def get_args():
""" Defines training-specific hyper-parameters. """
parser = argparse.ArgumentParser('Sequence to Sequence Model')
# Add data arguments
parser.add_argument('--data', default='europarl_prepared', help='path to data directory')
parser.add_argument('--source-lang', default='de', help='source language')
parser.add_argument('--target-lang', default='en', help='target language')
parser.add_argument('--max-tokens', default=None, type=int, help='maximum number of tokens in a batch')
parser.add_argument('--batch-size', default=10, type=int, help='maximum number of sentences in a batch')
parser.add_argument('--train-on-tiny', action='store_true', help='train model on a tiny dataset')
# Add model arguments
parser.add_argument('--device', default='cpu', choices=['cpu', 'cuda'], help='the device to carry out the training '
'either cpu or cuda aka gpu')
parser.add_argument('--arch', default='lstm', choices=ARCH_MODEL_REGISTRY.keys(), help='model architecture')
# Add optimization arguments
parser.add_argument('--max-epoch', default=100, type=int, help='force stop training at specified epoch')
parser.add_argument('--clip-norm', default=4.0, type=float, help='clip threshold of gradients')
parser.add_argument('--lr', default=0.0003, type=float, help='learning rate')
parser.add_argument('--patience', default=10, type=int,
help='number of epochs without improvement on validation set before early stopping')
# Add checkpoint arguments
parser.add_argument('--log-file', default=None, help='path to save logs')
parser.add_argument('--save-dir', default='checkpoints', help='path to save checkpoints')
parser.add_argument('--restore-file', default='checkpoint_last.pt', help='filename to load checkpoint')
parser.add_argument('--save-interval', type=int, default=1, help='save a checkpoint every N epochs')
parser.add_argument('--no-save', action='store_true', help='don\'t save models or checkpoints')
parser.add_argument('--epoch-checkpoints', action='store_true', help='store all epoch checkpoints')
# Parse twice as model arguments are not known the first time
args, _ = parser.parse_known_args()
model_parser = parser.add_argument_group(argument_default=argparse.SUPPRESS)
ARCH_MODEL_REGISTRY[args.arch].add_args(model_parser)
args = parser.parse_args()
ARCH_CONFIG_REGISTRY[args.arch](args)
return args
def get_args():
parser = argparse.ArgumentParser('Sequence to Sequence Model')
parser.add_argument('--seed',
default=42,
type=int,
help='pseudo random number generator seed')
parser.add_argument('--distributed-world-size',
default=torch.cuda.device_count(),
help='distributed world size')
parser.add_argument('--distributed-backend',
default='nccl',
help='distributed backend')
# Add data arguments
parser.add_argument('--data',
default='data-bin',
help='path to data directory')
parser.add_argument('--source-lang', default=None, help='source language')
parser.add_argument('--target-lang', default=None, help='target language')
parser.add_argument('--max-tokens',
default=16000,
type=int,
help='maximum number of tokens in a batch')
parser.add_argument('--batch-size',
default=None,
type=int,
help='maximum number of sentences in a batch')
parser.add_argument('--num-workers',
default=4,
type=int,
help='number of data workers')
# Add model arguments
parser.add_argument('--arch',
default='lstm',
choices=ARCH_MODEL_REGISTRY.keys(),
help='model architecture')
# Add optimization arguments
parser.add_argument('--max-epoch',
default=100,
type=int,
help='force stop training at specified epoch')
parser.add_argument('--clip-norm',
default=0.1,
type=float,
help='clip threshold of gradients')
parser.add_argument('--lr', default=0.25, type=float, help='learning rate')
parser.add_argument('--momentum',
default=0.99,
type=float,
help='momentum factor')
parser.add_argument('--weight-decay',
default=0.0,
type=float,
help='weight decay')
parser.add_argument('--lr-shrink',
default=0.1,
type=float,
help='learning rate shrink factor for annealing')
parser.add_argument('--min-lr',
default=1e-5,
type=float,
help='minimum learning rate')
# Add checkpoint arguments
parser.add_argument('--log-file', default=None, help='path to save logs')
parser.add_argument('--save-dir',
default='checkpoints',
help='path to save checkpoints')
parser.add_argument('--restore-file',
default='checkpoint_last.pt',
help='filename to load checkpoint')
parser.add_argument('--save-interval',
type=int,
default=1,
help='save a checkpoint every N epochs')
parser.add_argument('--no-save',
action='store_true',
help='don\'t save models or checkpoints')
parser.add_argument('--epoch-checkpoints',
action='store_true',
help='store all epoch checkpoints')
return parser.parse_args()
def get_args():
parser = argparse.ArgumentParser('Sequence to Sequence Model')
parser.add_argument('--seed',
default=42,
type=int,
help='pseudo random number generator seed')
parser.add_argument('--distributed-world-size',
default=torch.cuda.device_count(),
help='distributed world size')
parser.add_argument('--distributed-backend',
default='nccl',
help='distributed backend')
# Add data arguments
parser.add_argument('--data',
default='data-bin/how2',
help='path to data directory')
parser.add_argument('--max-tokens',
default=16000,
type=int,
help='maximum number of tokens in a batch')
parser.add_argument('--train_video_file',
default='../how2data/text/sum_train/tr_action.txt',
help='name of train video file')
parser.add_argument('--val_video_file',
default='../how2data/text/sum_cv/cv_action.txt',
help='name of val video file')
parser.add_argument('--video_dir',
default='../how2data/video_action_features',
help='path of video features')
parser.add_argument('--batch-size',
default=8,
type=int,
help='maximum number of sentences in a batch')
parser.add_argument('--num-workers',
default=2,
type=int,
help='number of data workers')
# Add model arguments
parser.add_argument('--arch',
default='MFtransformer',
choices=ARCH_MODEL_REGISTRY.keys(),
help='model architecture')
# Add optimization arguments
parser.add_argument('--max-epoch',
default=10,
type=int,
help='force stop training at specified epoch')
parser.add_argument('--clip-norm',
default=0.1,
type=float,
help='clip threshold of gradients')
parser.add_argument('--lr',
default=0.00015,
type=float,
help='learning rate')
parser.add_argument('--momentum',
default=0.99,
type=float,
help='momentum factor')
parser.add_argument('--weight-decay',
default=0.0,
type=float,
help='weight decay')
parser.add_argument('--lr-shrink',
default=0.1,
type=float,
help='learning rate shrink factor for annealing')
parser.add_argument('--min-lr',
default=1e-5,
type=float,
help='minimum learning rate')
# Add checkpoint arguments
parser.add_argument('--log-file',
default='./log.txt',
help='path to save logs')
parser.add_argument('--save-dir',
default='checkpoints/mfn',
help='path to save checkpoints')
parser.add_argument('--restore-file',
default='checkpoint_last.pt',
help='filename to load checkpoint')
parser.add_argument('--save-interval',
type=int,
default=1,
help='save a checkpoint every N epochs')
parser.add_argument('--no-save',
action='store_true',
help='don\'t save models or checkpoints')
parser.add_argument('--epoch-checkpoints',
action='store_true',
help='store all epoch checkpoints')
# Parse twice as model arguments are not known the first time
args, _ = parser.parse_known_args()
model_parser = parser.add_argument_group(
argument_default=argparse.SUPPRESS)
ARCH_MODEL_REGISTRY[args.arch].add_args(model_parser)
args = parser.parse_args()
ARCH_CONFIG_REGISTRY[args.arch](args)
return args