def benchmarkPredictExtractorManualActuation(self):
"""Benchmark PredictExtractorV2 "manually"."""
self._init_model()
records = self._readDatasetIntoExtracts()
extracts = []
for elem in records:
extracts.append(
input_extractor._ParseExample(elem, self._eval_config)) # pylint: disable=protected-access
prediction_do_fn = predict_extractor_v2._PredictionDoFn( # pylint: disable=protected-access
eval_config=self._eval_config,
eval_shared_models={"": self._eval_shared_model})
prediction_do_fn.setup()
start = time.time()
predict_result = []
predict_batch_size = 1000
for batch in benchmark_utils.batched_iterator(extracts,
predict_batch_size):
predict_result.extend(prediction_do_fn.process(batch))
end = time.time()
delta = end - start
self.report_benchmark(iters=1,
wall_time=delta,
extras={"num_examples": len(records)})
def benchmarkInputExtractorManualActuation(self):
"""Benchmark PredictExtractorV2 "manually"."""
self._init_model()
records = self._readDatasetIntoExtracts()
extracts = []
start = time.time()
for elem in records:
extracts.append(input_extractor._ParseExample(elem, self._eval_config)) # pylint: disable=protected-access
end = time.time()
delta = end - start
self.report_benchmark(
iters=1, wall_time=delta, extras={"num_examples": len(records)})
def _runMetricsAndPlotsEvaluatorManualActuation(self,
with_confidence_intervals,
metrics_specs=None):
"""Benchmark MetricsAndPlotsEvaluatorV2 "manually"."""
self._init_model()
if not metrics_specs:
metrics_specs = self._eval_config.metrics_specs
records = self._readDatasetIntoExtracts()
extracts = []
for elem in records:
extracts.append(
input_extractor._ParseExample(elem, self._eval_config)) # pylint: disable=protected-access
prediction_do_fn = predict_extractor_v2._PredictionDoFn( # pylint: disable=protected-access
eval_config=self._eval_config,
eval_shared_models={"": self._eval_shared_model})
prediction_do_fn.setup()
# Have to predict first
predict_result = []
predict_batch_size = 1000
for batch in benchmark_utils.batched_iterator(extracts,
predict_batch_size):
predict_result.extend(prediction_do_fn.process(batch))
# Now Evaluate
inputs_per_accumulator = 1000
start = time.time()
computations, _ = (
metrics_and_plots_evaluator_v2._filter_and_separate_computations( # pylint: disable=protected-access
metric_specs.to_computations(metrics_specs,
eval_config=self._eval_config)))
processed = []
for elem in predict_result:
processed.append(
next(
metrics_and_plots_evaluator_v2._PreprocessorDoFn( # pylint: disable=protected-access
computations).process(elem)))
combiner = metrics_and_plots_evaluator_v2._ComputationsCombineFn( # pylint: disable=protected-access
computations=computations,
compute_with_sampling=with_confidence_intervals)
accumulators = []
for batch in benchmark_utils.batched_iterator(processed,
inputs_per_accumulator):
accumulator = combiner.create_accumulator()
for elem in batch:
accumulator = combiner.add_input(accumulator, elem)
accumulators.append(accumulator)
final_accumulator = combiner.merge_accumulators(accumulators)
final_output = combiner.extract_output(final_accumulator)
end = time.time()
delta = end - start
# Sanity check the example count. This is not timed.
example_count_key = metric_types.MetricKey(name="example_count")
example_count = None
for x in final_output:
if example_count_key in x:
example_count = x[example_count_key]
break
if example_count is None:
raise ValueError(
"example_count was not in the final list of metrics. "
"metrics were: %s" % str(final_output))
if with_confidence_intervals:
# If we're computing using confidence intervals, the example count will
# not be exact.
lower_bound = int(0.9 * len(records))
upper_bound = int(1.1 * len(records))
if example_count < lower_bound or example_count > upper_bound:
raise ValueError("example count out of bounds: expecting "
"%d < example_count < %d, but got %d" %
(lower_bound, upper_bound, example_count))
else:
# If we're not using confidence intervals, we expect the example count to
# be exact.
if example_count != len(records):
raise ValueError(
"example count mismatch: expecting %d got %d" %
(len(records), example_count))
self.report_benchmark(iters=1,
wall_time=delta,
extras={
"inputs_per_accumulator":
inputs_per_accumulator,
"num_examples": len(records)
})