o ?e%@srdZddlZddlZddlmZddlmZddlmZ ddl m Z ddl m Z ddlmZGd d d ejZdS) z6Test utilities for tf.data benchmarking functionality.N)session) dataset_ops)options)nest)context)testc@sTeZdZdZddZ dddZdd d Z   dd d Z    dddZdS)DatasetBenchmarkBasez"Base class for dataset benchmarks.c Csng}ts tdt|D]#}|rt|}t|t|}t}t|t}|||qt |S)aJBenchmark the iterable in eager mode. Runs the iterable `iters` times. In each iteration, the benchmark measures the time it takes to go execute the iterable. Args: iterable: The tf op or tf.data Dataset to benchmark. iters: Number of times to repeat the timing. warmup: If true, warms up the session caches by running an untimed run. Returns: A float, representing the median time (with respect to `iters`) it takes for the iterable to be executed `iters` num of times. Raises: RuntimeError: When executed in graph mode. z7Eager mode benchmarking is not supported in graph mode.) rexecuting_eagerly RuntimeErrorrangeiternexttimeappendnpmedian) selfiterableiterswarmupdeltas_iteratorstartendrq/home/www/facesmatcher.com/pyenv/lib/python3.10/site-packages/tensorflow/python/data/benchmarks/benchmark_base.py_run_eager_benchmarks  z)DatasetBenchmarkBase._run_eager_benchmarkNc Csg}tr tdt|D]A}tj|d*}|r%|r |||||r,||t} ||t} Wdn1sCwY|| | qt |S)a.Benchmarks the iterable in graph mode. Runs the iterable `iters` times. In each iteration, the benchmark measures the time it takes to go execute the iterable. Args: iterable: The tf op or tf.data Dataset to benchmark. iters: Number of times to repeat the timing. warmup: If true, warms up the session caches by running an untimed run. session_config: A ConfigProto protocol buffer with configuration options for the session. Applicable only for benchmarking in graph mode. initializer: The initializer op required to initialize the iterable. Returns: A float, representing the median time (with respect to `iters`) it takes for the iterable to be executed `iters` num of times. Raises: RuntimeError: When executed in eager mode. z7Graph mode benchmarking is not supported in eager mode.)configN) rr r r rSessionrunrrrr) rrrrsession_config initializerrrsessrrrrr_run_graph_benchmarkCs&       z)DatasetBenchmarkBase._run_graph_benchmarkTcCs*tr |j|||dS|j||||dS)aBenchmarks the op. Runs the op `iters` times. In each iteration, the benchmark measures the time it takes to go execute the op. Args: op: The tf op to benchmark. iters: Number of times to repeat the timing. warmup: If true, warms up the session caches by running an untimed run. session_config: A ConfigProto protocol buffer with configuration options for the session. Applicable only for benchmarking in graph mode. Returns: A float, representing the per-execution wall time of the op in seconds. This is the median time (with respect to `iters`) it takes for the op to be executed `iters` num of times. rrr)rrrr!)rr rr$)roprrr!rrrrun_op_benchmarkss z%DatasetBenchmarkBase.run_op_benchmarkFc Cst}||j_||}||d}tr&|j|||d}|t |St |} | } t | dj} |j| |||| jd}|t |S)aBenchmarks the dataset. Runs the dataset `iters` times. In each iteration, the benchmark measures the time it takes to go through `num_elements` elements of the dataset. Args: dataset: Dataset to benchmark. num_elements: Number of dataset elements to iterate through each benchmark iteration. iters: Number of times to repeat the timing. warmup: If true, warms up the session caches by running an untimed run. apply_default_optimizations: Determines whether default optimizations should be applied. session_config: A ConfigProto protocol buffer with configuration options for the session. Applicable only for benchmarking in graph mode. Returns: A float, representing the per-element wall time of the dataset in seconds. This is the median time (with respect to `iters`) it takes for the dataset to go through `num_elements` elements, divided by `num_elements.` r%r&r)rrrr!r") options_libOptionsZexperimental_optimizationapply_default_optimizationsZ with_optionsskiprr rfloatrZmake_initializable_iteratorZget_nextrflattenr'r$r") rdataset num_elementsrrr+r!rZmedian_durationrZ next_elementr'rrr run_benchmarks*    z"DatasetBenchmarkBase.run_benchmarkc Csn|j||||||d} |duri}trd|}d|d<n d|}d|d<||d<|j| |||d | S) aBenchmarks the dataset and reports the stats. Runs the dataset `iters` times. In each iteration, the benchmark measures the time it takes to go through `num_elements` elements of the dataset. This is followed by logging/printing the benchmark stats. Args: dataset: Dataset to benchmark. num_elements: Number of dataset elements to iterate through each benchmark iteration. name: Name of the benchmark. iters: Number of times to repeat the timing. extras: A dict which maps string keys to additional benchmark info. warmup: If true, warms up the session caches by running an untimed run. apply_default_optimizations: Determines whether default optimizations should be applied. session_config: A ConfigProto protocol buffer with configuration options for the session. Applicable only for benchmarking in graph mode. Returns: A float, representing the per-element wall time of the dataset in seconds. This is the median time (with respect to `iters`) it takes for the dataset to go through `num_elements` elements, divided by `num_elements.` )r/r0rrr+r!Nz{}.eagereagerimplementationz{}.graphgraphr0) wall_timernameextras)r1rr formatZreport_benchmark) rr/r0r7rr8rr+r!r6rrrrun_and_report_benchmarks(!   z-DatasetBenchmarkBase.run_and_report_benchmark)N)r%TN)r%TFN)r2NTFN) __name__ __module__ __qualname____doc__rr$r(r1r:rrrrrs") 0 ?r)r>rnumpyrZtensorflow.python.clientrZtensorflow.python.data.opsrrr)Ztensorflow.python.data.utilrZtensorflow.python.eagerrZtensorflow.python.platformrZ Benchmarkrrrrrs