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 # Warmup means the number of logged step time entries that are excluded in # performance report. Default to exclude 1 FLAGS.log_steps time. super(Resnet50CtlBenchmarkBase, self)._report_benchmark(stats, wall_time_sec, total_batch_size=FLAGS.batch_size, log_steps=FLAGS.log_steps, warmup=1, start_time_sec=start_time_sec)
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)