o i e,@sPdZddlmZddlmZddZddZdd Zd d Zd d Z ddZ dS)aJUtilities for handling Keras model in graph plugin. Two canonical types of Keras model are Functional and Sequential. A model can be serialized as JSON and deserialized to reconstruct a model. This utility helps with dealing with the serialized Keras model. They have distinct structures to the configurations in shapes below: Functional: config name: Name of the model. If not specified, it is 'model' with an optional suffix if there are more than one instance. input_layers: Keras.layers.Inputs in the model. output_layers: Layer names that are outputs of the model. layers: list of layer configurations. layer: [*] inbound_nodes: inputs to this layer. Sequential: config name: Name of the model. If not specified, it is 'sequential' with an optional suffix if there are more than one instance. layers: list of layer configurations. layer: [*] [*]: Note that a model can be a layer. Please refer to https://github.com/tensorflow/tfjs-layers/blob/master/src/keras_format/model_serialization.ts for more complete definition. )GraphDef)dtypesccszd|fV|ddr9|dd}|ddD]}t|D]\}}|r0d||fn|}||fVq$qdSdS)aYWalks the nested keras layer configuration in preorder. Args: keras_layer: Keras configuration from model.to_json. Yields: A tuple of (name_scope, layer_config). name_scope: a string representing a scope name, similar to that of tf.name_scope. layer_config: a dict representing a Keras layer configuration. configlayersname%s/%sN)get _walk_layers) keras_layer name_scopelayersub_name_scopesublayerrS/var/www/myenv/lib/python3.10/site-packages/tensorboard/plugins/graph/keras_util.pyr 0s  r cCs|rd||fS|S)a/Returns scoped name for a node as a string in the form '/'. Args: name_scope: a string representing a scope name, similar to that of tf.name_scope. node_name: a string representing the current node name. Returns A string representing a scoped name. rr)r node_namerrr _scoped_nameHs rcCs|ddduS)zReturns True if layer is a model. Args: layer: a dict representing a Keras model configuration. Returns: bool: True if layer is a model. rrN)r )r rrr _is_modelXs rcCst|dtfr |S|gS)aNormalizes to a list of layers. Args: maybe_layers: A list of data[1] or a list of list of data. Returns: List of list of data. [1]: A Functional model has fields 'inbound_nodes' and 'output_layers' which can look like below: - ['in_layer_name', 0, 0] - [['in_layer_is_model', 1, 0], ['in_layer_is_model', 1, 1]] The data inside the list seems to describe [name, size, index]. r) isinstancelist) maybe_layersrrr_norm_to_list_of_layersdsrcsf|d}|dstdt||d|d}|d}|d}t|o)|} t|} | rN| rNt||D]\} } t| } t|| d}||| <q8n1| r^| s^t||ddd}n!| s|r| rt|d ksrJd t| |d} t| } ||| <| r|rt|}fd d |D}||<n|dd }|dd}t|}|g|<|||fS)aUpdates input_to_in_layer, model_name_to_output, and prev_node_name based on the model_layer. Args: name_scope: a string representing a scope name, similar to that of tf.name_scope. model_layer: a dict representing a Keras model configuration. input_to_in_layer: a dict mapping Keras.layers.Input to inbound layer. model_name_to_output: a dict mapping Keras Model name to output layer of the model. prev_node_name: a string representing a previous, in sequential model layout, node name. Returns: A tuple of (input_to_in_layer, model_name_to_output, prev_node_name). input_to_in_layer: a dict mapping Keras.layers.Input to inbound layer. model_name_to_output: a dict mapping Keras Model name to output layer of the model. prev_node_name: a string representing a previous, in sequential model layout, node name. rrzlayer is not a model.r input_layers output_layers inbound_nodesrzhCannot have multi-input Functional model when parent model is not Functional. Number of input layers: %dcsg|] }t|dqS)r)r).0r rrr sz!_update_dicts..)r ValueErrorrboolziplenr)r model_layerinput_to_in_layermodel_name_to_outputprev_node_name layer_configrrris_functional_modelis_parent_functional_model input_layer inbound_nodeinput_layer_nameinbound_node_namer layer_names last_layerlast_layer_name output_noderrr _update_dictsxsP             r4cCsi}i}t}d}t|D]\}}t|r!t|||||\}}}q |d}t||d}|j} || _|ddurK|d d} | | j d_ |d} | durht | t sht|d} | j| j d_|ddur|dD]/} t| }|D]&\}}}}t||}|||g}|t|kr||n|d }| j|q|qtn |dur| j|||vr| j||| j}q |S) aReturns a GraphDef representation of the Keras model in a dict form. Note that it only supports models that implemented to_json(). Args: keras_layer: A dict from Keras model.to_json(). Returns: A GraphDef representation of the layers in the model. Nrr class_nameascii keras_classdtyperr )rr rr4r rnodeaddrencodeattrsrdictras_dtypeas_datatype_enumtyperr$inputappend)r input_to_layerr'gr(r r r)rnode_defkeras_cls_namedtype_or_policytf_dtypemaybe_inbound_noderrsizeindex_ inbound_nameinbound_node_names input_namerrrkeras_model_to_graph_defsh         rQN) __doc__"tensorboard.compat.proto.graph_pb2r"tensorboard.compat.tensorflow_stubrr rrrr4rQrrrrs    I