def initialize(self, checkpoint_path=None):
"""Initialize from scratch or restore and keep restorer around."""
init_op = tf.variables_initializer(self.get_init_variables())
self.session.run(init_op)
if checkpoint_path is not None:
restorer = RestoreTFModelHook(
variables=self.get_restore_variables(),
checkpoint_path=ProjectManager.checkpoints,
global_step_setter=self.set_global_step,
)
with self.session.as_default():
restorer(checkpoint_path)
def initialize(self, checkpoint_path=None):
"""Overwrite default to make lazy"""
init_op = tf.variables_initializer(self.get_init_variables())
self.session.run(init_op)
if checkpoint_path is not None:
self.set_global_step(
RestoreTFModelHook.parse_global_step(checkpoint_path))
init_assign_op, init_feed_dict = slim.assign_from_checkpoint(
checkpoint_path,
self.get_restore_variables(),
ignore_missing_vars=True)
self.session.run(init_assign_op, feed_dict=init_feed_dict)
self.logger.info("Lazily restored from {}".format(checkpoint_path))
def initialize(self, checkpoint_path=None):
self.restore_variables = self.model.variables
# wait for new checkpoint and restore
if checkpoint_path:
restorer = RestoreCurrentCheckpointHook(
variables=self.restore_variables,
checkpoint_path=checkpoint_path,
global_step_setter=self.set_global_step,
)
self.hooks += [restorer]
else:
restorer = RestoreTFModelHook(
variables=self.restore_variables,
checkpoint_path=ProjectManager.checkpoints,
global_step_setter=self.set_global_step,
)
waiter = WaitForCheckpointHook(
checkpoint_root=ProjectManager.checkpoints,
callback=restorer,
eval_all=self.config.get("eval_all", False),
)
self.hooks += [waiter]