Source code for captum.attr._core.neuron.neuron_integrated_gradients

#!/usr/bin/env python3
from typing import Any, Callable, List, Tuple, Union

from captum._utils.gradient import construct_neuron_grad_fn
from captum._utils.typing import TensorOrTupleOfTensorsGeneric
from captum.attr._core.integrated_gradients import IntegratedGradients
from captum.attr._utils.attribution import GradientAttribution, NeuronAttribution
from captum.log import log_usage
from torch import Tensor
from torch.nn import Module

[docs] class NeuronIntegratedGradients(NeuronAttribution, GradientAttribution): r""" Approximates the integral of gradients for a particular neuron along the path from a baseline input to the given input. If no baseline is provided, the default baseline is the zero tensor. More details regarding the integrated gradient method can be found in the original paper here: Note that this method is equivalent to applying integrated gradients where the output is the output of the identified neuron. """ def __init__( self, forward_func: Callable, layer: Module, device_ids: Union[None, List[int]] = None, multiply_by_inputs: bool = True, ) -> None: r""" Args: forward_func (Callable): The forward function of the model or any modification of it layer (torch.nn.Module): Layer for which attributions are computed. Output size of attribute matches this layer's input or output dimensions, depending on whether we attribute to the inputs or outputs of the layer, corresponding to attribution of each neuron in the input or output of this layer. Currently, it is assumed that the inputs or the outputs of the layer, depending on which one is used for attribution, can only be a single tensor. device_ids (list[int]): Device ID list, necessary only if forward_func applies a DataParallel model. This allows reconstruction of intermediate outputs from batched results across devices. If forward_func is given as the DataParallel model itself, then it is not necessary to provide this argument. 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: In case of Neuron Integrated Gradients, if `multiply_by_inputs` is set to True, final sensitivity scores are being multiplied by (inputs - baselines). """ NeuronAttribution.__init__(self, forward_func, layer, device_ids) GradientAttribution.__init__(self, forward_func) self._multiply_by_inputs = multiply_by_inputs
[docs] @log_usage() def attribute( self, inputs: TensorOrTupleOfTensorsGeneric, neuron_selector: Union[int, Tuple[Union[int, slice], ...], Callable], baselines: Union[None, Tensor, Tuple[Tensor, ...]] = None, additional_forward_args: Any = None, n_steps: int = 50, method: str = "gausslegendre", internal_batch_size: Union[None, int] = None, attribute_to_neuron_input: bool = False, ) -> TensorOrTupleOfTensorsGeneric: r""" Args: inputs (Tensor or tuple[Tensor, ...]): Input for which neuron integrated gradients 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, and if multiple input tensors are provided, the examples must be aligned appropriately. neuron_selector (int, Callable, tuple[int], or slice): 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 independently. - 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 scalar, or Tensor, optional): Baselines define the starting point from which integral is computed. 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. For a tensor, the first dimension of the tensor must correspond to the number of examples. It will be repeated for each of `n_steps` along the integrated path. For all other types, the given argument is used for all forward evaluations. Note that attributions are not computed with respect to these arguments. Default: None n_steps (int, optional): The number of steps used by the approximation method. Default: 50. method (str, optional): Method for approximating the integral, one of `riemann_right`, `riemann_left`, `riemann_middle`, `riemann_trapezoid` or `gausslegendre`. Default: `gausslegendre` if no method is provided. internal_batch_size (int, optional): Divides total #steps * #examples data points into chunks of size at most internal_batch_size, which are computed (forward / backward passes) sequentially. internal_batch_size must be at least equal to #examples. For DataParallel models, each batch is split among the available devices, so evaluations on each available device contain internal_batch_size / num_devices examples. If internal_batch_size is None, then all evaluations are processed in one batch. 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 Returns: *Tensor* or *tuple[Tensor, ...]* of **attributions**: - **attributions** (*Tensor* or *tuple[Tensor, ...]*): Integrated gradients 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. >>> # It contains an attribute conv1, which is an instance of nn.conv2d, >>> # and the output of this layer has dimensions Nx12x32x32. >>> net = ImageClassifier() >>> neuron_ig = NeuronIntegratedGradients(net, net.conv1) >>> input = torch.randn(2, 3, 32, 32, requires_grad=True) >>> # To compute neuron attribution, we need to provide the neuron >>> # index for which attribution is desired. Since the layer output >>> # is Nx12x32x32, we need a tuple in the form (0..11,0..31,0..31) >>> # which indexes a particular neuron in the layer output. >>> # For this example, we choose the index (4,1,2). >>> # Computes neuron integrated gradients for neuron with >>> # index (4,1,2). >>> attribution = neuron_ig.attribute(input, (4,1,2)) """ ig = IntegratedGradients(self.forward_func, self.multiplies_by_inputs) ig.gradient_func = construct_neuron_grad_fn( self.layer, neuron_selector, self.device_ids, attribute_to_neuron_input ) # NOTE: using __wrapped__ to not log # Return only attributions and not delta return ig.attribute.__wrapped__( # type: ignore ig, # self inputs, baselines, additional_forward_args=additional_forward_args, n_steps=n_steps, method=method, internal_batch_size=internal_batch_size, )
@property def multiplies_by_inputs(self): return self._multiply_by_inputs