def test_rand_affine(self, input_param, input_data, expected_val): g = RandAffine(**input_param) g.set_random_state(123) result = g(**input_data) if input_param.get("cache_grid", False): self.assertTrue(g._cached_grid is not None) assert_allclose(result, expected_val, rtol=_rtol, atol=1e-4)
def test_rand_affine(self, input_param, input_data, expected_val): g = RandAffine(**input_param) g.set_random_state(123) result = g(**input_data) self.assertEqual(torch.is_tensor(result), torch.is_tensor(expected_val)) if torch.is_tensor(result): np.testing.assert_allclose(result.cpu().numpy(), expected_val.cpu().numpy(), rtol=1e-4, atol=1e-4) else: np.testing.assert_allclose(result, expected_val, rtol=1e-4, atol=1e-4)
def test_rand_affine(self, input_param, input_data, expected_val): g = RandAffine(**input_param) g.set_random_state(123) result = g(**input_data) if input_param.get("cache_grid", False): self.assertTrue(g._cached_grid is not None) self.assertEqual(isinstance(result, torch.Tensor), isinstance(expected_val, torch.Tensor)) if isinstance(result, torch.Tensor): np.testing.assert_allclose(result.cpu().numpy(), expected_val.cpu().numpy(), rtol=1e-4, atol=1e-4) else: np.testing.assert_allclose(result, expected_val, rtol=1e-4, atol=1e-4)