def create_many_oracle(from_a, to_b, num=1, save_path='../pretrain/'):
for i in range(num):
while True:
oracle = Oracle(cfg.gen_embed_dim,
cfg.gen_hidden_dim,
cfg.vocab_size,
cfg.max_seq_len,
cfg.padding_idx,
gpu=cfg.CUDA)
if cfg.CUDA:
oracle = oracle.cuda()
big_samples = oracle.sample(cfg.samples_num, 8 * cfg.batch_size)
small_samples = oracle.sample(cfg.samples_num // 2,
8 * cfg.batch_size)
oracle_data = GenDataIter(big_samples)
mle_criterion = nn.NLLLoss()
groud_truth = NLL.cal_nll(oracle, oracle_data.loader,
mle_criterion)
if from_a <= groud_truth <= to_b:
print('save ground truth: ', groud_truth)
prefix = 'oracle_lstm'
torch.save(oracle.state_dict(),
save_path + '{}.pt'.format(prefix))
torch.save(
big_samples, save_path +
'{}_samples_{}.pt'.format(prefix, cfg.samples_num))
torch.save(
small_samples, save_path +
'{}_samples_{}.pt'.format(prefix, cfg.samples_num // 2))
break
def create_multi_oracle(number):
for i in range(number):
print('Creating Oracle %d...' % i)
oracle = Oracle(cfg.gen_embed_dim,
cfg.gen_hidden_dim,
cfg.vocab_size,
cfg.max_seq_len,
cfg.padding_idx,
gpu=cfg.CUDA)
if cfg.CUDA:
oracle = oracle.cuda()
large_samples = oracle.sample(cfg.samples_num, 4 * cfg.batch_size)
small_samples = oracle.sample(cfg.samples_num // 2, 4 * cfg.batch_size)
torch.save(oracle.state_dict(),
cfg.multi_oracle_state_dict_path.format(i))
torch.save(large_samples,
cfg.multi_oracle_samples_path.format(i, cfg.samples_num))
torch.save(
small_samples,
cfg.multi_oracle_samples_path.format(i, cfg.samples_num // 2))
oracle_data = GenDataIter(large_samples)
mle_criterion = nn.NLLLoss()
groud_truth = NLL.cal_nll(oracle, oracle_data.loader, mle_criterion)
print('Oracle %d Groud Truth: %.4f' % (i, groud_truth))
def create_oracle():
"""Create a new Oracle model and Oracle's samples"""
import config as cfg
from models.Oracle import Oracle
print('Creating Oracle...')
oracle = Oracle(cfg.gen_embed_dim,
cfg.gen_hidden_dim,
cfg.vocab_size,
cfg.max_seq_len,
cfg.padding_idx,
gpu=cfg.CUDA)
if cfg.CUDA:
oracle = oracle.cuda()
torch.save(oracle.state_dict(), cfg.oracle_state_dict_path)
big_samples = oracle.sample(cfg.samples_num, 4 * cfg.batch_size)
# large
torch.save(big_samples, cfg.oracle_samples_path.format(cfg.samples_num))
# small
torch.save(oracle.sample(cfg.samples_num // 2, 4 * cfg.batch_size),
cfg.oracle_samples_path.format(cfg.samples_num // 2))
oracle_data = GenDataIter(big_samples)
mle_criterion = nn.NLLLoss()
groud_truth = NLL.cal_nll(oracle, oracle_data.loader, mle_criterion)
print('NLL_Oracle Groud Truth: %.4f' % groud_truth)
def create_oracle():
"""Create a new Oracle model and Oracle's samples"""
from models.Oracle import Oracle
print('Creating Oracle...')
oracle = Oracle(cfg.gen_embed_dim, cfg.gen_hidden_dim, cfg.vocab_size,
cfg.max_seq_len, cfg.padding_idx, gpu=cfg.CUDA)
oracle = oracle.cuda()
torch.save(oracle.state_dict(), cfg.oracle_state_dict_path)
# large
torch.save(oracle.sample(cfg.samples_num, 4 * cfg.batch_size),
cfg.oracle_samples_path.format(cfg.samples_num))
# small
torch.save(oracle.sample(cfg.samples_num // 2, 4 * cfg.batch_size),
cfg.oracle_samples_path.format(cfg.samples_num // 2))
def create_oracle():
oracle = Oracle(cfg.gen_embed_dim,
cfg.gen_hidden_dim,
cfg.vocab_size,
cfg.max_seq_len,
cfg.padding_idx,
gpu=cfg.CUDA)
oracle = oracle.cuda()
torch.save(oracle.state_dict(), cfg.oracle_state_dict_path)
# large
torch.save(oracle.sample(cfg.samples_num, 4 * cfg.batch_size),
cfg.oracle_samples_path.format(cfg.samples_num))
# small
torch.save(oracle.sample(cfg.samples_num // 2, 4 * cfg.batch_size),
cfg.oracle_samples_path.format(cfg.samples_num // 2))
def create_specific_oracle(from_a, to_b, num=1, save_path='../pretrain/'):
for i in range(num):
while True:
oracle = Oracle(cfg.gen_embed_dim,
cfg.gen_hidden_dim,
cfg.vocab_size,
cfg.max_seq_len,
cfg.padding_idx,
gpu=cfg.CUDA)
if cfg.CUDA:
oracle = oracle.cuda()
big_samples = oracle.sample(cfg.samples_num, 8 * cfg.batch_size)
small_samples = oracle.sample(cfg.samples_num // 2,
8 * cfg.batch_size)
oracle_data = GenDataIter(big_samples)
mle_criterion = nn.NLLLoss()
groud_truth = NLL.cal_nll(oracle, oracle_data.loader,
mle_criterion)
if from_a <= groud_truth <= to_b:
dir_path = save_path + 'oracle_data_gt{:.2f}_{}'.format(
groud_truth, strftime("%m%d_%H%M%S", localtime()))
if not os.path.exists(dir_path):
os.mkdir(dir_path)
print('save ground truth: ', groud_truth)
# prefix = 'oracle{}_lstm_gt{:.2f}_{}'.format(i, groud_truth, strftime("%m%d", localtime()))
prefix = dir_path + '/oracle_lstm'
torch.save(oracle.state_dict(), '{}.pt'.format(prefix))
torch.save(big_samples,
'{}_samples_{}.pt'.format(prefix, cfg.samples_num))
torch.save(
small_samples,
'{}_samples_{}.pt'.format(prefix, cfg.samples_num // 2))
break