def testLatencyStatsOptimizationV2(self):
aggregator = stats_aggregator.StatsAggregator()
dataset = dataset_ops.Dataset.from_tensors(1).apply(
optimization.assert_next([
"LatencyStats", "Map", "LatencyStats", "Prefetch",
"LatencyStats"
])).map(lambda x: x * x).prefetch(1)
options = dataset_ops.Options()
options.experimental_stats = stats_options.StatsOptions(aggregator)
dataset = dataset.with_options(options)
iterator = dataset.make_initializable_iterator()
get_next = iterator.get_next()
summary_t = aggregator.get_summary()
with self.cached_session() as sess:
sess.run(iterator.initializer)
self.assertEqual(1, sess.run(get_next))
with self.assertRaises(errors.OutOfRangeError):
sess.run(get_next)
summary_str = sess.run(summary_t)
self._assertSummaryHasCount(summary_str,
"record_latency_TensorDataset/_1", 1)
self._assertSummaryHasCount(summary_str,
"record_latency_MapDataset/_4", 1)
self._assertSummaryHasCount(summary_str,
"record_latency_PrefetchDataset/_6", 1)
def function_apply_options(dataset, aggregator, prefix="", counter_prefix=""):
options = dataset_ops.Options()
options.experimental_stats = stats_options.StatsOptions()
options.experimental_stats.aggregator = aggregator
options.experimental_stats.prefix = prefix
options.experimental_stats.counter_prefix = counter_prefix
options.experimental_stats.latency_all_edges = False
return dataset.with_options(options)
def testOptionsHaveDefaults(self):
options1 = dataset_ops.Options()
options2 = dataset_ops.Options()
self.assertIsNot(options1.experimental_optimization,
options2.experimental_optimization)
self.assertIsNot(options1.experimental_stats,
options2.experimental_stats)
self.assertIsNot(options1.experimental_threading,
options2.experimental_threading)
self.assertEqual(options1.experimental_optimization,
optimization_options.OptimizationOptions())
self.assertEqual(options1.experimental_stats, stats_options.StatsOptions())
self.assertEqual(options1.experimental_threading,
threading_options.ThreadingOptions())
def testLatencyStatsOptimizationV2(self):
aggregator = stats_aggregator.StatsAggregator()
dataset = dataset_ops.Dataset.from_tensors(1).apply(
optimization.assert_next([
"LatencyStats", "Map", "LatencyStats", "Prefetch",
"LatencyStats"
])).map(lambda x: x * x).prefetch(1)
options = dataset_ops.Options()
options.experimental_stats = stats_options.StatsOptions(aggregator)
dataset = dataset.with_options(options)
self.assertDatasetProduces(dataset,
expected_output=[1],
requires_initialization=True,
num_test_iterations=1)
summary_t = aggregator.get_summary()
summary_str = self.evaluate(summary_t)
self._assertSummaryHasCount(summary_str,
"record_latency_TensorDataset/_1", 1)
self._assertSummaryHasCount(summary_str,
"record_latency_MapDataset/_4", 1)
self._assertSummaryHasCount(summary_str,
"record_latency_PrefetchDataset/_6", 1)