# 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.
# ==============================================================================
"""Main function to train various object detection models."""

from __future__ import absolute_import
from __future__ import division
# from __future__ import google_type_annotations
from __future__ import print_function

import functools
import pprint

# pylint: disable=g-bad-import-order
import tensorflow as tf

from absl import app
from absl import flags
from absl import logging
# pylint: enable=g-bad-import-order

from official.modeling.hyperparams import params_dict
from official.modeling.training import distributed_executor as executor
from official.utils import hyperparams_flags
from official.utils.flags import core as flags_core
from official.utils.misc import distribution_utils
from official.utils.misc import keras_utils
from official.vision.detection.configs import factory as config_factory
from official.vision.detection.dataloader import input_reader
from official.vision.detection.dataloader import mode_keys as ModeKeys
from official.vision.detection.executor.detection_executor import DetectionDistributedExecutor
from official.vision.detection.modeling import factory as model_factory

hyperparams_flags.initialize_common_flags()
flags_core.define_log_steps()

flags.DEFINE_bool('enable_xla', default=False, help='Enable XLA for GPU')

flags.DEFINE_string(
    'mode', default='train', help='Mode to run: `train` or `eval`.')

flags.DEFINE_string(
    'model', default='retinanet',
    help='Model to run: `retinanet`, `mask_rcnn` or `shapemask`.')

flags.DEFINE_string('training_file_pattern', None,
                    'Location of the train data.')

flags.DEFINE_string('eval_file_pattern', None, 'Location of ther eval data')

flags.DEFINE_string(
    'checkpoint_path', None,
    'The checkpoint path to eval. Only used in eval_once mode.')

FLAGS = flags.FLAGS


def run_executor(params,
                 mode,
                 checkpoint_path=None,
                 train_input_fn=None,
                 eval_input_fn=None,
                 callbacks=None,
                 prebuilt_strategy=None):
  """Runs the object detection model on distribution strategy defined by the user."""

  if params.architecture.use_bfloat16:
    policy = tf.compat.v2.keras.mixed_precision.experimental.Policy(
        'mixed_bfloat16')
    tf.compat.v2.keras.mixed_precision.experimental.set_policy(policy)

  model_builder = model_factory.model_generator(params)

  if prebuilt_strategy is not None:
    strategy = prebuilt_strategy
  else:
    strategy_config = params.strategy_config
    distribution_utils.configure_cluster(strategy_config.worker_hosts,
                                         strategy_config.task_index)
    strategy = distribution_utils.get_distribution_strategy(
        distribution_strategy=params.strategy_type,
        num_gpus=strategy_config.num_gpus,
        all_reduce_alg=strategy_config.all_reduce_alg,
        num_packs=strategy_config.num_packs,
        tpu_address=strategy_config.tpu)

  num_workers = int(strategy.num_replicas_in_sync + 7) // 8
  is_multi_host = (int(num_workers) >= 2)

  if mode == 'train':

    def _model_fn(params):
      return model_builder.build_model(params, mode=ModeKeys.TRAIN)

    logging.info(
        'Train num_replicas_in_sync %d num_workers %d is_multi_host %s',
        strategy.num_replicas_in_sync, num_workers, is_multi_host)

    dist_executor = DetectionDistributedExecutor(
        strategy=strategy,
        params=params,
        model_fn=_model_fn,
        loss_fn=model_builder.build_loss_fn,
        is_multi_host=is_multi_host,
        predict_post_process_fn=model_builder.post_processing,
        trainable_variables_filter=model_builder
        .make_filter_trainable_variables_fn())

    if is_multi_host:
      train_input_fn = functools.partial(
          train_input_fn,
          batch_size=params.train.batch_size // strategy.num_replicas_in_sync)

    return dist_executor.train(
        train_input_fn=train_input_fn,
        model_dir=params.model_dir,
        iterations_per_loop=params.train.iterations_per_loop,
        total_steps=params.train.total_steps,
        init_checkpoint=model_builder.make_restore_checkpoint_fn(),
        custom_callbacks=callbacks,
        save_config=True)
  elif mode == 'eval' or mode == 'eval_once':

    def _model_fn(params):
      return model_builder.build_model(params, mode=ModeKeys.PREDICT_WITH_GT)

    logging.info('Eval num_replicas_in_sync %d num_workers %d is_multi_host %s',
                 strategy.num_replicas_in_sync, num_workers, is_multi_host)

    if is_multi_host:
      eval_input_fn = functools.partial(
          eval_input_fn,
          batch_size=params.eval.batch_size // strategy.num_replicas_in_sync)

    dist_executor = DetectionDistributedExecutor(
        strategy=strategy,
        params=params,
        model_fn=_model_fn,
        loss_fn=model_builder.build_loss_fn,
        is_multi_host=is_multi_host,
        predict_post_process_fn=model_builder.post_processing,
        trainable_variables_filter=model_builder
        .make_filter_trainable_variables_fn())

    if mode == 'eval':
      results = dist_executor.evaluate_from_model_dir(
          model_dir=params.model_dir,
          eval_input_fn=eval_input_fn,
          eval_metric_fn=model_builder.eval_metrics,
          eval_timeout=params.eval.eval_timeout,
          min_eval_interval=params.eval.min_eval_interval,
          total_steps=params.train.total_steps)
    else:
      # Run evaluation once for a single checkpoint.
      if not checkpoint_path:
        raise ValueError('checkpoint_path cannot be empty.')
      if tf.io.gfile.isdir(checkpoint_path):
        checkpoint_path = tf.train.latest_checkpoint(checkpoint_path)
      summary_writer = executor.SummaryWriter(params.model_dir, 'eval')
      results, _ = dist_executor.evaluate_checkpoint(
          checkpoint_path=checkpoint_path,
          eval_input_fn=eval_input_fn,
          eval_metric_fn=model_builder.eval_metrics,
          summary_writer=summary_writer)
    for k, v in results.items():
      logging.info('Final eval metric %s: %f', k, v)
    return results
  else:
    raise ValueError('Mode not found: %s.' % mode)


def run(callbacks=None):
  keras_utils.set_session_config(enable_xla=FLAGS.enable_xla)

  params = config_factory.config_generator(FLAGS.model)

  params = params_dict.override_params_dict(
      params, FLAGS.config_file, is_strict=True)

  params = params_dict.override_params_dict(
      params, FLAGS.params_override, is_strict=True)
  params.override(
      {
          'strategy_type': FLAGS.strategy_type,
          'model_dir': FLAGS.model_dir,
          'strategy_config': executor.strategy_flags_dict(),
      },
      is_strict=False)

  # Make sure use_tpu and strategy_type are in sync.
  params.use_tpu = (params.strategy_type == 'tpu')

  if not params.use_tpu:
    params.override({
        'architecture': {
            'use_bfloat16': False,
        },
        'norm_activation': {
            'use_sync_bn': False,
        },
    }, is_strict=True)

  params.validate()
  params.lock()
  pp = pprint.PrettyPrinter()
  params_str = pp.pformat(params.as_dict())
  logging.info('Model Parameters: %s', params_str)

  train_input_fn = None
  eval_input_fn = None
  training_file_pattern = FLAGS.training_file_pattern or params.train.train_file_pattern
  eval_file_pattern = FLAGS.eval_file_pattern or params.eval.eval_file_pattern
  if not training_file_pattern and not eval_file_pattern:
    raise ValueError('Must provide at least one of training_file_pattern and '
                     'eval_file_pattern.')

  if training_file_pattern:
    # Use global batch size for single host.
    train_input_fn = input_reader.InputFn(
        file_pattern=training_file_pattern,
        params=params,
        mode=input_reader.ModeKeys.TRAIN,
        batch_size=params.train.batch_size)

  if eval_file_pattern:
    eval_input_fn = input_reader.InputFn(
        file_pattern=eval_file_pattern,
        params=params,
        mode=input_reader.ModeKeys.PREDICT_WITH_GT,
        batch_size=params.eval.batch_size,
        num_examples=params.eval.eval_samples)

  if callbacks is None:
    callbacks = []

  if FLAGS.log_steps:
    callbacks.append(
        keras_utils.TimeHistory(
            batch_size=params.train.batch_size,
            log_steps=FLAGS.log_steps,
        ))

  return run_executor(
      params,
      FLAGS.mode,
      checkpoint_path=FLAGS.checkpoint_path,
      train_input_fn=train_input_fn,
      eval_input_fn=eval_input_fn,
      callbacks=callbacks)


def main(argv):
  del argv  # Unused.

  run()


if __name__ == '__main__':
  tf.config.set_soft_device_placement(True)
  app.run(main)
