在尝试eager execution的时候，突然发现一个tape.gradient()死活都是none的情况

当时的代码是这样的

def total_loss(pred, images, labels, bboxes, landmarks):
    """
    Return
    --------------------
        (total_loss, cls_loss, bbox_loss, landmark_loss)
    """
    c_loss = cls_loss(pred[0], labels)
    b_loss = bbox_loss(pred[1], bboxes, labels)
    l_loss = landmark_loss(pred[2], landmarks, labels)
    return c_loss + 0.5 * b_loss + 0.5 * l_loss#, c_loss, b_loss, l_loss

def grad(model, images, labels, bboxes, landmarks):
    pred = model(images)
    with tf.GradientTape() as tape:
        loss_value = total_loss(pred, images, labels, bboxes, landmarks)
    return loss_value, tape.gradient(loss_value, model.trainable_variables)

然后在多次尝试下，最终发现如果把代码改成下面这样，就能得到梯度

def total_loss(model, images, labels, bboxes, landmarks):
    """
    Return
    --------------------
        (total_loss, cls_loss, bbox_loss, landmark_loss)
    """
    pred = model(images)
    c_loss = cls_loss(pred[0], labels)
    b_loss = bbox_loss(pred[1], bboxes, labels)
    l_loss = landmark_loss(pred[2], landmarks, labels)
    return c_loss + 0.5 * b_loss + 0.5 * l_loss#, c_loss, b_loss, l_loss

def grad(model, images, labels, bboxes, landmarks):
    with tf.GradientTape() as tape:
        # must execute model(x) in the context of tf.GradientTape()
        loss_value = total_loss(model, images, labels, bboxes, landmarks)
    return loss_value, tape.gradient(loss_value, model.trainable_variables)

看到唯一的区别了吗？这也是根本原因所在。
计算model的输出必须在tf.GradientTape()的上下文中进行。
StackOverflow上有一个同样的问题，解释的很好