from __future__ import print_function

import argparse, os, sys, time
import numpy as np
from hparams import hparams
from emotion_inferring.train import er_train

HPARAMETER_FILE = ''


def get_arguments(hparams):
  parser = argparse.ArgumentParser(
      description='Train The Emotion Recognition System')
  parser.add_argument('--output-model-path',
                      dest='outputdir',
                      default=os.path.dirname(os.path.realpath(__file__)))
  parser.add_argument('--log-dir',
                      dest='philly_log_dir',
                      default=os.path.dirname(os.path.realpath(__file__)))
  parser.add_argument('--batch_size',
                      type=int,
                      default=hparams.batch_size,
                      help='Wav files numbers to process simultaneously')
  parser.add_argument('--data_dir',
                      type=str,
                      default=hparams.data_dir,
                      help='Speech data directory.')
  parser.add_argument('--logdir',
                      type=str,
                      default=None,
                      help='Directory in which to store the logging '
                      'information for TensorBoard. '
                      'If the model already exists, it will restore '
                      'the state and will continue training. '
                      'Cannot use with --logdir_root and --restore_from.')
  parser.add_argument('--logdir_root',
                      type=str,
                      default=hparams.log_dir_root,
                      help='Root directory to place the logging '
                      'output and generated model. These are stored '
                      'under the dated subdirectory of --logdir_root. '
                      'Cannot use with --logdir.')
  parser.add_argument(
      '--restore_from',
      type=str,
      default=None,
      help='Directory in which to restore the model from. '
      'This creates the new model under the dated directory in --logdir_root. '
      'Cannot use with --logdir.')
  parser.add_argument(
      '--checkpoint_every',
      type=int,
      default=hparams.checkpoint_every,
      help='How many steps to save each checkpoint after. Default: ' +
      str(hparams.checkpoint_every) + '.')
  parser.add_argument('--num_steps',
                      type=int,
                      default=hparams.num_steps,
                      help='Number of training steps. Default: ' +
                      str(hparams.num_steps) + '.')
  parser.add_argument('--learning_rate',
                      type=float,
                      default=hparams.learning_rate,
                      help='Learning rate for training. Default: ' +
                      str(hparams.learning_rate) + '.')
  parser.add_argument(
      '--optimizer',
      type=str,
      default='adam',
      help='Select the optimizer specified by this option. Default: adam.')
  parser.add_argument('--momentum', type=float, default=hparams.momentum)
  parser.add_argument('--device',
                      type=str,
                      default=-1,
                      help='The employed GPU ID for model training.')
  parser.add_argument('--database', type=str, default=hparams.database)
  return parser.parse_args()


def main():
  hparam = hparams.parse(HPARAMETER_FILE)
  args = get_arguments(hparam)
  os.environ["CUDA_VISIBLE_DEVICES"] = args.device
  er_train(args, hparam)


if __name__ == '__main__':
  main()

from __future__ import print_function

import argparse, os, sys, time
import numpy as np
from hparams import hparams
from emotion_inferring.train import er_train

HPARAMETER_FILE = ''


def get_arguments(hparams):
  parser = argparse.ArgumentParser(
      description='Train The Emotion Recognition System')
  parser.add_argument('--output-model-path',
                      dest='outputdir',
                      default=os.path.dirname(os.path.realpath(__file__)))
  parser.add_argument('--log-dir',
                      dest='philly_log_dir',
                      default=os.path.dirname(os.path.realpath(__file__)))
  parser.add_argument('--batch_size',
                      type=int,
                      default=hparams.batch_size,
                      help='Wav files numbers to process simultaneously')
  parser.add_argument('--data_dir',
                      type=str,
                      default=hparams.data_dir,
                      help='Speech data directory.')
  parser.add_argument('--logdir',
                      type=str,
                      default=None,
                      help='Directory in which to store the logging '
                      'information for TensorBoard. '
                      'If the model already exists, it will restore '
                      'the state and will continue training. '
                      'Cannot use with --logdir_root and --restore_from.')
  parser.add_argument('--logdir_root',
                      type=str,
                      default=hparams.log_dir_root,
                      help='Root directory to place the logging '
                      'output and generated model. These are stored '
                      'under the dated subdirectory of --logdir_root. '
                      'Cannot use with --logdir.')
  parser.add_argument(
      '--restore_from',
      type=str,
      default=None,
      help='Directory in which to restore the model from. '
      'This creates the new model under the dated directory in --logdir_root. '
      'Cannot use with --logdir.')
  parser.add_argument(
      '--checkpoint_every',
      type=int,
      default=hparams.checkpoint_every,
      help='How many steps to save each checkpoint after. Default: ' +
      str(hparams.checkpoint_every) + '.')
  parser.add_argument('--num_steps',
                      type=int,
                      default=hparams.num_steps,
                      help='Number of training steps. Default: ' +
                      str(hparams.num_steps) + '.')
  parser.add_argument('--learning_rate',
                      type=float,
                      default=hparams.learning_rate,
                      help='Learning rate for training. Default: ' +
                      str(hparams.learning_rate) + '.')
  parser.add_argument(
      '--optimizer',
      type=str,
      default='adam',
      help='Select the optimizer specified by this option. Default: adam.')
  parser.add_argument('--momentum', type=float, default=hparams.momentum)
  parser.add_argument('--device',
                      type=str,
                      default=-1,
                      help='The employed GPU ID for model training.')
  parser.add_argument('--database', type=str, default=hparams.database)
  return parser.parse_args()


def main():
  hparam = hparams.parse(HPARAMETER_FILE)
  args = get_arguments(hparam)
  os.environ["CUDA_VISIBLE_DEVICES"] = args.device
  er_train(args, hparam)


if __name__ == '__main__':
  main()
