bbbc006.py 文件源码

def loss(c_fuse, s_fuse, labels):
    """Add L2Loss to all the trainable variables.
    Add summary for "Loss" and "Loss/avg".
    Args:
        c_fuse: Contours output map from inference().
        s_fuse: Segments output map from inference().
        labels: Labels from distorted_inputs or inputs().

    Returns:
      Loss tensor of type float.
    """
    # Calculate the average cross entropy loss across the batch.

    # Split the labels tensor into contours and segments image tensors
    # Each has shape [FLAGS.batch_size, 696, 520, 1]
    contours_labels, segments_labels = tf.split(labels, 2, 3)

    _add_cross_entropy(contours_labels, c_fuse, 'c')
    _add_cross_entropy(segments_labels, s_fuse, 's')

    return tf.add_n(tf.get_collection('losses'), name='total_loss')