import tensorflow as tf

    'The parent directory where the model will be stored.')
tf.app.flags.DEFINE_integer(
    'log_every_n_steps', 10,
    'The frequency with which logs are print.')
tf.app.flags.DEFINE_integer(
    'save_summary_steps', 100,
    'The frequency with which summaries are saved, in seconds.')
tf.app.flags.DEFINE_integer(
    'save_checkpoints_secs', 3600,
    'The frequency with which the model is saved, in seconds.')
# model related configuration
tf.app.flags.DEFINE_integer(
    'train_image_size', 384,
    'The size of the input image for the model to use.')
tf.app.flags.DEFINE_integer(
    'heatmap_size', 96,
    'The size of the output heatmap of the model.')
tf.app.flags.DEFINE_string(
    'backbone', 'seresnext50',#or seresnext50 seresnet50
    'The backbone network to use for feature pyramid.')
tf.app.flags.DEFINE_float(
    'heatmap_sigma', 1.,
    'The sigma of Gaussian which generate the target heatmap.')
tf.app.flags.DEFINE_float(
    'bbox_border', 25.,
    'The nearest distance of the crop border to al keypoints.')
tf.app.flags.DEFINE_integer(
    'train_epochs', 50,
