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='regression_bbb', default=False, argv=None):
"""Parse command-line arguments.
Args:
mode: For what script should the parser assemble the set of command-line
parameters? Options:
- ``'regression_bbb'``
- ``'regression_avb'``
- ``'regression_ssge'``
default (optional): If True, command-line arguments will be ignored and
only the default values will be parsed.
argv (optional): If provided, it will be treated as a list of command-
line argument that is passed to the parser in place of sys.argv.
Returns:
The Namespace object containing argument names and values.
"""
if mode == 'regression_bbb':
description = 'Toy regression with tasks trained by BbB and ' + \
'protected by a hypernetwork'
elif mode == 'regression_avb':
description = 'Toy regression with tasks trained by implicit model ' \
'using AVB and protected by a hypernetwork'
elif mode == 'regression_ssge':
description = 'Toy regression with tasks trained by implicit model ' \
'using SSGE and protected by a hypernetwork'
else:
raise ValueError('Mode "%s" unknown.' % (mode))
parser = argparse.ArgumentParser(description=description)
# Default hnet keyword arguments.
hnet_args_kw = { # Function `cli.hnet_args`
# Note, the first list element denotes the default hnet.
'allowed_nets': ['hmlp', 'chunked_hmlp', 'hdeconv', 'chunked_hdeconv'],
'dhmlp_arch': '10,10',
'show_cond_emb_size': True,
'dcond_emb_size': 2,
'dchmlp_chunk_size': 64,
'dchunk_emb_size': 8,
'show_use_cond_chunk_embs': True,
'show_net_act': True,
'dnet_act': 'sigmoid',
'show_no_bias': True,
'show_dropout_rate': True,
'ddropout_rate': -1,
'show_specnorm': True,
'show_batchnorm': False,
'show_no_batchnorm': False
}
# If needed, add additional parameters.
if mode == 'regression_bbb':
dout_dir = './out_bbb/run_' + \
datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
cl_argroup = cli.cl_args(parser,
show_beta=True,
dbeta=0.005,
show_from_scratch=True,
show_multi_head=True)
train_argroup = cli.train_args(parser,
show_lr=True,
dn_iter=10001,
dlr=1e-2,
show_clip_grad_value=True,
show_clip_grad_norm=True,
show_momentum=False,
show_adam_beta1=True)
cli.main_net_args(parser,
allowed_nets=['mlp'],
dmlp_arch='10,10',
dnet_act='sigmoid',
show_no_bias=True)
cli.hnet_args(parser, **hnet_args_kw)
init_agroup = cli.init_args(parser, custom_option=False)
eval_agroup = cli.eval_args(parser, dval_iter=250)
data_args(parser)
misc_agroup = cli.miscellaneous_args(parser,
big_data=False,
synthetic_data=True,
show_plots=True,
no_cuda=True,
show_publication_style=True,
dout_dir=dout_dir)
mc_args(train_argroup, eval_agroup)
train_args(train_argroup,
show_local_reparam_trick=True,
show_radial_bnn=True)
cl_args(cl_argroup)
init_args(init_agroup)
miscellaneous_args(misc_agroup,
show_use_logvar_enc=True,
show_disable_lrt_test=True,
show_mean_only=True)
pmta.train_args(train_argroup,
show_init_with_prev_emb=False,
show_use_prev_post_as_prior=True,
show_num_kl_samples=True,
show_training_set_size=False)
#rtr config parameters for implicit model
else:
method = 'avb'
if mode == 'regression_ssge':
method = 'ssge'
dout_dir = './out_%s/run_' % method + \
datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
cl_argroup = cli.cl_args(parser,
show_beta=True,
dbeta=0.005,
show_from_scratch=True,
show_multi_head=True)
train_argroup = 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)
# Main network.
cli.main_net_args(parser,
allowed_nets=['mlp'],
dmlp_arch='10,10',
dnet_act='sigmoid',
show_no_bias=True)
if mode == 'regression_avb':
# Discriminator.
cli.main_net_args(parser,
allowed_nets=['mlp', 'chunked_mlp'],
dmlp_arch='10,10',
dcmlp_arch='10,10',
dcmlp_chunk_arch='10,10',
dcmlp_in_cdim=32,
dcmlp_out_cdim=8,
dcmlp_cemb_dim=8,
dnet_act='sigmoid',
show_no_bias=True,
prefix='dis_',
pf_name='discriminator')
# Hypernetwork (weight generator).
imp_hargs_kw = dict(hnet_args_kw)
imp_hargs_kw['show_cond_emb_size'] = False
imp_hargs_kw['show_use_cond_chunk_embs'] = False
imp_hargs_kw['dcond_emb_size'] = 0 # Not used for implicit hnet!
cli.hnet_args(parser,
**imp_hargs_kw,
prefix='imp_',
pf_name='implicit')
# Hyper-hypernetwork.
hhargs_kw = dict(hnet_args_kw)
cli.hnet_args(parser, **hhargs_kw, prefix='hh_', pf_name='hyper-hyper')
cli.init_args(parser, custom_option=False, show_hyper_fan_init=True)
eval_agroup = cli.eval_args(parser, dval_iter=250)
data_args(parser)
misc_agroup = cli.miscellaneous_args(parser,
big_data=False,
synthetic_data=True,
show_plots=True,
no_cuda=True,
show_publication_style=True,
dout_dir=dout_dir)
pmta.special_train_options(train_argroup, show_soft_targets=False)
mc_args(train_argroup, eval_agroup)
train_args(train_argroup,
show_local_reparam_trick=False,
show_kl_scale=True)
miscellaneous_args(misc_agroup, show_store_during_models=True)
pmta.train_args(train_argroup,
show_init_with_prev_emb=True,
show_use_prev_post_as_prior=True,
show_num_kl_samples=True,
show_calc_hnet_reg_targets_online=True,
show_hnet_reg_batch_size=True,
show_training_set_size=False)
pmta.ind_posterior_args(train_argroup, show_distill_iter=False)
pcta.miscellaneous_args(misc_agroup, show_no_hhnet=True)
pcta.imp_args(parser, dlatent_dim=8, show_prior_focused=True)
if mode == 'regression_avb':
pcta.avb_args(parser)
elif mode == 'regression_ssge':
pcta.ssge_args(parser)
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)
check_invalid_args_general(config)
pmta.check_invalid_args_general(config)
if mode == 'regression_bbb':
check_invalid_bbb_args(config)
else:
pass
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
def parse_cmd_arguments(default=False, argv=None):
"""Parse command-line arguments.
Args:
default (optional): If True, command-line arguments will be ignored and
only the default values will be parsed.
argv (optional): If provided, it will be treated as a list of command-
line argument that is passed to the parser in place of sys.argv.
Returns:
The Namespace object containing argument names and values.
"""
description = 'Continual learning on copy task.'
parser = argparse.ArgumentParser(description=description)
cli.cl_args(parser,
show_beta=True,
dbeta=0.005,
show_from_scratch=True,
show_multi_head=True,
show_split_head_cl3=False,
show_cl_scenario=False,
show_num_tasks=True,
dnum_tasks=6)
cli.train_args(parser,
show_lr=True,
show_epochs=False,
dbatch_size=64,
dn_iter=5000,
dlr=1e-3,
show_clip_grad_value=False,
show_clip_grad_norm=True,
show_momentum=False,
show_adam_beta1=True)
seq.rnn_args(parser, drnn_arch='256', dnet_act='tanh')
cli.hypernet_args(parser,
dhyper_chunks=-1,
dhnet_arch='10,10',
dtemb_size=2,
demb_size=32,
dhnet_act='sigmoid')
# Args of new hnets.
nhnet_args = cli.hnet_args(parser,
allowed_nets=[
'hmlp', 'chunked_hmlp', 'structured_hmlp',
'hdeconv', 'chunked_hdeconv'
],
dhmlp_arch='50,50',
show_cond_emb_size=True,
dcond_emb_size=32,
dchmlp_chunk_size=1000,
dchunk_emb_size=32,
show_use_cond_chunk_embs=True,
dhdeconv_shape='512,512,3',
prefix='nh_',
pf_name='new edition of a hyper-',
show_net_act=True,
dnet_act='relu',
show_no_bias=True,
show_dropout_rate=True,
ddropout_rate=-1,
show_specnorm=True,
show_batchnorm=False,
show_no_batchnorm=False)
seq.new_hnet_args(nhnet_args)
cli.init_args(parser,
custom_option=False,
show_normal_init=False,
show_hyper_fan_init=True)
cli.eval_args(parser, dval_iter=250)
magroup = cli.miscellaneous_args(parser,
big_data=False,
synthetic_data=True,
show_plots=True,
no_cuda=True,
show_publication_style=False)
seq.ewc_args(parser,
dewc_lambda=5000.,
dn_fisher=-1,
dtbptt_fisher=-1,
show_ts_weighting_fisher=False)
seq.si_args(parser, dsi_lambda=1.)
seq.context_mod_args(parser,
dsparsification_reg_type='l1',
dsparsification_reg_strength=1.,
dcontext_mod_init='constant')
seq.miscellaneous_args(magroup,
dmask_fraction=0.8,
dclassification=True,
show_ts_weighting=False,
show_use_ce_loss=False,
show_early_stopping_thld=True,
dearly_stopping_thld=-1)
copy_sequence_args(parser)
# Replay arguments.
rep_args = seq.replay_args(parser, show_all_task_softmax=False)
cli.generator_args(rep_args, dlatent_dim=100)
cli.main_net_args(parser,
allowed_nets=['simple_rnn'],
dsrnn_rec_layers='256',
dsrnn_pre_fc_layers='',
dsrnn_post_fc_layers='',
show_net_act=True,
dnet_act='tanh',
show_no_bias=True,
show_dropout_rate=False,
show_specnorm=False,
show_batchnorm=False,
prefix='dec_',
pf_name='replay decoder')
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)
seq.check_invalid_args_sequential(config)
check_invalid_args_sequential(config)
if config.train_from_scratch:
# FIXME We could get rid of this warning by properly checkpointing and
# loading all networks.
warnings.warn('When training from scratch, only during accuracies ' +
'make sense. All other outputs should be ignored!')
return config
def parse_cmd_arguments(mode='split_smnist', default=False, argv=None):
"""Parse command-line arguments.
Args:
mode (str): The mode of the experiment.
default (optional): If True, command-line arguments will be ignored and
only the default values will be parsed.
argv (optional): If provided, it will be treated as a list of command-
line argument that is passed to the parser in place of sys.argv.
Returns:
The Namespace object containing argument names and values.
"""
description = 'Continual learning on SMNIST task.'
parser = argparse.ArgumentParser(description=description)
dnum_tasks = 1
dnum_classes_per_task = 10
if mode == 'split_smnist':
dnum_tasks = 5
dnum_classes_per_task = 2
dval_set_size = 100
if mode == 'smnist':
dnum_tasks = 1
dnum_classes_per_task = 10
dval_set_size = 500
cli.cl_args(parser,
show_beta=True,
dbeta=0.005,
show_from_scratch=True,
show_multi_head=True,
show_split_head_cl3=False,
show_cl_scenario=False,
show_num_tasks=True,
dnum_tasks=dnum_tasks,
show_num_classes_per_task=True,
dnum_classes_per_task=dnum_classes_per_task)
cli.train_args(parser,
show_lr=True,
show_epochs=False,
dbatch_size=64,
dn_iter=5000,
dlr=1e-3,
show_clip_grad_value=False,
show_clip_grad_norm=True,
show_momentum=False,
show_adam_beta1=True)
seq.rnn_args(parser, drnn_arch='256', dnet_act='tanh')
cli.hypernet_args(parser,
dhyper_chunks=-1,
dhnet_arch='50,50',
dtemb_size=32,
demb_size=32,
dhnet_act='relu')
# Args of new hnets.
nhnet_args = cli.hnet_args(parser,
allowed_nets=[
'hmlp', 'chunked_hmlp', 'structured_hmlp',
'hdeconv', 'chunked_hdeconv'
],
dhmlp_arch='50,50',
show_cond_emb_size=True,
dcond_emb_size=32,
dchmlp_chunk_size=1000,
dchunk_emb_size=32,
show_use_cond_chunk_embs=True,
dhdeconv_shape='512,512,3',
prefix='nh_',
pf_name='new edition of a hyper-',
show_net_act=True,
dnet_act='relu',
show_no_bias=True,
show_dropout_rate=True,
ddropout_rate=-1,
show_specnorm=True,
show_batchnorm=False,
show_no_batchnorm=False)
seq.new_hnet_args(nhnet_args)
cli.init_args(parser,
custom_option=False,
show_normal_init=False,
show_hyper_fan_init=True)
cli.eval_args(parser,
dval_iter=250,
show_val_set_size=True,
dval_set_size=dval_set_size)
magroup = cli.miscellaneous_args(parser,
big_data=False,
synthetic_data=True,
show_plots=True,
no_cuda=True,
show_publication_style=False)
seq.ewc_args(parser,
dewc_lambda=5000.,
dn_fisher=-1,
dtbptt_fisher=-1,
dts_weighting_fisher='last')
seq.si_args(parser, dsi_lambda=1.)
seq.context_mod_args(parser,
dsparsification_reg_type='l1',
dsparsification_reg_strength=1.,
dcontext_mod_init='constant')
seq.miscellaneous_args(magroup,
dmask_fraction=0.8,
dclassification=True,
dts_weighting='last',
show_use_ce_loss=False)
# Replay arguments.
rep_args = seq.replay_args(parser)
cli.generator_args(rep_args, dlatent_dim=100)
cli.main_net_args(parser,
allowed_nets=['simple_rnn'],
dsrnn_rec_layers='256',
dsrnn_pre_fc_layers='',
dsrnn_post_fc_layers='',
show_net_act=True,
dnet_act='tanh',
show_no_bias=True,
show_dropout_rate=False,
show_specnorm=False,
show_batchnorm=False,
prefix='dec_',
pf_name='replay decoder')
rep_args.add_argument('--distill_across_time',
action='store_true',
help='The feature vector of an SMNIST sample ' +
'contains an "end-of-digit" bit, which in the ' +
'original data is only hot for 1 timestep. ' +
'However, in replayed data, this information ' +
'might be blurry. If this option is ' +
'activated, the distillation loss will be ' +
'applied to every timestep weighted by the ' +
'softmaxed "end-of-digit" feature from the ' +
'replayed input. Otherwise, the argmax ' +
'timestep of the "end-of-digit" feature is ' +
'considered.')
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)
config.mode = mode
### Check argument values!
cli.check_invalid_argument_usage(config)
seq.check_invalid_args_sequential(config)
if config.train_from_scratch:
# FIXME We could get rid of this warning by properly checkpointing and
# loading all networks.
warnings.warn('When training from scratch, only during accuracies ' +
'make sense. All other outputs should be ignored!')
return config
def collect_rp_cmd_arguments(mode='split', description=""):
"""Collect command-line arguments.
Args:
mode: For what script should the parser assemble the set of command-line
parameters? Options:
- "split"
- "perm"
Returns:
The Namespace object containing argument names and values.
"""
parser = argparse.ArgumentParser(description=description)
# If needed, add additional parameters.
if mode == 'split':
dout_dir = './out_split/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_num_tasks=True,
dnum_tasks=5)
train_argroup = cli.train_args(parser,
show_lr=False,
dbatch_size=128,
dn_iter=2000,
show_epochs=True)
cli.main_net_args(parser,
allowed_nets=['fc'],
dfc_arch='400,400',
dnet_act='relu',
prefix='enc_',
pf_name='encoder')
cli.main_net_args(parser,
allowed_nets=['fc'],
dfc_arch='400,400',
dnet_act='relu',
prefix='dec_',
pf_name='decoder')
cli.hypernet_args(parser,
dhyper_chunks=50000,
dhnet_arch='10,10',
dtemb_size=96,
demb_size=96,
prefix='rp_',
pf_name='replay',
dhnet_act='elu')
cli.init_args(parser, custom_option=False)
cli.miscellaneous_args(parser,
big_data=False,
synthetic_data=False,
show_plots=True,
no_cuda=False,
dout_dir=dout_dir)
cli.generator_args(parser, dlatent_dim=100)
cli.eval_args(parser, dval_iter=1000)
train_args_replay(parser, prefix='enc_', pf_name='encoder')
train_args_replay(parser,
show_emb_lr=True,
prefix='dec_',
pf_name='decoder')
split_args(parser)
elif mode == 'perm':
dout_dir = './out_permuted/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_num_tasks=True,
dnum_tasks=10)
train_argroup = cli.train_args(parser,
show_lr=False,
dbatch_size=128,
dn_iter=5000,
show_epochs=True)
cli.main_net_args(parser,
allowed_nets=['fc'],
dfc_arch='1000,1000',
dnet_act='relu',
prefix='enc_',
pf_name='encoder')
cli.main_net_args(parser,
allowed_nets=['fc'],
dfc_arch='1000,1000',
dnet_act='relu',
prefix='dec_',
pf_name='decoder')
cli.hypernet_args(parser,
dhyper_chunks=85000,
dhnet_arch='25,25',
dtemb_size=24,
demb_size=8,
prefix='rp_',
pf_name='replay',
dhnet_act='elu')
cli.init_args(parser, custom_option=False)
cli.miscellaneous_args(parser,
big_data=False,
synthetic_data=True,
show_plots=True,
no_cuda=False,
dout_dir=dout_dir)
cli.generator_args(parser, dlatent_dim=100)
cli.eval_args(parser, dval_iter=1000)
train_args_replay(parser, prefix='enc_', pf_name='Encoder', dlr=0.0001)
train_args_replay(parser,
show_emb_lr=True,
prefix='dec_',
dlr=0.0001,
dlr_emb=0.0001,
pf_name='Decoder')
perm_args(parser)
cl_arguments_replay(parser)
cl_arguments_general(parser)
data_args(parser)
return parser
def collect_rp_cmd_arguments(mode='mlp', description="", argv=0):
print('collect_rp_cmd_arguments')
"""Collect command-line arguments.
Args:
mode: For what script should the parser assemble the set of command-line
parameters? Options:
- "split"
- "perm"
Returns:
The Namespace object containing argument names and values.
"""
parser = argparse.ArgumentParser(description=description)
agroup = parser.add_argument_group('note')
agroup.add_argument('--note',
default='small,random,sep-emnist',
type=str,
required=False,
help='(default=%(default)d)')
agroup.add_argument('--dis_ntasks',
default=10,
type=int,
required=False,
help='(default=%(default)d)')
agroup.add_argument('--sim_ntasks',
default=10,
type=int,
required=False,
help='(default=%(default)d)')
agroup.add_argument('--idrandom',
default=0,
type=int,
required=False,
help='(default=%(default)d)')
agroup.add_argument('--classptask',
default=5,
type=int,
required=False,
help='(default=%(default)d)')
agroup.add_argument('--pdrop1',
default=-1,
type=float,
required=False,
help='(default=%(default)f)')
agroup.add_argument('--pdrop2',
default=-1,
type=float,
required=False,
help='(default=%(default)f)')
agroup.add_argument('--dims',
default=[],
type=list,
required=False,
help='(default=%(default)f)')
agroup.add_argument('--tasks_preserve',
type=int,
required=False,
help='(default=%(default)f)')
agroup.add_argument('--upper_bound',
action='store_true',
help='Train the classifier with "replay" data i.e ' +
'real data. This can be regarded an upper bound.')
agroup.add_argument("--num_class_femnist",
default=62,
type=int,
required=False,
help='(default=%(default)d)')
config = parser.parse_args(args=argv)
# If needed, add additional parameters.
if mode == 'mlp':
# dout_dir = './out_split/run_' + \
# datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
dout_dir = './out_split/' + config.note
cl_argroup = cli.cl_args(parser,
show_beta=False,
show_from_scratch=True,
show_multi_head=False,
show_cl_scenario=True,
show_num_tasks=True,
dnum_tasks=config.tasks_preserve)
train_argroup = cli.train_args(parser,
show_lr=False,
dbatch_size=128,
dn_iter=2000,
show_epochs=True)
cli.main_net_args(parser,
allowed_nets=['mlp'],
dfc_arch='400,400',
dnet_act='relu',
prefix='enc_',
pf_name='encoder')
cli.main_net_args(parser,
allowed_nets=['mlp'],
dfc_arch='400,400',
dnet_act='relu',
prefix='dec_',
pf_name='decoder')
cli.hypernet_args(parser,
dhyper_chunks=50000,
dhnet_arch='10,10',
dtemb_size=96,
demb_size=96,
prefix='rp_',
pf_name='replay',
dhnet_act='elu')
cli.init_args(parser, custom_option=False)
cli.miscellaneous_args(parser,
big_data=False,
synthetic_data=False,
show_plots=True,
no_cuda=False,
dout_dir=dout_dir)
cli.generator_args(parser, dlatent_dim=100)
cli.eval_args(parser, dval_iter=1000)
train_args_replay(parser, prefix='enc_', pf_name='encoder')
train_args_replay(parser,
show_emb_lr=True,
prefix='dec_',
pf_name='decoder')
split_args(parser)
cl_arguments_replay(parser)
cl_arguments_general(parser)
data_args(parser)
return parser
def collect_rp_cmd_arguments(mode='split', description="", argv=0):
"""Collect command-line arguments.
Args:
mode: For what script should the parser assemble the set of command-line
parameters? Options:
- "split"
- "perm"
Returns:
The Namespace object containing argument names and values.
"""
parser = argparse.ArgumentParser(description=description)
agroup = parser.add_argument_group('note')
agroup.add_argument('--note',
default='small,random,sep-emnist',
type=str,
required=False,
help='(default=%(default)d)')
agroup.add_argument('--ntasks',
default=10,
type=int,
required=False,
help='(default=%(default)d)')
agroup.add_argument('--idrandom',
default=0,
type=int,
required=False,
help='(default=%(default)d)')
agroup.add_argument('--classptask',
default=5,
type=int,
required=False,
help='(default=%(default)d)')
agroup.add_argument('--pdrop1',
default=-1,
type=float,
required=False,
help='(default=%(default)f)')
agroup.add_argument('--pdrop2',
default=-1,
type=float,
required=False,
help='(default=%(default)f)')
agroup.add_argument('--taskcla',
default=[],
type=list,
required=False,
help='(default=%(default)f)')
config = parser.parse_args(args=argv)
# If needed, add additional parameters.
if mode == 'split':
# dout_dir = './out_split/run_' + \
# datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
dout_dir = './out_split/' + config.note
cl_argroup = cli.cl_args(parser,
show_beta=False,
show_from_scratch=True,
show_multi_head=False,
show_cl_scenario=True,
show_num_tasks=True,
dnum_tasks=config.ntasks)
train_argroup = cli.train_args(parser,
show_lr=False,
dbatch_size=64,
dn_iter=2000,
show_epochs=True)
cli.main_net_args(parser,
allowed_nets=['fc'],
dfc_arch='400,400',
dnet_act='relu',
prefix='enc_',
pf_name='encoder')
cli.main_net_args(parser,
allowed_nets=['fc'],
dfc_arch='400,400',
dnet_act='relu',
prefix='dec_',
pf_name='decoder')
cli.hypernet_args(parser,
dhyper_chunks=50000,
dhnet_arch='10,10',
dtemb_size=96,
demb_size=96,
prefix='rp_',
pf_name='replay',
dhnet_act='elu')
cli.init_args(parser, custom_option=False)
cli.miscellaneous_args(parser,
big_data=False,
synthetic_data=False,
show_plots=True,
no_cuda=False,
dout_dir=dout_dir)
cli.generator_args(parser, dlatent_dim=100)
cli.eval_args(parser, dval_iter=1000)
train_args_replay(parser, prefix='enc_', pf_name='encoder')
train_args_replay(parser,
show_emb_lr=True,
prefix='dec_',
pf_name='decoder')
split_args(parser)
elif mode == 'perm':
dout_dir = './out_permuted/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_num_tasks=True,
dnum_tasks=config.ntasks)
train_argroup = cli.train_args(parser,
show_lr=False,
dbatch_size=64,
dn_iter=5000,
show_epochs=True)
cli.main_net_args(parser,
allowed_nets=['fc'],
dfc_arch='1000,1000',
dnet_act='relu',
prefix='enc_',
pf_name='encoder')
cli.main_net_args(parser,
allowed_nets=['fc'],
dfc_arch='1000,1000',
dnet_act='relu',
prefix='dec_',
pf_name='decoder')
cli.hypernet_args(parser,
dhyper_chunks=85000,
dhnet_arch='25,25',
dtemb_size=24,
demb_size=8,
prefix='rp_',
pf_name='replay',
dhnet_act='elu')
cli.init_args(parser, custom_option=False)
cli.miscellaneous_args(parser,
big_data=False,
synthetic_data=True,
show_plots=True,
no_cuda=False,
dout_dir=dout_dir)
cli.generator_args(parser, dlatent_dim=100)
cli.eval_args(parser, dval_iter=1000)
train_args_replay(parser, prefix='enc_', pf_name='Encoder', dlr=0.0001)
train_args_replay(parser,
show_emb_lr=True,
prefix='dec_',
dlr=0.0001,
dlr_emb=0.0001,
pf_name='Decoder')
perm_args(parser)
cl_arguments_replay(parser)
cl_arguments_general(parser)
data_args(parser)
return parser