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

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

import torch
from captum._utils.common import (
from captum._utils.gradient import _forward_layer_eval
from captum._utils.typing import BaselineType, TargetType
from captum.attr._core.feature_ablation import FeatureAblation
from captum.attr._utils.attribution import LayerAttribution, PerturbationAttribution
from captum.log import log_usage
from torch import Tensor
from torch.nn import Module
from torch.nn.parallel.scatter_gather import scatter

[docs] class LayerFeatureAblation(LayerAttribution, PerturbationAttribution): r""" A perturbation based approach to computing layer attribution, involving replacing values in the input / output of a layer with a given baseline / reference, and computing the difference in output. By default, each neuron (scalar input / output value) within the layer is replaced independently. Passing a layer mask allows grouping neurons to be ablated together. Each neuron in the group will be given the same attribution value equal to the change in target as a result of ablating the entire neuron group. """ def __init__( self, forward_func: Callable, layer: Module, 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): 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 (or otherwise has a device_ids attribute with the device ID list), then it is not necessary to provide this argument. """ LayerAttribution.__init__(self, forward_func, layer, device_ids) PerturbationAttribution.__init__(self, forward_func)
[docs] @log_usage() def attribute( self, inputs: Union[Tensor, Tuple[Tensor, ...]], layer_baselines: BaselineType = None, target: TargetType = None, additional_forward_args: Any = None, layer_mask: Union[None, Tensor, Tuple[Tensor, ...]] = None, attribute_to_layer_input: bool = False, perturbations_per_eval: int = 1, ) -> Union[Tensor, Tuple[Tensor, ...]]: r""" Args: inputs (Tensor or tuple[Tensor, ...]): Input for which layer 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. layer_baselines (scalar, Tensor, tuple of scalar, or Tensor, optional): Layer baselines define reference values which replace each layer input / output value when ablated. Layer baselines should be a single tensor with dimensions matching the input / output of the target layer (or broadcastable to match it), based on whether we are attributing to the input or output of the target layer. In the cases when `baselines` is not provided, we internally use zero as the baseline for each neuron. 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. Note that attributions are not computed with respect to these arguments. Default: None layer_mask (Tensor or tuple[Tensor, ...], optional): layer_mask defines a mask for the layer, grouping elements of the layer input / output which should be ablated together. layer_mask should be a single tensor with dimensions matching the input / output of the target layer (or broadcastable to match it), based on whether we are attributing to the input or output of the target layer. layer_mask should contain integers in the range 0 to num_groups - 1, and all elements with the same value are considered to be in the same group. If None, then a layer mask is constructed which assigns each neuron within the layer as a separate group, which is ablated independently. Default: None attribute_to_layer_input (bool, optional): Indicates whether to compute the attributions 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's inputs, otherwise it will be computed with respect to layer's outputs. Note that currently it is assumed that either the input or the output of the 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 perturbations_per_eval (int, optional): Allows ablation of multiple neuron (groups) to be processed simultaneously in one call to forward_fn. Each forward pass will contain a maximum of perturbations_per_eval * #examples samples. For DataParallel models, each batch is split among the available devices, so evaluations on each available device contain at most (perturbations_per_eval * #examples) / num_devices samples. Default: 1 Returns: *Tensor* or *tuple[Tensor, ...]* of **attributions**: - **attributions** (*Tensor* or *tuple[Tensor, ...]*): Attribution of each neuron in given layer input or output. Attributions will always be the same size as the input or output of the given layer, depending on whether we attribute to the inputs or outputs of the layer which is decided by the input flag `attribute_to_layer_input` Attributions are returned in a tuple if the layer inputs / outputs contain multiple tensors, otherwise a single tensor is returned. Examples:: >>> # SimpleClassifier takes a single input tensor of size Nx4x4, >>> # and returns an Nx3 tensor of class probabilities. >>> # It contains an attribute conv1, which is an instance of nn.conv2d, >>> # and the output of this layer has dimensions Nx12x3x3. >>> net = SimpleClassifier() >>> # Generating random input with size 2 x 4 x 4 >>> input = torch.randn(2, 4, 4) >>> # Defining LayerFeatureAblation interpreter >>> ablator = LayerFeatureAblation(net, net.conv1) >>> # Computes ablation attribution, ablating each of the 108 >>> # neurons independently. >>> attr = ablator.attribute(input, target=1) >>> # Alternatively, we may want to ablate neurons in groups, e.g. >>> # grouping all the layer outputs in the same row. >>> # This can be done by creating a layer mask as follows, which >>> # defines the groups of layer inputs / outouts, e.g.: >>> # +---+---+---+ >>> # | 0 | 0 | 0 | >>> # +---+---+---+ >>> # | 1 | 1 | 1 | >>> # +---+---+---+ >>> # | 2 | 2 | 2 | >>> # +---+---+---+ >>> # With this mask, all the 36 neurons in a row / channel are ablated >>> # simultaneously, and the attribution for each neuron in the same >>> # group (0 - 2) per example are the same. >>> # The attributions can be calculated as follows: >>> # layer mask has dimensions 1 x 3 x 3 >>> layer_mask = torch.tensor([[[0,0,0],[1,1,1], >>> [2,2,2]]]) >>> attr = ablator.attribute(input, target=1, >>> layer_mask=layer_mask) """ def layer_forward_func(*args): layer_length = args[-1] layer_input = args[:layer_length] original_inputs = args[layer_length:-1] device_ids = self.device_ids if device_ids is None: device_ids = getattr(self.forward_func, "device_ids", None) all_layer_inputs = {} if device_ids is not None: scattered_layer_input = scatter(layer_input, target_gpus=device_ids) for device_tensors in scattered_layer_input: all_layer_inputs[device_tensors[0].device] = device_tensors else: all_layer_inputs[layer_input[0].device] = layer_input def forward_hook(module, inp, out=None): device = _extract_device(module, inp, out) is_layer_tuple = ( isinstance(out, tuple) if out is not None else isinstance(inp, tuple) ) if device not in all_layer_inputs: raise AssertionError( "Layer input not placed on appropriate " "device. If using a DataParallel model, either provide the " "DataParallel model as forward_func or provide device ids" " to the constructor." ) if not is_layer_tuple: return all_layer_inputs[device][0] return all_layer_inputs[device] hook = None try: if attribute_to_layer_input: hook = self.layer.register_forward_pre_hook(forward_hook) else: hook = self.layer.register_forward_hook(forward_hook) eval = _run_forward(self.forward_func, original_inputs, target=target) finally: if hook is not None: hook.remove() return eval with torch.no_grad(): inputs = _format_tensor_into_tuples(inputs) additional_forward_args = _format_additional_forward_args( additional_forward_args ) 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, ) layer_eval_len = (len(layer_eval),) all_inputs = ( (inputs + additional_forward_args + layer_eval_len) if additional_forward_args is not None else inputs + layer_eval_len ) ablator = self.attributor(layer_forward_func) layer_attribs = ablator.attribute.__wrapped__( ablator, # self layer_eval, baselines=layer_baselines, additional_forward_args=all_inputs, feature_mask=layer_mask, perturbations_per_eval=perturbations_per_eval, ) _attr = _format_output(len(layer_attribs) > 1, layer_attribs) return _attr
@property def attributor(self): return FeatureAblation