def toShakespeare(self):
"""Given a line of text, return that text in the indicated style.
Args:
modern_text: (string) The input.
Returns:
string: The translated text, if generated.
"""
args = load_arguments()
vocab = Vocabulary(self.vocab_path, args.embedding, args.dim_emb)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
model = Model(args, vocab)
model.saver.restore(sess, args.model)
if args.beam > 1:
decoder = beam_search.Decoder(sess, args, vocab, model)
else:
decoder = greedy_decoding.Decoder(sess, args, vocab, model)
batch = get_batch([self.modern_text], [1], vocab.word2id)
ori, tsf = decoder.rewrite(batch)
out = ' '.join(w for w in tsf[0])
return out
def transform_text(text):
tf.compat.v1.disable_eager_execution()
args = load_arguments()
ah = vars(args)
ah['vocab'] = '../model/yelp.vocab'
ah['model'] = '../model/model'
ah['load_model'] = True
ah['beam'] = 8
ah['batch_size'] = 1
inp = [text]
vocab = Vocabulary(args.vocab, args.embedding, args.dim_emb)
print('vocabulary size:', vocab.size)
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
with tf.compat.v1.Session(config=config) as sess:
model = create_model(sess, args, vocab)
decoder = beam_search.Decoder(sess, args, vocab, model)
'''test_losses = transfer(model, decoder, sess, args, vocab,
test0, test1, args.output)'''
batches, order0, order1 = get_batches(inp, inp, vocab.word2id,
args.batch_size)
data0_tsf, data1_tsf = [], []
losses = Accumulator(len(batches), ['loss', 'rec', 'adv', 'd0', 'd1'])
# rec, tsf = decoder.rewrite(inp)
# print(rec)
# print(tsf)
for batch in batches:
rec, tsf = decoder.rewrite(batch)
half = batch['size'] // 2
print("rec:")
print(rec)
print("tsf:")
print(tsf)
data0_tsf += tsf[:half]
data1_tsf += tsf[half:]
n0, n1 = len(inp), len(inp)
data0_tsf = reorder(order0, data0_tsf)[:n0]
data1_tsf = reorder(order1, data1_tsf)[:n1]
print(data0_tsf)
print(data1_tsf)
if args.load_model:
print('Loading model from', args.model)
ckpt = tf.train.get_checkpoint_state(args.model)
if ckpt and ckpt.model_checkpoint_path:
try:
print("Trying to restore from a checkpoint...")
model.saver.restore(sess, ckpt.model_checkpoint_path)
print("Model is restored from checkpoint {}".format(
ckpt.model_checkpoint_path))
except Exception as e:
print("Cannot restore from checkpoint due to {}".format(e))
pass
# set type of decoding (is this after the very last layer?)
if args.beam > 1:
decoder = beam_search.Decoder(sess, args, vocab, model)
else:
decoder = greedy_decoding.Decoder(sess, args, vocab, model)
if args.train:
batches, _, _ = get_batches(train0,
train1,
vocab.word2id,
args.batch_size,
noisy=True,
unparallel=False,
max_seq_len=args.max_seq_length)
random.shuffle(batches)
start_time = time.time()
step = 0
