fix: resolve all 10 confirmed code-review findings (7-angle review, 20/20 verified)

wiflow_std: min_feature_width (default 15) replaces the keypoints->stride
coupling — for_keypoints(17) now provably builds the trained [2,2,2,2]
graph and pools 15->17, matching the validated Python protocol (pinned by
tests); param_count() total on invalid configs; random_mask returns Result
and rejects non-finite/out-of-range ratios; trainer checkpoints switched
to safetensors (.pt VarStore roundtrip broken on Windows torch 2.11).

ieee80211bf: SBP proxy now re-triggers instances and relays reports via
Action::RelaySbpReport -> SensingFrame::SbpReport (clients consume via
their existing path); missed_instances reset on success = consecutive
semantics; SessionTable gains a guarded SBP entry point + unknown-id drop
counter; initiator-role sessions reject inbound setup/SBP requests
(RejectedNotSupported) closing the idle hijack; StartSetup/StartSbp
outside Idle return InvalidStateForCommand; SBP validation unified
through evaluate_setup with a 1:1 SetupStatus->SbpStatus mapping.
events.rs split out to honor the 500-line cap.

calibration/cli: enrollment geometry now actually reaches trained banks —
both production call sites attach .with_geometry; --geometry flag on
train-room and POST /enroll/geometry + train-body geometry on
calibrate-serve give production a recording surface; geometry-free banks
log the ADR-152 §2.1.2 note.

benchmarks: corruption masks committed as ground truth (unregenerable
after in-place cleaning; verified bit-identical regeneration from the
pristine copy) + generate_corruption_masks.py producer; _bench_common.py
dedups the 5x-copied shim/evaluate/seed/remap (post-refactor PCK@20
re-verified equal to the last digit); remote scripts get the mmap patch;
tiny_edge --calib validated multiple-of-64; onnx_bench --help no longer
executes (and overwrote) the export — artifact restored byte-exact.

Workspace: 2,963 tests passed, 0 failed; Python proof PASS.

Co-Authored-By: claude-flow <ruv@ruv.net>

benchmarks/wiflow-std/eval_repro.py

@@ -15,56 +15,18 @@ Usage:
 import argparse
 import json
 import os
-import random
 import sys
-import time
 
-import numpy as np
 import torch
 from torch.utils.data import DataLoader
 
-UPSTREAM = os.path.join(os.path.dirname(os.path.abspath(__file__)), "upstream")
-sys.path.insert(0, UPSTREAM)
+from _bench_common import (UPSTREAM, evaluate, import_upstream,
+                           load_remapped_state, set_seed)
 
-# Upstream bug: models/__init__.py imports TemporalConvNet, which models/tcn.py
-# does not define (it defines TemporalBlock) — the package fails to import as
-# published. Register a stub package so the broken __init__ never executes;
-# submodules (models.pose_model etc.) still resolve via __path__.
-import types  # noqa: E402
+import_upstream()  # sys.path + models stub + >1GB np.load mmap patch
 
-_models_pkg = types.ModuleType("models")
-_models_pkg.__path__ = [os.path.join(UPSTREAM, "models")]
-sys.modules["models"] = _models_pkg
-
-import dataset as upstream_dataset  # noqa: E402
 from dataset import PreprocessedCSIKeypointsDataset, create_preprocessed_train_val_test_loaders  # noqa: E402
 from models.pose_model import WiFlowPoseModel  # noqa: E402
-from utils.metrics import calculate_pck, calculate_mpjpe  # noqa: E402
-
-# csi_windows.npy is ~13 GB; mmap large arrays instead of loading into RAM.
-_np_load = np.load
-
-
-def _np_load_mmap(path, *a, **kw):
-    if (isinstance(path, str) and path.endswith(".npy")
-            and os.path.getsize(path) > 1 << 30 and "mmap_mode" not in kw):
-        kw["mmap_mode"] = "r"
-    return _np_load(path, *a, **kw)
-
-
-upstream_dataset.np.load = _np_load_mmap
-
-
-def set_seed(seed=42):
-    # mirror upstream run.py exactly
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    if torch.cuda.is_available():
-        torch.cuda.manual_seed(seed)
-        torch.cuda.manual_seed_all(seed)
-    torch.backends.cudnn.deterministic = True
-    torch.backends.cudnn.benchmark = False
 
 
 def find_data_dir(root):
@@ -74,35 +36,6 @@ def find_data_dir(root):
     return None
 
 
-def evaluate(model, loader, device):
-    model.eval()
-    totals = {t: 0.0 for t in (0.1, 0.2, 0.3, 0.4, 0.5)}
-    total_mpe = 0.0
-    n = 0
-    t0 = time.time()
-    with torch.no_grad():
-        for batch_idx, (batch_x, batch_y) in enumerate(loader):
-            batch_x = batch_x.to(device)
-            batch_y = batch_y.to(device)
-            outputs = model(batch_x)
-            mpe = calculate_mpjpe(outputs, batch_y)
-            pck = calculate_pck(outputs, batch_y, thresholds=[0.1, 0.2, 0.3, 0.4, 0.5])
-            bs = batch_y.size(0)
-            total_mpe += mpe * bs
-            for t in totals:
-                totals[t] += pck[t] * bs
-            n += bs
-            if batch_idx % 50 == 0:
-                print(f"  batch {batch_idx}: n={n} pck20={totals[0.2]/n:.4f} "
-                      f"mpjpe={total_mpe/n:.4f} ({time.time()-t0:.0f}s)", flush=True)
-    return {
-        "samples": n,
-        "mpjpe": total_mpe / n,
-        **{f"pck@{int(t*100)}": totals[t] / n for t in totals},
-        "wall_seconds": time.time() - t0,
-    }
-
-
 def main():
     parser = argparse.ArgumentParser()
     parser.add_argument("--data-dir", required=True,
@@ -134,13 +67,9 @@ def main():
         dataset=dataset, batch_size=args.batch_size, num_workers=0, random_seed=42)
 
     model = WiFlowPoseModel(dropout=0.5).to(device)
-    state = torch.load(args.checkpoint, map_location=device, weights_only=True)
     # released checkpoint predates the published code: modules were renamed
     # att -> attention, final_conv -> decoder (param count identical, 2.23M)
-    renames = {"att.": "attention.", "final_conv.": "decoder."}
-    state = {next((new + k[len(old):] for old, new in renames.items()
-                   if k.startswith(old)), k): v
-             for k, v in state.items()}
+    state = load_remapped_state(args.checkpoint, map_location=device)
     model.load_state_dict(state, strict=True)
     n_params = sum(p.numel() for p in model.parameters())
     print(f"checkpoint: {args.checkpoint} ({n_params/1e6:.2f}M params)")
@@ -154,13 +83,13 @@ def main():
                "device": str(device)}
 
     print("=== test set (full, drop_last=False) ===")
-    results["test_full"] = evaluate(model, test_loader, device)
+    results["test_full"] = evaluate(model, test_loader, device=device)
     print(json.dumps(results["test_full"], indent=2))
 
     test_loader_dl = DataLoader(test_loader.dataset, batch_size=args.batch_size,
                                 shuffle=False, drop_last=True)
     print("=== test set (drop_last=True, as upstream train.py) ===")
-    results["test_drop_last"] = evaluate(model, test_loader_dl, device)
+    results["test_drop_last"] = evaluate(model, test_loader_dl, device=device)
     print(json.dumps(results["test_drop_last"], indent=2))
 
     os.makedirs(os.path.dirname(args.out), exist_ok=True)