def test_lrp_simple_attributions_AlphaBeta(self) -> None:
model, inputs = _get_simple_model()
with torch.no_grad():
model.linear.weight.data[0][0] = -2 # type: ignore
model.eval()
model.linear.rule = Alpha1_Beta0_Rule() # type: ignore
model.linear2.rule = Alpha1_Beta0_Rule() # type: ignore
lrp = LRP(model)
relevance = lrp.attribute(inputs)
assertTensorAlmostEqual(self, relevance,
torch.tensor([[12, 33.6, 50.4]]))
def test_lrp_simple_attributions_AlphaBeta(self):
model, inputs = _get_simple_model()
with torch.no_grad():
model.linear.weight.data[0][0] = -2
model.eval()
model.linear.rule = Alpha1_Beta0_Rule()
model.linear2.rule = Alpha1_Beta0_Rule()
lrp = LayerLRP(model, model.linear)
relevance = lrp.attribute(inputs)
assertTensorAlmostEqual(self, relevance[0],
torch.tensor([24.0, 36.0, 36.0]))
def test_lrp_simple_repeat_attributions(self) -> None:
model, inputs = _get_simple_model()
model.eval()
model.linear.rule = GammaRule() # type: ignore
model.linear2.rule = Alpha1_Beta0_Rule() # type: ignore
output = model(inputs)
lrp = LRP(model)
_ = lrp.attribute(inputs)
output_after = model(inputs)
assertTensorAlmostEqual(self, output, output_after)