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| from __future__ import print_function | |
| import argparse | |
| def if_use_feat(caption_model): | |
| # Decide if load attention feature according to caption model | |
| if caption_model in ['show_tell', 'all_img', 'fc', 'newfc']: | |
| use_att, use_fc = False, True | |
| elif caption_model == 'language_model': | |
| use_att, use_fc = False, False | |
| elif caption_model in ['updown', 'topdown']: | |
| use_fc, use_att = True, True | |
| else: | |
| use_att, use_fc = True, False | |
| return use_fc, use_att | |
| import pprint | |
| class Config(object): | |
| def __init__(self, **kwargs): | |
| """Configuration Class: set kwargs as class attributes with setattr""" | |
| for k, v in kwargs.items(): | |
| setattr(self, k, v) | |
| def config_str(self): | |
| return pprint.pformat(self.__dict__) | |
| def __repr__(self): | |
| """Pretty-print configurations in alphabetical order""" | |
| config_str = 'Configurations\n' | |
| config_str += self.config_str | |
| return config_str | |
| def parse_opt(parse=True, **optional_kwargs): | |
| parser = argparse.ArgumentParser() | |
| # Data input settings | |
| parser.add_argument('--input_json', type=str, default='data/coco.json', | |
| help='path to the json file containing additional info and vocab') | |
| parser.add_argument('--input_fc_dir', type=str, default='data/cocotalk_fc', | |
| help='path to the directory containing the preprocessed fc feats') | |
| parser.add_argument('--input_att_dir', type=str, default='data/cocotalk_att', | |
| help='path to the directory containing the preprocessed att feats') | |
| parser.add_argument('--input_box_dir', type=str, default='data/cocotalk_box', | |
| help='path to the directory containing the boxes of att feats') | |
| parser.add_argument('--input_label_h5', type=str, default='data/coco_label.h5', | |
| help='path to the h5file containing the preprocessed dataset') | |
| parser.add_argument('--data_in_memory', action='store_true', | |
| help='True if we want to save the features in memory') | |
| 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; | |
| 'model.pth' : weights | |
| """) | |
| 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="show_tell", | |
| help='show_tell, show_attend_tell, all_img, fc, att2in, att2in2, att2all2, adaatt, adaattmo, updown, stackatt, denseatt, transformer') | |
| 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('--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=-1, | |
| help='number of epochs') | |
| parser.add_argument('--batch_size', type=int, default=16, | |
| help='minibatch size') | |
| parser.add_argument('--grad_clip_mode', type=str, default='value', | |
| help='value or norm') | |
| parser.add_argument('--grad_clip_value', type=float, default=0.1, | |
| help='clip gradients at this value/max_norm, 0 means no clipping') | |
| 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_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('--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('--verbose', type=int, default=0) | |
| # Sample related | |
| add_eval_sample_opts(parser) | |
| #Optimization: for the Language Model | |
| parser.add_argument('--optim', type=str, default='adam', | |
| help='what update to use? rmsprop|sgd|sgdmom|adagrad|adam|adamw') | |
| 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') | |
| # 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('--reduce_on_plateau_factor', type=float, default=0.5, | |
| help='') | |
| parser.add_argument('--reduce_on_plateau_patience', type=int, default=3, | |
| help='') | |
| parser.add_argument('--cached_transformer', action='store_true', | |
| help='') | |
| parser.add_argument('--use_warmup', action='store_true', | |
| help='warm up the learing rate?') | |
| 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_images_use', type=int, default=3200, | |
| help='how many images to use when periodically evaluating the validation loss? (-1 = all)') | |
| parser.add_argument('--save_checkpoint_every', type=int, default=2500, | |
| help='how often to save a model checkpoint (in iterations)?') | |
| parser.add_argument('--save_every_epoch', action='store_true', | |
| help='Save checkpoint every epoch, will overwrite save_checkpoint_every') | |
| parser.add_argument('--save_history_ckpt', type=int, default=0, | |
| help='If save checkpoints at every save point') | |
| parser.add_argument('--checkpoint_path', type=str, default=None, | |
| help='directory to store checkpointed models') | |
| 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('--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=0, | |
| 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') | |
| # Reward | |
| parser.add_argument('--clipscore_reward_weight', type=float, default=1, | |
| help='The reward weight from clipscore') | |
| parser.add_argument('--use_clipscore', type=float, default=0, | |
| help='Use CLIPScore') | |
| parser.add_argument('--clipscore_mode', type=str, default='clip_s', | |
| help='Which CLIPScore to use: clip_s|refclip_s') | |
| # Structure_loss | |
| parser.add_argument('--structure_loss_weight', type=float, default=1, | |
| help='') | |
| parser.add_argument('--structure_after', type=int, default=-1, | |
| help='T') | |
| parser.add_argument('--structure_loss_type', type=str, default='seqnll', | |
| help='') | |
| 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') | |
| parser.add_argument('--self_cider_reward_weight', type=float, default=0, | |
| help='self cider reward') | |
| # Used for self critical or structure. Used when sampling is need during training | |
| parser.add_argument('--train_sample_n', type=int, default=16, | |
| help='The reward weight from cider') | |
| parser.add_argument('--train_sample_method', type=str, default='sample', | |
| help='') | |
| parser.add_argument('--train_beam_size', type=int, default=1, | |
| help='') | |
| # Used for self critical | |
| parser.add_argument('--sc_sample_method', type=str, default='greedy', | |
| help='') | |
| parser.add_argument('--sc_beam_size', type=int, default=1, | |
| help='') | |
| # For diversity evaluation during training | |
| add_diversity_opts(parser) | |
| # config | |
| parser.add_argument('--cfg', type=str, default=None, | |
| help='configuration; similar to what is used in detectron') | |
| parser.add_argument( | |
| '--set_cfgs', dest='set_cfgs', | |
| help='Set config keys. Key value sequence seperate by whitespace.' | |
| 'e.g. [key] [value] [key] [value]\n This has higher priority' | |
| 'than cfg file but lower than other args. (You can only overwrite' | |
| 'arguments that have alerady been defined in config file.)', | |
| default=[], nargs='+') | |
| # How will config be used | |
| # 1) read cfg argument, and load the cfg file if it's not None | |
| # 2) Overwrite cfg argument with set_cfgs | |
| # 3) parse config argument to args. | |
| # 4) in the end, parse command line argument and overwrite args | |
| # step 1: read cfg_fn | |
| # args = parser.parse_args() | |
| # Parse the arguments. | |
| if parse: | |
| args = parser.parse_args() | |
| # For interative engironmnet (ex. jupyter) | |
| else: | |
| args = parser.parse_known_args()[0] | |
| # print(args) | |
| # Namespace => Dictionary | |
| kwargs = vars(args) | |
| # for k, v in optional_kwargs.items(): | |
| # setattr(args, k, v) | |
| kwargs.update(optional_kwargs) | |
| args = Config(**kwargs) | |
| if args.cfg is not None or args.set_cfgs is not None: | |
| from .config import CfgNode | |
| if args.cfg is not None: | |
| # print('Read Cfg') | |
| cn = CfgNode(CfgNode.load_yaml_with_base(args.cfg)) | |
| # print(cn) | |
| else: | |
| cn = CfgNode() | |
| if args.set_cfgs is not None: | |
| cn.merge_from_list(args.set_cfgs) | |
| for k,v in cn.items(): | |
| if not hasattr(args, k): | |
| import os | |
| if 'LOCAL_RANK' in os.environ and os.environ['LOCAL_RANK'] != '0': | |
| pass | |
| else: | |
| print('Warning: key %s not in args' % k) | |
| setattr(args, k, v) | |
| if parse: | |
| args = parser.parse_args(namespace=args) | |
| else: | |
| args = parser.parse_known_args(namespace=args)[0] | |
| # 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" | |
| # default value for start_from and checkpoint_path | |
| args.checkpoint_path = args.checkpoint_path or './log_%s' %args.id | |
| args.start_from = args.start_from or args.checkpoint_path | |
| # Deal with feature things before anything | |
| args.use_fc, args.use_att = if_use_feat(args.caption_model) | |
| if args.use_box: args.att_feat_size = args.att_feat_size + 5 | |
| return args | |
| 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=0, | |
| 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 | |
| add_eval_sample_opts(parser) | |
| # 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='', | |
| help='path to the h5file containing the preprocessed dataset') | |
| parser.add_argument('--input_att_dir', type=str, default='', | |
| 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') | |
| 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, dgreedy, dsample, dtopk, dtopp') | |
| parser.add_argument('--eval_oracle', type=int, default=1, | |
| help='if we need to calculate loss.') | |
| # Sampling related options | |
| def add_eval_sample_opts(parser): | |
| parser.add_argument('--sample_method', type=str, default='greedy', | |
| help='greedy; sample; gumbel; top<int>, top<0-1>') | |
| parser.add_argument('--beam_size', type=int, default=1, | |
| help='used when sample_method = greedy, 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_method = sample). 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') | |
| parser.add_argument('--suppress_UNK', type=int, default=1, | |
| help='Not predicting UNK') | |
| if __name__ == '__main__': | |
| import sys | |
| sys.argv = [sys.argv[0]] | |
| args = parse_opt() | |
| print(args) | |
| print() | |
| sys.argv = [sys.argv[0], '--cfg', 'configs/updown_long.yml'] | |
| args1 = parse_opt() | |
| print(dict(set(vars(args1).items()) - set(vars(args).items()))) | |
| print() | |
| sys.argv = [sys.argv[0], '--cfg', 'configs/updown_long.yml', '--caption_model', 'att2in2'] | |
| args2 = parse_opt() | |
| print(dict(set(vars(args2).items()) - set(vars(args1).items()))) | |