# 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.
# ==============================================================================
"""Runs a ResNet model on the ImageNet dataset using custom training loops."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from absl import app
from absl import flags
from absl import logging
import tensorflow as tf

from official.modeling import performance
from official.staging.training import controller
from official.utils.flags import core as flags_core
from official.utils.logs import logger
from official.utils.misc import distribution_utils
from official.utils.misc import keras_utils
from official.utils.misc import model_helpers
from official.vision.image_classification.resnet import common
from official.vision.image_classification.resnet import imagenet_preprocessing
from official.vision.image_classification.resnet import resnet_runnable

flags.DEFINE_boolean(name='use_tf_function', default=True,
                     help='Wrap the train and test step inside a '
                     'tf.function.')
flags.DEFINE_boolean(name='single_l2_loss_op', default=False,
                     help='Calculate L2_loss on concatenated weights, '
                     'instead of using Keras per-layer L2 loss.')


def build_stats(runnable, time_callback):
  """Normalizes and returns dictionary of stats.

  Args:
    runnable: The module containing all the training and evaluation metrics.
    time_callback: Time tracking callback instance.

  Returns:
    Dictionary of normalized results.
  """
  stats = {}

  if not runnable.flags_obj.skip_eval:
    stats['eval_loss'] = runnable.test_loss.result().numpy()
    stats['eval_acc'] = runnable.test_accuracy.result().numpy()

    stats['train_loss'] = runnable.train_loss.result().numpy()
    stats['train_acc'] = runnable.train_accuracy.result().numpy()

  if time_callback:
    timestamp_log = time_callback.timestamp_log
    stats['step_timestamp_log'] = timestamp_log
    stats['train_finish_time'] = time_callback.train_finish_time
    if time_callback.epoch_runtime_log:
      stats['avg_exp_per_second'] = time_callback.average_examples_per_second

  return stats


def get_num_train_iterations(flags_obj):
  """Returns the number of training steps, train and test epochs."""
  train_steps = (
      imagenet_preprocessing.NUM_IMAGES['train'] // flags_obj.batch_size)
  train_epochs = flags_obj.train_epochs

  if flags_obj.train_steps:
    train_steps = min(flags_obj.train_steps, train_steps)
    train_epochs = 1

  eval_steps = (
      imagenet_preprocessing.NUM_IMAGES['validation'] // flags_obj.batch_size)

  return train_steps, train_epochs, eval_steps


def _steps_to_run(steps_in_current_epoch, steps_per_epoch, steps_per_loop):
  """Calculates steps to run on device."""
  if steps_per_loop <= 0:
    raise ValueError('steps_per_loop should be positive integer.')
  if steps_per_loop == 1:
    return steps_per_loop
  return min(steps_per_loop, steps_per_epoch - steps_in_current_epoch)


def run(flags_obj):
  """Run ResNet ImageNet training and eval loop using custom training loops.

  Args:
    flags_obj: An object containing parsed flag values.

  Raises:
    ValueError: If fp16 is passed as it is not currently supported.

  Returns:
    Dictionary of training and eval stats.
  """
  keras_utils.set_session_config(
      enable_eager=flags_obj.enable_eager,
      enable_xla=flags_obj.enable_xla)
  performance.set_mixed_precision_policy(flags_core.get_tf_dtype(flags_obj))

  # This only affects GPU.
  common.set_cudnn_batchnorm_mode()

  # TODO(anj-s): Set data_format without using Keras.
  data_format = flags_obj.data_format
  if data_format is None:
    data_format = ('channels_first'
                   if tf.test.is_built_with_cuda() else 'channels_last')
  tf.keras.backend.set_image_data_format(data_format)

  strategy = distribution_utils.get_distribution_strategy(
      distribution_strategy=flags_obj.distribution_strategy,
      num_gpus=flags_obj.num_gpus,
      all_reduce_alg=flags_obj.all_reduce_alg,
      num_packs=flags_obj.num_packs,
      tpu_address=flags_obj.tpu)

  per_epoch_steps, train_epochs, eval_steps = get_num_train_iterations(
      flags_obj)
  steps_per_loop = min(flags_obj.steps_per_loop, per_epoch_steps)

  logging.info(
      'Training %d epochs, each epoch has %d steps, '
      'total steps: %d; Eval %d steps', train_epochs, per_epoch_steps,
      train_epochs * per_epoch_steps, eval_steps)

  time_callback = keras_utils.TimeHistory(
      flags_obj.batch_size,
      flags_obj.log_steps,
      logdir=flags_obj.model_dir if flags_obj.enable_tensorboard else None)
  with distribution_utils.get_strategy_scope(strategy):
    runnable = resnet_runnable.ResnetRunnable(flags_obj, time_callback,
                                              per_epoch_steps)

  eval_interval = (
      flags_obj.epochs_between_evals *
      per_epoch_steps if not flags_obj.skip_eval else None)
  checkpoint_interval = (
      per_epoch_steps if flags_obj.enable_checkpoint_and_export else None)
  summary_interval = per_epoch_steps if flags_obj.enable_tensorboard else None

  checkpoint_manager = tf.train.CheckpointManager(
      runnable.checkpoint,
      directory=flags_obj.model_dir,
      max_to_keep=10,
      step_counter=runnable.global_step,
      checkpoint_interval=checkpoint_interval)

  resnet_controller = controller.Controller(
      strategy,
      runnable.train,
      runnable.evaluate,
      global_step=runnable.global_step,
      steps_per_loop=steps_per_loop,
      train_steps=per_epoch_steps * train_epochs,
      checkpoint_manager=checkpoint_manager,
      summary_interval=summary_interval,
      eval_steps=eval_steps,
      eval_interval=eval_interval)

  time_callback.on_train_begin()
  resnet_controller.train(evaluate=True)
  time_callback.on_train_end()

  stats = build_stats(runnable, time_callback)
  return stats


def main(_):
  model_helpers.apply_clean(flags.FLAGS)
  with logger.benchmark_context(flags.FLAGS):
    stats = run(flags.FLAGS)
  logging.info('Run stats:\n%s', stats)


if __name__ == '__main__':
  logging.set_verbosity(logging.INFO)
  common.define_keras_flags()
  app.run(main)
