diff --git a/Makefile b/Makefile index 3c2a230..53a5bf5 100644 --- a/Makefile +++ b/Makefile @@ -1,4 +1,4 @@ -PACKAGES := core train eval api benchmark monitor +PACKAGES := core train eval api benchmark monitor triage # Released model.zip version fetched from HuggingFace by `fetch-model`. # Pinned in api/MODEL_VERSION — the repo version and the model version are diff --git a/README.md b/README.md index caf6c89..43539f1 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,7 @@ through the full pipeline, with figures generated from real sequences. ## Packages -Six independent packages, each with its own `pyproject.toml` and `tests/`. +Seven independent packages, each with its own `pyproject.toml` and `tests/`. | Path | Import | Purpose | |------|--------|---------| @@ -23,6 +23,7 @@ Six independent packages, each with its own `pyproject.toml` and `tests/`. | `api/` | `temporal_model.api` | FastAPI serving layer, shipped as a Docker service. Depends on `core`. | | `benchmark/` | `temporal_model.benchmark` | Latency/throughput/resource benchmark with a per-stage `predict()` breakdown, runnable across VMs. Depends on `core`. | | `monitor/` | `temporal_model.monitor` | Production decision replay: import scored sequences from alert-api, re-run them through the pinned api+model release, view tubes in the eval viewer. | +| `triage/` | `temporal_model.triage` | Annotation-backlog triage: pull the pyro-annotator unannotated queue (read-only), score it, split into an unlabel worklist + a local-review viewer set. Depends on `core`. | `train`/`eval`/`api`/`benchmark` depend on `core` via a `uv` path source (`temporal-model-core = { path = "../core", editable = true }`). `core` and @@ -32,9 +33,9 @@ Six independent packages, each with its own `pyproject.toml` and `tests/`. ```bash make # list all available targets (same as `make help`) -make install # uv sync across all six packages -make test # pytest across all six packages -make lint # ruff check across all six packages + docs/assets/scripts +make install # uv sync across all seven packages +make test # pytest across all seven packages +make lint # ruff check across all seven packages + docs/assets/scripts ``` Per package, `cd && make install|lint|format|test`. @@ -67,6 +68,22 @@ the pyro-annotator sequence store and writes a self-describing report under VMs for comparison — see `benchmark/README.md` and the `scripts/` provision / push / pull helpers. +### Triage the annotation backlog + +```bash +cd triage && make install # uv sync (brings dvc[s3]) +AWS_PROFILE=pyronear uv run dvc pull # fetch data/02_shards from S3 +uv run temporal-triage unpack # tars → loose store + report +( cd ../viewer && npm install ) # first time only +make viewer # browse at localhost:3000 +``` + +`triage/` pulls the pyro-annotator's unannotated backlog (read-only), scores it +with the model, and splits it into **To Review** (worth a human's eyes) and +**Unlabel** (auto false-positive) buckets, browsable in the viewer. The commands +above are the *consumer* flow (no annotator credentials, no model, no Docker); +producing new data needs credentials — see `triage/README.md`. + ## Origin Ported from the Pyronear [`vision-rd`](https://github.com/pyronear/vision-rd) diff --git a/docs/specs/2026-06-16-triage-design.md b/docs/specs/2026-06-16-triage-design.md new file mode 100644 index 0000000..bf6b256 --- /dev/null +++ b/docs/specs/2026-06-16-triage-design.md @@ -0,0 +1,157 @@ +# triage/: Backlog Triage of the Annotation Queue + +**Date:** 2026-06-16 +**Status:** Draft + +## Motivation + +The pyro-annotator (https://annotator.pyronear.org) accumulates a large backlog +of **unannotated** sequences — alerts imported from the source APIs that no +human has labelled yet. Most are false positives, so a person grinding through +the whole queue spends most of their time discarding obvious non-smoke. + +`triage/` shrinks that queue. It pulls the unannotated backlog (read-only), +scores every sequence with the temporal smoke classifier in-process, and splits +the backlog at a configurable probability threshold: + +- **low (`< threshold`)** — almost certainly not smoke. Emitted as a read-only + **worklist** the team can later apply to the annotator as the `unlabeled` + false-positive type (the annotator's own concept for "false positive + discarded by auto-annotation without category"). +- **high (`>= threshold`)** — worth a human's eyes. Written into the eval-viewer + contract so they are reviewed locally in `viewer/`. + +The default threshold is **0.35**. + +## Scope & constraints + +- **API host.** The REST API is `annotationapi.pyronear.org` (the live FastAPI + service); `annotator.pyronear.org` is the frontend SPA, not the API. +- **Read-only against the pyro-annotator API.** `triage` only issues `GET`s + (plus the one `POST /api/v1/auth/login` to obtain a bearer token). It never + writes annotations to production. The low-score "assign to unlabeled" action is + emitted as an artifact (sequence ids + a ready-to-POST `bulk` payload) and + applied by a human in a separate, deliberate step — out of scope for this + package. +- **In-process scoring via `core`, no Docker.** We score for triage, not to + audit production parity, so we call `BboxTubeTemporalModel.predict()` + directly (like `benchmark`) rather than replaying through the pinned API + image (like `monitor`). +- **Test on small subsets first.** `pull` supports `--limit`, and the intended + workflow ramps `--limit 3` → `--limit ~50` → full pull. + +## Decisions (agreed in brainstorming) + +1. **New sibling package `triage/`** (`temporal_model.triage`, script + `temporal-triage`, distribution `temporal-model-triage`), depending on + `core` via the `uv` path source — same shape as `benchmark`/`monitor`. +2. **Pull scope = `processing_stage=ready_to_annotate`.** The deployed + annotator pre-creates a `SequenceAnnotation` record at `ready_to_annotate` + for the whole human queue, so `has_annotation=false` is near-empty (2 live) + while `ready_to_annotate` is the real backlog (21,489 live, matching the + UI's "Ready to Annotate" count). The `GET /sequences/` listing is not + implicitly org-scoped, so an account token pages the global backlog across + all organizations. The stage is a `--stage` flag (default + `ready_to_annotate`) so other stages can be pulled later. +3. **Read-only; low scorers become a worklist, not a write.** `triage` emits + `unlabeled.json` (ids + bulk-unlabel payload). Applying it is a separate + human step. +4. **In-process `core` scoring.** Sequence score = **max `probability` over + kept tubes** from `predict()`'s `details`. Sequences with no kept tubes (or + an uncalibrated model) score `0.0` and fall in the low bucket. +5. **Threshold is a DVC param.** `triage/params.yaml` holds + `triage.threshold: 0.35`, listed under the `score` stage's `params:` so a + change reruns the stage. A `--threshold` CLI flag overrides it for ad-hoc + runs. +6. **Reuse the Next.js eval viewer, no new UI.** The high bucket is written in + the exact reporting contract `viewer/` already reads + (`results.json`, `details/.json`, `sequences/.json`, + `model_config.json` under `data/08_reporting//vit_dinov2_finetune/`), + browsed with `DATA_ROOT=../triage`. + +## Architecture + +Two CLI commands. + +### `temporal-triage pull` (read-only) + +1. `POST /api/v1/auth/login` → bearer token (creds via `.envrc`, like + `monitor`). +2. Page `GET /api/v1/sequences/?processing_stage=ready_to_annotate` (newest + first), honouring `--stage` / `--limit` / `--page-size`. +3. For each sequence not already on disk: `GET /api/v1/detections/?sequence_id=…` + for its frames, `GET /api/v1/detections/{id}/url` for each signed image URL, + download the image, and write the store entry. +4. Incremental — sequences already present are skipped. + +Store layout (mirrors `monitor`/`benchmark`): + +``` +data/01_raw/sequences///seq_/ + meta.json # SequenceMeta: key, ids, org/camera, ordered frames + images/.jpg +``` + +`meta.json` carries the viewer join key (`pyro-annotator_`), the +ordered frame list (`file`, `detection_id`, `recorded_at`, `bucket_key`), and +provenance. There is **no ground-truth label** (these are unannotated) — the +store's `label` is `"unknown"`. + +Exposed as `make pull` → runs `pull`, then `dvc add data/01_raw/sequences` + +`dvc push`, so the pulled store is shareable. + +### `temporal-triage score` (DVC stage) + +1. Load each stored sequence into `core` `Frame`s (reuse `benchmark`'s + `dataset.py` loader shape). +2. `model = BboxTubeTemporalModel.from_package(model.zip, device=auto)`; + `output = model.predict(frames)`. +3. Sequence score = max kept-tube `probability` (`0.0` if none). +4. Split at `threshold` (param, default 0.35) into low / high. +5. Write outputs (below). + +`model.zip` is read from `api/models/` (populated by `make fetch-model`, same +as `benchmark`). + +### Module layout (one purpose each) + +| Module | Purpose | +|--------|---------| +| `annotator_api.py` | Read-only client: `login`, `iter_unannotated_sequences`, `list_detections`, `detection_image_url`. | +| `pull.py` | Orchestrate the pull; build `SequenceMeta`, download frames into the store. | +| `store.py` | `meta.json` schema (`SequenceMeta`/`FrameMeta`) + read/write/iter. | +| `score.py` | Load store → `predict` → sequence score; classify low/high. | +| `report.py` | Write eval-viewer contract for the high bucket + `unlabeled.json` / `review.json` worklists. | +| `cli.py` | `pull` / `score` subcommands. | + +## Outputs + +``` +data/08_reporting/pyro-annotator/vit_dinov2_finetune/ + results.json # eval-viewer rows (HIGH bucket): key, score, decision + details/.json # tubes/boxes/crops for the viewer (HIGH bucket) + sequences/.json # frame refs for the viewer + model_config.json # model provenance + unlabeled.json # LOW (=threshold): sequence_ids, scores + dropped.json # skip reasons (no_images, predict_failed, ...) +``` + +`unlabeled.json` records, per sequence: `sequence_id`, `key`, `score`, plus a +top-level `bulk_payload` block shaped for `POST /api/v1/annotations/sequences/bulk` +with `false_positive_type: "unlabeled"` — copy-pasteable for the separate apply +step, never sent by this package. + +## Testing + +Offline, like `monitor`: mocked HTTP for the client, a tiny on-disk fake store +for `score`/`report`, no network and no Docker. `score`'s model call is +exercised against a minimal packaged model or stubbed `predict` in unit tests; +a real end-to-end run is the `--limit 3` manual smoke test. + +## Out of scope + +- Writing annotations back to the annotator (the apply step). +- Production-parity scoring (ROI envelope / windowing) — that is `monitor`. +- Per-organization or per-camera filtering beyond what the listing offers + (can be added as `pull` flags later if needed). diff --git a/docs/specs/2026-06-16-triage-sharding-design.md b/docs/specs/2026-06-16-triage-sharding-design.md new file mode 100644 index 0000000..cd0387f --- /dev/null +++ b/docs/specs/2026-06-16-triage-sharding-design.md @@ -0,0 +1,148 @@ +# triage/: Tar-Sharded Frame Storage + +**Date:** 2026-06-16 +**Status:** Implemented (`shards.py`, `pack`/`unpack` CLI) + +## Implementation notes (deltas from the original draft) + +- **The report is sharded too.** At full scale `details/` + `sequences/` are + ~43k tiny files — not "small". `pack` bundles per-sequence `details.json` + + `sequence.json` into `report/` shards; only the ~6 aggregate files + (`results.json`/`.parquet`, worklists, `model_config`, `dropped`) stay loose. +- **Frames and predictions are separate shard sets** (`frames/` append-only and + model-independent; `report/` rebuilt per run) — a re-score never re-packs the + 26 GB of frames. +- **Every prediction is tagged with `model_version`** (the release version from + the model.zip manifest, e.g. `0.2.0`), so a future re-score's predictions stay + distinguishable. Multi-version report namespacing is deferred until a second + model run actually happens. +- **No `dvc.yaml` pipeline.** The 247k-file store is impractical as a stage dep + and the workflow is staged/manual, so the single DVC-tracked artifact is + `data/02_shards`, added with `dvc add`. + +## Motivation + +`triage` stores pulled frames as loose files under +`data/01_raw/sequences///seq_/images/*.jpg` and DVC-tracks the +directory. At the 500-sequence scale this is ~6,934 S3 objects — fine. At the +full `ready_to_annotate` backlog (~21,489 sequences, ~301k frames) it becomes +**~300k loose S3 objects / ~30 GB**: DVC content-addresses one object per file, +so push/pull is dominated by per-object overhead, the single `.dir` index grows +to ~300k entries (slow `status`/`checkout`), and per-request S3 cost climbs. + +The fix is to **pack frames into tar shards** so DVC tracks tens of objects +instead of hundreds of thousands. The scored outputs (`results.parquet` + +small JSON) are tiny and stay loose — only frames are sharded. + +## Decisions + +1. **Append-only sealed shards.** Each `pack` run writes the sequences not yet + in any shard into one or more **new, immutable** tar files + (`shard_0001.tar`, `shard_0002.tar`, …); existing shards are never rewritten. + A broad incremental pull therefore re-pushes only its new shard(s), not the + whole set. (Rejected: `sequence_id % N` bucketing — even and deterministic, + but adding any sequence rewrites a ~1 GB bucket tar, so a wide incremental + pull re-pushes ~everything. Rejected: keep loose — the ~300k-object problem.) +2. **Only frames are sharded.** Each shard holds, per sequence, both the frames + and the `meta.json` (so a shard is self-contained). The scored report + (`results.parquet`, `results.json`, `details/`, `sequences/`, + `model_config.json`) stays loose under `data/08_reporting/` — it is small and + the viewer reads per-key files. +3. **The loose store is a local, regenerable cache.** `data/01_raw/sequences/` + is gitignored and **not** DVC-tracked. It is produced either by `pull` + (producer) or by `unpack` (consumer). The DVC source of truth for frames is + `data/02_shards/`. +4. **A manifest drives incrementality.** `data/02_shards/manifest.json` maps + `sequence_id → shard_name` (plus `next_index`). `pack` packs only sequences + absent from the manifest; `unpack`/tooling use it to know what exists. +5. **Migration is a one-time, deliberate cleanup.** Cutting over from the loose + layout orphans the already-pushed loose frame objects. We wipe the + `triage/` remote prefix and re-push under the shard layout rather than rely + on `dvc gc` (see Migration). + +## Layout + +``` +data/ +├── 02_shards/ # DVC-tracked (cache:true) → pushed. Source of truth for frames. +│ ├── shard_0001.tar # immutable; each holds seq_/{meta.json, images/*.jpg} +│ ├── shard_0002.tar +│ └── manifest.json # {next_index, sequences: {: "shard_0001.tar", ...}} +├── 01_raw/sequences/ # LOCAL ONLY (gitignored, not DVC-tracked). +│ └── //seq_/… # produced by pull OR unpack; consumed by score + viewer. +└── 08_reporting/… # scored outputs (parquet + JSON), loose, unchanged. +``` + +Shard tar member paths mirror the store's per-sequence layout so `unpack` +restores it byte-for-byte: +`//seq_/meta.json` and `.../images/detection_*.jpg`. +Tars are **uncompressed** (JPEGs are already compressed). Each shard is sealed +at a target size (`SHARD_TARGET_BYTES`, default ~1 GB ≈ ~700 sequences); a +`pack` run that exceeds it rolls to the next index. + +## Commands (two new) + +- **`temporal-triage pack`** — read `data/01_raw/sequences`, select sequences + absent from `manifest.json`, append them into new sealed shard(s), update the + manifest. Idempotent: re-running with nothing new is a no-op. Does not delete + the loose copies (they stay as the local working set). +- **`temporal-triage unpack`** — extract every shard in `data/02_shards` into + `data/01_raw/sequences` (skips sequences already materialized). Reconstitutes + the loose store for `score` and the viewer from `dvc pull`-ed shards alone. + +`score`, `pull`, `report`, and the viewer are **unchanged** — they keep reading +the loose store by path. + +## Workflows + +**Producer (annotator creds):** +``` +make pull ARGS="--limit N" # loose store (incremental, parallel downloads) +temporal-triage pack # new sequences → new sealed shard(s) + manifest +dvc add data/02_shards # track shards +dvc push # upload only the new shard(s) +``` + +**Consumer (no creds, no Docker):** +``` +dvc pull # fetch shards (tens of objects) +temporal-triage unpack # shards → loose store +dvc repro # score → report (or: temporal-triage score) +cd ../viewer && DATA_ROOT=../triage npm run dev +``` + +## DVC pipeline changes + +- Untrack the loose store: remove `data/01_raw/sequences.dvc`. +- Track shards: `dvc add data/02_shards` (cache:true → pushed). +- `score` stage keeps `data/01_raw/sequences` as a **path dependency** (DVC + hashes it); the loose store is materialized by `pull` or `unpack` before + `score` runs. (Optionally an `unpack` stage with a `cache:false` output, so a + pure `dvc repro` on a fresh checkout unpacks then scores — to be decided in + implementation.) + +## Object-count impact (full backlog, ~21,489 seq / ~301k frames) + +| Layout | Frame S3 objects | Incremental pull of +500 seq | `.dir` size | +|--------|------------------|------------------------------|-------------| +| Loose (today) | ~300k | cheap (only new frames push) | ~300k entries | +| Append-only shards | **~30–40 tars** | **1 new ~1 GB tar** | tiny | + +## Migration (one-time) + +1. `temporal-triage pack` the current 500-sequence loose store → `shard_0001.tar` + manifest. +2. `aws s3 rm --recursive s3://pyro-vision-rd/dvc/temporal-model/triage/` — wipe the prefix (orphaned loose frames + report; both are regenerable). +3. Remove `data/01_raw/sequences.dvc`; `dvc add data/02_shards`; `dvc commit score` (report); `dvc push`. +4. Amend/replace the loose-layout pointer commit (`4ee716d`) so **no retained + commit references the orphaned loose frame objects** — this avoids `dvc gc` + reachability footguns entirely on the shared remote. + +## Out of scope + +- Compression of tars (JPEGs are already compressed). +- Deleting loose frames after `pack` (kept as the local working set; gitignored). +- Streaming frames directly from tars into the model (would require changing + `core`'s file-path `Frame` contract; `unpack` to loose files is non-invasive). +- Not storing frames at all (reference-only + on-demand refetch) — leaner, but + couples reuse to live annotator access; revisit separately if shard storage + proves heavy. diff --git a/triage/.dvc/.gitignore b/triage/.dvc/.gitignore new file mode 100644 index 0000000..20e0fb0 --- /dev/null +++ b/triage/.dvc/.gitignore @@ -0,0 +1 @@ +/config.local diff --git a/triage/.dvc/config b/triage/.dvc/config new file mode 100644 index 0000000..17987a4 --- /dev/null +++ b/triage/.dvc/config @@ -0,0 +1,5 @@ +[core] + remote = s3remote + analytics = false +['remote "s3remote"'] + url = s3://pyro-vision-rd/dvc/temporal-model/triage/ diff --git a/triage/.dvcignore b/triage/.dvcignore new file mode 100644 index 0000000..5197305 --- /dev/null +++ b/triage/.dvcignore @@ -0,0 +1,3 @@ +# Add patterns of files dvc should ignore, which could improve +# the performance. Learn more at +# https://dvc.org/doc/user-guide/dvcignore diff --git a/triage/.envrc.example b/triage/.envrc.example new file mode 100644 index 0000000..a39929e --- /dev/null +++ b/triage/.envrc.example @@ -0,0 +1,8 @@ +# Copy to triage/.envrc (untracked) and fill in; direnv loads it automatically. +# Read-only credentials for the pyro-annotator API. +# NB: the API is served at annotationapi.pyronear.org — annotator.pyronear.org +# is the frontend SPA, not the API. triage only ever issues GETs (plus the +# login POST that mints the token). +export ANNOTATOR_API_URL=https://annotationapi.pyronear.org +export ANNOTATOR_API_LOGIN=changeme +export ANNOTATOR_API_PASSWORD=changeme diff --git a/triage/.gitignore b/triage/.gitignore new file mode 100644 index 0000000..ae4022a --- /dev/null +++ b/triage/.gitignore @@ -0,0 +1,4 @@ +.venv/ +__pycache__/ +.pytest_cache/ +.ruff_cache/ diff --git a/triage/Makefile b/triage/Makefile new file mode 100644 index 0000000..a09018e --- /dev/null +++ b/triage/Makefile @@ -0,0 +1,27 @@ +.PHONY: install lint format test pull share unpack viewer + +install: ## uv sync + uv sync + +lint: ## ruff check + uv run ruff check . + +format: ## ruff format + uv run ruff format . + +test: ## pytest + uv run pytest tests/ -v + +pull: ## pull unannotated sequences from the annotator (read-only) + uv run temporal-triage pull $(ARGS) + +share: ## pack the scored store+report into shards, then dvc add + push + uv run temporal-triage pack + uv run dvc add data/02_shards + uv run dvc push + +unpack: ## restore the loose store + report from pulled shards + uv run temporal-triage unpack + +viewer: ## launch the eval viewer over this triage tree + cd ../viewer && DATA_ROOT=../triage npm run dev diff --git a/triage/README.md b/triage/README.md new file mode 100644 index 0000000..763d039 --- /dev/null +++ b/triage/README.md @@ -0,0 +1,171 @@ +# 🗂️🔥 triage — annotation-backlog triage + +Shrinks the pyro-annotator's **annotation backlog**. It pulls the unannotated +queue (sequences at `processing_stage=ready_to_annotate`) from the API +**read-only**, scores every sequence with the temporal smoke classifier +in-process, and splits the backlog at a threshold (default **0.35**): + +- **To Review (`score ≥ 0.35`)** → reviewed locally in the [`viewer/`](../viewer): + worth a human's eyes. +- **Unlabel (`score < 0.35`)** → `unlabeled.json`: a read-only worklist (sequence + ids + a ready-to-send `bulk` body) to later mark these as the `unlabeled` + false-positive type. triage **never writes** to the annotator. + +> **API host.** The REST API is **`annotationapi.pyronear.org`** — +> `annotator.pyronear.org` is the frontend SPA, not the API. + +Designs: [`docs/specs/2026-06-16-triage-design.md`](../docs/specs/2026-06-16-triage-design.md) +· [sharding](../docs/specs/2026-06-16-triage-sharding-design.md) + +## Read-only guarantee + +The only non-GET request triage sends to the annotator is the login POST that +mints a bearer token. The HTTP client has **no** patch/put/delete/non-login-post +method (enforced by `tests/test_annotator_api.py::test_client_is_read_only_only_login_posts`). +The `bulk` payload in `unlabeled.json` is written to disk only — applying it is a +separate, deliberate human step. + +--- + +## Just visualizing? Pull the shared data (no annotator creds, no model, no Docker) + +The scored set is pushed to the DVC S3 remote as **tar shards** (~36 objects, +not ~290k loose files). Browsing needs only S3 read access (`AWS_PROFILE=pyronear`) +— no annotator credentials, no `model.zip`, no GPU, no Docker. + +**Prerequisites:** this repo checked out on the branch holding `data/02_shards.dvc` +(until merged), `uv` + Node 22 + `npm`, the `pyronear` AWS profile configured, and +**~55 GB free disk** (26 GB DVC cache + 26 GB unpacked working tree). Then: + +```bash +cd triage +make install # uv sync (brings dvc[s3]) +AWS_PROFILE=pyronear uv run dvc pull # fetch data/02_shards from S3 (~26 GB) +uv run temporal-triage unpack # tars → loose store + report +( cd ../viewer && npm install ) # first time only +make viewer # browse at http://localhost:3000 +``` + +This exact sequence is verified end-to-end from a clean clone. + +`dvc pull` fetches `data/02_shards` (frame + report tars + manifests + the small +aggregate files); `unpack` restores the loose `data/01_raw/sequences` (frames) +and `data/08_reporting/.../` (per-key report) so the viewer reads them by path. +The viewer then renders the **triage view**: the **To Review / Unlabel** split, a +clickable **per-organization** breakdown, the threshold slider + sweep table, and +per-sequence tubes/boxes/crops. (DVC remote: +`s3://pyro-vision-rd/dvc/temporal-model/triage/`.) + +Everything below is only for **producing** new data. + +--- + +## Producing: pull (annotator credentials required) + +```bash +make install +cp .envrc.example .envrc # fill in read-only annotator credentials (direnv loads it) +``` + +`.envrc` sets `ANNOTATOR_API_URL=https://annotationapi.pyronear.org` plus the +read-only login/password. + +**Always test on a small subset first** (cheap, read-only): + +```bash +make pull ARGS="--limit 3" # smoke-test the client + store end-to-end +make pull ARGS="--limit 50" # sanity-check the split on a real sample +``` + +Then the full backlog (~21k sequences, ~30 GB, a few hours). Use concurrency — +`--seq-workers N` pulls N sequences in parallel, `--workers M` downloads M frames +per sequence; keep `N × M` near the HTTP pool (~32), which the API tolerates: + +```bash +make pull ARGS="--seq-workers 8 --workers 4" # ~3 seq/s, polite (≈32 in-flight) +``` + +The pull is **incremental** (sequences already on disk are skipped) and writes +each `meta.json` last, so an interrupted run is safely re-pulled. Override the +stage with `--stage ` (default `ready_to_annotate`). The loose +store is local-only (gitignored, not DVC-tracked) — it's shared via shards below. + +## Producing: score → pack → push + +```bash +make fetch-model # repo root: get ../api/models/model.zip +uv run temporal-triage score # score store → report + worklists (uses GPU if present) +uv run temporal-triage pack # tar-shard store + report → data/02_shards +AWS_PROFILE=pyronear uv run dvc add data/02_shards +AWS_PROFILE=pyronear uv run dvc push # share ~36 objects so teammates can dvc pull +``` + +Override the split ad-hoc with `temporal-triage score --threshold 0.5`; +re-bucketing is free (the `probability` values don't change). `pack` is +incremental for frames (model-independent, append-only) and rebuilds the report +shards (predictions are per model run); see the +[sharding design](../docs/specs/2026-06-16-triage-sharding-design.md). + +## CLI reference + +``` +temporal-triage pull [--store DIR] [--stage STAGE] [--limit N] + [--page-size N] [--workers M] [--seq-workers N] +temporal-triage score [--store DIR] [--output-dir DIR] [--model-zip ZIP] + [--threshold T] [--device cuda|mps|cpu] +temporal-triage pack [--store DIR] [--report-dir DIR] [--shards DIR] [--target-bytes N] +temporal-triage unpack [--shards DIR] [--store DIR] [--report-dir DIR] +``` + +## Outputs (`data/08_reporting/pyro-annotator/vit_dinov2_finetune/`) + +The eval-viewer contract (so the viewer / eval tooling read it unchanged), plus +triage worklists. All rows join on `key` = `pyro-annotator_`. + +| File | What it holds | +|------|----------------| +| `results.json` / `results.parquet` | one row per sequence: `triage_score`, `triage_bucket`, `decision`, `model_version` (e.g. `0.2.0`), `probability`, `num_tubes_kept`, `trigger_frame_index`, org/camera/time. Parquet is the columnar twin for analysis. | +| `details/.json` | full model output: tubes, per-frame bboxes, logits, calibrated probability, trigger, stabilized window. | +| `sequences/.json` | frame paths (relative to `triage/`) + metadata — the viewer join. | +| `unlabeled.json` | low bucket: `sequence_ids` + a ready-to-send `bulk` payload (`false_positive_type: "unlabeled"`) — **written to disk only, never sent**. | +| `review.json` | high bucket: `sequence_ids` + scores, highest first. | +| `model_config.json` | model provenance + the `threshold` used. | +| `dropped.json` | skipped sequences + reason (`no_images`, `predict_failed`). | + +## How it works (data layout) + +``` +data/01_raw/sequences///seq_/ # loose store — LOCAL only (gitignored) + meta.json # identity + ordered frames (detection_id, bucket_key) + images/detection_.jpg +data/08_reporting/pyro-annotator/vit_dinov2_finetune/ # loose report — LOCAL only +data/02_shards/ # the DVC-tracked, pushed artifact + frames/shard_*.tar + manifest.json # per-sequence images+meta (append-only, model-independent) + report/shard_*.tar + manifest.json # per-sequence details+sequence-view (per model run) + results.json, results.parquet, *.json # aggregate report files, loose +``` + +- **`pull`** (`pull.py` + `annotator_api.py`): login → page + `GET /sequences/?processing_stage=ready_to_annotate` → per sequence fetch + detections + signed image URLs → download into the store. Read-only. +- **`score`** (`score.py`): load each stored sequence into `core` `Frame`s, run + `BboxTubeTemporalModel.predict()`, sequence score = max kept-tube probability, + bucket at `threshold`; tag each prediction with the release `model_version`. +- **`report.py`** writes the eval-viewer contract + worklists. +- **`shards.py`** (`pack`/`unpack`): the loose store (~247k files) + report (~43k + files) would be a DVC object explosion, so `pack` bundles per-sequence data into + ~36 tar objects and `unpack` restores the loose tree. Frames and predictions + have separate lifecycles (frames immutable across model runs, predictions + per-run), so they get separate tars — re-scores never re-pack the 26 GB of + frames. See the [sharding design](../docs/specs/2026-06-16-triage-sharding-design.md). + +`triage` deliberately has **no `dvc.yaml` pipeline**: the store is too many files +to hash as a stage dep, and the workflow is inherently staged (pull needs +credentials, score needs a GPU). The single DVC-tracked artifact is +`data/02_shards`, added with `dvc add`. + +## Tests + +```bash +make test # offline: mocked HTTP, fake store, stub model — no network, no Docker +``` diff --git a/triage/data/02_shards.dvc b/triage/data/02_shards.dvc new file mode 100644 index 0000000..6730b6d --- /dev/null +++ b/triage/data/02_shards.dvc @@ -0,0 +1,6 @@ +outs: +- md5: 6e8fafcb53de71186e8506991a12069d.dir + size: 27337371501 + nfiles: 36 + hash: md5 + path: 02_shards diff --git a/triage/pyproject.toml b/triage/pyproject.toml new file mode 100644 index 0000000..66d0f4a --- /dev/null +++ b/triage/pyproject.toml @@ -0,0 +1,46 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "temporal-model-triage" +version = "0.1.0" +description = "Triage the pyro-annotator unannotated backlog: pull sequences read-only, score them with the temporal model, split at a threshold into an 'unlabel' worklist and a local-review viewer set" +requires-python = ">=3.11" +dependencies = [ + "temporal-model-core", + "pydantic>=2.7", + "requests>=2.31", + "pyyaml>=6", + "pandas>=2.0", + "pyarrow>=14", +] + +[project.scripts] +temporal-triage = "temporal_model.triage.cli:main" + +[tool.uv.sources] +temporal-model-core = { path = "../core", editable = true } + +[tool.hatch.build.targets.wheel] +packages = ["src/temporal_model"] + +[dependency-groups] +dev = [ + "dvc[s3]>=3.56", + "pytest>=8.0", + "ruff>=0.9", +] + +[tool.ruff] +target-version = "py311" +line-length = 88 + +[tool.ruff.lint] +select = ["E", "F", "I", "W", "UP", "B", "SIM", "PLC0415"] + +[tool.ruff.lint.isort] +known-first-party = ["temporal_model"] + +[tool.ruff.format] +quote-style = "double" diff --git a/triage/src/temporal_model/triage/__init__.py b/triage/src/temporal_model/triage/__init__.py new file mode 100644 index 0000000..c317963 --- /dev/null +++ b/triage/src/temporal_model/triage/__init__.py @@ -0,0 +1,2 @@ +"""Backlog triage: pull the pyro-annotator unannotated queue (read-only), +score it with the temporal model, split into an unlabel worklist + local review.""" diff --git a/triage/src/temporal_model/triage/annotator_api.py b/triage/src/temporal_model/triage/annotator_api.py new file mode 100644 index 0000000..cc47ce8 --- /dev/null +++ b/triage/src/temporal_model/triage/annotator_api.py @@ -0,0 +1,146 @@ +"""Read-only pyro-annotator HTTP client. + +INVARIANT: the only non-GET request this client issues is the login POST that +mints a bearer token (it writes no annotation data). There is deliberately no +post/patch/put/delete method here — triage never mutates annotator state. +Pagination is the annotator's ``page``/``size`` scheme returning +``{"items", "page", "pages", "total"}``. +""" + +from __future__ import annotations + +import os +from collections.abc import Iterator +from dataclasses import dataclass +from typing import Any + +import requests + +PAGE_SIZE = 100 # annotator enforces size <= 100 +TIMEOUT_S = 60 + + +@dataclass(frozen=True) +class AnnotatorApiConfig: + url: str + login: str + password: str + + @classmethod + def from_env(cls) -> AnnotatorApiConfig: + try: + return cls( + url=os.environ["ANNOTATOR_API_URL"].rstrip("/"), + login=os.environ["ANNOTATOR_API_LOGIN"], + password=os.environ["ANNOTATOR_API_PASSWORD"], + ) + except KeyError as exc: + raise SystemExit( + f"missing env var {exc.args[0]} (see triage/.envrc.example)" + ) from exc + + +class AnnotatorApiClient: + def __init__( + self, config: AnnotatorApiConfig, session: requests.Session | None = None + ) -> None: + self.config = config + if session is not None: + self.session = session + else: + self.session = requests.Session() + # Enlarge the pool so concurrent signed-URL fetches (the pull's + # ThreadPoolExecutor) don't exhaust urllib3's default 10 connections. + adapter = requests.adapters.HTTPAdapter(pool_connections=4, pool_maxsize=32) + self.session.mount("https://", adapter) + self.session.mount("http://", adapter) + self.token: str | None = None + + def login(self) -> None: + """POST /auth/login -> bearer token. The only non-GET call triage makes.""" + resp = self.session.post( + f"{self.config.url}/api/v1/auth/login", + json={"username": self.config.login, "password": self.config.password}, + timeout=TIMEOUT_S, + ) + resp.raise_for_status() + self.token = resp.json()["access_token"] + + def _get(self, path: str, params: dict[str, Any] | None = None) -> Any: + if self.token is None: + raise RuntimeError("call login() first") + resp = self.session.get( + f"{self.config.url}{path}", + headers={"Authorization": f"Bearer {self.token}"}, + params=params, + timeout=TIMEOUT_S, + ) + resp.raise_for_status() + return resp.json() + + def iter_unannotated_sequences( + self, + *, + processing_stage: str = "ready_to_annotate", + page_size: int = PAGE_SIZE, + limit: int | None = None, + ) -> Iterator[dict]: + """Yield the unannotated backlog, newest first. + + Defaults to ``processing_stage=ready_to_annotate`` — the deployed + annotator pre-creates a SequenceAnnotation record at that stage for the + whole human queue (so ``has_annotation=false`` is near-empty; the real + backlog lives here). Stops early once ``limit`` sequences have been + yielded — and does not fetch further pages — so ``--limit`` is cheap. + """ + yielded = 0 + page = 1 + size = min(page_size, PAGE_SIZE) + while True: + payload = self._get( + "/api/v1/sequences/", + { + "processing_stage": processing_stage, + "page": page, + "size": size, + "order_by": "created_at", + "order_direction": "desc", + }, + ) + items = payload.get("items", []) + for item in items: + yield item + yielded += 1 + if limit is not None and yielded >= limit: + return + # Stop on a short/empty page — robust even if the server omits + # ``pages`` (relying on it would stop after page 1 when absent). + if len(items) < size: + return + page += 1 + + def list_detections(self, sequence_id: int) -> list[dict]: + """All detections (frames) of a sequence, oldest first.""" + rows: list[dict] = [] + page = 1 + while True: + payload = self._get( + "/api/v1/detections/", + { + "sequence_id": sequence_id, + "page": page, + "size": PAGE_SIZE, + "order_by": "recorded_at", + "order_direction": "asc", + }, + ) + items = payload.get("items", []) + rows.extend(items) + # Short/empty page = last (no reliance on a possibly-absent `pages`). + if len(items) < PAGE_SIZE: + return rows + page += 1 + + def detection_image_url(self, detection_id: int) -> str: + """Signed, time-limited download URL for a detection's image.""" + return self._get(f"/api/v1/detections/{detection_id}/url")["url"] diff --git a/triage/src/temporal_model/triage/cli.py b/triage/src/temporal_model/triage/cli.py new file mode 100644 index 0000000..870c4e9 --- /dev/null +++ b/triage/src/temporal_model/triage/cli.py @@ -0,0 +1,155 @@ +"""triage CLI: ``pull`` (read-only fetch) and ``score`` (model + report).""" + +from __future__ import annotations + +import argparse +import json +import logging +from pathlib import Path + +from temporal_model.triage.annotator_api import AnnotatorApiClient, AnnotatorApiConfig +from temporal_model.triage.model_config import read_model_config +from temporal_model.triage.pull import ( + DEFAULT_SEQ_WORKERS, + DEFAULT_WORKERS, + pull_unannotated, +) +from temporal_model.triage.report import MODEL_DIR, SOURCE, write_triage_report +from temporal_model.triage.score import score_sequences +from temporal_model.triage.shards import DEFAULT_TARGET_BYTES, pack, unpack + +DEFAULT_STORE = Path("data/01_raw/sequences") +DEFAULT_OUTPUT = Path("data/08_reporting") +DEFAULT_REPORT = DEFAULT_OUTPUT / SOURCE / MODEL_DIR +DEFAULT_SHARDS = Path("data/02_shards") +DEFAULT_MODEL_ZIP = Path("../api/models/model.zip") +DEFAULT_THRESHOLD = 0.35 + + +def _build_client() -> AnnotatorApiClient: + client = AnnotatorApiClient(AnnotatorApiConfig.from_env()) + client.login() + return client + + +def _load_model(model_zip: Path, device: str | None): + # Local import: keep torch/ultralytics out of the `pull` path. + from temporal_model.core.model import BboxTubeTemporalModel # noqa: PLC0415 + + return BboxTubeTemporalModel.from_archive(model_zip, device=device) + + +def _cmd_pull(args: argparse.Namespace) -> None: + client = _build_client() + counts = pull_unannotated( + client, + args.store, + processing_stage=args.stage, + limit=args.limit, + page_size=args.page_size, + workers=args.workers, + seq_workers=args.seq_workers, + ) + print(json.dumps(counts, indent=2)) + + +def _cmd_score(args: argparse.Namespace) -> None: + model = _load_model(args.model_zip, args.device) + scored, dropped = score_sequences(model, args.store, threshold=args.threshold) + write_triage_report( + args.output_dir, + scored, + dropped=dropped, + threshold=args.threshold, + model_config=read_model_config(args.model_zip), + ) + n_low = sum(1 for s in scored if s.bucket == "unlabeled") + n_high = sum(1 for s in scored if s.bucket == "review") + print( + json.dumps( + { + "scored": len(scored), + "dropped": len(dropped), + "unlabeled": n_low, + "review": n_high, + "threshold": args.threshold, + }, + indent=2, + ) + ) + + +def _cmd_pack(args: argparse.Namespace) -> None: + counts = pack( + args.store, args.report_dir, args.shards, target_bytes=args.target_bytes + ) + print(json.dumps(counts, indent=2)) + + +def _cmd_unpack(args: argparse.Namespace) -> None: + unpack(args.shards, args.store, args.report_dir) + print( + json.dumps( + {"store": str(args.store), "report_dir": str(args.report_dir)}, indent=2 + ) + ) + + +def main(argv: list[str] | None = None) -> None: + logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s") + parser = argparse.ArgumentParser(prog="temporal-triage", description=__doc__) + sub = parser.add_subparsers(dest="command", required=True) + + p_pull = sub.add_parser("pull", help="fetch unannotated sequences (read-only)") + p_pull.add_argument("--store", type=Path, default=DEFAULT_STORE) + p_pull.add_argument( + "--stage", + default="ready_to_annotate", + help="annotator processing_stage to pull (default: ready_to_annotate)", + ) + p_pull.add_argument("--limit", type=int, default=None, help="cap sequences pulled") + p_pull.add_argument("--page-size", type=int, default=100) + p_pull.add_argument( + "--workers", + type=int, + default=DEFAULT_WORKERS, + help=f"concurrent frame downloads per sequence (default: {DEFAULT_WORKERS})", + ) + p_pull.add_argument( + "--seq-workers", + type=int, + default=DEFAULT_SEQ_WORKERS, + help="sequences pulled in parallel (default: %(default)s); total in-flight " + "requests ~= seq-workers * workers, keep near the HTTP pool size", + ) + p_pull.set_defaults(func=_cmd_pull) + + p_score = sub.add_parser("score", help="score the store + write report") + p_score.add_argument("--store", type=Path, default=DEFAULT_STORE) + p_score.add_argument("--output-dir", type=Path, default=DEFAULT_OUTPUT) + p_score.add_argument("--model-zip", type=Path, default=DEFAULT_MODEL_ZIP) + p_score.add_argument("--threshold", type=float, default=DEFAULT_THRESHOLD) + p_score.add_argument("--device", default=None, help="cuda/mps/cpu (default auto)") + p_score.set_defaults(func=_cmd_score) + + p_pack = sub.add_parser("pack", help="tar-shard the store + report for DVC") + p_pack.add_argument("--store", type=Path, default=DEFAULT_STORE) + p_pack.add_argument("--report-dir", type=Path, default=DEFAULT_REPORT) + p_pack.add_argument("--shards", type=Path, default=DEFAULT_SHARDS) + p_pack.add_argument("--target-bytes", type=int, default=DEFAULT_TARGET_BYTES) + p_pack.set_defaults(func=_cmd_pack) + + p_unpack = sub.add_parser( + "unpack", help="restore the loose store + report from shards" + ) + p_unpack.add_argument("--shards", type=Path, default=DEFAULT_SHARDS) + p_unpack.add_argument("--store", type=Path, default=DEFAULT_STORE) + p_unpack.add_argument("--report-dir", type=Path, default=DEFAULT_REPORT) + p_unpack.set_defaults(func=_cmd_unpack) + + args = parser.parse_args(argv) + args.func(args) + + +if __name__ == "__main__": + main() diff --git a/triage/src/temporal_model/triage/model_config.py b/triage/src/temporal_model/triage/model_config.py new file mode 100644 index 0000000..4f3b19d --- /dev/null +++ b/triage/src/temporal_model/triage/model_config.py @@ -0,0 +1,57 @@ +"""Read a packaged model.zip's metadata into one plain dict for the viewer. + +Mirrors ``eval/src/temporal_model/eval/model_config.py``: merges manifest.yaml +(detector provenance), config.yaml (decision/infer/model_input/tubes/classifier), +and logistic_calibrator.json. Tolerant: a missing or unreadable zip returns {} +(e.g. test runs that stub model loading); a missing member is omitted or None. +""" + +from __future__ import annotations + +import json +import zipfile +from pathlib import Path + +import yaml + + +def _read_member(z: zipfile.ZipFile, name: str): + """Parse a zip member by extension (yaml/json), or None if absent.""" + if name not in z.namelist(): + return None + raw = z.read(name).decode() + if name.endswith((".yaml", ".yml")): + return yaml.safe_load(raw) + return json.loads(raw) + + +def read_model_config(model_zip: Path) -> dict: + """Merged, JSON-serializable view of a packaged model's config. + + Returns {} when the zip is missing/unreadable. Keys: detector, variant, + train_git_sha (from manifest.yaml); decision, infer, model_input, tubes, + classifier (from config.yaml); calibrator (logistic_calibrator.json or None). + """ + model_zip = Path(model_zip) + if not model_zip.exists(): + return {} + try: + with zipfile.ZipFile(model_zip) as z: + manifest = _read_member(z, "manifest.yaml") or {} + config = _read_member(z, "config.yaml") or {} + calibrator = _read_member(z, "logistic_calibrator.json") + except (zipfile.BadZipFile, OSError): + return {} + provenance = manifest.get("provenance") or {} + return { + "model_version": manifest.get("model_version"), # release version, e.g. "0.2.0" + "detector": provenance.get("detector"), + "variant": manifest.get("variant"), + "train_git_sha": provenance.get("train_git_sha"), + "decision": config.get("decision"), + "infer": config.get("infer"), + "model_input": config.get("model_input"), + "tubes": config.get("tubes"), + "classifier": config.get("classifier"), + "calibrator": calibrator, + } diff --git a/triage/src/temporal_model/triage/pull.py b/triage/src/temporal_model/triage/pull.py new file mode 100644 index 0000000..34857bc --- /dev/null +++ b/triage/src/temporal_model/triage/pull.py @@ -0,0 +1,174 @@ +"""Pull the pyro-annotator unannotated backlog into the local store (read-only). + +Incremental: a sequence already on disk is skipped, so recurring pulls only pay +for new sequences. meta.json is written last — a crashed frame download leaves +the sequence dir without a meta.json, so the next run re-pulls it. +""" + +from __future__ import annotations + +import logging +import time +from collections.abc import Callable +from concurrent.futures import ThreadPoolExecutor, as_completed +from pathlib import Path + +import requests + +from temporal_model.triage.store import ( + IMAGES_DIR, + FrameRef, + SequenceMeta, + sequence_dir, + sequence_exists, + write_meta, +) + +logger = logging.getLogger(__name__) + +DOWNLOAD_ATTEMPTS = 3 +DEFAULT_WORKERS = 16 # per-sequence frame fetch+download concurrency +DEFAULT_SEQ_WORKERS = 1 # sequences processed in parallel (1 = serial) +PROGRESS_EVERY = 25 # log a progress line every N pulled sequences + + +def _default_download(url: str) -> bytes: + """Fetch one frame image over plain HTTP; retries absorb transient hiccups.""" + for attempt in range(1, DOWNLOAD_ATTEMPTS + 1): + try: + resp = requests.get(url, timeout=120) + resp.raise_for_status() + return resp.content + except requests.RequestException: + if attempt == DOWNLOAD_ATTEMPTS: + raise + logger.warning("frame download failed (attempt %d), retrying", attempt) + time.sleep(2**attempt) + raise AssertionError("unreachable") + + +def _pull_one_safe( + client, store_dir: Path, seq: dict, download: Callable[[str], bytes], workers: int +) -> bool: + """Pull one sequence; ANY error is logged and turned into False so the + sequence is retried next run (meta.json is written last, so it isn't marked + complete) without aborting the rest of the pull. Broad on purpose: a bad + detection record (e.g. null recorded_at) or a disk error on one sequence must + not kill a 20k-sequence run — especially in the concurrent path, where an + uncaught exception surfaces at ``fut.result()`` and abandons the queue.""" + try: + _pull_one(client, store_dir, seq, download, workers) + return True + except Exception: # noqa: BLE001 — record + continue; resilience over strictness + logger.exception("pull failed for sequence %s; will retry next run", seq["id"]) + return False + + +def pull_unannotated( + client, + store_dir: Path, + *, + processing_stage: str = "ready_to_annotate", + limit: int | None = None, + page_size: int = 100, + workers: int = DEFAULT_WORKERS, + seq_workers: int = DEFAULT_SEQ_WORKERS, + download: Callable[[str], bytes] = _default_download, +) -> dict[str, int]: + """Pull unannotated sequences + their frames. Returns {pulled, skipped}. + + ``seq_workers`` sequences are processed concurrently; within each, frames are + fetched + downloaded with ``workers`` threads. Total in-flight requests are + ~``seq_workers * workers`` — keep that near the client's HTTP pool size. A + progress line is logged every ``PROGRESS_EVERY`` pulled. + """ + store_dir.mkdir(parents=True, exist_ok=True) + start = time.monotonic() + pulled = skipped = 0 + + # Skip-existing in the listing pass (cheap glob); only new sequences download. + new_seqs: list[dict] = [] + for seq in client.iter_unannotated_sequences( + processing_stage=processing_stage, page_size=page_size, limit=limit + ): + if sequence_exists(store_dir, seq["id"]): + skipped += 1 + else: + new_seqs.append(seq) + + def _record(ok: bool) -> None: + nonlocal pulled + if not ok: + return + pulled += 1 + if pulled % PROGRESS_EVERY == 0: + elapsed = time.monotonic() - start + rate = pulled / elapsed if elapsed else 0.0 + logger.info( + "progress: %d/%d pulled, %d skipped (%.1f seq/s)", + pulled, + len(new_seqs), + skipped, + rate, + ) + + if seq_workers <= 1: + for seq in new_seqs: + _record(_pull_one_safe(client, store_dir, seq, download, workers)) + else: + with ThreadPoolExecutor(max_workers=seq_workers) as ex: + futures = [ + ex.submit(_pull_one_safe, client, store_dir, seq, download, workers) + for seq in new_seqs + ] + # as_completed runs in this (single) thread, so the counter + progress + # logging stay race-free. + for fut in as_completed(futures): + _record(fut.result()) + + logger.info("pull done: %d pulled, %d skipped", pulled, skipped) + return {"pulled": pulled, "skipped": skipped} + + +def _pull_one( + client, store_dir: Path, seq: dict, download: Callable[[str], bytes], workers: int +): + dets = client.list_detections(seq["id"]) + # recorded_at can be null/absent; sort tolerantly (None sorts first as ""). + dets = sorted(dets, key=lambda d: d.get("recorded_at") or "") + meta = SequenceMeta( + key=f"pyro-annotator_{seq['id']}", + sequence_id=seq["id"], + camera_id=seq.get("camera_id"), + camera_name=seq.get("camera_name"), + organization_id=seq.get("organisation_id"), + organization_name=seq.get("organisation_name"), + started_at=seq.get("recorded_at"), + frames=[ + FrameRef( + file=f"{IMAGES_DIR}/detection_{d['id']}.jpg", + detection_id=d["id"], + recorded_at=d.get("recorded_at"), + bucket_key=d.get("bucket_key"), + ) + for d in dets + ], + ) + seq_dir = sequence_dir(store_dir, meta) + images_dir = seq_dir / IMAGES_DIR + images_dir.mkdir(parents=True, exist_ok=True) + + def fetch(det: dict) -> None: + url = client.detection_image_url(det["id"]) + (images_dir / f"detection_{det['id']}.jpg").write_bytes(download(url)) + + if workers <= 1 or len(dets) <= 1: + for det in dets: + fetch(det) + else: + with ThreadPoolExecutor(max_workers=min(workers, len(dets))) as ex: + # list() drains the iterator so the first download error propagates + # (caught by pull_unannotated -> sequence re-pulled next run). + list(ex.map(fetch, dets)) + # meta.json last: its presence marks the sequence complete. + write_meta(seq_dir, meta) diff --git a/triage/src/temporal_model/triage/report.py b/triage/src/temporal_model/triage/report.py new file mode 100644 index 0000000..7a92ab4 --- /dev/null +++ b/triage/src/temporal_model/triage/report.py @@ -0,0 +1,153 @@ +"""Writers for the eval-viewer contract + the triage worklists. + +Layout (read by viewer/ with DATA_ROOT=../triage — see viewer/lib/paths.ts): +``data/08_reporting/pyro-annotator/vit_dinov2_finetune/{results.json, details/, +sequences/, model_config.json, dropped.json}`` plus the triage-specific +``unlabeled.json`` / ``review.json`` worklists. Row/detail shapes mirror +``eval/src/temporal_model/eval/evaluate.py`` and +``core/.../details_schema.py``; rows add ``triage_score``/``triage_bucket``. + +The ``unlabeled.json`` ``bulk_payload`` is a ready-to-send body for +``POST /api/v1/annotations/sequences/bulk`` — written to disk ONLY. triage never +transmits it; applying it is a separate, deliberate human step. +""" + +from __future__ import annotations + +import json +from pathlib import Path + +from temporal_model.triage.score import ScoredSequence + +SOURCE = "pyro-annotator" +MODEL_DIR = "vit_dinov2_finetune" # viewer/lib/paths.ts MODEL_NAME + + +def model_version_of(model_config: dict) -> str: + """Per-prediction model identity — the packaged release version (e.g. "0.2.0"). + + Lets a future re-score (likely a different model) keep its predictions + distinguishable. Falls back to ``@`` then + "unknown" when the packaged model carries no version/provenance. + """ + version = model_config.get("model_version") + if version: + return str(version) + variant = model_config.get("variant") or "unknown" + sha = model_config.get("train_git_sha") + return f"{variant}@{sha[:8]}" if sha else variant + + +def _result_row(s: ScoredSequence, model_version: str) -> dict: + kept = s.details.get("tubes", {}).get("kept", []) + decision = "keep" if s.bucket == "review" else "discard" + return { + "key": s.key, + "source": SOURCE, + "label": s.meta.label, # "unknown" — unannotated backlog + "decision": decision, + "outcome": "n/a", # no ground truth to score against + "triage_score": s.score, + "triage_bucket": s.bucket, + "model_version": model_version, + "score": max((t["logit"] for t in kept), default=None), + "probability": s.score, + "num_tubes_kept": len(kept), + "trigger_frame_index": s.trigger_frame_index, + "organization_name": s.meta.organization_name, + "camera_name": s.meta.camera_name, + "started_at": s.meta.started_at, + } + + +def _sequence_view(s: ScoredSequence) -> dict: + return { + "key": s.key, + "source": SOURCE, + "label": s.meta.label, + "organization_name": s.meta.organization_name, + "camera_name": s.meta.camera_name, + "started_at": s.meta.started_at, + "frames": [p.as_posix() for p in s.frame_paths], + } + + +def write_triage_report( + output_dir: Path, + scored: list[ScoredSequence], + *, + dropped: list[dict], + threshold: float, + model_config: dict, +) -> None: + out = output_dir / SOURCE / MODEL_DIR + (out / "details").mkdir(parents=True, exist_ok=True) + (out / "sequences").mkdir(parents=True, exist_ok=True) + + model_version = model_version_of(model_config) + rows = [_result_row(s, model_version) for s in scored] + (out / "results.json").write_text(json.dumps(rows, indent=2)) + # Columnar twin of results.json for analytical reuse (matches eval's + # results.parquet). Local import keeps pandas off the lightweight pull path. + import pandas as pd # noqa: PLC0415 + + pd.DataFrame(rows).to_parquet(out / "results.parquet") + (out / "model_config.json").write_text( + json.dumps( + {**model_config, "threshold": threshold, "model_version": model_version}, + indent=2, + default=str, + ) + ) + (out / "dropped.json").write_text(json.dumps(dropped, indent=2)) + for s in scored: + (out / "details" / f"{s.key}.json").write_text( + json.dumps(s.details, indent=2, default=str) + ) + (out / "sequences" / f"{s.key}.json").write_text( + json.dumps(_sequence_view(s), indent=2) + ) + + low = sorted((s for s in scored if s.bucket == "unlabeled"), key=lambda s: s.score) + high = sorted( + (s for s in scored if s.bucket == "review"), + key=lambda s: s.score, + reverse=True, + ) + low_ids = [s.sequence_id for s in low] + (out / "unlabeled.json").write_text( + json.dumps( + { + "threshold": threshold, + "count": len(low), + "sequence_ids": low_ids, + "items": [ + {"sequence_id": s.sequence_id, "key": s.key, "score": s.score} + for s in low + ], + # Ready-to-send body for POST /api/v1/annotations/sequences/bulk. + # WRITTEN TO DISK ONLY — triage never sends it. + "bulk_payload": { + "sequence_ids": low_ids, + "false_positive_type": "unlabeled", + "is_unsure": False, + "force": False, + }, + }, + indent=2, + ) + ) + (out / "review.json").write_text( + json.dumps( + { + "threshold": threshold, + "count": len(high), + "sequence_ids": [s.sequence_id for s in high], + "items": [ + {"sequence_id": s.sequence_id, "key": s.key, "score": s.score} + for s in high + ], + }, + indent=2, + ) + ) diff --git a/triage/src/temporal_model/triage/score.py b/triage/src/temporal_model/triage/score.py new file mode 100644 index 0000000..80ad239 --- /dev/null +++ b/triage/src/temporal_model/triage/score.py @@ -0,0 +1,101 @@ +"""Score stored sequences with the temporal model and bucket them by threshold. + +Pure helpers (``sequence_score``, ``bucket_for``) hold the triage policy and are +unit-tested without a model; ``score_sequences`` wires the loaded model over the +store. The triage score is the largest calibrated per-tube probability — the +same quantity ``eval`` reports as ``probability`` — thresholded independently of +the model's own keep/discard rule. +""" + +from __future__ import annotations + +import logging +import time +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +from temporal_model.triage.store import ( + SequenceMeta, + build_frames, + iter_sequence_dirs, + read_meta, +) + +logger = logging.getLogger(__name__) + +PROGRESS_EVERY = 250 # log a heartbeat every N scored sequences + + +@dataclass +class ScoredSequence: + key: str + sequence_id: int + score: float + bucket: str # "review" | "unlabeled" + meta: SequenceMeta + details: dict + trigger_frame_index: int | None + frame_paths: list[Path] + + +def sequence_score(details: dict[str, Any] | None) -> float: + """Largest calibrated probability across kept tubes; 0.0 if none.""" + kept = (details or {}).get("tubes", {}).get("kept", []) + probs = [t.get("probability") for t in kept if t.get("probability") is not None] + return max(probs) if probs else 0.0 + + +def bucket_for(score: float, threshold: float) -> str: + """'review' for high scorers (>= threshold), else 'unlabeled'.""" + return "review" if score >= threshold else "unlabeled" + + +def score_sequences( + model, store_dir: Path, *, threshold: float +) -> tuple[list[ScoredSequence], list[dict]]: + """Run predict() over every stored sequence; classify each by threshold.""" + seq_dirs = list(iter_sequence_dirs(store_dir)) + total = len(seq_dirs) + start = time.monotonic() + scored: list[ScoredSequence] = [] + dropped: list[dict] = [] + for i, seq_dir in enumerate(seq_dirs, 1): + meta = read_meta(seq_dir) + frame_paths = [seq_dir / f.file for f in meta.frames] + if not frame_paths: + dropped.append({"sequence_id": meta.key, "reason": "no_images"}) + continue + frames = build_frames(seq_dir, meta) + try: + output = model.predict(frames, compute_trigger=True) + except Exception as exc: # noqa: BLE001 — record + continue + logger.warning("predict failed for %s: %s", meta.key, exc) + dropped.append({"sequence_id": meta.key, "reason": "predict_failed"}) + continue + score = sequence_score(output.details) + scored.append( + ScoredSequence( + key=meta.key, + sequence_id=meta.sequence_id, + score=score, + bucket=bucket_for(score, threshold), + meta=meta, + details=output.details, + trigger_frame_index=output.trigger_frame_index, + frame_paths=frame_paths, + ) + ) + if i % PROGRESS_EVERY == 0: + elapsed = time.monotonic() - start + rate = i / elapsed if elapsed else 0.0 + review = sum(1 for s in scored if s.bucket == "review") + logger.info( + "scored %d/%d (%.1f seq/s, %d review / %d unlabel)", + i, + total, + rate, + review, + len(scored) - review, + ) + return scored, dropped diff --git a/triage/src/temporal_model/triage/shards.py b/triage/src/temporal_model/triage/shards.py new file mode 100644 index 0000000..c1ea5d3 --- /dev/null +++ b/triage/src/temporal_model/triage/shards.py @@ -0,0 +1,186 @@ +"""Tar-shard the triage working tree so DVC tracks ~tens of objects, not ~300k. + +At full scale the store is ~247k frame files and the report ~43k per-key JSON +files — a DVC object explosion. ``pack`` bundles per-sequence data into sealed +tar shards (frames + meta, and details + sequence-view), leaving only the +handful of aggregate report files loose; ``unpack`` restores the loose working +tree so ``score`` and the viewer read files by path, unchanged. + +Two shard sets with different lifecycles: +- ``frames/`` — append-only and model-independent (a sequence's images never + change); a re-score never rewrites them. +- ``report/`` — rebuilt each run (predictions are per model run). + +Each shard is sealed at ``target_bytes`` (~1 GB); tars are uncompressed (JPEGs +already are). A ``manifest.json`` per set maps unit id → shard for incremental +packs and tooling. +""" + +from __future__ import annotations + +import json +import shutil +import tarfile +from collections.abc import Callable +from pathlib import Path + +from temporal_model.triage.store import iter_sequence_dirs, read_meta + +FRAMES_DIR = "frames" +REPORT_DIR = "report" +MANIFEST = "manifest.json" +DEFAULT_TARGET_BYTES = 1_000_000_000 # ~1 GB per shard + +# Few, small, aggregate report files kept loose alongside the shards (and pushed). +AGGREGATE_FILES = [ + "results.json", + "results.parquet", + "model_config.json", + "dropped.json", + "unlabeled.json", + "review.json", +] + + +def _read_manifest(path: Path) -> dict: + if path.exists(): + return json.loads(path.read_text()) + return {"next_index": 1, "items": {}} + + +def _seal_units( + units: list, + *, + unit_id: Callable[[object], str], + unit_size: Callable[[object], int], + add_to_tar: Callable[[tarfile.TarFile, object], None], + shard_dir: Path, + manifest: dict, + target_bytes: int, +) -> None: + """Append ``units`` into new sealed tars, updating ``manifest`` in place. + + A unit is never split across shards; a shard is sealed once adding the next + unit would exceed ``target_bytes`` (a single oversized unit still gets its + own shard). + """ + if not units: + return + idx: int = manifest["next_index"] + tar: tarfile.TarFile | None = None + shard_name = "" + used = 0 + for u in units: + size = unit_size(u) + if tar is None or (used > 0 and used + size > target_bytes): + if tar is not None: + tar.close() + idx += 1 + shard_name = f"shard_{idx:04d}.tar" + # Lifecycle spans loop iterations (sealing on size), so no `with`. + tar = tarfile.open(shard_dir / shard_name, "w") # noqa: SIM115 + used = 0 + add_to_tar(tar, u) + manifest["items"][unit_id(u)] = shard_name + used += size + if tar is not None: + tar.close() + idx += 1 + manifest["next_index"] = idx + + +def _dir_size(path: Path) -> int: + return sum(p.stat().st_size for p in path.rglob("*") if p.is_file()) + + +def pack_frames(store_dir: Path, shards_dir: Path, *, target_bytes: int) -> dict: + """Append-only: pack store sequences absent from the manifest into new tars.""" + fdir = shards_dir / FRAMES_DIR + fdir.mkdir(parents=True, exist_ok=True) + manifest = _read_manifest(fdir / MANIFEST) + seq_dirs = [ + d + for d in iter_sequence_dirs(store_dir) + if str(read_meta(d).sequence_id) not in manifest["items"] + ] + _seal_units( + seq_dirs, + unit_id=lambda d: str(read_meta(d).sequence_id), + unit_size=_dir_size, + add_to_tar=lambda tar, d: tar.add( + d, arcname=d.relative_to(store_dir).as_posix() + ), + shard_dir=fdir, + manifest=manifest, + target_bytes=target_bytes, + ) + (fdir / MANIFEST).write_text(json.dumps(manifest, indent=2)) + return manifest + + +def pack_report(report_dir: Path, shards_dir: Path, *, target_bytes: int) -> dict: + """Rebuild the report shards from scratch (predictions are per run).""" + rdir = shards_dir / REPORT_DIR + if rdir.exists(): + shutil.rmtree(rdir) + rdir.mkdir(parents=True, exist_ok=True) + keys = sorted(p.stem for p in (report_dir / "details").glob("*.json")) + manifest = {"next_index": 1, "items": {}} + + def members(key: str) -> list[tuple[Path, str]]: + return [ + (report_dir / "details" / f"{key}.json", f"details/{key}.json"), + (report_dir / "sequences" / f"{key}.json", f"sequences/{key}.json"), + ] + + _seal_units( + keys, + unit_id=lambda k: k, + unit_size=lambda k: sum(p.stat().st_size for p, _ in members(k) if p.exists()), + add_to_tar=lambda tar, k: [ + tar.add(p, arcname=arc) for p, arc in members(k) if p.exists() + ], + shard_dir=rdir, + manifest=manifest, + target_bytes=target_bytes, + ) + (rdir / MANIFEST).write_text(json.dumps(manifest, indent=2)) + return manifest + + +def pack( + store_dir: Path, + report_dir: Path, + shards_dir: Path, + *, + target_bytes: int = DEFAULT_TARGET_BYTES, +) -> dict[str, int]: + """Pack the working tree into ``shards_dir``. Returns shard counts.""" + shards_dir.mkdir(parents=True, exist_ok=True) + frames = pack_frames(store_dir, shards_dir, target_bytes=target_bytes) + report = pack_report(report_dir, shards_dir, target_bytes=target_bytes) + for name in AGGREGATE_FILES: + src = report_dir / name + if src.exists(): + shutil.copy2(src, shards_dir / name) + return { + "frame_shards": frames["next_index"] - 1, + "report_shards": report["next_index"] - 1, + "sequences": len(frames["items"]), + } + + +def unpack(shards_dir: Path, store_dir: Path, report_dir: Path) -> None: + """Restore the loose store + report from ``shards_dir`` (idempotent extract).""" + store_dir.mkdir(parents=True, exist_ok=True) + report_dir.mkdir(parents=True, exist_ok=True) + for tar_path in sorted((shards_dir / FRAMES_DIR).glob("shard_*.tar")): + with tarfile.open(tar_path) as tar: + tar.extractall(store_dir, filter="data") + for tar_path in sorted((shards_dir / REPORT_DIR).glob("shard_*.tar")): + with tarfile.open(tar_path) as tar: + tar.extractall(report_dir, filter="data") + for name in AGGREGATE_FILES: + src = shards_dir / name + if src.exists(): + shutil.copy2(src, report_dir / name) diff --git a/triage/src/temporal_model/triage/store.py b/triage/src/temporal_model/triage/store.py new file mode 100644 index 0000000..77c8539 --- /dev/null +++ b/triage/src/temporal_model/triage/store.py @@ -0,0 +1,107 @@ +"""Sequence store: data/01_raw/sequences///seq_/{meta.json, images/}. + +Holds the pyro-annotator unannotated backlog. These sequences carry no human +label yet, so ``label`` is always ``"unknown"``; per-frame provenance keeps the +annotator ``detection_id``, capture time, and S3 ``bucket_key``. +""" + +from __future__ import annotations + +import re +import unicodedata +from collections.abc import Iterator +from datetime import datetime +from pathlib import Path + +from pydantic import BaseModel + +from temporal_model.core.protocol import Frame + +META_NAME = "meta.json" +IMAGES_DIR = "images" + + +class FrameRef(BaseModel): + file: str # relative to the sequence dir, e.g. "images/detection_7.jpg" + detection_id: int + recorded_at: str | None = None + bucket_key: str | None = None + + +class SequenceMeta(BaseModel): + key: str # "pyro-annotator_" — the viewer join key + sequence_id: int + source: str = "pyro-annotator" + label: str = "unknown" # unannotated backlog has no ground truth + camera_id: int | None = None + camera_name: str | None = None + organization_id: int | None = None + organization_name: str | None = None + started_at: str | None = None + frames: list[FrameRef] = [] + + +def slugify(value: str | None) -> str: + """Filesystem-safe lowercase ASCII slug; 'unknown' when nothing remains.""" + ascii_value = ( + unicodedata.normalize("NFKD", value or "").encode("ascii", "ignore").decode() + ) + slug = re.sub(r"[^a-z0-9]+", "-", ascii_value.lower()).strip("-") + return slug or "unknown" + + +def sequence_dir(store_dir: Path, meta: SequenceMeta) -> Path: + return ( + store_dir + / slugify(meta.organization_name) + / slugify(meta.camera_name) + / f"seq_{meta.sequence_id}" + ) + + +def write_meta(seq_dir: Path, meta: SequenceMeta) -> None: + seq_dir.mkdir(parents=True, exist_ok=True) + (seq_dir / META_NAME).write_text(meta.model_dump_json(indent=2)) + + +def read_meta(seq_dir: Path) -> SequenceMeta: + return SequenceMeta.model_validate_json((seq_dir / META_NAME).read_text()) + + +def find_sequence_dirs(store_dir: Path, sequence_id: int) -> list[Path]: + return sorted( + p.parent for p in store_dir.glob(f"*/*/seq_{sequence_id}/{META_NAME}") + ) + + +def sequence_exists(store_dir: Path, sequence_id: int) -> bool: + return bool(find_sequence_dirs(store_dir, sequence_id)) + + +def iter_sequence_dirs(store_dir: Path) -> Iterator[Path]: + """Yield every sequence directory under ``store_dir`` (recursive, sorted).""" + if not store_dir.exists(): + return + for meta_path in sorted(store_dir.glob(f"*/*/seq_*/{META_NAME}")): + yield meta_path.parent + + +def _parse_ts(value: str | None) -> datetime | None: + if not value: + return None + try: + return datetime.fromisoformat(value.replace("Z", "+00:00")) + except ValueError: + return None + + +def build_frames(seq_dir: Path, meta: SequenceMeta) -> list[Frame]: + """Build the ordered ``core`` Frame list the model consumes (meta order = time).""" + return [ + Frame( + frame_id=Path(ref.file).stem, + image_path=seq_dir / ref.file, + timestamp=_parse_ts(ref.recorded_at), + ) + for ref in meta.frames + ] diff --git a/triage/tests/test_annotator_api.py b/triage/tests/test_annotator_api.py new file mode 100644 index 0000000..9475957 --- /dev/null +++ b/triage/tests/test_annotator_api.py @@ -0,0 +1,80 @@ +import pytest + +from temporal_model.triage.annotator_api import AnnotatorApiClient, AnnotatorApiConfig + +CONFIG = AnnotatorApiConfig( + url="https://annotator.test", login="arthur", password="secret" +) + + +class FakeResponse: + def __init__(self, payload, status=200): + self._payload = payload + self.status_code = status + + def json(self): + return self._payload + + def raise_for_status(self): + if self.status_code >= 400: + raise AssertionError(f"HTTP {self.status_code}") + + +class RecordingSession: + """Records every HTTP call so we can assert the client is read-only.""" + + def __init__(self, pages): + self._pages = pages # list of payloads returned by successive GETs + self.calls = [] # (method, url) tuples + + def post(self, url, **kw): + self.calls.append(("POST", url)) + return FakeResponse({"access_token": "tok", "token_type": "bearer"}) + + def get(self, url, **kw): + self.calls.append(("GET", url)) + return FakeResponse(self._pages.pop(0)) + + +def test_login_then_paginate_unannotated(): + pages = [ + {"items": [{"id": 1}, {"id": 2}], "page": 1, "pages": 2, "total": 3}, + {"items": [{"id": 3}], "page": 2, "pages": 2, "total": 3}, + ] + session = RecordingSession(pages) + client = AnnotatorApiClient(CONFIG, session=session) + client.login() + seqs = list(client.iter_unannotated_sequences(page_size=2)) + assert [s["id"] for s in seqs] == [1, 2, 3] + + +def test_limit_caps_results_and_stops_paging(): + pages = [{"items": [{"id": 1}, {"id": 2}], "page": 1, "pages": 9, "total": 99}] + session = RecordingSession(pages) + client = AnnotatorApiClient(CONFIG, session=session) + client.login() + seqs = list(client.iter_unannotated_sequences(page_size=2, limit=2)) + assert [s["id"] for s in seqs] == [1, 2] + # only the first page was fetched (1 login POST + 1 GET) + assert session.calls == [ + ("POST", "https://annotator.test/api/v1/auth/login"), + ("GET", "https://annotator.test/api/v1/sequences/"), + ] + + +def test_client_is_read_only_only_login_posts(): + """The client must never expose a mutating verb, and the only POST is login.""" + session = RecordingSession([]) + client = AnnotatorApiClient(CONFIG, session=session) + client.login() + # No write methods exist on the client. + for verb in ("post", "patch", "put", "delete"): + assert not hasattr(client, verb) + # The single POST it makes is the auth login (writes no annotation data). + assert session.calls == [("POST", "https://annotator.test/api/v1/auth/login")] + + +def test_get_requires_login(): + client = AnnotatorApiClient(CONFIG, session=RecordingSession([])) + with pytest.raises(RuntimeError): + client.list_detections(1) diff --git a/triage/tests/test_cli.py b/triage/tests/test_cli.py new file mode 100644 index 0000000..b2e4f7d --- /dev/null +++ b/triage/tests/test_cli.py @@ -0,0 +1,54 @@ +from temporal_model.triage import cli + + +def test_pull_command_invokes_pull(monkeypatch, tmp_path): + calls = {} + fake_client = object() # _build_client (which logs in) is covered in Task 2 + + monkeypatch.setattr(cli, "_build_client", lambda: fake_client) + monkeypatch.setattr( + cli, + "pull_unannotated", + lambda client, store_dir, **kw: calls.update( + client=client, store=store_dir, kw=kw + ) + or {"pulled": 3, "skipped": 0}, + ) + cli.main(["pull", "--store", str(tmp_path), "--limit", "3"]) + assert calls["client"] is fake_client + assert calls["kw"]["limit"] == 3 + assert calls["kw"]["processing_stage"] == "ready_to_annotate" + assert calls["store"] == tmp_path + + +def test_score_command_threshold_flag_overrides(monkeypatch, tmp_path): + captured = {} + + monkeypatch.setattr(cli, "_load_model", lambda model_zip, device: object()) + monkeypatch.setattr( + cli, + "score_sequences", + lambda model, store, *, threshold: captured.update(threshold=threshold) + or ([], []), + ) + monkeypatch.setattr(cli, "read_model_config", lambda model_zip: {}) + monkeypatch.setattr( + cli, + "write_triage_report", + lambda *a, **k: captured.update(wrote=True), + ) + cli.main( + [ + "score", + "--store", + str(tmp_path), + "--output-dir", + str(tmp_path / "out"), + "--model-zip", + str(tmp_path / "model.zip"), + "--threshold", + "0.5", + ] + ) + assert captured["threshold"] == 0.5 + assert captured["wrote"] is True diff --git a/triage/tests/test_pull.py b/triage/tests/test_pull.py new file mode 100644 index 0000000..5cd901d --- /dev/null +++ b/triage/tests/test_pull.py @@ -0,0 +1,157 @@ +import threading + +from temporal_model.triage.pull import pull_unannotated +from temporal_model.triage.store import ( + SequenceMeta, + iter_sequence_dirs, + read_meta, + sequence_dir, + sequence_exists, +) + + +class FakeClient: + def __init__(self, sequences, detections): + self._sequences = sequences + self._detections = detections + self.detection_calls = 0 + + def iter_unannotated_sequences( + self, *, processing_stage="ready_to_annotate", page_size=100, limit=None + ): + self.last_stage = processing_stage + seqs = self._sequences if limit is None else self._sequences[:limit] + yield from seqs + + def list_detections(self, sequence_id): + self.detection_calls += 1 + return self._detections[sequence_id] + + def detection_image_url(self, detection_id): + return f"https://s3.test/det-{detection_id}.jpg?sig=x" + + +SEQ = { + "id": 42, + "camera_id": 1, + "camera_name": "cam-a", + "organisation_id": 9, + "organisation_name": "sis-67", + "recorded_at": "2026-06-01T10:00:00", +} +DETS = { + 42: [ + {"id": 7, "recorded_at": "2026-06-01T10:00:00", "bucket_key": "k/7.jpg"}, + {"id": 8, "recorded_at": "2026-06-01T10:01:00", "bucket_key": "k/8.jpg"}, + ] +} + + +def _expected_meta() -> SequenceMeta: + return SequenceMeta( + key="pyro-annotator_42", + sequence_id=42, + camera_name="cam-a", + organization_name="sis-67", + ) + + +def test_pull_writes_store_and_downloads_frames(tmp_path): + client = FakeClient([SEQ], DETS) + downloads = [] + counts = pull_unannotated( + client, tmp_path, download=lambda url: downloads.append(url) or b"img" + ) + assert counts == {"pulled": 1, "skipped": 0} + assert sequence_exists(tmp_path, 42) + seq_dir = sequence_dir(tmp_path, _expected_meta()) + meta = read_meta(seq_dir) + assert meta.key == "pyro-annotator_42" + assert [f.detection_id for f in meta.frames] == [7, 8] + assert (seq_dir / "images/detection_7.jpg").read_bytes() == b"img" + assert len(downloads) == 2 + + +def test_pull_is_incremental(tmp_path): + client = FakeClient([SEQ], DETS) + pull_unannotated(client, tmp_path, download=lambda url: b"img") + counts = pull_unannotated(client, tmp_path, download=lambda url: b"img") + assert counts == {"pulled": 0, "skipped": 1} + + +def test_pull_forwards_processing_stage(tmp_path): + client = FakeClient([SEQ], DETS) + pull_unannotated(client, tmp_path, download=lambda url: b"img") + assert client.last_stage == "ready_to_annotate" + + +def test_pull_parallel_downloads_every_frame(tmp_path): + dets = [ + {"id": i, "recorded_at": f"2026-06-01T10:{i:02d}:00", "bucket_key": f"k/{i}"} + for i in range(12) + ] + seq = dict(SEQ, id=99) + client = FakeClient([seq], {99: dets}) + + seen = set() + lock = threading.Lock() + + def dl(url): + with lock: + seen.add(url) + return b"img" + + counts = pull_unannotated(client, tmp_path, workers=6, download=dl) + assert counts == {"pulled": 1, "skipped": 0} + seq_dir = sequence_dir( + tmp_path, + SequenceMeta( + key="pyro-annotator_99", + sequence_id=99, + camera_name="cam-a", + organization_name="sis-67", + ), + ) + files = sorted(p.name for p in (seq_dir / "images").glob("*.jpg")) + assert len(files) == 12 # all frames written despite concurrency + assert len(seen) == 12 # each distinct signed URL fetched exactly once + + +def test_pull_sequence_level_concurrency(tmp_path): + seqs = [dict(SEQ, id=i, camera_name=f"cam-{i}") for i in range(5)] + dets = { + i: [ + { + "id": i * 10 + j, + "recorded_at": f"2026-06-01T10:0{j}:00", + "bucket_key": f"k/{i}-{j}", + } + for j in range(3) + ] + for i in range(5) + } + client = FakeClient(seqs, dets) + pulled_urls = set() + lock = threading.Lock() + + def dl(url): + with lock: + pulled_urls.add(url) + return b"img" + + counts = pull_unannotated( + client, tmp_path, seq_workers=3, workers=2, download=dl + ) + assert counts == {"pulled": 5, "skipped": 0} + assert sum(1 for _ in iter_sequence_dirs(tmp_path)) == 5 # all sequences landed + assert len(pulled_urls) == 15 # 5 sequences x 3 frames, no drops under concurrency + + +def test_limit_is_forwarded(tmp_path): + seqs = [dict(SEQ, id=i) for i in (42, 43)] + dets = {42: DETS[42], 43: DETS[42]} + client = FakeClient(seqs, dets) + counts = pull_unannotated(client, tmp_path, limit=1, download=lambda url: b"img") + assert counts == {"pulled": 1, "skipped": 0} + assert sequence_exists(tmp_path, 42) + assert not sequence_exists(tmp_path, 43) diff --git a/triage/tests/test_report.py b/triage/tests/test_report.py new file mode 100644 index 0000000..90f173c --- /dev/null +++ b/triage/tests/test_report.py @@ -0,0 +1,109 @@ +import json +from pathlib import Path + +import pandas as pd + +from temporal_model.triage.report import ( + MODEL_DIR, + model_version_of, + write_triage_report, +) +from temporal_model.triage.score import ScoredSequence +from temporal_model.triage.store import FrameRef, SequenceMeta + + +def test_model_version_prefers_release_then_falls_back(): + assert model_version_of({"model_version": "0.2.0"}) == "0.2.0" + assert ( + model_version_of( + {"variant": "vit_dinov2_finetune", "train_git_sha": "4b4d43ad77"} + ) + == "vit_dinov2_finetune@4b4d43ad" + ) + assert model_version_of({}) == "unknown" + + +def _scored(key, sid, score, bucket): + meta = SequenceMeta( + key=key, + sequence_id=sid, + camera_name="cam", + organization_name="org", + started_at="2026-06-01T10:00:00", + frames=[FrameRef(file="images/detection_1.jpg", detection_id=1)], + ) + return ScoredSequence( + key=key, + sequence_id=sid, + score=score, + bucket=bucket, + meta=meta, + details={ + "tubes": {"kept": [{"tube_id": 0, "logit": 1.0, "probability": score}]}, + "decision": {"aggregation": "logistic", "threshold": 0.5}, + }, + trigger_frame_index=0 if bucket == "review" else None, + frame_paths=[ + Path("data/01_raw/sequences/org/cam/seq_1/images/detection_1.jpg") + ], + ) + + +def test_write_triage_report_emits_contract_and_worklists(tmp_path): + scored = [ + _scored("pyro-annotator_1", 1, 0.92, "review"), + _scored("pyro-annotator_2", 2, 0.10, "unlabeled"), + ] + write_triage_report( + tmp_path, + scored, + dropped=[{"sequence_id": "pyro-annotator_9", "reason": "no_images"}], + threshold=0.35, + model_config={"model_version": "0.2.0", "variant": "vit_dinov2_finetune"}, + ) + out = tmp_path / "pyro-annotator" / MODEL_DIR + + rows = json.loads((out / "results.json").read_text()) + assert {r["key"]: r["triage_bucket"] for r in rows} == { + "pyro-annotator_1": "review", + "pyro-annotator_2": "unlabeled", + } + assert {r["key"]: r["decision"] for r in rows} == { + "pyro-annotator_1": "keep", + "pyro-annotator_2": "discard", + } + assert (out / "details" / "pyro-annotator_1.json").exists() + assert (out / "sequences" / "pyro-annotator_1.json").exists() + assert json.loads((out / "model_config.json").read_text())["threshold"] == 0.35 + assert json.loads((out / "dropped.json").read_text())[0]["reason"] == "no_images" + + unlabeled = json.loads((out / "unlabeled.json").read_text()) + assert unlabeled["threshold"] == 0.35 + assert unlabeled["sequence_ids"] == [2] + assert unlabeled["bulk_payload"]["false_positive_type"] == "unlabeled" + assert unlabeled["bulk_payload"]["sequence_ids"] == [2] + + review = json.loads((out / "review.json").read_text()) + assert review["sequence_ids"] == [1] + assert review["items"][0]["score"] == 0.92 + + # every prediction is tagged with the release model version + assert all(r["model_version"] == "0.2.0" for r in rows) + cfg = json.loads((out / "model_config.json").read_text()) + assert cfg["model_version"] == "0.2.0" + + +def test_results_parquet_mirrors_results_json(tmp_path): + scored = [ + _scored("pyro-annotator_1", 1, 0.92, "review"), + _scored("pyro-annotator_2", 2, 0.10, "unlabeled"), + ] + write_triage_report(tmp_path, scored, dropped=[], threshold=0.35, model_config={}) + out = tmp_path / "pyro-annotator" / MODEL_DIR + df = pd.read_parquet(out / "results.parquet") + rows = json.loads((out / "results.json").read_text()) + assert len(df) == len(rows) == 2 + assert dict(zip(df["key"], df["triage_bucket"], strict=True)) == { + "pyro-annotator_1": "review", + "pyro-annotator_2": "unlabeled", + } diff --git a/triage/tests/test_scaffold.py b/triage/tests/test_scaffold.py new file mode 100644 index 0000000..63ba0bf --- /dev/null +++ b/triage/tests/test_scaffold.py @@ -0,0 +1,5 @@ +from temporal_model.triage import __doc__ as pkg_doc + + +def test_package_importable(): + assert "triage" in pkg_doc.lower() diff --git a/triage/tests/test_score.py b/triage/tests/test_score.py new file mode 100644 index 0000000..d190b6a --- /dev/null +++ b/triage/tests/test_score.py @@ -0,0 +1,74 @@ +from dataclasses import dataclass + +from temporal_model.triage.score import ( + ScoredSequence, + bucket_for, + score_sequences, + sequence_score, +) +from temporal_model.triage.store import ( + FrameRef, + SequenceMeta, + sequence_dir, + write_meta, +) + + +def _details(probs): + return {"tubes": {"kept": [{"probability": p} for p in probs]}} + + +def test_sequence_score_is_max_tube_probability(): + assert sequence_score(_details([0.1, 0.8, 0.3])) == 0.8 + + +def test_sequence_score_zero_when_no_tubes(): + assert sequence_score(_details([])) == 0.0 + + +def test_sequence_score_zero_when_all_probabilities_none(): + assert sequence_score({"tubes": {"kept": [{"probability": None}]}}) == 0.0 + + +def test_bucket_split_at_threshold(): + assert bucket_for(0.35, 0.35) == "review" # boundary is inclusive (>=) + assert bucket_for(0.34, 0.35) == "unlabeled" + assert bucket_for(0.9, 0.35) == "review" + + +@dataclass +class _Output: + is_positive: bool + trigger_frame_index: int | None + details: dict + + +class _FakeModel: + def __init__(self, prob): + self._prob = prob + + def predict(self, frames, compute_trigger=True): + return _Output(self._prob >= 0.5, 0, _details([self._prob])) + + +def test_score_sequences_classifies_and_carries_through(tmp_path): + meta = SequenceMeta( + key="pyro-annotator_1", + sequence_id=1, + camera_name="cam", + organization_name="org", + frames=[FrameRef(file="images/detection_1.jpg", detection_id=1)], + ) + seq_dir = sequence_dir(tmp_path, meta) + (seq_dir / "images").mkdir(parents=True) + (seq_dir / "images/detection_1.jpg").write_bytes(b"x") + write_meta(seq_dir, meta) + + scored, dropped = score_sequences(_FakeModel(0.8), tmp_path, threshold=0.35) + assert dropped == [] + assert len(scored) == 1 + s = scored[0] + assert isinstance(s, ScoredSequence) + assert s.key == "pyro-annotator_1" + assert s.score == 0.8 + assert s.bucket == "review" diff --git a/triage/tests/test_shards.py b/triage/tests/test_shards.py new file mode 100644 index 0000000..a239e74 --- /dev/null +++ b/triage/tests/test_shards.py @@ -0,0 +1,85 @@ +import json + +from temporal_model.triage.shards import AGGREGATE_FILES, pack, unpack +from temporal_model.triage.store import ( + IMAGES_DIR, + FrameRef, + SequenceMeta, + sequence_dir, + write_meta, +) + + +def _meta(i: int) -> SequenceMeta: + return SequenceMeta( + key=f"pyro-annotator_{i}", + sequence_id=i, + camera_name=f"cam{i}", + organization_name="org", + frames=[FrameRef(file=f"{IMAGES_DIR}/detection_{i}.jpg", detection_id=i)], + ) + + +def _add_sequence(store, report, i: int) -> SequenceMeta: + meta = _meta(i) + sd = sequence_dir(store, meta) + (sd / IMAGES_DIR).mkdir(parents=True) + (sd / IMAGES_DIR / f"detection_{i}.jpg").write_bytes(b"img" * 100) + write_meta(sd, meta) + (report / "details" / f"{meta.key}.json").write_text( + json.dumps({"tubes": {"kept": []}}) + ) + (report / "sequences" / f"{meta.key}.json").write_text( + json.dumps({"key": meta.key}) + ) + return meta + + +def _make(root): + store, report = root / "store", root / "report" + (report / "details").mkdir(parents=True) + (report / "sequences").mkdir(parents=True) + for i in (1, 2, 3): + _add_sequence(store, report, i) + for agg in AGGREGATE_FILES: + (report / agg).write_bytes(b"AGG") + return store, report + + +def test_pack_then_unpack_roundtrip(tmp_path): + store, report = _make(tmp_path) + shards = tmp_path / "shards" + pack(store, report, shards, target_bytes=500) # tiny → forces multiple shards + + assert (shards / "frames" / "manifest.json").exists() + assert len(list((shards / "frames").glob("shard_*.tar"))) >= 2 + assert len(list((shards / "report").glob("shard_*.tar"))) >= 1 + assert (shards / "results.parquet").read_bytes() == b"AGG" + + store2, report2 = tmp_path / "store2", tmp_path / "report2" + unpack(shards, store2, report2) + + sd = sequence_dir(store2, _meta(1)) + assert (sd / "meta.json").exists() + assert (sd / "images" / "detection_1.jpg").read_bytes() == b"img" * 100 + assert (report2 / "details" / "pyro-annotator_1.json").exists() + assert (report2 / "sequences" / "pyro-annotator_2.json").exists() + assert (report2 / "results.parquet").read_bytes() == b"AGG" + + +def test_pack_frames_is_append_only(tmp_path): + store, report = _make(tmp_path) + shards = tmp_path / "shards" + pack(store, report, shards, target_bytes=10**9) + m1 = json.loads((shards / "frames" / "manifest.json").read_text()) + tars1 = {p.name for p in (shards / "frames").glob("shard_*.tar")} + + _add_sequence(store, report, 4) # a new sequence arrives + pack(store, report, shards, target_bytes=10**9) + m2 = json.loads((shards / "frames" / "manifest.json").read_text()) + + assert set(m1["items"]) == {"1", "2", "3"} + assert set(m2["items"]) == {"1", "2", "3", "4"} + # existing sequences keep their original shard (append-only, not rewritten) + assert m2["items"]["1"] == m1["items"]["1"] + assert tars1 <= {p.name for p in (shards / "frames").glob("shard_*.tar")} diff --git a/triage/tests/test_store.py b/triage/tests/test_store.py new file mode 100644 index 0000000..6eea471 --- /dev/null +++ b/triage/tests/test_store.py @@ -0,0 +1,65 @@ +from pathlib import Path + +from temporal_model.triage.store import ( + IMAGES_DIR, + FrameRef, + SequenceMeta, + build_frames, + iter_sequence_dirs, + read_meta, + sequence_dir, + sequence_exists, + slugify, + write_meta, +) + + +def _meta() -> SequenceMeta: + return SequenceMeta( + key="pyro-annotator_42", + sequence_id=42, + camera_name="Donon / Sarrebourg 01", + organization_name="SIS 67", + started_at="2026-06-01T10:00:00", + frames=[ + FrameRef( + file=f"{IMAGES_DIR}/detection_7.jpg", + detection_id=7, + recorded_at="2026-06-01T10:00:00", + bucket_key="cam1/frame-7.jpg", + ) + ], + ) + + +def test_slugify_is_filesystem_safe(): + assert slugify("SIS 67 / Est") == "sis-67-est" + assert slugify(None) == "unknown" + + +def test_sequence_dir_layout(): + meta = _meta() + d = sequence_dir(Path("/store"), meta) + assert d == Path("/store/sis-67/donon-sarrebourg-01/seq_42") + + +def test_write_then_read_roundtrip(tmp_path): + meta = _meta() + seq_dir = sequence_dir(tmp_path, meta) + write_meta(seq_dir, meta) + assert sequence_exists(tmp_path, 42) + back = read_meta(seq_dir) + assert back.key == "pyro-annotator_42" + assert back.label == "unknown" + assert back.frames[0].detection_id == 7 + assert [d for d in iter_sequence_dirs(tmp_path)] == [seq_dir] + + +def test_build_frames_orders_and_parses_timestamps(tmp_path): + meta = _meta() + seq_dir = sequence_dir(tmp_path, meta) + frames = build_frames(seq_dir, meta) + assert len(frames) == 1 + assert frames[0].frame_id == "detection_7" + assert frames[0].image_path == seq_dir / 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BboxTubeDetails, ModelConfig, @@ -43,6 +49,9 @@ export default function Page() { }); const defaultThr = useMemo(() => { + // Triage trees write a top-level `threshold` (the triage split, 0.35) — + // prefer it so the slider opens at the triage default, not the model's. + if (typeof cfg.threshold === "number") return cfg.threshold; const v = cfg.decision?.logistic_threshold; return typeof v === "number" ? v : DEFAULT_LOGISTIC_THRESHOLD; }, [cfg]); @@ -76,15 +85,28 @@ export default function Page() { () => sourceRows.some((r) => r.replayed_probability !== undefined), [sourceRows], ); - // Monitor rows carry production's verdict — never re-decided locally, so - // the threshold slider is eval-only. + // Triage rows carry a fixed-threshold bucket (triage_bucket); their presence + // switches the rail/table/filters to triage mode. + const triageMode = useMemo( + () => sourceRows.some((r) => r.triage_bucket !== undefined), + [sourceRows], + ); + // Monitor rows carry production's verdict (never re-decided locally). Eval and + // triage both re-bucket live from probability, so they get the slider — triage + // uses it to explore how the threshold trades off To Review vs Unlabel. const showSlider = !monitorMode && - cfg.decision?.aggregation === "logistic" && + (triageMode || cfg.decision?.aggregation === "logistic") && sourceRows.some((r) => r.probability != null); + // Defer the THRESHOLD INPUT (not the output): the slider value updates every + // tick (smooth), but the expensive applyThreshold + table reconciliation run + // off the deferred value, so React skips intermediate drag values. No manual + // debounce; the rail/table catch up when the drag slows. + const deferredThreshold = useDeferredValue(threshold); const rows = useMemo( - () => (showSlider ? applyThreshold(sourceRows, threshold) : sourceRows), - [sourceRows, showSlider, threshold], + () => + showSlider ? applyThreshold(sourceRows, deferredThreshold) : sourceRows, + [sourceRows, showSlider, deferredThreshold], ); // Filters reset when the source changes (camera options are source-specific). @@ -110,6 +132,11 @@ export default function Page() { [sourceRows], ); const [sort, setSort] = useState(null); + // Stable callback so React.memo(SequenceTable) isn't defeated each render. + const handleSort = useCallback( + (col: SortCol) => setSort((cur) => nextSort(cur, col)), + [], + ); const tableRows = useMemo( () => sortRows(applyFilters(rows, filters), sort), [rows, filters, sort], @@ -144,6 +171,7 @@ export default function Page() { onThreshold={setThreshold} onReset={() => setThreshold(defaultThr)} monitorMode={monitorMode} + triageMode={triageMode} selectedOrganization={filters.organization} onSelectOrganization={(org) => // mirror the FilterBar's org change: camera resets with the org @@ -159,6 +187,7 @@ export default function Page() { shownCount={tableRows.length} totalCount={rows.length} monitorMode={monitorMode} + triageMode={triageMode} />
setSort((cur) => nextSort(cur, col))} + onSort={handleSort} monitorMode={monitorMode} + triageMode={triageMode} />
diff --git a/viewer/components/ControlRail.tsx b/viewer/components/ControlRail.tsx index 258dcd9..0b617e8 100644 --- a/viewer/components/ControlRail.tsx +++ b/viewer/components/ControlRail.tsx @@ -3,6 +3,8 @@ import { MonitorCards } from "@/components/MonitorCards"; import { PerfCards } from "@/components/PerfCards"; import { SourceSelect } from "@/components/SourceSelect"; import { ThresholdSlider } from "@/components/ThresholdSlider"; +import { ThresholdSweep } from "@/components/ThresholdSweep"; +import { TriageCards } from "@/components/TriageCards"; import type { ModelConfig, ResultRow } from "@/lib/types"; export function ControlRail(props: { @@ -17,6 +19,7 @@ export function ControlRail(props: { onThreshold: (v: number) => void; onReset: () => void; monitorMode?: boolean; + triageMode?: boolean; selectedOrganization?: string; onSelectOrganization?: (org: string) => void; }) { @@ -30,7 +33,14 @@ export function ControlRail(props: { onChange={props.onSource} /> )} - {props.monitorMode ? ( + {props.triageMode ? ( + + ) : props.monitorMode ? ( + )} + {props.triageMode && props.showSlider && ( + )}
diff --git a/viewer/components/FilterBar.tsx b/viewer/components/FilterBar.tsx index bfe62be..909f438 100644 --- a/viewer/components/FilterBar.tsx +++ b/viewer/components/FilterBar.tsx @@ -45,6 +45,7 @@ export function FilterBar({ shownCount, totalCount, monitorMode = false, + triageMode = false, }: { filters: Filters; cameras: string[]; @@ -53,7 +54,11 @@ export function FilterBar({ shownCount: number; totalCount: number; monitorMode?: boolean; + triageMode?: boolean; }) { + // Triage shares the monitor layout (no correctness/GT, has an org filter); it + // only relabels the verdict options as Review / Unlabel. + const breakdownMode = monitorMode || triageMode; const toggleOutcome = (o: Outcome) => { const on = filters.outcomes.includes(o); const outcomes = on @@ -68,7 +73,7 @@ export function FilterBar({ {shownCount}/{totalCount} sequences - {!monitorMode && ( + {!breakdownMode && (
{ALL_OUTCOMES.map((o) => { const on = filters.outcomes.includes(o); @@ -94,7 +99,7 @@ export function FilterBar({
)} - {!monitorMode && ( + {!breakdownMode && ( label="GT" value={filters.label} @@ -107,7 +112,7 @@ export function FilterBar({ ]} /> )} - {monitorMode && organizations.length > 0 && ( + {breakdownMode && organizations.length > 0 && ( label="organization" value={filters.organization} @@ -121,13 +126,13 @@ export function FilterBar({ /> )} - label="verdict" + label={triageMode ? "bucket" : "verdict"} value={filters.verdict} onChange={(v) => onChange({ ...filters, verdict: v })} options={[ { value: "all", label: "all" }, - { value: "keep", label: "keep" }, - { value: "discard", label: "discard" }, + { value: "keep", label: triageMode ? "to review" : "keep" }, + { value: "discard", label: triageMode ? "unlabel" : "discard" }, ]} /> {cameras.length > 0 && ( diff --git a/viewer/components/SequenceTable.tsx b/viewer/components/SequenceTable.tsx index e5dac63..417efe9 100644 --- a/viewer/components/SequenceTable.tsx +++ b/viewer/components/SequenceTable.tsx @@ -1,5 +1,5 @@ "use client"; -import { useMemo, useRef, type ReactNode } from "react"; +import { memo, useMemo, useRef, type ReactNode } from "react"; import { correctnessLabel, outcomeTokens, rowTokens } from "@/lib/correctness"; import type { Sort, SortCol } from "@/lib/sort"; import type { ResultRow } from "@/lib/types"; @@ -53,13 +53,59 @@ const MONITOR_COLUMNS: Column[] = [ { header: "prob", sortCol: "probability", render: (r) => num(r.probability) }, ]; -export function SequenceTable({ +// review = keep (>= threshold), unlabel = discard. No correctness column: +// triage rows are unlabeled (label "unknown"), so there is no ground truth. +const triageBucket = (r: ResultRow): { label: string; color: string } => + r.decision === "keep" + ? { label: "to review", color: "#047857" } + : { label: "unlabel", color: "#64748b" }; + +const TRIAGE_COLUMNS: Column[] = [ + { + header: "started", + sortCol: "started", + render: (r) => + r.started_at ? r.started_at.slice(0, 16).replace("T", " ") : "—", + }, + { + header: "organization", + sortCol: "organization", + render: (r) => r.organization_name ?? "—", + }, + { header: "camera", sortCol: "camera", render: (r) => r.camera_name ?? "—" }, + { + header: "bucket", + sortCol: "decision", + cellStyle: (r) => ({ color: triageBucket(r).color }), + render: (r) => { + const b = triageBucket(r); + return ( + <> + + {b.label} + + ); + }, + }, + { header: "tubes", sortCol: "tubes", render: (r) => r.num_tubes_kept }, + { + header: "score", + sortCol: "probability", + render: (r) => num(r.triage_score ?? r.probability), + }, +]; + +function SequenceTableInner({ rows, selectedKey, onSelect, sort = null, onSort, monitorMode = false, + triageMode = false, }: { rows: ResultRow[]; selectedKey: string | null; @@ -67,10 +113,15 @@ export function SequenceTable({ sort?: Sort | null; onSort?: (col: SortCol) => void; monitorMode?: boolean; + triageMode?: boolean; }) { const ref = useRef(null); const order = useMemo(() => rows.map((r) => r.key), [rows]); - const columns = monitorMode ? MONITOR_COLUMNS : EVAL_COLUMNS; + const columns = triageMode + ? TRIAGE_COLUMNS + : monitorMode + ? MONITOR_COLUMNS + : EVAL_COLUMNS; function move(delta: number) { if (!order.length) return; @@ -144,3 +195,7 @@ export function SequenceTable({
); } + +// Memoized: with stable props (deferred tableRows + useCallback handlers) the +// 21k-row table is skipped entirely while the threshold slider is dragged. +export const SequenceTable = memo(SequenceTableInner); diff --git a/viewer/components/ThresholdSlider.tsx b/viewer/components/ThresholdSlider.tsx index cce817d..90c5e55 100644 --- a/viewer/components/ThresholdSlider.tsx +++ b/viewer/components/ThresholdSlider.tsx @@ -5,17 +5,21 @@ export function ThresholdSlider({ defaultValue, onChange, onReset, + label = "logistic threshold", + defaultLabel = "model default", }: { value: number; defaultValue: number; onChange: (v: number) => void; onReset: () => void; + label?: string; + defaultLabel?: string; }) { return (
); diff --git a/viewer/components/ThresholdSweep.tsx b/viewer/components/ThresholdSweep.tsx new file mode 100644 index 0000000..44eba65 --- /dev/null +++ b/viewer/components/ThresholdSweep.tsx @@ -0,0 +1,82 @@ +import { useMemo } from "react"; +import type { ResultRow } from "@/lib/types"; + +// Dense 0.05 steps around the ~0.45 operating point, bigger gaps at the extremes. +const THRESHOLDS = [ + 0.1, 0.2, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.7, 0.8, 0.9, +]; + +/** + * Compact tradeoff table under the triage slider: at each standard threshold, + * how many sequences land in To Review vs Unlabel. Counts depend only on the + * scores (not the live slider), so they're stable while dragging; the row + * nearest the current threshold is highlighted. + */ +export function ThresholdSweep({ + rows, + current, + onSelect, +}: { + rows: ResultRow[]; + current: number; + onSelect?: (threshold: number) => void; +}) { + const total = rows.length; + const counts = useMemo(() => { + const probs = rows + .map((r) => r.probability) + .filter((p): p is number => p != null); + return THRESHOLDS.map((t) => { + const review = probs.filter((p) => p >= t).length; + return { t, review, unlabel: total - review }; + }); + }, [rows, total]); + + // Highlight the standard threshold closest to the live slider value. + const nearest = THRESHOLDS.reduce((a, b) => + Math.abs(b - current) < Math.abs(a - current) ? b : a, + ); + + return ( +
+
+ Threshold sweep +
+ + + + + + + + + + + {counts.map(({ t, review, unlabel }) => { + const active = t === nearest; + const pct = total ? Math.round((review / total) * 100) : 0; + return ( + onSelect?.(t)} + aria-current={active ? "true" : undefined} + className={`${ + onSelect ? "cursor-pointer hover:bg-slate-50" : "" + } ${active ? "bg-slate-100 font-medium" : ""}`} + > + + + + + + ); + })} + +
thrreviewunlabel%
{t.toFixed(2)} + {review.toLocaleString()} + + {unlabel.toLocaleString()} + {pct}%
+
+ ); +} diff --git a/viewer/components/TriageCards.tsx b/viewer/components/TriageCards.tsx new file mode 100644 index 0000000..b33cdb5 --- /dev/null +++ b/viewer/components/TriageCards.tsx @@ -0,0 +1,168 @@ +import { useMemo } from "react"; +import type { ResultRow } from "@/lib/types"; + +/** + * Left-rail cards for the triage reporting tree: the Review / Unlabel split, a + * per-organization breakdown, and a triage-specific explainer. Mirrors + * MonitorCards structurally; "keep" maps to Review (>= threshold, needs a human) + * and "discard" to Unlabel (< threshold, auto false-positive). + */ +export function TriageCards({ + rows, + threshold, + selectedOrganization = "all", + onSelectOrganization, +}: { + rows: ResultRow[]; + threshold?: number | null; + selectedOrganization?: string; + onSelectOrganization?: (org: string) => void; +}) { + // Memoized on rows so re-renders for the live header value (threshold) don't + // re-scan all rows each tick. + const { review, unlabel, orgs } = useMemo(() => { + const review = rows.filter((r) => r.decision === "keep").length; + // Per-org breakdown (review/unlabel), sorted by total desc. + const orgMap = new Map(); + for (const r of rows) { + const org = r.organization_name ?? "—"; + const entry = orgMap.get(org) ?? { review: 0, unlabel: 0 }; + if (r.decision === "keep") entry.review++; + else entry.unlabel++; + orgMap.set(org, entry); + } + const orgs = [...orgMap.entries()].sort( + (a, b) => b[1].review + b[1].unlabel - (a[1].review + a[1].unlabel), + ); + return { review, unlabel: rows.length - review, orgs }; + }, [rows]); + + const pctOf = (n: number) => + rows.length ? Math.round((n / rows.length) * 100) : 0; + + return ( +
+
+
+ Backlog +
+
+ {rows.length} sequences + {threshold != null && ( + + · threshold {threshold} + + )} +
+
+ +
+
+
+ To Review +
+
+ {review} + {rows.length > 0 && ( + + {pctOf(review)}% + + )} +
+
+
+
+
+
+
+ Unlabel +
+
+ {unlabel} + {rows.length > 0 && ( + + {pctOf(unlabel)}% + + )} +
+
+
+
+
+
+ + {orgs.length > 0 && ( +
+
+ By organization +
+ {orgs.map(([org, counts]) => { + const total = counts.review + counts.unlabel; + const pct = total ? Math.round((counts.review / total) * 100) : 0; + const clickable = onSelectOrganization && org !== "—"; + const active = org === selectedOrganization; + return ( + + ); + })} +
+ )} + +
+ + How this view works + +
+

+ These are unannotated sequences pulled from the pyro-annotator + (read-only), each scored by the temporal model. +

+

+ To review = score ≥ + threshold: worth a human's eyes.{" "} + Unlabel = score < + threshold: almost certainly a false positive, queued to be marked{" "} + unlabeled in the annotator. +

+

+ Verdicts are the model's; the per-tube boxes and crops on the + right show why each sequence scored as it did. +

+
+
+
+ ); +} diff --git a/viewer/components/__tests__/ThresholdSweep.test.tsx b/viewer/components/__tests__/ThresholdSweep.test.tsx new file mode 100644 index 0000000..ff110d9 --- /dev/null +++ b/viewer/components/__tests__/ThresholdSweep.test.tsx @@ -0,0 +1,55 @@ +import { fireEvent, render, screen, within } from "@testing-library/react"; +import { describe, expect, it, vi } from "vitest"; +import { ThresholdSweep } from "@/components/ThresholdSweep"; +import type { ResultRow } from "@/lib/types"; + +const row = (probability: number | null): ResultRow => ({ + key: `k${probability}`, + source: "pyro-annotator", + label: "unknown", + decision: "keep", + outcome: "n/a", + score: probability, + probability, + triage_score: probability, + triage_bucket: "review", + num_tubes_kept: 1, + trigger_frame_index: 0, + organization_name: "adf", + camera_name: null, + started_at: null, +}); + +// probabilities: 0.05, 0.30, 0.40, 0.60, 0.95 (n=5) +const rows = [row(0.05), row(0.3), row(0.4), row(0.6), row(0.95)]; + +describe("ThresholdSweep", () => { + it("counts To Review (prob >= threshold) at each standard threshold", () => { + render(); + // threshold 0.35 -> review = 0.40, 0.60, 0.95 = 3, unlabel = 2 + const row035 = screen.getByText("0.35").closest("tr")!; + expect(within(row035).getByText("3")).toBeInTheDocument(); + expect(within(row035).getByText("2")).toBeInTheDocument(); + expect(within(row035).getByText("60%")).toBeInTheDocument(); + }); + + it("highlights the standard threshold nearest the current slider value", () => { + render(); + const row035 = screen.getByText("0.35").closest("tr")!; + expect(row035).toHaveAttribute("aria-current", "true"); + const row070 = screen.getByText("0.70").closest("tr")!; + expect(row070).not.toHaveAttribute("aria-current"); + }); + + it("calls onSelect with the row's threshold when a row is clicked", () => { + const onSelect = vi.fn(); + render(); + fireEvent.click(screen.getByText("0.50").closest("tr")!); + expect(onSelect).toHaveBeenCalledWith(0.5); + }); + + it("ignores null probabilities and renders without crashing when empty", () => { + render(); + expect(screen.getByText("Threshold sweep")).toBeInTheDocument(); + }); +}); diff --git a/viewer/components/__tests__/TriageCards.test.tsx b/viewer/components/__tests__/TriageCards.test.tsx new file mode 100644 index 0000000..2398c86 --- /dev/null +++ b/viewer/components/__tests__/TriageCards.test.tsx @@ -0,0 +1,67 @@ +import { render, screen } from "@testing-library/react"; +import { describe, expect, it } from "vitest"; +import { TriageCards } from "@/components/TriageCards"; +import type { ResultRow } from "@/lib/types"; + +const r = ( + key: string, + decision: ResultRow["decision"], + organization_name: string | null = null, +): ResultRow => ({ + key, + source: "pyro-annotator", + label: "unknown", + decision, + outcome: "n/a", + score: 1, + probability: 0.9, + triage_score: 0.9, + triage_bucket: decision === "keep" ? "review" : "unlabeled", + num_tubes_kept: 1, + trigger_frame_index: 0, + organization_name, + camera_name: null, + started_at: null, +}); + +const rows: ResultRow[] = [ + r("a", "keep", "adf"), + r("b", "keep", "adf"), + r("c", "discard", "adf"), + r("d", "keep", "sis-67"), + r("e", "discard", "sis-67"), + r("f", "discard", null), +]; + +describe("TriageCards", () => { + it("labels the buckets Review and Unlabel", () => { + render(); + // "Review"/"Unlabel" appear both as card labels and in the explainer text. + expect(screen.getAllByText("To Review").length).toBeGreaterThanOrEqual(1); + expect(screen.getAllByText("Unlabel").length).toBeGreaterThanOrEqual(1); + }); + + it("shows correct review (3) and unlabel (3) counts", () => { + render(); + expect(screen.getAllByText("3").length).toBeGreaterThanOrEqual(2); + }); + + it("renders the backlog total and threshold", () => { + render(); + expect(screen.getByText(/6 sequences/)).toBeInTheDocument(); + expect(screen.getByText(/threshold 0\.35/)).toBeInTheDocument(); + }); + + it("renders a per-org breakdown sorted by total desc (adf first)", () => { + render(); + const items = screen.getAllByText(/adf|sis-67/); + expect(items[0].textContent).toBe("adf"); + expect(items[1].textContent).toBe("sis-67"); + }); + + it("renders empty without crashing", () => { + render(); + expect(screen.getAllByText("To Review").length).toBeGreaterThanOrEqual(1); + expect(screen.getAllByText("Unlabel").length).toBeGreaterThanOrEqual(1); + }); +}); diff --git a/viewer/components/detail/DetailPanel.tsx b/viewer/components/detail/DetailPanel.tsx index e1f7887..4e90440 100644 --- a/viewer/components/detail/DetailPanel.tsx +++ b/viewer/components/detail/DetailPanel.tsx @@ -144,19 +144,32 @@ export function DetailPanel({
- {row.replayed_probability === undefined && ( - - )} + {/* Eval-only: triage rows are unlabeled (no ground truth), monitor + rows carry production's verdict — neither has a correctness. */} + {row.replayed_probability === undefined && + row.triage_bucket === undefined && ( + + )} {(row.replayed_probability === undefined || trig != null) && ( | null; calibrator?: unknown; + // Triage-only: the fixed triage split threshold written into model_config.json. + threshold?: number | null; }