Source code for captum.attr._core.neuron.neuron_conductance

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

import torch
from captum._utils.common import (
    _expand_additional_forward_args,
    _expand_target,
    _format_additional_forward_args,
    _format_output,
    _is_tuple,
    _verify_select_neuron,
)
from captum._utils.gradient import compute_layer_gradients_and_eval
from captum._utils.typing import BaselineType, TargetType, TensorOrTupleOfTensorsGeneric
from captum.attr._utils.approximation_methods import approximation_parameters
from captum.attr._utils.attribution import GradientAttribution, NeuronAttribution
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
from torch import Tensor
from torch.nn import Module


[docs]class NeuronConductance(NeuronAttribution, GradientAttribution): r""" Computes conductance with respect to particular hidden neuron. The returned output is in the shape of the input, showing the attribution / conductance of each input feature to the selected hidden layer neuron. The details of the approach can be found here: https://arxiv.org/abs/1805.12233 """ 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 neuron attributions are computed. Attributions for a particular neuron in the input or output of this layer are computed using the argument neuron_selector in the attribute method. Currently, only layers with a single tensor input or output are supported. 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. Currently, it is assumed that the inputs or the outputs of the layer, depending on which one is used for attribution, can only be a single tensor. 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 that 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 Neuron Conductance, if `multiply_by_inputs` is set to True, final sensitivity scores are being multiplied by (inputs - baselines). """ NeuronAttribution.__init__(self, forward_func, layer, device_ids) GradientAttribution.__init__(self, forward_func) self._multiply_by_inputs = multiply_by_inputs
[docs] @log_usage() def attribute( self, inputs: TensorOrTupleOfTensorsGeneric, neuron_selector: Union[int, Tuple[int, ...], Callable], baselines: BaselineType = None, target: TargetType = None, additional_forward_args: Any = None, n_steps: int = 50, method: str = "riemann_trapezoid", internal_batch_size: Union[None, int] = None, attribute_to_neuron_input: bool = False, ) -> TensorOrTupleOfTensorsGeneric: r""" Args: inputs (Tensor or tuple[Tensor, ...]): Input for which neuron 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. neuron_selector (int, Callable, tuple[int], or slice): Selector for neuron in given layer for which attribution is desired. Neuron selector can be provided as: - a single integer, if the layer output is 2D. This integer selects the appropriate neuron column in the layer input or output - a tuple of integers. Length of this tuple must be one less than the number of dimensions in the input / output of the given layer (since dimension 0 corresponds to number of examples). This can be used as long as the layer input / output is a single tensor. - a callable, which should take the target layer as input (single tensor or tuple if multiple tensors are in layer) and return a selected neuron - output shape should be 1D with length equal to batch_size (one scalar per input example) NOTE: Callables applicable for neuron conductance are less general than those of other methods and should NOT aggregate values of the layer, only return a specific output. This option should only be used in cases where the layer input / output is a tuple of tensors, where the other options would not suffice. This limitation is necessary since neuron conductance, unlike other neuron methods, also utilizes the gradient of output with respect to the intermedite neuron, which cannot be computed for aggregations of multiple intemediate neurons. baselines (scalar, Tensor, tuple of scalar, or Tensor, 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 (str, 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 #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 attribute_to_neuron_input (bool, optional): Indicates whether to compute the attributions with respect to the neuron input or output. If `attribute_to_neuron_input` is set to True then the attributions will be computed with respect to neuron's inputs, otherwise it will be computed with respect to neuron's outputs. Note that currently it is assumed that either the input or the output of internal neuron, 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[Tensor, ...]* of **attributions**: - **attributions** (*Tensor* or *tuple[Tensor, ...]*): Conductance for particular neuron with respect to each input feature. Attributions will always be the same size as the provided inputs, with each value providing the attribution of the corresponding input index. If a single tensor is provided as inputs, a single tensor is returned. If a tuple is provided for inputs, a tuple of corresponding sized tensors is returned. 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() >>> neuron_cond = NeuronConductance(net, net.conv1) >>> input = torch.randn(2, 3, 32, 32, requires_grad=True) >>> # To compute neuron attribution, we need to provide the neuron >>> # index for which attribution is desired. Since the layer output >>> # is Nx12x32x32, we need a tuple in the form (0..11,0..31,0..31) >>> # which indexes a particular neuron in the layer output. >>> # Computes neuron conductance for neuron with >>> # index (4,1,2). >>> attribution = neuron_cond.attribute(input, (4,1,2)) """ if callable(neuron_selector): warnings.warn( "The neuron_selector provided is a callable. Please ensure that this" " function only selects neurons from the given layer; aggregating" " or performing other operations on the tensor may lead to inaccurate" " results." ) is_inputs_tuple = _is_tuple(inputs) 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, inputs=inputs, baselines=baselines, neuron_selector=neuron_selector, target=target, additional_forward_args=additional_forward_args, method=method, attribute_to_neuron_input=attribute_to_neuron_input, ) else: attrs = self._attribute( inputs=inputs, neuron_selector=neuron_selector, baselines=baselines, target=target, additional_forward_args=additional_forward_args, n_steps=n_steps, method=method, attribute_to_neuron_input=attribute_to_neuron_input, ) return _format_output(is_inputs_tuple, attrs)
def _attribute( self, inputs: Tuple[Tensor, ...], neuron_selector: Union[int, Tuple[int, ...], Callable], baselines: Tuple[Union[Tensor, int, float], ...], target: TargetType = None, additional_forward_args: Any = None, n_steps: int = 50, method: str = "riemann_trapezoid", attribute_to_neuron_input: bool = False, step_sizes_and_alphas: Union[None, Tuple[List[float], List[float]]] = None, ) -> Tuple[Tensor, ...]: num_examples = inputs[0].shape[0] total_batch = num_examples * n_steps if step_sizes_and_alphas is None: # retrieve step size and scaling factor for specified approximation method step_sizes_func, alphas_func = approximation_parameters(method) step_sizes, alphas = step_sizes_func(n_steps), alphas_func(n_steps) else: step_sizes, 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) if additional_forward_args is not None else None ) expanded_target = _expand_target(target, n_steps) # Conductance Gradients - Returns gradient of output with respect to # hidden layer and hidden layer evaluated at each input. layer_gradients, layer_eval, input_grads = compute_layer_gradients_and_eval( forward_fn=self.forward_func, layer=self.layer, inputs=scaled_features_tpl, target_ind=expanded_target, additional_forward_args=input_additional_args, gradient_neuron_selector=neuron_selector, device_ids=self.device_ids, attribute_to_layer_input=attribute_to_neuron_input, ) mid_grads = _verify_select_neuron(layer_gradients, neuron_selector) scaled_input_gradients = tuple( input_grad * mid_grads.reshape((total_batch,) + (1,) * (len(input_grad.shape) - 1)) for input_grad in input_grads ) # Mutliplies by appropriate step size. scaled_grads = tuple( scaled_input_gradient.contiguous().view(n_steps, -1) * torch.tensor(step_sizes).view(n_steps, 1).to(scaled_input_gradient.device) for scaled_input_gradient in scaled_input_gradients ) # Aggregates across all steps for each tensor in the input tuple total_grads = tuple( _reshape_and_sum(scaled_grad, n_steps, num_examples, input_grad.shape[1:]) for (scaled_grad, input_grad) in zip(scaled_grads, input_grads) ) if self.multiplies_by_inputs: # computes attribution for each tensor in input tuple # attributions has the same dimensionality as inputs attributions = tuple( total_grad * (input - baseline) for total_grad, input, baseline in zip(total_grads, inputs, baselines) ) else: attributions = total_grads return attributions @property def multiplies_by_inputs(self): return self._multiply_by_inputs