o i ek5@sdZddlZddlZddlmmZddlmZ ddlm Z ddlm Z ddlm Z ddl mZddlmZddlmZdd lmZdd lmZdd lmZd d Zd(ddZddZd)ddZd*ddZddZd)ddZ ddZ!ddZ"d d!Z#d"d#Z$d$d%Z%d&d'Z&dS)+z$Utils related to keras model saving.N)backend)losses) optimizers)base_layer_utils) optimizer_v1) serialization) version_utils)ask_to_proceed_with_overwrite) tf_loggingcCs t|ddrdd|jDSdS)aPConvert metrics from a Keras model `compile` API to dictionary. This is used for converting Keras models to Estimators and SavedModels. Args: model: A `tf.keras.Model` object. Returns: Dictionary mapping metric names to metric instances. May return `None` if the model does not contain any metrics. _compile_metricsNcSsi|]}|j|qS)name).0mr r S/var/www/myenv/lib/python3.10/site-packages/keras/src/saving/legacy/saving_utils.py 4sz)extract_model_metrics..)getattr_compile_metric_functionsmodelr r rextract_model_metrics$s rFcCs&|j| d}|dur dSt|}|S)atInspect model to get its input signature. The model's input signature is a list with a single (possibly-nested) object. This is due to the Keras-enforced restriction that tensor inputs must be passed in as the first argument. For example, a model with input {'feature1': , 'feature2': } will have input signature: [{'feature1': TensorSpec, 'feature2': TensorSpec}] Args: model: Keras Model object. keep_original_batch_size: A boolean indicating whether we want to keep using the original batch size or set it to None. Default is `False`, which means that the batch dim of the returned input signature will always be set to `None`. Returns: A tuple containing `(args, kwargs)` TensorSpecs of the model call function inputs. `kwargs` does not contain the `training` argument. ) dynamic_batchN)NN) save_spec_enforce_names_consistency)rkeep_original_batch_size input_specsr r rmodel_call_inputs8s rcCs.t|tjjrtd|dtd|d)NzModel z cannot be saved because the input shape is not available. Please specify an input shape either by calling `build(input_shape)` directly, or by calling the model on actual data using `Model()`, `Model.fit()`, or `Model.predict()`.a cannot be saved either because the input shape is not available or because the forward pass of the model is not defined.To define a forward pass, please override `Model.call()`. To specify an input shape, either call `build(input_shape)` directly, or call the model on actual data using `Model()`, `Model.fit()`, or `Model.predict()`. If you have a custom training step, please make sure to invoke the forward pass in train step through `Model.__call__`, i.e. `model(inputs)`, as opposed to `model.call()`.) isinstancekerasmodels Sequential ValueErrorrr r rraise_model_input_errorVs  r"csn|durtjtjjjrjj}|r|}i}nt\}}|dur&ttjfdd}|j |i|S)aTrace the model call to create a tf.function for exporting a Keras model. Args: model: A Keras model. input_signature: optional, a list of tf.TensorSpec objects specifying the inputs to the model. Returns: A tf.function wrapping the model's call function with input signatures set. Raises: ValueError: if input signature cannot be inferred from the model. Ncsjjdd||dd\}}tjddddd|i|}Wdn1s+wYj}|durBddlm}||}t j |}d d t ||DS) z._wrapped_model..) _call_spec set_arg_valuer call_contextenter output_nameskeras.src.enginer(create_pseudo_output_namestfnestflattenzip)argskwargsoutputsr.r(rr r_wrapped_models      z(trace_model_call.._wrapped_model) rcallr1 __internal__functionFunctioninput_signaturerr"get_concrete_function)rr= model_args model_kwargsr8r rrtrace_model_callms rATc Cs ddlm}ddlm}d|jji}z||d<Wnty0}z |r&|WYd}~nd}~wwtt |t |d}|j r|rt |j t jrOtd|S|jr|jd d }|d dt||d <t |j |jrotd tj|j j|j d} | |d d<|S)z3Returns a dictionary containing the model metadata.r) __version__) optimizer_v2 class_nameconfigN) keras_versionr model_configa<TensorFlow optimizers do not make it possible to access optimizer attributes or optimizer state after instantiation. As a result, we cannot save the optimizer as part of the model save file. You will have to compile your model again after loading it. Prefer using a Keras optimizer instead (see keras.io/optimizers).F) user_metrics optimizertraining_configzOptimizers loaded from a SavedModel cannot be saved. If you are calling `model.save` or `tf.keras.models.save_model`, please set the `include_optimizer` option to `False`. For `tf.saved_model.save`, delete the optimizer from the model.)rDrEoptimizer_config) keras.srcrBkeras.src.optimizers.legacyrC __class____name__ get_configNotImplementedErrordictstrrrIrr TFOptimizerloggingwarning_compile_was_called_get_compile_argspop_serialize_nested_configRestoredOptimizerrutilsget_registered_name) rinclude_optimizerrequire_configrFrCrGemetadatarJrKr r rmodel_metadatasN     "    rbcCs|s tj|r t|SdS)z3Returns whether the filepath should be overwritten.T)ospathisfiler )filepath overwriter r rshould_overwritesrhc Cs|duri}tj|T|d}t|}d}|dd}|dur(ttj|}d}|dd}|dur9tt|}d}|dd} | durJtt| }t |drS|dnd} |d} Wdn1scwYt ||||| | dS) z4Return model.compile arguments from training config.NrKlossmetricsweighted_metricssample_weight_mode loss_weights)rIrirjrkrmrl) rr\CustomObjectScoper deserializeget_deserialize_nested_configr_deserialize_metrichasattrrR) rJcustom_objectsrKrIri loss_configrjmetrics_configrkweighted_metrics_configrlrmr r r!compile_args_from_training_configsF       !rxcstdd}|dur dS||r|St|tr"fdd|DSt|ttfr2fdd|DStd|d ) z=Deserializes arbitrary Keras `config` using `deserialize_fn`.cSs(t|tr d|vr dSt|trdSdS)NrDTF)rrRrSobjr r r_is_single_objects  z5_deserialize_nested_config.._is_single_objectNcsi|] \}}|t|qSr rq)rkvdeserialize_fnr rr s z._deserialize_nested_config..csg|]}t|qSr r|)rrzrr r %s z._deserialize_nested_config..zrSaved configuration not understood. Configuration should be a dictionary, string, tuple or list. Received: config=.)rrRitemstuplelistr!)rrEr{r rrrqs$   rqcCsdd}tj||S)z/Serialized a nested structure of Keras objects.cSst|r t|S|SN)callablerserialize_keras_objectryr r r _serialize_fn2s z/_serialize_nested_config.._serialize_fn)r1r2 map_structure)rErr r rrZ/srZcCs"ddlm}|dvr |S||S)z7Deserialize metrics, leaving special strings untouched.r)rj)accuracyacc crossentropyce)rLrjro) metric_configmetrics_moduler r rrr:s  rrcs`dddd}tj|}tfdd|Do$tfdd|D }|r.tj||}|S)z5Enforces that either all specs have names or none do.cSs|dup t|do |jduSNr )rsr specr r r _has_nameIsz-_enforce_names_consistency.._has_namecSst|}t|dr d|_|Sr)copydeepcopyrs_namerr r r _clear_nameLs  z/_enforce_names_consistency.._clear_namec3s|]}|VqdSrr )rsrr r Ssz-_enforce_names_consistency..)r1r2r3anyallr)specsr flat_specsname_inconsistencyr rrrFs " rcCspt|s4|jdur6z|jjs|j|j|jjs&|j|j|jWdSWdStdYdSdSdS)NzCompiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.) ris_v1_layer_or_modelr7 compiled_lossbuiltbuildcompiled_metricsrUrVrr r rtry_build_compiled_arguments\s  rcCs|dp|dp|dS)Nz.h5z.kerasz.hdf5)endswith)rfr r ris_hdf5_filepathns r)Fr)TT)'__doc__rrctensorflow.compat.v2compatv2r1rLsrcrrrrr/rkeras.src.optimizersrkeras.src.saving.legacyrkeras.src.utilsrkeras.src.utils.io_utilsr tensorflow.python.platformr rUrrr"rArbrhrxrqrZrrrrrr r r rs6             48 0