def translate_and_compute_bleu(model, params, subtokenizer, bleu_source,
bleu_ref):
"""Translate file and report the cased and uncased bleu scores.
Args:
model: A Keras model, used to generate the translations.
params: A dictionary, containing the translation related parameters.
subtokenizer: A subtokenizer object, used for encoding and decoding source
and translated lines.
bleu_source: A file containing source sentences for translation.
bleu_ref: A file containing the reference for the translated sentences.
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.
"""
# Create temporary file to store translation.
output = 'out'
translate.translate_file(model,
params,
subtokenizer,
bleu_source,
output_file=output,
print_all_translations=False)
# Compute uncased and cased bleu scores.
uncased_score = compute_bleu.bleu_wrapper(bleu_ref, output, False)
cased_score = compute_bleu.bleu_wrapper(bleu_ref, output, True)
os.remove(output)
return uncased_score, cased_score
def test_bleu_different(self):
ref = self._create_temp_file("Testing\nmore tests!")
hyp = self._create_temp_file("Dog\nCat")
uncased_score = compute_bleu.bleu_wrapper(ref, hyp, False)
cased_score = compute_bleu.bleu_wrapper(ref, hyp, True)
self.assertLess(uncased_score, 100)
self.assertLess(cased_score, 100)
def test_bleu_same_different_case(self):
ref = self._create_temp_file("Test 1 two 3\nmore tests!")
hyp = self._create_temp_file("test 1 two 3\nMore tests!")
uncased_score = compute_bleu.bleu_wrapper(ref, hyp, False)
cased_score = compute_bleu.bleu_wrapper(ref, hyp, True)
self.assertEqual(100, uncased_score)
self.assertLess(cased_score, 100)
def translate_and_compute_bleu(estimator, subtokenizer, bleu_source, bleu_ref):
"""Translate file and report the cased and uncased bleu scores."""
# Create temporary file to store translation.
tmp = tempfile.NamedTemporaryFile(delete=False)
tmp_filename = tmp.name
translate.translate_file(
estimator, subtokenizer, bleu_source, output_file=tmp_filename,
print_all_translations=False)
# Compute uncased and cased bleu scores.
uncased_score = compute_bleu.bleu_wrapper(bleu_ref, tmp_filename, False)
cased_score = compute_bleu.bleu_wrapper(bleu_ref, tmp_filename, True)
os.remove(tmp_filename)
return uncased_score, cased_score
def translate_and_compute_bleu(estimator, subtokenizer, bleu_source, bleu_ref):
"""Translate file and report the cased and uncased bleu scores."""
# Create temporary file to store translation.
tmp = tempfile.NamedTemporaryFile(delete=False)
tmp_filename = tmp.name
translate.translate_file(
estimator, subtokenizer, bleu_source, output_file=tmp_filename,
print_all_translations=False)
# Compute uncased and cased bleu scores.
uncased_score = compute_bleu.bleu_wrapper(bleu_ref, tmp_filename, False)
cased_score = compute_bleu.bleu_wrapper(bleu_ref, tmp_filename, True)
os.remove(tmp_filename)
return uncased_score, cased_score
def translate_and_compute_bleu(estimator, subtokenizer, subtokenizer_target, bleu_source, bleu_ref):
"""Translate file and report the cased and uncased bleu scores."""
# Create temporary file to store translation.
tmp = tempfile.NamedTemporaryFile(delete=False)
tmp_filename = tmp.name
#如果不用close()，remove时会出现PermissionError另一个程序正在使用此文件，进程无法访问。
tmp.close()
translate.translate_file(
estimator, subtokenizer, subtokenizer_target, bleu_source, output_file=tmp_filename,
print_all_translations=False)
# Compute uncased and cased bleu scores.
uncased_score = compute_bleu.bleu_wrapper(bleu_ref, tmp_filename, False)
cased_score = compute_bleu.bleu_wrapper(bleu_ref, tmp_filename, True)
# tmp.close()
os.remove(tmp_filename)
return uncased_score, cased_score
def translate_and_compute_bleu(model,
params,
subtokenizer,
bleu_source,
bleu_ref,
distribution_strategy=None):
"""Translate file and report the cased and uncased bleu scores.
Args:
model: A Keras model, used to generate the translations.
params: A dictionary, containing the translation related parameters.
subtokenizer: A subtokenizer object, used for encoding and decoding source
and translated lines.
bleu_source: A file containing source sentences for translation.
bleu_ref: A file containing the reference for the translated sentences.
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.
"""
# Create temporary file to store translation.
tmp = tempfile.NamedTemporaryFile(delete=False)
tmp_filename = tmp.name
translate.translate_file(model,
params,
subtokenizer,
bleu_source,
output_file=tmp_filename,
print_all_translations=False,
distribution_strategy=distribution_strategy)
# Compute uncased and cased bleu scores.
uncased_score = compute_bleu.bleu_wrapper(bleu_ref, tmp_filename, False)
cased_score = compute_bleu.bleu_wrapper(bleu_ref, tmp_filename, True)
# os.remove(tmp_filename)
try:
os.remove(tmp_filename)
except OSError:
pass
return uncased_score, cased_score
def translate_and_compute_bleu(model, subtokenizer, bleu_source, bleu_ref):
"""
Translate file and report the cased and uncased bleu scores
"""
tmp = tempfile.NamedTemporaryFile(delete=False)
tmp_filename = tmp.name
translate.translate_file(model, subtokenizer, bleu_source, output_file=tmp_filename,
print_all_translations=False)
uncased_score = compute_bleu.bleu_wrapper(bleu_ref, tmp_filename, False)
os.remove(tmp_filename)
return uncased_score
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')
def train_model(input_params):
params = models_params.BASE_PARAMS
subtokenizer = tokenizer.Subtokenizer("data/data/" + VOCAB_FILE)
input_file = "data/raw/dev/newstest2013.en"
output_file = "./output/dev_tf.de"
ref_filename = "data/raw/dev/newstest2013.de"
trace_path = "checkpoints/logging/"
dataset = train_input_fn(input_params)
num_epochs = 50
# @tf.function
def train_step(inputs, targets):
with tf.GradientTape() as tape:
#print(inputs)
logits = model(inputs=[inputs, targets], training=True)
logits = metrics.MetricLayer(
params["vocab_size"])([logits, targets])
logits, loss = metrics.LossLayer(params["vocab_size"],
0.1)([logits, targets])
gradients = tape.gradient(loss, model.trainable_weights)
optimizer_.apply_gradients(zip(gradients, model.trainable_weights))
return loss
model = Transformer(params)
# model.load_weights('./checkpoints/my_checkpoint')
learning_rate = CustomSchedule(
params["hidden_size"],
warmup_steps=params["learning_rate_warmup_steps"])
# optimizer = tf.optimizers.Adam(learning_rate=0.001)
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)
if (i % 100 == 0):
print('Batch ID {} at Epoch [{}/{}]: loss {:.4f}, time = {}'.
format(i, epoch + 1, num_epochs, loss,
(time.time() - time_) / 100))
time_ = time.time()
if ((i + 1) % 2000 == 0):
model.save_weights('./checkpoints/my_checkpoint')
with tf.io.gfile.GFile(trace_path + "loss", "ab+") as trace_file:
trace_file.write(str(loss.numpy()) + '\n')
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))
model.save_weights('./checkpoints/my_checkpoint')
