def _run_and_report_benchmark(self):
        start_time_sec = time.time()
        stats = resnet_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 = resnet_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)