def _run_and_report_benchmark(self):
start_time_sec = time.time()
stats = ctl_imagenet_main.run(flags.FLAGS)
wall_time_sec = time.time() - start_time_sec
super(Resnet50CtlAccuracy,
self)._report_benchmark(stats,
wall_time_sec,
top_1_min=MIN_TOP_1_ACCURACY,
top_1_max=MAX_TOP_1_ACCURACY,
total_batch_size=FLAGS.batch_size,
log_steps=100)
def _run_and_report_benchmark(self):
start_time_sec = time.time()
stats = ctl_imagenet_main.run(FLAGS)
wall_time_sec = time.time() - start_time_sec
# Number of logged step time entries that are excluded in performance
# report. We keep results from last 100 batches in this case.
warmup = (FLAGS.train_steps - 100) // FLAGS.log_steps
super(Resnet50CtlBenchmarkBase,
self)._report_benchmark(stats,
wall_time_sec,
total_batch_size=FLAGS.batch_size,
log_steps=FLAGS.log_steps,
warmup=warmup)