loss_func.py 文件源码

def mean_IoU(y_true, y_pred):
    s = K.shape(y_true)

    # reshape such that w and h dim are multiplied together
    y_true_reshaped = K.reshape( y_true, tf.stack( [-1, s[1]*s[2], s[-1]] ) )
    y_pred_reshaped = K.reshape( y_pred, tf.stack( [-1, s[1]*s[2], s[-1]] ) )

    # correctly classified
    clf_pred = K.one_hot( K.argmax(y_pred_reshaped), nb_classes = s[-1])
    equal_entries = K.cast(K.equal(clf_pred,y_true_reshaped), dtype='float32') * y_true_reshaped

    intersection = K.sum(equal_entries, axis=1)
    union_per_class = K.sum(y_true_reshaped,axis=1) + K.sum(y_pred_reshaped,axis=1)

    iou = intersection / (union_per_class - intersection)
    iou_mask = tf.is_finite(iou)
    iou_masked = tf.boolean_mask(iou,iou_mask)

    return K.mean( iou_masked )