def test_filter_ips_white_list(self):
        CONF.network_label_regex = '.*'
        CONF.ip_regex = '^(15.|123.)'
        CONF.black_list_regex = '^10.123.123.*'
        ip = self.instance.get_visible_ip_addresses()
        ip = filter_ips(
            ip, CONF.ip_regex, CONF.black_list_regex)
        self.assertEqual(2, len(ip))
        self.assertTrue('123.123.123.123' in ip)
        self.assertTrue('15.123.123.123' in ip)
def gradient(self, idx, inputs):
        self.model.zero_grad()
        X = [x.requires_grad_() for x in inputs]
        outputs = self.model(*X)
        selected = [val for val in outputs[:, idx]]
        grads = []
        if self.interim:
            interim_inputs = self.layer.target_input
            for idx, input in enumerate(interim_inputs):
                grad = torch.autograd.grad(selected, input,
                                           retain_graph=True if idx + 1 < len(interim_inputs) else None,
                                           allow_unused=True)[0]
                if grad is not None:
                    grad = grad.cpu().numpy()
                else:
                    grad = torch.zeros_like(X[idx]).cpu().numpy()
                grads.append(grad)
            del self.layer.target_input
            return grads, [i.detach().cpu().numpy() for i in interim_inputs]
        else:
            for idx, x in enumerate(X):
                grad = torch.autograd.grad(selected, x,
                                           retain_graph=True if idx + 1 < len(X) else None,
                                           allow_unused=True)[0]
                if grad is not None:
                    grad = grad.cpu().numpy()
                else:
                    grad = torch.zeros_like(X[idx]).cpu().numpy()
                grads.append(grad)
            return grads