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Bind the ADR-027 MERIDIAN cross-environment domain-generalization surface
into the wheel behind a gated [meridian] extra / Cargo `meridian` feature.
Inference/adaptation path only (tch-free), per ADR-185 section 3.3.
Surface (bound against the REAL code at HEAD, not the ADR wishlist):
- HardwareType / HardwareNormalizer / CanonicalCsiFrame (from
wifi-densepose-signal::hardware_norm)
- MeridianGeometryConfig / GeometryEncoder (64-dim, permutation-invariant)
- RapidAdaptation / AdaptationResult (push_frame + adapt, LoRA deltas)
- CrossDomainEvaluator + mpjpe (from wifi-densepose-train, NO tch-backend)
All compute paths GIL-released (py.allow_threads).
Honest deviations from ADR section 3.3 (documented in the module header):
- ADR's RapidAdaptation.calibrate(windows) and AdaptationResult.converged
DO NOT EXIST. Real API is push_frame + adapt(); result carries
{lora_weights, final_loss, frames_used, adaptation_epochs}. Bound as-is.
- ADR's HardwareType.detect exposed as a staticmethod delegating to the
real HardwareNormalizer::detect_hardware.
- ADR's normalize(frame: CsiFrame, hw) is really normalize(amplitude,
phase, hw) over f64 vectors returning Result; bound faithfully.
- CanonicalCsiFrame fields are singular amplitude/phase (ADR said plural).
Training-time types (DomainFactorizer, GradientReversalLayer,
VirtualDomainAugmentor) are out of P6 scope (need the libtorch tier).
Parity (section 4.1, release-blocking): committed fixture
meridian_input.json -> native Rust reference (tests/meridian_parity.rs,
calls hardware_norm + geometry + rapid_adapt directly) locks
tests/golden/meridian_output.sha256 over the concatenated f32 outputs
(esp32+intel canonical frames, 64-dim geometry vector, rapid-adapt LoRA
weights); pytest (tests/test_meridian.py) runs the same fixture through
the binding and asserts the identical SHA-256. Both pass.
Verified:
cargo test --features meridian --test meridian_parity -> 2/2 pass
maturin develop --features meridian + pytest tests/test_meridian.py
-> 13/13 pass
default cargo build clean, 0 train/signal/sensing-server refs in the
default dep graph (gate keeps the base wheel lean).
WHEEL-SIZE FINDING (ADR-185 section 9 / section 1.2): the libtorch risk
the ADR feared is AVOIDED -- wifi-densepose-train's `tch` dep is properly
optional (feature tch-backend, OFF), so no libtorch links. BUT train
still carries NON-optional deps: tokio (rt subset), the five ruvector-*
crates, and wifi-densepose-nn (which itself pulls `ort` / ONNX Runtime +
reqwest/hyper). So a [meridian] wheel exceeds the ADR-117 section 5.4
<=5 MB budget (though lighter than AETHER's axum/tokio server tree). The
clean fix is the same leaf-crate hoist: move the pure inference modules
(geometry, rapid_adapt, eval, hardware_norm) into a tch/tokio/ort-free
leaf crate. A required pre-release follow-up, not a functional blocker;
P2 binds real code and proves parity today.
61 lines
2.2 KiB
Python
61 lines
2.2 KiB
Python
"""MERIDIAN — cross-environment domain generalization (ADR-027, ADR-185 P2).
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Hardware-invariant CSI normalization, geometry-conditioned deployment,
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few-shot room adaptation, and cross-domain evaluation — the tch-free
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inference/adaptation path of Project MERIDIAN, computed by the Rust core.
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Available **only** when the wheel was built with the ``[meridian]`` extra::
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pip install wifi-densepose[meridian]
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Quick start::
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from wifi_densepose.meridian import HardwareNormalizer, HardwareType
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norm = HardwareNormalizer() # canonical 56 subcarriers
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hw = HardwareType.detect(64) # -> HardwareType.Esp32S3
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frame = norm.normalize(amplitude, phase, hw) # -> CanonicalCsiFrame
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print(len(frame.amplitude), frame.hardware_type)
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Note (honest scope, ADR-185 §3.3): the ADR's ``RapidAdaptation.calibrate``
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/ ``AdaptationResult.converged`` do not exist in the Rust core — use
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``push_frame(...)`` then ``adapt()``; the result exposes ``final_loss``,
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``frames_used``, ``adaptation_epochs``. Training-time types
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(DomainFactorizer, GradientReversalLayer, VirtualDomainAugmentor) are
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out of scope for P6 (they need the deferred libtorch training tier).
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"""
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from __future__ import annotations
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from wifi_densepose import _native
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# MERIDIAN symbols are compiled into `_native` only under the Rust
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# `meridian` feature; absent in a base wheel (ADR-185 §6 acceptance).
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if not hasattr(_native, "HardwareNormalizer"):
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raise ImportError(
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"wifi_densepose.meridian is not available in this wheel. "
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"It requires the 'meridian' extra: pip install wifi-densepose[meridian]"
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)
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HardwareType = _native.HardwareType
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CanonicalCsiFrame = _native.CanonicalCsiFrame
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HardwareNormalizer = _native.HardwareNormalizer
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MeridianGeometryConfig = _native.MeridianGeometryConfig
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GeometryEncoder = _native.GeometryEncoder
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RapidAdaptation = _native.RapidAdaptation
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AdaptationResult = _native.AdaptationResult
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CrossDomainEvaluator = _native.CrossDomainEvaluator
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mpjpe = _native.mpjpe
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__all__ = [
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"HardwareType",
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"CanonicalCsiFrame",
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"HardwareNormalizer",
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"MeridianGeometryConfig",
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"GeometryEncoder",
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"RapidAdaptation",
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"AdaptationResult",
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"CrossDomainEvaluator",
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"mpjpe",
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]
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