I need to implement custom loss function and I saw following tutorial.
class LinearFunction(Function):

    @staticmethod
    def forward(ctx, input, weight, bias=None):
        ctx.save_for_backward(input, weight, bias)
        output = input.mm(weight.t())
        if bias is not None:
            output += bias.unsqueeze(0).expand_as(output)
        return output

    @staticmethod
    def backward(ctx, grad_output):
        input, weight, bias = ctx.saved_tensors
        grad_input = grad_weight = grad_bias = None

        if ctx.needs_input_grad[0]:
            grad_input = grad_output.mm(weight)
        if ctx.needs_input_grad[1]:
            grad_weight = grad_output.t().mm(input)
        if bias is not None and ctx.needs_input_grad[2]:
            grad_bias = grad_output.sum(0).squeeze(0)

        return grad_input, grad_weight, grad_bias

But I don’t know how to use above guide for my situation.
I have ground truth data, but it’s not a label data.
I perform math formulas with GT data through all image pixels and I obtain one float number.
And I want to update weights in CNN as above float number loss value minimizes.
How to implement forward() and backward() in this case?