import tensorflow as tf

    'Model scope name used to replace the name_scope in checkpoint.')
tf.app.flags.DEFINE_string(
    'checkpoint_exclude_scopes', 'xdet_resnet/xdet_head, xdet_resnet/xdet_multi_path, xdet_resnet/xdet_additional_conv',#None
    'Comma-separated list of scopes of variables to exclude when restoring from a checkpoint.')
tf.app.flags.DEFINE_boolean(
    'ignore_missing_vars', True,
    'When restoring a checkpoint would ignore missing variables.')
tf.app.flags.DEFINE_boolean(
    'run_on_cloud', True,
    'Wether we will train on cloud (pre-trained model will be placed in the "data_dir/cloud_checkpoint_path").')
tf.app.flags.DEFINE_string(
    'cloud_checkpoint_path', 'resnet50/model.ckpt',
    'The path to a checkpoint from which to fine-tune.')

FLAGS = tf.app.flags.FLAGS

def input_pipeline():
    image_preprocessing_fn = lambda image_, shape_, glabels_, gbboxes_ : preprocessing_factory.get_preprocessing(
        'xdet_resnet', is_training=True)(image_, glabels_, gbboxes_, out_shape=[FLAGS.train_image_size] * 2, data_format=('NCHW' if FLAGS.data_format=='channels_first' else 'NHWC'))

