o ci@sddlZddlZddlZddlmZddlmZmZddlZ ddlm Z ddl m Z ddl mZer8ddlmZedd Gd d d e ZdS) N)Path) TYPE_CHECKINGOptional)keras)FrameworkCheckpoint) PublicAPI) Preprocessorbeta) stabilityc@seZdZdZdZedddejdedddfd d Z eddd e dedddfd d Z eddde dedddfddZ de jjfddZdS)TensorflowCheckpointzKA :py:class:`~ray.train.Checkpoint` with TensorFlow-specific functionality._model_filenameN) preprocessormodelr rreturncCsLt}d}|t||||}|r||||j|i|S)auCreate a :py:class:`~ray.train.Checkpoint` that stores a Keras model. The checkpoint created with this method needs to be paired with `model` when used. Args: model: The Keras model, whose weights are stored in the checkpoint. preprocessor: A fitted preprocessor to be applied before inference. Returns: A :py:class:`TensorflowCheckpoint` containing the specified model. Examples: .. testcode:: from ray.train.tensorflow import TensorflowCheckpoint import tensorflow as tf model = tf.keras.applications.resnet.ResNet101() checkpoint = TensorflowCheckpoint.from_model(model) .. testoutput:: :options: +MOCK :hide: ... # Model may or may not be downloaded z model.keras) tempfilemkdtempsaveras_posixfrom_directoryset_preprocessorupdate_metadataMODEL_FILENAME_KEY)clsrr tempdirfilename checkpointr^/home/ubuntu/.local/lib/python3.10/site-packages/ray/train/tensorflow/tensorflow_checkpoint.py from_models$  zTensorflowCheckpoint.from_model file_pathcCsxtj|r |dstdt}tj|}t|| }t ||| |}|r2| |||j|i|S)aTCreate a :py:class:`~ray.train.Checkpoint` that stores a Keras model from H5 format. The checkpoint generated by this method contains all the information needed. Thus no `model` is needed to be supplied when using this checkpoint. Args: file_path: The path to the .h5 file to load model from. This is the same path that is used for ``model.save(path)``. preprocessor: A fitted preprocessor to be applied before inference. Returns: A :py:class:`TensorflowCheckpoint` converted from h5 format. z.h5zAPlease supply a h5 file path to `TensorflowCheckpoint.from_h5()`.)ospathisfileendswith ValueErrorrrbasenamerrshutilcopyrrrr)rrr rrnew_checkpoint_filerrrrfrom_h5Es    zTensorflowCheckpoint.from_h5dir_pathcCs^tj|s tdt}t|t||| |}|r%| || |j di|S)acCreate a :py:class:`~ray.train.Checkpoint` that stores a Keras model from SavedModel format. The checkpoint generated by this method contains all the information needed. Thus no `model` is needed to be supplied when using this checkpoint. Args: dir_path: The directory containing the saved model. This is the same directory as used by ``model.save(dir_path)``. preprocessor: A fitted preprocessor to be applied before inference. Returns: A :py:class:`TensorflowCheckpoint` converted from SavedModel format. zDPlease supply a directory to `TensorflowCheckpoint.from_saved_model`.) r r!isdirr$rrrmdirr&copytreerrrr)rr*r rrrrrfrom_saved_modelgs     z%TensorflowCheckpoint.from_saved_modelcCsj|}|j|vr td||j}|}t||}tj|WdS1s.wYdS)zRetrieve the model stored in this checkpoint. Returns: The Tensorflow Keras model stored in the checkpoint. z`TensorflowCheckpoint` cannot retrieve the model if you override the checkpoint metadata. Please use `Checkpoint.update_metadata` instead.N) get_metadatarr$ as_directoryrrrmodels load_model)selfmetadatamodel_filenamecheckpoint_dir model_pathrrr get_models    $zTensorflowCheckpoint.get_model)__name__ __module__ __qualname____doc__r classmethodrModelrrstrr)r/tfr9rrrrr sB - ! "r )r r&rpathlibrtypingrr tensorflowrAr(ray.train._internal.framework_checkpointrray.util.annotationsrray.data.preprocessorrr rrrrs