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

#!/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.gradient_shap import GradientShap
from captum.attr._utils.attribution import GradientAttribution, NeuronAttribution
from captum.log import log_usage
from torch.nn import Module

[docs] class NeuronGradientShap(NeuronAttribution, GradientAttribution): r""" Implements gradient SHAP for a neuron in a hidden layer based on the implementation from SHAP's primary author. For reference, please, view:\ #deep-learning-example-with-gradientexplainer-tensorflowkeraspytorch-models A Unified Approach to Interpreting Model Predictions\ 7062-a-unified-approach-to-interpreting-model-predictions GradientShap approximates SHAP values by computing the expectations of gradients by randomly sampling from the distribution of baselines/references. It adds white noise to each input sample `n_samples` times, selects a random baseline from baselines' distribution and a random point along the path between the baseline and the input, and computes the gradient of the neuron with index `neuron_selector` with respect to those selected random points. The final SHAP values represent the expected values of `gradients * (inputs - baselines)`. GradientShap makes an assumption that the input features are independent and that the explanation model is linear, meaning that the explanations are modeled through the additive composition of feature effects. Under those assumptions, SHAP value can be approximated as the expectation of gradients that are computed for randomly generated `n_samples` input samples after adding gaussian noise `n_samples` times to each input for different baselines/references. In some sense it can be viewed as an approximation of integrated gradients by computing the expectations of gradients for different baselines. Current implementation uses Smoothgrad from :class:`.NoiseTunnel` in order to randomly draw samples from the distribution of baselines, add noise to input samples and compute the expectation (smoothgrad). """ 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 neuron attributions are computed. The output size of the attribute method matches the dimensions of the inputs or outputs of the neuron with index `neuron_selector` in this layer, depending on whether we attribute to the inputs or outputs of the neuron. Currently, it is assumed that the inputs or the outputs of the neurons in this 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 Gradient SHAP, if `multiply_by_inputs` is set to True, the sensitivity scores for scaled inputs 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[ TensorOrTupleOfTensorsGeneric, Callable[..., TensorOrTupleOfTensorsGeneric] ], n_samples: int = 5, stdevs: float = 0.0, additional_forward_args: Any = None, attribute_to_neuron_input: bool = False, ) -> TensorOrTupleOfTensorsGeneric: r""" Args: inputs (Tensor or tuple[Tensor, ...]): Input for which SHAP attribution values 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 (Tensor, tuple[Tensor, ...], or Callable): Baselines define the starting point from which expectation is computed and can be provided as: - a single tensor, if inputs is a single tensor, with the first dimension equal to the number of examples in the baselines' distribution. The remaining dimensions must match with input tensor's dimension starting from the second dimension. - a tuple of tensors, if inputs is a tuple of tensors, with the first dimension of any tensor inside the tuple equal to the number of examples in the baseline's distribution. The remaining dimensions must match the dimensions of the corresponding input tensor starting from the second dimension. - callable function, optionally takes `inputs` as an argument and either returns a single tensor or a tuple of those. It is recommended that the number of samples in the baselines' tensors is larger than one. n_samples (int, optional): The number of randomly generated examples per sample in the input batch. Random examples are generated by adding gaussian random noise to each sample. Default: `5` if `n_samples` is not provided. stdevs (float or tuple of float, optional): The standard deviation of gaussian noise with zero mean that is added to each input in the batch. If `stdevs` is a single float value then that same value is used for all inputs. If it is a tuple, then it must have the same length as the inputs tuple. In this case, each stdev value in the stdevs tuple corresponds to the input with the same index in the inputs tuple. Default: 0.0 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 can contain a tuple of ND tensors or any arbitrary python type of any shape. In case of the ND tensor the first dimension of the tensor must correspond to the batch size. It will be repeated for each `n_steps` for each randomly generated input sample. Note that the gradients 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 Returns: **attributions** or 2-element tuple of **attributions**, **delta**: - **attributions** (*Tensor* or *tuple[Tensor, ...]*): Attribution score computed based on GradientSHAP 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() >>> neuron_grad_shap = NeuronGradientShap(net, net.linear2) >>> input = torch.randn(3, 3, 32, 32, requires_grad=True) >>> # choosing baselines randomly >>> baselines = torch.randn(20, 3, 32, 32) >>> # Computes gradient SHAP of first neuron in linear2 layer >>> # with respect to the input's of the network. >>> # Attribution size matches input size: 3x3x32x32 >>> attribution = neuron_grad_shap.attribute(input, neuron_ind=0 baselines) """ gs = GradientShap(self.forward_func, self.multiplies_by_inputs) gs.gradient_func = construct_neuron_grad_fn( self.layer, neuron_selector, self.device_ids, attribute_to_neuron_input=attribute_to_neuron_input, ) # NOTE: using __wrapped__ to not log return gs.attribute.__wrapped__( # type: ignore gs, # self inputs, baselines, n_samples=n_samples, stdevs=stdevs, additional_forward_args=additional_forward_args, )
@property def multiplies_by_inputs(self): return self._multiply_by_inputs