def batch_size(features, labels):
"""Returns the batch size of the current batch based on the passed features.
Args:
features: The features.
labels: The labels
"""
return extract_batch_length(features)
def batch_size(features, labels):
"""Returns the batch size of the current batch based on the passed features.
Args:
features: The features.
labels: The labels
"""
return extract_batch_length(features)
def _infer_model(self, mode, input_fn=None, predict_keys=None, hooks=None, checkpoint_path=None):
"""Returns predictions for given features given an inference mode.
Args:
mode: The inference to use, possible values: PREDICT, GENERATE, ENCODE.
input_fn: Input function returning features which is a dictionary of
string feature name to `Tensor` or `SparseTensor`. If it returns a
tuple, first item is extracted as features. Prediction continues until
`input_fn` raises an end-of-input exception (`OutOfRangeError` or `StopIteration`).
predict_keys: list of `str`, name of the keys to predict. It is used if
the `EstimatorSpec.predictions` is a `dict`. If `predict_keys` is used then rest
of the predictions will be filtered from the dictionary. If `None`, returns all.
hooks: List of `SessionRunHook` subclass instances. Used for callbacks
inside the prediction call.
checkpoint_path: Path of a specific checkpoint to predict. If `None`, the
latest checkpoint in `model_dir` is used.
Yields:
Evaluated values of `predictions` tensors.
Raises:
ValueError: Could not find a trained model in model_dir.
ValueError: if batch length of predictions are not same.
ValueError: If there is a conflict between `predict_keys` and `predictions`.
For example if `predict_keys` is not `None`
but `EstimatorSpec.predictions` is not a `dict`.
"""
hooks = self._check_hooks(hooks)
# Check that model has been trained.
if not checkpoint_path:
checkpoint_path = saver.latest_checkpoint(self._model_dir)
if not checkpoint_path:
raise ValueError("Could not find trained model at %s." % self._model_dir)
with ops.Graph().as_default() as g:
random_seed.set_random_seed(self._config.tf_random_seed)
training.get_or_create_global_step(g)
features = self._get_features_from_input_fn(input_fn)
estimator_spec = self._call_model_fn(features, None, mode)
predictions = self._extract_keys(estimator_spec.predictions, predict_keys)
with monitored_session.MonitoredSession(
session_creator=monitored_session.ChiefSessionCreator(
checkpoint_filename_with_path=checkpoint_path,
scaffold=estimator_spec.scaffold,
config=self._session_config),
hooks=hooks) as mon_sess:
while not mon_sess.should_stop():
preds_evaluated = mon_sess.run(predictions)
if not isinstance(predictions, dict):
for pred in preds_evaluated:
yield pred
else:
for i in xrange(extract_batch_length(preds_evaluated)):
yield {key: value[i] for key, value in six.iteritems(preds_evaluated)}
def _infer_model(self,
mode,
input_fn=None,
predict_keys=None,
hooks=None,
checkpoint_path=None):
"""Returns predictions for given features given an inference mode.
Args:
mode: The inference to use, possible values: PREDICT, GENERATE, ENCODE.
input_fn: Input function returning features which is a dictionary of
string feature name to `Tensor` or `SparseTensor`. If it returns a
tuple, first item is extracted as features. Prediction continues until
`input_fn` raises an end-of-input exception (`OutOfRangeError` or `StopIteration`).
predict_keys: list of `str`, name of the keys to predict. It is used if
the `EstimatorSpec.predictions` is a `dict`. If `predict_keys` is used then rest
of the predictions will be filtered from the dictionary. If `None`, returns all.
hooks: List of `SessionRunHook` subclass instances. Used for callbacks
inside the prediction call.
checkpoint_path: Path of a specific checkpoint to predict. If `None`, the
latest checkpoint in `model_dir` is used.
Yields:
Evaluated values of `predictions` tensors.
Raises:
ValueError: Could not find a trained model in model_dir.
ValueError: if batch length of predictions are not same.
ValueError: If there is a conflict between `predict_keys` and `predictions`.
For example if `predict_keys` is not `None`
but `EstimatorSpec.predictions` is not a `dict`.
"""
hooks = self._check_hooks(hooks)
# Check that model has been trained.
if not checkpoint_path:
checkpoint_path = saver.latest_checkpoint(self._model_dir)
if not checkpoint_path:
raise ValueError(
'Could not find trained model in model_dir: {}.'.format(
self._model_dir))
with ops.Graph().as_default() as g:
random_seed.set_random_seed(self._config.tf_random_seed)
self._create_and_assert_global_step(g)
features = self._get_features_from_input_fn(input_fn, mode)
estimator_spec = self._call_model_fn(features, None, mode)
predictions = self._extract_keys(estimator_spec.predictions,
predict_keys)
with monitored_session.MonitoredSession(
session_creator=monitored_session.ChiefSessionCreator(
checkpoint_filename_with_path=checkpoint_path,
scaffold=estimator_spec.scaffold,
config=self._session_config),
hooks=hooks) as mon_sess:
while not mon_sess.should_stop():
preds_evaluated = mon_sess.run(predictions)
if not isinstance(predictions, dict):
for pred in preds_evaluated:
yield pred
else:
for i in xrange(extract_batch_length(preds_evaluated)):
yield {
key: value[i]
for key, value in six.iteritems(
preds_evaluated)
}
