Source code for captum.metrics._core.infidelity

#!/usr/bin/env python3

from typing import Any, Callable, Tuple, Union, cast

import torch
from captum._utils.common import (
from captum._utils.typing import BaselineType, TargetType, TensorOrTupleOfTensorsGeneric
from captum.log import log_usage
from captum.metrics._utils.batching import _divide_and_aggregate_metrics
from torch import Tensor

def infidelity_perturb_func_decorator(multipy_by_inputs: bool = True) -> Callable:
    r"""An auxiliary, decorator function that helps with computing
    perturbations given perturbed inputs. It can be useful for cases
    when `pertub_func` returns only perturbed inputs and we
    internally compute the perturbations as
    (input - perturbed_input) / (input - baseline) if
    multipy_by_inputs is set to True and
    (input - perturbed_input) otherwise.

    If users decorate their `pertub_func` with
    `@infidelity_perturb_func_decorator` function then their `pertub_func`
    needs to only return perturbed inputs.


        multipy_by_inputs (bool): Indicates whether model inputs'
                multiplier is factored in the computation of
                attribution scores.


    def sub_infidelity_perturb_func_decorator(pertub_func: Callable) -> Callable:

            pertub_func(callable): Input perturbation function that takes inputs
                and optionally baselines and returns perturbed inputs


            default_perturb_func(callable): Internal default perturbation
            function that computes the perturbations internally and returns
            perturbations and perturbed inputs.

            >>> @infidelity_perturb_func_decorator(True)
            >>> def perturb_fn(inputs):
            >>>    noise = torch.tensor(np.random.normal(0, 0.003,
            >>>                         inputs.shape)).float()
            >>>    return inputs - noise
            >>> # Computes infidelity score using `perturb_fn`
            >>> infidelity = infidelity(model, perturb_fn, input, ...)


        def default_perturb_func(
            inputs: TensorOrTupleOfTensorsGeneric, baselines: BaselineType = None
            r""" """
            inputs_perturbed = (
                pertub_func(inputs, baselines)
                if baselines is not None
                else pertub_func(inputs)
            inputs_perturbed = _format_input(inputs_perturbed)
            inputs = _format_input(inputs)
            baselines = _format_baseline(baselines, inputs)
            if baselines is None:
                perturbations = tuple(
                        input - input_perturbed,
                    if multipy_by_inputs
                    else input - input_perturbed
                    for input, input_perturbed in zip(inputs, inputs_perturbed)
                perturbations = tuple(
                        input - input_perturbed,
                        input - baseline,
                    if multipy_by_inputs
                    else input - input_perturbed
                    for input, input_perturbed, baseline in zip(
                        inputs, inputs_perturbed, baselines
            return perturbations, inputs_perturbed

        return default_perturb_func

    return sub_infidelity_perturb_func_decorator

[docs]@log_usage() def infidelity( forward_func: Callable, perturb_func: Callable, inputs: TensorOrTupleOfTensorsGeneric, attributions: TensorOrTupleOfTensorsGeneric, baselines: BaselineType = None, additional_forward_args: Any = None, target: TargetType = None, n_perturb_samples: int = 10, max_examples_per_batch: int = None, normalize: bool = False, ) -> Tensor: r""" Explanation infidelity represents the expected mean-squared error between the explanation multiplied by a meaningful input perturbation and the differences between the predictor function at its input and perturbed input. More details about the measure can be found in the following paper: It is derived from the completeness property of well-known attribution algorithms and is a computationally more efficient and generalized notion of Sensitivy-n. The latter measures correlations between the sum of the attributions and the differences of the predictor function at its input and fixed baseline. More details about the Sensitivity-n can be found here: The users can perturb the inputs any desired way by providing any perturbation function that takes the inputs (and optionally baselines) and returns perturbed inputs or perturbed inputs and corresponding perturbations. This specific implementation is primarily tested for attribution-based explanation methods but the idea can be expanded to use for non attribution-based interpretability methods as well. Args: forward_func (callable): The forward function of the model or any modification of it. perturb_func (callable): The perturbation function of model inputs. This function takes model inputs and optionally baselines as input arguments and returns either a tuple of perturbations and perturbed inputs or just perturbed inputs. For example: >>> def my_perturb_func(inputs): >>> <MY-LOGIC-HERE> >>> return perturbations, perturbed_inputs If we want to only return perturbed inputs and compute perturbations internally then we can wrap perturb_func with `infidelity_perturb_func_decorator` decorator such as: >>> from captum.metrics import infidelity_perturb_func_decorator >>> @infidelity_perturb_func_decorator(<multipy_by_inputs flag>) >>> def my_perturb_func(inputs): >>> <MY-LOGIC-HERE> >>> return perturbed_inputs In case `multipy_by_inputs` is False we compute perturbations by `input - perturbed_input` difference and in case `multipy_by_inputs` flag is True we compute it by dividing (input - perturbed_input) by (input - baselines). The user needs to only return perturbed inputs in `perturb_func` as described above. `infidelity_perturb_func_decorator` needs to be used with `multipy_by_inputs` flag set to False in case infidelity score is being computed for attribution maps that are local aka that do not factor in inputs in the final attribution score. Such attribution algorithms include Saliency, GradCam, Guided Backprop, or Integrated Gradients and DeepLift attribution scores that are already computed with `multipy_by_inputs=False` flag. If there are more than one inputs passed to infidelity function those will be passed to `perturb_func` as tuples in the same order as they are passed to infidelity function. If inputs - is a single tensor, the function needs to return a tuple of perturbations and perturbed input such as: perturb, perturbed_input and only perturbed_input in case `infidelity_perturb_func_decorator` is used. - is a tuple of tensors, corresponding perturbations and perturbed inputs must be computed and returned as tuples in the following format: (perturb1, perturb2, ... perturbN), (perturbed_input1, perturbed_input2, ... perturbed_inputN) Similar to previous case here as well we need to return only perturbed inputs in case `infidelity_perturb_func_decorator` decorates out `perturb_func`. It is important to note that for performance reasons `perturb_func` isn't called for each example individually but on a batch of input examples that are repeated `max_examples_per_batch / batch_size` times within the batch. inputs (tensor or tuple of tensors): 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. baselines (scalar, tensor, tuple of scalars or tensors, optional): Baselines define reference values which sometimes represent ablated values and are used to compare with the actual inputs to compute importance scores in attribution algorithms. They can be represented 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. Default: None attributions (tensor or tuple of tensors): Attribution scores computed based on an attribution algorithm. This attribution scores can be computed using the implementations provided in the `captum.attr` package. Some of those attribution approaches are so called global methods, which means that they factor in model inputs' multiplier, as described in: Many global attribution algorithms can be used in local modes, meaning that the inputs multiplier isn't factored in the attribution scores. This can be done duing the definition of the attribution algorithm by passing `multipy_by_inputs=False` flag. For example in case of Integrated Gradients (IG) we can obtain local attribution scores if we define the constructor of IG as: ig = IntegratedGradients(multipy_by_inputs=False) Some attribution algorithms are inherently local. Examples of inherently local attribution methods include: Saliency, Guided GradCam, Guided Backprop and Deconvolution. For local attributions we can use real-valued perturbations whereas for global attributions that perturbation is binary. If we want to compute the infidelity of global attributions we can use a binary perturbation matrix that will allow us to select a subset of features from `inputs` or `inputs - baselines` space. This will allow us to approximate sensitivity-n for a global attribution algorithm. `infidelity_perturb_func_decorator` function decorator is a helper function that computes perturbations under the hood if perturbed inputs are provided. For more details about how to use `infidelity_perturb_func_decorator`, please, read the documentation about `perturb_func` Attributions have the same shape and dimensionality as the inputs. If inputs is a single tensor then the attributions is a single tensor as well. If inputs is provided as a tuple of tensors then attributions will be tuples of tensors as well. 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 the perturbations are not computed with respect to these arguments. This means that these arguments aren't being passed to `perturb_func` as an input argument. Default: None target (int, tuple, tensor or list, optional): Indices for selecting predictions from output(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 n_perturb_samples (int, optional): The number of times input tensors are perturbed. Each input example in the inputs tensor is expanded `n_perturb_samples` times before calling `perturb_func` function. Default: 10 max_examples_per_batch (int, optional): The number of maximum input examples that are processed together. In case the number of examples (`input batch size * n_perturb_samples`) exceeds `max_examples_per_batch`, they will be sliced into batches of `max_examples_per_batch` examples and processed in a sequential order. If `max_examples_per_batch` is None, all examples are processed together. `max_examples_per_batch` should at least be equal `input batch size` and at most `input batch size * n_perturb_samples`. Default: None normalize (bool, optional): Normalize the dot product of the input perturbation and the attribution so the infidelity value is invariant to constant scaling of the attribution values. The normalization factor beta is defined as the ratio of two mean values:: \beta = \frac{ \mathbb{E}_{I \sim \mu_I} [ I^T \Phi(f, x) (f(x) - f(x - I)) ] }{ \mathbb{E}_{I \sim \mu_I} [ (I^T \Phi(f, x))^2 ] } Please refer the original paper for the meaning of the symbols. Same normalization can be found in the paper's official implementation Default: False Returns: infidelities (tensor): A tensor of scalar infidelity scores per input example. The first dimension is equal to the number of examples in the input batch and the second dimension is one. Examples:: >>> # ImageClassifier takes a single input tensor of images Nx3x32x32, >>> # and returns an Nx10 tensor of class probabilities. >>> net = ImageClassifier() >>> saliency = Saliency(net) >>> input = torch.randn(2, 3, 32, 32, requires_grad=True) >>> # Computes saliency maps for class 3. >>> attribution = saliency.attribute(input, target=3) >>> # define a perturbation function for the input >>> def perturb_fn(inputs): >>> noise = torch.tensor(np.random.normal(0, 0.003, inputs.shape)).float() >>> return noise, inputs - noise >>> # Computes infidelity score for saliency maps >>> infid = infidelity(net, perturb_fn, input, attribution) """ def _generate_perturbations( current_n_perturb_samples: int, ) -> Tuple[TensorOrTupleOfTensorsGeneric, TensorOrTupleOfTensorsGeneric]: r""" The perturbations are generated for each example `current_n_perturb_samples` times. For performance reasons we are not calling `perturb_func` on each example but on a batch that contains `current_n_perturb_samples` repeated instances per example. """ def call_perturb_func(): r""" """ baselines_pert = None inputs_pert: Union[Tensor, Tuple[Tensor, ...]] if len(inputs_expanded) == 1: inputs_pert = inputs_expanded[0] if baselines_expanded is not None: baselines_pert = cast(Tuple, baselines_expanded)[0] else: inputs_pert = inputs_expanded baselines_pert = baselines_expanded return ( perturb_func(inputs_pert, baselines_pert) if baselines_pert is not None else perturb_func(inputs_pert) ) inputs_expanded = tuple( torch.repeat_interleave(input, current_n_perturb_samples, dim=0) for input in inputs ) baselines_expanded = baselines if baselines is not None: baselines_expanded = tuple( baseline.repeat_interleave(current_n_perturb_samples, dim=0) if isinstance(baseline, torch.Tensor) and baseline.shape[0] == input.shape[0] and baseline.shape[0] > 1 else baseline for input, baseline in zip(inputs, cast(Tuple, baselines)) ) return call_perturb_func() def _validate_inputs_and_perturbations( inputs: Tuple[Tensor, ...], inputs_perturbed: Tuple[Tensor, ...], perturbations: Tuple[Tensor, ...], ) -> None: # asserts the sizes of the perturbations and inputs assert len(perturbations) == len(inputs), ( """The number of perturbed inputs and corresponding perturbations must have the same number of elements. Found number of inputs is: {} and perturbations: {}""" ).format(len(perturbations), len(inputs)) # asserts the shapes of the perturbations and perturbed inputs for perturb, input_perturbed in zip(perturbations, inputs_perturbed): assert perturb[0].shape == input_perturbed[0].shape, ( """Perturbed input and corresponding perturbation must have the same shape and dimensionality. Found perturbation shape is: {} and the input shape is: {}""" ).format(perturb[0].shape, input_perturbed[0].shape) def _next_infidelity_tensors( current_n_perturb_samples: int, ) -> Union[Tuple[Tensor], Tuple[Tensor, Tensor, Tensor]]: perturbations, inputs_perturbed = _generate_perturbations( current_n_perturb_samples ) perturbations = _format_tensor_into_tuples(perturbations) inputs_perturbed = _format_tensor_into_tuples(inputs_perturbed) _validate_inputs_and_perturbations( cast(Tuple[Tensor, ...], inputs), cast(Tuple[Tensor, ...], inputs_perturbed), cast(Tuple[Tensor, ...], perturbations), ) targets_expanded = _expand_target( target, current_n_perturb_samples, expansion_type=ExpansionTypes.repeat_interleave, ) additional_forward_args_expanded = _expand_additional_forward_args( additional_forward_args, current_n_perturb_samples, expansion_type=ExpansionTypes.repeat_interleave, ) inputs_perturbed_fwd = _run_forward( forward_func, inputs_perturbed, targets_expanded, additional_forward_args_expanded, ) inputs_fwd = _run_forward(forward_func, inputs, target, additional_forward_args) inputs_fwd = torch.repeat_interleave( inputs_fwd, current_n_perturb_samples, dim=0 ) perturbed_fwd_diffs = inputs_fwd - inputs_perturbed_fwd attributions_expanded = tuple( torch.repeat_interleave(attribution, current_n_perturb_samples, dim=0) for attribution in attributions ) attributions_times_perturb = tuple( (attribution_expanded * perturbation).view(attribution_expanded.size(0), -1) for attribution_expanded, perturbation in zip( attributions_expanded, perturbations ) ) attr_times_perturb_sums = sum( torch.sum(attribution_times_perturb, dim=1) for attribution_times_perturb in attributions_times_perturb ) attr_times_perturb_sums = cast(Tensor, attr_times_perturb_sums) # reshape as Tensor(bsz, current_n_perturb_samples) attr_times_perturb_sums = attr_times_perturb_sums.view(bsz, -1) perturbed_fwd_diffs = perturbed_fwd_diffs.view(bsz, -1) if normalize: # in order to normalize, we have to aggregate the following tensors # to calculate MSE in its polynomial expansion: # (a-b)^2 = a^2 - 2ab + b^2 return ( attr_times_perturb_sums.pow(2).sum(-1), (attr_times_perturb_sums * perturbed_fwd_diffs).sum(-1), perturbed_fwd_diffs.pow(2).sum(-1), ) else: # returns (a-b)^2 if no need to normalize return ((attr_times_perturb_sums - perturbed_fwd_diffs).pow(2).sum(-1),) def _sum_infidelity_tensors(agg_tensors, tensors): return tuple(agg_t + t for agg_t, t in zip(agg_tensors, tensors)) # perform argument formattings inputs = _format_input(inputs) # type: ignore if baselines is not None: baselines = _format_baseline(baselines, cast(Tuple[Tensor, ...], inputs)) additional_forward_args = _format_additional_forward_args(additional_forward_args) attributions = _format_tensor_into_tuples(attributions) # type: ignore # Make sure that inputs and corresponding attributions have matching sizes. assert len(inputs) == len(attributions), ( """The number of tensors in the inputs and attributions must match. Found number of tensors in the inputs is: {} and in the attributions: {}""" ).format(len(inputs), len(attributions)) for inp, attr in zip(inputs, attributions): assert inp.shape == attr.shape, ( """Inputs and attributions must have matching shapes. One of the input tensor's shape is {} and the attribution tensor's shape is: {}""" ).format(inp.shape, attr.shape) bsz = inputs[0].size(0) with torch.no_grad(): # if not normalize, directly return aggrgated MSE ((a-b)^2,) # else return aggregated MSE's polynomial expansion tensors (a^2, ab, b^2) agg_tensors = _divide_and_aggregate_metrics( cast(Tuple[Tensor, ...], inputs), n_perturb_samples, _next_infidelity_tensors, agg_func=_sum_infidelity_tensors, max_examples_per_batch=max_examples_per_batch, ) if normalize: beta_num = agg_tensors[1] beta_denorm = agg_tensors[0] beta = safe_div(beta_num, beta_denorm) infidelity_values = ( beta ** 2 * agg_tensors[0] - 2 * beta * agg_tensors[1] + agg_tensors[2] ) else: infidelity_values = agg_tensors[0] infidelity_values /= n_perturb_samples return infidelity_values