def _assert_attributions(
self,
model,
layer,
inputs,
baselines,
target,
expected,
expected_delta=None,
n_samples=5,
attribute_to_layer_input=False,
add_args=None,
):
lgs = LayerGradientShap(model, layer)
attrs, delta = lgs.attribute(
inputs,
baselines=baselines,
target=target,
additional_forward_args=add_args,
n_samples=n_samples,
stdevs=0.0009,
return_convergence_delta=True,
attribute_to_layer_input=attribute_to_layer_input,
)
if isinstance(attrs, tuple):
for attr, exp in zip(attrs, expected):
assertTensorAlmostEqual(self, attr, exp, 0.005)
else:
assertTensorAlmostEqual(self, attrs, expected, 0.005)
if expected_delta is None:
_assert_attribution_delta(self, inputs, attrs, n_samples, delta, True)
else:
for delta_i, expected_delta_i in zip(delta, expected_delta):
self.assertAlmostEqual(delta_i, expected_delta_i, delta=0.01)
def test_basic_multilayer_wo_multiplying_by_inputs(self) -> None:
model = BasicModel_MultiLayer(inplace=True)
model.eval()
inputs = torch.tensor([[1.0, -20.0, 10.0]])
baselines = torch.zeros(3, 3)
lgs = LayerGradientShap(model, model.linear2, multiply_by_inputs=False)
attrs = lgs.attribute(inputs, baselines, target=0, stdevs=0.0,)
assertTensorAlmostEqual(self, attrs, torch.tensor([[1.0, 0.0]]))
def _assert_attributions(
self,
model: Module,
layer: Module,
inputs: TensorOrTupleOfTensorsGeneric,
baselines: Union[TensorOrTupleOfTensorsGeneric, Callable],
target: TargetType,
expected: Union[
Tensor,
Tuple[Tensor, ...],
List[float],
List[List[float]],
Tuple[List[float], ...],
Tuple[List[List[float]], ...],
],
expected_delta: Tensor = None,
n_samples: int = 5,
attribute_to_layer_input: bool = False,
add_args: Any = None,
) -> None:
lgs = LayerGradientShap(model, layer)
attrs, delta = lgs.attribute(
inputs,
baselines,
target=target,
additional_forward_args=add_args,
n_samples=n_samples,
stdevs=0.0009,
return_convergence_delta=True,
attribute_to_layer_input=attribute_to_layer_input,
)
assertTensorTuplesAlmostEqual(self, attrs, expected, delta=0.005)
if expected_delta is None:
_assert_attribution_delta(self, inputs, attrs, n_samples, delta, True)
else:
for delta_i, expected_delta_i in zip(delta, expected_delta):
assertTensorAlmostEqual(self, delta_i, expected_delta_i, delta=0.01)