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)