def test_relu_deeplift_exact_match_wo_mutliplying_by_inputs(self) -> None:
x1 = torch.tensor([1.0])
x2 = torch.tensor([2.0])
inputs = (x1, x2)
model = ReLUDeepLiftModel()
dl = DeepLift(model, multiply_by_inputs=False)
attributions = dl.attribute(inputs)
self.assertEqual(attributions[0][0], 2.0)
self.assertEqual(attributions[1][0], 0.5)
def test_relu_deeplift_with_hypothetical_contrib_func(self) -> None:
model = Conv1dDeepLiftModel()
rand_seq_data = torch.abs(torch.randn(2, 4, 1000))
rand_seq_ref = torch.abs(torch.randn(2, 4, 1000))
dls = DeepLift(model)
attr = dls.attribute(
rand_seq_data,
rand_seq_ref,
custom_attribution_func=_hypothetical_contrib_func,
target=(1, 0),
)
self.assertEqual(attr.shape, rand_seq_data.shape)
def test_lin_maxpool_lin_classification(self) -> None:
inputs = torch.ones(2, 4)
baselines = torch.tensor([[1, 2, 3, 9], [4, 8, 6, 7]]).float()
model = LinearMaxPoolLinearModel()
dl = DeepLift(model)
attrs, delta = dl.attribute(
inputs, baselines, target=0, return_convergence_delta=True
)
expected = [[0.0, 0.0, 0.0, -8.0], [0.0, -7.0, 0.0, 0.0]]
expected_delta = [0.0, 0.0]
assertArraysAlmostEqual(attrs.detach().numpy(), expected)
assertArraysAlmostEqual(delta.detach().numpy(), expected_delta)
def test_lin_maxpool_lin_classification(self) -> None:
inputs = torch.ones(2, 4)
baselines = torch.tensor([[1, 2, 3, 9], [4, 8, 6, 7]]).float()
model = LinearMaxPoolLinearModel()
dl = DeepLift(model)
attrs, delta = dl.attribute(inputs,
baselines,
target=0,
return_convergence_delta=True)
expected = torch.Tensor([[0.0, 0.0, 0.0, -8.0], [0.0, -7.0, 0.0, 0.0]])
expected_delta = torch.Tensor([0.0, 0.0])
assertTensorAlmostEqual(self, attrs, expected, 0.0001)
assertTensorAlmostEqual(self, delta, expected_delta, 0.0001)
def test_relu_deeplift_exact_match(self) -> None:
x1 = torch.tensor([1.0], requires_grad=True)
x2 = torch.tensor([2.0], requires_grad=True)
b1 = torch.tensor([0.0], requires_grad=True)
b2 = torch.tensor([0.0], requires_grad=True)
inputs = (x1, x2)
baselines = (b1, b2)
model = ReLUDeepLiftModel()
dl = DeepLift(model)
attributions, delta = dl.attribute(
inputs, baselines, return_convergence_delta=True
)
self.assertEqual(attributions[0][0], 2.0)
self.assertEqual(attributions[1][0], 1.0)
self.assertEqual(delta[0], 0.0)
def test_sigmoid_classification(self):
num_in = 20
input = torch.arange(0.0, num_in * 1.0, requires_grad=True).unsqueeze(0)
baseline = 0 * input
target = torch.tensor(0)
# TODO add test cases for multiple different layers
model = SigmoidDeepLiftModel(num_in, 5, 1)
dl = DeepLift(model)
model.zero_grad()
attributions, delta = dl.attribute(
input, baseline, target=target, return_convergence_delta=True
)
self._assert_attributions(model, attributions, input, baseline, delta, target)
# compare with integrated gradients
ig = IntegratedGradients(model)
attributions_ig = ig.attribute(input, baseline, target=target)
assertAttributionComparision(self, (attributions,), (attributions_ig,))
def test_reusable_modules(self) -> None:
model = BasicModelWithReusableModules()
input = torch.rand(1, 3)
dl = DeepLift(model)
with self.assertRaises(RuntimeError):
dl.attribute(input, target=0)
