BVh+dZddlZddlZddlmZddlmZddlmZddlmZddlm Z ddlm Z dd lm Z dd l m Zdd lmZdd lmZdd lmZdZdZGddej*ZGddej*Z ddZy)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_hookc tj}|jtjj}t |dk(r|dSt |dkDr t dtjdgtjtjdtjjtjjg}|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_stepscounters U/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/training/evaluation.py_get_or_create_eval_stepr$!s    !%##CMM$;$;<*_ a= : G HH))ll..0]]22CMM4K4KL NG Nct|trt|j}t j |5t jtjcdddS#1swYyxYw)aGets the eval step `Tensor` value after running `update_ops`. Args: update_ops: A list of `Tensors` or a dictionary of names to `Tensors`, which are run before reading the eval step value. Returns: A `Tensor` representing the value for the evaluation step. N) isinstancedictlistvaluesrcontrol_dependenciesridentityr$ read_value) update_opss r#_get_latest_eval_step_valuer/<sc D!j'')*J  +G   68CCE FGGGs +A44A=c6eZdZdZd dZdZdZdZdZdZ y) _MultiStepStopAfterNEvalsHookGRun hook used by the evaluation routines to run the `eval_ops` N times.c.||_d|_||_y)zConstructs the run hook. Args: num_evals: The number of evaluations to run for. if set to None, will iterate the dataset until all inputs are exhausted. steps_per_run: Number of steps executed per run call. N) _num_evals_evals_completed_steps_per_run_initial_value)self num_evals steps_per_runs r#__init__z&_MultiStepStopAfterNEvalsHook.__init__Ps DO D(5D%r%c||_yNr5r7updated_eval_steps r#_set_evals_completed_tensorz9_MultiStepStopAfterNEvalsHook._set_evals_completed_tensor\ -Dr%c6tj|_yr<)r $get_or_create_steps_per_run_variable_steps_per_run_variable)r7s r#beginz#_MultiStepStopAfterNEvalsHook.begin_sDDF  r%c|j |j}n t|j|j}|jj ||y)Nsession)r4r6minrDload)r7rHcoordstepss r#after_create_sessionz2_MultiStepStopAfterNEvalsHook.after_create_sessioncsF //e$33T__Ee  %%eW%=r%cDtjd|jiSNevals_completedr SessionRunArgsr5r7 run_contexts r# before_runz(_MultiStepStopAfterNEvalsHook.before_runk#  * * D112 44r%c|jd}|j |j}n#t|j|z |j}|jj ||j |jtjd|n!tjd||j|j!||jk\r|jyyy)NrPrGEvaluation [%d]Evaluation [%d/%d]) resultsr4r6rIrDrJrHlogginginfo request_stop)r7rT run_valuesrPrLs r# after_runz'_MultiStepStopAfterNEvalsHook.after_runos (():;O //e$//O3335e  %%e[5H5H%I  ll$o6 ll'$//J "$//'I (J"r%N)r) __name__ __module__ __qualname____doc__r:r@rErMrUr_r%r#r1r1Ms$O 6.G>4!r%r1c*eZdZdZddZdZdZdZy)_StopAfterNEvalsHookr2c||_d|_||_||dkrd|_ytj|dz |_y)zConstructs the run hook. Args: num_evals: The number of evaluations to run for. if set to None, will iterate the dataset until all inputs are exhausted. log_progress: Whether to log evaluation progress, defaults to True. Nrg$@)r4r5 _log_progressmathfloor_log_frequency)r7r8 log_progresss r#r:z_StopAfterNEvalsHook.__init__sF DO D%D!*!2i"n1D::i#o6 r%c||_yr<r=r>s r#r@z0_StopAfterNEvalsHook._set_evals_completed_tensorrAr%cDtjd|jiSrOrQrSs r#rUz_StopAfterNEvalsHook.before_runrVr%c^|jd}|jre|jtjd|nB||j zdk(s|j|k(r!tjd||j|j!||jk\r|j yyy)NrPrXrrY)rZrir4r[r\rlr])r7rTr^rPs r#r_z_StopAfterNEvalsHook.after_runs (():;O    &8 t22 2q 8 __ / ,,+_doo N "$//'I (J"r%N)T)r`rarbrcr:r@rUr_rdr%r#rfrfsO8 .4 !r%rfc t} t|xsg}|td|DrLtj} t j | tj| | jd} nt j | dd} t|tr| |d<n*t|ttfrt|| gz}n|| g}t|} |D]*} t| ttfs| j!| ,t#j$dt'j(d t'j*zt'j&}t-j.|||| }tj0||}|j3|t-j4|| 5}|3|j7s#|j9|||j7s#dddt#j$d j;t'j&|z t#j$d t'j(dt'j*z|j<S#1swYxYw)ay Evaluates the model at the given checkpoint path. During a single evaluation, the `eval_ops` is run until the session is interrupted or requested to finish. 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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