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='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='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