def _run_and_report_benchmark(self): start_time_sec = time.time() stats = resnet_imagenet_main.run(FLAGS) wall_time_sec = time.time() - start_time_sec super(TrivialKerasBenchmarkReal, self)._report_benchmark( stats, wall_time_sec, total_batch_size=FLAGS.batch_size, log_steps=FLAGS.log_steps)
def _run_and_report_benchmark(self, top_1_min=MIN_TOP_1_ACCURACY, top_1_max=MAX_TOP_1_ACCURACY): start_time_sec = time.time() stats = resnet_imagenet_main.run(flags.FLAGS) wall_time_sec = time.time() - start_time_sec super(Resnet50KerasAccuracy, self)._report_benchmark( stats, wall_time_sec, top_1_min=top_1_min, top_1_max=top_1_max, total_batch_size=FLAGS.batch_size, log_steps=100)
def _run_and_report_benchmark(self): start_time_sec = time.time() stats = resnet_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(Resnet50KerasBenchmarkBase, self)._report_benchmark( stats, wall_time_sec, total_batch_size=FLAGS.batch_size, log_steps=FLAGS.log_steps, warmup=warmup)
def _run_and_report_benchmark(self, top_1_min=MODEL_OPTIMIZATION_TOP_1_ACCURACY[ 'RESNET50_FINETUNE_PRUNING'][0], top_1_max=MODEL_OPTIMIZATION_TOP_1_ACCURACY[ 'RESNET50_FINETUNE_PRUNING'][1]): start_time_sec = time.time() stats = resnet_imagenet_main.run(flags.FLAGS) wall_time_sec = time.time() - start_time_sec super(KerasPruningAccuracyBase, self)._report_benchmark( stats, wall_time_sec, top_1_min=top_1_min, top_1_max=top_1_max, total_batch_size=FLAGS.batch_size, log_steps=100)