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

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

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

from captum._utils.common import (
    ExpansionTypes,
    _expand_target,
    _format_additional_forward_args,
    _format_baseline,
    _format_input,
)
from captum._utils.gradient import (
    apply_gradient_requirements,
    compute_layer_gradients_and_eval,
    undo_gradient_requirements,
)
from captum._utils.typing import (
    BaselineType,
    Literal,
    TargetType,
    TensorOrTupleOfTensorsGeneric,
)
from captum.attr._core.deep_lift import DeepLift, DeepLiftShap
from captum.attr._utils.attribution import LayerAttribution
from captum.attr._utils.common import (
    _call_custom_attribution_func,
    _compute_conv_delta_and_format_attrs,
    _format_callable_baseline,
    _tensorize_baseline,
    _validate_input,
)
from captum.log import log_usage


[docs]class LayerDeepLift(LayerAttribution, DeepLift): r""" Implements DeepLIFT algorithm for the layer based on the following paper: Learning Important Features Through Propagating Activation Differences, Avanti Shrikumar, et. al. https://arxiv.org/abs/1704.02685 and the gradient formulation proposed in: Towards better understanding of gradient-based attribution methods for deep neural networks, Marco Ancona, et.al. https://openreview.net/pdf?id=Sy21R9JAW This implementation supports only Rescale rule. RevealCancel rule will be supported in later releases. Although DeepLIFT's(Rescale Rule) attribution quality is comparable with Integrated Gradients, it runs significantly faster than Integrated Gradients and is preferred for large datasets. Currently we only support a limited number of non-linear activations but the plan is to expand the list in the future. Note: As we know, currently we cannot access the building blocks, of PyTorch's built-in LSTM, RNNs and GRUs such as Tanh and Sigmoid. Nonetheless, it is possible to build custom LSTMs, RNNS and GRUs with performance similar to built-in ones using TorchScript. More details on how to build custom RNNs can be found here: https://pytorch.org/blog/optimizing-cuda-rnn-with-torchscript/ """ def __init__( self, model: Module, layer: Module, multiply_by_inputs: bool = True, ): r""" Args: model (torch.nn.Module): The reference to PyTorch model instance. layer (torch.nn.Module): Layer for which attributions are computed. The size and dimensionality of the attributions corresponds to the size and dimensionality of the layer's input or output depending on whether we attribute to the inputs or outputs of the layer. 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 Layer DeepLift, if `multiply_by_inputs` is set to True, final sensitivity scores are being multiplied by layer activations for inputs - layer activations for baselines. This flag applies only if `custom_attribution_func` is set to None. """ LayerAttribution.__init__(self, model, layer) DeepLift.__init__(self, model) self.model = model self._multiply_by_inputs = multiply_by_inputs # Ignoring mypy error for inconsistent signature with DeepLift @typing.overload # type: ignore def attribute( self, inputs: Union[Tensor, Tuple[Tensor, ...]], baselines: BaselineType = None, target: TargetType = None, additional_forward_args: Any = None, return_convergence_delta: Literal[False] = False, attribute_to_layer_input: bool = False, custom_attribution_func: Union[None, Callable[..., Tuple[Tensor, ...]]] = None, ) -> Union[Tensor, Tuple[Tensor, ...]]: ... @typing.overload def attribute( self, inputs: Union[Tensor, Tuple[Tensor, ...]], baselines: BaselineType = None, target: TargetType = None, additional_forward_args: Any = None, *, return_convergence_delta: Literal[True], attribute_to_layer_input: bool = False, custom_attribution_func: Union[None, Callable[..., Tuple[Tensor, ...]]] = None, ) -> Tuple[Union[Tensor, Tuple[Tensor, ...]], Tensor]: ...
[docs] @log_usage() def attribute( self, inputs: Union[Tensor, Tuple[Tensor, ...]], baselines: BaselineType = None, target: TargetType = None, additional_forward_args: Any = None, return_convergence_delta: bool = False, attribute_to_layer_input: bool = False, custom_attribution_func: Union[None, Callable[..., Tuple[Tensor, ...]]] = None, ) -> Union[ Tensor, Tuple[Tensor, ...], Tuple[Union[Tensor, Tuple[Tensor, ...]], Tensor] ]: r""" Args: inputs (tensor or tuple of tensors): 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 (aka batch size), and if multiple input tensors are provided, the examples must be aligned appropriately. baselines (scalar, tensor, tuple of scalars or tensors, optional): Baselines define reference samples that are compared with the inputs. In order to assign attribution scores DeepLift computes the differences between the inputs/outputs and corresponding references. Baselines 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. Note that attributions are not computed with respect to these arguments. 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 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 custom_attribution_func (callable, optional): A custom function for computing final attribution scores. This function can take at least one and at most three arguments with the following signature: - custom_attribution_func(multipliers) - custom_attribution_func(multipliers, inputs) - custom_attribution_func(multipliers, inputs, baselines) In case this function is not provided, we use the default logic defined as: multipliers * (inputs - baselines) It is assumed that all input arguments, `multipliers`, `inputs` and `baselines` are provided in tuples of same length. `custom_attribution_func` returns a tuple of attribution tensors that have the same length as the `inputs`. Default: None Returns: **attributions** or 2-element tuple of **attributions**, **delta**: - **attributions** (*tensor* or tuple of *tensors*): Attribution score computed based on DeepLift's rescale rule with respect to layer's inputs or outputs. Attributions will always be the same size as the provided layer's inputs or outputs, depending on whether we attribute to the inputs or outputs of the layer. If the layer input / output is a single tensor, then just a tensor is returned; if the layer input / output has multiple tensors, then a corresponding tuple of tensors is returned. - **delta** (*tensor*, returned if return_convergence_delta=True): 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 computed based on DeepLift's rescale rule. Delta is calculated per example, meaning that the number of elements in returned delta tensor is equal to the number of of examples in input. Note that the logic described for deltas is guaranteed when the default logic for attribution computations is used, meaning that the `custom_attribution_func=None`, otherwise it is not guaranteed and depends on the specifics of the `custom_attribution_func`. Examples:: >>> # ImageClassifier takes a single input tensor of images Nx3x32x32, >>> # and returns an Nx10 tensor of class probabilities. >>> net = ImageClassifier() >>> # creates an instance of LayerDeepLift to interpret target >>> # class 1 with respect to conv4 layer. >>> dl = LayerDeepLift(net, net.conv4) >>> input = torch.randn(1, 3, 32, 32, requires_grad=True) >>> # Computes deeplift attribution scores for conv4 layer and class 3. >>> attribution = dl.attribute(input, target=1) """ inputs = _format_input(inputs) baselines = _format_baseline(baselines, inputs) gradient_mask = apply_gradient_requirements(inputs) _validate_input(inputs, baselines) baselines = _tensorize_baseline(inputs, baselines) main_model_hooks = [] try: main_model_hooks = self._hook_main_model() self.model.apply( lambda mod: self._register_hooks( mod, attribute_to_layer_input=attribute_to_layer_input ) ) additional_forward_args = _format_additional_forward_args( additional_forward_args ) expanded_target = _expand_target( target, 2, expansion_type=ExpansionTypes.repeat ) wrapped_forward_func = self._construct_forward_func( self.model, (inputs, baselines), expanded_target, additional_forward_args, ) def chunk_output_fn(out: TensorOrTupleOfTensorsGeneric) -> Sequence: if isinstance(out, Tensor): return out.chunk(2) return tuple(out_sub.chunk(2) for out_sub in out) gradients, attrs = compute_layer_gradients_and_eval( wrapped_forward_func, self.layer, inputs, attribute_to_layer_input=attribute_to_layer_input, output_fn=lambda out: chunk_output_fn(out), ) attr_inputs = tuple(map(lambda attr: attr[0], attrs)) attr_baselines = tuple(map(lambda attr: attr[1], attrs)) gradients = tuple(map(lambda grad: grad[0], gradients)) if custom_attribution_func is None: if self.multiplies_by_inputs: attributions = tuple( (input - baseline) * gradient for input, baseline, gradient in zip( attr_inputs, attr_baselines, gradients ) ) else: attributions = gradients else: attributions = _call_custom_attribution_func( custom_attribution_func, gradients, attr_inputs, attr_baselines ) finally: # remove hooks from all activations self._remove_hooks(main_model_hooks) undo_gradient_requirements(inputs, gradient_mask) return _compute_conv_delta_and_format_attrs( self, return_convergence_delta, attributions, baselines, inputs, additional_forward_args, target, cast(Union[Literal[True], Literal[False]], len(attributions) > 1), )
@property def multiplies_by_inputs(self): return self._multiply_by_inputs
[docs]class LayerDeepLiftShap(LayerDeepLift, DeepLiftShap): r""" Extends LayerDeepLift and DeepLiftShap algorithms and approximates SHAP values for given input `layer`. For each input sample - baseline pair it computes DeepLift attributions with respect to inputs or outputs of given `layer` averages resulting attributions across baselines. Whether to compute the attributions with respect to the inputs or outputs of the layer is defined by the input flag `attribute_to_layer_input`. More details about the algorithm can be found here: http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf Note that the explanation model: 1. Assumes that input features are independent of one another 2. Is linear, meaning that the explanations are modeled through the additive composition of feature effects. Although, it assumes a linear model for each explanation, the overall model across multiple explanations can be complex and non-linear. """ def __init__( self, model: Module, layer: Module, multiply_by_inputs: bool = True, ) -> None: r""" Args: model (torch.nn.Module): The reference to PyTorch model instance. layer (torch.nn.Module): Layer for which attributions are computed. The size and dimensionality of the attributions corresponds to the size and dimensionality of the layer's input or output depending on whether we attribute to the inputs or outputs of the layer. 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 LayerDeepLiftShap, if `multiply_by_inputs` is set to True, final sensitivity scores are being multiplied by layer activations for inputs - layer activations for baselines This flag applies only if `custom_attribution_func` is set to None. """ LayerDeepLift.__init__(self, model, layer) DeepLiftShap.__init__(self, model, multiply_by_inputs) # Ignoring mypy error for inconsistent signature with DeepLiftShap @typing.overload # type: ignore def attribute( self, inputs: Union[Tensor, Tuple[Tensor, ...]], baselines: Union[ Tensor, Tuple[Tensor, ...], Callable[..., Union[Tensor, Tuple[Tensor, ...]]] ], target: TargetType = None, additional_forward_args: Any = None, return_convergence_delta: Literal[False] = False, attribute_to_layer_input: bool = False, custom_attribution_func: Union[None, Callable[..., Tuple[Tensor, ...]]] = None, ) -> Union[Tensor, Tuple[Tensor, ...]]: ... @typing.overload def attribute( self, inputs: Union[Tensor, Tuple[Tensor, ...]], baselines: Union[ Tensor, Tuple[Tensor, ...], Callable[..., Union[Tensor, Tuple[Tensor, ...]]] ], target: TargetType = None, additional_forward_args: Any = None, *, return_convergence_delta: Literal[True], attribute_to_layer_input: bool = False, custom_attribution_func: Union[None, Callable[..., Tuple[Tensor, ...]]] = None, ) -> Tuple[Union[Tensor, Tuple[Tensor, ...]], Tensor]: ...
[docs] @log_usage() def attribute( self, inputs: Union[Tensor, Tuple[Tensor, ...]], baselines: Union[ Tensor, Tuple[Tensor, ...], Callable[..., Union[Tensor, Tuple[Tensor, ...]]] ], target: TargetType = None, additional_forward_args: Any = None, return_convergence_delta: bool = False, attribute_to_layer_input: bool = False, custom_attribution_func: Union[None, Callable[..., Tuple[Tensor, ...]]] = None, ) -> Union[ Tensor, Tuple[Tensor, ...], Tuple[Union[Tensor, Tuple[Tensor, ...]], Tensor] ]: r""" Args: inputs (tensor or tuple of tensors): 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 (aka batch size), and if multiple input tensors are provided, the examples must be aligned appropriately. baselines (tensor, tuple of tensors, callable): Baselines define reference samples that are compared with the inputs. In order to assign attribution scores DeepLift computes the differences between the inputs/outputs and corresponding references. Baselines can be provided as: - a single tensor, if inputs is a single tensor, with the first dimension equal to the number of examples in the baselines' distribution. The remaining dimensions must match with input tensor's dimension starting from the second dimension. - a tuple of tensors, if inputs is a tuple of tensors, with the first dimension of any tensor inside the tuple equal to the number of examples in the baseline's distribution. The remaining dimensions must match the dimensions of the corresponding input tensor starting from the second dimension. - callable function, optionally takes `inputs` as an argument and either returns a single tensor or a tuple of those. It is recommended that the number of samples in the baselines' tensors is larger than one. 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 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 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 inputs, otherwise it will be computed with respect to layer outputs. Note that currently it assumes that both the inputs and outputs of internal layers are single tensors. Support for multiple tensors will be added later. Default: False custom_attribution_func (callable, optional): A custom function for computing final attribution scores. This function can take at least one and at most three arguments with the following signature: - custom_attribution_func(multipliers) - custom_attribution_func(multipliers, inputs) - custom_attribution_func(multipliers, inputs, baselines) In case this function is not provided, we use the default logic defined as: multipliers * (inputs - baselines) It is assumed that all input arguments, `multipliers`, `inputs` and `baselines` are provided in tuples of same length. `custom_attribution_func` returns a tuple of attribution tensors that have the same length as the `inputs`. Default: None Returns: **attributions** or 2-element tuple of **attributions**, **delta**: - **attributions** (*tensor* or tuple of *tensors*): Attribution score computed based on DeepLift's rescale rule with respect to layer's inputs or outputs. Attributions will always be the same size as the provided layer's inputs or outputs, depending on whether we attribute to the inputs or outputs of the layer. Attributions are returned in a tuple based on whether the layer inputs / outputs are contained in a tuple from a forward hook. For standard modules, inputs of a single tensor are usually wrapped in a tuple, while outputs of a single tensor are not. - **delta** (*tensor*, returned if return_convergence_delta=True): This is computed using the property that the total sum of forward_func(inputs) - forward_func(baselines) must be very close to the total sum of attributions computed based on approximated SHAP values using DeepLift's rescale rule. Delta is calculated for each example input and baseline pair, meaning that the number of elements in returned delta tensor is equal to the `number of examples in input` * `number of examples in baseline`. The deltas are ordered in the first place by input example, followed by the baseline. Note that the logic described for deltas is guaranteed when the default logic for attribution computations is used, meaning that the `custom_attribution_func=None`, otherwise it is not guaranteed and depends on the specifics of the `custom_attribution_func`. Examples:: >>> # ImageClassifier takes a single input tensor of images Nx3x32x32, >>> # and returns an Nx10 tensor of class probabilities. >>> net = ImageClassifier() >>> # creates an instance of LayerDeepLift to interpret target >>> # class 1 with respect to conv4 layer. >>> dl = LayerDeepLiftShap(net, net.conv4) >>> input = torch.randn(2, 3, 32, 32, requires_grad=True) >>> # Computes shap values using deeplift for class 3. >>> attribution = dl.attribute(input, target=3) """ inputs = _format_input(inputs) baselines = _format_callable_baseline(baselines, inputs) assert isinstance(baselines[0], torch.Tensor) and baselines[0].shape[0] > 1, ( "Baselines distribution has to be provided in form of a torch.Tensor" " with more than one example but found: {}." " If baselines are provided in shape of scalars or with a single" " baseline example, `LayerDeepLift`" " approach can be used instead.".format(baselines[0]) ) # batch sizes inp_bsz = inputs[0].shape[0] base_bsz = baselines[0].shape[0] ( exp_inp, exp_base, exp_target, exp_addit_args, ) = DeepLiftShap._expand_inputs_baselines_targets( self, baselines, inputs, target, additional_forward_args ) attributions = LayerDeepLift.attribute.__wrapped__( # type: ignore self, exp_inp, exp_base, target=exp_target, additional_forward_args=exp_addit_args, return_convergence_delta=cast( Literal[True, False], return_convergence_delta ), attribute_to_layer_input=attribute_to_layer_input, custom_attribution_func=custom_attribution_func, ) if return_convergence_delta: attributions, delta = attributions if isinstance(attributions, tuple): attributions = tuple( DeepLiftShap._compute_mean_across_baselines( self, inp_bsz, base_bsz, cast(Tensor, attrib) ) for attrib in attributions ) else: attributions = DeepLiftShap._compute_mean_across_baselines( self, inp_bsz, base_bsz, attributions ) if return_convergence_delta: return attributions, delta else: return attributions
@property def multiplies_by_inputs(self): return self._multiply_by_inputs