def _assert_multi_variable(
self,
type: str,
approximation_method: str = "gausslegendre",
multiply_by_inputs: bool = True,
) -> None:
model = BasicModel2()
input1 = torch.tensor([3.0])
input2 = torch.tensor([1.0], requires_grad=True)
baseline1 = torch.tensor([0.0])
baseline2 = torch.tensor([0.0])
attributions1 = self._compute_attribution_and_evaluate(
model,
(input1, input2),
(baseline1, baseline2),
type=type,
approximation_method=approximation_method,
multiply_by_inputs=multiply_by_inputs,
)
if type == "vanilla":
assertArraysAlmostEqual(
attributions1[0].tolist(),
[1.5] if multiply_by_inputs else [0.5],
delta=0.05,
)
assertArraysAlmostEqual(
attributions1[1].tolist(),
[-0.5] if multiply_by_inputs else [-0.5],
delta=0.05,
)
model = BasicModel3()
attributions2 = self._compute_attribution_and_evaluate(
model,
(input1, input2),
(baseline1, baseline2),
type=type,
approximation_method=approximation_method,
multiply_by_inputs=multiply_by_inputs,
)
if type == "vanilla":
assertArraysAlmostEqual(
attributions2[0].tolist(),
[1.5] if multiply_by_inputs else [0.5],
delta=0.05,
)
assertArraysAlmostEqual(
attributions2[1].tolist(),
[-0.5] if multiply_by_inputs else [-0.5],
delta=0.05,
)
# Verifies implementation invariance
self.assertEqual(
sum(attribution for attribution in attributions1),
sum(attribution for attribution in attributions2),
)
def test_simple_multi_input(self) -> None:
net = BasicModel3()
inp1 = torch.tensor([[-10.0], [3.0]])
inp2 = torch.tensor([[-5.0], [1.0]])
self._occlusion_test_assert(
net,
(inp1, inp2),
([0.0, 1.0], [0.0, -1.0]),
sliding_window_shapes=((1, ), (1, )),
)
def test_simple_multi_input_int_to_float(self) -> None:
net = BasicModel3()
def wrapper_func(*inp):
return net(*inp).float()
inp1 = torch.tensor([[-10], [3]])
inp2 = torch.tensor([[-5], [1]])
self._occlusion_test_assert(
wrapper_func,
(inp1, inp2),
([0.0, 1.0], [0.0, -1.0]),
sliding_window_shapes=((1, ), (1, )),
)