def __init__(self, models, configs, beam_size, beta):
"""Sets some things up then calls _beam_search() to do the real work.
Args:
models: a sequence of RNN or Transformer objects.
configs: a sequence of model configs (argparse.Namespace objects).
beam_size: an integer specifying the beam width.
beta: a float between 0.0 and 1.0 specifying the value of bias
"""
self._models = models
self._configs = configs
self._beam_size = beam_size
self.beta = beta
with tf.name_scope('beam_search'):
# Define placeholders.
self.inputs = sampler_inputs.SamplerInputs()
# Create model adapters to get a consistent interface to
# Transformer and RNN models.
model_adapters = []
for i, (model, config) in enumerate(zip(models, configs)):
with tf.name_scope('model_adapter_{}'.format(i)) as scope:
if config.model_type == 'transformer':
adapter = transformer_inference.ModelAdapter(
model, config, scope)
else:
assert config.model_type == 'rnn'
adapter = rnn_inference.ModelAdapter(
model, config, scope)
model_adapters.append(adapter)
# Check that individual models are compatible with each other.
vocab_sizes = [a.target_vocab_size for a in model_adapters]
if len(set(vocab_sizes)) > 1:
raise exception.Error('Cannot ensemble models with different '
'target vocabulary sizes')
target_vocab_size = vocab_sizes[0]
# Build the graph to do the actual work.
sequences, scores = _beam_search(
model_adapters=model_adapters,
beam_size=beam_size,
batch_size_x=self.inputs.batch_size_x,
max_translation_len=self.inputs.max_translation_len,
normalization_alpha=self.inputs.normalization_alpha,
vocab_size=target_vocab_size,
eos_id=0,
last_translation=self.inputs.last_translation,
last_translation_len=self.inputs.last_translation_len,
beta=self.beta)
# print(sequences, scores)
self._outputs = sequences, scores
def __init__(self, models, configs, beam_size):
"""Sets some things up then calls _random_sample() to do the real work.
Args:
models: a sequence of RNN or Transformer objects.
configs: a sequence of model configs (argparse.Namespace objects).
beam_size: integer specifying the beam width.
"""
self._models = models
self._configs = configs
self._beam_size = beam_size
with tf.compat.v1.name_scope('random_sampler'):
# Define placeholders.
self.inputs = sampler_inputs.SamplerInputs()
# Create an adapter to get a consistent interface to
# Transformer and RNN models.
model_adapters = []
for i, (model, config) in enumerate(zip(models, configs)):
with tf.compat.v1.name_scope(
'model_adapter_{}'.format(i)) as scope:
if config.model_type == 'transformer':
adapter = transformer_inference.ModelAdapter(
model, config, scope)
else:
assert config.model_type == 'rnn'
adapter = rnn_inference.ModelAdapter(
model, config, scope)
model_adapters.append(adapter)
# Build the graph to do the actual work.
sequences, scores = _random_sample(
model_adapters=model_adapters,
beam_size=beam_size,
batch_size_x=self.inputs.batch_size_x,
max_translation_len=self.inputs.max_translation_len,
normalization_alpha=self.inputs.normalization_alpha,
eos_id=0)
self._outputs = sequences, scores
