def load_run_config(self, print_info=False, dataset='C10+'):
if os.path.isfile(self.run_config_path):
run_config = json.load(open(self.run_config_path, 'r'))
else:
print('Use Default Run Config for %s' % dataset)
run_config = RunConfig.get_default_run_config(dataset)
if print_info:
print('Run config:')
for k, v in run_config.items():
print('\t%s: %s' % (k, v))
return RunConfig(**run_config)
type=str,
default='C10+',
choices=['C10', 'C10+', 'C100', 'C100+'],
)
parser.add_argument('--path', type=str, default='')
parser.add_argument('--save_config',
action='store_true',
help='Whether to save config in the path')
parser.add_argument('--save_init', action='store_true')
parser.add_argument('--load_model', action='store_true')
args = parser.parse_args()
if args.dataset in ['C10', 'C100', 'C10+', 'C100+']:
run_config_cifar['dataset'] = args.dataset
run_config = RunConfig(**run_config_cifar)
net_config = standard_net_config_cifar
else:
raise ValueError
if len(args.path) == 0:
args.path = '../trained_nets/DenseNet/vs=%s_%s_%s_L=%d_K=%d_%s' % \
(run_config.validation_size, os.uname()[1], net_config['model_type'], net_config['depth'],
net_config['growth_rate'], run_config.dataset)
if run_config.dataset in ['C10+', 'C100+']:
net_config['keep_prob'] = 1.0
if standard_net_config_cifar['model_type'] == 'DenseNet':
net_config['reduction'] = 1.0
if args.test: args.load_model = True
# print configurations
'--dataset',
type=str,
default='C10+',
choices=['C10', 'C10+', 'C100', 'C100+', 'SVHN', 'MNIST'],
)
parser.add_argument('--path', type=str, default='')
parser.add_argument('--save_config',
action='store_true',
help='Whether to save config in the path')
parser.add_argument('--save_init', action='store_true')
parser.add_argument('--load_model', action='store_true')
args = parser.parse_args()
if args.dataset in ['C10', 'C100', 'C10+', 'C100+']:
run_config_cifar['dataset'] = args.dataset
run_config = RunConfig(**run_config_cifar)
elif args.dataset in ['SVHN']:
run_config = RunConfig(**run_config_svhn)
elif args.dataset in ['MNIST']:
run_config = RunConfig(**run_config_mnist)
else:
raise ValueError
if len(args.path) == 0:
args.path = '../trained_nets/Convnet/vs=%s_Convnet_%s_%s_%s' % \
(run_config.validation_size, os.uname()[1], run_str, run_config.dataset)
if args.test: args.load_model = True
# print configurations
print('Run config:')
for k, v in run_config.get_config().items():
print('\t%s: %s' % (k, v))