pulsatrix::Saliency Class Reference

Raw-gradient saliency: d(output[target_index])/d(input), computed by seeding ExplainerContext::backward_pass with a one-hot vector at target_index. More...

#include <saliency.hpp>

Public Member Functions
Attribution	explain (ExplainerContext &ctx, const Tensor &input, int64_t target_index, DeviceBackend *backend) const
	Computes the saliency map for one output index.

Detailed Description

Raw-gradient saliency: d(output[target_index])/d(input), computed by seeding ExplainerContext::backward_pass with a one-hot vector at target_index.

Note

A pure graph walker built entirely against ExplainerContext's public interface – no core (Tensor/ComputationGraph/Autograd/Module) changes needed, per the charter's own red-flag check for explainer additions.

Device-generic: forward/backward run on the network's device; only the one-hot seed is built on the host and uploaded beside the output (explainer_detail host boundary).

Member Function Documentation

explain()

Attribution pulsatrix::Saliency::explain ( ExplainerContext &  ctx, const Tensor &  input, int64_t  target_index, DeviceBackend *  backend  ) const

inline

Computes the saliency map for one output index.

Parameters

ctx	Context to run the forward/backward pass through.
input	Input to explain.
target_index	Which output element's gradient to compute (0-based, flat index into the network's output).
backend	Backend to allocate the one-hot seed tensor through (the output's own backend is used instead when this one serves a different device).

Returns

An Attribution with method "saliency", values = the input gradient, and metadata recording the target index used.

Note

Assumes a rank-2 (N, num_classes) network output – migrated by campaign_exai_dl_library_batch_dimension_support from the original rank-1 assumption. target_index selects the same class for every example in the batch (seeding a one-hot at [n, target_index] for every row n), not a per-example target vector – a deliberate scope boundary (this migration fixes the shape contract, it doesn't add per-example target selection as a new capability).

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The documentation for this class was generated from the following file:

• include/pulsatrix/saliency.hpp