def make_features(self, element=None, features=None, training=None):
if self.combine_features:
if features is None:
features = {}
for i, inputter in enumerate(self.inputters):
prefix = "inputter_%d_" % i
sub_features = misc.extract_prefixed_keys(features, prefix)
if not sub_features:
# Also try to read the format produced by the serving features.
sub_features = misc.extract_suffixed_keys(
features, "_%d" % i)
sub_features = inputter.make_features(
element=element[i] if element is not None else None,
features=sub_features,
training=training)
for key, value in sub_features.items():
features["%s%s" % (prefix, key)] = value
return features
else:
if features is None:
features = [{} for _ in self.inputters]
else:
features = list(features)
for i, inputter in enumerate(self.inputters):
features[i] = inputter.make_features(
element=element[i] if element is not None else None,
features=features[i],
training=training)
return tuple(features)
def _transform_data(self, data, mode):
transformed = []
for i, inputter in enumerate(self.inputters):
with tf.variable_scope("inputter_{}".format(i)):
sub_data = extract_prefixed_keys(data, "inputter_{}_".format(i))
transformed.append(inputter._transform_data(sub_data, mode)) # pylint: disable=protected-access
if self.reducer is not None:
transformed = self.reducer(transformed)
return transformed
def get_length(self, data):
lengths = []
for i, inputter in enumerate(self.inputters):
sub_data = extract_prefixed_keys(data, "inputter_{}_".format(i))
lengths.append(inputter.get_length(sub_data))
if self.reducer is None:
return lengths
else:
return lengths[0]
def call(self, features, training=None):
transformed = []
for i, inputter in enumerate(self.inputters):
if self.combine_features:
sub_features = misc.extract_prefixed_keys(features, "inputter_{}_".format(i))
else:
sub_features = features[i]
transformed.append(inputter(sub_features, training=training))
if self.reducer is not None:
transformed = self.reducer(transformed)
return transformed
def get_length(self, features):
lengths = []
for i, inputter in enumerate(self.inputters):
if self.combine_features:
sub_features = extract_prefixed_keys(features, "inputter_{}_".format(i))
else:
sub_features = features[i]
lengths.append(inputter.get_length(sub_features))
if self.reducer is None:
return lengths
else:
return lengths[0]
def make_inputs(self, features, training=None):
transformed = []
for i, inputter in enumerate(self.inputters):
with tf.variable_scope("inputter_{}".format(i)):
if self.combine_features:
sub_features = extract_prefixed_keys(
features, "inputter_{}_".format(i))
else:
sub_features = features[i]
transformed.append(
inputter.make_inputs(sub_features, training=training))
if self.reducer is not None:
transformed = self.reducer(transformed)
return transformed
def make_inputs(self, features, training=None):
if not self.built:
self.build()
transformed = []
for i, (inputter, scope) in enumerate(zip(self.inputters, self._get_scopes())):
with compat.tf_compat(v1="variable_scope")(scope):
if self.combine_features:
sub_features = extract_prefixed_keys(features, "inputter_{}_".format(i))
else:
sub_features = features[i]
transformed.append(inputter.make_inputs(sub_features, training=training))
if self.reducer is not None:
transformed = self.reducer(transformed)
return transformed
def get_padded_shapes(self, element_spec, maximum_length=None):
if maximum_length is None:
maximum_length = [None for _ in self.inputters]
elif not isinstance(
maximum_length,
(list, tuple)) or len(maximum_length) != len(self.inputters):
raise ValueError(
"A maximum length should be set for each parallel inputter")
if self.combine_features:
shapes = {}
for i, (inputter,
length) in enumerate(zip(self.inputters, maximum_length)):
prefix = "inputter_%d_" % i
spec = misc.extract_prefixed_keys(element_spec, prefix)
sub_shapes = inputter.get_padded_shapes(spec,
maximum_length=length)
for key, value in sub_shapes.items():
shapes["%s%s" % (prefix, key)] = value
return shapes
else:
return type(element_spec)(
inputter.get_padded_shapes(spec, maximum_length=length)
for inputter, spec, length in zip(self.inputters, element_spec,
maximum_length))
def __init__(
self,
model,
features_file,
labels_file,
batch_size,
batch_type="examples",
length_bucket_width=None,
scorers=None,
save_predictions=False,
early_stopping=None,
model_dir=None,
export_on_best=None,
exporter=None,
max_exports_to_keep=5,
):
"""Initializes the evaluator.
Args:
model: A :class:`opennmt.models.Model` to evaluate.
features_file: Path to the evaluation features.
labels_file: Path to the evaluation labels.
batch_size: The evaluation batch size.
batch_type: The batching strategy to use: can be "examples" or "tokens".
length_bucket_width: The width of the length buckets to select batch
candidates from (for efficiency). Set ``None`` to not constrain batch
formation.
scorers: A list of scorers, callables taking the path to the reference and
the hypothesis and return one or more scores.
save_predictions: Save evaluation predictions to a file. This is ``True``
when :obj:`scorers` is set.
early_stopping: An ``EarlyStopping`` instance.
model_dir: The active model directory.
export_on_best: Export a model when this evaluation metric has the
best value so far.
exporter: A :class:`opennmt.utils.Exporter` instance to export the model.
Defaults to :class:`opennmt.utils.SavedModelExporter`.
max_exports_to_keep: Maximum number of exports to keep. Older exports will
be garbage collected. Set to ``None`` to keep all exports.
Raises:
ValueError: If :obj:`save_predictions` is set but the model is not compatible.
ValueError: If :obj:`save_predictions` is set but :obj:`model_dir` is ``None``.
ValueError: If :obj:`export_on_best` is set but :obj:`model_dir` is ``None``.
ValueError: If the :obj:`early_stopping` configuration is invalid.
"""
if model_dir is not None:
export_dir = os.path.join(model_dir, "export")
eval_dir = os.path.join(model_dir, "eval")
else:
if save_predictions:
raise ValueError(
"Saving evaluation predictions requires model_dir to be set"
)
if export_on_best is not None:
raise ValueError(
"Exporting models requires model_dir to be set")
export_dir = None
eval_dir = None
if scorers is None:
scorers = []
if scorers:
save_predictions = True
if save_predictions:
if model.unsupervised:
raise ValueError(
"This model does not support saving evaluation predictions"
)
if not tf.io.gfile.exists(eval_dir):
tf.io.gfile.makedirs(eval_dir)
self._model = model
self._labels_file = labels_file
self._save_predictions = save_predictions
self._scorers = scorers
self._eval_dir = eval_dir
self._metrics_history = []
if eval_dir is not None:
self._summary_writer = tf.summary.create_file_writer(eval_dir)
summaries = misc.read_summaries(eval_dir)
for step, values in summaries:
metrics = misc.extract_prefixed_keys(values,
_SUMMARIES_SCOPE + "/")
self._metrics_history.append((step, metrics))
else:
self._summary_writer = tf.summary.create_noop_writer()
dataset = model.examples_inputter.make_evaluation_dataset(
features_file,
labels_file,
batch_size,
batch_type=batch_type,
length_bucket_width=length_bucket_width,
num_threads=1,
prefetch_buffer_size=1,
)
self._eval_fn = tf.function(model.evaluate,
input_signature=dataset.element_spec)
self._dataset = dataset
self._metrics_name = {"loss", "perplexity"}
for scorer in self._scorers:
self._metrics_name.update(scorer.scores_name)
model_metrics = self._model.get_metrics()
if model_metrics:
self._metrics_name.update(set(model_metrics.keys()))
if early_stopping is not None:
if early_stopping.metric not in self._metrics_name:
raise ValueError(
"Invalid early stopping metric '%s', expected one in %s" %
(early_stopping.metric, str(self._metrics_name)))
if early_stopping.steps <= 0:
raise ValueError("Early stopping steps should greater than 0")
self._early_stopping = early_stopping
self._export_on_best = export_on_best
self._exporter = exporter
self._export_dir = export_dir
self._max_exports_to_keep = max_exports_to_keep
def __init__(self,
model,
features_file,
labels_file,
batch_size,
scorers=None,
save_predictions=False,
early_stopping=None,
eval_dir=None):
"""Initializes the evaluator.
Args:
model: A :class:`opennmt.models.model.Model` to evaluate.
features_file: Path to the evaluation features.
labels_file: Path to the evaluation labels.
batch_size: The evaluation batch size.
scorers: A list of scorers, callables taking the path to the reference and
the hypothesis and return one or more scores.
save_predictions: Save evaluation predictions to a file. This is ``True``
when :obj:`external_evaluator` is set.
early_stopping: An ``EarlyStopping`` instance.
eval_dir: Directory where predictions can be saved.
Raises:
ValueError: If predictions should be saved but the model is not compatible.
ValueError: If predictions should be saved but :obj:`eval_dir` is ``None``.
ValueError: If the :obj:`early_stopping` configuration is invalid.
"""
if scorers is None:
scorers = []
if scorers:
save_predictions = True
if save_predictions:
if model.unsupervised:
raise ValueError("This model does not support saving evaluation predictions")
if eval_dir is None:
raise ValueError("Saving evaluation predictions requires eval_dir to be set")
if not tf.io.gfile.exists(eval_dir):
tf.io.gfile.makedirs(eval_dir)
self._model = model
self._labels_file = labels_file
self._save_predictions = save_predictions
self._scorers = scorers
self._eval_dir = eval_dir
self._metrics_history = []
if eval_dir is not None:
self._summary_writer = tf.summary.create_file_writer(eval_dir)
summaries = misc.read_summaries(eval_dir)
for step, values in summaries:
metrics = misc.extract_prefixed_keys(values, _SUMMARIES_SCOPE + "/")
self._metrics_history.append((step, metrics))
else:
self._summary_writer = tf.summary.create_noop_writer()
dataset = model.examples_inputter.make_evaluation_dataset(
features_file,
labels_file,
batch_size,
num_threads=1,
prefetch_buffer_size=1)
@dataset_lib.function_on_next(dataset)
def _eval(next_fn):
source, target = next_fn()
outputs, predictions = model(source, labels=target)
loss = model.compute_loss(outputs, target, training=False)
return loss, predictions, target
self._eval = _eval
self._metrics_name = {"loss", "perplexity"}
for scorer in self._scorers:
self._metrics_name.update(scorer.scores_name)
model_metrics = self._model.get_metrics()
if model_metrics:
self._metrics_name.update(set(six.iterkeys(model_metrics)))
if early_stopping is not None:
if early_stopping.metric not in self._metrics_name:
raise ValueError("Invalid early stopping metric '%s', expected one in %s" % (
early_stopping.metric, str(self._metrics_name)))
if early_stopping.steps <= 0:
raise ValueError("Early stopping steps should greater than 0")
self._early_stopping = early_stopping
def _index_features(self, features, index):
if self.combine_features:
return misc.extract_prefixed_keys(features, "inputter_{}_".format(index))
else:
return features[index]
