from typing import Any, List, Union
from captum._utils.common import _format_output, _format_tensor_into_tuples, _is_tuple
from captum._utils.typing import TargetType, TensorOrTupleOfTensorsGeneric
from captum.attr._core.guided_backprop_deconvnet import GuidedBackprop
from captum.attr._core.layer.grad_cam import LayerGradCam
from captum.attr._utils.attribution import GradientAttribution, LayerAttribution
from captum.log import log_usage
from torch import Tensor
from torch.nn import Module
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
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:
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.
self, model: Module, layer: Module, device_ids: Union[None, List[int]] = None
) -> None:
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
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.
self.grad_cam = LayerGradCam(model, layer, device_ids)
self.guided_backprop = GuidedBackprop(model)
target: TargetType = None,
additional_forward_args: Any = None,
interpolate_mode: str = "nearest",
attribute_to_layer_input: bool = False,
) -> TensorOrTupleOfTensorsGeneric:
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.
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.
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.
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.
*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
>>> # 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_tensor_into_tuples(inputs)
grad_cam_attr = self.grad_cam.attribute.__wrapped__(
self.grad_cam, # self
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
guided_backprop_attr = self.guided_backprop.attribute.__wrapped__(
self.guided_backprop, # self
output_attr: List[Tensor] = 
for i in range(len(inputs)):
"Couldn't appropriately interpolate GradCAM attributions for some "
"input tensors, returning empty tensor for corresponding "
return _format_output(is_inputs_tuple, tuple(output_attr))