def _assert_attributions(
self,
model: Module,
layer: Module,
inputs: Tensor,
baselines: Union[Tensor, Callable[..., Tensor]],
neuron_ind: Union[int, Tuple[Union[int, slice], ...]],
n_samples: int = 5,
) -> None:
ngs = NeuronGradientShap(model, layer)
nig = NeuronIntegratedGradients(model, layer)
attrs_gs = ngs.attribute(
inputs, neuron_ind, baselines=baselines, n_samples=n_samples, stdevs=0.09
)
if callable(baselines):
baselines = baselines(inputs)
attrs_ig = []
for baseline in torch.unbind(baselines):
attrs_ig.append(
nig.attribute(inputs, neuron_ind, baselines=baseline.unsqueeze(0))
)
combined_attrs_ig = torch.stack(attrs_ig, dim=0).mean(dim=0)
self.assertTrue(ngs.multiplies_by_inputs)
assertTensorAlmostEqual(self, attrs_gs, combined_attrs_ig, 0.5)
def _assert_attributions(self,
model,
layer,
inputs,
baselines,
neuron_ind,
n_samples=5):
ngs = NeuronGradientShap(model, layer)
nig = NeuronIntegratedGradients(model, layer)
attrs_gs = ngs.attribute(inputs,
neuron_ind,
baselines=baselines,
n_samples=n_samples,
stdevs=0.09)
if callable(baselines):
baselines = baselines(inputs)
attrs_ig = []
for baseline in baselines:
attrs_ig.append(
nig.attribute(inputs,
neuron_ind,
baselines=baseline.unsqueeze(0)))
attrs_ig = torch.stack(attrs_ig, axis=0).mean(axis=0)
assertTensorAlmostEqual(self, attrs_gs, attrs_ig, 0.5)
def test_basic_multilayer(self) -> None:
model = BasicModel_MultiLayer(inplace=True)
model.eval()
inputs = torch.tensor([[1.0, 20.0, 10.0]])
baselines = torch.zeros(2, 3)
ngs = NeuronGradientShap(model, model.linear1, multiply_by_inputs=False)
attr = ngs.attribute(inputs, 0, baselines=baselines, stdevs=0.0)
self.assertFalse(ngs.multiplies_by_inputs)
assertTensorAlmostEqual(self, attr, [1.0, 1.0, 1.0])