forked from jiasenlu/NeuralBabyTalk
-
Notifications
You must be signed in to change notification settings - Fork 0
/
opts.py
161 lines (153 loc) · 9.93 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
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
import argparse
def parse_opt():
parser = argparse.ArgumentParser()
# # Data input settings
parser.add_argument('--path_opt', type=str, default='cfgs/coco.yml',
help='')
parser.add_argument('--dataset', type=str, default='coco',
help='')
parser.add_argument('--input_json', type=str, default='data/coco/cap_coco.json',
help='path to the json file containing additional info and vocab')
parser.add_argument('--input_dic', type=str, default='data/coco/dic_coco.json',
help='path to the json containing the preprocessed dataset')
parser.add_argument('--image_path', type=str, default='/srv/share/datasets/coco/images',
help='path to the h5file containing the image data')
parser.add_argument('--proposal_h5', type=str, default='data/coco/coco_detection.h5',
help='path to the json containing the detection result.')
parser.add_argument('--cnn_backend', type=str, default='res101',
help='res101 or vgg16')
parser.add_argument('--data_path', type=str, default='',
help='')
parser.add_argument('--decode_noc', type=bool, default=True,
help='decoding option: normal | noc')
parser.add_argument('--att_model', type=str, default='topdown',
help='different attention model, now supporting topdown | att2in2')
parser.add_argument('--num_workers', dest='num_workers',
help='number of worker to load data',
default=10, type=int)
parser.add_argument('--cuda', type=bool, default=True,
help='whether use cuda')
parser.add_argument('--mGPUs', type=bool, default=False,
help='whether use multiple GPUs')
parser.add_argument('--cached_tokens', type=str, default='dataset/coco-train-idxs',
help='Cached token file for calculating cider score during self critical training.')
# Model settings
parser.add_argument('--rnn_size', type=int, default=1024,
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('--input_encoding_size', type=int, default=512,
help='the encoding size of each token in the vocabulary, and the image.')
parser.add_argument('--att_hid_size', type=int, default=512,
help='the hidden size of the attention MLP; only useful in show_attend_tell; 0 if not using hidden layer')
parser.add_argument('--fc_feat_size', type=int, default=2048,
help='2048 for resnet, 4096 for vgg')
parser.add_argument('--att_feat_size', type=int, default=2048,
help='2048 for resnet, 512 for vgg')
parser.add_argument('--image_size', type=int, default=576,
help='image random crop size')
parser.add_argument('--image_crop_size', type=int, default=512,
help='image random crop size')
# Optimization: General
parser.add_argument('--max_epochs', type=int, default=30,
help='number of epochs')
parser.add_argument('--batch_size', type=int, default=10,
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')
parser.add_argument('--self_critical', type=bool, default=False,
help='whether use self critical training.')
parser.add_argument('--seq_per_img', type=int, default=5,
help='number of captions to sample for each image during training. Done for efficiency since CNN forward pass is expensive. E.g. coco has 5 sents/image')
parser.add_argument('--seq_length', type=int, default=20, help='')
parser.add_argument('--beam_size', type=int, default=1,
help='used when sample_max = 1, indicates number of beams in beam search. Usually 2 or 3 works well. More is not better. Set this to 1 for faster runtime but a bit worse performance.')
# Schedule Sampling.
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.')
#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=5e-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')
# Optimization: for the CNN
parser.add_argument('--finetune_cnn', action='store_true',
help='finetune CNN')
parser.add_argument('--fixed_block', type=float, default=1,
help='fixed cnn block when training. [0-4] \
0:finetune all block, 4: fix all block')
parser.add_argument('--cnn_optim', type=str, default='adam',
help='what update to use? rmsprop|sgd|sgdmom|adagrad|adam')
parser.add_argument('--cnn_optim_alpha', type=float, default=0.8,
help='cnn alpha for adam')
parser.add_argument('--cnn_optim_beta', type=float, default=0.999,
help='beta used for adam')
parser.add_argument('--cnn_learning_rate', type=float, default=1e-5,
help='cnn learning rate')
parser.add_argument('--cnn_weight_decay', type=float, default=0,
help='weight_decay')
# set training session
parser.add_argument('--start_from', type=str, default=None,
help="""continue training from saved model at this path. Path must contain files saved by previous training process:
'infos.pkl' : configuration;
'checkpoint' : paths to model file(s) (created by tf).
Note: this file contains absolute paths, be careful when moving files around;
'model.ckpt-*' : file(s) with model definition (created by tf)
""")
parser.add_argument('--id', type=str, default='',
help='an id identifying this run/job. used in cross-val and appended when writing progress files')
# Evaluation/Checkpointing
parser.add_argument('--cider_df', type=str, default='corpus',
help='')
parser.add_argument('--val_split', type=str, default='test',
help='')
parser.add_argument('--inference_only', type=bool, default=False,
help='')
parser.add_argument('--val_images_use', type=int, default=5000,
help='how many images to use when periodically evaluating the validation loss? (-1 = all)')
parser.add_argument('--val_every_epoch', type=int, default=3,
help='how many images to use when periodically evaluating the validation loss? (-1 = all)')
parser.add_argument('--checkpoint_path', type=str, default='save',
help='directory to store checkpointed models')
parser.add_argument('--language_eval', type=int, default=1,
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.')
parser.add_argument('--disp_interval', type=int, default=100,
help='how many iteration to display an loss.')
parser.add_argument('--losses_log_every', type=int, default=10,
help='how many iteration for log.')
parser.add_argument('--cbs', type=bool, default=False,
help='whether use constraint beam search.')
parser.add_argument('--cbs_tag_size', type=int, default=3,
help='whether use constraint beam search.')
parser.add_argument('--cbs_mode', type=str, default='all',
help='which cbs mode to use in the decoding stage. cbs_mode: all|unique|novel')
parser.add_argument('--det_oracle', type=bool, default=False,
help='whether use oracle bounding box.')
args = parser.parse_args()
return args