def main():
args = parser.parse_args()
pp.pprint(vars(args))
config = vars(args)
# train with different datasets
if args.dataset == 'oracle':
oracle_model = OracleLstm(num_vocabulary=args.vocab_size, batch_size=args.batch_size, emb_dim=args.gen_emb_dim,
hidden_dim=args.hidden_dim, sequence_length=args.seq_len,
start_token=args.start_token)
oracle_loader = OracleDataLoader(args.batch_size, args.seq_len)
gen_loader = OracleDataLoader(args.batch_size, args.seq_len)
generator = models.get_generator(args.g_architecture, vocab_size=args.vocab_size, batch_size=args.batch_size,
seq_len=args.seq_len, gen_emb_dim=args.gen_emb_dim, mem_slots=args.mem_slots,
head_size=args.head_size, num_heads=args.num_heads, hidden_dim=args.hidden_dim,
start_token=args.start_token)
discriminator = models.get_discriminator(args.d_architecture, batch_size=args.batch_size, seq_len=args.seq_len,
vocab_size=args.vocab_size, dis_emb_dim=args.dis_emb_dim,
num_rep=args.num_rep, sn=args.sn)
oracle_train(generator, discriminator, oracle_model, oracle_loader, gen_loader, config)
elif args.dataset in ['image_coco', 'emnlp_news']:
data_file = os.path.join(args.data_dir, '{}.txt'.format(args.dataset))
seq_len, vocab_size = text_precess(data_file)
config['seq_len'] = seq_len # override the sequence length
config['vocab_size'] = vocab_size
print('seq_len: %d, vocab_size: %d' % (seq_len, vocab_size))
oracle_loader = RealDataLoader(args.batch_size, args.seq_len)
generator = models.get_generator(args.g_architecture, vocab_size=vocab_size, batch_size=args.batch_size,
seq_len=seq_len, gen_emb_dim=args.gen_emb_dim, mem_slots=args.mem_slots,
head_size=args.head_size, num_heads=args.num_heads, hidden_dim=args.hidden_dim,
start_token=args.start_token)
discriminator = models.get_discriminator(args.d_architecture, batch_size=args.batch_size, seq_len=seq_len,
vocab_size=vocab_size, dis_emb_dim=args.dis_emb_dim,
num_rep=args.num_rep, sn=args.sn)
f_classifier = models.get_classifier(args.f_architecture, scope="f_classifier", batch_size=args.batch_size, seq_len=seq_len,
vocab_size=vocab_size, dis_emb_dim=args.f_emb_dim,
num_rep=args.num_rep, sn=args.sn)
real_train(generator, discriminator, f_classifier, oracle_loader, config)
else:
raise NotImplementedError('{}: unknown dataset!'.format(args.dataset))
def main():
args = parser.parse_args()
pp.pprint(vars(args))
config = vars(args)
# train with different datasets
if args.dataset == 'oracle':
oracle_model = OracleLstm(num_vocabulary=args.vocab_size,
batch_size=args.batch_size,
emb_dim=args.gen_emb_dim,
hidden_dim=args.hidden_dim,
sequence_length=args.seq_len,
start_token=args.start_token)
oracle_loader = OracleDataLoader(args.batch_size, args.seq_len)
gen_loader = OracleDataLoader(args.batch_size, args.seq_len)
generator = models.get_generator(args.g_architecture,
vocab_size=args.vocab_size,
batch_size=args.batch_size,
seq_len=args.seq_len,
gen_emb_dim=args.gen_emb_dim,
mem_slots=args.mem_slots,
head_size=args.head_size,
num_heads=args.num_heads,
hidden_dim=args.hidden_dim,
start_token=args.start_token)
discriminator = models.get_discriminator(args.d_architecture,
batch_size=args.batch_size,
seq_len=args.seq_len,
vocab_size=args.vocab_size,
dis_emb_dim=args.dis_emb_dim,
num_rep=args.num_rep,
sn=args.sn)
oracle_train(generator, discriminator, oracle_model, oracle_loader,
gen_loader, config)
elif args.dataset in ['image_coco', 'emnlp_news']:
# custom dataset selected
data_file = resources_path(args.data_dir,
'{}.txt'.format(args.dataset))
sample_dir = resources_path(config['sample_dir'])
oracle_file = os.path.join(sample_dir,
'oracle_{}.txt'.format(args.dataset))
data_dir = resources_path(config['data_dir'])
if args.dataset == 'image_coco':
test_file = os.path.join(data_dir, 'testdata/test_coco.txt')
elif args.dataset == 'emnlp_news':
test_file = os.path.join(data_dir, 'testdata/test_emnlp.txt')
else:
raise NotImplementedError('Unknown dataset!')
if args.dataset == 'emnlp_news':
data_file, lda_file = create_subsample_data_file(data_file)
else:
lda_file = data_file
seq_len, vocab_size, word_index_dict, index_word_dict = text_precess(
data_file, test_file, oracle_file=oracle_file)
config['seq_len'] = seq_len
config['vocab_size'] = vocab_size
print('seq_len: %d, vocab_size: %d' % (seq_len, vocab_size))
config['topic_loss_weight'] = args.topic_loss_weight
if config['LSTM']:
if config['topic']:
topic_number = config['topic_number']
oracle_loader = RealDataTopicLoader(args.batch_size,
args.seq_len)
oracle_loader.set_dataset(args.dataset)
oracle_loader.set_files(data_file, lda_file)
oracle_loader.topic_num = topic_number
oracle_loader.set_dictionaries(word_index_dict,
index_word_dict)
generator = models.get_generator(
args.g_architecture,
vocab_size=vocab_size,
batch_size=args.batch_size,
seq_len=seq_len,
gen_emb_dim=args.gen_emb_dim,
mem_slots=args.mem_slots,
head_size=args.head_size,
num_heads=args.num_heads,
hidden_dim=args.hidden_dim,
start_token=args.start_token,
TopicInMemory=args.topic_in_memory,
NoTopic=args.no_topic)
from real.real_gan.real_topic_train_NoDiscr import real_topic_train_NoDiscr
real_topic_train_NoDiscr(generator, oracle_loader, config,
args)
else:
generator = models.get_generator(args.g_architecture,
vocab_size=vocab_size,
batch_size=args.batch_size,
seq_len=seq_len,
gen_emb_dim=args.gen_emb_dim,
mem_slots=args.mem_slots,
head_size=args.head_size,
num_heads=args.num_heads,
hidden_dim=args.hidden_dim,
start_token=args.start_token)
oracle_loader = RealDataLoader(args.batch_size, args.seq_len)
oracle_loader.set_dictionaries(word_index_dict,
index_word_dict)
oracle_loader.set_dataset(args.dataset)
oracle_loader.set_files(data_file, lda_file)
oracle_loader.topic_num = config['topic_number']
from real.real_gan.real_train_NoDiscr import real_train_NoDiscr
real_train_NoDiscr(generator, oracle_loader, config, args)
else:
if config['topic']:
topic_number = config['topic_number']
oracle_loader = RealDataTopicLoader(args.batch_size,
args.seq_len)
oracle_loader.set_dataset(args.dataset)
oracle_loader.set_files(data_file, lda_file)
oracle_loader.topic_num = topic_number
oracle_loader.set_dictionaries(word_index_dict,
index_word_dict)
generator = models.get_generator(
args.g_architecture,
vocab_size=vocab_size,
batch_size=args.batch_size,
seq_len=seq_len,
gen_emb_dim=args.gen_emb_dim,
mem_slots=args.mem_slots,
head_size=args.head_size,
num_heads=args.num_heads,
hidden_dim=args.hidden_dim,
start_token=args.start_token,
TopicInMemory=args.topic_in_memory,
NoTopic=args.no_topic)
discriminator = models.get_discriminator(
args.d_architecture,
batch_size=args.batch_size,
seq_len=seq_len,
vocab_size=vocab_size,
dis_emb_dim=args.dis_emb_dim,
num_rep=args.num_rep,
sn=args.sn)
if not args.no_topic:
topic_discriminator = models.get_topic_discriminator(
args.topic_architecture,
batch_size=args.batch_size,
seq_len=seq_len,
vocab_size=vocab_size,
dis_emb_dim=args.dis_emb_dim,
num_rep=args.num_rep,
sn=args.sn,
discriminator=discriminator)
else:
topic_discriminator = None
from real.real_gan.real_topic_train import real_topic_train
real_topic_train(generator, discriminator, topic_discriminator,
oracle_loader, config, args)
else:
generator = models.get_generator(args.g_architecture,
vocab_size=vocab_size,
batch_size=args.batch_size,
seq_len=seq_len,
gen_emb_dim=args.gen_emb_dim,
mem_slots=args.mem_slots,
head_size=args.head_size,
num_heads=args.num_heads,
hidden_dim=args.hidden_dim,
start_token=args.start_token)
discriminator = models.get_discriminator(
args.d_architecture,
batch_size=args.batch_size,
seq_len=seq_len,
vocab_size=vocab_size,
dis_emb_dim=args.dis_emb_dim,
num_rep=args.num_rep,
sn=args.sn)
oracle_loader = RealDataLoader(args.batch_size, args.seq_len)
from real.real_gan.real_train import real_train
real_train(generator, discriminator, oracle_loader, config,
args)
elif args.dataset in ['Amazon_Attribute']:
# custom dataset selected
data_dir = resources_path(config['data_dir'], "Amazon_Attribute")
sample_dir = resources_path(config['sample_dir'])
oracle_file = os.path.join(sample_dir,
'oracle_{}.txt'.format(args.dataset))
train_file = os.path.join(data_dir, 'train.csv')
dev_file = os.path.join(data_dir, 'dev.csv')
test_file = os.path.join(data_dir, 'test.csv')
# create_tokens_files(data_files=[train_file, dev_file, test_file])
config_file = load_json(os.path.join(data_dir, 'config.json'))
config = {**config, **config_file} # merge dictionaries
from real.real_gan.loaders.amazon_loader import RealDataAmazonLoader
oracle_loader = RealDataAmazonLoader(args.batch_size, args.seq_len)
oracle_loader.create_batches(
data_file=[train_file, dev_file, test_file])
oracle_loader.model_index_word_dict = load_json(
join(data_dir, 'index_word_dict.json'))
oracle_loader.model_word_index_dict = load_json(
join(data_dir, 'word_index_dict.json'))
generator = models.get_generator("amazon_attribute",
vocab_size=config['vocabulary_size'],
batch_size=args.batch_size,
seq_len=config['seq_len'],
gen_emb_dim=args.gen_emb_dim,
mem_slots=args.mem_slots,
head_size=args.head_size,
num_heads=args.num_heads,
hidden_dim=args.hidden_dim,
start_token=args.start_token,
user_num=config['user_num'],
product_num=config['product_num'],
rating_num=5)
discriminator = models.get_discriminator(
"amazon_attribute",
batch_size=args.batch_size,
seq_len=config['seq_len'],
vocab_size=config['vocabulary_size'],
dis_emb_dim=args.dis_emb_dim,
num_rep=args.num_rep,
sn=args.sn)
from real.real_gan.amazon_attribute_train import amazon_attribute_train
amazon_attribute_train(generator, discriminator, oracle_loader, config,
args)
elif args.dataset in ['CustomerReviews', 'imdb']:
from real.real_gan.loaders.custom_reviews_loader import RealDataCustomerReviewsLoader
from real.real_gan.customer_reviews_train import customer_reviews_train
# custom dataset selected
if args.dataset == 'CustomerReviews':
data_dir = resources_path(config['data_dir'], "MovieReviews", "cr")
elif args.dataset == 'imdb':
data_dir = resources_path(config['data_dir'], "MovieReviews",
'movie', 'sstb')
else:
raise ValueError
sample_dir = resources_path(config['sample_dir'])
oracle_file = os.path.join(sample_dir,
'oracle_{}.txt'.format(args.dataset))
train_file = os.path.join(data_dir, 'train.csv')
# create_tokens_files(data_files=[train_file, dev_file, test_file])
config_file = load_json(os.path.join(data_dir, 'config.json'))
config = {**config, **config_file} # merge dictionaries
oracle_loader = RealDataCustomerReviewsLoader(args.batch_size,
args.seq_len)
oracle_loader.create_batches(data_file=[train_file])
oracle_loader.model_index_word_dict = load_json(
join(data_dir, 'index_word_dict.json'))
oracle_loader.model_word_index_dict = load_json(
join(data_dir, 'word_index_dict.json'))
generator = models.get_generator("CustomerReviews",
vocab_size=config['vocabulary_size'],
batch_size=args.batch_size,
start_token=args.start_token,
seq_len=config['seq_len'],
gen_emb_dim=args.gen_emb_dim,
mem_slots=args.mem_slots,
head_size=args.head_size,
num_heads=args.num_heads,
hidden_dim=args.hidden_dim,
sentiment_num=config['sentiment_num'])
discriminator_positive = models.get_discriminator(
"CustomerReviews",
scope="discriminator_positive",
batch_size=args.batch_size,
seq_len=config['seq_len'],
vocab_size=config['vocabulary_size'],
dis_emb_dim=args.dis_emb_dim,
num_rep=args.num_rep,
sn=args.sn)
discriminator_negative = models.get_discriminator(
"CustomerReviews",
scope="discriminator_negative",
batch_size=args.batch_size,
seq_len=config['seq_len'],
vocab_size=config['vocabulary_size'],
dis_emb_dim=args.dis_emb_dim,
num_rep=args.num_rep,
sn=args.sn)
customer_reviews_train(generator, discriminator_positive,
discriminator_negative, oracle_loader, config,
args)
else:
raise NotImplementedError('{}: unknown dataset!'.format(args.dataset))
print("RUN FINISHED")
return
def main():
args = parser.parse_args()
# pp.pprint(vars(args))
config = vars(args)
# train with different datasets
if args.dataset == 'oracle':
oracle_model = OracleLstm(num_vocabulary=args.vocab_size,
batch_size=args.batch_size,
emb_dim=args.gen_emb_dim,
hidden_dim=args.hidden_dim,
sequence_length=args.seq_len,
start_token=args.start_token)
oracle_loader = OracleDataLoader(args.batch_size, args.seq_len)
gen_loader = OracleDataLoader(args.batch_size, args.seq_len)
generator = models.get_generator(args.g_architecture,
vocab_size=args.vocab_size,
batch_size=args.batch_size,
seq_len=args.seq_len,
gen_emb_dim=args.gen_emb_dim,
mem_slots=args.mem_slots,
head_size=args.head_size,
num_heads=args.num_heads,
hidden_dim=args.hidden_dim,
start_token=args.start_token)
discriminator = models.get_discriminator(args.d_architecture,
batch_size=args.batch_size,
seq_len=args.seq_len,
vocab_size=args.vocab_size,
dis_emb_dim=args.dis_emb_dim,
num_rep=args.num_rep,
sn=args.sn)
oracle_train(generator, discriminator, oracle_model, oracle_loader,
gen_loader, config)
elif args.dataset in ['image_coco', 'emnlp_news', 'emnlp_news_small']:
data_file = os.path.join(args.data_dir, '{}.txt'.format(args.dataset))
seq_len, vocab_size, word_index_dict, index_word_dict = text_precess(
data_file)
config['seq_len'] = seq_len
config['vocab_size'] = vocab_size
# print('seq_len: %d, vocab_size: %d' % (seq_len, vocab_size))
oracle_loader = RealDataLoader(args.batch_size, args.seq_len)
generator = models.get_generator(args.g_architecture,
vocab_size=vocab_size,
batch_size=args.batch_size,
seq_len=seq_len,
gen_emb_dim=args.gen_emb_dim,
mem_slots=args.mem_slots,
head_size=args.head_size,
num_heads=args.num_heads,
hidden_dim=args.hidden_dim,
start_token=args.start_token)
discriminator = models.get_discriminator(args.d_architecture,
batch_size=args.batch_size,
seq_len=seq_len,
vocab_size=vocab_size,
dis_emb_dim=args.dis_emb_dim,
num_rep=args.num_rep,
sn=args.sn)
# print("gen params = ", count_params(generator.trainable_variables))
# print("disc params = ", count_params(discriminator.trainable_variables))
# sys.stdout.flush()
load_model = False
if config['load_saved_model'] != "":
log_dir_path = os.path.dirname(config['load_saved_model'])
config['log_dir'] = log_dir_path
config['sample_dir'] = os.path.join(
os.path.split(log_dir_path)[0], 'samples')
index_word_dict = load_index_to_word_dict(
os.path.join(config['log_dir'], "index_to_word_dict.json"))
word_index_dict = {v: k for k, v in index_word_dict.items()}
load_model = True
else:
if not os.path.exists(config['log_dir']):
os.makedirs(config['log_dir'])
json.dump(
index_word_dict,
open(
os.path.join(config['log_dir'], "index_to_word_dict.json"),
'w'))
json.dump(
word_index_dict,
open(
os.path.join(config['log_dir'], "word_to_index_dict.json"),
'w'))
pp.pprint(config)
print('seq_len: %d, vocab_size: %d' % (seq_len, vocab_size))
sys.stdout.flush()
real_train(generator,
discriminator,
oracle_loader,
config,
word_index_dict,
index_word_dict,
load_model=load_model)
if args.dataset == "emnlp_news" or args.dataset == "emnlp_news_small":
call([
"python", 'bleu_post_training_emnlp.py',
os.path.join(os.path.split(config['log_dir'])[0], 'samples'),
'na'
],
cwd=".")
elif args.dataset == "image_coco":
call([
"python", 'bleu_post_training.py',
os.path.join(os.path.split(config['log_dir'])[0], 'samples'),
'na'
],
cwd=".")
elif args.dataset in ['ace0_small']:
# data_file = os.path.join(args.data_dir, '{}.txt'.format(args.dataset))
# seq_len, vocab_size, word_index_dict, index_word_dict = text_precess(data_file)
seq_len = config['seq_len']
vocab_size = config['vocab_size']
# # print('seq_len: %d, vocab_size: %d' % (seq_len, vocab_size))
# oracle_loader = RealDataLoader(args.batch_size, args.seq_len)
generator = models.get_generator(args.g_architecture,
vocab_size=config['vocab_size'],
batch_size=args.batch_size,
seq_len=config['seq_len'],
gen_emb_dim=args.gen_emb_dim,
mem_slots=args.mem_slots,
head_size=args.head_size,
num_heads=args.num_heads,
hidden_dim=args.hidden_dim,
start_token=args.start_token)
discriminator = models.get_discriminator(
args.d_architecture,
batch_size=args.batch_size,
seq_len=config['seq_len'],
vocab_size=config['vocab_size'],
dis_emb_dim=args.dis_emb_dim,
num_rep=args.num_rep,
sn=args.sn)
# print("gen params = ", count_params(generator.trainable_variables))
# print("disc params = ", count_params(discriminator.trainable_variables))
# sys.stdout.flush()
load_model = False
if config['load_saved_model'] != "":
log_dir_path = os.path.dirname(config['load_saved_model'])
config['log_dir'] = log_dir_path
config['sample_dir'] = os.path.join(
os.path.split(log_dir_path)[0], 'samples')
index_word_dict = load_index_to_word_dict(
os.path.join(config['log_dir'], "index_to_word_dict.json"))
word_index_dict = {v: k for k, v in index_word_dict.items()}
load_model = True
else:
if not os.path.exists(config['log_dir']):
os.makedirs(config['log_dir'])
# json.dump(index_word_dict, open(os.path.join(config['log_dir'], "index_to_word_dict.json"), 'w'))
# json.dump(word_index_dict, open(os.path.join(config['log_dir'], "word_to_index_dict.json"), 'w'))
pp.pprint(config)
print('seq_len: %d, vocab_size: %d' % (seq_len, vocab_size))
sys.stdout.flush()
real_train_traj(generator,
discriminator,
None,
config,
None,
None,
load_model=load_model)
# if args.dataset == "emnlp_news" or args.dataset == "emnlp_news_small":
# call(["python", 'bleu_post_training_emnlp.py', os.path.join(os.path.split(config['log_dir'])[0], 'samples'), 'na'], cwd=".")
# elif args.dataset == "image_coco":
# call(["python", 'bleu_post_training.py', os.path.join(os.path.split(config['log_dir'])[0], 'samples'), 'na'], cwd=".")
else:
raise NotImplementedError('{}: unknown dataset!'.format(args.dataset))