o ?elP@sdZddlZddlZddlmZddlmZddlmZddlm Z ddlm Z ddl m Z dd l mZdd lmZdd lmZdd lmZdd lmZddlmZddlmZddlmZddlmZddlmZddlm Z ddl!m"Z"dZ#ddZ$e"dgdddZ%d6ddZ&ej'fddZ(d d!Z)d"d#Z*d6d$d%Z+d6d&d'Z,e"gd(de -d)d*d+Z.e"d,gdd-d.Z/e"d/d0gde 0dd1d6d2d3Z1Gd4d5d5e2Z3dS)7zNLoader implementation for SavedModel with hermetic, language-neutral exports. N)message) text_format)graph_debug_info_pb2)meta_graph_pb2)saved_model_pb2)ops)file_io) variables) tf_logging) constants) path_helpers)signature_def_utils) utils_impl)metrics)saver)compat) deprecation) tf_exportloaderc Cst|}tt|tj}t}t |rRt |d-}z | | Wnt jy?}z td|d|dd}~wwWd||fS1sMwY||fS)a`Reads the savedmodel as well as the graph debug info. Args: export_dir: Directory containing the SavedModel and GraphDebugInfo files. Returns: `SavedModel` and `GraphDebugInfo` protocol buffers. Raises: IOError: If the saved model file does not exist, or cannot be successfully parsed. Missing graph debug info file is fine. rbCannot parse file : .N)parse_saved_modelrjoinr Z get_debug_dirr ZDEBUG_INFO_FILENAME_PBrZGraphDebugInfo file_existsFileIOParseFromStringreadr DecodeErrorIOError) export_dir saved_modelZdebug_info_pathZ debug_infoZ debug_fileer$j/home/www/facesmatcher.com/pyenv/lib/python3.10/site-packages/tensorflow/python/saved_model/loader_impl.py!parse_saved_model_with_debug_info.s&   r&z*__internal__.saved_model.parse_saved_model)v1c Csttt|ttj}ttt|ttj}ttt|ttj}t }t |rwt |d }| }Wdn1sNwYz||W|Stjyv}ztd|dt|d|d}~wwt |rt |d }| }Wdn1swYz t|d|W|Stjy}ztd|dt|d|d}~wwtd|tjjdtjd tjd ) a9Reads the savedmodel.pb or savedmodel.pbtxt file containing `SavedModel`. Args: export_dir: String or Pathlike, path to the directory containing the SavedModel file. Returns: A `SavedModel` protocol buffer. Raises: IOError: If the file does not exist, or cannot be successfully parsed. rNrrrzutf-8z#SavedModel file does not exist at: {|})rrras_bytesZ path_to_strr SAVED_MODEL_FILENAME_PBTXTSAVED_MODEL_FILENAME_PBZSAVED_MODEL_FILENAME_CPBrZ SavedModelrrrrrrr strrParsedecodeZ ParseErrorospathsep)r!Z path_to_pbtxtZ path_to_pbZ path_to_cpbr"fZ file_contentr#r$r$r%rKsT        rc Cs|j}i}g}|jr|j}ntj|vr-|tjjj}|D]}t}||| |qt t |t tj} |D]}|jj} |rJd|| f} t t | t |j|| <q<|S)aGets the asset tensors, if defined in the meta graph def to load. Args: export_dir: Directory where the SavedModel is located. meta_graph_def_to_load: The meta graph def from the SavedModel to be loaded. import_scope: Optional `string` -- if specified, prepend this followed by '/' to all returned asset tensor names. Returns: A dictionary of asset tensors, keyed by the name of the asset tensor. The value in the map corresponds to the absolute path of the asset file. z%s/%s)collection_defZasset_file_defr Z ASSETS_KEYZany_listvaluerZ AssetFileDefZUnpackappendrrrr+ZASSETS_DIRECTORYZ tensor_infonamefilename) r!meta_graph_def_to_load import_scoper5Zasset_tensor_dictZ asset_protosZassets_any_protoZasset_any_protoZ asset_protoZassets_directoryZ tensor_namer$r$r%get_asset_tensors~s.      r<cCsV|j}d}||vr)||jj}t|dkr"tdt|d|dt|d}|S)aGets the main op tensor, if one exists. Args: meta_graph_def_to_load: The meta graph def from the SavedModel to be loaded. init_op_key: name of the collection to check; should be one of MAIN_OP_KEY or the deprecated LEGACY_INIT_OP_KEY Returns: The main op tensor, if it exists and `None` otherwise. Raises: RuntimeError: If the collection def corresponding to the main op key has other than exactly one tensor. Nz/Expected exactly one SavedModel init op. Found rrr)r5Z node_listr6len RuntimeErrorrZget_collection)r:Z init_op_keyr5init_opZ init_op_listr$r$r%_get_main_op_tensors   rAcCs t||SN)rA)meta_graph_defZop_keyr$r$r%_get_op_from_collections rDcCs"||jvrt|j|||SdS)z>Retrieve op stored in the imported meta graph's signature def.N)Z signature_defr Zload_op_from_signature_def)rCZop_signature_keyr;r$r$r%_get_op_from_signature_defs  rEcCs&t|tj|pt|tjpt|tjSrB)rEr ZINIT_OP_SIGNATURE_KEYrD MAIN_OP_KEYZLEGACY_INIT_OP_KEY)rCr;r$r$r% get_init_ops  rGcCs&t|tj|}|durt|tj}|SrB)rEr ZTRAIN_OP_SIGNATURE_KEYrDZ TRAIN_OP_KEY)rCr;Ztrain_opr$r$r% get_train_ops  rH) saved_model.contains_saved_modelz'saved_model.maybe_saved_model_directory.saved_model.loader.maybe_saved_model_directoryrJcCs0t|tj}t|tj}t|pt|S)a8Checks whether the provided export directory could contain a SavedModel. Note that the method does not load any data by itself. If the method returns `false`, the export directory definitely does not contain a SavedModel. If the method returns `true`, the export directory may contain a SavedModel but provides no guarantee that it can be loaded. Args: export_dir: Absolute string path to possible export location. For example, '/my/foo/model'. Returns: True if the export directory contains SavedModel files, False otherwise. )rrr r,r-r)r!Ztxt_pathZpb_pathr$r$r%maybe_saved_model_directorysrKrIcCst|tjr t|}t|S)a1Checks whether the provided export directory could contain a SavedModel. Note that the method does not load any data by itself. If the method returns `false`, the export directory definitely does not contain a SavedModel. If the method returns `true`, the export directory may contain a SavedModel but provides no guarantee that it can be loaded. Args: export_dir: Absolute path to possible export location. For example, '/my/foo/model'. Returns: True if the export directory contains SavedModel files, False otherwise. ) isinstancer1PathLikefspathrK)r!r$r$r%contains_saved_models  rOzsaved_model.loadzsaved_model.loader.loadz"Use `tf.saved_model.load` instead.cKst|}|j|||fi|S)ax Loads the model from a SavedModel as specified by tags. Args: sess: The TensorFlow session to restore the variables. tags: Set of string tags to identify the required MetaGraphDef. These should correspond to the tags used when saving the variables using the SavedModel `save()` API. export_dir: Directory in which the SavedModel protocol buffer and variables to be loaded are located. import_scope: Optional `string` -- if specified, prepend this string followed by '/' to all loaded tensor names. This scope is applied to tensor instances loaded into the passed session, but it is *not* written through to the static `MetaGraphDef` protocol buffer that is returned. **saver_kwargs: Optional keyword arguments passed through to Saver. Returns: The `MetaGraphDef` protocol buffer loaded in the provided session. This can be used to further extract signature-defs, collection-defs, etc. Raises: RuntimeError: MetaGraphDef associated with the tags cannot be found. @compatibility(TF2) `tf.compat.v1.saved_model.load` or `tf.compat.v1.saved_model.loader.load` is not compatible with eager execution. Please use `tf.saved_model.load` instead to load your model. You can refer to the [SavedModel guide] (https://www.tensorflow.org/guide/saved_model) for more information as well as "Importing SavedModels from TensorFlow 1.x" in the [`tf.saved_model.load`] (https://www.tensorflow.org/api_docs/python/tf/saved_model/load) docstring. #### How to Map Arguments | TF1 Arg Name | TF2 Arg Name | Note | | :-------------------- | :-------------- | :------------------------- | | `sess` | Not supported | - | | `tags` | `tags` | - | | `export_dir` | `export_dir` | - | | `import_scope` | Not supported | Name scopes are not needed. : : : By default, variables are : : : : associated with the loaded : : : : object and function names : : : : are deduped. : | `saver_kwargs` | Not supported | - | #### Before & After Usage Example Before: ``` with tf.compat.v1.Session(graph=tf.Graph()) as sess: tf.compat.v1.saved_model.loader.load(sess, ["foo-tag"], export_dir) ``` After: ``` model = tf.saved_model.load(export_dir, tags=["foo-tag"]) ``` @end_compatibility )SavedModelLoaderload)sesstagsr!r; saver_kwargsrr$r$r%rQsBrQc@sleZdZdZddZeddZeddZedd Zd d Z dd dZ dddZ dddZ dddZ d S)rPzr^ZHasFieldZ IncrementRead) rXrRrSr;rTZsaved_model_protor_rCr$r$r%rQs"       zSavedModelLoader.loadrB)__name__ __module__ __qualname____doc__rYpropertyr!r]r"rbrkrprrrQr$r$r$r%rPYs      rPrB)4rzr1reZgoogle.protobufrrZtensorflow.core.frameworkrZtensorflow.core.protobufrrZtensorflow.python.frameworkrZtensorflow.python.lib.iorZtensorflow.python.opsr Ztensorflow.python.platformr Ztensorflow.python.saved_modelr r r rrgZ0tensorflow.python.saved_model.pywrap_saved_modelrZtensorflow.python.trainingrriZtensorflow.python.utilrrZ tensorflow.python.util.tf_exportrrur&rr<rFrArDrErGrHZdeprecated_endpointsrKrO deprecatedrQobjectrPr$r$r$r%s\                     2,       B