def _guided_backprop_matching_assert(
self, model: Module, output_layer: Module, test_input: TensorOrTupleOfTensors
):
out = model(test_input)
attrib = GuidedBackprop(model)
neuron_attrib = NeuronGuidedBackprop(model, output_layer)
for i in range(out.shape[1]):
gbp_vals = attrib.attribute(test_input, target=i)
neuron_gbp_vals = neuron_attrib.attribute(test_input, (i,))
assertTensorAlmostEqual(self, gbp_vals, neuron_gbp_vals, delta=0.01)
def _guided_backprop_test_assert(self,
model,
test_input,
expected,
additional_input=None):
guided_backprop = GuidedBackprop(model)
attributions = guided_backprop.attribute(
test_input, target=0, additional_forward_args=additional_input)
for i in range(len(test_input)):
assertTensorAlmostEqual(self,
attributions[i],
expected[i],
delta=0.01)
def _guided_backprop_test_assert(
self,
model: Module,
test_input: TensorOrTupleOfTensorsGeneric,
expected: Tuple[List[List[float]], ...],
additional_input: Any = None,
) -> None:
guided_backprop = GuidedBackprop(model)
attributions = guided_backprop.attribute(
test_input, target=0, additional_forward_args=additional_input
)
for i in range(len(test_input)):
assertTensorAlmostEqual(self, attributions[i], expected[i], delta=0.01)
def _guided_backprop_matching_assert(
self,
model: Module,
output_layer: Module,
test_input: TensorOrTupleOfTensorsGeneric,
):
out = model(test_input)
model_copy = copy.deepcopy(model)
attrib = GuidedBackprop(model_copy)
self.assertFalse(attrib.multiplies_by_inputs)
neuron_attrib = NeuronGuidedBackprop(model, output_layer)
for i in range(out.shape[1]):
gbp_vals = attrib.attribute(test_input, target=i)
neuron_gbp_vals = neuron_attrib.attribute(test_input, (i, ))
assertTensorAlmostEqual(self,
gbp_vals,
neuron_gbp_vals,
delta=0.01)