-
Notifications
You must be signed in to change notification settings - Fork 7
/
opts.py
144 lines (133 loc) · 9.18 KB
/
opts.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
import argparse
def parse_opt():
parser = argparse.ArgumentParser()
# Data input settings
parser.add_argument('--input_json', type=str,
help='path to the json file containing additional info and vocab (img/video)')
parser.add_argument('--input_fc_dir', type=str,
help='path to the directory containing the preprocessed fc video features')
parser.add_argument('--input_img_dir', type=str,
help='path to the directory containing the image features')
parser.add_argument('--input_box_dir', type=str,
help='path to the directory containing the boxes of att img feats (img)')
parser.add_argument('--input_label_h5', type=str,
help='path to the h5file containing the preprocessed dataset (img/video)')
parser.add_argument('--g_start_from', type=str, default=None,
help="""skip pre training step and continue training from saved generator model at this path.
'infos_{id}.pkl' : configuration;
'gen_optimizer_{epoch}.pth' : optimizer;
'gen_{epoch}.pth' : model
""")
parser.add_argument('--g_start_epoch', type=str, default="latest",
help="""start training generator at epoch (int, latest, latest_ce, latest_scst)
""")
parser.add_argument('--cached_tokens', type=str, default='coco-train-idxs',
help='Cached token file for calculating cider score during self critical training.')
# Model settings
parser.add_argument('--caption_model', type=str, default="video",
help='fc, show_tell, adaatt, topdown, s2vt, paragraph show_attend_tell, all_img, att2in, att2in2, att2all2, stackatt, denseatt')
parser.add_argument('--g_context_epoch', type=int, default=10,
help='epoch to start incorporating context for generator (-1 = dont use context)')
parser.add_argument('--rnn_size', type=int, default=512,
help='size of the rnn in number of hidden nodes in each layer')
parser.add_argument('--num_layers', type=int, default=1,
help='number of layers in the RNN')
parser.add_argument('--rnn_type', type=str, default='lstm',
help='rnn, gru, or lstm')
parser.add_argument('--video_encoding_size', type=int, default=256,
help='the encoding size of each frame of visual features.')
parser.add_argument('--input_encoding_size', type=int, default=512,
help='the encoding size of each token in the vocabulary, and the image.')
parser.add_argument('--feat_type', type=str, default='i3d',
help='feat type for video (i3d, c3d, resnext101-64f)')
parser.add_argument('--fc_feat_size', type=int, default=1024,
help='1024 for i3d, 2048 for resnet, 4096 for vgg (img) \
500 for c3d, 8192 for r3d (video')
parser.add_argument('--img_feat_size', type=int, default=2048,
help='img feat size')
parser.add_argument('--box_feat_size', type=int, default=15461,
help='box feat size')
parser.add_argument('--logit_layers', type=int, default=1,
help='number of layers in the RNN')
parser.add_argument('--use_bn', type=int, default=0,
help='If 1, then do batch_normalization first in att_embed, if 2 then do bn both in the beginning and the end of att_embed')
parser.add_argument('--glove', type=str, default=None,
help='text or npy containing glove vector associated with word_idx labels. \
builds a npy file in the same directory if text file is given')
# input settings
parser.add_argument('--use_video', type=int, default=1,
help='use video features (c3d/resnext101-64f) specified in input_fc_dir')
parser.add_argument('--use_img', type=int, default=1,
help='use resnet features specified in input_img_dir')
parser.add_argument('--use_box', type=int, default=0,
help='use bottomup features sepcified in input_box_dir')
parser.add_argument('--max_seg', type=int, default=3,
help='number of segments to divide the temporal visual features')
parser.add_argument('--max_sent_num', type=int, default=5,
help='max number of sentences per group (LSMDC has a group of 5 clips)')
parser.add_argument('--use_mean', type=int, default=0)
# Optimization: General
parser.add_argument('--g_pre_nepoch', type=int, default=80,
help='number of epochs to pre-train generator with cross entropy')
parser.add_argument('--batch_size', type=int, default=16,
help='minibatch size')
parser.add_argument('--grad_clip', type=float, default=0.1, #5.,
help='clip gradients at this value')
parser.add_argument('--drop_prob_lm', type=float, default=0.5,
help='strength of dropout in the Language Model RNN')
# Optimization: for the Language Model
parser.add_argument('--optim', type=str, default='adam',
help='what update to use? rmsprop|sgd|sgdmom|adagrad|adam')
parser.add_argument('--learning_rate', type=float, default=4e-4,
help='learning rate')
parser.add_argument('--learning_rate_decay_start', type=int, default=-1,
help='at what iteration to start decaying learning rate? (-1 = dont) (in epoch)')
parser.add_argument('--learning_rate_decay_every', type=int, default=3,
help='every how many iterations thereafter to drop LR?(in epoch)')
parser.add_argument('--learning_rate_decay_rate', type=float, default=0.8,
help='every how many iterations thereafter to drop LR?(in epoch)')
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=0,
help='weight_decay')
parser.add_argument('--scheduled_sampling_start', type=int, default=-1,
help='at what iteration to start decay gt probability')
parser.add_argument('--scheduled_sampling_increase_every', type=int, default=5,
help='every how many iterations thereafter to gt probability')
parser.add_argument('--scheduled_sampling_increase_prob', type=float, default=0.05,
help='How much to update the prob')
parser.add_argument('--scheduled_sampling_max_prob', type=float, default=0.25,
help='Maximum scheduled sampling prob.')
# Evaluation/Checkpointing
parser.add_argument('--val_id', type=str, default='',
help='id to use to save captions for validation')
parser.add_argument('--val_videos_use', type=int, default=-1,
help='how many videos to use when periodically evaluating the validation loss? (-1 = all)')
parser.add_argument('--losses_print_every', type=int, default=10,
help='How often do we want to print losses? (0 = disable)')
parser.add_argument('--save_checkpoint_every', type=int, default=1,
help='how often to save a model checkpoint in iterations? the code already saves checkpoint every epoch (0 = dont save; 1 = every epoch)')
parser.add_argument('--checkpoint_path', type=str, default='save',
help='directory to store checkpointed models')
parser.add_argument('--losses_log_every', type=int, default=25,
help='How often do we snapshot losses, for inclusion in the progress dump? (0 = disable)')
parser.add_argument('--language_eval', type=int, default=0,
help='Evaluate language as well (1 = yes, 0 = no)? BLEU/CIDEr/METEOR/ROUGE_L? requires coco-caption code from Github.')
parser.add_argument('--load_best_score', type=int, default=1,
help='Do we load previous best score when resuming training.')
args = parser.parse_args()
# Check if args are valid
assert args.rnn_size > 0, "rnn_size should be greater than 0"
assert args.num_layers > 0, "num_layers should be greater than 0"
assert args.input_encoding_size > 0, "input_encoding_size should be greater than 0"
assert args.batch_size > 0, "batch_size should be greater than 0"
assert args.drop_prob_lm >= 0 and args.drop_prob_lm < 1, "drop_prob_lm should be between 0 and 1"
assert args.losses_log_every > 0, "losses_log_every should be greater than 0"
assert args.language_eval == 0 or args.language_eval == 1, "language_eval should be 0 or 1"
assert args.load_best_score == 0 or args.load_best_score == 1, "language_eval should be 0 or 1"
assert args.save_checkpoint_every >= 0, "saving checkpoint at every $epoch should be non-negative"
return args