def _layer_activation_test_assert(
self,
model: Module,
target_layer: Module,
test_input: Union[Tensor, Tuple[Tensor, ...]],
expected_activation: Union[List[float], Tuple[List[float], ...]],
additional_input: Any = None,
) -> None:
layer_act = LayerGradientXActivation(model, target_layer)
self.assertTrue(layer_act.multiplies_by_inputs)
attributions = layer_act.attribute(
test_input, target=0, additional_forward_args=additional_input)
assertTensorTuplesAlmostEqual(self,
attributions,
expected_activation,
delta=0.01)
# test Layer Gradient without multiplying with activations
layer_grads = LayerGradientXActivation(model,
target_layer,
multiply_by_inputs=False)
layer_act = LayerActivation(model, target_layer)
self.assertFalse(layer_grads.multiplies_by_inputs)
grads = layer_grads.attribute(test_input,
target=0,
additional_forward_args=additional_input)
acts = layer_act.attribute(test_input,
additional_forward_args=additional_input)
assertTensorTuplesAlmostEqual(
self,
attributions,
tuple(act * grad for act, grad in zip(acts, grads)),
delta=0.01,
)
def test_gradient_activation_embedding(self) -> None:
input1 = torch.tensor([2, 5, 0, 1])
input2 = torch.tensor([3, 0, 0, 2])
model = BasicEmbeddingModel()
layer_act = LayerGradientXActivation(model, model.embedding1)
self.assertEqual(
list(layer_act.attribute(inputs=(input1, input2)).shape), [4, 100])
def test_gradient_activation_embedding_no_grad(self) -> None:
input1 = torch.tensor([2, 5, 0, 1])
input2 = torch.tensor([3, 0, 0, 2])
model = BasicEmbeddingModel()
for param in model.parameters():
param.requires_grad = False
with torch.no_grad():
layer_act = LayerGradientXActivation(model, model.embedding1)
self.assertEqual(
list(layer_act.attribute(inputs=(input1, input2)).shape),
[4, 100])
def _layer_activation_test_assert(
self,
model,
target_layer,
test_input,
expected_activation,
additional_input=None,
):
layer_act = LayerGradientXActivation(model, target_layer)
attributions = layer_act.attribute(
test_input, target=0, additional_forward_args=additional_input)
assertArraysAlmostEqual(attributions.squeeze(0).tolist(),
expected_activation,
delta=0.01)
def _layer_activation_test_assert(
self,
model,
target_layer,
test_input,
expected_activation,
additional_input=None,
):
layer_act = LayerGradientXActivation(model, target_layer)
attributions = layer_act.attribute(
test_input, target=0, additional_forward_args=additional_input
)
assertTensorTuplesAlmostEqual(
self, attributions, expected_activation, delta=0.01
)
def _layer_activation_test_assert(
self,
model: Module,
target_layer: Module,
test_input: Union[Tensor, Tuple[Tensor, ...]],
expected_activation: Union[List[float], Tuple[List[float], ...]],
additional_input: Any = None,
) -> None:
layer_act = LayerGradientXActivation(model, target_layer)
attributions = layer_act.attribute(
test_input, target=0, additional_forward_args=additional_input)
assertTensorTuplesAlmostEqual(self,
attributions,
expected_activation,
delta=0.01)