def __init__(self, resnet_size_choices=None):
super(ResnetArgParser, self).__init__(parents=[
parsers.BaseParser(),
parsers.PerformanceParser(),
parsers.ImageModelParser(),
parsers.ExportParser(),
parsers.BenchmarkParser(),
])
self.add_argument(
'--version',
'-v',
type=int,
choices=[1, 2],
default=resnet_model.DEFAULT_VERSION,
help='Version of ResNet. (1 or 2) See README.md for details.')
self.add_argument(
'--resnet_size',
'-rs',
type=int,
default=50,
choices=resnet_size_choices,
help='[default: %(default)s] The size of the ResNet model to use.',
metavar='<RS>' if resnet_size_choices is None else None)
def __init__(self):
super(DANArgParser, self).__init__(parents=[
parsers.BaseParser(),
parsers.PerformanceParser(),
parsers.ImageModelParser(),
])
self.add_argument(
"--data_dir_test",
"-ddt",
default=None,
help="[default: %(default)s] The location of the test data.",
metavar="<DD>",
)
self.add_argument(
'--dan_stage',
'-ds',
type=int,
default=1,
choices=[1, 2],
help='[default: %(default)s] The stage of the DAN model.')
self.add_argument('--mode',
'-mode',
type=str,
default='train',
choices=['train', 'eval', 'predict'])
self.add_argument('--num_lmark', '-nlm', type=int, default=68)
def __init__(self):
super(SimpNetArgParser, self).__init__(parents=[
parsers.BaseParser(),
parsers.PerformanceParser(),
parsers.ImageModelParser(),
parsers.BenchmarkParser(),
])
def __init__(self):
super(TestParser, self).__init__(parents=[
parsers.BaseParser(),
parsers.PerformanceParser(num_parallel_calls=True, inter_op=True,
intra_op=True, use_synthetic_data=True),
parsers.ImageModelParser(data_format=True),
parsers.BenchmarkParser(benchmark_log_dir=True)
])
def __init__(self):
super(MNISTArgParser, self).__init__(parents=[
parsers.BaseParser(),
parsers.ImageModelParser(),
])
self.set_defaults(data_dir='/tmp/mnist_data',
model_dir='/tmp/mnist_model',
batch_size=100,
train_epochs=40)
def __init__(self):
super(MNISTArgParser, self).__init__(parents=[
parsers.BaseParser(),
parsers.ImageModelParser(),
parsers.ExportParser(),
])
self.set_defaults(
data_dir='/home/jcf/models-master/official/mnist/mnist_data',
model_dir='/tmp/mnist_model',
batch_size=100,
train_epochs=40)
def __init__(self, resnet_size_choices=None):
super(ResnetArgParser, self).__init__(parents=[
parsers.BaseParser(),
parsers.PerformanceParser(),
parsers.ImageModelParser(),
])
self.add_argument(
'--resnet_size', '-rs', type=int, default=50,
choices=resnet_size_choices,
help='[default: %(default)s]The size of the ResNet model to use.',
metavar='<RS>'
)
def __init__(self, resnet_size_choices=None):
super(ResnetArgParser, self).__init__(parents=[
parsers.BaseParser(),
parsers.PerformanceParser(),
parsers.ImageModelParser(),
parsers.ExportParser(),
parsers.BenchmarkParser(),
])
self.add_argument('--dataset','-d',default="cifar10",
help='Which dataset to use (currently cifar10/cifar100)'
)
self.add_argument(
'--version', '-v', type=int, choices=[1, 2],
default=rncm.RESNET_DEFAULT_VERSION,
help='Version of ResNet. (1 or 2) See README.md for details.'
)
self.add_argument(
'--resnet_size', '-rs', type=int, default=50,
choices=resnet_size_choices,
help='[default: %(default)s] The size of the ResNet model to use.',
metavar='<RS>' if resnet_size_choices is None else None
)
self.add_argument(
'--continu',type=int,default=0,
help='Continue with an existing model, or start from scratch'
)
self.add_argument(
'--scratch',type=int,default=0,
help='Start from scratch even if model exist'
)
self.add_argument(
'--ncmmethod', default=rncm.NCM_DEFAULT_METHOD,
help='[default: %(default)s] Which NCM method to use',
)
self.add_argument(
'--ncmparam', default=rncm.NCM_DEFAULT_PARAMETER, type=float,
help='[default: %(default)s] additional NCM parameter to use',
)
self.add_argument(
'--initial_learning_scale', '-l', default=0.1, type=float,
help='Intial Learning Scale (default: %(default)s)',
)
def __init__(self):
super(MNISTEagerArgParser, self).__init__(parents=[
parsers.BaseParser(
epochs_between_evals=False, multi_gpu=False, hooks=False),
parsers.ImageModelParser()
])
self.add_argument(
'--log_interval',
'-li',
type=int,
default=10,
metavar='N',
help=
'[default: %(default)s] batches between logging training status')
self.add_argument(
'--output_dir',
'-od',
type=str,
default=None,
metavar='<OD>',
help=
'[default: %(default)s] Directory to write TensorBoard summaries')
self.add_argument('--lr',
'-lr',
type=float,
default=0.01,
metavar='<LR>',
help='[default: %(default)s] learning rate')
self.add_argument('--momentum',
'-m',
type=float,
default=0.5,
metavar='<M>',
help='[default: %(default)s] SGD momentum')
self.add_argument('--no_gpu',
'-nogpu',
action='store_true',
default=False,
help='disables GPU usage even if a GPU is available')
self.set_defaults(
data_dir='/tmp/tensorflow/mnist/input_data',
model_dir='/tmp/tensorflow/mnist/checkpoints/',
batch_size=100,
train_epochs=10,
)
def __init__(self):
super(MNISTArgParser, self).__init__(
parents=[parsers.BaseParser(),
parsers.ImageModelParser()])
self.add_argument(
'--export_dir',
type=str,
help=
'[default: %(default)s] If set, a SavedModel serialization of the '
'model will be exported to this directory at the end of training. '
'See the README for more details and relevant links.')
self.set_defaults(data_dir='/tmp/mnist_data',
model_dir='/tmp/mnist_model',
batch_size=100,
train_epochs=40)
def __init__(self, resnet_size_choices=None):
super(ResnetArgParser, self).__init__(parents=[
parsers.BaseParser(),
parsers.PerformanceParser(),
parsers.ImageModelParser(),
parsers.ExportParser(),
parsers.BenchmarkParser(),
])
self.add_argument(
'--version',
'-v',
type=int,
choices=[1, 2],
default=resnet_model.DEFAULT_VERSION,
help='Version of ResNet. (1 or 2) See README.md for details.')
self.add_argument(
'--resnet_size',
'-rs',
type=int,
default=50,
choices=resnet_size_choices,
help='[default: %(default)s] The size of the ResNet model to use.',
metavar='<RS>' if resnet_size_choices is None else None)
self.add_argument(
'--enable_ml_comm',
'-mc',
type=int,
choices=[0, 1],
default=1,
help=
'[default: %(default)s] Whether to use Cray ML-Comm Distributed Training Plugin'
)
self.add_argument(
'--global_perf_log_freq',
'-pf',
type=int,
default=50,
help=
'[default: %(default)s] Number of steps after which to report global (all process averages) training loss and performance'
)
self.add_argument(
'--warmup_epochs',
'-we',
type=int,
default=0,
help=
'[default: %(default)s] Number of warmup epochs when using LARS')
self.add_argument(
'--base_lr',
'-blr',
type=float,
default=1.0,
help=
'[default: %(default)s] Learning rate to start after warmup epochs finish when using LARS'
)
self.add_argument(
'--init_lr',
'-ilr',
type=float,
default=0.1,
help=
'[default: %(default)s] Learning rate to start warmup with when using LARS'
)
self.add_argument(
'--weight_decay',
'-wd',
type=float,
default=1e-4,
help='[default: %(default)s] Weight decay to use during training')