o 2h+@sdZddlZddlZddlmZddlmZddlmZddlmZddlm Z ddlm Z dd lm Z dd l m Zdd lmZdd lmZdd lmZddZddZGdddejZGdddejZ        dddZdS)z?Contains functions for evaluation and summarization of metrics.N)dtypes)ops) array_ops)init_ops)math_ops) state_ops)variable_scope) tf_logging)basic_session_run_hooks)monitored_session)session_run_hookcCsjt}|tjj}t|dkr|dSt|dkrtdtjdgt j t dtjj tjjgd}|S)zGets or creates the eval step `Tensor`. Returns: A `Tensor` representing a counter for the evaluation step. Raises: ValueError: If multiple `Tensors` have been added to the `tf.GraphKeys.EVAL_STEP` collection. rz0Multiple tensors added to tf.GraphKeys.EVAL_STEP eval_stepF)shapedtype initializer trainable collections)rget_default_graphget_collection GraphKeys EVAL_STEPlen ValueErrorr get_variablerint64rzeros_initializerLOCAL_VARIABLES)graph eval_stepscounterr!`/var/www/html/chatgem/venv/lib/python3.10/site-packages/tensorflow/python/training/evaluation.py_get_or_create_eval_step!s   r#cCsTt|tr t|}t|tt WdS1s#wYdS)aGets the eval step `Tensor` value after running `update_ops`. Args: update_ops: A list of `Tensors` or a dictionary of names to `Tensors`, which are run before reading the eval step value. Returns: A `Tensor` representing the value for the evaluation step. 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This is typically requested via a `tf.contrib.training.StopAfterNEvalsHook` which results in `eval_ops` running the requested number of times. Optionally, a user can pass in `final_ops`, a single `Tensor`, a list of `Tensors` or a dictionary from names to `Tensors`. The `final_ops` is evaluated a single time after `eval_ops` has finished running and the fetched values of `final_ops` are returned. If `final_ops` is left as `None`, then `None` is returned. One may also consider using a `tf.contrib.training.SummaryAtEndHook` to record summaries after the `eval_ops` have run. If `eval_ops` is `None`, the summaries run immediately after the model checkpoint has been restored. Note that `evaluate_once` creates a local variable used to track the number of evaluations run via `tf.contrib.training.get_or_create_eval_step`. Consequently, if a custom local init op is provided via a `scaffold`, the caller should ensure that the local init op also initializes the eval step. Args: checkpoint_path: The path to a checkpoint to use for evaluation. master: The BNS address of the TensorFlow master. scaffold: An tf.compat.v1.train.Scaffold instance for initializing variables and restoring variables. Note that `scaffold.init_fn` is used by the function to restore the checkpoint. If you supply a custom init_fn, then it must also take care of restoring the model from its checkpoint. eval_ops: A single `Tensor`, a list of `Tensors` or a dictionary of names to `Tensors`, which is run until the session is requested to stop, commonly done by a `tf.contrib.training.StopAfterNEvalsHook`. feed_dict: The feed dictionary to use when executing the `eval_ops`. final_ops: A single `Tensor`, a list of `Tensors` or a dictionary of names to `Tensors`. final_ops_feed_dict: A feed dictionary to use when evaluating `final_ops`. hooks: List of `tf.estimator.SessionRunHook` callbacks which are run inside the evaluation loop. config: An instance of `tf.compat.v1.ConfigProto` that will be used to configure the `Session`. If left as `None`, the default will be used. Returns: The fetched values of `final_ops` or `None` if `final_ops` is `None`. 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