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# Lint as: python3
# Copyright 2020 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 benchmark testing for 3D Unet model."""
# pylint: disable=line-too-long
from __future__ import print_function
import functools
import os
import time
from typing import Optional
from absl import flags
import tensorflow as tf # pylint: disable=g-bad-import-order
from official.benchmark import benchmark_wrappers
from official.benchmark import keras_benchmark
from official.benchmark import owner_utils
from official.vision.segmentation import unet_main as unet_training_lib
from official.vision.segmentation import unet_model as unet_model_lib
UNET3D_MIN_ACCURACY = 0.90
UNET3D_MAX_ACCURACY = 0.98
UNET_TRAINING_FILES = 'gs://mlcompass-data/unet3d/train_data/*'
UNET_EVAL_FILES = 'gs://mlcompass-data/unet3d/eval_data/*'
UNET_MODEL_CONFIG_FILE = 'gs://mlcompass-data/unet3d/config/unet_config.yaml'
FLAGS = flags.FLAGS
class Unet3DAccuracyBenchmark(keras_benchmark.KerasBenchmark):
"""Benchmark accuracy tests for UNet3D model in Keras."""
def __init__(self,
output_dir: Optional[str] = None,
root_data_dir: Optional[str] = None,
**kwargs):
"""A benchmark class.
Args:
output_dir: directory where to output e.g. log files
root_data_dir: directory under which to look for dataset
**kwargs: arbitrary named arguments. This is needed to make the
constructor forward compatible in case PerfZero provides more named
arguments before updating the constructor.
"""
flag_methods = [unet_training_lib.define_unet3d_flags]
# UNet3D model in Keras."""
self.training_file_pattern = UNET_TRAINING_FILES
self.eval_file_pattern = UNET_EVAL_FILES
# TODO(hongjunchoi): Create and use shared config file instead.
self.config_file = UNET_MODEL_CONFIG_FILE
super(Unet3DAccuracyBenchmark, self).__init__(
output_dir=output_dir, flag_methods=flag_methods)
def _set_benchmark_parameters(self, experiment_name):
"""Overrides training parameters for benchmark tests."""
FLAGS.model_dir = self._get_model_dir(experiment_name)
FLAGS.mode = 'train'
FLAGS.training_file_pattern = self.training_file_pattern
FLAGS.eval_file_pattern = self.eval_file_pattern
FLAGS.config_file = self.config_file
FLAGS.lr_init_value = 0.00005
FLAGS.lr_decay_rate = 0.5
FLAGS.epochs = 3
@benchmark_wrappers.enable_runtime_flags
def _run_and_report_benchmark(self,
experiment_name: str,
min_accuracy: float = UNET3D_MIN_ACCURACY,
max_accuracy: float = UNET3D_MAX_ACCURACY,
distribution_strategy: str = 'tpu',
epochs: int = 10,
steps: int = 0,
epochs_between_evals: int = 1,
dtype: str = 'float32',
enable_xla: bool = False,
run_eagerly: bool = False):
"""Runs and reports the benchmark given the provided configuration."""
params = unet_training_lib.extract_params(FLAGS)
strategy = unet_training_lib.create_distribution_strategy(params)
if params.use_bfloat16:
policy = tf.keras.mixed_precision.experimental.Policy('mixed_bfloat16')
tf.keras.mixed_precision.experimental.set_policy(policy)
stats = {}
start_time_sec = time.time()
with strategy.scope():
unet_model = unet_model_lib.build_unet_model(params)
history = unet_training_lib.train(
params, strategy, unet_model,
functools.partial(unet_training_lib.get_train_dataset, params),
functools.partial(unet_training_lib.get_eval_dataset, params))
stats['accuracy_top_1'] = history.history['val_metric_accuracy'][-1]
stats['training_accuracy_top_1'] = history.history['metric_accuracy'][-1]
wall_time_sec = time.time() - start_time_sec
super(Unet3DAccuracyBenchmark, self)._report_benchmark(
stats,
wall_time_sec,
top_1_min=min_accuracy,
top_1_max=max_accuracy,
total_batch_size=params.train_batch_size)
def _get_model_dir(self, folder_name):
return os.path.join(self.output_dir, folder_name)
@owner_utils.Owner('tf-model-garden')
def benchmark_4x4_tpu_bf16(self):
"""Test Keras model with 4x4 TPU, fp16."""
experiment_name = 'benchmark_4x4_tpu_fp16'
self._setup()
self._set_benchmark_parameters(experiment_name)
self._run_and_report_benchmark(
experiment_name=experiment_name,
dtype='bfloat16',
distribution_strategy='tpu')
@owner_utils.Owner('tf-graph-compiler')
def benchmark_4x4_tpu_bf16_mlir(self):
"""Test Keras model with 4x4 TPU, fp16 and MLIR enabled."""
experiment_name = 'benchmark_4x4_tpu_fp16_mlir'
tf.config.experimental.enable_mlir_bridge()
self._setup()
self._set_benchmark_parameters(experiment_name)
self._run_and_report_benchmark(
experiment_name=experiment_name,
dtype='bfloat16',
distribution_strategy='tpu')
if __name__ == '__main__':
tf.test.main()
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