在MASK-RCNN中使用的CIOU损失函数代码

以下是使用CIOU损失函数的MASK-RCNN的代码示例：

import tensorflow as tf
from tensorflow.keras.losses import Loss


def compute_ciou(box1, box2):
    # 计算两个边界框的IOU
    x1, y1, w1, h1 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3]
    x2, y2, w2, h2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3]
    area1, area2 = w1 * h1, w2 * h2
    x_min, y_min, x_max, y_max = tf.maximum(x1 - w1 / 2, x2 - w2 / 2), \
                                 tf.maximum(y1 - h1 / 2, y2 - h2 / 2), \
                                 tf.minimum(x1 + w1 / 2, x2 + w2 / 2), \
                                 tf.minimum(y1 + h1 / 2, y2 + h2 / 2)
    intersection, union = tf.maximum((x_max - x_min), 0) * tf.maximum((y_max - y_min), 0), \
                          tf.maximum((area1 + area2 - intersection), 1e-7)
    iou = intersection / union

    # 计算两个边界框的中心点距离
    center_distance = tf.square(x1 - x2) + tf.square(y1 - y2)

    # 计算两个边界框的对角线长度平方
    box1_diag, box2_diag = tf.square(w1) + tf.square(h1), tf.square(w2) + tf.square(h2)

    # 计算CIOU
    v = 4 * tf.square(tf.math.atan2(w2, h2) - tf.math.atan2(w1, h1)) / (tf.math.pi ** 2)
    with tf.device('/cpu:0'):
        alpha = v / (1 - iou + v)
    ciou = iou - (center_distance / box2_diag) - alpha * (1 - union / (area1 + area2 - union))
    return ciou


class CIOULoss(Loss):
    def __init__(self, weight=1.0, **kwargs):
        super(CIOULoss, self).__init__(**kwargs)
        self.weight = weight

    def call(self, y_true, y_pred):
        box1, box2 = y_true, y_pred
        ciou = tf.clip_by_value(compute_ciou(box1, box2), clip_value_min=-1.0, clip_value_max=1.0)
        loss = 1 - ciou
        loss = tf.reduce_mean(loss) * self.weight
        return loss

在这里，我们定义了一个`compute_ciou`函数来计算两个边界框的CIOU值，并在`CIOULoss`类中使用该函数来计算CIOU损失。最后，我们将损失乘以权重作为最终的损失。