metrics.py 文件源码

def dice_core(y_true, y_pred):
    """
    Computes the Sorensen-Dice metric, where P come from class 1,2,3,4,5
                    TP
        Dice = 2 -------
                  T + P
    Parameters
    ----------
    y_true : keras.placeholder
        Placeholder that contains the ground truth labels of the classes
    y_pred : keras.placeholder
        Placeholder that contains the class prediction

    Returns
    -------
    scalar
        Dice metric
    """

    y_pred_decision = tf.floor((y_pred  + K.epsilon()) / K.max(y_pred, axis=4, keepdims=True))

    mask_true1 = y_true[:, :, :, :, 3:]
    mask_true2 = y_true[:, :, :, :, 1:2]
    mask_true = K.sum(K.concatenate([mask_true1, mask_true2], axis=4), axis=4)
    mask_pred1 = y_pred_decision[:, :, :, :, 3:]
    mask_pred2 = y_pred_decision[:, :, :, :, 1:2]
    mask_pred = K.sum(K.concatenate([mask_pred1, mask_pred2], axis=4), axis=4)

    y_sum = K.sum(mask_true * mask_pred)

    return (2. * y_sum + K.epsilon()) / (K.sum(mask_true) + K.sum(mask_pred) + K.epsilon())