def load(cls, basename: str, **kwargs) -> 'ClassifierModelBase':
"""Reload the model from a graph file and a checkpoint
The model that is loaded is independent of the pooling and stacking layers, making this class reusable
by sub-classes.
:param basename: The base directory to load from
:param kwargs: See below
:Keyword Arguments:
* *sess* -- An optional tensorflow session. If not passed, a new session is
created
:return: A restored model
"""
_state = read_json("{}.state".format(basename))
if __version__ != _state['version']:
logger.warning(
"Loaded model is from baseline version %s, running version is %s",
_state['version'], __version__)
if not tf.executing_eagerly():
_state['sess'] = kwargs.pop('sess', create_session())
with _state['sess'].graph.as_default():
embeddings_info = _state.pop('embeddings')
embeddings = reload_embeddings(embeddings_info, basename)
# If there is a kwarg that is the same name as an embedding object that
# is taken to be the input of that layer. This allows for passing in
# subgraphs like from a tf.split (for data parallel) or preprocessing
# graphs that convert text to indices
for k in embeddings_info:
if k in kwargs:
_state[k] = kwargs[k]
labels = read_json("{}.labels".format(basename))
model = cls.create(embeddings, labels, **_state)
model._state = _state
if kwargs.get('init', True):
model.sess.run(tf.compat.v1.global_variables_initializer())
model.saver = tf.compat.v1.train.Saver()
model.saver.restore(model.sess, basename)
else:
embeddings_info = _state.pop('embeddings')
embeddings = reload_embeddings(embeddings_info, basename)
# If there is a kwarg that is the same name as an embedding object that
# is taken to be the input of that layer. This allows for passing in
# subgraphs like from a tf.split (for data parallel) or preprocessing
# graphs that convert text to indices
for k in embeddings_info:
if k in kwargs:
_state[k] = kwargs[k]
# TODO: convert labels into just another vocab and pass number of labels to models.
labels = read_json("{}.labels".format(basename))
model = cls.create(embeddings, labels, **_state)
model._state = _state
model.load_weights(f"{basename}.wgt")
return model
def load(cls, basename: str, **kwargs) -> 'DependencyParserModelBase':
"""Reload the model from a graph file and a checkpoint
The model that is loaded is independent of the pooling and stacking layers, making this class reusable
by sub-classes.
:param basename: The base directory to load from
:param kwargs: See below
:Keyword Arguments:
* *sess* -- An optional tensorflow session. If not passed, a new session is
created
:return: A restored model
"""
_state = read_json("{}.state".format(basename))
if __version__ != _state['version']:
logger.warning(
"Loaded model is from baseline version %s, running version is %s",
_state['version'], __version__)
embeddings_info = _state.pop('embeddings')
embeddings = reload_embeddings(embeddings_info, basename)
# If there is a kwarg that is the same name as an embedding object that
# is taken to be the input of that layer. This allows for passing in
# subgraphs like from a tf.split (for data parallel) or preprocessing
# graphs that convert text to indices
for k in embeddings_info:
if k in kwargs:
_state[k] = kwargs[k]
labels = {"labels": read_json("{}.labels".format(basename))}
model = cls.create(embeddings, labels, **_state)
model._state = _state
model.load_weights(f"{basename}.wgt")
return model
def load(cls, basename, **kwargs):
_state = read_json("{}.state".format(basename))
if __version__ != _state['version']:
bl_logger.warning("Loaded model is from baseline version %s, running version is %s", _state['version'], __version__)
_state['sess'] = kwargs.pop('sess', create_session())
with _state['sess'].graph.as_default():
embeddings_info = _state.pop('embeddings')
embeddings = reload_embeddings(embeddings_info, basename)
for k in embeddings_info:
if k in kwargs:
_state[k] = kwargs[k]
model = cls.create(embeddings, init=kwargs.get('init', True), **_state)
model._state = _state
model.saver = tf.train.Saver()
model.saver.restore(model.sess, basename)
return model
def load(cls, basename, **kwargs):
"""Reload the model from a graph file and a checkpoint
The model that is loaded is independent of the pooling and stacking layers, making this class reusable
by sub-classes.
:param basename: The base directory to load from
:param kwargs: See below
:Keyword Arguments:
* *sess* -- An optional tensorflow session. If not passed, a new session is
created
:return: A restored model
"""
_state = read_json("{}.state".format(basename))
if __version__ != _state['version']:
logger.warning("Loaded model is from baseline version %s, running version is %s", _state['version'], __version__)
_state['sess'] = kwargs.pop('sess', tf.Session())
with _state['sess'].graph.as_default():
embeddings_info = _state.pop('embeddings')
embeddings = reload_embeddings(embeddings_info, basename)
# If there is a kwarg that is the same name as an embedding object that
# is taken to be the input of that layer. This allows for passing in
# subgraphs like from a tf.split (for data parallel) or preprocessing
# graphs that convert text to indices
for k in embeddings_info:
if k in kwargs:
_state[k] = kwargs[k]
# TODO: convert labels into just another vocab and pass number of labels to models.
labels = read_json("{}.labels".format(basename))
model = cls.create(embeddings, labels, **_state)
model._state = _state
if kwargs.get('init', True):
model.sess.run(tf.global_variables_initializer())
model.saver = tf.train.Saver()
model.saver.restore(model.sess, basename)
return model
