o Ne1@sdZddlZddlZddlZddlmmZddl m Z ddl m Z e eeefZddZddZd d Zdd d Z dd dZdS)z6Object config serialization and deserialization logic.N)object_registration) tf_exportcCs|dur|St|tr |St|ttfrdd|DSt|tr$t|St|tr3dd|didSt|tj r=| St|tj rQd| |jjd dSt|jtjkrot|tjrkd | |jjd dS|St|tjrx|jStj|jd d }|durt|tjr|j}n|jj}|jj}|d krd}n't|tjrt|}nt|j}n|d}d|dd}|d}d}||t ||dS)aRetrieve the config dict by serializing the Keras object. `serialize_keras_object()` serializes a Keras object to a python dictionary that represents the object, and is a reciprocal function of `deserialize_keras_object()`. See `deserialize_keras_object()` for more information about the config format. Args: obj: the Keras object to serialize. Returns: A python dict that represents the object. The python dict can be deserialized via `deserialize_keras_object()`. NcSsg|]}t|qSserialize_keras_object.0xrr[D:\Projects\ConvertPro\env\Lib\site-packages\keras/saving/experimental/serialization_lib.py 4sz*serialize_keras_object.. __bytes__valueutf-8) class_nameconfig __tensor__)r dtype __numpy__keras)Zapi_namebuiltins.)modulerrregistered_name)! isinstance PLAIN_TYPESlisttupledictserialize_dictbytesdecodetfZ TensorShapeas_listZTensornumpytolistrnametype __module__np__name__ZndarrayitemZDTyperZget_canonical_name_for_symbol __class__types FunctionTyperZget_registered_namesplitjoin_get_class_or_fn_config)objZkeras_api_namerrrpartsrrr rsl             rcCst|tjrt|ddkrtd||jSt|dr3|}t|ts/td|d|t |Std|dt |d ) z1Return the object's config depending on its type.r*zz`lambda` objects cannot be serialized. Make sure there are no `lambda` objects being returned by a `get_config()` method. Received the following: get_configzThe `get_config()` method of z$ should return a dict. It returned: zCannot serialize object z of type zG. To be serializable, a class must implement the `get_config()` method.) rr-r.getattr TypeErrorr*hasattrr4rrr')r2rrrr r1ys(   r1cCsdd|DS)NcSsi|] \}}|t|qSrrrkeyr rrr sz"serialize_dict..)items)r2rrr rsrcspi|dur dSt|tr!t|tr|dur|S|St|ttfr1fdd|DSt|ts=td|d|vsEd|vrPfdd|DS|d}|d}|d krgt j |d |d d S|d krvt j |d |d d S|ddkr|d  dS|dd}|d|}|dkr|}t|||d|dSt|||d|d}t|dstd|d|t ||WdS1swYdS)a Retrieve the object by deserializing the config dict. The config dict is a Python dictionary that consists of a set of key-value pairs, and represents a Keras object, such as an `Optimizer`, `Layer`, `Metrics`, etc. The saving and loading library uses the following keys to record information of a Keras object: - `class_name`: String. This is the name of the class, as exactly defined in the source code, such as "LossesContainer". - `config`: Dict. Library-defined or user-defined key-value pairs that store the configuration of the object, as obtained by `object.get_config()`. - `module`: String. The path of the python module, such as "keras.engine.compile_utils". Built-in Keras classes expect to have prefix `keras`. - `registered_name`: String. The key the class is registered under via `keras.utils.register_keras_serializable(package, name)` API. The key has the format of '{package}>{name}', where `package` and `name` are the arguments passed to `register_keras_serializable()`. If `name` is not provided, it defaults to the class name. If `registered_name` successfully resolves to a class (that was registered), the `class_name` and `config` values in the dict will not be used. `registered_name` is only used for non-built-in classes. For example, the following dictionary represents the built-in Adam optimizer with the relevant config: ```python dict_structure = { "class_name": "Adam", "config": { "amsgrad": false, "beta_1": 0.8999999761581421, "beta_2": 0.9990000128746033, "decay": 0.0, "epsilon": 1e-07, "learning_rate": 0.0010000000474974513, "name": "Adam" }, "module": "keras.optimizers", "registered_name": None } # Returns an `Adam` instance identical to the original one. deserialize_keras_object(dict_structure) ``` If the class does not have an exported Keras namespace, the library tracks it by its `module` and `class_name`. For example: ```python dict_structure = { "class_name": "LossesContainer", "config": { "losses": [...], "total_loss_mean": {...}, }, "module": "keras.engine.compile_utils", "registered_name": "LossesContainer" } # Returns a `LossesContainer` instance identical to the original one. deserialize_keras_object(dict_structure) ``` And the following dictionary represents a user-customized `MeanSquaredError` loss: ```python @keras.utils.register_keras_serializable(package='my_package') class ModifiedMeanSquaredError(keras.losses.MeanSquaredError): ... dict_structure = { "class_name": "ModifiedMeanSquaredError", "config": { "fn": "mean_squared_error", "name": "mean_squared_error", "reduction": "auto" }, "registered_name": "my_package>ModifiedMeanSquaredError" } # Returns the `ModifiedMeanSquaredError` object deserialize_keras_object(dict_structure) ``` Args: config_dict: the python dict structure to deserialize the Keras object from. Returns: The object described by the `config` dictionary. Ncsg|]}t|dqScustom_objectsdeserialize_keras_objectrr=rr r s z,deserialize_keras_object..zCould not parse config: rrcsi|] \}}|t|dqSr<r?r8r=rr r: s z,deserialize_keras_object..rr r)rrr rrrfunction)obj_type full_configr>class from_configz&Unable to reconstruct an instance of 'zM' because the class is missing a `from_config()` method. Full object config: )rrstrgetrrrr6r;r"Zconstantr)arrayencode_retrieve_class_or_fnr7rZcustom_object_scoperE)rr>rZ inner_configrrfn_nameclsrr=r r@sl^          $r@c Cstj||d}|dur |S|rT|dks|dr't|d|}|dur'|Szt|}WntyEtd|d|d|d|wt | |d}|durT|Std |d|d |) Nr=rzkeras.rzCould not deserialize z 'z' because its parent module z) cannot be imported. Full object config: zCould not locate zo'. Make sure custom classes are decorated with `@keras.utils.register_keras_serializable`. Full object config: ) rZget_registered_object startswithrZget_symbol_from_name importlib import_moduleModuleNotFoundErrorr6varsrG) r&rrrBrCr>Z custom_objr2modrrr rJAs: rJ)N)__doc__rNr-r$r)Ztensorflow.compat.v2compatv2r"Z keras.savingrZtensorflow.python.utilrrFintfloatboolrrr1rr@rJrrrr s   Z )