captum/attr/_core/layer/grad_cam.py [86:198]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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 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 the layer input, otherwise it will be computed with respect to layer output. Note that currently it is assumed that either the input or the outputs of internal layers, depending on whether we attribute to the input or output, are single tensors. Support for multiple tensors will be added later. Default: False relu_attributions (bool, optional): Indicates whether to apply a ReLU operation on the final attribution, returning only non-negative attributions. Setting this flag to True matches the original GradCAM algorithm, otherwise, by default, both positive and negative attributions are returned. Default: False Returns: *tensor* or tuple of *tensors* of **attributions**: - **attributions** (*tensor* or tuple of *tensors*): Attributions based on GradCAM method. Attributions will be the same size as the output of the given layer, except for dimension 2, which will be 1 due to summing over channels. Attributions are returned in a tuple if the layer inputs / outputs contain multiple tensors, otherwise a single tensor is returned. Examples:: >>> # ImageClassifier takes a single input tensor of images Nx3x32x32, >>> # and returns an Nx10 tensor of class probabilities. >>> # It contains a layer 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 GradCAM. >>> net = ImageClassifier() >>> layer_gc = LayerGradCam(net, net.conv4) >>> input = torch.randn(2, 3, 32, 32, requires_grad=True) >>> # Computes layer GradCAM for class 3. >>> # attribution size matches layer output except for dimension >>> # 1, so dimensions of attr would be Nx1x8x8. >>> attr = layer_gc.attribute(input, 3) >>> # GradCAM attributions are often upsampled and viewed as a >>> # mask to the input, since the convolutional layer output >>> # spatially matches the original input image. >>> # This can be done with LayerAttribution's interpolate method. >>> upsampled_attr = LayerAttribution.interpolate(attr, (32, 32)) """ inputs = _format_tensor_into_tuples(inputs) additional_forward_args = _format_additional_forward_args( additional_forward_args ) # Returns gradient of output with respect to # hidden layer and hidden layer evaluated at each input. layer_gradients, layer_evals = compute_layer_gradients_and_eval( self.forward_func, self.layer, inputs, target, additional_forward_args, device_ids=self.device_ids, attribute_to_layer_input=attribute_to_layer_input, ) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - captum/attr/_core/layer/layer_gradient_x_activation.py [80:178]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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 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. Default: False Returns: *tensor* or tuple of *tensors* or *list* of **attributions**: - **attributions** (*tensor* or tuple of *tensors* or *list*): Product of gradient and activation for each neuron in given layer output. Attributions will always be the same size as the output of the given layer. Attributions are returned in a tuple if the layer inputs / outputs contain multiple tensors, otherwise a single tensor is returned. If multiple layers are provided, attributions are returned as a list, each element corresponding to the activations of the corresponding layer. 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() >>> layer_ga = LayerGradientXActivation(net, net.conv1) >>> input = torch.randn(2, 3, 32, 32, requires_grad=True) >>> # Computes layer activation x gradient for class 3. >>> # attribution size matches layer output, Nx12x32x32 >>> attribution = layer_ga.attribute(input, 3) """ inputs = _format_tensor_into_tuples(inputs) additional_forward_args = _format_additional_forward_args( additional_forward_args ) # Returns gradient of output with respect to # hidden layer and hidden layer evaluated at each input. layer_gradients, layer_evals = compute_layer_gradients_and_eval( self.forward_func, self.layer, inputs, target, additional_forward_args, device_ids=self.device_ids, attribute_to_layer_input=attribute_to_layer_input, ) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -