def testMlPerfCompliance(self):
string_io = six.StringIO()
handler = logging.StreamHandler(string_io)
data_dir = test_util.create_black_and_white_images()
try:
mlperf_log.LOGGER.addHandler(handler)
params = benchmark_cnn.make_params(
data_dir=data_dir,
data_name='imagenet',
batch_size=2,
num_warmup_batches=0,
num_batches=2,
num_eval_batches=3,
eval_during_training_every_n_steps=1,
distortions=False,
weight_decay=0.5,
optimizer='momentum',
momentum=0.5,
stop_at_top_1_accuracy=2.0,
tf_random_seed=9876,
ml_perf=True)
with mlperf.mlperf_logger(use_mlperf_logger=True,
model='resnet50_v1.5'):
bench_cnn = benchmark_cnn.BenchmarkCNN(
params, model=_MlPerfTestModel())
bench_cnn.run()
logs = string_io.getvalue().splitlines()
log_regexes = Counter()
for log in logs:
for regex in self.EXPECTED_LOG_REGEXES:
if regex.search(log):
log_regexes[regex] += 1
if log_regexes != self.EXPECTED_LOG_REGEXES:
diff_counter = Counter(log_regexes)
diff_counter.subtract(self.EXPECTED_LOG_REGEXES)
differences = []
for regex in (k for k in diff_counter.keys()
if diff_counter[k]):
found_count = log_regexes[regex]
expected_count = self.EXPECTED_LOG_REGEXES[regex]
differences.append(
' For regex %s: Found %d lines matching but '
'expected to find %d' %
(regex.pattern, found_count, expected_count))
raise AssertionError(
'Logs did not match expected logs. Differences:\n'
'%s' % '\n'.join(differences))
finally:
mlperf_log.LOGGER.removeHandler(handler)
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(absl_flags.FLAGS.ml_perf_compliance_logging,
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()