Python util.ParseConfigsToLuaTable方法代碼示例

# 需要導入模塊: from utils import util [as 別名]
# 或者: from utils.util import ParseConfigsToLuaTable [as 別名]
def main(_):
  # Parse config dict from yaml config files / command line flags.
  config = util.ParseConfigsToLuaTable(FLAGS.config_paths, FLAGS.model_params)
  num_views = config.data.num_views

  validation_records = util.GetFilesRecursively(config.data.validation)
  batch_size = config.data.batch_size

  checkpointdir = FLAGS.checkpointdir

  # If evaluating a specific checkpoint, do that.
  if FLAGS.checkpoint_iter:
    checkpoint_path = os.path.join(
        '%s/model.ckpt-%s' % (checkpointdir, FLAGS.checkpoint_iter))
    evaluate_once(
        config, checkpointdir, validation_records, checkpoint_path, batch_size,
        num_views)
  else:
    for checkpoint_path in tf.contrib.training.checkpoints_iterator(
        checkpointdir):
      evaluate_once(
          config, checkpointdir, validation_records, checkpoint_path,
          batch_size, num_views) 

# 需要導入模塊: from utils import util [as 別名]
# 或者: from utils.util import ParseConfigsToLuaTable [as 別名]
def main(_):
  """Runs main eval loop."""
  # Parse config dict from yaml config files / command line flags.
  logdir = FLAGS.logdir
  config = util.ParseConfigsToLuaTable(FLAGS.config_paths, FLAGS.model_params)

  # Choose an estimator based on training strategy.
  estimator = get_estimator(config, logdir)

  # Wait for the first checkpoint file to be written.
  while not tf.train.latest_checkpoint(logdir):
    tf.logging.info('Waiting for a checkpoint file...')
    time.sleep(10)

  # Run validation.
  while True:
    estimator.evaluate() 

# 需要導入模塊: from utils import util [as 別名]
# 或者: from utils.util import ParseConfigsToLuaTable [as 別名]
def main(_):
  # Parse config dict from yaml config files / command line flags.
  config = util.ParseConfigsToLuaTable(FLAGS.config_paths, FLAGS.model_params)

  # Get tables to embed.
  query_records_dir = FLAGS.query_records_dir
  query_records = util.GetFilesRecursively(query_records_dir)

  target_records_dir = FLAGS.target_records_dir
  target_records = util.GetFilesRecursively(target_records_dir)

  height = config.data.raw_height
  width = config.data.raw_width
  mode = FLAGS.mode
  if mode == 'multi':
    # Generate videos where target set is composed of multiple videos.
    MultiImitationVideos(query_records, target_records, config,
                         height, width)
  elif mode == 'single':
    # Generate videos where target set is a single video.
    SingleImitationVideos(query_records, target_records, config,
                          height, width)
  elif mode == 'same':
    # Generate videos where target set is the same as query, but diff view.
    SameSequenceVideos(query_records, config, height, width)
  else:
    raise ValueError('Unknown mode %s' % mode) 

# 需要導入模塊: from utils import util [as 別名]
# 或者: from utils.util import ParseConfigsToLuaTable [as 別名]
def main(_):
  """Runs main training loop."""
  # Parse config dict from yaml config files / command line flags.
  config = util.ParseConfigsToLuaTable(
      FLAGS.config_paths, FLAGS.model_params, save=True, logdir=FLAGS.logdir)

  # Choose an estimator based on training strategy.
  estimator = get_estimator(config, FLAGS.logdir)

  # Run training
  estimator.train() 

# 需要導入模塊: from utils import util [as 別名]
# 或者: from utils.util import ParseConfigsToLuaTable [as 別名]
def main(_):
  """Runs main labeled eval loop."""
  # Parse config dict from yaml config files / command line flags.
  config = util.ParseConfigsToLuaTable(FLAGS.config_paths, FLAGS.model_params)

  # Choose an estimator based on training strategy.
  checkpointdir = FLAGS.checkpointdir
  estimator = get_estimator(config, checkpointdir)

  # Get data configs.
  image_attr_keys = config.data.labeled.image_attr_keys
  label_attr_keys = config.data.labeled.label_attr_keys
  embedding_size = config.embedding_size
  num_views = config.data.num_views
  k_list = config.val.recall_at_k_list
  batch_size = config.data.batch_size

  # Get either labeled validation or test tables.
  labeled_tables = get_labeled_tables(config)

  def input_fn_by_view(view_index):
    """Returns an input_fn for use with a tf.Estimator by view."""
    def input_fn():
      # Get raw labeled images.
      (preprocessed_images, labels,
       tasks) = data_providers.labeled_data_provider(
           labeled_tables,
           estimator.preprocess_data, view_index, image_attr_keys,
           label_attr_keys, batch_size=batch_size)
      return {
          'batch_preprocessed': preprocessed_images,
          'tasks': tasks,
          'classification_labels': labels,
      }, None
    return input_fn

  # If evaluating a specific checkpoint, do that.
  if FLAGS.checkpoint_iter:
    checkpoint_path = os.path.join(
        '%s/model.ckpt-%s' % (checkpointdir, FLAGS.checkpoint_iter))
    evaluate_once(
        estimator, input_fn_by_view, batch_size, checkpoint_path,
        label_attr_keys, embedding_size, num_views, k_list)
  else:
    for checkpoint_path in tf.contrib.training.checkpoints_iterator(
        checkpointdir):
      evaluate_once(
          estimator, input_fn_by_view, batch_size, checkpoint_path,
          label_attr_keys, embedding_size, num_views, k_list)