def main(unused_argv):
import transformer_rl_main
tf.logging.set_verbosity(tf.logging.INFO)
if FLAGS.text is None and FLAGS.file is None:
tf.logging.warn(
"Nothing to translate. Make sure to call this script using "
"flags --text or --file.")
return
subtokenizer_source = tokenizer.Subtokenizer(
os.path.join(FLAGS.data_dir,
'vocab' + '.bpe.' + str(FLAGS.search) + '.' + FLAGS.fro))
subtokenizer_target = tokenizer.Subtokenizer(
os.path.join(FLAGS.data_dir,
'vocab' + '.bpe.' + str(FLAGS.search) + '.' + FLAGS.to))
# Set up estimator and params
params = transformer_rl_main.PARAMS_MAP[FLAGS.param_set]
params.beam_size = _BEAM_SIZE
params.alpha = _ALPHA
params.extra_decode_length = _EXTRA_DECODE_LENGTH
params.batch_size = _DECODE_BATCH_SIZE
fp = open(
os.path.join(FLAGS.data_dir,
'vocab.bpe.' + str(FLAGS.search) + "." + FLAGS.fro), 'r')
lines = fp.readlines()
params.source_vocab_size = len(lines)
fp = open(
os.path.join(FLAGS.data_dir,
'vocab.bpe.' + str(FLAGS.search) + "." + FLAGS.to), 'r')
lines = fp.readlines()
params.target_vocab_size = len(lines)
estimator = tf.estimator.Estimator(
model_fn=transformer_rl_main.gan_model_fn,
model_dir=FLAGS.model_dir,
params=params)
if FLAGS.text is not None:
tf.logging.info("Translating text: %s" % FLAGS.text)
translate_text(estimator, subtokenizer_source, subtokenizer_target,
FLAGS.text)
if FLAGS.file is not None:
input_file = os.path.abspath(FLAGS.file)
tf.logging.info("Translating file: %s" % input_file)
if not tf.gfile.Exists(FLAGS.file):
raise ValueError("File does not exist: %s" % input_file)
output_file = None
if FLAGS.file_out is not None:
output_file = os.path.abspath(FLAGS.file_out)
tf.logging.info("File output specified: %s" % output_file)
translate_file(estimator, subtokenizer_source, subtokenizer_target,
input_file, output_file)
def evaluate_and_log_bleu(estimator, bleu_source, bleu_ref, vocab_file, vocab_file_target):
"""Calculate and record the BLEU score."""
subtokenizer = tokenizer.Subtokenizer(vocab_file)
subtokenizer_target = tokenizer.Subtokenizer(vocab_file_target)
uncased_score, cased_score = translate_and_compute_bleu(
estimator, subtokenizer, subtokenizer_target, bleu_source, bleu_ref)
tf.logging.info("Bleu score (uncased): %d", uncased_score)
tf.logging.info("Bleu score (cased): %d", cased_score)
return uncased_score, cased_score
def evaluate_and_log_bleu(model,
params,
bleu_source,
bleu_ref,
vocab_file,
distribution_strategy=None):
"""Calculate and record the BLEU score.
Args:
model: A Keras model, used to generate the translations.
params: A dictionary, containing the translation related parameters.
bleu_source: A file containing source sentences for translation.
bleu_ref: A file containing the reference for the translated sentences.
vocab_file: A file containing the vocabulary for translation.
distribution_strategy: A platform distribution strategy, used for TPU based
translation.
Returns:
uncased_score: A float, the case insensitive BLEU score.
cased_score: A float, the case sensitive BLEU score.
"""
subtokenizer = tokenizer.Subtokenizer(vocab_file)
uncased_score, cased_score = translate_and_compute_bleu(
model, params, subtokenizer, bleu_source, bleu_ref,
distribution_strategy)
logging.info("Bleu score (uncased): %s", uncased_score)
logging.info("Bleu score (cased): %s", cased_score)
return uncased_score, cased_score
def _init_subtokenizer(self, vocab_list):
temp_file = tempfile.NamedTemporaryFile(delete=False)
with tf.gfile.Open(temp_file.name, 'w') as w:
for subtoken in vocab_list:
w.write("'%s'" % subtoken)
w.write("\n")
return tokenizer.Subtokenizer(temp_file.name, reserved_tokens=[])
def evaluate_and_log_bleu(model, bleu_source, bleu_ref, vocab_file):
"""Calculate and record the BLEU score."""
subtokenizer = tokenizer.Subtokenizer(vocab_file)
uncased_score, cased_score = translate_and_compute_bleu(
model, subtokenizer, bleu_source, bleu_ref)
tf.compat.v1.logging.info("Bleu score (uncased): %s", uncased_score)
tf.compat.v1.logging.info("Bleu score (cased): %s", cased_score)
return uncased_score, cased_score
def main(unused_argv):
import transformer_main
tf.logging.set_verbosity(tf.logging.INFO)
if FLAGS.text is None and FLAGS.file is None:
tf.logging.warn(
"Nothing to translate. Make sure to call this script using "
"flags --text or --file.")
return
subtokenizer = tokenizer.Subtokenizer(FLAGS.vocab_file)
subtokenizer_target = tokenizer.Subtokenizer(FLAGS.vocab_file_en)
# Set up estimator and params
params = transformer_main.PARAMS_MAP[FLAGS.param_set]
params["beam_size"] = _BEAM_SIZE
params["alpha"] = _ALPHA
params["extra_decode_length"] = _EXTRA_DECODE_LENGTH
params["batch_size"] = _DECODE_BATCH_SIZE
estimator = tf.estimator.Estimator(model_fn=transformer_main.model_fn,
model_dir=FLAGS.model_dir,
params=params)
if FLAGS.text is not None:
tf.logging.info("Translating text: %s" % FLAGS.text)
translate_text(estimator, subtokenizer, subtokenizer_target,
FLAGS.text)
if FLAGS.file is not None:
input_file = os.path.abspath(FLAGS.file)
tf.logging.info("Translating file: %s" % input_file)
if not tf.gfile.Exists(FLAGS.file):
raise ValueError("File does not exist: %s" % input_file)
output_file = None
if FLAGS.file_out is not None:
output_file = os.path.abspath(FLAGS.file_out)
tf.logging.info("File output specified: %s" % output_file)
translate_file(estimator, subtokenizer, input_file, output_file)
def main(unused_argv):
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
if FLAGS.text is None and FLAGS.file is None:
tf.compat.v1.logging.warn("Nothing to translate. Make sure to call this script using "
"flags --text or --file.")
return
subtokenizer = tokenizer.Subtokenizer(
os.path.join(FLAGS.data_dir, FLAGS.vocab_file))
if FLAGS.params == "base":
params = model_params.TransformerBaseParams
elif FLAGS.params == "big":
params = model_params.TransformerBigParams
else:
raise ValueError("Invalid parameter set defined: %s."
"Expected 'base' or 'big.'" % FLAGS.params)
# Set up estimator and params
params.beam_size = _BEAM_SIZE
params.alpha = _ALPHA
params.extra_decode_length = _EXTRA_DECODE_LENGTH
params.batch_size = FLAGS.batch_size
params.frozen_graph = FLAGS.input_graph
# Add inter_op and intra_op parallelism thread
session_config = tf.compat.v1.ConfigProto(
inter_op_parallelism_threads=FLAGS.inter_op_parallelism_threads,
intra_op_parallelism_threads=FLAGS.intra_op_parallelism_threads,
allow_soft_placement=True)
run_config = tf.estimator.RunConfig(session_config=session_config)
estimator = tf.estimator.Estimator(
model_fn=transformer_main.model_fn, model_dir=FLAGS.model_dir, params=params,
config=run_config)
if FLAGS.text is not None:
tf.compat.v1.logging.info("Translating text: %s" % FLAGS.text)
translate_text(estimator, subtokenizer, FLAGS.text)
if FLAGS.file is not None:
input_file = os.path.abspath(FLAGS.file)
tf.compat.v1.logging.info("Translating file: %s" % input_file)
if not tf.io.gfile.exists(FLAGS.file):
raise ValueError("File does not exist: %s" % input_file)
output_file = None
if FLAGS.file_out is not None:
output_file = os.path.abspath(FLAGS.file_out)
tf.compat.v1.logging.info("File output specified: %s" % output_file)
translate_file(estimator, subtokenizer, input_file, output_file,
FLAGS.batch_size, False)
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
if FLAGS.text is None and FLAGS.file is None:
tf.logging.warn(
"Nothing to translate. Make sure to call this script using "
"flags --text or --file.")
return
subtokenizer = tokenizer.Subtokenizer(
os.path.join(FLAGS.data_dir, FLAGS.vocab_file))
if FLAGS.params == "base":
params = model_params.TransformerBaseParams
elif FLAGS.params == "big":
params = model_params.TransformerBigParams
else:
raise ValueError("Invalid parameter set defined: %s."
"Expected 'base' or 'big.'" % FLAGS.params)
# Set up estimator and params
params.beam_size = _BEAM_SIZE
params.alpha = _ALPHA
params.extra_decode_length = _EXTRA_DECODE_LENGTH
params.batch_size = _DECODE_BATCH_SIZE
estimator = tf.estimator.Estimator(model_fn=transformer_main.model_fn,
model_dir=FLAGS.model_dir,
params=params)
if FLAGS.text is not None:
tf.logging.info("Translating text: %s" % FLAGS.text)
translate_text(estimator, subtokenizer, FLAGS.text)
if FLAGS.file is not None:
input_file = os.path.abspath(FLAGS.file)
tf.logging.info("Translating file: %s" % input_file)
if not tf.gfile.Exists(FLAGS.file):
raise ValueError("File does not exist: %s" % input_file)
output_file = None
if FLAGS.file_out is not None:
output_file = os.path.abspath(FLAGS.file_out)
tf.logging.info("File output specified: %s" % output_file)
translate_file(estimator, subtokenizer, input_file, output_file)
def predict(self):
"""Predicts result from the model."""
params, flags_obj, is_train = self.params, self.flags_obj, False
with tf.name_scope("model"):
model = transformer.create_model(params, is_train)
self._load_weights_if_possible(
model, tf.train.latest_checkpoint(self.flags_obj.model_dir))
model.summary()
subtokenizer = tokenizer.Subtokenizer(flags_obj.vocab_file)
ds = data_pipeline.eval_input_fn(params)
ds = ds.map(lambda x, y: x).take(_SINGLE_SAMPLE)
ret = model.predict(ds)
val_outputs, _ = ret
length = len(val_outputs)
for i in range(length):
translate.translate_from_input(val_outputs[i], subtokenizer)
def evaluate_and_log_bleu(estimator, bleu_writer, bleu_source, bleu_ref):
"""Calculate and record the BLEU score."""
subtokenizer = tokenizer.Subtokenizer(
os.path.join(FLAGS.data_dir, FLAGS.vocab_file))
uncased_score, cased_score = translate_and_compute_bleu(
estimator, subtokenizer, bleu_source, bleu_ref)
print("Bleu score (uncased):", uncased_score)
print("Bleu score (cased):", cased_score)
summary = tf.Summary(value=[
tf.Summary.Value(tag="bleu/uncased", simple_value=uncased_score),
tf.Summary.Value(tag="bleu/cased", simple_value=cased_score),
])
bleu_writer.add_summary(summary, get_global_step(estimator))
bleu_writer.flush()
return uncased_score, cased_score
def train_schedule(train_eval_iterations, single_iteration_train_steps, params,
bleu_source=None, bleu_ref=None, bleu_threshold=None):
"""
Train and evaluate model
:param model: model to train
:param train_eval_iterations: Number of times to repeat the train-eval iteration
:param single_iteration_train_steps: Number of steps to train in one iteration
:param bleu_source:File containing text to be translated for BLEU calculation.
:param bleu_ref:File containing reference translations for BLEU calculation.
:param bleu_threshold:minimum BLEU score before training is stopped.
"""
print('Training schedule:')
print('\t1.Train for %d iterations' % train_eval_iterations)
print('\t2.Each iteration for %d steps.' % single_iteration_train_steps)
print('\t3.Compute BLEU score.')
'''if bleu_threshold is not None:
print("Repeat above steps until the BLEU score reaches", bleu_threshold)
train_eval_iterations = INF
else:
print("Repeat above steps %d times." % train_eval_iterations)'''
# Loop training/evaluation/bleu cycles
subtokenizer = tokenizer.Subtokenizer(vocab_file='vocab.ende.32768')
dataset_train = dataset.TranslationDataset(dir_lang1='wmt32k-train.lang1',
dir_lang2='wmt32k-train.lang2',
subtokenizer=subtokenizer)
global_step = 0
best_bleu_score = 0
net = transformer.Transformer(params=params, train=1)
net.initialize(init=init.Xavier(), ctx=ctx, force_reinit=True)
learning_rate = get_learning_rate(params.learning_rate, params.hidden_size,
params.learning_rate_warmup_steps, global_step)
optimizer = mx.optimizer.Adam(learning_rate=learning_rate, beta1=params.optimizer_adam_beta1,
beta2=params.optimizer_adam_beta2, epsilon=params.optimizer_adam_epsilon)
trainer = gluon.Trainer(net.collect_params(), optimizer=optimizer)
bleu_score_file = open('blue_score_file', w+)
def train_model(input_params):
params = model_params.EXAMPLE_PARAMS_v4
dataset = train_input_fn(input_params)
subtokenizer = tokenizer.Subtokenizer("data/data/" + VOCAB_FILE)
input_file = "data/raw/dev/newstest2013.en"
output_file = "./output/dev.de"
ref_filename = "data/raw/dev/newstest2013.de"
trace_path = "checkpoints_v4/logging/"
num_epochs = 10
def train_step(inputs, targets):
model.train()
with tf.GradientTape() as tape:
#print(inputs)
logits = model(inputs=inputs, targets=targets)
logits = metrics.MetricLayer(params.vocab_size)([logits, targets])
logits, loss = metrics.LossLayer(params.vocab_size,
0.1)([logits, targets])
gradients = tape.gradient(loss, model.all_weights)
optimizer_.apply_gradients(zip(gradients, model.all_weights))
return loss
model = Transformer(params)
# load_weights = tl.files.load_npz(name='./checkpoints_v4/model.npz')
# tl.files.assign_weights(load_weights, model)
learning_rate = CustomSchedule(
params.hidden_size, warmup_steps=params.learning_rate_warmup_steps)
optimizer_ = optimizer.LazyAdam(learning_rate,
beta_1=0.9,
beta_2=0.98,
epsilon=1e-9)
time_ = time.time()
for epoch in range(num_epochs):
total_loss, n_iter = 0, 0
for i, [inputs, targets] in enumerate(dataset):
loss = train_step(inputs, targets)
with tf.io.gfile.GFile(trace_path + "loss", "ab+") as trace_file:
trace_file.write(str(loss.numpy()) + '\n')
if (i % 100 == 0):
print('Batch ID {} at Epoch [{}/{}]: loss {:.4f} using {:.4f}'.
format(i, epoch + 1, num_epochs, loss,
(time.time() - time_) / 100))
time_ = time.time()
if (i % 2000 == 0):
tl.files.save_npz(model.all_weights,
name='./checkpoints_v4/model.npz')
if (i == 5000):
translate_file(model,
subtokenizer,
input_file=input_file,
output_file=output_file)
try:
insensitive_score = bleu_wrapper(ref_filename, output_file,
False)
sensitive_score = bleu_wrapper(ref_filename, output_file,
True)
with tf.io.gfile.GFile(trace_path + "bleu_insensitive",
"ab+") as trace_file:
trace_file.write(str(insensitive_score) + '\n')
with tf.io.gfile.GFile(trace_path + "bleu_sensitive",
"ab+") as trace_file:
trace_file.write(str(sensitive_score) + '\n')
except:
print("An exception occurred")
total_loss += loss
n_iter += 1
# printing average loss after every epoch
print('Epoch [{}/{}]: loss {:.4f}'.format(epoch + 1, num_epochs,
total_loss / n_iter))
# save model weights after every epoch
tl.files.save_npz(model.all_weights, name='./checkpoints_v4/model.npz')
model.eval()
for i, text in enumerate(input_fn()):
prediction = model(inputs=text)
for i, single in enumerate(prediction["outputs"]):
translation = _trim_and_decode(single, subtokenizer)
translations.append(translation)
# Write translations in the order they appeared in the original file.
if output_file is not None:
if tf.io.gfile.isdir(output_file):
raise ValueError(
"File output is a directory, will not save outputs to "
"file.")
# tf.logging.info("Writing to file %s" % output_file)
with tf.io.gfile.GFile(output_file, "w") as f:
for i in sorted_keys:
f.write("%s\n" % translations[i])
if __name__ == "__main__":
subtokenizer = tokenizer.Subtokenizer("data/data/" + VOCAB_FILE)
params = model_params.EXAMPLE_PARAMS
model = Transformer(params)
load_weights = tl.files.load_npz(name='./checkpoints_light/model.npz')
tl.files.assign_weights(load_weights, model)
input_file = "./data/raw/dev/newstest2013.en"
translate_file(model,
subtokenizer,
input_file,
output_file="./output/out.de")
tf.logging.info("Translation of \"%s\": \"%s\"" % (txt, translation))
return translation
tf.logging.set_verbosity(tf.logging.INFO)
params = transformer_main.PARAMS_MAP["tiny"]
params["beam_size"] = _BEAM_SIZE
params["alpha"] = _ALPHA
params["extra_decode_length"] = _EXTRA_DECODE_LENGTH
params["batch_size"] = _DECODE_BATCH_SIZE
estimator = tf.estimator.Estimator(
model_fn=transformer_main.model_fn, model_dir="./tiny-model/",
params=params)
subtokenizer = tokenizer.Subtokenizer("./tiny-model/vocab.ende.32768")
estimator=FastPredict(estimator,get_input_fn("بس",subtokenizer))
input_data = "حبيبي يا عاشق"
tf.logging.info("Translating text: %s" % input_data)
start = time.time()
print("started timing")
output_data = translate_text(estimator, subtokenizer, input_data)
end = time.time()
print("translate took %f seconds" % (end - start))
