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)
@property
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())))