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

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

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

from captum._utils.common import (
    _expand_additional_forward_args,
    _expand_target,
    _format_additional_forward_args,
    _format_output,
)
from captum._utils.gradient import compute_layer_gradients_and_eval
from captum._utils.typing import BaselineType, Literal, TargetType
from captum.attr._utils.approximation_methods import approximation_parameters
from captum.attr._utils.attribution import GradientAttribution, LayerAttribution
from captum.attr._utils.batching import _batch_attribution
from captum.attr._utils.common import (
    _format_input_baseline,
    _reshape_and_sum,
    _validate_input,
)
from captum.log import log_usage


[docs]class LayerConductance(LayerAttribution, GradientAttribution): r""" Computes conductance with respect to the given layer. The returned output is in the shape of the layer's output, showing the total conductance of each hidden layer neuron. The details of the approach can be found here: https://arxiv.org/abs/1805.12233 https://arxiv.org/pdf/1807.09946.pdf Note that this provides the total conductance of each neuron in the layer's output. To obtain the breakdown of a neuron's conductance by input features, utilize NeuronConductance instead, and provide the target neuron index. """ 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, then it is not necessary to provide this argument. """ LayerAttribution.__init__(self, forward_func, layer, device_ids) GradientAttribution.__init__(self, forward_func)
[docs] def has_convergence_delta(self) -> bool: return True
@typing.overload def attribute( self, inputs: Union[Tensor, Tuple[Tensor, ...]], baselines: BaselineType = None, target: TargetType = None, additional_forward_args: Any = None, n_steps: int = 50, method: str = "gausslegendre", internal_batch_size: Union[None, int] = None, *, return_convergence_delta: Literal[True], attribute_to_layer_input: bool = False, ) -> Tuple[Union[Tensor, Tuple[Tensor, ...]], Tensor]: ... @typing.overload def attribute( self, inputs: Union[Tensor, Tuple[Tensor, ...]], baselines: BaselineType = None, target: TargetType = None, additional_forward_args: Any = None, n_steps: int = 50, method: str = "gausslegendre", internal_batch_size: Union[None, int] = None, return_convergence_delta: Literal[False] = False, attribute_to_layer_input: bool = False, ) -> Union[Tensor, Tuple[Tensor, ...]]: ...
[docs] @log_usage() def attribute( self, inputs: Union[Tensor, Tuple[Tensor, ...]], baselines: Union[ None, int, float, Tensor, Tuple[Union[int, float, Tensor], ...] ] = None, target: TargetType = None, additional_forward_args: Any = None, n_steps: int = 50, method: str = "gausslegendre", internal_batch_size: Union[None, int] = 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 for which layer conductance 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. baselines (scalar, tensor, tuple of scalars or tensors, optional): Baselines define the starting point from which integral is computed and can be provided 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. In the cases when `baselines` is not provided, we internally use zero scalar corresponding to each input tensor. 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. For a tensor, the first dimension of the tensor must correspond to the number of examples. It will be repeated for each of `n_steps` along the integrated path. For all other types, the given argument is used for all forward evaluations. Note that attributions are not computed with respect to these arguments. Default: None n_steps (int, optional): The number of steps used by the approximation method. Default: 50. method (string, optional): Method for approximating the integral, one of `riemann_right`, `riemann_left`, `riemann_middle`, `riemann_trapezoid` or `gausslegendre`. Default: `gausslegendre` if no method is provided. internal_batch_size (int, optional): Divides total #steps * #examples data points into chunks of size at most internal_batch_size, which are computed (forward / backward passes) sequentially. internal_batch_size must be at least equal to 2 * #examples. For DataParallel models, each batch is split among the available devices, so evaluations on each available device contain internal_batch_size / num_devices examples. If internal_batch_size is None, then all evaluations are processed in one batch. 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 inputs, otherwise it will be computed with respect to layer outputs. 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*): Conductance 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. - **delta** (*tensor*, returned if return_convergence_delta=True): The difference between the total approximated and true conductance. This is computed using the property that the total sum of forward_func(inputs) - forward_func(baselines) must equal the total sum of the attributions. Delta is calculated per example, meaning that the number of elements in returned delta tensor is equal to the number of of examples in inputs. 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_cond = LayerConductance(net, net.conv1) >>> input = torch.randn(2, 3, 32, 32, requires_grad=True) >>> # Computes layer conductance for class 3. >>> # attribution size matches layer output, Nx12x32x32 >>> attribution = layer_cond.attribute(input, target=3) """ inputs, baselines = _format_input_baseline(inputs, baselines) _validate_input(inputs, baselines, n_steps, method) num_examples = inputs[0].shape[0] if internal_batch_size is not None: num_examples = inputs[0].shape[0] attrs = _batch_attribution( self, num_examples, internal_batch_size, n_steps + 1, include_endpoint=True, inputs=inputs, baselines=baselines, target=target, additional_forward_args=additional_forward_args, method=method, attribute_to_layer_input=attribute_to_layer_input, ) else: attrs = self._attribute( inputs=inputs, baselines=baselines, target=target, additional_forward_args=additional_forward_args, n_steps=n_steps, method=method, attribute_to_layer_input=attribute_to_layer_input, ) is_layer_tuple = isinstance(attrs, tuple) attributions = attrs if is_layer_tuple else (attrs,) if return_convergence_delta: start_point, end_point = baselines, inputs delta = self.compute_convergence_delta( attributions, start_point, end_point, target=target, additional_forward_args=additional_forward_args, ) return _format_output(is_layer_tuple, attributions), delta return _format_output(is_layer_tuple, attributions)
def _attribute( self, inputs: Tuple[Tensor, ...], baselines: Tuple[Union[Tensor, int, float], ...], target: TargetType = None, additional_forward_args: Any = None, n_steps: int = 50, method: str = "gausslegendre", attribute_to_layer_input: bool = False, step_sizes_and_alphas: Union[None, Tuple[List[float], List[float]]] = None, ) -> Union[Tensor, Tuple[Tensor, ...]]: num_examples = inputs[0].shape[0] if step_sizes_and_alphas is None: # Retrieve scaling factors for specified approximation method step_sizes_func, alphas_func = approximation_parameters(method) alphas = alphas_func(n_steps + 1) else: _, alphas = step_sizes_and_alphas # Compute scaled inputs from baseline to final input. scaled_features_tpl = tuple( torch.cat( [baseline + alpha * (input - baseline) for alpha in alphas], dim=0 ).requires_grad_() for input, baseline in zip(inputs, baselines) ) additional_forward_args = _format_additional_forward_args( additional_forward_args ) # apply number of steps to additional forward args # currently, number of steps is applied only to additional forward arguments # that are nd-tensors. It is assumed that the first dimension is # the number of batches. # dim -> (#examples * #steps x additional_forward_args[0].shape[1:], ...) input_additional_args = ( _expand_additional_forward_args(additional_forward_args, n_steps + 1) if additional_forward_args is not None else None ) expanded_target = _expand_target(target, n_steps + 1) # Conductance Gradients - 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( forward_fn=self.forward_func, layer=self.layer, inputs=scaled_features_tpl, additional_forward_args=input_additional_args, target_ind=expanded_target, device_ids=self.device_ids, attribute_to_layer_input=attribute_to_layer_input, ) # Compute differences between consecutive evaluations of layer_eval. # This approximates the total input gradient of each step multiplied # by the step size. grad_diffs = tuple( layer_eval[num_examples:] - layer_eval[:-num_examples] for layer_eval in layer_evals ) # Element-wise multiply gradient of output with respect to hidden layer # and summed gradients with respect to input (chain rule) and sum # across stepped inputs. attributions = tuple( _reshape_and_sum( grad_diff * layer_gradient[:-num_examples], n_steps, num_examples, layer_eval.shape[1:], ) for layer_gradient, layer_eval, grad_diff in zip( layer_gradients, layer_evals, grad_diffs ) ) return _format_output(len(attributions) > 1, attributions) @property def multiplies_by_inputs(self): return True