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

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

from torch import Tensor
from torch.nn import Module

from captum._utils.common import (
    _format_additional_forward_args,
    _format_input,
    _format_output,
)
from captum._utils.gradient import (
    apply_gradient_requirements,
    compute_layer_gradients_and_eval,
    undo_gradient_requirements,
)
from captum._utils.typing import ModuleOrModuleList, TargetType
from captum.attr._utils.attribution import GradientAttribution, LayerAttribution
from captum.log import log_usage


[docs]class LayerGradientXActivation(LayerAttribution, GradientAttribution): r""" Computes element-wise product of gradient and activation for selected layer on given inputs. """ def __init__( self, forward_func: Callable, layer: ModuleOrModuleList, 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 or list(torch.nn.Module)): Layer or layers 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. If multiple layers are provided, attributions are returned as a list, each element corresponding to the attributions of the corresponding 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 x activation, if `multiply_by_inputs` is set to True, final sensitivity scores are being multiplied by layer activations for inputs. """ LayerAttribution.__init__(self, forward_func, layer, device_ids) GradientAttribution.__init__(self, forward_func) self._multiply_by_inputs = multiply_by_inputs @property def multiplies_by_inputs(self): return self._multiply_by_inputs
[docs] @log_usage() def attribute( self, inputs: Union[Tensor, Tuple[Tensor, ...]], target: TargetType = None, additional_forward_args: Any = None, attribute_to_layer_input: bool = False, ) -> Union[Tensor, Tuple[Tensor, ...], List[Union[Tensor, Tuple[Tensor, ...]]]]: r""" Args: 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, and if multiple input tensors are provided, the examples must be aligned appropriately. 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. Note that attributions are not computed with respect to these arguments. Default: None 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. Default: False Returns: *tensor* or tuple of *tensors* or *list* of **attributions**: - **attributions** (*tensor* or tuple of *tensors* or *list*): Product of gradient and activation for each neuron in given layer output. Attributions will always be the same size as the output of the given layer. Attributions are returned in a tuple if the layer inputs / outputs contain multiple tensors, otherwise a single tensor is returned. If multiple layers are provided, attributions are returned as a list, each element corresponding to the activations of the corresponding layer. 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() >>> layer_ga = LayerGradientXActivation(net, net.conv1) >>> input = torch.randn(2, 3, 32, 32, requires_grad=True) >>> # Computes layer activation x gradient for class 3. >>> # attribution size matches layer output, Nx12x32x32 >>> attribution = layer_ga.attribute(input, 3) """ inputs = _format_input(inputs) additional_forward_args = _format_additional_forward_args( additional_forward_args ) gradient_mask = apply_gradient_requirements(inputs) # Returns gradient of output with respect to # hidden layer and hidden layer evaluated at each input. layer_gradients, layer_evals = compute_layer_gradients_and_eval( self.forward_func, self.layer, inputs, target, additional_forward_args, device_ids=self.device_ids, attribute_to_layer_input=attribute_to_layer_input, ) undo_gradient_requirements(inputs, gradient_mask) if isinstance(self.layer, Module): return _format_output( len(layer_evals) > 1, self.multiply_gradient_acts(layer_gradients, layer_evals), ) else: return [ _format_output( len(layer_evals[i]) > 1, self.multiply_gradient_acts(layer_gradients[i], layer_evals[i]), ) for i in range(len(self.layer)) ]
def multiply_gradient_acts( self, gradients: Tuple[Tensor, ...], evals: Tuple[Tensor, ...] ) -> Tuple[Tensor, ...]: return tuple( single_gradient * single_eval if self.multiplies_by_inputs else single_gradient for single_gradient, single_eval in zip(gradients, evals) )