def __init__(self, src_vocab_size, trg_vocab_size, model_name,
hparams_set_name, checkpoint_dir, single_cpu_thread,
nizza_unk_id=None):
"""Initializes a nizza predictor.
Args:
src_vocab_size (int): Source vocabulary size (called inputs_vocab_size
in nizza)
trg_vocab_size (int): Target vocabulary size (called targets_vocab_size
in nizza)
model_name (string): Name of the nizza model
hparams_set_name (string): Name of the nizza hyper-parameter set
checkpoint_dir (string): Path to the Nizza checkpoint directory. The
predictor will load the top most checkpoint in
the `checkpoints` file.
single_cpu_thread (bool): If true, prevent tensorflow from
doing multithreading.
nizza_unk_id (int): If set, use this as UNK id. Otherwise, the
nizza is assumed to have no UNKs
Raises:
IOError if checkpoint file not found.
"""
super(BaseNizzaPredictor, self).__init__()
if not os.path.isfile("%s/checkpoint" % checkpoint_dir):
logging.fatal("Checkpoint file %s/checkpoint not found!"
% checkpoint_dir)
raise IOError
self._single_cpu_thread = single_cpu_thread
self._checkpoint_dir = checkpoint_dir
self._nizza_unk_id = nizza_unk_id
predictor_graph = tf.Graph()
with predictor_graph.as_default() as g:
hparams = registry.get_registered_hparams_set(hparams_set_name)
hparams.add_hparam("inputs_vocab_size", src_vocab_size)
hparams.add_hparam("targets_vocab_size", trg_vocab_size)
run_config = tf.contrib.learn.RunConfig()
run_config = run_config.replace(model_dir=checkpoint_dir)
model = registry.get_registered_model(model_name, hparams, run_config)
self._inputs_var = tf.placeholder(dtype=tf.int32, shape=[None],
name="sgnmt_inputs")
self._targets_var = tf.placeholder(dtype=tf.int32, shape=[None],
name="sgnmt_targets")
features = {"inputs": tf.expand_dims(self._inputs_var, 0),
"targets": tf.expand_dims(self._targets_var, 0)}
mode = tf.estimator.ModeKeys.PREDICT
self.precomputed = model.precompute(features, mode, hparams)
self.log_probs = tf.squeeze(
model.predict_next_word(features, hparams, self.precomputed), 0)
self.mon_sess = self.create_session(self._checkpoint_dir)
def get_hparams():
"""Gets the hyperparameters from command line arguments.
Returns:
An HParams instance.
Throws:
ValueError if FLAGS.hparams_set could not be found
in the registry.
"""
hparams = registry.get_registered_hparams_set(FLAGS.hparams_set)
hparams.add_hparam("inputs_vocab_size", FLAGS.inputs_vocab_size)
hparams.add_hparam("targets_vocab_size", FLAGS.targets_vocab_size)
hparams.parse(FLAGS.hparams)
return hparams
def __init__(self,
src_vocab_size,
trg_vocab_size,
model_name,
hparams_set_name,
checkpoint_dir,
single_cpu_thread,
nizza_unk_id=None):
"""Initializes a nizza predictor.
Args:
src_vocab_size (int): Source vocabulary size (called inputs_vocab_size
in nizza)
trg_vocab_size (int): Target vocabulary size (called targets_vocab_size
in nizza)
model_name (string): Name of the nizza model
hparams_set_name (string): Name of the nizza hyper-parameter set
checkpoint_dir (string): Path to the Nizza checkpoint directory. The
predictor will load the top most checkpoint in
the `checkpoints` file.
single_cpu_thread (bool): If true, prevent tensorflow from
doing multithreading.
nizza_unk_id (int): If set, use this as UNK id. Otherwise, the
nizza is assumed to have no UNKs
Raises:
IOError if checkpoint file not found.
"""
super(BaseNizzaPredictor, self).__init__()
if not os.path.isfile("%s/checkpoint" % checkpoint_dir):
logging.fatal("Checkpoint file %s/checkpoint not found!" %
checkpoint_dir)
raise IOError
self._single_cpu_thread = single_cpu_thread
self._checkpoint_dir = checkpoint_dir
self._nizza_unk_id = nizza_unk_id
predictor_graph = tf.Graph()
with predictor_graph.as_default() as g:
hparams = registry.get_registered_hparams_set(hparams_set_name)
hparams.add_hparam("inputs_vocab_size", src_vocab_size)
hparams.add_hparam("targets_vocab_size", trg_vocab_size)
run_config = tf.contrib.learn.RunConfig()
run_config = run_config.replace(model_dir=checkpoint_dir)
model = registry.get_registered_model(model_name, hparams,
run_config)
self._inputs_var = tf.placeholder(dtype=tf.int32,
shape=[None],
name="sgnmt_inputs")
self._targets_var = tf.placeholder(dtype=tf.int32,
shape=[None],
name="sgnmt_targets")
features = {
"inputs": tf.expand_dims(self._inputs_var, 0),
"targets": tf.expand_dims(self._targets_var, 0)
}
mode = tf.estimator.ModeKeys.PREDICT
self.precomputed = model.precompute(features, mode, hparams)
self.log_probs = tf.squeeze(
model.predict_next_word(features, hparams, self.precomputed),
0)
self.mon_sess = self.create_session(self._checkpoint_dir)
def __init__(self,
src_vocab_size,
trg_vocab_size,
model_name,
hparams_set_name,
checkpoint_dir,
single_cpu_thread,
alpha,
beta,
shortlist_strategies,
trg2src_model_name="",
trg2src_hparams_set_name="",
trg2src_checkpoint_dir="",
max_shortlist_length=0,
min_id=0,
nizza_unk_id=None):
"""Initializes a nizza predictor.
Args:
src_vocab_size (int): Source vocabulary size (called inputs_vocab_size
in nizza)
trg_vocab_size (int): Target vocabulary size (called targets_vocab_size
in nizza)
model_name (string): Name of the nizza model
hparams_set_name (string): Name of the nizza hyper-parameter set
checkpoint_dir (string): Path to the Nizza checkpoint directory. The
predictor will load the top most checkpoint in
the `checkpoints` file.
single_cpu_thread (bool): If true, prevent tensorflow from
doing multithreading.
alpha (float): Score for each matching word
beta (float): Penalty for each uncovered word at the end
shortlist_strategies (string): Comma-separated list of shortlist
strategies.
trg2src_model_name (string): Name of the target2source nizza model
trg2src_hparams_set_name (string): Name of the nizza hyper-parameter set
for the target2source model
trg2src_checkpoint_dir (string): Path to the Nizza checkpoint directory
for the target2source model. The
predictor will load the top most checkpoint in
the `checkpoints` file.
max_shortlist_length (int): If a shortlist exceeds this limit,
initialize the initial coverage with 1 at this position. If
zero, do not apply any limit
min_id (int): Do not use IDs below this threshold (filters out most
frequent words).
nizza_unk_id (int): If set, use this as UNK id. Otherwise, the
nizza is assumed to have no UNKs
Raises:
IOError if checkpoint file not found.
"""
super(LexNizzaPredictor, self).__init__(src_vocab_size,
trg_vocab_size,
model_name,
hparams_set_name,
checkpoint_dir,
single_cpu_thread,
nizza_unk_id=nizza_unk_id)
self.alpha = alpha
self.alpha_is_zero = alpha == 0.0
self.beta = beta
self.shortlist_strategies = utils.split_comma(shortlist_strategies)
self.max_shortlist_length = max_shortlist_length
self.min_id = min_id
if trg2src_checkpoint_dir:
self.use_trg2src = True
predictor_graph = tf.Graph()
with predictor_graph.as_default() as g:
hparams = registry.get_registered_hparams_set(
trg2src_hparams_set_name)
hparams.add_hparam("inputs_vocab_size", trg_vocab_size)
hparams.add_hparam("targets_vocab_size", src_vocab_size)
run_config = tf.contrib.learn.RunConfig()
run_config = run_config.replace(
model_dir=trg2src_checkpoint_dir)
model = registry.get_registered_model(trg2src_model_name,
hparams, run_config)
features = {
"inputs": tf.expand_dims(tf.range(trg_vocab_size), 0)
}
mode = tf.estimator.ModeKeys.PREDICT
trg2src_lex_logits = model.precompute(features, mode, hparams)
# Precompute trg2src partitions
partitions = tf.reduce_logsumexp(trg2src_lex_logits, axis=-1)
self._trg2src_src_words_var = tf.placeholder(
dtype=tf.int32,
shape=[None],
name="sgnmt_trg2src_src_words")
# trg2src_lex_logits has shape [1, trg_vocab_size, src_vocab_size]
self.trg2src_logits = tf.gather(
tf.transpose(trg2src_lex_logits[0, :, :]),
self._trg2src_src_words_var)
# trg2src_logits has shape [len(src_words), trg_vocab_size]
self.trg2src_mon_sess = self.create_session(
trg2src_checkpoint_dir)
logging.debug("Precomputing lexnizza trg2src partitions...")
self.trg2src_partitions = self.trg2src_mon_sess.run(partitions)
else:
self.use_trg2src = False
logging.warn("No target-to-source model specified for lexnizza.")
def __init__(self, src_vocab_size, trg_vocab_size, model_name,
hparams_set_name, checkpoint_dir, single_cpu_thread,
alpha, beta, shortlist_strategies,
trg2src_model_name="", trg2src_hparams_set_name="",
trg2src_checkpoint_dir="",
max_shortlist_length=0,
min_id=0,
nizza_unk_id=None):
"""Initializes a nizza predictor.
Args:
src_vocab_size (int): Source vocabulary size (called inputs_vocab_size
in nizza)
trg_vocab_size (int): Target vocabulary size (called targets_vocab_size
in nizza)
model_name (string): Name of the nizza model
hparams_set_name (string): Name of the nizza hyper-parameter set
checkpoint_dir (string): Path to the Nizza checkpoint directory. The
predictor will load the top most checkpoint in
the `checkpoints` file.
single_cpu_thread (bool): If true, prevent tensorflow from
doing multithreading.
alpha (float): Score for each matching word
beta (float): Penalty for each uncovered word at the end
shortlist_strategies (string): Comma-separated list of shortlist
strategies.
trg2src_model_name (string): Name of the target2source nizza model
trg2src_hparams_set_name (string): Name of the nizza hyper-parameter set
for the target2source model
trg2src_checkpoint_dir (string): Path to the Nizza checkpoint directory
for the target2source model. The
predictor will load the top most checkpoint in
the `checkpoints` file.
max_shortlist_length (int): If a shortlist exceeds this limit,
initialize the initial coverage with 1 at this position. If
zero, do not apply any limit
min_id (int): Do not use IDs below this threshold (filters out most
frequent words).
nizza_unk_id (int): If set, use this as UNK id. Otherwise, the
nizza is assumed to have no UNKs
Raises:
IOError if checkpoint file not found.
"""
super(LexNizzaPredictor, self).__init__(
src_vocab_size, trg_vocab_size, model_name, hparams_set_name,
checkpoint_dir, single_cpu_thread, nizza_unk_id=nizza_unk_id)
self.alpha = alpha
self.alpha_is_zero = alpha == 0.0
self.beta = beta
self.shortlist_strategies = utils.split_comma(shortlist_strategies)
self.max_shortlist_length = max_shortlist_length
self.min_id = min_id
if trg2src_checkpoint_dir:
self.use_trg2src = True
predictor_graph = tf.Graph()
with predictor_graph.as_default() as g:
hparams = registry.get_registered_hparams_set(trg2src_hparams_set_name)
hparams.add_hparam("inputs_vocab_size", trg_vocab_size)
hparams.add_hparam("targets_vocab_size", src_vocab_size)
run_config = tf.contrib.learn.RunConfig()
run_config = run_config.replace(model_dir=trg2src_checkpoint_dir)
model = registry.get_registered_model(trg2src_model_name, hparams, run_config)
features = {"inputs": tf.expand_dims(tf.range(trg_vocab_size), 0)}
mode = tf.estimator.ModeKeys.PREDICT
trg2src_lex_logits = model.precompute(features, mode, hparams)
# Precompute trg2src partitions
partitions = tf.reduce_logsumexp(trg2src_lex_logits, axis=-1)
self._trg2src_src_words_var = tf.placeholder(dtype=tf.int32, shape=[None],
name="sgnmt_trg2src_src_words")
# trg2src_lex_logits has shape [1, trg_vocab_size, src_vocab_size]
self.trg2src_logits = tf.gather(tf.transpose(trg2src_lex_logits[0, :, :]), self._trg2src_src_words_var)
# trg2src_logits has shape [len(src_words), trg_vocab_size]
self.trg2src_mon_sess = self.create_session(trg2src_checkpoint_dir)
logging.debug("Precomputing lexnizza trg2src partitions...")
self.trg2src_partitions = self.trg2src_mon_sess.run(partitions)
else:
self.use_trg2src = False
logging.warn("No target-to-source model specified for lexnizza.")