o ?e /@sdZddlZddlmZddlmZddlmZddlm Z ddl m Z ddl mZdd lmZdd lmZed gd Gd ddeZdS)zManages a Checkpoint View.N)trackable_object_graph_pb2)trackable_view) errors_impl) tf_logging)base)py_checkpoint_reader)object_identity) tf_exportztrain.CheckpointView)v1c@s@eZdZdZddZddZddZdd Zd d Zd d Z dS)CheckpointViewaaGathers and serializes a checkpoint view. This is for loading specific portions of a module from a checkpoint, and be able to compare two modules by matching components. Example usage: >>> class SimpleModule(tf.Module): ... def __init__(self, name=None): ... super().__init__(name=name) ... self.a_var = tf.Variable(5.0) ... self.b_var = tf.Variable(4.0) ... self.vars = [tf.Variable(1.0), tf.Variable(2.0)] >>> root = SimpleModule(name="root") >>> root.leaf = SimpleModule(name="leaf") >>> ckpt = tf.train.Checkpoint(root) >>> save_path = ckpt.save('/tmp/tf_ckpts') >>> checkpoint_view = tf.train.CheckpointView(save_path) Pass `node_id=0` to `tf.train.CheckpointView.children()` to get the dictionary of all children directly linked to the checkpoint root. >>> for name, node_id in checkpoint_view.children(0).items(): ... print(f"- name: '{name}', node_id: {node_id}") - name: 'a_var', node_id: 1 - name: 'b_var', node_id: 2 - name: 'vars', node_id: 3 - name: 'leaf', node_id: 4 - name: 'root', node_id: 0 - name: 'save_counter', node_id: 5 c Cslt|}z|tj}Wntjy'}z td|dtjd|d}~wwt }| |||_ dS)zConfigure the checkpoint view. Args: save_path: The path to the checkpoint. Raises: ValueError: If the save_path does not lead to a TF2 checkpoint. zThe specified checkpoint "zS" does not appear to be object-based (saved with TF2) since it is missing the key "zg". Likely it was created with the TF1 name-based saver and does not contain an object dependency graph.N) rZNewCheckpointReaderZ get_tensorrZOBJECT_GRAPH_PROTO_KEYrZ NotFoundError ValueErrorrZTrackableObjectGraphZParseFromString_object_graph_proto)selfZ save_pathreaderZobject_graph_stringZnot_found_errorZobject_graph_protorm/home/www/facesmatcher.com/pyenv/lib/python3.10/site-packages/tensorflow/python/checkpoint/checkpoint_view.py__init__@s   zCheckpointView.__init__cCsdd|jj|jDS)zReturns all child trackables attached to obj. Args: node_id: Id of the node to return its children. Returns: Dictionary of all children attached to the object with name to node_id. cSsi|]}|j|jqSr) local_namenode_id).0childrrr asz+CheckpointView.children..)r nodeschildren)rrrrrrXs zCheckpointView.childrencCst|S)z8Returns a list of trackables by node_id attached to obj.)list_descendants_with_pathskeys)rrrr descendantsfszCheckpointView.descendantscCsi}tdg}d|d<|d}|rN|}|jj|}|jD]+}|jdks.|j|vr/q ||}|j|vrA| |j|d|j ||j<q |s|S)zReturns a dict of descendants by node_id and paths to node. The names returned by this private method are subject to change. rroot.) collectionsdequegetpopleftr rrrrappendr)rZall_nodes_with_pathsto_visitpathrobjrrrrrks        z&CheckpointView._descendants_with_pathsc Cs t|tjstd|dt|di}||d<td|fg}t}t |}|r| \}}| |}| | D]>\} } | |vsI| dkrJq=| |vr{| | } | durg|| || <|| || fq=| || ur{td| d|| dq=|||s+|S) a>Returns all matching trackables between CheckpointView and Trackable. Matching trackables represents trackables with the same name and position in graph. Args: obj: `Trackable` root. Returns: Dictionary containing all overlapping trackables that maps `node_id` to `Trackable`. Example usage: >>> class SimpleModule(tf.Module): ... def __init__(self, name=None): ... super().__init__(name=name) ... self.a_var = tf.Variable(5.0) ... self.b_var = tf.Variable(4.0) ... self.vars = [tf.Variable(1.0), tf.Variable(2.0)] >>> root = SimpleModule(name="root") >>> leaf = root.leaf = SimpleModule(name="leaf") >>> leaf.leaf3 = tf.Variable(6.0, name="leaf3") >>> leaf.leaf4 = tf.Variable(7.0, name="leaf4") >>> ckpt = tf.train.Checkpoint(root) >>> save_path = ckpt.save('/tmp/tf_ckpts') >>> checkpoint_view = tf.train.CheckpointView(save_path) >>> root2 = SimpleModule(name="root") >>> leaf2 = root2.leaf2 = SimpleModule(name="leaf2") >>> leaf2.leaf3 = tf.Variable(6.0) >>> leaf2.leaf4 = tf.Variable(7.0) Pass `node_id=0` to `tf.train.CheckpointView.children()` to get the dictionary of all children directly linked to the checkpoint root. >>> checkpoint_view_match = checkpoint_view.match(root2).items() >>> for item in checkpoint_view_match: ... print(item) (0, ...) (1, ) (2, ) (3, ListWrapper([, ])) (6, ) (7, ) zExpected a Trackable, got z of type rrNzjInconsistent references when matching the checkpoint into this object graph. The referenced objects are: (z and z).) isinstancerZ Trackabler typer r!setr TrackableViewr#ritemsr"r$loggingwarningadd) rr'overlapping_nodesr%visitedviewZcurrent_node_idZcurrent_trackableZtrackable_childrenZ child_nameZ child_node_idZcurrent_assignmentrrrmatchs< 2       zCheckpointView.matchcCsp||}g}g}|D] }||vr||q t|D]}|t|vr2||q"|||fS)a: Returns diff between CheckpointView and Trackable. This method is intended to be used to compare the object stored in a checkpoint vs a live model in Python. For example, if checkpoint restoration fails the `assert_consumed()` or `assert_existing_objects_matched()` checks, you can use this to list out the objects/checkpoint nodes which were not restored. Example Usage: >>> class SimpleModule(tf.Module): ... def __init__(self, name=None): ... super().__init__(name=name) ... self.a_var = tf.Variable(5.0) ... self.b_var = tf.Variable(4.0) ... self.vars = [tf.Variable(1.0), tf.Variable(2.0)] >>> root = SimpleModule(name="root") >>> leaf = root.leaf = SimpleModule(name="leaf") >>> leaf.leaf3 = tf.Variable(6.0, name="leaf3") >>> leaf.leaf4 = tf.Variable(7.0, name="leaf4") >>> ckpt = tf.train.Checkpoint(root) >>> save_path = ckpt.save('/tmp/tf_ckpts') >>> checkpoint_view = tf.train.CheckpointView(save_path) >>> root2 = SimpleModule(name="root") >>> leaf2 = root2.leaf2 = SimpleModule(name="leaf2") >>> leaf2.leaf3 = tf.Variable(6.0) >>> leaf2.leaf4 = tf.Variable(7.0) Pass `node_id=0` to `tf.train.CheckpointView.children()` to get the dictionary of all children directly linked to the checkpoint root. >>> checkpoint_view_diff = checkpoint_view.diff(root2) >>> checkpoint_view_match = checkpoint_view_diff[0].items() >>> for item in checkpoint_view_match: ... print(item) (0, ...) (1, ) (2, ) (3, ListWrapper([, ])) (6, ) (7, ) >>> only_in_checkpoint_view = checkpoint_view_diff[1] >>> print(only_in_checkpoint_view) [4, 5, 8, 9, 10, 11, 12, 13, 14] >>> only_in_trackable = checkpoint_view_diff[2] >>> print(only_in_trackable) [..., , , ListWrapper([, ]), , , , ] Args: obj: `Trackable` root. Returns: Tuple of ( - Overlaps: Dictionary containing all overlapping trackables that maps `node_id` to `Trackable`, same as CheckpointView.match(). - Only in CheckpointView: List of `node_id` that only exist in CheckpointView. - Only in Trackable: List of `Trackable` that only exist in Trackable. ) ) r3rrr$rr+rZObjectIdentitySetvalues)rr'r0Zonly_in_checkpoint_viewZonly_in_trackablerZ trackablerrrdiffs K     zCheckpointView.diffN) __name__ __module__ __qualname____doc__rrrrr3r5rrrrr s" Vr )r9r Ztensorflow.core.protobufrZtensorflow.python.checkpointrZtensorflow.python.frameworkrZtensorflow.python.platformrr-Ztensorflow.python.trackablerZtensorflow.python.trainingrZtensorflow.python.utilrZ tensorflow.python.util.tf_exportr objectr rrrrs