# 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)

    input_dtype = params.dtype
    if input_dtype == 'float16' or input_dtype == 'bfloat16':
      policy = tf.keras.mixed_precision.experimental.Policy(
          'mixed_bfloat16' if input_dtype == 'bfloat16' else 'mixed_float16')
      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()

# 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)

    input_dtype = params.dtype
    if input_dtype == 'float16' or input_dtype == 'bfloat16':
      policy = tf.keras.mixed_precision.experimental.Policy(
          'mixed_bfloat16' if input_dtype == 'bfloat16' else 'mixed_float16')
      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()

# 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 # pylint: disable=g-bad-import-order Benchmark accuracy tests for UNet3D model in Keras. 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. # UNet3D model in Keras.""" # TODO(hongjunchoi): Create and use shared config file instead. Overrides training parameters for benchmark tests. Runs and reports the benchmark given the provided configuration. Test Keras model with 4x4 TPU, fp16. Test Keras model with 4x4 TPU, fp16 and MLIR enabled.