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def get_parser():
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# parameter priority: command line > config > default
parser = argparse.ArgumentParser(
description='The pytorch implementation for Visual Alignment Constraint '
'for Continuous Sign Language Recognition.')
parser.add_argument(
'--work-dir',
default='./work_dir/temp',
help='the work folder for storing results')
parser.add_argument(
'--config',
default='./configs/baseline.yaml',
help='path to the configuration file')
parser.add_argument(
'--random_fix',
type=str2bool,
default=True,
help='fix random seed or not')
parser.add_argument(
'--device',
type=str,
default=0,
help='the indexes of GPUs for training or testing')
parser.add_argument(
'--num-feature-aug',
type=int,
default=-1,
help='number of feature duplicates, by default -1 no duplication.')
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# processor
parser.add_argument(
'--phase', default='train', help='can be train, test and features')
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# debug
parser.add_argument(
'--save-interval',
type=int,
default=200,
help='the interval for storing models (#epochs)')
parser.add_argument(
'--random-seed',
type=int,
default=0,
help='the default value for random seed.')
parser.add_argument(
'--eval-interval',
type=int,
default=100,
help='the interval for evaluating models (#epochs)')
parser.add_argument(
'--print-log',
type=str2bool,
default=True,
help='print logging or not')
parser.add_argument(
'--log-interval',
type=int,
default=20,
help='the interval for printing messages (#iteration)')
parser.add_argument(
'--evaluate-tool', default="python", help='sclite or python')
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# feeder
parser.add_argument(
'--feeder', default='dataloader_video.BaseFeeder', help='data loader will be used')
parser.add_argument(
'--dataset',
default=None,
help='data loader will be used'
)
parser.add_argument(
'--dataset-info',
default=dict(),
help='data loader will be used'
)
parser.add_argument(
'--preprocess-sample-ratio',
type=float,
default=1.0,
help='preprocess-only: fraction of manifest to sample (unused by training)')
parser.add_argument(
'--label-column',
default='TEXT',
help='preprocess-only: manifest label column (unused by training)')
parser.add_argument(
'--scale-update-cycle',
type=str2bool,
default=False,
help='slt-only: scale update_cycle by GPU count (unused by SMKD)')
parser.add_argument(
'--dev-from-train-count',
type=int,
default=0,
help='preprocess-only: override dev split with N samples from train (unused by training)')
parser.add_argument(
'--dev-from-train-keep',
type=str2bool,
default=False,
help='preprocess-only: keep dev samples in train (unused by training)')
parser.add_argument(
'--eval-use-conv',
type=str2bool,
default=False,
help='evaluate using conv head outputs instead of sequence head')
parser.add_argument(
'--debug-seq-topk',
type=int,
default=0,
help='print top-k sequence head average probs on first eval batch (0 disables)')
parser.add_argument(
'--debug-conv-topk',
type=int,
default=0,
help='print top-k conv head average probs on first eval batch (0 disables)')
parser.add_argument(
'--num-worker',
type=int,
default=4,
help='the number of worker for data loader')
parser.add_argument(
'--feeder-args',
default=dict(),
help='the arguments of data loader')
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# model
parser.add_argument('--model', default=None, help='the model will be used')
parser.add_argument(
'--model-args',
type=dict,
default=dict(),
help='the arguments of model')
parser.add_argument(
'--load-weights',
default=None,
help='load weights for network initialization')
parser.add_argument(
'--load-checkpoints',
default=None,
help='load checkpoints for continue training')
parser.add_argument(
'--decode-mode',
default="max",
help='search mode for decode, max or beam')
parser.add_argument(
'--ignore-weights',
type=str,
default=[],
nargs='+',
help='the name of weights which will be ignored in the initialization')
parser.add_argument(
'--skip-sample-file',
default=None,
help='path to a newline separated list of sample IDs to ignore')
parser.add_argument(
'--disable-bad-sample-filter',
type=str2bool,
default=False,
help='set true to skip recording/removing samples with non-finite loss')
parser.add_argument(
'--debug-save-batch',
type=str2bool,
default=True,
help='save first batch frames for debugging')
parser.add_argument(
'--debug-stats',
type=str2bool,
default=True,
help='log first batch stats for debugging')
parser.add_argument(
'--debug-save-dir',
default=None,
help='override debug frame output directory')
parser.add_argument(
'--debug-stats-path',
default=None,
help='override debug stats output file path')
parser.add_argument(
'--debug-save-frames',
type=int,
default=8,
help='max frames to save from first batch')
parser.add_argument(
'--debug-save-samples',
type=int,
default=1,
help='max samples to save from first batch')
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# optim
parser.add_argument(
'--batch-size', type=int, default=16, help='training batch size')
parser.add_argument(
'--test-batch-size', type=int, default=8, help='test batch size')
default_optimizer_dict = {
"base_lr": 1e-2,
"optimizer": "SGD",
"nesterov": False,
"step": [5, 10],
"weight_decay": 0.00005,
"start_epoch": 1,
}
default_loss_dict = {
"SeqCTC": 1.0,
}
parser.add_argument(
'--loss-weights',
default=default_loss_dict,
help='loss selection'
)
parser.add_argument(
'--optimizer-args',
default=default_optimizer_dict,
help='the arguments of optimizer')
parser.add_argument(
'--num-epoch',
type=int,
default=80,
help='stop training in which epoch')
return parser
def str2bool(v):
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
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