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# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Executes RetinaNet benchmarks and accuracy tests."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# pylint: disable=g-bad-import-order
import json
import time
from absl import flags
from absl.testing import flagsaver
import tensorflow as tf
# pylint: enable=g-bad-import-order
from official.benchmark import benchmark_wrappers
from official.benchmark import perfzero_benchmark
from official.utils.flags import core as flags_core
from official.utils.misc import keras_utils
from official.vision.detection import main as detection
from official.vision.detection.configs import base_config
FLAGS = flags.FLAGS
# pylint: disable=line-too-long
COCO_TRAIN_DATA = 'gs://tf-perfzero-data/coco/train*'
COCO_EVAL_DATA = 'gs://tf-perfzero-data/coco/val*'
COCO_EVAL_JSON = 'gs://tf-perfzero-data/coco/instances_val2017.json'
RESNET_CHECKPOINT_PATH = 'gs://cloud-tpu-checkpoints/retinanet/resnet50-checkpoint-2018-02-07'
# pylint: enable=line-too-long
class DetectionBenchmarkBase(perfzero_benchmark.PerfZeroBenchmark):
"""Base class to hold methods common to test classes."""
def __init__(self, **kwargs):
super(DetectionBenchmarkBase, self).__init__(**kwargs)
self.timer_callback = None
def _report_benchmark(self, stats, start_time_sec, wall_time_sec, min_ap,
max_ap, warmup):
"""Report benchmark results by writing to local protobuf file.
Args:
stats: dict returned from Detection models with known entries.
start_time_sec: the start of the benchmark execution in seconds
wall_time_sec: the duration of the benchmark execution in seconds
min_ap: Minimum detection AP constraint to verify correctness of the
model.
max_ap: Maximum detection AP accuracy constraint to verify correctness of
the model.
warmup: Number of time log entries to ignore when computing examples/sec.
"""
metrics = [{
'name': 'total_loss',
'value': stats['total_loss'],
}]
if self.timer_callback:
metrics.append({
'name': 'exp_per_second',
'value': self.timer_callback.get_examples_per_sec(warmup)
})
metrics.append({
'name': 'startup_time',
'value': self.timer_callback.get_startup_time(start_time_sec)
})
else:
metrics.append({
'name': 'exp_per_second',
'value': 0.0,
})
if 'eval_metrics' in stats:
metrics.append({
'name': 'AP',
'value': stats['AP'],
'min_value': min_ap,
'max_value': max_ap,
})
flags_str = flags_core.get_nondefault_flags_as_str()
self.report_benchmark(
iters=stats['total_steps'],
wall_time=wall_time_sec,
metrics=metrics,
extras={'flags': flags_str})
class RetinanetBenchmarkBase(DetectionBenchmarkBase):
"""Base class to hold methods common to test classes in the module."""
def __init__(self, **kwargs):
self.train_data_path = COCO_TRAIN_DATA
self.eval_data_path = COCO_EVAL_DATA
self.eval_json_path = COCO_EVAL_JSON
self.resnet_checkpoint_path = RESNET_CHECKPOINT_PATH
super(RetinanetBenchmarkBase, self).__init__(**kwargs)
def _run_detection_main(self):
"""Starts detection job."""
if self.timer_callback:
FLAGS.log_steps = 0 # prevent detection.run from adding the same callback
return detection.run(callbacks=[self.timer_callback])
else:
return detection.run()
class RetinanetAccuracy(RetinanetBenchmarkBase):
"""Accuracy test for RetinaNet model.
Tests RetinaNet detection task model accuracy. The naming
convention of below test cases follow
`benchmark_(number of gpus)_gpu_(dataset type)` format.
"""
@benchmark_wrappers.enable_runtime_flags
def _run_and_report_benchmark(self,
params,
min_ap=0.325,
max_ap=0.35,
do_eval=True,
warmup=1):
"""Starts RetinaNet accuracy benchmark test."""
FLAGS.params_override = json.dumps(params)
# Need timer callback to measure performance
self.timer_callback = keras_utils.TimeHistory(
batch_size=params['train']['batch_size'],
log_steps=FLAGS.log_steps,
)
start_time_sec = time.time()
FLAGS.mode = 'train'
summary, _ = self._run_detection_main()
wall_time_sec = time.time() - start_time_sec
if do_eval:
FLAGS.mode = 'eval'
eval_metrics = self._run_detection_main()
summary.update(eval_metrics)
summary['total_steps'] = params['train']['total_steps']
self._report_benchmark(summary, start_time_sec, wall_time_sec, min_ap,
max_ap, warmup)
def _setup(self):
super(RetinanetAccuracy, self)._setup()
FLAGS.model = 'retinanet'
def _params(self):
return {
'architecture': {
'use_bfloat16': True,
},
'train': {
'batch_size': 64,
'iterations_per_loop': 100,
'total_steps': 22500,
'train_file_pattern': self.train_data_path,
'checkpoint': {
'path': self.resnet_checkpoint_path,
'prefix': 'resnet50/'
},
# Speed up ResNet training when loading from the checkpoint.
'frozen_variable_prefix': base_config.RESNET_FROZEN_VAR_PREFIX,
},
'eval': {
'batch_size': 8,
'eval_samples': 5000,
'val_json_file': self.eval_json_path,
'eval_file_pattern': self.eval_data_path,
},
}
@flagsaver.flagsaver
def benchmark_8_gpu_coco(self):
"""Run RetinaNet model accuracy test with 8 GPUs."""
self._setup()
params = self._params()
FLAGS.num_gpus = 8
FLAGS.model_dir = self._get_model_dir('benchmark_8_gpu_coco')
FLAGS.strategy_type = 'mirrored'
self._run_and_report_benchmark(params)
class RetinanetBenchmarkReal(RetinanetAccuracy):
"""Short benchmark performance tests for RetinaNet model.
Tests RetinaNet performance in different GPU configurations.
The naming convention of below test cases follow
`benchmark_(number of gpus)_gpu` format.
"""
def _setup(self):
super(RetinanetBenchmarkReal, self)._setup()
# Use negative value to avoid saving checkpoints.
FLAGS.save_checkpoint_freq = -1
@flagsaver.flagsaver
def benchmark_8_gpu_coco(self):
"""Run RetinaNet model accuracy test with 8 GPUs."""
self._setup()
params = self._params()
params['architecture']['use_bfloat16'] = False
params['train']['total_steps'] = 1875 # One epoch.
# The iterations_per_loop must be one, otherwise the number of examples per
# second would be wrong. Currently only support calling callback per batch
# when each loop only runs on one batch, i.e. host loop for one step. The
# performance of this situation might be lower than the case of
# iterations_per_loop > 1.
# Related bug: b/135933080
params['train']['iterations_per_loop'] = 1
params['eval']['eval_samples'] = 8
FLAGS.num_gpus = 8
FLAGS.model_dir = self._get_model_dir('real_benchmark_8_gpu_coco')
FLAGS.strategy_type = 'mirrored'
self._run_and_report_benchmark(params)
@flagsaver.flagsaver
def benchmark_1_gpu_coco(self):
"""Run RetinaNet model accuracy test with 1 GPU."""
self._setup()
params = self._params()
params['architecture']['use_bfloat16'] = False
params['train']['batch_size'] = 8
params['train']['total_steps'] = 200
params['train']['iterations_per_loop'] = 1
params['eval']['eval_samples'] = 8
FLAGS.num_gpus = 1
FLAGS.model_dir = self._get_model_dir('real_benchmark_1_gpu_coco')
FLAGS.strategy_type = 'one_device'
self._run_and_report_benchmark(params)
@flagsaver.flagsaver
def benchmark_xla_1_gpu_coco(self):
"""Run RetinaNet model accuracy test with 1 GPU and XLA enabled."""
self._setup()
params = self._params()
params['architecture']['use_bfloat16'] = False
params['train']['batch_size'] = 8
params['train']['total_steps'] = 200
params['train']['iterations_per_loop'] = 1
params['eval']['eval_samples'] = 8
FLAGS.num_gpus = 1
FLAGS.model_dir = self._get_model_dir('real_benchmark_xla_1_gpu_coco')
FLAGS.strategy_type = 'one_device'
FLAGS.enable_xla = True
self._run_and_report_benchmark(params)
@flagsaver.flagsaver
def benchmark_2x2_tpu_coco(self):
"""Run RetinaNet model accuracy test with 4 TPUs."""
self._setup()
params = self._params()
params['train']['batch_size'] = 64
params['train']['total_steps'] = 1875 # One epoch.
params['train']['iterations_per_loop'] = 500
FLAGS.model_dir = self._get_model_dir('real_benchmark_2x2_tpu_coco')
FLAGS.strategy_type = 'tpu'
self._run_and_report_benchmark(params, do_eval=False, warmup=0)
if __name__ == '__main__':
tf.test.main()
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