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opts.py
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opts.py
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import argparse
def parse_opt():
parser = argparse.ArgumentParser()
# Data input settings
parser.add_argument(
'--input_json',
type=str,
default='data/videodatainfo_2017.json',
help='path to the json file containing video info')
parser.add_argument(
'--info_json',
type=str,
default='data/info.json',
help='path to the json file containing additional info and vocab')
parser.add_argument(
'--caption_json',
type=str,
default='data/caption.json',
help='path to the processed video caption json')
parser.add_argument(
'--feats_dir',
nargs='*',
type=str,
default=['data/feats/resnet152/'],
help='path to the directory containing the preprocessed fc feats')
parser.add_argument('--c3d_feats_dir', type=str, default='data/c3d_feats')
parser.add_argument(
'--with_c3d', type=int, default=0, help='whether to use c3d features')
parser.add_argument(
'--cached_tokens',
type=str,
default='msr-all-idxs',
help='Cached token file for calculating cider score \
during self critical training.')
# Model settings
parser.add_argument(
"--model", type=str, default='S2VTModel', help="with model to use")
parser.add_argument(
"--max_len",
type=int,
default=28,
help='max length of captions(containing <sos>,<eos>)')
parser.add_argument(
"--bidirectional",
type=int,
default=0,
help="0 for disable, 1 for enable. encoder/decoder bidirectional.")
parser.add_argument(
'--dim_hidden',
type=int,
default=512,
help='size of the rnn hidden layer')
parser.add_argument(
'--num_layers', type=int, default=1, help='number of layers in the RNN')
parser.add_argument(
'--input_dropout_p',
type=float,
default=0.2,
help='strength of dropout in the Language Model RNN')
parser.add_argument(
'--rnn_type', type=str, default='gru', help='lstm or gru')
parser.add_argument(
'--rnn_dropout_p',
type=float,
default=0.5,
help='strength of dropout in the Language Model RNN')
parser.add_argument(
'--dim_word',
type=int,
default=512,
help='the encoding size of each token in the vocabulary, and the video.'
)
parser.add_argument(
'--dim_vid',
type=int,
default=2048,
help='dim of features of video frames')
# Optimization: General
parser.add_argument(
'--epochs', type=int, default=6001, help='number of epochs')
parser.add_argument(
'--batch_size', type=int, default=128, help='minibatch size')
parser.add_argument(
'--grad_clip',
type=float,
default=5, # 5.,
help='clip gradients at this value')
parser.add_argument(
'--self_crit_after',
type=int,
default=-1,
help='After what epoch do we start finetuning the CNN? \
(-1 = disable; never finetune, 0 = finetune from start)'
)
parser.add_argument(
'--learning_rate', type=float, default=4e-4, help='learning rate')
parser.add_argument(
'--learning_rate_decay_every',
type=int,
default=200,
help='every how many iterations thereafter to drop LR?(in epoch)')
parser.add_argument('--learning_rate_decay_rate', type=float, default=0.8)
parser.add_argument(
'--optim_alpha', type=float, default=0.9, help='alpha for adam')
parser.add_argument(
'--optim_beta', type=float, default=0.999, help='beta used for adam')
parser.add_argument(
'--optim_epsilon',
type=float,
default=1e-8,
help='epsilon that goes into denominator for smoothing')
parser.add_argument(
'--weight_decay',
type=float,
default=5e-4,
help='weight_decay. strength of weight regularization')
parser.add_argument(
'--save_checkpoint_every',
type=int,
default=50,
help='how often to save a model checkpoint (in epoch)?')
parser.add_argument(
'--checkpoint_path',
type=str,
default='save',
help='directory to store checkpointed models')
parser.add_argument(
'--gpu', type=str, default='0', help='gpu device number')
args = parser.parse_args()
return args