def test_convnet_maxpool3d_classification(self) -> None:
inputs = 100 * torch.randn(2, 1, 10, 10, 10)
model = BasicModel_ConvNet_MaxPool3d()
ndl = NeuronDeepLift(model, model.pool1)
attr = ndl.attribute(inputs, neuron_index=(0, 0, 0, 0))
ndl2 = NeuronDeepLift(model, model.conv2)
attr2 = ndl2.attribute(inputs,
neuron_index=(0, 0, 0, 0),
attribute_to_neuron_input=True)
assertTensorAlmostEqual(self, attr.sum(), attr2.sum())
def test_deeplift_compare_with_and_without_inplace(self) -> None:
model1 = ReLULinearDeepLiftModel(inplace=True)
model2 = ReLULinearDeepLiftModel()
x1 = torch.tensor([[-10.0, 1.0, -5.0]], requires_grad=True)
x2 = torch.tensor([[3.0, 3.0, 1.0]], requires_grad=True)
inputs = (x1, x2)
neuron_dl1 = NeuronDeepLift(model1, model1.relu)
attributions1 = neuron_dl1.attribute(inputs, 0, attribute_to_neuron_input=False)
neuron_dl2 = NeuronDeepLift(model2, model2.relu)
attributions2 = neuron_dl2.attribute(inputs, 0, attribute_to_neuron_input=False)
assertTensorAlmostEqual(self, attributions1[0], attributions2[0])
assertTensorAlmostEqual(self, attributions1[1], attributions2[1])
def test_lin_maxpool_lin_classification(self) -> None:
inputs = torch.ones(2, 4)
baselines = torch.tensor([[1, 2, 3, 9], [4, 8, 6, 7]]).float()
model = LinearMaxPoolLinearModel()
ndl = NeuronDeepLift(model, model.pool1)
attr = ndl.attribute(inputs, neuron_index=(0), baselines=baselines)
ndl2 = NeuronDeepLift(model, model.lin2)
attr2 = ndl2.attribute(
inputs,
neuron_index=(0),
baselines=baselines,
attribute_to_neuron_input=True,
)
assertTensorAlmostEqual(self, attr, attr2)
def test_relu_deeplift_with_custom_attr_func(self) -> None:
model = ReLULinearModel()
inputs, baselines = _create_inps_and_base_for_deeplift_neuron_layer_testing(
)
neuron_dl = NeuronDeepLift(model, model.l3)
expected = ([0.0, 0.0, 0.0], [0.0, 0.0, 0.0])
self._relu_custom_attr_func_assert(neuron_dl, inputs, baselines,
expected)
def test_relu_neuron_deeplift(self) -> None:
model = ReLULinearDeepLiftModel(inplace=True)
x1 = torch.tensor([[-10.0, 1.0, -5.0]], requires_grad=True)
x2 = torch.tensor([[3.0, 3.0, 1.0]], requires_grad=True)
inputs = (x1, x2)
neuron_dl = NeuronDeepLift(model, model.relu)
attributions = neuron_dl.attribute(inputs, 0, attribute_to_neuron_input=False)
assertTensorAlmostEqual(self, attributions[0], [[0.0, 0.0, 0.0]])
assertTensorAlmostEqual(self, attributions[1], [[0.0, 0.0, 0.0]])
def test_linear_neuron_deeplift_wo_inp_marginal_effects(self) -> None:
model = ReLULinearModel()
inputs, baselines = _create_inps_and_base_for_deeplift_neuron_layer_testing(
)
neuron_dl = NeuronDeepLift(model, model.l3, multiply_by_inputs=False)
attributions = neuron_dl.attribute(inputs,
0,
baselines,
attribute_to_neuron_input=False)
assertTensorAlmostEqual(self, attributions[0], [[-0.0, 0.0, -0.0]])
assertTensorAlmostEqual(self, attributions[1], [[2.0, 3.0, 0.0]])
def test_linear_neuron_deeplift(self) -> None:
model = ReLULinearDeepLiftModel()
inputs, baselines = _create_inps_and_base_for_deeplift_neuron_layer_testing()
neuron_dl = NeuronDeepLift(model, model.l3)
attributions = neuron_dl.attribute(
inputs, 0, baselines, attribute_to_neuron_input=True
)
assertTensorAlmostEqual(self, attributions[0], [[-0.0, 0.0, -0.0]])
assertTensorAlmostEqual(self, attributions[1], [[0.0, 0.0, 0.0]])
attributions = neuron_dl.attribute(
inputs, 0, baselines, attribute_to_neuron_input=False
)
assertTensorAlmostEqual(self, attributions[0], [[-0.0, 0.0, -0.0]])
assertTensorAlmostEqual(self, attributions[1], [[6.0, 9.0, 0.0]])