o biy@sfddlmZddlmZddlmZddlmZddlmZddl m Z edGdd d ej Z d S) )backend)layers) keras_export)_list_variables_used_by_fns)serialization_lib) tensorflowzkeras.layers.TFSMLayercsXeZdZdZ     dfdd ZddZdd d Zfd d ZedddZ Z S) TFSMLayeragReload a Keras model/layer that was saved via SavedModel / ExportArchive. Arguments: filepath: `str` or `pathlib.Path` object. The path to the SavedModel. call_endpoint: Name of the endpoint to use as the `call()` method of the reloaded layer. If the SavedModel was created via `model.export()`, then the default endpoint name is `'serve'`. In other cases it may be named `'serving_default'`. Example: ```python model.export("path/to/artifact") reloaded_layer = TFSMLayer("path/to/artifact") outputs = reloaded_layer(inputs) ``` The reloaded object can be used like a regular Keras layer, and supports training/fine-tuning of its trainable weights. Note that the reloaded object retains none of the internal structure or custom methods of the original object -- it's a brand new layer created around the saved function. **Limitations:** * Only call endpoints with a single `inputs` tensor argument (which may optionally be a dict/tuple/list of tensors) are supported. For endpoints with multiple separate input tensor arguments, consider subclassing `TFSMLayer` and implementing a `call()` method with a custom signature. * If you need training-time behavior to differ from inference-time behavior (i.e. if you need the reloaded object to support a `training=True` argument in `__call__()`), make sure that the training-time call function is saved as a standalone endpoint in the artifact, and provide its name to the `TFSMLayer` via the `call_training_endpoint` argument. serveNTc sRtdkr tdtj|||dtj||_||_||_ ||_ t |j|r1t |j||_ n||jjvr?|jj||_ ntd|dt|jj|r}t |j|r_t |j||_n||jjvrm|jj||_ntd|dt|jj|j g}|r||jt|\}} |D]} || q| D]} || q|dS)NrzFThe TFSMLayer is only currently supported with the TensorFlow backend.) trainablenamedtypezThe endpoint 'z' is neither an attribute of the reloaded SavedModel, nor an entry in the `signatures` field of the reloaded SavedModel. Select another endpoint via the `call_endpoint` argument. Available endpoints for this SavedModel: z' is neither an attribute of the reloaded SavedModel, nor an entry in the `signatures` field of the reloaded SavedModel. Available endpoints for this SavedModel: )rNotImplementedErrorsuper__init__tf saved_modelload _reloaded_objfilepath call_endpointcall_training_endpointhasattrgetattrcall_endpoint_fn signatures ValueErrorlistkeyscall_training_endpoint_fnappendr_add_existing_weight_build_at_init) selfrrrr r r all_fnstvsntvsv __class__O/home/ubuntu/.local/lib/python3.10/site-packages/keras/src/export/tfsm_layer.pyr1sR           zTFSMLayer.__init__c Cs2tj||j|j|j|jddd}||dS)zTracks an existing weight./_) initializerr r shaper N)rVariabler r r.r replace_track_variable)r"weightvariabler)r)r*r {s zTFSMLayer._add_existing_weightFcKs.|r|jr|j|fi|S|j|fi|S)N)rrr)r"inputstrainingkwargsr)r)r*callszTFSMLayer.callcs(t}|j|j|jd}i||S)N)rrr)r get_configrrr)r" base_configconfigr'r)r*r8s  zTFSMLayer.get_configcCs2|dur|nt}|durtd|di|S)a/Creates a TFSMLayer from its config. Args: config: A Python dictionary, typically the output of `get_config`. custom_objects: Optional dictionary mapping names to custom objects. safe_mode: Boolean, whether to disallow loading TFSMLayer. When `safe_mode=True`, loading is disallowed because TFSMLayer loads external SavedModels that may contain attacker-controlled executable graph code. Defaults to `True`. Returns: A TFSMLayer instance. NFahRequested the deserialization of a `TFSMLayer`, which loads an external SavedModel. This carries a potential risk of arbitrary code execution and thus it is disallowed by default. If you trust the source of the artifact, you can override this error by passing `safe_mode=False` to the loading function, or calling `keras.config.enable_unsafe_deserialization().r))r in_safe_moder)clsr:custom_objects safe_modeeffective_safe_moder)r)r* from_configs zTFSMLayer.from_config)r NTNN)F)NN) __name__ __module__ __qualname____doc__rr r7r8 classmethodr@ __classcell__r)r)r'r*r s)J  rN) keras.srcrrkeras.src.api_exportrkeras.src.export.saved_modelrkeras.src.savingrkeras.src.utils.module_utilsrrLayerrr)r)r)r*s