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ADR-152: WiFi-Pose SOTA 2026 intake — WiFlow-STD benchmark, Rust integrations, ADR-153 802.11bf layer, efficiency frontier (#1008)
* feat(calibration): NodeGeometry transceiver-geometry recording (ADR-152 §2.1.1) PerceptAlign-motivated geometry capture at enrollment: per-node optional records (position, antenna orientation, inter-node distances, acquisition method) — recorded when known, never required. Event-sourced via EnrollmentEvent::GeometryRecorded (latest recording wins); persisted on SpecialistBank with serde defaults so pre-ADR-152 bank JSON loads cleanly (fixture-proven, and geometry-free banks serialize byte-shape-identical to the old schema); threaded through MultiNodeMixture as data only — the learned geometry embeddings and algorithmic fusion use are §2.1.2, deliberately deferred until the ADR-151 P6 LoRA heads exist. Geometry recorded from now on means banks captured today remain usable for layout-conditioned training later — you can't retroactively add geometry to data you didn't record. 8 new tests (3 geometry, 2 anchor, 2 bank, 1 multistatic) + full-loop extension (2-node geometry, one tape-measured + one unknown, surviving the bank JSON round-trip the runtime loads from). 50/50 calibration (both feature configs) + 23 CLI tests green. Co-Authored-By: RuFlo <ruv@ruv.net> * feat(training): two-checkerboard camera↔room calibration for ADR-079 labels (ADR-152 §2.1.3) Defends the camera-supervised pipeline against PerceptAlign's "coordinate overfitting": MediaPipe keypoints were emitted in raw camera coordinates with no shared frame and no transceiver-geometry metadata — the exact label shape that memorizes deployment layout and collapses cross-layout. - scripts/calibrate-camera-room.py + calibration_lib.py: OpenCV two-checkerboard calibration → versioned bundle JSON (intrinsics, camera→room extrinsics, checkerboard spec, transceiver geometry, sha256 calibration_id). Intrinsics resolve from file > cache > multi-view computation > loud-warning 2-view fallback. - collect-ground-truth.py --calibration <bundle>: every sample gains keypoints_room (unit bearing rays from the camera center in the room frame — documented projective alignment; raw image coords preserved so training chooses), camera_origin_room, calibration_id, and the transceiver geometry stamp. Without the flag, output is byte-identical to before (tested) + a one-line ADR-152 warning. Design finding (recorded for ADR-152): a single planar checkerboard's corner grid is centrosymmetric — the reversed corner ordering fits a ghost camera pose with IDENTICAL reprojection error, so per-board flip disambiguation is mathematically ill-posed. solve_two_board_extrinsics solves the joint wall+floor set over all 4 flip combinations, where the minimum is unique — an independent reason the TWO-checkerboard method is required, beyond what PerceptAlign states. 15 headless pytest tests green (synthetic corners: extrinsics recovery incl. ghost resolution, bundle round-trip + hash stability, ray transforms w/ distortion + cross-resolution, no-calibration byte identity). Co-Authored-By: RuFlo <ruv@ruv.net> * feat(benchmarks): WiFlow-STD reproduction harness + measurement (a) results (ADR-152 §2.2) Shipped checkpoint REFUTED (0.08% PCK@20, wrong keypoint normalization); 6 reproducibility defects documented (broken imports, corrupted dataset tail with float32-max garbage that NaN-poisons fp16 BatchNorm, unreachable test phase). After repairs, retraining with upstream defaults reproduces 96.09% PCK@20 full-test / 96.61% corruption-free (published 97.25%) on RTX 5080. Claims graded MEASURED-EQUIVALENT; 2.23M params + ~0.055 GFLOPs verified. Third-party code/weights/data stay out of tree (gitignored). Co-Authored-By: claude-flow <ruv@ruv.net> * feat: ADR-152 Rust integrations + ADR-153 802.11bf protocol model - calibration: GeometryEmbedding — 32-slot permutation-invariant NodeGeometry featurization for future LoRA-head conditioning (ADR-152 §2.1.2); derived SpecialistBank::geometry_embedding() accessor; 59 tests - train: MaePretrainConfig + patchify/random-mask with UNSW measured recipe (80% masking, (30,3) patches; ADR-152 §2.3, arXiv 2511.18792); strict no-truncate/no-NaN policy; proptest properties - train: WiFlowStdModel — tch-gated port of the verified ~96%-PCK@20 WiFlow-STD architecture (ADR-152 §2.2 beyond-SOTA); ungated param formula pinned to 2,225,042; 15/17-keypoint support; 239 crate tests - hardware: ieee80211bf forward-compatibility protocol model (ADR-153): SpecProfile gates, SensingCapabilities negotiation, required ConsentMode, session FSM, SensingTransport + SimTransport + OpportunisticCsiBridge; full acceptance checklist covered; 156+4 tests - deps: ruvector bumps per ADR-152 §2.6 survey (mincut/solver 2.0.6, attention 2.1.0, gnn 2.2.0); vendor/ruvector synced to a083bd77f - docs: ADR-153 accepted; ADR-152 §2.2 status, §2.4 amendment, §2.6 added Workspace: 162 test suites green (--no-default-features); Python proof PASS. Known pre-existing flake: homecore-api env_empty_falls_back_to_defaults (unserialized env-var mutation) — untouched, follow-up. Co-Authored-By: claude-flow <ruv@ruv.net> * docs: CHANGELOG + CLAUDE.md entries for ADR-152 integrations and ADR-153 Co-Authored-By: claude-flow <ruv@ruv.net> * fix(train): repair tch-backend bit-rot — gated path compiles and tests run again Mechanical API refresh against current tch: Vec::from(Tensor) -> try_from (+ explicit flatten), numel() usize cast, Rem/div ops -> remainder() / divide_scalar_mode(floor) — the latter fixed a silent true-division bug in heatmap argmax decoding; clamp(1.0, f64::MAX) -> clamp_min (torch 2.x scalar overflow panic); petgraph EdgeRef import; missing EvalMetrics and verify_checkpoint_dir APIs that tests documented. wiflow_std roundtrip test uses safetensors (.pt _save_parameters roundtrip broken in torch 2.11 Windows). Gated: 349 passed (incl. all 20 wiflow_std); ungated: unchanged. Known pre-existing: gaussian-heatmap convention mismatch (2 tests), proof seed race under parallel threads — documented, deliberate follow-ups. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(train): WiFlow-STD PyTorch->tch weight import + numerical parity proof export_to_safetensors.py maps the retrained checkpoint (295 tensors -> 248 mapped, param sum exactly 2,225,042; num_batches_tracked dropped) into a tch-loadable safetensors plus a deterministic parity fixture. Gated #[ignore] integration test loads it strictly and asserts forward-pass agreement: max abs diff 1.192e-7 on the seed-42 fixture. dump_variable_names test makes the tch name layout authoritative. Zero architecture discrepancies found. Co-Authored-By: claude-flow <ruv@ruv.net> * fix: workflow-review findings — BN gamma init, ThresholdParams serde, init docs Concurrent validation workflow (2 review lanes + adversarial verification, 13 agents): 5 confirmed findings, 3 refuted. Fixes: - wiflow_std: pin BatchNorm gamma to 1.0 (tch default draws Uniform(0,1) — silently halves activations in from-scratch training; loaded checkpoints unaffected, parity re-verified after the change) - wiflow_std: document the conv-init divergences vs the reference's effective kaiming_normal(fan_out) re-init (from-scratch dynamics only) - ieee80211bf: ThresholdParams deserialization validates via try_from so the <=100 invariant holds for untrusted payloads (+ rejection test) Benchmarks (release, ruvzen): GeometryEmbedding 1.84us/call (542k/s), MAE tokenization 7.38us/window (135k/s), 802.11bf FSM 8.9M events/s — nothing suspicious. Co-Authored-By: claude-flow <ruv@ruv.net> * docs(adr): ADR-152 §2.1.4 gate resolved — PerceptAlign repo MIT, dataset on HF Co-Authored-By: claude-flow <ruv@ruv.net> * feat(benchmarks): edge optimization measured + measurement (b) blocked + 92.9% retraction Edge optimization (ADR-152 optimize track): ONNX Runtime fp32 is the CPU latency win (3.2 ms/window, ~3.4x faster than torch, parity 2.4e-7); ORT dynamic int8 reaches 2.44 MB (paper's ~2.2 MB claim plausible only via conv-capable toolchains; -0.16pt PCK@20, +18% MPJPE, 2x slower); torch dynamic quant converts 0% of this conv-only model; fp16 halves storage free but is slower on CPU. Measurement (b) BLOCKED-ON-DATA: only 1,077 paired ESP32 windows exist (stop rule <2k). Forensic recheck of the surviving April holdout RETRACTS the ADR-079 '92.9% PCK@20' figure: constant-output model, absolute (not torso) threshold, 69 near-static frames — mean predictor scores 100% under that protocol; torso-PCK@20 is 19.1%. Corroborates PR #535. Stale citations removed from user-guide, readme-details, ADR-152 §2.1.3; no-citation rule extended to ADR-079 accuracy claims. Unblock: >=2k-window multi-pose paired session + torso-PCK re-baseline. Co-Authored-By: claude-flow <ruv@ruv.net> * docs(user-guide): corrected camera-supervised collection tutorial Step 0 CSI-rate check + session-length math (window yield = frames/20 — the May session's 8x under-delivery was a ~12 Hz CSI rate, not an aligner bug); two-checkerboard calibration step (ADR-152 §2.1.3); pose-variety and confidence guidance; torso-normalized PCK + temporal-split + pred-variance eval protocol (lessons from the 92.9% retraction); scale presets re-keyed to realistic window counts. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(benchmarks): static PTQ int8 (calibrated) results + overnight capture script Conv-only static QDQ beats dynamic int8 on accuracy (PCK@20 96.61-96.63% vs 96.52%, MPJPE +10% vs +18% over fp32) at ~equal size/latency; all-ops QDQ strictly worse (int8 activations through attention glue). Entropy calibration verified bit-identical to MinMax on this data. Deployment: ONNX fp32 for speed (3.2ms), static conv-only QDQ for smallest (2.53MB). Also: scripts/overnight-empty-capture.py — segmented UDP CSI recorder for empty-room baselines (no glob collisions, detach-safe). Co-Authored-By: claude-flow <ruv@ruv.net> * feat(benchmarks): measurement (b) MEASURED — optimization transfer only, mean-pose baseline wins WiFlow-STD fine-tuned on 2,046 fresh single-room ESP32 paired windows (temporal 70/15/15, 70->540 adapter, K=17): pretrained-init 65% PCK@20 vs scratch 0% (optimization transfer) but frozen-trunk ~0% (no feature transfer), and NOTHING beats the mean-pose baseline (95.9% PCK@20 — single subject, near-static normalized coords). Honesty gates held: pred std 0.0113 (non-constant model) but mean-baseline dominance means no citable CSI->pose capability from this data. ADR-152 open question 1 answered partially; definitive answer needs multi-subject/position data. Two new aligner findings: heterogeneous csi_shape with silent zero-padding (~20%), and extractCsiMatrix's transposed shape label (frame-major data, [nSc, nFrames] label) — fixes pending. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(benchmarks): efficiency sweep MEASURED — half model dominates full reference Compact WiFlow-STD variants on the same data/split/protocol: half (843,834 params, 0.38x) strictly dominates the 2.23M reference (PCK@20 96.62 vs 96.61, PCK@50 99.47 vs 99.11, MPJPE 0.00898 vs 0.0094) — the published architecture is over-parameterized for its own benchmark. quarter (338k) 96.05%; tiny (56,290 params, 1/39.5) holds 94.11% — a ~220KB fp32 edge candidate. In-domain caveats recorded; cross-domain untested. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(train): compact WiFlow-STD presets in Rust + tiny edge artifact (ADR-152) WiFlowStdConfig gains half()/quarter()/tiny() mirroring the overnight sweep exactly: TcnGroupsMode (Fixed/Gcd/Depthwise), input_pw_groups, derived stride schedule and decoder-mid (all default to upstream behavior; legacy serde JSON unaffected). Param formulas pin to trained ground truth first try: 843,834 / 338,600 / 56,290; default 2,225,042 pin and 1.192e-7 parity unchanged. 248 tests green. Tiny edge artifact (tiny_edge_bench.py): ONNX fp32 = 295 KB, 0.66 ms/win (~1,500/s CPU), 94.11% PCK@20 (matches sweep clean-test exactly; parity 1.49e-7). Static int8 is a bad trade at this scale (-1.43pt, +19% MPJPE, -16% size, slower) — recorded as negative result. Export note: width-16 breaks AdaptiveAvgPool((15,1)) TorchScript export; replaced by exact mean+matmul equivalent, proven by parity. Co-Authored-By: claude-flow <ruv@ruv.net> * 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> * ci: build workspace tests without debuginfo — runner disk exhaustion The combined 38-crate debug target exceeds the GitHub runner's disk ('final link failed: No space left on device'); the same tree measured 151GB locally with full debuginfo. CARGO_PROFILE_{DEV,TEST}_DEBUG=0 shrinks the target ~5-10x; debuginfo serves no purpose in CI test runs. Co-Authored-By: claude-flow <ruv@ruv.net>
This commit is contained in:
rUv
@@ -39,7 +39,8 @@ use tokio::sync::{mpsc, oneshot, RwLock};
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use tower_http::cors::CorsLayer;
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use wifi_densepose_calibration::extract::{AnchorFeature, Features};
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use wifi_densepose_calibration::{
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AnchorLabel, AnchorQualityGate, AnchorRecorder, MixtureOfSpecialists, SpecialistBank,
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AnchorLabel, AnchorQualityGate, AnchorRecorder, MixtureOfSpecialists, NodeGeometry,
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SpecialistBank,
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};
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use wifi_densepose_core::types::CsiFrame;
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use wifi_densepose_signal::{BaselineCalibration, CalibrationRecorder};
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@@ -207,6 +208,9 @@ struct RoomEnroll {
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baseline_id: String,
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fs_hz: f32,
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anchors: Vec<AnchorFeature>,
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/// Transceiver geometry recorded via `POST /enroll/geometry` (ADR-152
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/// §2.1.1); latest recording wins. Snapshotted into the bank at train time.
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geometry: Vec<NodeGeometry>,
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}
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/// Result of capturing one anchor (`POST /enroll/anchor`).
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@@ -299,6 +303,7 @@ fn build_router(state: ApiState) -> Router {
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.route("/api/v1/room/state", get(room_state))
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.route("/api/v1/room/train", post(train_room))
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.route("/api/v1/enroll/anchor", post(enroll_anchor))
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.route("/api/v1/enroll/geometry", post(enroll_geometry))
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.route("/api/v1/enroll/status", get(enroll_status))
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.layer(CorsLayer::permissive())
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.with_state(state)
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@@ -670,8 +675,9 @@ async fn descriptor() -> impl IntoResponse {
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"GET /api/v1/calibration/result": "last finalized baseline summary",
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"GET /api/v1/calibration/baselines": "list persisted baseline files",
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"GET /api/v1/room/state?bank=<name>": "live mixture-of-specialists RoomState over the CSI window",
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"POST /api/v1/room/train": "{ room_id, baseline_id, anchors[]? } → train + persist a specialist bank (anchors[] optional if enrolled in-server)",
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"POST /api/v1/room/train": "{ room_id, baseline_id, anchors[]?, geometry[]? } → train + persist a specialist bank (anchors[]/geometry[] optional if enrolled in-server)",
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"POST /api/v1/enroll/anchor": "{ room_id, baseline, label, duration_s? } → capture one guided anchor (blocks for the capture)",
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"POST /api/v1/enroll/geometry": "{ room_id, geometry: [NodeGeometry…] } → record transceiver geometry for the room (ADR-152 §2.1.1; latest wins)",
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"GET /api/v1/enroll/status?room=<id>": "enrollment progress (accepted anchors, next, complete)"
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}
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}))
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@@ -740,11 +746,18 @@ struct TrainRequest {
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baseline_id: String,
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#[serde(default)]
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anchors: Vec<AnchorFeature>,
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/// Optional transceiver geometry (ADR-152 §2.1.1). Falls back to the
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/// geometry recorded in-server via `POST /enroll/geometry`; absent both,
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/// the bank trains geometry-free (valid, but no geometry conditioning).
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#[serde(default)]
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geometry: Vec<NodeGeometry>,
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}
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/// Train a per-room specialist bank and persist it as `<output_dir>/<room_id>.json`
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/// (the name `room-state` reads back). Uses the posted `anchors` if present, else
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/// falls back to the in-server enrollment accumulated via `POST /enroll/anchor`.
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/// The enrollment's transceiver-geometry snapshot (posted `geometry` or the
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/// `POST /enroll/geometry` record) is threaded into the bank (ADR-152 §2.1.1).
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async fn train_room(State(st): State<ApiState>, Json(req): Json<TrainRequest>) -> impl IntoResponse {
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let (anchors, baseline_id) = if !req.anchors.is_empty() {
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(req.anchors.clone(), req.baseline_id.clone())
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@@ -756,11 +769,25 @@ async fn train_room(State(st): State<ApiState>, Json(req): Json<TrainRequest>) -
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}
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}
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};
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let geometry = if !req.geometry.is_empty() {
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req.geometry.clone()
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} else {
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st.enroll.read().await.get(&req.room_id).map(|re| re.geometry.clone()).unwrap_or_default()
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};
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let at = (unix_ms() / 1000) as i64;
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let bank = match SpecialistBank::train(&req.room_id, &baseline_id, &anchors, at) {
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Ok(b) => b,
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Err(e) => return (StatusCode::BAD_REQUEST, Json(serde_json::json!({"error": format!("training failed: {e}")}))).into_response(),
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};
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let bank = if geometry.is_empty() {
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eprintln!(
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"[calibrate-serve] no transceiver geometry recorded for room '{}' — bank will not support geometry conditioning (ADR-152 §2.1.2)",
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req.room_id
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);
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bank
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} else {
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bank.with_geometry(geometry)
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};
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let name = sanitize_room_id(&req.room_id);
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let dir = { st.status.read().await.output_dir.clone() };
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let path = format!("{dir}/{name}.json");
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@@ -777,10 +804,37 @@ async fn train_room(State(st): State<ApiState>, Json(req): Json<TrainRequest>) -
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"bank": name, // pass as ?bank=<name> to /room/state
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"anchor_count": bank.anchor_count,
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"specialists": kinds,
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"geometry_nodes": bank.geometry.len(),
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"path": path,
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}))).into_response()
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}
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/// Body for `POST /api/v1/enroll/geometry`.
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#[derive(Deserialize)]
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struct EnrollGeometryBody {
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room_id: String,
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/// Per-node transceiver geometry records (ADR-152 §2.1.1).
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geometry: Vec<NodeGeometry>,
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}
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/// Record the room's transceiver geometry (ADR-152 §2.1.1) into the in-server
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/// enrollment; the next `POST /room/train` snapshots it into the bank. A later
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/// POST supersedes an earlier one (latest wins), mirroring
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/// `EnrollmentSession::record_geometry`.
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async fn enroll_geometry(State(st): State<ApiState>, Json(b): Json<EnrollGeometryBody>) -> impl IntoResponse {
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if b.geometry.is_empty() {
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return (StatusCode::BAD_REQUEST, Json(serde_json::json!({"error":"geometry must be a non-empty array of NodeGeometry records"}))).into_response();
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}
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let nodes = b.geometry.len();
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{
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let mut map = st.enroll.write().await;
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let re = map.entry(b.room_id.clone()).or_insert_with(RoomEnroll::default);
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re.geometry = b.geometry;
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}
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eprintln!("[calibrate-serve] enroll geometry room={} nodes={nodes}", b.room_id);
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(StatusCode::OK, Json(serde_json::json!({"room_id": b.room_id, "geometry_nodes": nodes}))).into_response()
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}
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/// Body for `POST /api/v1/enroll/anchor`.
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#[derive(Deserialize)]
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struct EnrollAnchorBody {
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@@ -1086,6 +1140,59 @@ mod tests {
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);
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}
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/// ADR-152 §2.1.1: geometry threads into the trained bank through both API
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/// paths — inline in the train request, or recorded via /enroll/geometry —
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/// and a geometry-free train still produces a valid (unconditioned) bank.
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#[tokio::test]
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async fn train_threads_geometry_into_bank() {
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let dir = tempfile::tempdir().unwrap();
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let app = build_router(test_state(dir.path().to_str().unwrap()));
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let anchors = r#"[
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{"room_id":"g","label":"empty","features":{"mean":1.0,"variance":1.0,"motion":0.1,"breathing_score":0.0,"breathing_hz":0.0,"heart_score":0.0,"heart_hz":0.0}},
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{"room_id":"g","label":"stand_still","features":{"mean":1.0,"variance":10.0,"motion":0.2,"breathing_score":0.0,"breathing_hz":0.0,"heart_score":0.0,"heart_hz":0.0}}
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]"#;
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let load_bank = |name: &str| {
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let raw = std::fs::read_to_string(dir.path().join(format!("{name}.json"))).unwrap();
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SpecialistBank::from_json(&raw).unwrap()
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};
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// (1) geometry inline in the train request.
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let body = format!(
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r#"{{"room_id":"g1","baseline_id":"b","anchors":{anchors},
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"geometry":[{{"node_id":1,"position":{{"x_m":0.0,"y_m":0.0,"z_m":1.0}},"method":"tape-measure"}},{{"node_id":2}}]}}"#
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);
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assert_eq!(req(app.clone(), "POST", "/api/v1/room/train", Some(&body)).await, StatusCode::OK);
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let bank = load_bank("g1");
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assert_eq!(bank.geometry.len(), 2);
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assert_eq!(bank.geometry[0].method, "tape-measure");
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assert_eq!(bank.geometry[1].node_id, 2);
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// (2) geometry recorded via /enroll/geometry; train body omits it.
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assert_eq!(
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req(app.clone(), "POST", "/api/v1/enroll/geometry",
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Some(r#"{"room_id":"g2","geometry":[{"node_id":7,"method":"floor-plan"}]}"#)).await,
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StatusCode::OK
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);
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let body2 = format!(r#"{{"room_id":"g2","baseline_id":"b","anchors":{anchors}}}"#);
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assert_eq!(req(app.clone(), "POST", "/api/v1/room/train", Some(&body2)).await, StatusCode::OK);
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let bank2 = load_bank("g2");
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assert_eq!(bank2.geometry.len(), 1);
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assert_eq!(bank2.geometry[0].node_id, 7);
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// (3) no geometry anywhere → valid geometry-free bank (note logged).
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let body3 = format!(r#"{{"room_id":"g3","baseline_id":"b","anchors":{anchors}}}"#);
|
||||
assert_eq!(req(app.clone(), "POST", "/api/v1/room/train", Some(&body3)).await, StatusCode::OK);
|
||||
let bank3 = load_bank("g3");
|
||||
assert!(bank3.geometry.is_empty());
|
||||
assert!(bank3.presence.is_some(), "bank still trains without geometry");
|
||||
|
||||
// (4) empty geometry array is rejected.
|
||||
assert_eq!(
|
||||
req(app, "POST", "/api/v1/enroll/geometry", Some(r#"{"room_id":"g4","geometry":[]}"#)).await,
|
||||
StatusCode::BAD_REQUEST
|
||||
);
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn enroll_status_empty_and_bad_label() {
|
||||
let dir = tempfile::tempdir().unwrap();
|
||||
|
||||
Reference in New Issue
Block a user