def __init__( self, forward_func: Callable, layer: Module, device_ids: Optional[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 the 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=device_ids) GradientAttribution.__init__(self, forward_func) self.ig = IntegratedGradients(forward_func)
def __init__(self, model: Module, layer: ModuleOrModuleList) -> None: """ Args: model (module): The forward function of the model or any modification of it. Custom rules for a given layer need to be defined as attribute `module.rule` and need to be of type PropagationRule. Model cannot contain any in-place nonlinear submodules; these are not supported by the register_full_backward_hook PyTorch API starting from PyTorch v1.9. layer (torch.nn.Module or list(torch.nn.Module)): Layer or layers 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. If value is None, the relevance for all layers is returned in attribution. """ LayerAttribution.__init__(self, model, layer) LRP.__init__(self, model) if hasattr(self.model, "device_ids"): self.device_ids = cast(List[int], self.model.device_ids)
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)
def __init__(self, model: Module, layer: Module): 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. """ LayerAttribution.__init__(self, model, layer) DeepLift.__init__(self, model) self.model = model
def __init__(self, model: Module, layer: ModuleOrModuleList) -> None: """ Args: model (module): The forward function of the model or any modification of it. Custom rules for a given layer need to be defined as attribute `module.rule` and need to be of type PropagationRule. layer (torch.nn.Module or list(torch.nn.Module)): Layer or layers 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. If value is None, the relevance for all layers is returned in attribution. """ LayerAttribution.__init__(self, model, layer) LRP.__init__(self, model)
def __init__( self, forward_func: Callable, layer: ModuleOrModuleList, 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 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 attributions 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. 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 this 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 gradient x activation, if `multiply_by_inputs` is set to True, final sensitivity scores are being multiplied by layer activations for inputs. """ LayerAttribution.__init__(self, forward_func, layer, device_ids) GradientAttribution.__init__(self, forward_func) self._multiply_by_inputs = multiply_by_inputs
def __init__( self, model: Module, layer: Module, multiply_by_inputs: bool = True, ) -> None: r""" Args: model (nn.Module): The reference to PyTorch model instance. Model cannot contain any in-place nonlinear submodules; these are not supported by the register_full_backward_hook PyTorch API starting from PyTorch v1.9. 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
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 output dimensions, except for dimension 2, which will be 1, since GradCAM sums over channels. 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)
def __init__( self, forward_func: Callable, layer: ModuleOrModuleList, 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 (ModuleOrModuleList): Layer or list of layers for which attributions are computed. For each layer the output size of the attribute matches this layer's input or output dimensions, depending on whether we attribute to the inputs or outputs of the layer, corresponding to the attribution of each neuron in the input or output of this layer. Please note that layers to attribute on cannot be dependent on each other. That is, a subset of layers in `layer` cannot produce the inputs for another layer. For example, if your model is of a simple linked-list based graph structure (think nn.Sequence), e.g. x -> l1 -> l2 -> l3 -> output. If you pass in any one of those layers, you cannot pass in another due to the dependence, e.g. if you pass in l2 you cannot pass in l1 or l3. 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 this 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 integrated gradients, if `multiply_by_inputs` is set to True, final sensitivity scores are being multiplied by layer activations for inputs - layer activations for baselines. """ LayerAttribution.__init__(self, forward_func, layer, device_ids=device_ids) GradientAttribution.__init__(self, forward_func) self.ig = IntegratedGradients(forward_func, multiply_by_inputs) if isinstance(layer, list) and len(layer) > 1: warnings.warn( "Multiple layers provided. Please ensure that each layer is" "**not** solely solely dependent on the outputs of" "another layer. Please refer to the documentation for more" "detail.")
def attribute( self, inputs: TensorOrTupleOfTensorsGeneric, target: TargetType = None, additional_forward_args: Any = None, interpolate_mode: str = "nearest", attribute_to_layer_input: bool = False, ) -> TensorOrTupleOfTensorsGeneric: r""" Args: inputs (tensor or tuple of tensors): Input for which 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. 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 interpolate_mode (str, optional): Method for interpolation, which must be a valid input interpolation mode for torch.nn.functional. These methods are "nearest", "area", "linear" (3D-only), "bilinear" (4D-only), "bicubic" (4D-only), "trilinear" (5D-only) based on the number of dimensions of the chosen layer output (which must also match the number of dimensions for the input tensor). Note that the original GradCAM paper uses "bilinear" interpolation, but we default to "nearest" for applicability to any of 3D, 4D or 5D tensors. Default: "nearest" attribute_to_layer_input (bool, optional): Indicates whether to compute the attribution with respect to the layer input or output in `LayerGradCam`. 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: *tensor* of **attributions**: - **attributions** (*tensor*): Element-wise product of (upsampled) GradCAM and Guided Backprop attributions. 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. Attributions will be the same size as the provided inputs, with each value providing the attribution of the corresponding input index. If the GradCAM attributions cannot be upsampled to the shape of a given input tensor, None is returned in the corresponding index position. Examples:: >>> # ImageClassifier takes a single input tensor of images Nx3x32x32, >>> # and returns an Nx10 tensor of class probabilities. >>> # It contains an attribute conv4, which is an instance of nn.conv2d, >>> # and the output of this layer has dimensions Nx50x8x8. >>> # It is the last convolution layer, which is the recommended >>> # use case for GuidedGradCAM. >>> net = ImageClassifier() >>> guided_gc = GuidedGradCam(net, net.conv4) >>> input = torch.randn(2, 3, 32, 32, requires_grad=True) >>> # Computes guided GradCAM attributions for class 3. >>> # attribution size matches input size, Nx3x32x32 >>> attribution = guided_gc.attribute(input, 3) """ is_inputs_tuple = _is_tuple(inputs) inputs = _format_input(inputs) grad_cam_attr = self.grad_cam.attribute.__wrapped__( self.grad_cam, # self inputs=inputs, target=target, additional_forward_args=additional_forward_args, attribute_to_layer_input=attribute_to_layer_input, relu_attributions=True, ) if isinstance(grad_cam_attr, tuple): assert len(grad_cam_attr) == 1, ( "GuidedGradCAM attributions for layer with multiple inputs / " "outputs is not supported.") grad_cam_attr = grad_cam_attr[0] guided_backprop_attr = self.guided_backprop.attribute.__wrapped__( self.guided_backprop, # self inputs=inputs, target=target, additional_forward_args=additional_forward_args, ) output_attr: List[Tensor] = [] for i in range(len(inputs)): try: output_attr.append(guided_backprop_attr[i] * LayerAttribution.interpolate( grad_cam_attr, inputs[i].shape[2:], interpolate_mode=interpolate_mode, )) except Exception: warnings.warn( "Couldn't appropriately interpolate GradCAM attributions for some " "input tensors, returning empty tensor for corresponding " "attributions.") output_attr.append(torch.empty(0)) return _format_output(is_inputs_tuple, tuple(output_attr))