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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('--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