def init_real_training(self, data_loc=None, with_image=True):
self.sequence_length, self.vocab_size, vocabulary = process_train_data(
self.config, data_loc, has_image=with_image)
##self.sequence_length, self.vocab_size, index_word_dict = text_precess(data_loc, oracle_file=self.config.temp_oracle_file)
print("sequence length:", self.sequence_length, " vocab size:",
self.vocab_size)
goal_out_size = sum(self.num_filters)
discriminator = Discriminator(self.config)
self.set_discriminator(discriminator)
generator = Generator(self.config, D_model=discriminator)
self.set_generator(generator)
# data loader for generator and discriminator
gen_dataloader = DataLoader(self.config,
batch_size=self.batch_size,
seq_length=self.sequence_length)
gen_dataloader.create_shuffled_batches(with_image)
#gen_dataloader.create_shuffled_batches()
oracle_dataloader = None
dis_dataloader = DisDataloader(self.config,
batch_size=self.batch_size,
seq_length=self.sequence_length)
self.set_data_loader(gen_loader=gen_dataloader,
dis_loader=dis_dataloader,
oracle_loader=oracle_dataloader)
#print("done initializing training")
return vocabulary
def init_oracle_trainng(self, oracle=None):
goal_out_size = sum(self.num_filters)
if oracle is None:
oracle = OracleLstm(num_vocabulary=self.vocab_size, batch_size=self.batch_size, emb_dim=self.emb_dim,
hidden_dim=self.hidden_dim, sequence_length=self.sequence_length,
start_token=self.start_token)
self.set_oracle(oracle)
discriminator = Discriminator(sequence_length=self.sequence_length, num_classes=2, vocab_size=self.vocab_size,
dis_emb_dim=self.dis_embedding_dim, filter_sizes=self.filter_size,
num_filters=self.num_filters,
batch_size=self.batch_size, hidden_dim=self.hidden_dim,
start_token=self.start_token,
goal_out_size=goal_out_size, step_size=4,
l2_reg_lambda=self.l2_reg_lambda)
self.set_discriminator(discriminator)
generator = Generator(num_classes=2, num_vocabulary=self.vocab_size, batch_size=self.batch_size,
emb_dim=self.emb_dim, dis_emb_dim=self.dis_embedding_dim, goal_size=self.goal_size,
hidden_dim=self.hidden_dim, sequence_length=self.sequence_length,
filter_sizes=self.filter_size, start_token=self.start_token,
num_filters=self.num_filters, goal_out_size=goal_out_size, D_model=discriminator,
step_size=4)
self.set_generator(generator)
gen_dataloader = DataLoader(batch_size=self.batch_size, seq_length=self.sequence_length)
oracle_dataloader = DataLoader(batch_size=self.batch_size, seq_length=self.sequence_length)
dis_dataloader = DisDataloader(batch_size=self.batch_size, seq_length=self.sequence_length)
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 0.5
self.sess = tf.compat.v1.Session(config=config)
self.set_data_loader(gen_loader=gen_dataloader, dis_loader=dis_dataloader, oracle_loader=oracle_dataloader)
def init_cfg_training(self, grammar=None):
from utils.oracle.OracleCfg import OracleCfg
oracle = OracleCfg(sequence_length=self.sequence_length, cfg_grammar=grammar)
self.set_oracle(oracle)
self.oracle.generate_oracle()
self.vocab_size = self.oracle.vocab_size + 1
goal_out_size = sum(self.num_filters)
discriminator = Discriminator(sequence_length=self.sequence_length, num_classes=2, vocab_size=self.vocab_size,
dis_emb_dim=self.dis_embedding_dim, filter_sizes=self.filter_size,
num_filters=self.num_filters,
batch_size=self.batch_size, hidden_dim=self.hidden_dim,
start_token=self.start_token,
goal_out_size=goal_out_size, step_size=4,
l2_reg_lambda=self.l2_reg_lambda)
self.set_discriminator(discriminator)
generator = Generator(num_classes=2, num_vocabulary=self.vocab_size, batch_size=self.batch_size,
emb_dim=self.emb_dim, dis_emb_dim=self.dis_embedding_dim, goal_size=self.goal_size,
hidden_dim=self.hidden_dim, sequence_length=self.sequence_length,
filter_sizes=self.filter_size, start_token=self.start_token,
num_filters=self.num_filters, goal_out_size=goal_out_size, D_model=discriminator,
step_size=4)
self.set_generator(generator)
gen_dataloader = DataLoader(batch_size=self.batch_size, seq_length=self.sequence_length)
oracle_dataloader = DataLoader(batch_size=self.batch_size, seq_length=self.sequence_length)
dis_dataloader = DisDataloader(batch_size=self.batch_size, seq_length=self.sequence_length)
self.set_data_loader(gen_loader=gen_dataloader, dis_loader=dis_dataloader, oracle_loader=oracle_dataloader)
return oracle.wi_dict, oracle.iw_dict
def init_real_training(self, data_loc=None, with_image=True):
from utils.text_process import text_precess, text_to_code, process_train_data
from utils.text_process import get_tokenlized, get_word_list, get_dict
self.sequence_length, self.vocab_size, vocabulary = process_train_data(
self.config, data_loc, has_image=with_image)
##self.sequence_length, self.vocab_size, index_word_dict = text_precess(data_loc, oracle_file=self.config.temp_oracle_file)
print("sequence length:", self.sequence_length, " vocab size:",
self.vocab_size)
goal_out_size = sum(self.num_filters)
discriminator = Discriminator(sequence_length=self.sequence_length,
num_classes=2,
vocab_size=self.vocab_size,
dis_emb_dim=self.dis_embedding_dim,
filter_sizes=self.filter_size,
num_filters=self.num_filters,
batch_size=self.batch_size,
hidden_dim=self.hidden_dim,
start_token=self.start_token,
goal_out_size=goal_out_size,
step_size=4,
l2_reg_lambda=self.l2_reg_lambda)
self.set_discriminator(discriminator)
generator = Generator(num_classes=2,
num_vocabulary=self.vocab_size,
batch_size=self.batch_size,
emb_dim=self.emb_dim,
dis_emb_dim=self.dis_embedding_dim,
goal_size=self.goal_size,
hidden_dim=self.hidden_dim,
sequence_length=self.sequence_length,
filter_sizes=self.filter_size,
start_token=self.start_token,
num_filters=self.num_filters,
goal_out_size=goal_out_size,
D_model=discriminator,
step_size=4)
self.set_generator(generator)
# data loader for generator and discriminator
gen_dataloader = DataLoader(self.config,
batch_size=self.batch_size,
seq_length=self.sequence_length)
gen_dataloader.create_batches_v2(self.config, with_image)
oracle_dataloader = None
dis_dataloader = DisDataloader(self.config,
batch_size=self.batch_size,
seq_length=self.sequence_length)
self.set_data_loader(gen_loader=gen_dataloader,
dis_loader=dis_dataloader,
oracle_loader=oracle_dataloader)
#print("done initializing training")
return vocabulary
def init_real_trainng(self, data_loc=None):
from utils.text_process import text_precess, text_to_code
from utils.text_process import get_tokenlized, get_word_list, get_dict
if data_loc is None:
data_loc = 'data/image_coco.txt'
self.sequence_length, self.vocab_size = text_precess(data_loc)
goal_out_size = sum(self.num_filters)
discriminator = Discriminator(sequence_length=self.sequence_length, num_classes=2, vocab_size=self.vocab_size,
dis_emb_dim=self.dis_embedding_dim, filter_sizes=self.filter_size,
num_filters=self.num_filters,
batch_size=self.batch_size, hidden_dim=self.hidden_dim,
start_token=self.start_token,
goal_out_size=goal_out_size, step_size=4,
l2_reg_lambda=self.l2_reg_lambda)
self.set_discriminator(discriminator)
generator = Generator(num_classes=2, num_vocabulary=self.vocab_size, batch_size=self.batch_size,
emb_dim=self.emb_dim, dis_emb_dim=self.dis_embedding_dim, goal_size=self.goal_size,
hidden_dim=self.hidden_dim, sequence_length=self.sequence_length,
filter_sizes=self.filter_size, start_token=self.start_token,
num_filters=self.num_filters, goal_out_size=goal_out_size, D_model=discriminator,
step_size=4)
self.set_generator(generator)
gen_dataloader = DataLoader(batch_size=self.batch_size, seq_length=self.sequence_length)
oracle_dataloader = None
dis_dataloader = DisDataloader(batch_size=self.batch_size, seq_length=self.sequence_length)
self.set_data_loader(gen_loader=gen_dataloader, dis_loader=dis_dataloader, oracle_loader=oracle_dataloader)
tokens = get_tokenlized(data_loc)
word_set = get_word_list(tokens)
[word_index_dict, index_word_dict] = get_dict(word_set)
with open(self.oracle_file, 'w') as outfile:
outfile.write(text_to_code(tokens, word_index_dict, self.sequence_length))
return word_index_dict, index_word_dict
