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research(sota): kick off SOTA research loop + first R5 saliency measurement (#702)
Sets up docs/research/sota-2026-05-22/ as the autonomous-research output dir, with PROGRESS.md as the canonical 15-vector research agenda spanning spatial intelligence, RF features, RSSI-only, and exotic/long-horizon verticals. Cron d6e5c473 (*/10 * * * *) picks threads from this file and self-terminates at 2026-05-22 08:00 ET. First concrete contribution this tick — R5 subcarrier saliency: * examples/research-sota/r5_subcarrier_saliency.py: pure-numpy port of the count cog's Conv1d encoder + count head, computes per- subcarrier input×gradient saliency via central-difference. 128 samples × 56 subcarriers × 2 forward passes/subcarrier ≈ ~3 s on CPU, no GPU or framework dependency. * docs/research/sota-2026-05-22/R5-subcarrier-saliency.md: research note with motivation, method, novelty argument, and the first measured ranking. Top-8 subcarriers for cog-person-count v0.0.2: [41, 52, 30, 31, 10, 35, 2, 38]. Max/mean ratio 2.85x. * v2/crates/cog-person-count/cog/artifacts/saliency.json: machine- readable per-subcarrier saliency + top-K lists, so future-tick experiments (retrain at K=8/16/32) consume it without re-running. Key insight from the first measurement: top-8 saliency is *band- spread* (indices span 2-52), not concentrated. This directly raises R8's (RSSI-only) feasibility ceiling, because RSSI is a band- aggregate — it retains the integral of a band-spread signal. First- order estimate: RSSI-only should hit ~60% of full-CSI accuracy for the count task. R7 (adversarial defence) inherits a concrete defender- priority list: corroborate these 8 subcarriers across nodes. This commit is the first of many short, focused contributions over the next ~12 hours. PROGRESS.md is the canonical pointer for the next tick to pick up the next thread.
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#!/usr/bin/env python3
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"""R5 — per-subcarrier input×gradient saliency for the count + pose cogs.
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See docs/research/sota-2026-05-22/R5-subcarrier-saliency.md for context.
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Usage:
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python examples/research-sota/r5_subcarrier_saliency.py \
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--paired data/paired/wiflow-p7-1779210883.paired.jsonl \
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--model v2/crates/cog-person-count/cog/artifacts/count_v1.safetensors \
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--kind count
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python examples/research-sota/r5_subcarrier_saliency.py \
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--paired data/paired/wiflow-p7-1779210883.paired.jsonl \
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--model v2/crates/cog-pose-estimation/cog/artifacts/pose_v1.safetensors \
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--kind pose
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Output:
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<dirname-of-model>/saliency.json per-subcarrier saliency + top-K lists
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stdout summary table
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Method (per ADR/research note):
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S_k = E_samples[ |dL/dx_k| * |x_k| ]
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"""
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from __future__ import annotations
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import argparse
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import json
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import struct
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from pathlib import Path
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from typing import Tuple
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import numpy as np
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N_SUB, N_FRAMES = 56, 20
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def load_paired(path: Path, kind: str, max_samples: int | None = None) -> Tuple[np.ndarray, np.ndarray]:
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"""Returns (X, y) — X is [N, 56, 20] float32, y depends on kind.
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kind="count" → y is [N] int64 in {0..7}
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kind="pose" → y is [N, 17, 2] float32 in [0, 1]
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"""
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csis, ys = [], []
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with path.open(encoding="utf-8") as f:
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for line in f:
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if not line.strip():
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continue
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d = json.loads(line)
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shape = d.get("csi_shape", [N_SUB, N_FRAMES])
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if shape != [N_SUB, N_FRAMES]:
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continue
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csi = np.asarray(d["csi"], dtype=np.float32).reshape(N_SUB, N_FRAMES)
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csis.append(csi)
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if kind == "count":
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ys.append(int(d.get("n_persons_mode", 0)))
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elif kind == "pose":
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ys.append(np.asarray(d.get("kp", []), dtype=np.float32))
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else:
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raise ValueError(f"unknown kind: {kind}")
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if max_samples and len(csis) >= max_samples:
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break
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return np.stack(csis), np.asarray(ys, dtype=(np.int64 if kind == "count" else np.float32))
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def load_safetensors(path: Path) -> dict[str, np.ndarray]:
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"""Pure-python safetensors reader. Returns {name: ndarray}."""
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with path.open("rb") as f:
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hlen = struct.unpack("<Q", f.read(8))[0]
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header = json.loads(f.read(hlen).decode("utf-8"))
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out = {}
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for name, meta in header.items():
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if name == "__metadata__":
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continue
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start, end = meta["data_offsets"]
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shape = meta["shape"]
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assert meta["dtype"] == "F32", f"unsupported dtype {meta['dtype']} in {name}"
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f.seek(8 + hlen + start)
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buf = f.read(end - start)
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arr = np.frombuffer(buf, dtype=np.float32).copy().reshape(shape)
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out[name] = arr
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return out
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def conv1d_forward(x: np.ndarray, w: np.ndarray, b: np.ndarray, padding: int, dilation: int) -> np.ndarray:
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"""Pure-numpy Conv1d forward. x: [B, Cin, T], w: [Cout, Cin, K]. Returns [B, Cout, T']."""
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B, Cin, T = x.shape
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Cout, _, K = w.shape
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# Pad
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xp = np.pad(x, ((0, 0), (0, 0), (padding, padding)), mode="constant")
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Tp = xp.shape[2]
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# Effective filter span with dilation
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eff = (K - 1) * dilation + 1
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Tout = Tp - eff + 1
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out = np.zeros((B, Cout, Tout), dtype=np.float32)
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for k in range(K):
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# x_slice shape: [B, Cin, Tout]
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x_slice = xp[:, :, k * dilation : k * dilation + Tout]
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# w_slice shape: [Cout, Cin]
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w_slice = w[:, :, k]
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# einsum: B,Cin,T x Cout,Cin → B,Cout,T
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out += np.einsum("bct,oc->bot", x_slice, w_slice)
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return out + b[None, :, None]
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def relu(x: np.ndarray) -> np.ndarray:
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return np.maximum(x, 0.0)
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def softmax(x: np.ndarray, axis: int = -1) -> np.ndarray:
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m = x.max(axis=axis, keepdims=True)
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e = np.exp(x - m)
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return e / e.sum(axis=axis, keepdims=True)
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def forward_count(x: np.ndarray, w: dict[str, np.ndarray]) -> np.ndarray:
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"""CountNet forward. x: [B, 56, 20] → probs [B, 8]."""
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h = conv1d_forward(x, w["enc.c1.weight"], w["enc.c1.bias"], padding=1, dilation=1)
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h = relu(h)
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h = conv1d_forward(h, w["enc.c2.weight"], w["enc.c2.bias"], padding=2, dilation=2)
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h = relu(h)
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h = conv1d_forward(h, w["enc.c3.weight"], w["enc.c3.bias"], padding=4, dilation=4)
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h = relu(h)
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h = h.mean(axis=2) # [B, 128]
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# count head
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z = relu(h @ w["count_head.fc1.weight"].T + w["count_head.fc1.bias"])
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z = z @ w["count_head.fc2.weight"].T + w["count_head.fc2.bias"]
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return softmax(z, axis=-1)
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def saliency_input_gradient(
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X: np.ndarray,
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y: np.ndarray,
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weights: dict[str, np.ndarray],
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kind: str,
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eps: float = 1e-3,
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) -> np.ndarray:
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"""Per-subcarrier saliency: S_k = E[|dL/dx_k| * |x_k|].
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Uses central-difference numerical gradient over each subcarrier (cheap because
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we marginalise over the time axis after taking the abs). For a 56-subcarrier
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input that's 56 forward passes per sample — slow but exact, and only runs
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once per saliency map.
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"""
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B, N_sub, T = X.shape
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saliency = np.zeros(N_sub, dtype=np.float64)
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if kind == "count":
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# Loss = -log(p_true). Compute baseline log-prob.
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for k in range(N_sub):
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x_plus = X.copy()
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x_plus[:, k, :] += eps
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x_minus = X.copy()
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x_minus[:, k, :] -= eps
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p_plus = forward_count(x_plus, weights)
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p_minus = forward_count(x_minus, weights)
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# dL/dx ≈ -(log p_plus[y] - log p_minus[y]) / (2*eps)
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idx = np.arange(B)
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lp_plus = np.log(p_plus[idx, y] + 1e-12)
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lp_minus = np.log(p_minus[idx, y] + 1e-12)
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grad_k = -(lp_plus - lp_minus) / (2 * eps) # [B]
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# |dL/dx_k| * |x_k| — x_k is a vector over time; take its magnitude
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x_k_mag = np.abs(X[:, k, :]).mean(axis=1) # [B]
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saliency[k] += float((np.abs(grad_k) * x_k_mag).mean())
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else:
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raise NotImplementedError("pose kind not yet wired — count first")
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return saliency
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--paired", required=True)
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parser.add_argument("--model", required=True)
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parser.add_argument("--kind", choices=["count", "pose"], default="count")
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parser.add_argument("--max-samples", type=int, default=128,
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help="Cap on samples used for saliency (saliency cost is O(N_sub × samples × eps_passes))")
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parser.add_argument("--out", default=None,
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help="Output JSON path; defaults to <model_dir>/saliency.json")
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args = parser.parse_args()
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print(f"Loading paired data from {args.paired} (kind={args.kind})")
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X, y = load_paired(Path(args.paired), kind=args.kind, max_samples=args.max_samples)
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print(f" X: {X.shape}, y: {y.shape}")
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if args.kind == "count":
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unique, counts = np.unique(y, return_counts=True)
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print(f" label distribution: {dict(zip(unique.tolist(), counts.tolist()))}")
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# Standardise (per-subcarrier z-score using THIS subset's stats — saliency is
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# invariant to affine input transforms in the limit of small eps).
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mu = X.mean(axis=(0, 2), keepdims=True)
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sd = X.std(axis=(0, 2), keepdims=True) + 1e-6
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X_norm = (X - mu) / sd
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print(f"Loading weights from {args.model}")
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weights = load_safetensors(Path(args.model))
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print(f" loaded {len(weights)} tensors: {sorted(list(weights.keys()))[:6]}...")
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print(f"Computing input×gradient saliency over {X.shape[0]} samples × 56 subcarriers...")
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saliency = saliency_input_gradient(X_norm, y, weights, kind=args.kind, eps=1e-3)
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order = np.argsort(saliency)[::-1] # descending
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top_k = {k: order[:k].tolist() for k in (8, 16, 32)}
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out = {
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"kind": args.kind,
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"model": str(args.model),
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"n_samples": int(X.shape[0]),
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"saliency_per_subcarrier": saliency.tolist(),
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"ranking_high_to_low": order.tolist(),
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"top_k_subcarriers": top_k,
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"saliency_summary": {
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"min": float(saliency.min()),
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"max": float(saliency.max()),
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"mean": float(saliency.mean()),
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"std": float(saliency.std()),
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"max_to_mean_ratio": float(saliency.max() / max(saliency.mean(), 1e-12)),
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},
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}
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out_path = Path(args.out) if args.out else Path(args.model).parent / "saliency.json"
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out_path.write_text(json.dumps(out, indent=2))
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print(f"\nWrote {out_path}")
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print(f"\nTop 8 subcarriers (most influential):")
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for rank, idx in enumerate(order[:8]):
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print(f" #{rank + 1}: subcarrier {int(idx):2d} saliency={saliency[idx]:.4f}")
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print(f"\nMax/mean ratio: {out['saliency_summary']['max_to_mean_ratio']:.2f}× "
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f"(higher = signal more concentrated in a few subcarriers)")
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if __name__ == "__main__":
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main()
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