Which true gradient does HSJA estimated for? (in function "approximate_gradient")

[machanic]

Dear Sir: In your code def approximate_gradient(model, sample, num_evals, delta, params), https://github.com/Jianbo-Lab/HSJA/blob/daecd5c7055d5214b39c34a7a28a98acd3557fbc/hsja.py#L165

I thought this code is used for estimating the true gradient as the following code:

    def get_grad(self, model, x, true_labels, target_labels):
        with torch.enable_grad():
            # x = torch.clamp(x,min=0,max=1.0).cuda()
            x = x.cuda()
            x.requires_grad_()
            logits = torch.softmax(model(x),dim=1)
            if true_labels is not None:
                true_labels = true_labels.cuda()
            if target_labels is not None:
                target_labels = target_labels.cuda()
            loss = self.cw_loss(logits, true_labels, target_labels)
            gradient = torch.autograd.grad(loss, x,retain_graph=True)[0].cpu().detach()
        return gradient

where cw_loss is defined as the following:

    def cw_loss(self, logit, label, target=None):
        if target is not None:
            # targeted cw loss: logit_t - max_{i\neq t}logit_i
            _, argsort = logit.sort(dim=1, descending=True)
            target_is_max = argsort[:, 0].eq(target).long()
            second_max_index = target_is_max.long() * argsort[:, 1] + (1 - target_is_max).long() * argsort[:, 0]
            target_logit = logit[torch.arange(logit.shape[0]), target]
            second_max_logit = logit[torch.arange(logit.shape[0]), second_max_index]
            return target_logit - second_max_logit
        else:
            # untargeted cw loss: max_{i\neq y}logit_i - logit_y
            _, argsort = logit.sort(dim=1, descending=True)
            gt_is_max = argsort[:, 0].eq(label).long()
            second_max_index = gt_is_max.long() * argsort[:, 1] + (1 - gt_is_max).long() * argsort[:, 0]
            gt_logit = logit[torch.arange(logit.shape[0]), label]
            second_max_logit = logit[torch.arange(logit.shape[0]), second_max_index]
            return second_max_logit - gt_logit

But when I compute the cosine simillarity between two gradient, I found it was very low, about 0.02? Which true gradient does approximate_gradient approximate? Can you give me some code or example please?