Computes element-wise product of guided backpropagation attributions with upsampled (non-negative) GradCAM attributions. GradCAM attributions are computed with respect to the layer provided in the constructor, and attributions are upsampled to match the input size. GradCAM is designed for convolutional neural networks, and is usually applied to the last convolutional layer.

Note that if multiple input tensors are provided, attributions for each input tensor are computed by upsampling the GradCAM attributions to match that input’s dimensions. If interpolation is not possible for the input tensor dimensions and interpolation mode, then an empty tensor is returned in the attributions for the corresponding position of that input tensor. This can occur if the input tensor does not have the same number of dimensions as the chosen layer’s output or is not either 3D, 4D or 5D.

Note that attributions are only meaningful for input tensors which are spatially alligned with the chosen layer, e.g. an input image tensor for a convolutional layer.

More details regarding GuidedGradCAM can be found in the original GradCAM paper here: https://arxiv.org/abs/1610.02391

Warning: Ensure that all ReLU operations in the forward function of the given model are performed using a module (nn.module.ReLU). If nn.functional.ReLU is used, gradients are not overridden appropriately.

Parameters
• model (nn.Module) – The reference to PyTorch model instance.

• layer (torch.nn.Module) – Layer for which GradCAM attributions are computed. Currently, only layers with a single tensor output are supported.

• 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.

Parameters
• inputs (Tensor or tuple[Tensor, ...]) – 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

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.

Return type

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()