def execute(self, batch_size): base = jt.log(self.vertices.abs() / (1 - self.vertices.abs())) centroid = jt.tanh(self.center) vertices = (base + self.displace).sigmoid() * nn.sign(self.vertices) vertices = nn.relu(vertices) * (1 - centroid) - nn.relu(-vertices) * (centroid + 1) vertices = vertices + centroid # apply Laplacian and flatten geometry constraints laplacian_loss = self.laplacian_loss(vertices).mean() flatten_loss = self.flatten_loss(vertices).mean() return jr.Mesh(vertices.repeat(batch_size, 1, 1), self.faces.repeat(batch_size, 1, 1), dr_type='n3mr'), laplacian_loss, flatten_loss在每行代码后添加注释

以下是每行代码的注释：

def execute(self, batch_size):
    # 计算顶点的基础值，用于后续位移和形变的计算
    base = jt.log(self.vertices.abs() / (1 - self.vertices.abs()))
    # 计算几何中心点
    centroid = jt.tanh(self.center)
    # 计算顶点的位移和形变
    vertices = (base + self.displace).sigmoid() * nn.sign(self.vertices)
    vertices = nn.relu(vertices) * (1 - centroid) - nn.relu(-vertices) * (centroid + 1)
    vertices = vertices + centroid
    # 应用 Laplacian 和 flatten 约束损失
    laplacian_loss = self.laplacian_loss(vertices).mean()
    flatten_loss = self.flatten_loss(vertices).mean()
    # 返回优化后的模型、Laplacian 约束损失和 flatten 约束损失
    return jr.Mesh(vertices.repeat(batch_size, 1, 1), 
                   self.faces.repeat(batch_size, 1, 1), dr_type='n3mr'), laplacian_loss, flatten_loss

希望这些注释能够帮助您更好地理解这段代码的作用和功能。