Input X Gradient¶
- class captum.attr.InputXGradient(forward_func)[source]¶
A baseline approach for computing the attribution. It multiplies input with the gradient with respect to input. https://arxiv.org/abs/1605.01713
- Parameters
forward_func (Callable) – The forward function of the model or any modification of it
- attribute(inputs, target=None, additional_forward_args=None)[source]¶
- Parameters
inputs (Tensor or tuple[Tensor, ...]) – Input for which 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.
target (int, tuple, Tensor, or list, optional) –
Output indices for which gradients are computed (for classification cases, this is usually the target class). If the network returns a scalar value per example, no target index is necessary. For general 2D outputs, targets can be either:
a single integer or a tensor containing a single integer, which is applied to all input examples
a list of integers or a 1D tensor, with length matching the number of examples in inputs (dim 0). Each integer is applied as the target for the corresponding example.
For outputs with > 2 dimensions, targets can be either:
A single tuple, which contains #output_dims - 1 elements. This target index is applied to all examples.
A list of tuples with length equal to the number of examples in inputs (dim 0), and each tuple containing #output_dims - 1 elements. Each tuple is applied as the target for the corresponding example.
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
- Returns
- attributions (Tensor or tuple[Tensor, …]):
The input x gradient 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.
- Return type
Tensor or tuple[Tensor, …] of attributions
Examples:
>>> # ImageClassifier takes a single input tensor of images Nx3x32x32, >>> # and returns an Nx10 tensor of class probabilities. >>> net = ImageClassifier() >>> # Generating random input with size 2x3x3x32 >>> input = torch.randn(2, 3, 32, 32, requires_grad=True) >>> # Defining InputXGradient interpreter >>> input_x_gradient = InputXGradient(net) >>> # Computes inputXgradient for class 4. >>> attribution = input_x_gradient.attribute(input, target=4)