def restore(self, save_path, session=None):
"""Restore a training checkpoint.
Restores `root_checkpointable` and any objects that it tracks
(transitive). Either assigns values immediately if variables to restore have
been created already, or defers restoration until the variables are
created. Dependencies added to the `root_checkpointable` passed to the
constructor after this call will be matched if they have a corresponding
object in the checkpoint.
When building a graph, restorations are added to the graph but not run. A
session is required to retrieve checkpoint metadata.
To disallow deferred loading, assert immediately that all checkpointed
variables have been matched to variable objects:
```python
saver = Saver(root)
saver.restore(path).assert_consumed()
```
An exception will be raised unless every object was matched and its
variables already exist.
When graph building, `assert_consumed()` indicates that all of the restore
ops which will be created for this checkpoint have been created. They can be
run via the `run_restore_ops()` function of the status object:
```python
saver.restore(path).assert_consumed().run_restore_ops()
```
If the checkpoint has not been consumed completely, then the list of restore
ops will grow as more objects are added to the dependency graph.
Name-based `tf.train.Saver` checkpoints can be loaded using this
method. There is no deferred loading, and names are used to match
variables. No restore ops are created/run until `run_restore_ops()` or
`initialize_or_restore()` are called on the returned status object, even
when executing eagerly. Re-encode name-based checkpoints using this
object-based `Saver.save` as soon as possible.
Args:
save_path: The path to the checkpoint, as returned by `save` or
`tf.train.latest_checkpoint`. If None (as when there is no latest
checkpoint for `tf.train.latest_checkpoint` to return), returns an
object which may run initializers for objects in the dependency
graph. If the checkpoint was written by the name-based `tf.train.Saver`,
names are used to match variables.
session: The session to retrieve metadata with. Ignored when executing
eagerly. If not provided when graph building, the default session is
used.
Returns:
A load status object, which can be used to make assertions about the
status of checkpoint restoration and run initialization/restore ops
(of type `CheckpointLoadStatus`, or `InitializationOnlyStatus` if
`save_path` is `None`).
If `save_path` points to a name-based checkpoint, a `NameBasedSaverStatus`
object is returned which runs restore ops from a name-based saver.
"""
if save_path is None:
return InitializationOnlyStatus(self._root_checkpointable)
in_graph_mode = context.in_graph_mode()
if in_graph_mode:
if session is None:
session = ops.get_default_session()
file_prefix_tensor = self._file_prefix_placeholder
file_prefix_feed_dict = {self._file_prefix_placeholder: save_path}
else:
session = None
file_prefix_tensor = constant_op.constant(save_path)
file_prefix_feed_dict = None
try:
if not in_graph_mode or self._object_graph_restore_tensor is None:
object_graph_string, = io_ops.restore_v2(
prefix=file_prefix_tensor,
tensor_names=[_OBJECT_GRAPH_PROTO_KEY],
shape_and_slices=[""],
dtypes=[dtypes.string],
name="object_graph_proto_read")
if in_graph_mode:
self._object_graph_restore_tensor = object_graph_string
if in_graph_mode:
object_graph_string = session.run(
self._object_graph_restore_tensor,
feed_dict=file_prefix_feed_dict)
else:
object_graph_string = object_graph_string.numpy()
except errors_impl.NotFoundError:
# The object graph proto does not exist in this checkpoint. Try again with
# name-based saving.
return NameBasedSaverStatus(self, save_path)
object_graph_proto = (
checkpointable_object_graph_pb2.CheckpointableObjectGraph())
object_graph_proto.ParseFromString(object_graph_string)
if in_graph_mode and object_graph_proto == self._last_restore_object_graph:
checkpoint = self._last_restore_checkpoint
else:
if in_graph_mode:
dtype_map = None
else:
reader = pywrap_tensorflow.NewCheckpointReader(save_path)
dtype_map = reader.get_variable_to_dtype_map()
checkpoint = core_checkpointable_utils._Checkpoint( # pylint: disable=protected-access
object_graph_proto=object_graph_proto,
save_path=file_prefix_tensor,
dtype_map=dtype_map)
if in_graph_mode:
if self._last_restore_object_graph is not None:
raise NotImplementedError(
"Using a single Saver to restore different object graphs is not "
"currently supported when graph building. Use a different Saver "
"for each object graph (restore ops will be duplicated), or "
"file a feature request if this limitation bothers you."
)
self._last_restore_checkpoint = checkpoint
self._last_restore_object_graph = object_graph_proto
core_checkpointable._CheckpointPosition( # pylint: disable=protected-access
checkpoint=checkpoint,
proto_id=0).restore(self._root_checkpointable)
load_status = CheckpointLoadStatus(checkpoint,
feed_dict=file_prefix_feed_dict)
return load_status
def restore(self, save_path, session=None):
"""Restore a training checkpoint.
Restores `root_checkpointable` and any objects that it tracks
(transitive). Either assigns values immediately if variables to restore have
been created already, or defers restoration until the variables are
created. Dependencies added to the `root_checkpointable` passed to the
constructor after this call will be matched if they have a corresponding
object in the checkpoint.
When building a graph, restorations are added to the graph but not run. A
session is required to retrieve checkpoint metadata.
To disallow deferred loading, assert immediately that all checkpointed
variables have been matched to variable objects:
```python
saver = Saver(root)
saver.restore(path).assert_consumed()
```
An exception will be raised unless every object was matched and its
variables already exist.
When graph building, `assert_consumed()` indicates that all of the restore
ops which will be created for this checkpoint have been created. They can be
run via the `run_restore_ops()` function of the status object:
```python
saver.restore(path).assert_consumed().run_restore_ops()
```
If the checkpoint has not been consumed completely, then the list of restore
ops will grow as more objects are added to the dependency graph.
Name-based `tf.train.Saver` checkpoints can be loaded using this
method. There is no deferred loading, and names are used to match
variables. No restore ops are created/run until `run_restore_ops()` or
`initialize_or_restore()` are called on the returned status object, even
when executing eagerly. Re-encode name-based checkpoints using this
object-based `Saver.save` as soon as possible.
Args:
save_path: The path to the checkpoint, as returned by `save` or
`tf.train.latest_checkpoint`. If None (as when there is no latest
checkpoint for `tf.train.latest_checkpoint` to return), returns an
object which may run initializers for objects in the dependency
graph. If the checkpoint was written by the name-based `tf.train.Saver`,
names are used to match variables.
session: The session to retrieve metadata with. Ignored when executing
eagerly. If not provided when graph building, the default session is
used.
Returns:
A load status object, which can be used to make assertions about the
status of checkpoint restoration and run initialization/restore ops
(of type `CheckpointLoadStatus`, or `InitializationOnlyStatus` if
`save_path` is `None`).
If `save_path` points to a name-based checkpoint, a `NameBasedSaverStatus`
object is returned which runs restore ops from a name-based saver.
"""
if save_path is None:
return InitializationOnlyStatus(self._root_checkpointable)
in_graph_mode = context.in_graph_mode()
if in_graph_mode:
if session is None:
session = ops.get_default_session()
file_prefix_tensor = self._file_prefix_placeholder
file_prefix_feed_dict = {self._file_prefix_placeholder: save_path}
else:
session = None
file_prefix_tensor = constant_op.constant(save_path)
file_prefix_feed_dict = None
try:
if not in_graph_mode or self._object_graph_restore_tensor is None:
object_graph_string, = io_ops.restore_v2(
prefix=file_prefix_tensor,
tensor_names=[_OBJECT_GRAPH_PROTO_KEY],
shape_and_slices=[""],
dtypes=[dtypes.string],
name="object_graph_proto_read")
if in_graph_mode:
self._object_graph_restore_tensor = object_graph_string
if in_graph_mode:
object_graph_string = session.run(
self._object_graph_restore_tensor,
feed_dict=file_prefix_feed_dict)
else:
object_graph_string = object_graph_string.numpy()
except errors_impl.NotFoundError:
# The object graph proto does not exist in this checkpoint. Try again with
# name-based saving.
return NameBasedSaverStatus(self, save_path)
object_graph_proto = (
checkpointable_object_graph_pb2.CheckpointableObjectGraph())
object_graph_proto.ParseFromString(object_graph_string)
if in_graph_mode and object_graph_proto == self._last_restore_object_graph:
checkpoint = self._last_restore_checkpoint
else:
if in_graph_mode:
dtype_map = None
else:
reader = pywrap_tensorflow.NewCheckpointReader(save_path)
dtype_map = reader.get_variable_to_dtype_map()
checkpoint = core_checkpointable_utils._Checkpoint( # pylint: disable=protected-access
object_graph_proto=object_graph_proto,
save_path=file_prefix_tensor,
dtype_map=dtype_map)
if in_graph_mode:
if self._last_restore_object_graph is not None:
raise NotImplementedError(
"Using a single Saver to restore different object graphs is not "
"currently supported when graph building. Use a different Saver "
"for each object graph (restore ops will be duplicated), or "
"file a feature request if this limitation bothers you.")
self._last_restore_checkpoint = checkpoint
self._last_restore_object_graph = object_graph_proto
core_checkpointable._CheckpointPosition( # pylint: disable=protected-access
checkpoint=checkpoint, proto_id=0).restore(self._root_checkpointable)
load_status = CheckpointLoadStatus(
checkpoint, feed_dict=file_prefix_feed_dict)
return load_status