opts.py

import argparse

def parse_opt():
    parser = argparse.ArgumentParser()
    # Data input settings
    parser.add_argument('--input_json', type=str, default='data/cocotalk_with_cc_vocab_rm_8_same_order_for_self_critical.json',
                    help='path to the json file containing additional info and vocab')
    parser.add_argument('--val_json', type=str, default='data/cocotalk_coco8_split_as_train_val.json')    
    parser.add_argument('--input_fc_dir', type=str, default='/srv/share2/sgondala/data_for_captioning_models/trainval_resnet101_faster_rcnn_genome_36.tsv',
                    help='path to the directory containing the preprocessed fc feats')
    parser.add_argument('--input_att_dir', type=str, default='/srv/share2/sgondala/data_for_captioning_models/trainval_resnet101_faster_rcnn_genome_36.tsv',
                    help='path to the directory containing the preprocessed att feats')
    parser.add_argument('--input_box_dir', type=str, default='/srv/share2/sgondala/data_for_captioning_models/',
                    help='path to the directory containing the boxes of att feats')
    parser.add_argument('--input_label_h5', type=str, default='data/cocotalk_with_cc_vocab.h5',
                    help='path to the h5file containing the preprocessed dataset')
    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('--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="att2in2",
                    help='show_tell, show_attend_tell, all_img, fc, att2in, att2in2, att2all2, adaatt, adaattmo, topdown, stackatt, denseatt, transformer')
    parser.add_argument('--rnn_size', type=int, default=2400,
                    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('--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')

    # feature manipulation
    parser.add_argument('--norm_att_feat', type=int, default=0,
                    help='If normalize attention features')
    parser.add_argument('--use_box', type=int, default=0,
                    help='If use box features')
    parser.add_argument('--norm_box_feat', type=int, default=0,
                    help='If use box, do we normalize box feature')

    # Optimization: General
    parser.add_argument('--max_epochs', type=int, default=3,
                    help='number of epochs')
    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,
                    help='strength of dropout in the Language Model RNN')
    parser.add_argument('--self_critical_after', type=int, default=0,
                    help='After what epoch do we start finetuning the CNN? (-1 = disable; never finetune, 0 = finetune from start)')
    parser.add_argument('--seq_per_img', type=int, default=1,
                    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')

    # Sample related
    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.')
    parser.add_argument('--max_length', type=int, default=20,
                    help='Maximum length during sampling')
    parser.add_argument('--length_penalty', type=str, default='',
                    help='wu_X or avg_X, X is the alpha')
    parser.add_argument('--block_trigrams', type=int, default=0,
                    help='block repeated trigram.')
    parser.add_argument('--remove_bad_endings', type=int, default=0,
                    help='Remove bad endings')

    #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-5,
                    help='learning rate')
    parser.add_argument('--learning_rate_decay_start', type=int, default=0, 
                    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')
    # Transformer
    parser.add_argument('--label_smoothing', type=float, default=0,
                    help='')
    parser.add_argument('--noamopt', action='store_true',
                    help='')
    parser.add_argument('--noamopt_warmup', type=int, default=2000,
                    help='')
    parser.add_argument('--noamopt_factor', type=float, default=1,
                    help='')
    parser.add_argument('--reduce_on_plateau', action='store_true',
                    help='')

    parser.add_argument('--scheduled_sampling_start', type=int, default=0, 
                    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_images_use', type=int, default=5000,
                    help='how many images to use when periodically evaluating the validation loss? (-1 = all)')
    parser.add_argument('--train_images_use_for_val', type=int, default=-1)                
    parser.add_argument('--save_checkpoint_every', type=int, default=2500,
                    help='how often to save a model checkpoint (in iterations)?')
    parser.add_argument('--save_all_checkpoints', action='store_true')
    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('--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('--load_best_score', type=int, default=1,
                    help='Do we load previous best score when resuming training.')       

    # misc
    parser.add_argument('--id', type=str, default='',
                    help='an id identifying this run/job. used in cross-val and appended when writing progress files')
    parser.add_argument('--train_only', type=int, default=1,
                    help='if true then use 80k, else use 110k')


    # Reward
    parser.add_argument('--cider_reward_weight', type=float, default=1,
                    help='The reward weight from cider')
    parser.add_argument('--bleu_reward_weight', type=float, default=0,
                    help='The reward weight from bleu4')


    # Structure_loss
    parser.add_argument('--structure_loss_weight', type=float, default=1,
                    help='The reward weight from cider')
    parser.add_argument('--structure_after', type=float, default=-1,
                    help='The reward weight from cider')
    parser.add_argument('--structure_sample_n', type=float, default=16,
                    help='The reward weight from cider')
    parser.add_argument('--structure_loss_type', type=str, default='seqnll',
                    help='The reward weight from cider')
    parser.add_argument('--struc_use_logsoftmax', action='store_true', help='')
    parser.add_argument('--entropy_reward_weight', type=float, default=0,
                    help='Entropy reward, seems very interesting')

    # Training techniques
    parser.add_argument('--drop_worst_after', type=float, default=-1,
                    help='')
    parser.add_argument('--drop_worst_rate', type=float, default=0,
                    help='')

    # CIDEr scores
    parser.add_argument('--cider_model', type=str, default='')
    parser.add_argument('--config_file', type=str, default='config/bert_base_6layer_6conect.json')
    parser.add_argument('--use_cider', action='store_true')

    # Solr scores
    parser.add_argument('--unigram_prob_file', type=str, default='data/unigram_prob_cc.json')
    parser.add_argument('--use_slor', action='store_true')

    # Vifidel scores
    parser.add_argument('--glove_vectors', type=str, default='data/glove_vectors.pt')
    parser.add_argument('--glove_word_to_ix', type=str, default='data/glove_stoi.json')
    parser.add_argument('--ground_truth_object_annotations', type=str, default='data/coco_gt_objs_modified.json')
    parser.add_argument('--use_vifidel', action='store_true')

    # Other
    parser.add_argument('--use_ref_caps', action='store_true')
    parser.add_argument('--save_all_train_captions', action='store_true')
    parser.add_argument('--eval_split_during_train', type=str, default='val')
    # parser.add_argument('--use_base_model_for_greedy', action='store_true')
    parser.add_argument('--id_language_eval', type=str, default='')
    parser.add_argument('--is_classification_cider_model', type=int, default=0)
    parser.add_argument('--classification_threshold', type=float, default=0.999)
    parser.add_argument('--do_not_generate_cider_plots', action='store_true')
    parser.add_argument('--train_split', type=str, default='train')
    parser.add_argument('--enable_cbs_support', action='store_true')
    parser.add_argument('--nocaps_eval', action='store_true')
    parser.add_argument('--do_not_eval_val', action='store_true')
    parser.add_argument('--use_nocaps_for_early_stopping', action='store_true')
   # parser.add_argument('--id_language_eval', type=str, default='')
    
    # PPO
    parser.add_argument('--do_not_use_ppo', action='store_true')
    parser.add_argument('--ppo_iters', type=int, default=4)
    parser.add_argument('--ppo_clip_param', type=float, default=0.1)

    add_vse_options(parser)

    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.seq_per_img > 0, "seq_per_img should be greater than 0"
    assert args.beam_size > 0, "beam_size should be greater than 0"
    assert args.save_checkpoint_every > 0, "save_checkpoint_every should be greater than 0"
    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.train_only == 0 or args.train_only == 1, "language_eval should be 0 or 1"

    return args

def add_vse_options(parser):
    parser.add_argument('--initialize_retrieval', type=str, default=None,
	                help="""xxxx.pth""")
    # vse
    parser.add_argument('--vse_model', type=str, default="None",
                    help='fc, None')
    parser.add_argument('--vse_rnn_type', type=str, default='gru',
                    help='rnn, gru, or lstm')
    parser.add_argument('--vse_margin', default=0.2, type=float,
                    help='Rank loss margin; when margin is -1, it means use binary cross entropy (usually works with MLP).')
    parser.add_argument('--vse_word_dim', default=300, type=int,
                    help='Dimensionality of the word embedding.')
    parser.add_argument('--vse_embed_size', default=1024, type=int,
                    help='Dimensionality of the joint embedding.')
    parser.add_argument('--vse_num_layers', default=1, type=int,
                    help='Number of GRU layers.')
    parser.add_argument('--vse_max_violation', default=1, type=int,
                    help='Use max instead of sum in the rank loss.')
    parser.add_argument('--vse_measure', default='cosine',
                    help='Similarity measure used (cosine|order|MLP)')
    parser.add_argument('--vse_use_abs', default=0, type=int,
                    help='Take the absolute value of embedding vectors.')
    parser.add_argument('--vse_no_imgnorm', default=0, type=int,
                    help='Do not normalize the image embeddings.')
    parser.add_argument('--vse_loss_type', default='contrastive', type=str,
                    help='contrastive or pair')
    parser.add_argument('--vse_pool_type', default='last', type=str,
                    help='last, mean, max')

    parser.add_argument('--retrieval_reward_weight', default=0, type=float,
                    help='gumbel, reinforce')


def add_eval_options(parser):
    # Basic options
    parser.add_argument('--batch_size', type=int, default=0,
                    help='if > 0 then overrule, otherwise load from checkpoint.')
    parser.add_argument('--num_images', type=int, default=-1,
                    help='how many images to use when periodically evaluating the loss? (-1 = all)')
    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('--dump_images', type=int, default=1,
                    help='Dump images into vis/imgs folder for vis? (1=yes,0=no)')
    parser.add_argument('--dump_json', type=int, default=1,
                    help='Dump json with predictions into vis folder? (1=yes,0=no)')
    parser.add_argument('--dump_path', type=int, default=0,
                    help='Write image paths along with predictions into vis json? (1=yes,0=no)')

    # Sampling options
    parser.add_argument('--sample_max', type=int, default=1,
                    help='1 = sample argmax words. 0 = sample from distributions. 2 = gumbel softmax sample. negative means topk sampling')
    parser.add_argument('--beam_size', type=int, default=2,
                    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.')
    parser.add_argument('--max_length', type=int, default=20,
                    help='Maximum length during sampling')
    parser.add_argument('--length_penalty', type=str, default='',
                    help='wu_X or avg_X, X is the alpha')
    parser.add_argument('--group_size', type=int, default=1,
                    help='used for diverse beam search. if group_size is 1, then it\'s normal beam search')
    parser.add_argument('--diversity_lambda', type=float, default=0.5,
                    help='used for diverse beam search. Usually from 0.2 to 0.8. Higher value of lambda produces a more diverse list')
    parser.add_argument('--temperature', type=float, default=1.0,
                    help='temperature when sampling from distributions (i.e. when sample_max = 0). Lower = "safer" predictions.')
    parser.add_argument('--decoding_constraint', type=int, default=0,
                    help='If 1, not allowing same word in a row')
    parser.add_argument('--block_trigrams', type=int, default=0,
                    help='block repeated trigram.')
    parser.add_argument('--remove_bad_endings', type=int, default=0,
                    help='Remove bad endings')
    # For evaluation on a folder of images:
    parser.add_argument('--image_folder', type=str, default='', 
                    help='If this is nonempty then will predict on the images in this folder path')
    parser.add_argument('--image_root', type=str, default='', 
                    help='In case the image paths have to be preprended with a root path to an image folder')
    # For evaluation on MSCOCO images from some split:
    parser.add_argument('--input_fc_dir', type=str, default='/srv/share2/sgondala/data_for_captioning_models/trainval_resnet101_faster_rcnn_genome_36.tsv',
                    help='path to the h5file containing the preprocessed dataset')
    parser.add_argument('--input_att_dir', type=str, default='/srv/share2/sgondala/data_for_captioning_models/trainval_resnet101_faster_rcnn_genome_36.tsv',
                    help='path to the h5file containing the preprocessed dataset')
    parser.add_argument('--input_box_dir', type=str, default='',
                    help='path to the h5file containing the preprocessed dataset')
    parser.add_argument('--input_label_h5', type=str, default='',
                    help='path to the h5file containing the preprocessed dataset')
    parser.add_argument('--input_json', type=str, default='', 
                    help='path to the json file containing additional info and vocab. empty = fetch from model checkpoint.')
    parser.add_argument('--split', type=str, default='test', 
                    help='if running on MSCOCO images, which split to use: val|test|train')
    parser.add_argument('--coco_json', type=str, default='', 
                    help='if nonempty then use this file in DataLoaderRaw (see docs there). Used only in MSCOCO test evaluation, where we have a specific json file of only test set images.')
    # misc
    parser.add_argument('--id', type=str, default='', 
                    help='an id identifying this run/job. used only if language_eval = 1 for appending to intermediate files')
    parser.add_argument('--verbose_beam', type=int, default=1, 
                    help='if we need to print out all beam search beams.')
    parser.add_argument('--verbose_loss', type=int, default=0, 
                    help='If calculate loss using ground truth during evaluation')
    
    # post processing
    parser.add_argument('--post_processing', type=int, default=0, help='nasty post processing')

    # Other
    parser.add_argument('--enable_cbs_support', action='store_true')
    parser.add_argument('--nocaps_eval', action='store_true')
    parser.add_argument('--id_language_eval', type=str, default='')

def add_diversity_opts(parser):
    parser.add_argument('--sample_n', type=int, default=1,
                    help='Diverse sampling')
    parser.add_argument('--sample_n_method', type=str, default='sample',
                    help='sample, bs, dbs, gumbel, topk')
    parser.add_argument('--eval_oracle', type=int, default=1)