metrics.py 文件源码

def dice_whole_mod(y_true, y_pred):
    """
    Computes the Sorensen-Dice metric, where P come from class 1,2,3,4,0
                    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
    """
    # mask = K.expand_dims(K.sum(y_true,axis=4),axis=4)
    # cmp_mask = K.concatenate([K.ones_like(mask) - mask,K.zeros_like(mask), K.zeros_like(mask)],axis=4)
    # y_pred = y_pred + cmp_mask

    y_true = y_true[:,:,:,:,:3]
    y_pred_decision = tf.floor((y_pred  + K.epsilon()) / K.max(y_pred, axis=4, keepdims=True))

    mask_true = K.sum(y_true, axis=4)
    mask_pred = K.sum(y_pred_decision, axis=4) * K.sum(y_true, 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())