a Sic@s~dZddlmmZddlmZddlmZddl m Z ddl m Z ddl m Z ddl m Z dd lmZdd lmZdd lmZdd lmZdd lmZddlmZddlmZddlmZddlmZddlmZe j Z e j!Z!ddZ"ddZ#ddZ$ddZ%de#fddZ&ddZ'dd Z(de#fd!d"Z)ed#d3d$d%Z*d&d'Z+d(d)Z,ed*gd+d,d-Z-ed.gd+d4d1d2Z.dS)5z7Code for model cloning, plus model-related API entries.N)backend)metrics) functional) sequential)training) training_v1) AddMetric)Layer)Input) InputLayer) optimizer_v1) generic_utils) version_utils)CustomObjectScope) tf_logging) keras_exportcCs|SNlayerrrP/var/www/html/django/DPS/env/lib/python3.9/site-packages/keras/models/cloning.py share_weights-srcCs|j|Sr __class__ from_config get_configrrrr _clone_layer1srcsJdd|D}|jfddddd|D|}|j|t|ddS) z>Inserts ancillary layers into the model with the proper order.cSsg|]}t|tr|qSr isinstancer.0rrrr 8sz,_insert_ancillary_layers..cs |jSr)index metric_namer metrics_namesrr;z*_insert_ancillary_layers..)keycSsg|]}t|ts|qSrrrrrrr!<s)relevant_nodesN)sort_insert_layerslist)modelancillary_layersr% new_nodes metric_layersrr$r_insert_ancillary_layers5sr1cs*t}t|}|jdd|D]}||}|D]}|j} | |vr\|| } | || <| } n|| } t| trpq4tfddtj |j Dr4tj fdd|j } tj fdd|j} | | i| } tj | d}|| j|jjttj |jtj | D]\}}||<qq4q"|S) aUses the layers in `layer_map` to make new nodes based on `nodes_by_depth`. Args: nodes_by_depth: Provides structure information to create new nodes. layer_fn: Function to clone layers. layer_map: Map from layers in `model` to new layers. tensor_map: Map from tensors in `model` to newly compute tensors. Returns: A set of new nodes. `layer_map` and `tensor_map` are updated. Treversec3s|]}|vVqdSrr)r tensor tensor_maprr fsz"_make_new_nodes..cs ||Srgettr5rrr&lr'z!_make_new_nodes..cs ||Srr8r:r5rrr&or'r)setr,keysr*outbound_layerrr alltfnestflatten input_tensors map_structure call_args call_kwargsadd_inbound_nodes_keras_history node_indexzipoutput_tensors)nodes_by_depthlayer_fn layer_mapr6r/ depth_keysdepthnodesnoder new_layerargskwargsrLfirst_output_tensorxyrr5r_make_new_nodesBsH         rZcsNttstdttr0tdjsDtdi}|durtj|}t|D]P\}}j |}t |s|j }t |d|d}|jj}|||<qd|jj||<qdt|std|t||\} } tj| | d\}} } j} t|| j d fd d | D} | rJtjd d | D}t| | |S) aClone a functional `Model` instance. Model cloning is similar to calling a model on new inputs, except that it creates new layers (and thus new weights) instead of sharing the weights of the existing layers. Input layers are always cloned. Args: model: Instance of `Model`. input_tensors: optional list of input tensors to build the model upon. If not provided, placeholders will be created. layer_fn: callable to be applied on non-input layers in the model. By default it clones the layer. Another example is to preserve the layer to share the weights. This is required when we create a per-replica copy of the model with distribution strategy; we want the weights to be shared but still feed inputs separately so we create new input layers. Returns: An instance of `Model` reproducing the behavior of the original model, on top of new inputs tensors, using newly instantiated weights. Raises: ValueError: in case of invalid `model` argument value or `layer_fn` argument value. zDExpected `model` argument to be a `Model` instance. Received: model=zdExpected `model` argument to be a functional `Model` instance, got a `Sequential` instance instead: zcExpected `model` argument to be a functional `Model` instance, but got a subclassed model instead: Ninput_wrapper_for_r4nameBExpected `layer_fn` argument to be a callable. Received: layer_fn=)created_layers)r]csg|]}|jvr|qSr)layersrr-rrr!sz+_clone_functional_model..cSs*g|]"}t|r |jddn|jqS)N)r_should_skip_first_node inbound_nodesrrrrr!s)rModel ValueError Sequential_is_graph_networkr@rArB enumerate _input_layersris_keras_tensorr]r rIrcallable_clone_layers_and_model_configrreconstruct_from_configr%valuesr1)r-rCrNnew_input_layersi input_tensororiginal_input_layerr]newly_created_input_layer model_configsr_rLr%r.r/rrar_clone_functional_models      rvcs,ifdd}tj|d}|fS)aClones all layers, and returns the model config without serializing layers. This function ensures that only the node graph is retrieved when getting the model config. The `layer_fn` used to clone layers might not rely on `layer.get_config()`, so some custom layers do not define `get_config`. Trying to retrieve the config results in errors. Args: model: A Functional model. input_layers: Dictionary mapping input layers in `model` to new input layers. layer_fn: Function used to clone all non-input layers. Returns: Model config object, and a dictionary of newly created layers. csN|vr||j<n2|jvr._copy_layer)serialize_layer_fn)rget_network_config)r-rxrNryconfigrrwrrms  rmcsrg|js|fSdd|jD}|jdd|D]$}|j|D]}||jqDq6fdd|DfS)aRemoves and returns any ancillary layers from `layers` based on `model`. Ancillary layers are part of the model topology but not used to compute the model outputs, e.g., layers from `add_loss` and `add_metric`. Args: model: A Keras Model. layer_map: A map to from layers in the `model` to those in `layers`. layers: A list of all layers. Returns: Two lists of layers: (1) `layers` with the ancillary layers removed, and (2) the ancillary layers. cSsg|]}|dkr|qSrr)r rQrrrr!+r'z,_remove_ancillary_layers..Tr2csg|]}|vr|qSrr)r lr.rrr!1r')rh_nodes_by_depthr=r*appendr>)r-rOr`depthsrQrSrrr_remove_ancillary_layerss rcCs"t|tstd|t|s.td|g}i}|jdddD]D}t|tr\|dur\qDt|trnt|n||}|||||<qDt|||\}}|durt||j d}nt t |dkrtd|nt|t rt|}t |d } t| r:| jj} t| tr*t| g||j d}ntd |n2t| d t| j d } | jj} t| g||j d}|sv|Si} |jD]j\}}|j|}t||D]J\}}t|jtrt|jD]\}}|| |j|<qn |j| |j<qqtd d|jD||| }t|||j||S)aqClone a `Sequential` model instance. Model cloning is similar to calling a model on new inputs, except that it creates new layers (and thus new weights) instead of sharing the weights of the existing layers. Args: model: Instance of `Sequential`. input_tensors: optional list of input tensors to build the model upon. If not provided, placeholders will be created. layer_fn: callable to be applied on non-input layers in the model. By default it clones the layer. Another example is to preserve the layer to share the weights. This is required when we create a per-replica copy of the model with distribution strategy; we want the weights to be shared but still feed inputs separately so we create new input layers. Returns: An instance of `Sequential` reproducing the behavior of the original model, on top of new inputs tensors, using newly instantiated weights. Raises: ValueError: in case of invalid `model` argument value or `layer_fn` argument value. zOExpected `model` argument to be a `Sequential` model instance. Received: model=r^F include_self recursiveN)r`r]rbzpTo clone a `Sequential` model, we expect at most one tensor as part of `input_tensors`. Received: input_tensors=rzCannot clone a `Sequential` model on top of a tensor that comes from a Keras layer other than an `InputLayer`. Use the Functional API instead. Received: input_tensors=r[r\cSsi|]\}}|dkr||qSr}r)r rQrRrrr sz+_clone_sequential_model..)rrgrfrl_flatten_layersr rrrr]lenr to_listtupler,rrkrIrr strritemsrKrLrirZr1r%)r-rCrNr`rOr cloned_layerr. cloned_modelrX origin_layerrr input_layerr6rQ cloned_nodesrR cloned_noderSj output_tensorr/rrr_clone_sequential_model4s            rzkeras.models.clone_modelcCszt^|durt}t|tr.zWe do not support the use of list-of-layers attributes in subclassed models used with `model_to_estimator` at this time. Found list model: cSsi|]\}}||qSrr)r r(valuerrrrDr'z4_in_place_subclassed_model_reset..FrzpWe do not support the use of nested subclassed models in `model_to_estimator` at this time. Found nested model: _original_attributes_cacheN) inputsoutputs total_loss optimizertrain_function test_functionpredict_function_training_endpoints_collected_trainable_weights _feed_inputs_feed_input_names_feed_input_shapes)"rhr swap_classrrrerr@compatv1#executing_eagerly_outside_functionsdirgetattrAttributeErrorrf TypeErrorrr r`hasattrr,rr?rr_setattr_trackingrrrsetattrrrbuilt_reset_build_compile_trackers) r-attributes_cacher]rlayers_to_namesoriginal_layerssetattr_trackingrr| fresh_layerattributes_to_cacherrr _in_place_subclassed_model_resets|        rcCs4d|_d|_d|_d|_tjjs*d|_d|_ dS)aReset state trackers for model. Note that we do not actually zero out attributes such as optimizer, but instead rely on the expectation that all of the attrs will be over-written on calling build/compile/etc. This is somewhat fragile, insofar as we check elsewhere for the presence of these attributes as evidence of having been built/compiled/etc. Pending a better way to do this, we reset key attributes here to allow building and compiling. Args: model: the model that is being reset FN) rrr _is_compiledr@rrr_v1_compile_was_calledrrarrrrvs rzEkeras.__internal__.models.in_place_subclassed_model_state_restoration)rcCs|jr Jt|drt|jdurt|j}d|_g|_|jD]*\}}t|||t|tr:|j |q:d|_||_nt |dS)anRestores the original state of a model after it was "reset". This undoes this action of `_in_place_subclassed_model_reset`, which is called in `clone_and_build_model` if `in_place_reset` is set to True. Args: model: Instance of a Keras model created via subclassing, on which `_in_place_subclassed_model_reset` was previously called. rNF) rhrrrrrrrr rr)r-rr]rrrr+in_place_subclassed_model_state_restorations   rz/keras.__internal__.models.clone_and_build_modelTFc s~|j}|r|std||r6|fdd|_t|p>i|jrZt||d} nt|trt||d} | js|jdurt j j r| |jn| tj|j|jdjdnz|j|} Wn>ty td|std |d |} t| Yn0|durHt|ttfr>t|d kr>|d}| |Wdn1s^0Y|rzt|tjrt|j|} t| nt|ttfs|g}|durd d |D} n4t|t r|dj|g} ndd t!||D} |dur| D] } || _"qt| d kr,| d} | d<|durF|d<t#$dd<t#$dd<| j%fi| S)aGClone a `Model` and build/compile it with the same settings used before. This function can be run in the same graph or in a separate graph from the model. When using a separate graph, `in_place_reset` must be `False`. Note that, currently, the clone produced from this function may not work with TPU DistributionStrategy. Try at your own risk. Args: model: `tf.keras.Model` object. Can be Functional, Sequential, or sub-classed. input_tensors: Optional list or dictionary of input tensors to build the model upon. If not provided, placeholders will be created. target_tensors: Optional list of target tensors for compiling the model. If not provided, placeholders will be created. custom_objects: Optional dictionary mapping string names to custom classes or functions. compile_clone: Boolean, whether to compile model clone (default `True`). in_place_reset: Boolean, whether to reset the model in place. Only used if the model is a subclassed model. In the case of a subclassed model, this argument must be set to `True` (default `False`). To restore the original model, use the function `in_place_subclassed_model_state_restoration(model)`. optimizer_iterations: An iterations variable that will be incremented by the optimizer if the clone is compiled. This argument is used when a Keras model is cloned into an Estimator model function, because Estimators create their own global step variable. optimizer_config: Optimizer config dictionary or list of dictionary returned from `get_config()`. This argument should be defined if `clone_and_build_model` is called in a different graph or session from the original model, and the optimizer is an instance of `OptimizerV2`. Returns: Clone of the model. Raises: ValueError: Cloning fails in the following cases - cloning a subclassed model with `in_place_reset` set to False. - compiling the clone when the original model has not been compiled. zyError when cloning model: `compile_clone` was set to True, but the original model has not been compiled. Received: model=csSrrr compile_argsrrr&r'z'clone_and_build_model..)rCNr)dtypezvThis model is a subclassed model. Please implement `get_config` and `from_config` to better support cloning the model.z This model (aA) is a subclassed model. Such a model cannot be cloned, but there is a workaround where the model is reset in-place. To use this, please set the argument `in_place_reset` to `True`. This will reset the attributes in the original model. To restore the attributes, call `in_place_subclassed_model_state_restoration(model)`.rbcSsg|]}|j|qSrr)r optrrrr!1sz)clone_and_build_model..cSsg|]\}}|j|qSr)rr)r r opt_configrrrr!<srtarget_tensorsrweighted_metrics)&rrf_get_compile_argsrrhrrrg_build_input_shaper@rrrbuild _set_inputsr placeholderrrrrrNotImplementedErrorloggingwarningrr,rrr TFOptimizertrack_tf_optimizerdictrK iterationsmetrics_module clone_metricscompile) r-rCrcustom_objects compile_clonein_place_resetoptimizer_iterationsoptimizer_configorig_optimizerclonerrrrrclone_and_build_models5         *      r)NN)NNNTFNN)/__doc__Ztensorflow.compat.v2rv2r@kerasrrr keras.enginerrrrZkeras.engine.base_layerrr keras.engine.input_layerr r Zkeras.optimizersr keras.utilsr rkeras.utils.generic_utilsrtensorflow.python.platformrr tensorflow.python.util.tf_exportrrergrrr1rZrvrmrrrrrrrrrrrs\                 Ap$ Jw "