArgumentParser(description=description)
general_options(parser)
if mode == 'resnet_cifar':
dout_dir = './out_resnet/run_' + \
datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
cl_group = cli.cl_args(parser,
show_beta=True,
dbeta=0.05,
show_from_scratch=True,
show_multi_head=False,
show_cl_scenario=True,
show_split_head_cl3=False,
show_num_tasks=True,
dnum_tasks=6)
cli.main_net_args(parser,
allowed_nets=['resnet'],
show_batchnorm=False,
show_no_batchnorm=True,
show_bn_no_running_stats=True,
show_bn_distill_stats=True,
show_bn_no_stats_checkpointing=True,
show_specnorm=False,
show_dropout_rate=False,
show_net_act=False)
cli.hypernet_args(parser,
dhyper_chunks=7000,
dhnet_arch='',
dtemb_size=32,
demb_size=32)
cli.data_args(parser, show_disable_data_augmentation=True)
train_agroup = cli.train_args(parser,
show_lr=True,
dlr=0.001,
show_epochs=True,
depochs=200,
dbatch_size=32,
dn_iter=2000,
show_use_adam=True,
show_use_rmsprop=True,
show_use_adadelta=False,
show_use_adagrad=False,
show_clip_grad_value=False,
show_clip_grad_norm=False)
elif mode == 'zenke_cifar':
dout_dir = './out_zenke/run_' + \
datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
cl_group = cli.cl_args(parser,
show_beta=True,
dbeta=0.01,
show_from_scratch=True,
show_multi_head=False,
show_cl_scenario=True,
show_split_head_cl3=False,
show_num_tasks=True,
dnum_tasks=6)
cli.main_net_args(parser,
allowed_nets=['zenke'],
show_batchnorm=False,
show_no_batchnorm=False,
show_dropout_rate=True,
ddropout_rate=0.25,
show_specnorm=False,
show_net_act=False)
cli.hypernet_args(parser,
dhyper_chunks=5500,
dhnet_arch='100,150,200',
dtemb_size=48,
demb_size=80)
cli.data_args(parser, show_disable_data_augmentation=True)
train_agroup = cli.train_args(parser,
show_lr=True,
dlr=0.0001,
show_epochs=True,
depochs=80,
dbatch_size=256,
dn_iter=2000,
show_use_adam=True,
dadam_beta1=0.5,
show_use_rmsprop=True,
show_use_adadelta=False,
show_use_adagrad=False,
show_clip_grad_value=False,
show_clip_grad_norm=False)
special_cl_options(cl_group)
special_train_options(train_agroup)
init_group = cli.init_args(parser, custom_option=True)
special_init_options(init_group)
cli.eval_args(parser, show_val_batch_size=True, dval_batch_size=1000)
cli.miscellaneous_args(parser,
big_data=False,
synthetic_data=False,
show_plots=False,
no_cuda=False,
dout_dir=dout_dir)
args = None
if argv is not None:
if default:
warnings.warn('Provided "argv" will be ignored since "default" ' +
'option was turned on.')
args = argv
if default:
args = []
config = parser.parse_args(args=args)
### Check argument values!
cli.check_invalid_argument_usage(config)
### ... insert additional checks if necessary
if config.num_tasks < 1 or config.num_tasks > 11:
raise ValueError('Argument "num_tasks" must be between 1 and 11!')
if config.cl_scenario != 1:
raise NotImplementedError('CIFAR experiments are currently only ' +
'implemented for CL1.')
if config.plateau_lr_scheduler and config.epochs == -1:
raise ValueError('Flag "plateau_lr_scheduler" can only be used if ' +
'"epochs" was set.')
if config.lambda_lr_scheduler and config.epochs == -1:
raise ValueError('Flag "lambda_lr_scheduler" can only be used if ' +
'"epochs" was set.')
if config.no_lookahead and config.backprop_dt:
raise ValueError('Can\'t activate "no_lookahead" and "backprop_dt" ' +
'simultaneously.')
return config