tensorflow下使用CIOU损失函数的Mask RCNN的代码

由于CIOU损失函数不是TensorFlow自带的损失函数，需要自定义实现。以下是在Mask RCNN中使用CIOU损失函数的代码：

import tensorflow as tf
import numpy as np

def compute_ciou(box1, box2):
    """
    计算CIOU
    """
    def cal_area(box):
        """
        计算框的面积
        """
        return (box[..., 2] - box[..., 0]) * (box[..., 3] - box[..., 1])

    # 计算两个框的面积
    box1_area = cal_area(box1)
    box2_area = cal_area(box2)

    # 计算相交矩形的坐标
    lt = tf.maximum(box1[..., :2], box2[..., :2])
    rb = tf.minimum(box1[..., 2:], box2[..., 2:])

    # 计算相交矩形的面积
    wh = tf.maximum(0.0, rb - lt)
    inter_area = wh[..., 0] * wh[..., 1]

    # 计算并集矩形的面积
    union_area = box1_area + box2_area - inter_area

    # 计算CIOU
    iou = inter_area / union_area
    v = tf.pow((1 - iou), 2)
    alpha = v / (1 - iou + v)
    center_distance = tf.reduce_sum(tf.square((box1[..., :2] + box1[..., 2:]) / 2 - (box2[..., :2] + box2[..., 2:]) / 2), axis=-1)
    diagonal_distance = tf.reduce_sum(tf.square(box1[..., 2:] - box1[..., :2]) + tf.square(box2[..., 2:] - box2[..., :2]), axis=-1)
    ciou = iou - (center_distance + alpha * diagonal_distance) / union_area
    return ciou

def ciou_loss(y_true, y_pred):
    """
    计算CIOU损失函数
    """
    box_true, class_true, mask_true = y_true
    box_pred, class_pred, mask_pred = y_pred

    # 计算CIOU
    ciou = compute_ciou(box_true, box_pred)

    # 计算类别损失
    class_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=class_true, logits=class_pred))

    # 计算掩码损失
    mask_loss = tf.reduce_mean(tf.boolean_mask(tf.nn.sigmoid_cross_entropy_with_logits(labels=mask_true, logits=mask_pred), mask_true))

    # 计算总损失
    loss = class_loss + ciou + mask_loss

    return loss

# 在Mask RCNN中使用CIOU损失函数
model.compile(optimizer=optimizer, loss=[ciou_loss, rpn_class_loss, rpn_bbox_loss, mrcnn_class_loss, mrcnn_bbox_loss, mrcnn_mask_loss])

在上面的代码中，`compute_ciou`函数用于计算CIOU，`ciou_loss`函数用于计算CIOU损失函数，`model.compile`函数中将CIOU损失函数作为第一个损失函数。由于Mask RCNN的损失函数需要分为多个部分，因此在`ciou_loss`函数中，还需要计算类别损失和掩码损失，并将三者相加得到总损失。