91b0d220c8e816766fd4565e1d2f5115d3afbefe,test/functions/test_inv_quad_log_det.py,TestInvQuadLogDetBatch,setUp,#TestInvQuadLogDetBatch#,115

Before Change

            self.rng_state = torch.get_rng_state()
            torch.manual_seed(1)

        mats = [[[3, -1, 0], [-1, 3, 0], [0, 0, 3]], [[10, -2, 1], [-2, 10, 0], [1, 0, 10]]]
        self.mats_var = torch.tensor(mats, dtype=torch.float, requires_grad=True)
        self.mats_var_clone = self.mats_var.clone().detach().requires_grad_(True)
        self.vecs_var = torch.randn(2, 3, 4, requires_grad=True)
        self.vecs_var_clone = self.vecs_var.clone().detach().requires_grad_(True)

    def tearDown(self):
        if hasattr(self, "rng_state"):

After Change

        if hasattr(self, "rng_state"):
            torch.set_rng_state(self.rng_state)

    def setUp(self):
        if os.getenv("unlock_seed") is None or os.getenv("unlock_seed").lower() == "false":
            self.rng_state = torch.get_rng_state()
            torch.manual_seed(1)
            if torch.cuda.is_available():
                torch.cuda.manual_seed_all(1)
            random.seed(1)

        mats = torch.randn(5, 4, 4)
        mats = mats @ mats.transpose(-1, -2)
        mats.div_(5).add_(torch.eye(4).unsqueeze_(0))
        vecs = torch.randn(5, 4, 6)
        self.mats = mats.detach().clone().requires_grad_(True)
        self.mats_clone = mats.detach().clone().requires_grad_(True)
        self.vecs = vecs.detach().clone().requires_grad_(True)
        self.vecs_clone = vecs.detach().clone().requires_grad_(True)

    def test_inv_quad_log_det_many_vectors(self):
        // Forward pass

Instances