import tensorflow as tf

# checkpoint related configuration
tf.app.flags.DEFINE_string(
    'checkpoint_path', './model/resnet50',#None,
    'The path to a checkpoint from which to fine-tune.')
tf.app.flags.DEFINE_string(
    'checkpoint_model_scope', '',
    'Model scope in the checkpoint. None if the same as the trained model.')
tf.app.flags.DEFINE_string(
    'model_scope', 'xdet_resnet',
    '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():
