feat: ADR-024 Contrastive CSI Embedding Model — all 7 phases (#52)

Full implementation of Project AETHER — Contrastive CSI Embedding Model.

## Phases Delivered
1. ProjectionHead (64→128→128) + L2 normalization
2. CsiAugmenter (5 physically-motivated augmentations)
3. InfoNCE contrastive loss + SimCLR pretraining
4. FingerprintIndex (4 index types: env, activity, temporal, person)
5. RVF SEG_EMBED (0x0C) + CLI integration
6. Cross-modal alignment (PoseEncoder + InfoNCE)
7. Deep RuVector: MicroLoRA, EWC++, drift detection, hard-negative mining, SEG_LORA

## Stats
- 276 tests passing (191 lib + 51 bin + 16 rvf + 18 vitals)
- 3,342 additions across 8 files
- Zero unsafe/unwrap/panic/todo stubs
- ~55KB INT8 model for ESP32 edge deployment

Also fixes deprecated GitHub Actions (v3→v4) and adds feat/* branch CI triggers.

Closes #50

rust-port/wifi-densepose-rs/crates/wifi-densepose-nn/README.md

@@ -0,0 +1,89 @@
+# wifi-densepose-nn
+
+[![Crates.io](https://img.shields.io/crates/v/wifi-densepose-nn.svg)](https://crates.io/crates/wifi-densepose-nn)
+[![Documentation](https://docs.rs/wifi-densepose-nn/badge.svg)](https://docs.rs/wifi-densepose-nn)
+[![License](https://img.shields.io/crates/l/wifi-densepose-nn.svg)](LICENSE)
+
+Multi-backend neural network inference for WiFi-based DensePose estimation.
+
+## Overview
+
+`wifi-densepose-nn` provides the inference engine that maps processed WiFi CSI features to
+DensePose body surface predictions. It supports three backends -- ONNX Runtime (default),
+PyTorch via `tch-rs`, and Candle -- so models can run on CPU, CUDA GPU, or TensorRT depending
+on the deployment target.
+
+The crate implements two key neural components:
+
+- **DensePose Head** -- Predicts 24 body part segmentation masks and per-part UV coordinate
+  regression.
+- **Modality Translator** -- Translates CSI feature embeddings into visual feature space,
+  bridging the domain gap between WiFi signals and image-based pose estimation.
+
+## Features
+
+- **ONNX Runtime backend** (default) -- Load and run `.onnx` models with CPU or GPU execution
+  providers.
+- **PyTorch backend** (`tch-backend`) -- Native PyTorch inference via libtorch FFI.
+- **Candle backend** (`candle-backend`) -- Pure-Rust inference with `candle-core` and
+  `candle-nn`.
+- **CUDA acceleration** (`cuda`) -- GPU execution for supported backends.
+- **TensorRT optimization** (`tensorrt`) -- INT8/FP16 optimized inference via ONNX Runtime.
+- **Batched inference** -- Process multiple CSI frames in a single forward pass.
+- **Model caching** -- Memory-mapped model weights via `memmap2`.
+
+### Feature flags
+
+| Flag              | Default | Description                         |
+|-------------------|---------|-------------------------------------|
+| `onnx`            | yes     | ONNX Runtime backend                |
+| `tch-backend`     | no      | PyTorch (tch-rs) backend            |
+| `candle-backend`  | no      | Candle pure-Rust backend            |
+| `cuda`            | no      | CUDA GPU acceleration               |
+| `tensorrt`        | no      | TensorRT via ONNX Runtime           |
+| `all-backends`    | no      | Enable onnx + tch + candle together |
+
+## Quick Start
+
+```rust
+use wifi_densepose_nn::{InferenceEngine, DensePoseConfig, OnnxBackend};
+
+// Create inference engine with ONNX backend
+let config = DensePoseConfig::default();
+let backend = OnnxBackend::from_file("model.onnx")?;
+let engine = InferenceEngine::new(backend, config)?;
+
+// Run inference on a CSI feature tensor
+let input = ndarray::Array4::zeros((1, 256, 64, 64));
+let output = engine.infer(&input)?;
+
+println!("Body parts: {}", output.body_parts.shape()[1]); // 24
+```
+
+## Architecture
+
+```text
+wifi-densepose-nn/src/
+  lib.rs          -- Re-exports, constants (NUM_BODY_PARTS=24), prelude
+  densepose.rs    -- DensePoseHead, DensePoseConfig, DensePoseOutput
+  inference.rs    -- Backend trait, InferenceEngine, InferenceOptions
+  onnx.rs         -- OnnxBackend, OnnxSession (feature-gated)
+  tensor.rs       -- Tensor, TensorShape utilities
+  translator.rs   -- ModalityTranslator (CSI -> visual space)
+  error.rs        -- NnError, NnResult
+```
+
+## Related Crates
+
+| Crate | Role |
+|-------|------|
+| [`wifi-densepose-core`](../wifi-densepose-core) | Foundation types and `NeuralInference` trait |
+| [`wifi-densepose-signal`](../wifi-densepose-signal) | Produces CSI features consumed by inference |
+| [`wifi-densepose-train`](../wifi-densepose-train) | Trains the models this crate loads |
+| [`ort`](https://crates.io/crates/ort) | ONNX Runtime Rust bindings |
+| [`tch`](https://crates.io/crates/tch) | PyTorch Rust bindings |
+| [`candle-core`](https://crates.io/crates/candle-core) | Hugging Face pure-Rust ML framework |
+
+## License
+
+MIT OR Apache-2.0