def infer(self,
features_file,
predictions_file=None,
checkpoint_path=None,
log_time=False):
"""Runs inference.
Args:
features_file: The file(s) to infer from.
predictions_file: If set, predictions are saved in this file.
checkpoint_path: Path of a specific checkpoint to predict. If ``None``,
the latest is used.
log_time: If ``True``, several time metrics will be printed in the logs at
the end of the inference loop.
"""
if "infer" not in self._config:
self._config["infer"] = {}
if checkpoint_path is not None and tf.gfile.IsDirectory(
checkpoint_path):
checkpoint_path = tf.train.latest_checkpoint(checkpoint_path)
input_fn = self._model.input_fn(
tf.estimator.ModeKeys.PREDICT,
self._config["infer"]["batch_size"],
self._config["data"],
features_file,
bucket_width=self._config["infer"]["bucket_width"],
num_threads=self._config["infer"].get("num_threads"),
prefetch_buffer_size=self._config["infer"].get(
"prefetch_buffer_size"))
if predictions_file:
stream = io.open(predictions_file, encoding="utf-8", mode="w")
else:
stream = sys.stdout
infer_hooks = []
if log_time:
infer_hooks.append(hooks.LogPredictionTimeHook())
ordered_writer = None
write_fn = lambda prediction: (self._model.print_prediction(
prediction, params=self._config["infer"], stream=stream))
for prediction in self._estimator.predict(
input_fn=input_fn,
checkpoint_path=checkpoint_path,
hooks=infer_hooks):
# If the index is part of the prediction, they may be out of order.
if "index" in prediction:
if ordered_writer is None:
ordered_writer = OrderRestorer(
index_fn=lambda prediction: prediction["index"],
callback_fn=write_fn)
ordered_writer.push(prediction)
else:
write_fn(prediction)
if predictions_file:
stream.close()
def get_alignment(self,
features_file,
checkpoint_path=None,
log_time=False):
"""Runs inference.
Args:
features_file: The file(s) to infer from.
checkpoint_path: Path of a specific checkpoint to predict. If ``None``,
the latest is used.
log_time: If ``True``, several time metrics will be printed in the logs at
the end of the inference loop.
"""
if "infer" not in self._config:
self._config["infer"] = {}
if checkpoint_path is not None and os.path.isdir(checkpoint_path):
checkpoint_path = tf.train.latest_checkpoint(checkpoint_path)
batch_size = self._config["infer"].get("batch_size", 1)
input_fn = self._model.input_fn(
tf.estimator.ModeKeys.PREDICT,
batch_size,
self._config["data"],
features_file,
num_threads=self._config["infer"].get("num_threads"),
prefetch_buffer_size=self._config["infer"].get("prefetch_buffer_size"))
infer_hooks = []
if log_time:
infer_hooks.append(hooks.LogPredictionTimeHook())
def _parse_prediction(prediction, params=None):
n_best = params and params.get("n_best")
n_best = n_best or 1
if n_best > len(prediction["tokens"]):
raise ValueError("n_best cannot be greater than beam_width")
n_best_sentences = []
n_alignments = []
for i in range(n_best):
tokens = prediction["tokens"][i][:prediction["length"][i] - 1] # Ignore </s>.
sentence = self._model.target_inputter.tokenizer.detokenize(tokens)
n_best_sentences.append(sentence)
n_alignments.append(prediction["alignment"])
return n_best_sentences, n_alignments
sentences = []
alignments = []
for prediction in self._estimator.predict(
input_fn=input_fn,
checkpoint_path=checkpoint_path,
hooks=infer_hooks):
n_best_sentences, n_alignments = _parse_prediction(prediction, params=self._config["infer"])
sentences.extend(n_best_sentences)
alignments.extend(n_alignments)
return sentences, alignments
def infer(self,
features_file,
predictions_file=None,
checkpoint_path=None,
log_time=False):
"""Runs inference.
Args:
features_file: The file(s) to infer from.
predictions_file: If set, predictions are saved in this file.
checkpoint_path: Path of a specific checkpoint to predict. If ``None``,
the latest is used.
log_time: If ``True``, several time metrics will be printed in the logs at
the end of the inference loop.
"""
if "infer" not in self._config:
self._config["infer"] = {}
if checkpoint_path is not None and os.path.isdir(checkpoint_path):
checkpoint_path = tf.train.latest_checkpoint(checkpoint_path)
batch_size = self._config["infer"].get("batch_size", 1)
input_fn = self._model.input_fn(
tf.estimator.ModeKeys.PREDICT,
batch_size,
self._config["data"],
features_file,
num_threads=self._config["infer"].get("num_threads"),
prefetch_buffer_size=self._config["infer"].get(
"prefetch_buffer_size"))
if predictions_file:
stream = io.open(predictions_file, encoding="utf-8", mode="w")
else:
stream = sys.stdout
infer_hooks = []
if log_time:
infer_hooks.append(hooks.LogPredictionTimeHook())
for prediction in self._estimator.predict(
input_fn=input_fn,
checkpoint_path=checkpoint_path,
hooks=infer_hooks):
self._model.print_prediction(prediction,
params=self._config["infer"],
stream=stream)
if predictions_file:
stream.close()
def infer(self,
features_file,
predictions_file=None,
checkpoint_path=None,
log_time=False):
"""Runs inference.
Args:
features_file: The file(s) to infer from.
predictions_file: If set, predictions are saved in this file.
checkpoint_path: Path of a specific checkpoint to predict. If ``None``,
the latest is used.
log_time: If ``True``, several time metrics will be printed in the logs at
the end of the inference loop.
"""
need_ae = False
if self._ae_model != None:
need_ae = True
if checkpoint_path is not None and tf.gfile.IsDirectory(
checkpoint_path):
checkpoint_path = tf.train.latest_checkpoint(checkpoint_path)
input_fn = estimator_util.make_input_fn(
self._model,
tf.estimator.ModeKeys.PREDICT,
self._config["infer"]["batch_size"],
features_file=features_file,
bucket_width=self._config["infer"]["bucket_width"],
num_threads=self._config["infer"].get("num_threads"),
prefetch_buffer_size=self._config["infer"].get(
"prefetch_buffer_size"),
return_dataset=False,
need_ae=need_ae)
if predictions_file:
stream = io.open(predictions_file, encoding="utf-8", mode="w")
else:
stream = sys.stdout
infer_hooks = []
if log_time:
infer_hooks.append(hooks.LogPredictionTimeHook())
ordered_writer = None
write_fn = lambda prediction: (self._model.print_prediction(
prediction, params=self._config["infer"], stream=stream))
estimator = self._make_estimator()
sample_count = 0
for prediction in estimator.predict(input_fn=input_fn,
checkpoint_path=checkpoint_path,
hooks=infer_hooks):
if sample_count % 1000 == 0:
now_time = time.strftime('%Y-%m-%d %H:%M:%S',
time.localtime(time.time()))
tf.logging.info("{}:{}".format(now_time, sample_count))
sample_count += 1
# If the index is part of the prediction, they may be out of order.
if "index" in prediction:
if ordered_writer is None:
ordered_writer = OrderRestorer(
index_fn=lambda prediction: prediction["index"],
callback_fn=write_fn)
ordered_writer.push(prediction)
else:
write_fn(prediction)
if predictions_file:
stream.close()
