Source code for captum.attr._core.layer.layer_gradient_shap

#!/usr/bin/env python3

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

import numpy as np
import torch
from torch import Tensor
from torch.nn import Module

from captum._utils.gradient import _forward_layer_eval, compute_layer_gradients_and_eval
from captum._utils.typing import Literal, TargetType, TensorOrTupleOfTensorsGeneric
from captum.attr._core.gradient_shap import _scale_input
from captum.attr._core.noise_tunnel import NoiseTunnel
from captum.attr._utils.attribution import GradientAttribution, LayerAttribution
from captum.attr._utils.common import (
    _compute_conv_delta_and_format_attrs,
    _format_callable_baseline,
    _format_input_baseline,
)
from captum.log import log_usage


[docs]class LayerGradientShap(LayerAttribution, GradientAttribution): r""" Implements gradient SHAP for layer based on the implementation from SHAP's primary author. For reference, please, view: https://github.com/slundberg/shap\ #deep-learning-example-with-gradientexplainer-tensorflowkeraspytorch-models A Unified Approach to Interpreting Model Predictions http://papers.nips.cc/paper\ 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 outputs with respect to selected random points in chosen `layer`. The final SHAP values represent the expected values of `gradients * (layer_attr_inputs - layer_attr_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 `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 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. 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 this 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: https://arxiv.org/abs/1711.06104 In case of layer gradient shap, if `multiply_by_inputs` is set to True, the sensitivity scores for scaled inputs are being multiplied by layer activations for inputs - layer activations for baselines. """ LayerAttribution.__init__(self, forward_func, layer, device_ids) GradientAttribution.__init__(self, forward_func) self._multiply_by_inputs = multiply_by_inputs @typing.overload def attribute( self, inputs: TensorOrTupleOfTensorsGeneric, baselines: Union[TensorOrTupleOfTensorsGeneric, Callable], n_samples: int = 5, stdevs: Union[float, Tuple[float, ...]] = 0.0, target: TargetType = None, additional_forward_args: Any = None, *, return_convergence_delta: Literal[True], attribute_to_layer_input: bool = False, ) -> Tuple[Union[Tensor, Tuple[Tensor, ...]], Tensor]: ... @typing.overload def attribute( self, inputs: TensorOrTupleOfTensorsGeneric, baselines: Union[TensorOrTupleOfTensorsGeneric, Callable], n_samples: int = 5, stdevs: Union[float, Tuple[float, ...]] = 0.0, target: TargetType = None, additional_forward_args: Any = None, return_convergence_delta: Literal[False] = False, attribute_to_layer_input: bool = False, ) -> Union[Tensor, Tuple[Tensor, ...]]: ...
[docs] @log_usage() def attribute( self, inputs: TensorOrTupleOfTensorsGeneric, baselines: Union[TensorOrTupleOfTensorsGeneric, Callable], n_samples: int = 5, stdevs: Union[float, Tuple[float, ...]] = 0.0, target: TargetType = None, additional_forward_args: Any = None, return_convergence_delta: bool = False, attribute_to_layer_input: bool = False, ) -> Union[ Tensor, Tuple[Tensor, ...], Tuple[Union[Tensor, Tuple[Tensor, ...]], Tensor] ]: r""" Args: inputs (tensor or tuple of tensors): Input which are used to compute SHAP attribution values for a given `layer`. 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 (tensor, tuple of tensors, 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 a tuple of floats 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 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 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 attributions are not computed with respect to these arguments. 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 attribute_to_layer_input (bool, optional): Indicates whether to compute the attribution with respect to the layer input or output. If `attribute_to_layer_input` is set to True then the attributions will be computed with respect to layer input, otherwise it will be computed with respect to layer output. Note that currently it is assumed that either the input or the output of internal layer, 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 of *tensors*): Attribution score computed based on GradientSHAP with respect to layer's input or output. Attributions will always be the same size as the provided layer's inputs or outputs, depending on whether we attribute to the inputs or outputs of the layer. Attributions are returned in a tuple if the layer inputs / outputs contain multiple tensors, otherwise a single tensor is returned. - **delta** (*tensor*, returned if return_convergence_delta=True): This is computed using the property that the total sum of forward_func(inputs) - forward_func(baselines) must be very close to the total sum of the attributions based on layer gradient SHAP. Delta is calculated for each example in the input after adding `n_samples` times gaussian noise to each of them. Therefore, the dimensionality of the deltas tensor is equal to the `number of examples in the input` * `n_samples` The deltas are ordered by each input example and `n_samples` noisy samples generated for it. Examples:: >>> # ImageClassifier takes a single input tensor of images Nx3x32x32, >>> # and returns an Nx10 tensor of class probabilities. >>> net = ImageClassifier() >>> layer_grad_shap = LayerGradientShap(net, net.linear1) >>> input = torch.randn(3, 3, 32, 32, requires_grad=True) >>> # choosing baselines randomly >>> baselines = torch.randn(20, 3, 32, 32) >>> # Computes gradient SHAP of output layer when target is equal >>> # to 0 with respect to the layer linear1. >>> # Attribution size matches to the size of the linear1 layer >>> attribution = layer_grad_shap.attribute(input, baselines, target=5) """ # since `baselines` is a distribution, we can generate it using a function # rather than passing it as an input argument baselines = _format_callable_baseline(baselines, inputs) assert isinstance(baselines[0], torch.Tensor), ( "Baselines distribution has to be provided in a form " "of a torch.Tensor {}.".format(baselines[0]) ) input_min_baseline_x_grad = LayerInputBaselineXGradient( self.forward_func, self.layer, device_ids=self.device_ids, multiply_by_inputs=self.multiplies_by_inputs, ) nt = NoiseTunnel(input_min_baseline_x_grad) attributions = nt.attribute.__wrapped__( nt, # self inputs, nt_type="smoothgrad", n_samples=n_samples, stdevs=stdevs, draw_baseline_from_distrib=True, baselines=baselines, target=target, additional_forward_args=additional_forward_args, return_convergence_delta=return_convergence_delta, attribute_to_layer_input=attribute_to_layer_input, ) return attributions
[docs] def has_convergence_delta(self) -> bool: return True
@property def multiplies_by_inputs(self): return self._multiply_by_inputs
class LayerInputBaselineXGradient(LayerAttribution, GradientAttribution): def __init__( self, forward_func: Callable, layer: Module, device_ids: Union[None, List[int]] = None, multiply_by_inputs: bool = True, ): 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. 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 this 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: https://arxiv.org/abs/1711.06104 In case of layer input minus baseline x gradient, if `multiply_by_inputs` is set to True, the sensitivity scores for scaled inputs are being multiplied by layer activations for inputs - layer activations for baselines. """ LayerAttribution.__init__(self, forward_func, layer, device_ids) GradientAttribution.__init__(self, forward_func) self._multiply_by_inputs = multiply_by_inputs @typing.overload def attribute( self, inputs: Union[Tensor, Tuple[Tensor, ...]], baselines: Union[Tensor, Tuple[Tensor, ...]], target: TargetType = None, additional_forward_args: Any = None, return_convergence_delta: Literal[False] = False, attribute_to_layer_input: bool = False, ) -> Union[Tensor, Tuple[Tensor, ...]]: ... @typing.overload def attribute( self, inputs: Union[Tensor, Tuple[Tensor, ...]], baselines: Union[Tensor, Tuple[Tensor, ...]], target: TargetType = None, additional_forward_args: Any = None, *, return_convergence_delta: Literal[True], attribute_to_layer_input: bool = False, ) -> Tuple[Union[Tensor, Tuple[Tensor, ...]], Tensor]: ... @log_usage() def attribute( # type: ignore self, inputs: Union[Tensor, Tuple[Tensor, ...]], baselines: Union[Tensor, Tuple[Tensor, ...]], target: TargetType = None, additional_forward_args: Any = None, return_convergence_delta: bool = False, attribute_to_layer_input: bool = False, ) -> Union[ Tensor, Tuple[Tensor, ...], Tuple[Union[Tensor, Tuple[Tensor, ...]], Tensor] ]: inputs, baselines = _format_input_baseline(inputs, baselines) rand_coefficient = torch.tensor( np.random.uniform(0.0, 1.0, inputs[0].shape[0]), device=inputs[0].device, dtype=inputs[0].dtype, ) input_baseline_scaled = tuple( _scale_input(input, baseline, rand_coefficient) for input, baseline in zip(inputs, baselines) ) grads, _ = compute_layer_gradients_and_eval( self.forward_func, self.layer, input_baseline_scaled, target, additional_forward_args, device_ids=self.device_ids, attribute_to_layer_input=attribute_to_layer_input, ) attr_baselines = _forward_layer_eval( self.forward_func, baselines, self.layer, additional_forward_args=additional_forward_args, device_ids=self.device_ids, attribute_to_layer_input=attribute_to_layer_input, ) attr_inputs = _forward_layer_eval( self.forward_func, inputs, self.layer, additional_forward_args=additional_forward_args, device_ids=self.device_ids, attribute_to_layer_input=attribute_to_layer_input, ) if self.multiplies_by_inputs: input_baseline_diffs = tuple( input - baseline for input, baseline in zip(attr_inputs, attr_baselines) ) attributions = tuple( input_baseline_diff * grad for input_baseline_diff, grad in zip(input_baseline_diffs, grads) ) else: attributions = grads return _compute_conv_delta_and_format_attrs( self, return_convergence_delta, attributions, baselines, inputs, additional_forward_args, target, cast(Union[Literal[True], Literal[False]], len(attributions) > 1), ) def has_convergence_delta(self) -> bool: return True @property def multiplies_by_inputs(self): return self._multiply_by_inputs