Source code for captum.attr._core.integrated_gradients

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

import torch
from captum._utils.common import (
from captum._utils.typing import (
from captum.attr._utils.approximation_methods import approximation_parameters
from captum.attr._utils.attribution import GradientAttribution
from captum.attr._utils.batching import _batch_attribution
from captum.attr._utils.common import (
from captum.log import log_usage
from torch import Tensor

[docs] class IntegratedGradients(GradientAttribution): r""" Integrated Gradients is an axiomatic model interpretability algorithm that assigns an importance score to each input feature by approximating the integral of gradients of the model's output with respect to the inputs along the path (straight line) from given baselines / references to inputs. Baselines can be provided as input arguments to attribute method. To approximate the integral we can choose to use either a variant of Riemann sum or Gauss-Legendre quadrature rule. More details regarding the integrated gradients method can be found in the original paper: """ def __init__( self, forward_func: Callable, multiply_by_inputs: bool = True, ) -> None: r""" Args: forward_func (Callable): The forward function of the model or any modification of it 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 integrated gradients, if `multiply_by_inputs` is set to True, final sensitivity scores are being multiplied by (inputs - baselines). """ GradientAttribution.__init__(self, forward_func) self._multiply_by_inputs = multiply_by_inputs # The following overloaded method signatures correspond to the case where # return_convergence_delta is False, then only attributions are returned, # and when return_convergence_delta is True, the return type is # a tuple with both attributions and deltas. @typing.overload def attribute( self, inputs: TensorOrTupleOfTensorsGeneric, baselines: BaselineType = None, target: TargetType = None, additional_forward_args: Any = None, n_steps: int = 50, method: str = "gausslegendre", internal_batch_size: Union[None, int] = None, return_convergence_delta: Literal[False] = False, ) -> TensorOrTupleOfTensorsGeneric: ... @typing.overload def attribute( self, inputs: TensorOrTupleOfTensorsGeneric, baselines: BaselineType = None, target: TargetType = None, additional_forward_args: Any = None, n_steps: int = 50, method: str = "gausslegendre", internal_batch_size: Union[None, int] = None, *, return_convergence_delta: Literal[True], ) -> Tuple[TensorOrTupleOfTensorsGeneric, Tensor]: ...
[docs] @log_usage() def attribute( # type: ignore self, inputs: TensorOrTupleOfTensorsGeneric, baselines: BaselineType = None, target: TargetType = None, additional_forward_args: Any = None, n_steps: int = 50, method: str = "gausslegendre", internal_batch_size: Union[None, int] = None, return_convergence_delta: bool = False, ) -> Union[ TensorOrTupleOfTensorsGeneric, Tuple[TensorOrTupleOfTensorsGeneric, Tensor] ]: r""" This method attributes the output of the model with given target index (in case it is provided, otherwise it assumes that output is a scalar) to the inputs of the model using the approach described above. In addition to that it also returns, if `return_convergence_delta` is set to True, integral approximation delta based on the completeness property of integrated gradients. Args: inputs (Tensor or tuple[Tensor, ...]): Input for which 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. baselines (scalar, Tensor, tuple of scalar, or Tensor, optional): Baselines define the starting point from which integral is computed and 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 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. 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 return_convergence_delta (bool, optional): Indicates whether to return convergence delta or not. If `return_convergence_delta` is set to True convergence delta will be returned in a tuple following attributions. Default: False Returns: **attributions** or 2-element tuple of **attributions**, **delta**: - **attributions** (*Tensor* or *tuple[Tensor, ...]*): Integrated gradients 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. - **delta** (*Tensor*, returned if return_convergence_delta=True): The difference between the total approximated and true integrated gradients. This is computed using the property that the total sum of forward_func(inputs) - forward_func(baselines) must equal the total sum of the integrated gradient. Delta is calculated per example, meaning that the number of elements in returned delta tensor is equal to the number of examples in inputs. Examples:: >>> # ImageClassifier takes a single input tensor of images Nx3x32x32, >>> # and returns an Nx10 tensor of class probabilities. >>> net = ImageClassifier() >>> ig = IntegratedGradients(net) >>> input = torch.randn(2, 3, 32, 32, requires_grad=True) >>> # Computes integrated gradients for class 3. >>> attribution = ig.attribute(input, target=3) """ # Keeps track whether original input is a tuple or not before # converting it into a tuple. is_inputs_tuple = _is_tuple(inputs) inputs, baselines = _format_input_baseline(inputs, baselines) _validate_input(inputs, baselines, n_steps, method) if internal_batch_size is not None: num_examples = inputs[0].shape[0] attributions = _batch_attribution( self, num_examples, internal_batch_size, n_steps, inputs=inputs, baselines=baselines, target=target, additional_forward_args=additional_forward_args, method=method, ) else: attributions = self._attribute( inputs=inputs, baselines=baselines, target=target, additional_forward_args=additional_forward_args, n_steps=n_steps, method=method, ) if return_convergence_delta: start_point, end_point = baselines, inputs # computes approximation error based on the completeness axiom delta = self.compute_convergence_delta( attributions, start_point, end_point, additional_forward_args=additional_forward_args, target=target, ) return _format_output(is_inputs_tuple, attributions), delta return _format_output(is_inputs_tuple, attributions)
def _attribute( self, inputs: Tuple[Tensor, ...], baselines: Tuple[Union[Tensor, int, float], ...], target: TargetType = None, additional_forward_args: Any = None, n_steps: int = 50, method: str = "gausslegendre", step_sizes_and_alphas: Union[None, Tuple[List[float], List[float]]] = None, ) -> Tuple[Tensor, ...]: if step_sizes_and_alphas is None: # retrieve step size and scaling factor for specified # approximation method step_sizes_func, alphas_func = approximation_parameters(method) step_sizes, alphas = step_sizes_func(n_steps), alphas_func(n_steps) else: step_sizes, alphas = step_sizes_and_alphas # scale features and compute gradients. (batch size is abbreviated as bsz) # scaled_features' dim -> (bsz * #steps x inputs[0].shape[1:], ...) scaled_features_tpl = tuple( [baseline + alpha * (input - baseline) for alpha in alphas], dim=0 ).requires_grad_() for input, baseline in zip(inputs, baselines) ) additional_forward_args = _format_additional_forward_args( additional_forward_args ) # apply number of steps to additional forward args # currently, number of steps is applied only to additional forward arguments # that are nd-tensors. It is assumed that the first dimension is # the number of batches. # dim -> (bsz * #steps x additional_forward_args[0].shape[1:], ...) input_additional_args = ( _expand_additional_forward_args(additional_forward_args, n_steps) if additional_forward_args is not None else None ) expanded_target = _expand_target(target, n_steps) # grads: dim -> (bsz * #steps x inputs[0].shape[1:], ...) grads = self.gradient_func( forward_fn=self.forward_func, inputs=scaled_features_tpl, target_ind=expanded_target, additional_forward_args=input_additional_args, ) # flattening grads so that we can multilpy it with step-size # calling contiguous to avoid `memory whole` problems scaled_grads = [ grad.contiguous().view(n_steps, -1) * torch.tensor(step_sizes).float().view(n_steps, 1).to(grad.device) for grad in grads ] # aggregates across all steps for each tensor in the input tuple # total_grads has the same dimensionality as inputs total_grads = tuple( _reshape_and_sum( scaled_grad, n_steps, grad.shape[0] // n_steps, grad.shape[1:] ) for (scaled_grad, grad) in zip(scaled_grads, grads) ) # computes attribution for each tensor in input tuple # attributions has the same dimensionality as inputs if not self.multiplies_by_inputs: attributions = total_grads else: attributions = tuple( total_grad * (input - baseline) for total_grad, input, baseline in zip(total_grads, inputs, baselines) ) return attributions
[docs] def has_convergence_delta(self) -> bool: return True
@property def multiplies_by_inputs(self): return self._multiply_by_inputs