def test_relu_deeplift_exact_match_wo_mutliplying_by_inputs(self) -> None: x1 = torch.tensor([1.0]) x2 = torch.tensor([2.0]) inputs = (x1, x2) model = ReLUDeepLiftModel() dl = DeepLift(model, multiply_by_inputs=False) attributions = dl.attribute(inputs) self.assertEqual(attributions[0][0], 2.0) self.assertEqual(attributions[1][0], 0.5)
def test_relu_linear_deeplift_compare_inplace(self) -> None: model1 = ReLULinearDeepLiftModel(inplace=True) x1 = torch.tensor([[-10.0, 1.0, -5.0], [2.0, 3.0, 4.0]], requires_grad=True) x2 = torch.tensor([[3.0, 3.0, 1.0], [2.3, 5.0, 4.0]], requires_grad=True) inputs = (x1, x2) attributions1 = DeepLift(model1).attribute(inputs) model2 = ReLULinearDeepLiftModel() attributions2 = DeepLift(model2).attribute(inputs) assertTensorAlmostEqual(self, attributions1[0], attributions2[0]) assertTensorAlmostEqual(self, attributions1[1], attributions2[1])
def test_relu_deeplift_with_hypothetical_contrib_func(self) -> None: model = Conv1dDeepLiftModel() rand_seq_data = torch.abs(torch.randn(2, 4, 1000)) rand_seq_ref = torch.abs(torch.randn(2, 4, 1000)) dls = DeepLift(model) attr = dls.attribute( rand_seq_data, rand_seq_ref, custom_attribution_func=_hypothetical_contrib_func, target=(1, 0), ) self.assertEqual(attr.shape, rand_seq_data.shape)
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() dl = DeepLift(model) attrs, delta = dl.attribute( inputs, baselines, target=0, return_convergence_delta=True ) expected = [[0.0, 0.0, 0.0, -8.0], [0.0, -7.0, 0.0, 0.0]] expected_delta = [0.0, 0.0] assertArraysAlmostEqual(attrs.detach().numpy(), expected) assertArraysAlmostEqual(delta.detach().numpy(), expected_delta)
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() dl = DeepLift(model) attrs, delta = dl.attribute(inputs, baselines, target=0, return_convergence_delta=True) expected = torch.Tensor([[0.0, 0.0, 0.0, -8.0], [0.0, -7.0, 0.0, 0.0]]) expected_delta = torch.Tensor([0.0, 0.0]) assertTensorAlmostEqual(self, attrs, expected, 0.0001) assertTensorAlmostEqual(self, delta, expected_delta, 0.0001)
def test_relu_deeplift_exact_match(self) -> None: x1 = torch.tensor([1.0], requires_grad=True) x2 = torch.tensor([2.0], requires_grad=True) b1 = torch.tensor([0.0], requires_grad=True) b2 = torch.tensor([0.0], requires_grad=True) inputs = (x1, x2) baselines = (b1, b2) model = ReLUDeepLiftModel() dl = DeepLift(model) attributions, delta = dl.attribute( inputs, baselines, return_convergence_delta=True ) self.assertEqual(attributions[0][0], 2.0) self.assertEqual(attributions[1][0], 1.0) self.assertEqual(delta[0], 0.0)
def test_convnet_with_maxpool2d(self): input = 100 * torch.randn(2, 1, 10, 10, requires_grad=True) baseline = 20 * torch.randn(2, 1, 10, 10) model = BasicModel_ConvNet() dl = DeepLift(model) self.softmax_classification(model, dl, input, baseline)
def test_convnet_with_maxpool1d_large_baselines(self) -> None: input = 100 * torch.randn(2, 1, 10, requires_grad=True) baseline = 500 * torch.randn(2, 1, 10) model = BasicModel_ConvNet_MaxPool1d() dl = DeepLift(model) self.softmax_classification(model, dl, input, baseline, torch.tensor(2))
def test_relu_linear_deeplift_batch(self) -> None: model = ReLULinearDeepLiftModel(inplace=True) x1 = torch.tensor([[-10.0, 1.0, -5.0], [2.0, 3.0, 4.0]], requires_grad=True) x2 = torch.tensor([[3.0, 3.0, 1.0], [2.3, 5.0, 4.0]], requires_grad=True) inputs = (x1, x2) baselines = (torch.zeros(1, 3), torch.rand(1, 3) * 0.001) # expected = [[[0.0, 0.0]], [[6.0, 2.0]]] self._deeplift_assert(model, DeepLift(model), inputs, baselines)
def test_softmax_classification_zero_baseline(self) -> None: num_in = 20 input = torch.arange(0.0, num_in * 1.0, requires_grad=True).unsqueeze(0) baselines = 0.0 model = SoftmaxDeepLiftModel(num_in, 20, 10) dl = DeepLift(model) self.softmax_classification(model, dl, input, baselines, torch.tensor(2))
def test_softmax_classification_batch_zero_baseline(self): num_in = 40 input = torch.arange(0.0, num_in * 3.0, requires_grad=True).reshape(3, num_in) baselines = 0 * input model = SoftmaxDeepLiftModel(num_in, 20, 10) dl = DeepLift(model) self.softmax_classification(model, dl, input, baselines, torch.tensor(2))
def test_sigmoid_classification(self): num_in = 20 input = torch.arange(0.0, num_in * 1.0, requires_grad=True).unsqueeze(0) baseline = 0 * input target = torch.tensor(0) # TODO add test cases for multiple different layers model = SigmoidDeepLiftModel(num_in, 5, 1) dl = DeepLift(model) model.zero_grad() attributions, delta = dl.attribute( input, baseline, target=target, return_convergence_delta=True ) self._assert_attributions(model, attributions, input, baseline, delta, target) # compare with integrated gradients ig = IntegratedGradients(model) attributions_ig = ig.attribute(input, baseline, target=target) assertAttributionComparision(self, (attributions,), (attributions_ig,))
def test_softmax_classification_batch_multi_target(self) -> None: num_in = 40 inputs = torch.arange(0.0, num_in * 3.0, requires_grad=True).reshape(3, num_in) baselines = torch.arange(1.0, num_in + 1).reshape(1, num_in) model = SoftmaxDeepLiftModel(num_in, 20, 10) dl = DeepLift(model) self.softmax_classification(model, dl, inputs, baselines, torch.tensor([2, 2, 2]))
def test_relu_linear_deeplift(self) -> None: model = ReLULinearModel(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) baselines = (0, 0.0001) # expected = [[[0.0, 0.0]], [[6.0, 2.0]]] self._deeplift_assert(model, DeepLift(model), inputs, baselines)
def test_relu_deeplift(self): x1 = torch.tensor([1.0], requires_grad=True) x2 = torch.tensor([2.0], requires_grad=True) b1 = torch.tensor([0.0], requires_grad=True) b2 = torch.tensor([0.0], requires_grad=True) inputs = (x1, x2) baselines = (b1, b2) model = ReLUDeepLiftModel() self._deeplift_helper(model, DeepLift(model), inputs, baselines)
def test_relu_deeplift_batch(self) -> None: x1 = torch.tensor([[1.0], [1.0], [1.0], [1.0]], requires_grad=True) x2 = torch.tensor([[2.0], [2.0], [2.0], [2.0]], requires_grad=True) b1 = torch.tensor([[0.0], [0.0], [0.0], [0.0]], requires_grad=True) b2 = torch.tensor([[0.0], [0.0], [0.0], [0.0]], requires_grad=True) inputs = (x1, x2) baselines = (b1, b2) model = ReLUDeepLiftModel() self._deeplift_assert(model, DeepLift(model), inputs, baselines)
def test_tanh_deeplift(self) -> None: x1 = torch.tensor([-1.0], requires_grad=True) x2 = torch.tensor([-2.0], requires_grad=True) b1 = torch.tensor([0.0], requires_grad=True) b2 = torch.tensor([0.0], requires_grad=True) inputs = (x1, x2) baselines = (b1, b2) model = TanhDeepLiftModel() self._deeplift_assert(model, DeepLift(model), inputs, baselines)
def test_relu_linear_deeplift(self): model = 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) b1 = torch.tensor([[0.0, 0.0, 0.0]], requires_grad=True) b2 = torch.tensor([[0.0, 0.0, 0.0]], requires_grad=True) inputs = (x1, x2) baselines = (b1, b2) # expected = [[[0.0, 0.0]], [[6.0, 2.0]]] self._deeplift_helper(model, DeepLift(model), inputs, baselines)
def __init__( self, model: Module, layer: Module, multiply_by_inputs: bool = True, ) -> None: r""" Args: model (nn.Module): The reference to PyTorch model instance. Model cannot contain any in-place nonlinear submodules; these are not supported by the register_full_backward_hook PyTorch API starting from PyTorch v1.9. layer (torch.nn.Module): Layer for which attributions are computed. The size and dimensionality of the attributions corresponds to the size and dimensionality of the layer's input or output depending on whether we attribute to the inputs or outputs of the layer. multiply_by_inputs (bool, optional): Indicates whether to factor model inputs' multiplier in the final attribution scores. In the literature this is also known as local vs global attribution. If inputs' multiplier isn't factored in then that type of attribution method is also called local attribution. If it is, then that type of attribution method is called global. More detailed can be found here: https://arxiv.org/abs/1711.06104 In case of Layer DeepLift, if `multiply_by_inputs` is set to True, final sensitivity scores are being multiplied by layer activations for inputs - layer activations for baselines. This flag applies only if `custom_attribution_func` is set to None. """ LayerAttribution.__init__(self, model, layer) DeepLift.__init__(self, model) self.model = model self._multiply_by_inputs = multiply_by_inputs
def attribute( self, inputs: TensorOrTupleOfTensorsGeneric, neuron_selector: Union[int, Tuple[Union[int, slice], ...], Callable], baselines: BaselineType = None, additional_forward_args: Any = None, attribute_to_neuron_input: bool = False, custom_attribution_func: Union[None, Callable[..., Tuple[Tensor, ...]]] = None, ) -> TensorOrTupleOfTensorsGeneric: r""" Args: inputs (tensor or tuple of tensors): Input for which layer attributions are computed. If forward_func takes a single tensor as input, a single input tensor should be provided. If forward_func takes multiple tensors as input, a tuple of the input tensors should be provided. It is assumed that for all given input tensors, dimension 0 corresponds to the number of examples (aka batch size), and if multiple input tensors are provided, the examples must be aligned appropriately. neuron_selector (int, callable, or tuple of ints or slices): Selector for neuron in given layer for which attribution is desired. Neuron selector can be provided as: - a single integer, if the layer output is 2D. This integer selects the appropriate neuron column in the layer input or output - a tuple of integers or slice objects. Length of this tuple must be one less than the number of dimensions in the input / output of the given layer (since dimension 0 corresponds to number of examples). The elements of the tuple can be either integers or slice objects (slice object allows indexing a range of neurons rather individual ones). If any of the tuple elements is a slice object, the indexed output tensor is used for attribution. Note that specifying a slice of a tensor would amount to computing the attribution of the sum of the specified neurons, and not the individual neurons independantly. - a callable, which should take the target layer as input (single tensor or tuple if multiple tensors are in layer) and return a neuron or aggregate of the layer's neurons for attribution. For example, this function could return the sum of the neurons in the layer or sum of neurons with activations in a particular range. It is expected that this function returns either a tensor with one element or a 1D tensor with length equal to batch_size (one scalar per input example) baselines (scalar, tensor, tuple of scalars or tensors, optional): Baselines define reference samples that are compared with the inputs. In order to assign attribution scores DeepLift computes the differences between the inputs/outputs and corresponding references. Baselines can be provided as: - a single tensor, if inputs is a single tensor, with exactly the same dimensions as inputs or the first dimension is one and the remaining dimensions match with inputs. - a single scalar, if inputs is a single tensor, which will be broadcasted for each input value in input tensor. - a tuple of tensors or scalars, the baseline corresponding to each tensor in the inputs' tuple can be: - either a tensor with matching dimensions to corresponding tensor in the inputs' tuple or the first dimension is one and the remaining dimensions match with the corresponding input tensor. - or a scalar, corresponding to a tensor in the inputs' tuple. This scalar value is broadcasted for corresponding input tensor. In the cases when `baselines` is not provided, we internally use zero scalar corresponding to each input tensor. Default: None additional_forward_args (any, optional): If the forward function requires additional arguments other than the inputs for which attributions should not be computed, this argument can be provided. It must be either a single additional argument of a Tensor or arbitrary (non-tuple) type or a tuple containing multiple additional arguments including tensors or any arbitrary python types. These arguments are provided to forward_func in order, following the arguments in inputs. Note that attributions are not computed with respect to these arguments. Default: None attribute_to_neuron_input (bool, optional): Indicates whether to compute the attributions with respect to the neuron input or output. If `attribute_to_neuron_input` is set to True then the attributions will be computed with respect to neuron's inputs, otherwise it will be computed with respect to neuron's outputs. Note that currently it is assumed that either the input or the output of internal neuron, depending on whether we attribute to the input or output, is a single tensor. Support for multiple tensors will be added later. Default: False custom_attribution_func (callable, optional): A custom function for computing final attribution scores. This function can take at least one and at most three arguments with the following signature: - custom_attribution_func(multipliers) - custom_attribution_func(multipliers, inputs) - custom_attribution_func(multipliers, inputs, baselines) In case this function is not provided, we use the default logic defined as: multipliers * (inputs - baselines) It is assumed that all input arguments, `multipliers`, `inputs` and `baselines` are provided in tuples of same length. `custom_attribution_func` returns a tuple of attribution tensors that have the same length as the `inputs`. Default: None Returns: **attributions** or 2-element tuple of **attributions**, **delta**: - **attributions** (*tensor* or tuple of *tensors*): Computes attributions using Deeplift's rescale rule for particular neuron with respect to each input feature. Attributions will always be the same size as the provided inputs, with each value providing the attribution of the corresponding input index. If a single tensor is provided as inputs, a single tensor is returned. If a tuple is provided for inputs, a tuple of corresponding sized tensors is returned. Examples:: >>> # ImageClassifier takes a single input tensor of images Nx3x32x32, >>> # and returns an Nx10 tensor of class probabilities. >>> net = ImageClassifier() >>> # creates an instance of LayerDeepLift to interpret target >>> # class 1 with respect to conv4 layer. >>> dl = NeuronDeepLift(net, net.conv4) >>> input = torch.randn(1, 3, 32, 32, requires_grad=True) >>> # Computes deeplift attribution scores for conv4 layer and neuron >>> # index (4,1,2). >>> attribution = dl.attribute(input, (4,1,2)) """ dl = DeepLift(cast(Module, self.forward_func), self.multiplies_by_inputs) dl.gradient_func = construct_neuron_grad_fn( self.layer, neuron_selector, attribute_to_neuron_input=attribute_to_neuron_input, ) # NOTE: using __wrapped__ to not log return dl.attribute.__wrapped__( # type: ignore dl, # self inputs, baselines, additional_forward_args=additional_forward_args, custom_attribution_func=custom_attribution_func, )
def test_reusable_modules(self) -> None: model = BasicModelWithReusableModules() input = torch.rand(1, 3) dl = DeepLift(model) with self.assertRaises(RuntimeError): dl.attribute(input, target=0)