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

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

import torch
from torch import Tensor
from torch.nn import Module

from captum._utils.common import _format_output
from captum._utils.gradient import _forward_layer_eval
from captum._utils.typing import ModuleOrModuleList
from captum.attr._utils.attribution import LayerAttribution
from captum.log import log_usage


[docs]class LayerActivation(LayerAttribution): r""" Computes activation of selected layer for given input. """ def __init__( self, forward_func: Callable, layer: ModuleOrModuleList, device_ids: Union[None, List[int]] = None, ) -> 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 activations 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. """ LayerAttribution.__init__(self, forward_func, layer, device_ids)
[docs] @log_usage() def attribute( self, inputs: Union[Tensor, Tuple[Tensor, ...]], 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 layer activation is 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. 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. 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: *tensor* or tuple of *tensors* or *list* of **attributions**: - **attributions** (*tensor* or tuple of *tensors* or *list*): Activation of 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_act = LayerActivation(net, net.conv1) >>> input = torch.randn(2, 3, 32, 32, requires_grad=True) >>> # Computes layer activation. >>> # attribution is layer output, with size Nx12x32x32 >>> attribution = layer_cond.attribute(input) """ with torch.no_grad(): layer_eval = _forward_layer_eval( self.forward_func, inputs, self.layer, additional_forward_args, device_ids=self.device_ids, attribute_to_layer_input=attribute_to_layer_input, ) if isinstance(self.layer, Module): return _format_output(len(layer_eval) > 1, layer_eval) else: return [ _format_output(len(single_layer_eval) > 1, single_layer_eval) for single_layer_eval in layer_eval ]
@property def multiplies_by_inputs(self): return True