def get_parser() -> argparse.ArgumentParser:
parser = options.get_trainer_options()
# General opts
parser.add_argument('-v',
'--verbose',
action='store_true',
default=False,
help='Set verbose mode')
# Data options
parser.add_argument('--checkpoint-dir',
type=str,
default='./checkpoint',
help='Set directory to place training snapshots into')
parser.add_argument('--checkpoint-name',
type=str,
default='mnist_autoencoder',
help='Name to prepend to all checkpoints')
parser.add_argument('--load-checkpoint',
type=str,
default=None,
help='Load a given checkpoint')
parser.add_argument('--overwrite',
action='store_true',
default=False,
help='Overwrite existing processed data files')
return parser
def get_parser() -> argparse.ArgumentParser:
parser = options.get_trainer_options()
parser.add_argument('-v',
'--verbose',
action='store_true',
default=False,
help='Set verbose mode')
parser.add_argument('--find-lr',
action='store_true',
default=False,
help='Search for optimal learning rate')
parser.add_argument('--num-layers',
type=int,
default=28,
help='Number of layers for Resnet model')
parser.add_argument('--num-classes',
type=int,
default=10,
help='Number of output classes for classification')
parser.add_argument('--widen-factor',
type=int,
default=1,
help='Widen factor for wide Resnet')
# Data options
parser.add_argument('--checkpoint-dir',
type=str,
default='./checkpoint',
help='Set directory to place training snapshots into')
parser.add_argument('--checkpoint-name',
type=str,
default='resnet-cifar10',
help='Name to prepend to all checkpoints')
return parser
def get_parser() -> argparse.ArgumentParser:
parser = options.get_trainer_options()
# add some extra options for this particular example
parser.add_argument('-v',
'--verbose',
action='store_true',
default=False,
help='Set verbose mode')
parser.add_argument('--find-lr',
action='store_true',
default=False,
help='Search for optimal learning rate')
# Figure output
parser.add_argument('--fig-name',
type=str,
default='figures/cifar10net_train.png',
help='Name of file to place output figure into')
# Checkpoint options
parser.add_argument('--checkpoint-dir',
type=str,
default='./checkpoint',
help='Set directory to place training snapshots into')
parser.add_argument('--checkpoint-name',
type=str,
default='cifar10',
help='Name to prepend to all checkpoints')
# TODO : implement this...
parser.add_argument('--load-checkpoint',
type=str,
default=None,
help='Load a given checkpoint')
return parser
def get_parser():
parser = options.get_trainer_options()
parser = options.get_lr_finder_options(parser)
# General opts
parser.add_argument('-v',
'--verbose',
action='store_true',
default=False,
help='Set verbose mode')
parser.add_argument('--draw-plot',
default=False,
action='store_true',
help='Display plots')
parser.add_argument(
'--find-only',
action='store_true',
default=False,
help='Only perform the parameter find step (no training)')
# model, size options
parser.add_argument(
'--model',
type=str,
default='resnet',
help='Type of model to use in example. (default: resnet)')
parser.add_argument('--resnet-depth',
type=int,
default=58,
help='Depth of resnet to use for resnet models')
# Schedule options
parser.add_argument('--exp-decay',
type=float,
default=0.001,
help='Exponential decay term')
parser.add_argument('--sched-stepsize',
type=int,
default=0,
help='Size of step for learning rate scheduler')
# Data options
parser.add_argument('--checkpoint-dir',
type=str,
default='./checkpoint',
help='Set directory to place training snapshots into')
parser.add_argument('--checkpoint-name',
type=str,
default='lr_find_ex_cifar10',
help='Name to prepend to all checkpoints')
parser.add_argument('--load-checkpoint',
type=str,
default=None,
help='Load a given checkpoint')
parser.add_argument('--overwrite',
action='store_true',
default=False,
help='Overwrite existing processed data files')
return parser
def get_parser() -> argparse.ArgumentParser:
parser = options.get_trainer_options()
# General opts
parser.add_argument('-v',
'--verbose',
action='store_true',
default=False,
help='Set verbose mode')
# Network options
parser.add_argument('--zvec-dim',
type=int,
default=128,
help='Dimension of z vector')
parser.add_argument('--g-num-filters',
type=int,
default=64,
help='Number of filters to use in generator')
parser.add_argument('--d-num-filters',
type=int,
default=64,
help='Number of filters to use in discriminator')
# DCGAN trainer options
parser.add_argument('--beta1',
type=float,
default=0.5,
help='beta1 parameter for ADAM optimizer')
# Data options
parser.add_argument(
'--image-size',
type=int,
default=64,
help=
'Resize all images to this size using a transformer before training')
parser.add_argument('--dataset-root',
type=str,
default='/mnt/ml-data/datasets/celeba/',
help='Path to root of dataset')
parser.add_argument('--dataset',
type=str,
default=None,
help='Path to dataset in LMDB format (default: None)')
# checkpoint options
parser.add_argument('--checkpoint-dir',
type=str,
default='./checkpoint/',
help='Set directory to place training snapshots into')
parser.add_argument('--checkpoint-name',
type=str,
default='lmdb_dcgan',
help='Name to prepend to all checkpoints')
parser.add_argument('--load-checkpoint',
type=str,
default=None,
help='Load a given checkpoint')
return parser
def get_parser() -> argparse.ArgumentParser:
parser = options.get_trainer_options()
# General opts
parser.add_argument('-v',
'--verbose',
action='store_true',
default=False,
help='Set verbose mode')
parser.add_argument(
'--infer',
action='store_true',
default=False,
help='Do an inference pass on the models after training')
parser.add_argument(
'--data-dir',
type=str,
default='./data',
help='Path to location where data will be downloaded (default: ./data)'
)
parser.add_argument(
'--loss-history-file',
type=str,
default='figures/dae_loss_history.png',
help=
'File to write loss history to (default: figures/dae_loss_history.png')
# Noise options
parser.add_argument(
'--noise-bias',
type=float,
default=0.25,
help='Amount to bias image by during noise application (default: 0.25)'
)
parser.add_argument(
'--noise-factor',
type=float,
default=0.1,
help='Amount of noise to add to image overall (default: 0.1)')
# Data options
parser.add_argument('--checkpoint-dir',
type=str,
default='./checkpoint',
help='Set directory to place training snapshots into')
parser.add_argument('--checkpoint-name',
type=str,
default='denoise_auto_mnist',
help='Name to prepend to all checkpoints')
return parser
def get_parser() -> argparse.ArgumentParser:
parser = options.get_trainer_options()
parser = options.get_lr_finder_options(parser)
# General opts
parser.add_argument('-v',
'--verbose',
action='store_true',
default=False,
help='Set verbose mode')
parser.add_argument('--find-lr',
action='store_true',
default=False,
help='Search for optimal learning rate')
parser.add_argument('--checkpoint-dir',
type=str,
default='./checkpoint',
help='Set directory to place training snapshots into')
parser.add_argument('--checkpoint-name',
type=str,
default='mnist',
help='Name to prepend to all checkpoints')
return parser
def get_parser() -> argparse.ArgumentParser:
parser = options.get_trainer_options()
parser = options.get_lr_finder_options(parser)
# General opts
parser.add_argument('-v', '--verbose',
action='store_true',
default=False,
help='Set verbose mode'
)
parser.add_argument('--draw-plot',
default=False,
action='store_true',
help='Display plots'
)
# Data options
parser.add_argument('--checkpoint-dir',
type=str,
default='./checkpoint',
help='Set directory to place training snapshots into'
)
parser.add_argument('--load-checkpoint',
type=str,
default=None,
help='Load a given checkpoint'
)
parser.add_argument('--tensorboard-dir',
default=None,
type=str,
help='Directory to save tensorboard runs to. If None, tensorboard is not used. (default: None)'
)
parser.add_argument('--sched-type',
type=str,
default='TriangularScheduler',
help='Type of learning rate scheduler to use. Must be the name of a class in lernomatic.train.schedule. (default: TriangularScheduler)'
)
return parser
def get_parser() -> argparse.ArgumentParser:
parser = options.get_trainer_options()
# General opts
parser.add_argument('-v',
'--verbose',
action='store_true',
default=False,
help='Set verbose mode')
parser.add_argument('--find-lr',
action='store_true',
default=False,
help='Search for optimal learning rate')
# Dataset options
parser.add_argument('--train-dataset',
type=str,
default='hdf5/cvd_train.h5',
help='Path to training dataset')
parser.add_argument('--test-dataset',
type=str,
default='hdf5/cvd_test.h5',
help='Path to test dataset')
parser.add_argument('--val-dataset',
type=str,
default='hdf5/cvd_val.h5',
help='Path to validation dataset')
# Data options
parser.add_argument('--checkpoint-dir',
type=str,
default='./checkpoint',
help='Set directory to place training snapshots into')
parser.add_argument('--checkpoint-name',
type=str,
default='cvd_',
help='Name to prepend to all checkpoints')
return parser
def get_parser() -> argparse.ArgumentParser:
parser = options.get_trainer_options()
# General opts
parser.add_argument('-v', '--verbose',
action='store_true',
default=False,
help='Set verbose mode'
)
# Network options
parser.add_argument('--gen-type',
type=str,
default='resnet',
help='Type of generator to use (resnet or unet)'
)
parser.add_argument('--disc-type',
type=str,
default='pixel',
help='Type of discriminator to use (nlayer or pixel)'
)
# Data options
parser.add_argument('--train-dataset-h5',
type=str,
default=None,
help='If specified, load this *.h5 dataset as training data'
)
parser.add_argument('--val-dataset-h5',
type=str,
default=None,
help='If specified, load this *.h5 dataset as validation data'
)
parser.add_argument('--train-data-path',
type=str,
default='/home/kreshnik/ml-data/night2day/train/',
help='Path to training data'
)
parser.add_argument('--val-data-path',
type=str,
default='/home/kreshnik/ml-data/night2day/val/',
help='Path to training data'
)
parser.add_argument('--checkpoint-dir',
type=str,
default='./checkpoint',
help='Set directory to place training snapshots into'
)
parser.add_argument('--checkpoint-name',
type=str,
default='pix2pix',
help='Name to prepend to all checkpoints'
)
parser.add_argument('--load-checkpoint',
type=str,
default=None,
help='Load a given checkpoint'
)
parser.add_argument('--overwrite',
action='store_true',
default=False,
help='Overwrite existing processed data files'
)
return parser