o ?e+@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. 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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. 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