def test_lrp_simple_attributions(self) -> None:
model, inputs = _get_simple_model()
model.eval()
model.linear.rule = EpsilonRule() # type: ignore
model.linear2.rule = EpsilonRule() # type: ignore
lrp = LRP(model)
relevance = lrp.attribute(inputs)
assertTensorAlmostEqual(self, relevance,
torch.tensor([18.0, 36.0, 54.0]))
def test_lrp_simple_attributions_all_layers(self):
model, inputs = _get_simple_model(inplace=False)
model.eval()
model.linear.rule = EpsilonRule()
model.linear2.rule = EpsilonRule()
layers = [model.linear, model.linear2]
lrp = LayerLRP(model, layers)
relevance = lrp.attribute(inputs, attribute_to_layer_input=True)
self.assertEqual(len(relevance), 2)
assertTensorAlmostEqual(self, relevance[0][0],
torch.tensor([[[18.0, 36.0, 54.0]]]))
def test_lrp_simple_inplaceReLU(self) -> None:
model_default, inputs = _get_simple_model()
model_inplace, _ = _get_simple_model(inplace=True)
for model in [model_default, model_inplace]:
model.eval()
model.linear.rule = EpsilonRule() # type: ignore
model.linear2.rule = EpsilonRule() # type: ignore
lrp_default = LRP(model_default)
lrp_inplace = LRP(model_inplace)
relevance_default = lrp_default.attribute(inputs)
relevance_inplace = lrp_inplace.attribute(inputs)
assertTensorAlmostEqual(self, relevance_default, relevance_inplace)
def test_lrp_simple_attributions(self):
model, inputs = _get_simple_model(inplace=False)
model.eval()
model.linear.rule = EpsilonRule()
model.linear2.rule = EpsilonRule()
lrp_upper = LayerLRP(model, model.linear2)
relevance_upper, delta = lrp_upper.attribute(
inputs,
attribute_to_layer_input=True,
return_convergence_delta=True)
lrp_lower = LayerLRP(model, model.linear)
relevance_lower = lrp_lower.attribute(inputs)
assertTensorAlmostEqual(self, relevance_lower[0], relevance_upper[0])
self.assertEqual(delta.item(), 0)
def test_lrp_simple_attributions_batch(self) -> None:
model, inputs = _get_simple_model()
model.eval()
model.linear.rule = EpsilonRule() # type: ignore
model.linear2.rule = EpsilonRule() # type: ignore
lrp = LRP(model)
inputs = torch.cat((inputs, 3 * inputs))
relevance, delta = lrp.attribute(inputs,
target=0,
return_convergence_delta=True)
self.assertEqual(relevance.shape, inputs.shape) # type: ignore
self.assertEqual(delta.shape[0], inputs.shape[0]) # type: ignore
assertTensorAlmostEqual(
self, relevance,
torch.Tensor([[18.0, 36.0, 54.0], [54.0, 108.0, 162.0]]))
def test_lrp_simple_inplaceReLU(self):
model_default, inputs = _get_simple_model()
model_inplace, _ = _get_simple_model(inplace=True)
for model in [model_default, model_inplace]:
model.eval()
model.linear.rule = EpsilonRule()
model.linear2.rule = EpsilonRule()
lrp_default = LayerLRP(model_default, model_default.linear2)
lrp_inplace = LayerLRP(model_inplace, model_inplace.linear2)
relevance_default = lrp_default.attribute(
inputs, attribute_to_layer_input=True)
relevance_inplace = lrp_inplace.attribute(
inputs, attribute_to_layer_input=True)
assertTensorAlmostEqual(self, relevance_default[0],
relevance_inplace[0])
def test_lrp_skip_connection(self) -> None:
# A custom addition module needs to be used so that relevance is
# propagated correctly.
class Addition_Module(nn.Module):
def __init__(self) -> None:
super().__init__()
def forward(self, x1: Tensor, x2: Tensor) -> Tensor:
return x1 + x2
class SkipConnection(nn.Module):
def __init__(self) -> None:
super().__init__()
self.linear = nn.Linear(2, 2, bias=False)
self.linear.weight.data.fill_(5)
self.add = Addition_Module()
def forward(self, input: Tensor) -> Module:
x = self.add(self.linear(input), input)
return x
model = SkipConnection()
input = torch.Tensor([[2, 3]])
model.add.rule = EpsilonRule() # type: ignore
lrp = LRP(model)
relevance = lrp.attribute(input, target=1)
assertTensorAlmostEqual(self, relevance, torch.Tensor([[10, 18]]))
def test_lrp_simple_attributions_all_layers_delta(self):
model, inputs = _get_simple_model(inplace=False)
model.eval()
model.linear.rule = EpsilonRule()
model.linear2.rule = EpsilonRule()
layers = [model.linear, model.linear2]
lrp = LayerLRP(model, layers)
inputs = torch.cat((inputs, 2 * inputs))
relevance, delta = lrp.attribute(inputs,
attribute_to_layer_input=True,
return_convergence_delta=True)
self.assertEqual(len(relevance), len(delta))
assertTensorAlmostEqual(
self,
relevance[0],
torch.tensor([[18.0, 36.0, 54.0], [36.0, 72.0, 108.0]]),
)
def test_lrp_Identity(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 = IdentityRule() # type: ignore
model.linear2.rule = EpsilonRule() # type: ignore
lrp = LRP(model)
relevance = lrp.attribute(inputs)
assertTensorAlmostEqual(self, relevance,
torch.tensor([24.0, 36.0, 36.0]))