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)
