tf_cnn_benchmarks.py

# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

"""Benchmark script for TensorFlow.

See the README for more information.
"""

from absl import app
from absl import flags as absl_flags
import tensorflow.compat.v1 as tf

import benchmark_cnn
import cnn_util
import flags
import mlperf
from cnn_util import log_fn


flags.define_flags()
for name in flags.param_specs.keys():
  absl_flags.declare_key_flag(name)

_ML_PERF_COMPLIANCE_LOGGING = absl_flags.DEFINE_boolean(
    'ml_perf_compliance_logging', False,
    'Print logs required to be compliant with MLPerf. If set, must clone the '
    'MLPerf training repo https://github.com/mlperf/training and add '
    'https://github.com/mlperf/training/tree/master/compliance to the '
    'PYTHONPATH')


def main(positional_arguments):
  # Command-line arguments like '--distortions False' are equivalent to
  # '--distortions=True False', where False is a positional argument. To prevent
  # this from silently running with distortions, we do not allow positional
  # arguments.
  assert len(positional_arguments) >= 1
  if len(positional_arguments) > 1:
    raise ValueError('Received unknown positional arguments: %s'
                     % positional_arguments[1:])

  params = benchmark_cnn.make_params_from_flags()
  with mlperf.mlperf_logger(_ML_PERF_COMPLIANCE_LOGGING.value, params.model):
    params = benchmark_cnn.setup(params)
    bench = benchmark_cnn.BenchmarkCNN(params)

    tfversion = cnn_util.tensorflow_version_tuple()
    log_fn('TensorFlow:  %i.%i' % (tfversion[0], tfversion[1]))

    bench.print_info()
    bench.run()


if __name__ == '__main__':
  tf.disable_v2_behavior()
  app.run(main)  # Raises error on invalid flags, unlike tf.app.run()