def _fn():
self._initialize(metadata)
dataset = inputter.make_dataset(data_file, training=training)
if training:
batch_size_multiple = 1
if batch_type == "tokens" and self.dtype == tf.float16:
batch_size_multiple = 8
dataset = data.training_pipeline(
dataset,
batch_size,
batch_type=batch_type,
batch_multiplier=batch_multiplier,
bucket_width=bucket_width,
single_pass=single_pass,
process_fn=process_fn,
num_threads=num_threads,
shuffle_buffer_size=sample_buffer_size,
prefetch_buffer_size=prefetch_buffer_size,
dataset_size=self.features_inputter.get_dataset_size(
features_file),
maximum_features_length=maximum_features_length,
maximum_labels_length=maximum_labels_length,
features_length_fn=self.features_inputter.get_length,
labels_length_fn=self.labels_inputter.get_length,
batch_size_multiple=batch_size_multiple,
num_shards=num_shards,
shard_index=shard_index)
else:
dataset = data.inference_pipeline(
dataset,
batch_size,
process_fn=process_fn,
num_threads=num_threads,
prefetch_buffer_size=prefetch_buffer_size,
bucket_width=bucket_width,
length_fn=self.features_inputter.get_length)
iterator = dataset.make_initializable_iterator()
# Add the initializer to a standard collection for it to be initialized.
tf.add_to_collection(tf.GraphKeys.TABLE_INITIALIZERS,
iterator.initializer)
return iterator.get_next()
def make_training_dataset(self,
features_file,
labels_file,
batch_size,
batch_type="examples",
batch_multiplier=1,
batch_size_multiple=1,
shuffle_buffer_size=None,
bucket_width=None,
maximum_features_length=None,
maximum_labels_length=None,
single_pass=False,
num_shards=1,
shard_index=0,
num_threads=4,
prefetch_buffer_size=None):
"""See :meth:`opennmt.inputters.inputter.ExampleInputter.make_training_dataset`."""
_ = labels_file
dataset = self.make_dataset(features_file, training=True)
dataset = data.training_pipeline(
dataset,
batch_size,
batch_type=batch_type,
batch_multiplier=batch_multiplier,
bucket_width=bucket_width,
single_pass=single_pass,
process_fn=lambda x: self._generate_example(x, training=True),
num_threads=num_threads,
shuffle_buffer_size=shuffle_buffer_size,
prefetch_buffer_size=prefetch_buffer_size,
maximum_features_length=maximum_features_length,
maximum_labels_length=maximum_labels_length,
features_length_fn=self.get_length,
batch_size_multiple=batch_size_multiple,
num_shards=num_shards,
shard_index=shard_index)
return dataset
def _input_fn_impl(self,
mode,
batch_size,
metadata,
features_file,
labels_file=None,
batch_type="examples",
batch_multiplier=1,
bucket_width=None,
single_pass=False,
num_threads=None,
sample_buffer_size=None,
prefetch_buffer_size=None,
maximum_features_length=None,
maximum_labels_length=None):
"""See ``input_fn``."""
self._initialize(metadata)
feat_dataset, feat_process_fn = self._get_features_builder(
features_file)
if labels_file is None:
dataset = feat_dataset
# Parallel inputs must be catched in a single tuple and not considered as multiple arguments.
process_fn = lambda *arg: feat_process_fn(item_or_tuple(arg))
else:
labels_dataset, labels_process_fn = self._get_labels_builder(
labels_file)
dataset = tf.data.Dataset.zip((feat_dataset, labels_dataset))
process_fn = lambda features, labels: (feat_process_fn(features),
labels_process_fn(labels))
dataset, process_fn = self._augment_parallel_dataset(dataset,
process_fn,
mode=mode)
if mode == tf.estimator.ModeKeys.TRAIN:
dataset = data.training_pipeline(
dataset,
batch_size,
batch_type=batch_type,
batch_multiplier=batch_multiplier,
bucket_width=bucket_width,
single_pass=single_pass,
process_fn=process_fn,
num_threads=num_threads,
shuffle_buffer_size=sample_buffer_size,
prefetch_buffer_size=prefetch_buffer_size,
dataset_size=self._get_dataset_size(features_file),
maximum_features_length=maximum_features_length,
maximum_labels_length=maximum_labels_length,
features_length_fn=self._get_features_length,
labels_length_fn=self._get_labels_length)
else:
dataset = data.inference_pipeline(
dataset,
batch_size,
process_fn=process_fn,
num_threads=num_threads,
prefetch_buffer_size=prefetch_buffer_size)
iterator = dataset.make_initializable_iterator()
# Add the initializer to a standard collection for it to be initialized.
tf.add_to_collection(tf.GraphKeys.TABLE_INITIALIZERS,
iterator.initializer)
return iterator.get_next()
def make_training_dataset(self,
features_file,
labels_file,
batch_size,
batch_type="examples",
batch_multiplier=1,
batch_size_multiple=1,
shuffle_buffer_size=None,
bucket_width=None,
maximum_features_length=None,
maximum_labels_length=None,
single_pass=False,
num_shards=1,
shard_index=0,
num_threads=4,
prefetch_buffer_size=None):
"""Builds a dataset to be used for training. It supports the full training
pipeline, including:
* sharding
* shuffling
* filtering
* bucketing
* prefetching
Args:
features_file: The evaluation source file.
labels_file: The evaluation target file.
batch_size: The batch size to use.
batch_type: The training batching stragety to use: can be "examples" or
"tokens".
batch_multiplier: The batch size multiplier to prepare splitting accross
replicated graph parts.
batch_size_multiple: When :obj:`batch_type` is "tokens", ensure that the
result batch size is a multiple of this value.
shuffle_buffer_size: The number of elements from which to sample.
bucket_width: The width of the length buckets to select batch candidates
from (for efficiency). Set ``None`` to not constrain batch formation.
maximum_features_length: The maximum length or list of maximum lengths of
the features sequence(s). ``None`` to not constrain the length.
maximum_labels_length: The maximum length of the labels sequence.
``None`` to not constrain the length.
single_pass: If ``True``, makes a single pass over the training data.
num_shards: The number of data shards (usually the number of workers in a
distributed setting).
shard_index: The shard index this data pipeline should read from.
num_threads: The number of elements processed in parallel.
prefetch_buffer_size: The number of batches to prefetch asynchronously. If
``None``, use an automatically tuned value on TensorFlow 1.8+ and 1 on
older versions.
Returns:
A ``tf.data.Dataset``.
"""
dataset_size = self.features_inputter.get_dataset_size(features_file)
map_func = lambda *arg: self.make_features(arg, training=True)
dataset = self.make_dataset([features_file, labels_file],
training=True)
dataset = training_pipeline(
dataset,
batch_size,
batch_type=batch_type,
batch_multiplier=batch_multiplier,
bucket_width=bucket_width,
single_pass=single_pass,
process_fn=map_func,
num_threads=num_threads,
shuffle_buffer_size=shuffle_buffer_size,
prefetch_buffer_size=prefetch_buffer_size,
dataset_size=dataset_size,
maximum_features_length=maximum_features_length,
maximum_labels_length=maximum_labels_length,
features_length_fn=self.features_inputter.get_length,
labels_length_fn=self.labels_inputter.get_length,
batch_size_multiple=batch_size_multiple,
num_shards=num_shards,
shard_index=shard_index)
return dataset
