Question

在尝试实现Intersection over Union（IoU）时，我遇到了一个我似乎无法放置的python / keras错误。在单独的文件中，我定义了以下指标：

def computeIoU(y_pred_batch, y_true_batch):
    print y_true_batch.shape[0]
    return np.mean(np.asarray([imageIoU(y_pred_batch[i], y_true_batch[i]) for i in range(y_true_batch.shape[0])]))

def imageIoU(y_pred, y_true):
    y_pred = np.argmax(y_pred, axis=2)
    y_true = np.argmax(y_true, axis=2)
    inter = 0
    union = 0
    for x in range(imCols):
        for y in range(imRows):
            for i in range(num_classes):
                inter += (y_pred[y][x] == y_true[y][x] == i)
                union += (y_pred[y][x] == i or y_true[y][x] == i)
    print inter
    print union
    return float(inter)/union

在主文件中，我导入了该函数，并按如下方式使用指标：

fcn32_model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy', computeIoU])

引发的错误是

TypeError: __int__ should return int object

使用建议的Keras / tf语法从这里的答案和其他问题实现上述算法后，代码更改为：

def iou(y_pred_batch, y_true_batch):
    intersection = tf.zeros(())
    union = tf.zeros(())
    y_pred_batch = K.argmax(y_pred_batch, axis=-1)
    y_true_batch = K.argmax(y_true_batch, axis=-1)
    for i in range(num_classes):
        iTensor = tf.to_int64(tf.fill(y_pred_batch.shape, i))
        intersection = tf.add(intersection, tf.to_float(tf.count_nonzero(tf.logical_and(K.equal(y_true_batch, y_pred_batch), K.equal(y_true_batch, iTensor)))))
        union = tf.add(union, tf.to_float(tf.count_nonzero(tf.logical_or(K.equal(y_true_batch, iTensor), K.equal(y_pred_batch, iTensor)))))
    return intersection/union

Answer 1

问题似乎是你试图用普通整数计算，而不是keras变量。

intersection = K.sum(K.abs(y_true * y_pred), axis=-1)
union_sum = K.sum(K.abs(y_true) + K.abs(y_pred), axis=-1)
IOU = (intersection) / (union_sum- intersection)

自定义Keras公制投掷错误

1 个答案: