diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index facd8b8..c89b360 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -11,7 +11,7 @@ jobs: strategy: fail-fast: false matrix: - package: [core, train, eval, api] + package: [core, train, eval, api, monitor] defaults: run: working-directory: ${{ matrix.package }} diff --git a/.gitignore b/.gitignore index 86a98a7..d9e25ae 100644 --- a/.gitignore +++ b/.gitignore @@ -28,3 +28,6 @@ api/models/ # brainstorming visual companion .superpowers/ + +# direnv credentials (per-package .envrc.example documents the variables) +.envrc diff --git a/Makefile b/Makefile index d14a180..a198010 100644 --- a/Makefile +++ b/Makefile @@ -1,4 +1,4 @@ -PACKAGES := core train eval api benchmark +PACKAGES := core train eval api benchmark monitor # 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 a716a8c..caf6c89 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,7 @@ through the full pipeline, with figures generated from real sequences. ## Packages -Five independent packages, each with its own `pyproject.toml` and `tests/`. +Six independent packages, each with its own `pyproject.toml` and `tests/`. | Path | Import | Purpose | |------|--------|---------| @@ -22,6 +22,7 @@ Five independent packages, each with its own `pyproject.toml` and `tests/`. | `eval/` | `temporal_model.eval` | DVC evaluation pipeline (packaged-model protocol metrics). Depends on `core`. | | `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. | `train`/`eval`/`api`/`benchmark` depend on `core` via a `uv` path source (`temporal-model-core = { path = "../core", editable = true }`). `core` and @@ -31,9 +32,9 @@ Five 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 five packages -make test # pytest across all five packages -make lint # ruff check across all five packages + docs/assets/scripts +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 ``` Per package, `cd && make install|lint|format|test`. diff --git a/docs/specs/2026-06-12-monitor-design.md b/docs/specs/2026-06-12-monitor-design.md new file mode 100644 index 0000000..74c9356 --- /dev/null +++ b/docs/specs/2026-06-12-monitor-design.md @@ -0,0 +1,325 @@ +# monitor/: Production Decision Replay & Viewer + +**Date:** 2026-06-12 +**Status:** Approved + +## Motivation + +The temporal model API is deployed and alert-api calls it on every sequence +needing validation. Production stores the lean verdict only — a probability +plus the `version` provenance block — so nobody can see *why* the model kept +or discarded a sequence: no tubes, no per-frame boxes, no trigger frame. + +`monitor/` answers "what did the model decide in production, and why?": it +imports recently scored sequences from alert-api, replays them through the +**exact** api + model release that scored them, captures the verbose tube +details, and serves them to the existing `viewer/`. + +## Decisions (agreed in brainstorming) + +1. **Replay through the pinned release Docker image, locally.** Each + sequence records `temporal_api_version` (alert-api now returns the real + tag, not `latest`). Release images on DockerHub + (`pyronear/temporal-model-api:`) bake both the serving code and the + exact `model.zip` (`api/Dockerfile` `COPY api/models/model.zip`), so one + `docker pull` reproduces code + weights. No load or auth against the + production instance; old versions stay replayable forever. + (Alternatives rejected: calling the deployed API directly — adds prod + load, needs the prod token, breaks on upgrade; running `core` directly — + matches the model but not the api code path.) +2. **Reuse the Next.js eval viewer, no new UI.** Monitor writes the exact + reporting contract `viewer/` already reads + (`results.json`, `details/.json`, `sequences/.json`, + `model_config.json` under + `data/08_reporting//vit_dinov2_finetune/`) and the viewer points + at it with `DATA_ROOT=../monitor`. Tube timeline, bbox overlay and + stabilized crops come for free. Monitor-specific columns are additive. +3. **Frames are served to the pinned image via MinIO, uniformly.** + Production sequences were scored by releases ≤ v0.3.1, which predate the + local-frames feature (PR #49) — and an old image *silently ignores* an + unknown `source: "local"` field and would hit S3 with those paths. A + monitor-owned compose stack (pinned api image + MinIO) with frames + uploaded under their original `bucket_key`s works identically against + every version: one code path. A mount-the-store fast path for ≥ #49 + releases is a possible follow-up, not v1. +4. **Always request `?verbose=true&compute_trigger=true`.** Releases with + PR #51 return `trigger_frame_index` / `trigger_tube_id` / + `first_crossing_frame`; older pinned images ignore the unknown query + param and the viewer renders "no trigger". Replay is offline, so the + trigger search's latency cost is irrelevant. +5. **`stabilized_window` is recomputed client-side.** The verbose response + does not include it. It is deterministic geometry (union of a tube's + observed bboxes), so monitor derives it when writing + `details/.json`, keeping the viewer's crop panel working for every + api version. +6. **Provenance and recorded score come from alert-api's `SequenceRead`**, + which now exposes `temporal_model_score`, `temporal_model_version` and + `temporal_api_version`. The recorded score doubles as a consistency + check against the replayed probability. +7. **Credentials via `.envrc` (direnv), like `api/`.** Monitor reads + `ALERT_API_URL`, `ALERT_API_LOGIN`, `ALERT_API_PASSWORD` from the + environment; a committed `.envrc.example` documents them and the real + `.envrc` stays untracked. +8. **DVC tracks both the store and the replay artifacts**, following the + `train/`/`eval/` setup exactly: a per-package DVC repo (`.dvc/config` + with `analytics = false` and an `s3remote` at + `s3://pyro-vision-rd/dvc/temporal-model/monitor/`) and Kedro-style + layer dirs. The sequence store is `dvc add`-tracked as + `data/01_raw/sequences.dvc` (imports extend it, then `dvc add` + + `dvc push`, like eval's `data/01_raw/pyro-annotator.dvc`); the replay + inference is a `dvc.yaml` stage whose outputs are **cached and + pushed** — a deliberate divergence from eval's `cache: false` + reporting outs, because sharing is the point: a teammate runs + `dvc pull` and opens the viewer with no alert-api credentials and no + Docker. Import itself stays a CLI command outside the pipeline (it + talks to a live API and is append-only, not reproducible). +9. **No `core` dependency.** Monitor talks HTTP and JSON and does small + geometry; pulling torch/ultralytics for that would be waste. + +## Architecture + +A sixth uv package, `temporal_model.monitor`, exposing a `temporal-monitor` +CLI with two commands forming a pipeline, plus the existing viewer: + +``` +alert-api ──import──▶ data/01_raw/sequences/ ──dvc repro──▶ data/08_reporting/ + (frames + meta.json, (replay (eval-viewer contract, + dvc add + push) stage) cached outs, dvc push) + │ + viewer/ (DATA_ROOT=../monitor) +``` + +``` +monitor/ +├── pyproject.toml # uv package: temporal-model-monitor +├── Makefile # install | lint | format | test (mirrors siblings) +├── README.md +├── .envrc.example # ALERT_API_URL / ALERT_API_LOGIN / ALERT_API_PASSWORD +├── .dvc/config # s3remote, like train/ and eval/ +├── .dvcignore +├── dvc.yaml # replay stage (see below) +├── docker-compose.yml # pinned api image + MinIO (no build:) +├── src/temporal_model/monitor/ +│ ├── cli.py # temporal-monitor import|replay +│ ├── alert_api.py # login, sequences, detections client +│ ├── store.py # sequence store read/write (meta.json) +│ ├── reconstruct.py # frame selection + ROI, mirroring pyro-api +│ ├── stack.py # compose lifecycle, MinIO upload, health checks +│ ├── replay.py # /predict calls + consistency checks +│ ├── geometry.py # stabilized_window recompute +│ └── report.py # eval-viewer contract writers +├── tests/ +└── data/ # DVC-managed (gitignored content) + ├── 01_raw/sequences///seq_/{meta.json, images/} + ├── 01_raw/sequences.dvc + └── 08_reporting/alert-api/vit_dinov2_finetune/... +``` + +The replay stage in `dvc.yaml`, in the house style (explicit code deps, +`uv run python -m` cmd): + +```yaml +stages: + replay: + cmd: >- + uv run python -m temporal_model.monitor.cli replay + --store data/01_raw/sequences + --output-dir data/08_reporting + --trigger-image temporal-model-api:dev + deps: + - src/temporal_model/monitor/cli.py + - src/temporal_model/monitor/store.py + - src/temporal_model/monitor/reconstruct.py + - src/temporal_model/monitor/stack.py + - src/temporal_model/monitor/replay.py + - src/temporal_model/monitor/geometry.py + - src/temporal_model/monitor/report.py + - docker-compose.yml + - data/01_raw/sequences + outs: + - data/08_reporting # cached → dvc push shares it +``` + +## `temporal-monitor import` + +Modeled on vision-rd's `temporal-model-explorer` import, modernized: + +- Login: `POST /api/v1/login/creds` → bearer token. +- Sequences: `GET /api/v1/sequences/all/fromdate?from_date=...&limit=100&offset=...` + for each day in `--date-from/--date-to` (default: yesterday..today). +- Per sequence: `GET /api/v1/sequences/{id}/detections?limit=100&desc=false (paginated until a short page)`, + download each detection's full frame via its presigned `url`, write + `images/detection_.jpg`. +- **Incremental:** a sequence already present in the store is skipped + (`--force` re-downloads). The explorer re-downloaded everything; monitor + runs recurringly. +- After an import, the store is synced with + `dvc add data/01_raw/sequences && dvc push` (a `make import` convenience + target wraps import + add + push). + +`meta.json` extends the explorer's schema with everything replay needs: + +```json +{ + "key": "alert-api_42307", + "sequence_id": 42307, + "source": "alert-api", + "label": "smoke" | "fp" | "unknown", + "label_detail": "wildfire_smoke" | "other_smoke" | "other" | null, + "camera_id": 122, "camera_name": "...", "organization_id": 11, + "organization_name": "...", "started_at": "...", + "temporal_model_score": 0.9867, + "temporal_model_version": "0.1.0", + "temporal_api_version": "0.3.1", + "frames": [ + { + "file": "images/detection_14549.jpg", + "detection_id": 14549, + "created_at": "...", + "bucket_key": "", + "bboxes": "" + } + ] +} +``` + +Label mapping is fixed (no config): `is_wildfire` ∈ +{`wildfire_smoke`, `other_smoke`} → `smoke`; `other` → `fp`; `null` → +`unknown`. + +## `temporal-monitor replay` + +Runs as the `replay` stage via `dvc repro` (directly invocable too); a +store change (new import) makes the stage stale, `dvc repro` re-runs it, +`dvc push` shares the artifacts. + +1. **Group** stored sequences by `temporal_api_version`. Sequences with a + null version (never scored, or pre-provenance) go to `dropped.json` with + a reason; so do groups whose image tag cannot be pulled. +2. **Per group:** `docker pull pyronear/temporal-model-api:`, + `docker compose up` (api + MinIO), wait for `GET /health` to report + `model_loaded: true`. +3. **Sanity check:** `/health.model_version` must equal each sequence's + recorded `temporal_model_version`; a mismatch drops the sequence with + reason `model_version_mismatch` (the image's baked model is not the one + that scored it — should not happen, must not pass silently). +4. **Upload** the sequence's frames to MinIO under their original + `bucket_key`s. +5. **Reconstruct the production call** exactly as alert-api's validation + worker does (`pyro-api/src/app/services/validation.py`): distinct + `bucket_key`s ordered by `created_at` ascending, truncated to the **last + 10**; skip (→ `dropped.json`) if fewer than 4 distinct frames; + `roi_xyxyn` = union envelope of the kept detections' primary bboxes, + `null` if none parse. The reconstruction logic mirrors pyro-api and is + fixture-tested against it. +6. **Call** `POST /predict?verbose=true&compute_trigger=true` with + `{bucket, frames, roi_xyxyn}`. +7. **Consistency check:** replayed `probability` vs recorded + `temporal_model_score` (tolerance 1e-5: cross-hardware float noise). Recorded per sequence as + `replay_matches`; mismatches are summarized at the end of the run. + Known, accepted limitation: the detection set may have grown after the + last production scoring, so reconstruction can legitimately differ — + `replay_matches` makes that visible instead of silent. +8. **Write the reporting tree** — a single source named `alert-api` + containing all organizations' rows (superseded per-org design after live + use; see "Source naming in the viewer" below): + - `results.json` — eval columns (`key`, `source`, `label`, `decision`, + `outcome`, `score`, `probability`, `num_tubes_kept`, + `trigger_frame_index`, `organization_name`, `camera_name`, + `started_at`) carrying production's verdict (`decision`, + `probability` = recorded score) plus monitor extras: + `replayed_probability`, `replayed_decision`, `replay_matches`, + `matched_window_frames`, `temporal_model_version`, + `temporal_api_version`. + `source` is always the fixed slug `"alert-api"`; `organization_name` + carries the raw org name and is shown as a table column in the viewer. + - `details/.json` — verbose `details` reshaped to the eval + `BboxTubeDetails` shape (`tubes.kept`, `decision`, `preprocessing`), + with `stabilized_window` recomputed and trigger fields when the + pinned version returns them. + - `sequences/.json` — `SequenceView` with frame paths relative to + `monitor/` (e.g. + `data/01_raw/sequences///seq_/images/...`). + The store layout (per-org dirs) is unchanged; only the reporting + tree root changes. + - `model_config.json` — versions from `/health` + the decision block + from a verbose response (the full training config is not available + over HTTP; the viewer tolerates missing keys). + +Outcome mapping reuses eval's semantics: `smoke`+keep → kept-smoke, +`fp`+keep → kept-fp, etc.; `unknown` → n/a. + +## Source naming in the viewer + +*This section supersedes the per-org design originally sketched here.* + +All replayed sequences land in a single reporting tree at +`data/08_reporting/alert-api/vit_dinov2_finetune/`. The viewer's source +selector shows one entry ("alert-api") for monitor data instead of one +entry per organization. The `organization_name` field on each row carries +the raw org name and is rendered as a dedicated "organization" column in +the sequence table (shown only when the viewer is in monitor mode, detected +by the presence of `replayed_probability` on the rows). This design was +adopted after live use revealed that per-org sources added friction when +comparing sequences across organizations. + +Frame paths inside `sequences/.json` still point into the per-org +store dirs (`data/01_raw/sequences///...`); the store layout +is unchanged. + +## Viewing + +```bash +cd viewer && DATA_ROOT=../monitor npm run dev +``` + +No structural viewer changes beyond the organization column and slim filter +bar (outcome chips and GT dropdown hidden in monitor mode). Eval flows are +untouched. + +## Error handling + +- Every skipped sequence lands in `dropped.json` with a machine-readable + reason: `no_temporal_version`, `no_recorded_score`, `invalid_api_version`, + `image_pull_failed`, `stack_unhealthy`, `model_version_mismatch`, + `too_few_frames`, `no_images`, `predict_failed`. +- Import is resumable: a partially-downloaded sequence (images missing vs + meta) is re-fetched on the next run. +- Replay failures on one sequence (HTTP error, timeout) are logged, the + sequence is dropped with reason `predict_failed`, and the run continues. + +## Conventions + +- `monitor/` mirrors sibling packages: own `pyproject.toml` (uv, Python + 3.11+), `Makefile` with `install|lint|format|test`, `tests/`, README. +- Added to the root `Makefile` `PACKAGES` list and to the CI matrix in + `.github/workflows/ci.yml`. +- Dependencies: `requests`, `pydantic` (schemas), `dvc[s3]>=3.56` (same + pin as train/eval), `docker` via subprocess (no docker SDK). No torch, + no `core`. + +## Testing + +All tests run offline (mocked HTTP, no Docker): + +- `alert_api`: auth flow, pagination, endpoint URLs (mocked `requests`). +- `store`: meta.json round-trip, incremental skip, partial-download repair. +- `reconstruct`: frame selection (distinct, ordered, last-10, min-4) and + ROI envelope against fixtures mirroring pyro-api's validation worker + tests. +- `geometry`: stabilized_window union matches eval's values on a golden + details fixture. +- `report`: written artifacts validate against golden files copied from an + eval reporting tree (schema compatibility is the contract). +- `replay`: grouping, drop reasons, consistency check (stubbed predict). + +The compose stack itself is exercised by a documented manual e2e recipe in +the README (import one day, replay, open viewer), not CI. + +## Out of scope + +- Local-frames fast path (`source: "local"`) for ≥ PR #49 releases. +- Any api/ or core/ changes (e.g. adding `stabilized_window` to verbose). +- Aggregate monitoring dashboards (alerting, drift charts over time) — the + viewer's per-source performance cards are the v1 summary. +- pyro-annotator imports (eval already covers labeled offline data). diff --git a/monitor/.dvc/.gitignore b/monitor/.dvc/.gitignore new file mode 100644 index 0000000..20e0fb0 --- /dev/null +++ b/monitor/.dvc/.gitignore @@ -0,0 +1 @@ +/config.local diff --git a/monitor/.dvc/config b/monitor/.dvc/config new file mode 100644 index 0000000..8597384 --- /dev/null +++ b/monitor/.dvc/config @@ -0,0 +1,5 @@ +[core] + remote = s3remote + analytics = false +['remote "s3remote"'] + url = s3://pyro-vision-rd/dvc/temporal-model/monitor/ diff --git a/monitor/.dvcignore b/monitor/.dvcignore new file mode 100644 index 0000000..5197305 --- /dev/null +++ b/monitor/.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/monitor/.envrc.example b/monitor/.envrc.example new file mode 100644 index 0000000..ebf63a0 --- /dev/null +++ b/monitor/.envrc.example @@ -0,0 +1,5 @@ +# Copy to monitor/.envrc (untracked) and fill in; direnv loads it automatically. +# Credentials for the alert-api (the platform that calls /predict in production). +export ALERT_API_URL=https://alertapi.pyronear.org +export ALERT_API_LOGIN=changeme +export ALERT_API_PASSWORD=changeme diff --git a/monitor/Makefile b/monitor/Makefile new file mode 100644 index 0000000..94cab33 --- /dev/null +++ b/monitor/Makefile @@ -0,0 +1,18 @@ +.PHONY: install lint format test import + +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 + +import: ## import new sequences from alert-api, then dvc add + push the store + uv run temporal-monitor import $(ARGS) + uv run dvc add data/01_raw/sequences + uv run dvc push diff --git a/monitor/README.md b/monitor/README.md new file mode 100644 index 0000000..0589ae5 --- /dev/null +++ b/monitor/README.md @@ -0,0 +1,120 @@ +# 🔎🔥 monitor — production decision replay + +Answers "what did the temporal model decide in production, and why?". +Imports sequences scored by the deployed API (alert-api records the +probability + `version` provenance per sequence), replays them through the +**exact** pinned release image with `verbose=true&compute_trigger=true`, and +writes the eval-viewer contract so tubes/boxes/crops are explorable in +[`viewer/`](../viewer). + +Design: [`docs/specs/2026-06-12-monitor-design.md`](../docs/specs/2026-06-12-monitor-design.md) + +## Just browsing? Pull the shared data (no credentials, no Docker) + +The store and the replay artifacts are pushed to the DVC remote, so viewing +what production decided requires neither alert-api credentials nor Docker: + +```bash +make install # uv sync (brings dvc[s3]) +uv run dvc pull # fetch store + reports from S3 +cd ../viewer && DATA_ROOT=../monitor npm run dev # browse at localhost:3000 +``` + +Everything below is only for IMPORTING new sequences and RE-RUNNING the +model — the producer workflow. + +## Importing new data (alert-api credentials required) + +```bash +make install +cp .envrc.example .envrc # fill in alert-api credentials (direnv loads it) +``` + +```bash +make import # 1. fetch new sequences (incremental), + # dvc add + push the store +make import ARGS="--date-from 2026-06-01 --date-to 2026-06-10" # backfill a range +make import ARGS="--all-orgs --exclude-org pyroadmins" # admin token: every org, skipping CI org +``` + +The alert-api listing (`/sequences/all/fromdate`) is limited to the authenticated account's own +organization; `--all-orgs` scans the global sequence-id space instead (admin token required), so +every organization (sdis-07, sdis-77, ...) lands in the single `alert-api` +source, distinguishable via the organization column/filter in the viewer. +`--exclude-org` skips an organization (accepts a slug or raw name, e.g. `--exclude-org pyroadmins` +for the CI camera org); pass it multiple times to exclude several. Exclusion affects future imports +only — already-imported directories must be removed manually. + +## Replaying (Docker required) + +```bash +uv run dvc repro # 2. replay through pinned releases +uv run dvc push # 3. share store + artifacts so + # teammates can just `dvc pull` +cd ../viewer && DATA_ROOT=../monitor npm run dev # 4. browse at localhost:3000 +``` + +`replay` groups sequences by their recorded `temporal_api_version`, runs +`pyronear/temporal-model-api:` (model.zip baked in) + a throwaway MinIO +on ports 18000/19000 (offset from `api/`'s 8000/9000), uploads each +sequence's frames under their original S3 keys, and reconstructs the exact +production call (last ≤10 distinct frames oldest-first, ROI = envelope of +the detections' primary bboxes — mirrors pyro-api's validation worker). +Docker must be running; each version group costs one image pull. + +## Outputs (`data/08_reporting/alert-api/vit_dinov2_finetune/`) + +All organizations land in one reporting tree; organizations are a table column in the viewer. + +- `results.json` — eval columns carrying PRODUCTION's verdict (`decision`, + `probability` = the recorded score) + monitor extras: `replayed_probability` + / `replayed_decision` (the local re-run), `replay_matches` (|Δ| ≤ 1e-5), + `matched_window_frames` (when a mismatch is window drift: the sequence + length at which production's recorded score is reproduced; null = no window + matched, genuine drift), `temporal_model_version`, `temporal_api_version`. +- `details/.json` — tubes in the eval shape; `stabilized_window` is + recomputed client-side; trigger fields appear for releases shipping + `compute_trigger` (older images ignore the flag → "no trigger" in the + viewer). +- `sequences/.json`, `model_config.json`, `dropped.json` (skip reasons: + `no_temporal_version`, `no_recorded_score`, `invalid_api_version`, + `image_pull_failed`, `stack_unhealthy`, `model_version_mismatch`, + `too_few_frames`, `no_images`, `predict_failed`). + +A `replay_matches: false` row means the reconstruction diverged from the +recorded score. When this happens, the replay automatically probes candidate +windows (ascending distinct-frame counts from MIN_FRAMES) to check whether +the mismatch is window drift — production scores a sequence early and stops +once it validates, while detections keep arriving. If a shorter prefix of the +sequence reproduces the recorded score, `matched_window_frames` is set to +that count (window drift, not model drift); if no prefix matches, it remains +null (genuine drift — see the spec). + +## Trigger frames + +Sequences scored by api v0.3.1 and earlier predate the `compute_trigger` +feature, so their replays produce no `trigger_frame_index` or +`first_crossing_frame` data. The enrichment pass fills these fields using a +newer serving image (the same `model.zip` baked in) while keeping the pinned +replay authoritative for tubes and probability. + +Pass `--trigger-image ` to `replay` (or leave the default in dvc.yaml). +The enrichment probability must reproduce the pinned replay's within +`SCORE_TOLERANCE` (1e-5) — a disagreement means the scoring path changed and +the trigger fields are left empty for that sequence. + +Build the dev image from the repo root: + +```bash +make fetch-model MODEL_VERSION=0.2.0 # repo root: same model production runs +docker build -f api/Dockerfile --build-arg VERSION=dev -t temporal-model-api:dev . +``` + +Teammates who only need the artifacts can skip the build and use `dvc pull` +instead of `dvc repro`. + +## Tests + +```bash +make test # offline: mocked HTTP, fake docker stack — no Docker needed +``` diff --git a/monitor/data/01_raw/.gitkeep b/monitor/data/01_raw/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/monitor/data/01_raw/sequences.dvc b/monitor/data/01_raw/sequences.dvc new file mode 100644 index 0000000..c42e9e6 --- /dev/null +++ b/monitor/data/01_raw/sequences.dvc @@ -0,0 +1,6 @@ +outs: +- md5: 8b9d7b77dd2a2dacd4f67fe623c7f323.dir + size: 448152089 + nfiles: 3214 + hash: md5 + path: sequences diff --git a/monitor/docker-compose.yml b/monitor/docker-compose.yml new file mode 100644 index 0000000..cda07d4 --- /dev/null +++ b/monitor/docker-compose.yml @@ -0,0 +1,44 @@ +# Replay stack: the pinned release image (model.zip baked in) + throwaway MinIO. +# Image tag comes from MONITOR_API_IMAGE (set by `temporal-monitor replay` per +# api-version group). Ports are offset from api/docker-compose.yml so both +# stacks can run side by side. +services: + api: + image: ${MONITOR_API_IMAGE:?set by temporal-monitor replay} + ports: + - "18000:8000" + environment: + - TEMPORAL_API_S3_BUCKET=frames + - TEMPORAL_API_S3_ENDPOINT_URL=http://minio:9000 + - TEMPORAL_API_S3_REGION=us-east-1 + # local replay only — never reused for shared environments + - AWS_ACCESS_KEY_ID=minioadmin + - AWS_SECRET_ACCESS_KEY=minioadmin + depends_on: + minio: + condition: service_started + createbuckets: + condition: service_completed_successfully + + minio: + image: minio/minio:latest + command: server /data + ports: + - "19000:9000" + environment: + - MINIO_ROOT_USER=minioadmin + - MINIO_ROOT_PASSWORD=minioadmin + + # One-shot: wait for MinIO, then create the bucket the API reads from. + createbuckets: + image: minio/mc:latest + depends_on: + - minio + entrypoint: > + /bin/sh -c " + until mc alias set local http://minio:9000 minioadmin minioadmin; do + echo 'waiting for minio...'; sleep 1; + done; + mc mb --ignore-existing local/frames; + echo 'bucket frames ready'; + " diff --git a/monitor/dvc.lock b/monitor/dvc.lock new file mode 100644 index 0000000..2978224 --- /dev/null +++ b/monitor/dvc.lock @@ -0,0 +1,50 @@ +schema: '2.0' +stages: + replay: + cmd: uv run python -m temporal_model.monitor.cli replay --store + data/01_raw/sequences --output-dir data/08_reporting --trigger-image + temporal-model-api:dev + deps: + - path: data/01_raw/sequences + hash: md5 + md5: 8b9d7b77dd2a2dacd4f67fe623c7f323.dir + size: 448152089 + nfiles: 3214 + - path: docker-compose.yml + hash: md5 + md5: 985531d975ed5a5d3f2daf2ea5083d4a + size: 1403 + - path: src/temporal_model/monitor/cli.py + hash: md5 + md5: 1715ab3cbe191376fb8493d7928f7e63 + size: 4196 + - path: src/temporal_model/monitor/geometry.py + hash: md5 + md5: 7f811828367c67fd9115ad5e6b69956d + size: 1485 + - path: src/temporal_model/monitor/reconstruct.py + hash: md5 + md5: fa83924a8b64a59e2e9ba7ade4c601b4 + size: 3351 + - path: src/temporal_model/monitor/replay.py + hash: md5 + md5: 087c2ed380aff23a6032b45574a7edfd + size: 15782 + - path: src/temporal_model/monitor/report.py + hash: md5 + md5: 3726aeaa208d275c6e1f8100d329861e + size: 6579 + - path: src/temporal_model/monitor/stack.py + hash: md5 + md5: a46cd818f3301d3470e8cd3e3f9f8864 + size: 2955 + - path: src/temporal_model/monitor/store.py + hash: md5 + md5: 8ebb0731d13c1b9903e80b3cfb08b546 + size: 3363 + outs: + - path: data/08_reporting + hash: md5 + md5: 7b461fc059991bf2eb18fd7dff356982.dir + size: 342568 + nfiles: 185 diff --git a/monitor/dvc.yaml b/monitor/dvc.yaml new file mode 100644 index 0000000..a11c5d7 --- /dev/null +++ b/monitor/dvc.yaml @@ -0,0 +1,19 @@ +stages: + replay: + cmd: >- + uv run python -m temporal_model.monitor.cli replay + --store data/01_raw/sequences + --output-dir data/08_reporting + --trigger-image temporal-model-api:dev + deps: + - src/temporal_model/monitor/cli.py + - src/temporal_model/monitor/store.py + - src/temporal_model/monitor/reconstruct.py + - src/temporal_model/monitor/stack.py + - src/temporal_model/monitor/replay.py + - src/temporal_model/monitor/geometry.py + - src/temporal_model/monitor/report.py + - docker-compose.yml + - data/01_raw/sequences + outs: + - data/08_reporting # cached → dvc push shares it diff --git a/monitor/pyproject.toml b/monitor/pyproject.toml new file mode 100644 index 0000000..30e57b0 --- /dev/null +++ b/monitor/pyproject.toml @@ -0,0 +1,40 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "temporal-model-monitor" +version = "0.1.0" +description = "Production decision replay for the temporal smoke classifier: import scored sequences from alert-api, re-run them with verbose details through the exact pinned api+model release, view tubes in the eval viewer" +requires-python = ">=3.11" +dependencies = [ + "pydantic>=2.7", + "requests>=2.31", + "boto3>=1.34", +] + +[project.scripts] +temporal-monitor = "temporal_model.monitor.cli:main" + +[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/monitor/src/temporal_model/monitor/__init__.py b/monitor/src/temporal_model/monitor/__init__.py new file mode 100644 index 0000000..7fb41dc --- /dev/null +++ b/monitor/src/temporal_model/monitor/__init__.py @@ -0,0 +1 @@ +"""Production decision replay: alert-api import + pinned-release re-run.""" diff --git a/monitor/src/temporal_model/monitor/alert_api.py b/monitor/src/temporal_model/monitor/alert_api.py new file mode 100644 index 0000000..518bae1 --- /dev/null +++ b/monitor/src/temporal_model/monitor/alert_api.py @@ -0,0 +1,109 @@ +"""Thin alert-api HTTP client: login, sequences, detections, cameras, orgs. + +Pagination: the sequences/detections list endpoints page at 100 rows; helpers +loop with ``offset`` until a short page. ``/cameras/`` and ``/organizations/`` +return everything in one response (no limit param server-side). Detections are +fetched COMPLETELY and oldest first — production ROI reconstruction uses every +detection of a sequence (pyro-api ``validation._sequence_frames_and_roi`` runs +an unbounded ``fetch_all``), so a truncated import would change the replayed +call. +""" + +from __future__ import annotations + +import os +from dataclasses import dataclass +from typing import Any + +import requests + +PAGE_SIZE = 100 # detections endpoint enforces le=100; elsewhere just our batch size +TIMEOUT_S = 60 + + +@dataclass(frozen=True) +class AlertApiConfig: + url: str + login: str + password: str + + @classmethod + def from_env(cls) -> AlertApiConfig: + try: + return cls( + url=os.environ["ALERT_API_URL"].rstrip("/"), + login=os.environ["ALERT_API_LOGIN"], + password=os.environ["ALERT_API_PASSWORD"], + ) + except KeyError as exc: + raise SystemExit( + f"missing env var {exc.args[0]} (see monitor/.envrc.example)" + ) from exc + + +class AlertApiClient: + def __init__( + self, config: AlertApiConfig, session: requests.Session | None = None + ) -> None: + self.config = config + self.session = session if session is not None else requests.Session() + self.token: str | None = None + + def login(self) -> None: + resp = self.session.post( + f"{self.config.url}/api/v1/login/creds", + data={"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 _get_paginated(self, path: str, params: dict[str, Any]) -> list[dict]: + rows: list[dict] = [] + offset = 0 + while True: + page = self._get(path, {**params, "limit": PAGE_SIZE, "offset": offset}) + rows.extend(page) + if len(page) < PAGE_SIZE: + return rows + offset += PAGE_SIZE + + def list_sequences_for_date(self, day: str) -> list[dict]: + """All sequences started on ``day`` (YYYY-MM-DD). + + The server scopes results to the authenticated account's organization. + """ + return self._get_paginated("/api/v1/sequences/all/fromdate", {"from_date": day}) + + def list_sequence_detections(self, sequence_id: int) -> list[dict]: + """ALL detections of a sequence, oldest first (see module docstring).""" + return self._get_paginated( + f"/api/v1/sequences/{sequence_id}/detections", {"desc": False} + ) + + def list_cameras(self) -> list[dict]: + return self._get("/api/v1/cameras/", {"include_non_trustable": True}) + + def list_organizations(self) -> list[dict]: + return self._get("/api/v1/organizations/") + + def get_sequence(self, sequence_id: int) -> dict | None: + """One sequence by id (admin: any org), or None when it doesn't exist.""" + try: + return self._get(f"/api/v1/sequences/{sequence_id}") + except requests.HTTPError as exc: + if exc.response is not None and exc.response.status_code == 404: + return None + raise diff --git a/monitor/src/temporal_model/monitor/cli.py b/monitor/src/temporal_model/monitor/cli.py new file mode 100644 index 0000000..43d9cf1 --- /dev/null +++ b/monitor/src/temporal_model/monitor/cli.py @@ -0,0 +1,121 @@ +"""temporal-monitor CLI: import sequences from alert-api, replay them locally.""" + +from __future__ import annotations + +import argparse +import datetime as dt +import logging +from pathlib import Path + +DEFAULT_STORE = Path("data/01_raw/sequences") +DEFAULT_OUTPUT = Path("data/08_reporting") + + +def _parse_args(argv: list[str] | None = None) -> argparse.Namespace: + parser = argparse.ArgumentParser(prog="temporal-monitor", description=__doc__) + sub = parser.add_subparsers(dest="command", required=True) + + imp = sub.add_parser("import", help="fetch scored sequences from alert-api") + today = dt.date.today().isoformat() + yesterday = (dt.date.today() - dt.timedelta(days=1)).isoformat() + imp.add_argument("--date-from", default=yesterday, help="YYYY-MM-DD (inclusive)") + imp.add_argument("--date-to", default=today, help="YYYY-MM-DD (inclusive)") + imp.add_argument("--store", type=Path, default=DEFAULT_STORE) + imp.add_argument( + "--force", action="store_true", help="re-download already-stored sequences" + ) + imp.add_argument( + "--all-orgs", + action="store_true", + help="scan the global sequence-id space to import every organization " + "(admin token required); default imports only the account's own org", + ) + imp.add_argument( + "--seed-id", + type=int, + default=None, + help="recent sequence id to seed the --all-orgs scan (only needed on " + "an empty store when the own-org listing is empty)", + ) + imp.add_argument( + "--exclude-org", + action="append", + metavar="ORG", + default=None, + help="organization to skip (slug or name); repeatable", + ) + + rep = sub.add_parser( + "replay", help="re-run stored sequences through their pinned api release" + ) + rep.add_argument("--store", type=Path, default=DEFAULT_STORE) + rep.add_argument("--output-dir", type=Path, default=DEFAULT_OUTPUT) + rep.add_argument( + "--compose-file", + type=Path, + # cli.py -> monitor(pkg) -> temporal_model -> src -> monitor/ root + default=Path(__file__).resolve().parents[3] / "docker-compose.yml", + ) + rep.add_argument( + "--trigger-image", + default=None, + help=( + "newer serving image used ONLY to compute trigger fields (same model.zip); " + "merged only when its probability reproduces the pinned replay's" + ), + ) + return parser.parse_args(argv) + + +def main(argv: list[str] | None = None) -> None: + logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s") + args = _parse_args(argv) + if args.command == "import": + # Imported lazily so `replay` does not need alert-api credentials. + from temporal_model.monitor.alert_api import ( # noqa: PLC0415 + AlertApiClient, + AlertApiConfig, + ) + from temporal_model.monitor.import_alert_api import ( # noqa: PLC0415 + import_alert_api, + import_all_orgs, + ) + from temporal_model.monitor.store import slugify # noqa: PLC0415 + + exclude_orgs = ( + {slugify(o) for o in args.exclude_org} if args.exclude_org else None + ) + client = AlertApiClient(AlertApiConfig.from_env()) + client.login() + if args.all_orgs: + import_all_orgs( + client, + args.store, + args.date_from, + args.date_to, + force=args.force, + seed_id=args.seed_id, + exclude_orgs=exclude_orgs, + ) + else: + import_alert_api( + client, + args.store, + args.date_from, + args.date_to, + force=args.force, + exclude_orgs=exclude_orgs, + ) + elif args.command == "replay": + from temporal_model.monitor.replay import run_replay # noqa: PLC0415 + + run_replay( + store_dir=args.store, + output_dir=args.output_dir, + compose_file=args.compose_file, + trigger_image=args.trigger_image, + ) + + +if __name__ == "__main__": + main() diff --git a/monitor/src/temporal_model/monitor/geometry.py b/monitor/src/temporal_model/monitor/geometry.py new file mode 100644 index 0000000..50d4c71 --- /dev/null +++ b/monitor/src/temporal_model/monitor/geometry.py @@ -0,0 +1,35 @@ +"""Stabilized crop window, recomputed from verbose-response tube entries. + +Port of ``core/src/temporal_model/core/stabilize.py`` (the API's verbose +details omit ``stabilized_window``; the viewer's crop panel needs it). Same +policy: union of the tube's observed (non-gap) boxes, falling back to the +union of all available boxes; None when the tube has no usable detection. +Operates on the API's entry dicts rather than core dataclasses. +""" + +from __future__ import annotations + +Box = tuple[float, float, float, float] # normalized (cx, cy, w, h) + + +def union_window(boxes: list[Box]) -> Box: + """Enclosing box of ``boxes``; raises ValueError on empty input.""" + if not boxes: + raise ValueError("union_window requires at least one box") + x0 = min(cx - w / 2 for cx, _, w, _ in boxes) + y0 = min(cy - h / 2 for _, cy, _, h in boxes) + x1 = max(cx + w / 2 for cx, _, w, _ in boxes) + y1 = max(cy + h / 2 for _, cy, _, h in boxes) + return (x0 + x1) / 2, (y0 + y1) / 2, x1 - x0, y1 - y0 + + +def tube_stabilized_window(entries: list[dict]) -> Box | None: + """Fixed crop window for one verbose-response tube, or None.""" + boxes = [ + (tuple(e["bbox"]) if e.get("bbox") is not None else None, bool(e["is_gap"])) + for e in entries + ] + available = [b for b, _ in boxes if b is not None] + observed = [b for b, is_gap in boxes if b is not None and not is_gap] + chosen = observed or available + return union_window(chosen) if chosen else None diff --git a/monitor/src/temporal_model/monitor/import_alert_api.py b/monitor/src/temporal_model/monitor/import_alert_api.py new file mode 100644 index 0000000..3de35ee --- /dev/null +++ b/monitor/src/temporal_model/monitor/import_alert_api.py @@ -0,0 +1,284 @@ +"""Import scored sequences (frames + provenance) from alert-api into the store. + +Incremental by design: a sequence already in the store is skipped, so a +recurring import only pays for new sequences. Detections arrive oldest first +and are stored one FrameMeta per detection (a bucket_key can repeat when +several detections share a frame; replay deduplicates, mirroring production). +""" + +from __future__ import annotations + +import datetime as dt +import logging +import shutil +import time +from collections.abc import Callable +from pathlib import Path + +import requests + +from temporal_model.monitor.store import ( + IMAGES_DIR, + FrameMeta, + SequenceMeta, + find_sequence_dirs, + iter_metas, + label_from_is_wildfire, + sequence_dir, + sequence_exists, + slugify, + write_meta, +) + +logger = logging.getLogger(__name__) + + +DOWNLOAD_ATTEMPTS = 3 + + +def _default_download(url: str) -> bytes: + """Fetch one frame; retries absorb transient S3/CDN 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 _date_range(day_from: str, day_to: str) -> list[str]: + start = dt.date.fromisoformat(day_from) + end = dt.date.fromisoformat(day_to) + return [ + (start + dt.timedelta(days=i)).isoformat() + for i in range((end - start).days + 1) + ] + + +def _camera_index(client) -> dict[int, dict]: + return {cam["id"]: cam for cam in client.list_cameras()} + + +def _org_names(client) -> dict[int, str]: + try: + return {org["id"]: org["name"] for org in client.list_organizations()} + except Exception: # noqa: BLE001 — org listing may need admin scope + logger.warning("organizations endpoint unavailable; using org- names") + return {} + + +def _org_name( + seq: dict, cameras: dict[int, dict], org_names: dict[int, str] +) -> str | None: + """Resolve the org name for a sequence (same logic used by _import_one).""" + camera = cameras.get(seq.get("camera_id")) or {} + org_id = camera.get("organization_id") + return org_names.get(org_id) or (f"org-{org_id}" if org_id is not None else None) + + +def import_alert_api( + client, + store_dir: Path, + day_from: str, + day_to: str, + *, + force: bool = False, + download: Callable[[str], bytes] = _default_download, + exclude_orgs: set[str] | None = None, +) -> dict[str, int]: + """Import all sequences in [day_from, day_to] (inclusive). Returns counts.""" + store_dir.mkdir(parents=True, exist_ok=True) + cameras = _camera_index(client) + org_names = _org_names(client) + imported = skipped = excluded = 0 + for day in _date_range(day_from, day_to): + for seq in client.list_sequences_for_date(day): + org_slug = slugify(_org_name(seq, cameras, org_names)) + if exclude_orgs and org_slug in exclude_orgs: + excluded += 1 + continue + if not force and sequence_exists(store_dir, seq["id"]): + skipped += 1 + continue + try: + _import_one(client, store_dir, seq, cameras, org_names, download) + except requests.RequestException: + logger.exception( + "import failed for sequence %s; will retry next run", seq["id"] + ) + continue + imported += 1 + logger.info( + "import done: %d imported, %d skipped, %d excluded", imported, skipped, excluded + ) + return {"imported": imported, "skipped": skipped, "excluded": excluded} + + +def _import_one( + client, + store_dir: Path, + seq: dict, + cameras: dict[int, dict], + org_names: dict[int, str], + download: Callable[[str], bytes], +) -> None: + dets = client.list_sequence_detections(seq["id"]) + dets = sorted(dets, key=lambda d: d["created_at"]) + camera = cameras.get(seq.get("camera_id")) or {} + org_id = camera.get("organization_id") + label, label_detail = label_from_is_wildfire(seq.get("is_wildfire")) + meta = SequenceMeta( + key=f"alert-api_{seq['id']}", + sequence_id=seq["id"], + label=label, + label_detail=label_detail, + camera_id=seq.get("camera_id"), + camera_name=camera.get("name"), + organization_id=org_id, + organization_name=_org_name(seq, cameras, org_names), + started_at=seq.get("started_at"), + temporal_model_score=seq.get("temporal_model_score"), + temporal_model_version=seq.get("temporal_model_version"), + temporal_api_version=seq.get("temporal_api_version"), + frames=[ + FrameMeta( + file=f"{IMAGES_DIR}/detection_{d['id']}.jpg", + detection_id=d["id"], + created_at=d["created_at"], + bucket_key=d["bucket_key"], + bbox=d.get("bbox") or "", + ) + for d in dets + ], + ) + seq_dir = sequence_dir(store_dir, meta) + # Remove stale dirs from a previous import under a different org/camera name. + for stale in find_sequence_dirs(store_dir, seq["id"]): + if stale != seq_dir: + shutil.rmtree(stale) + images_dir = seq_dir / IMAGES_DIR + images_dir.mkdir(parents=True, exist_ok=True) + for det in dets: + (images_dir / f"detection_{det['id']}.jpg").write_bytes(download(det["url"])) + # meta.json last: its presence marks the sequence complete, so a crashed + # download is re-fetched (not skipped) on the next run. + write_meta(seq_dir, meta) + + +HEAD_GAP = 25 # consecutive missing IDs that mean "past the newest sequence" +OLDER_STOP = 25 # consecutive too-old sequences that mean "past the date range" +# Mid-walk 404 holes (deleted sequences) are a different concern from head +# detection: a bulk deletion can leave a far wider gap than HEAD_GAP without +# meaning the scan is done — only the date rule ends the downward walk. +WALK_GAP = 500 + + +def import_all_orgs( + client, + store_dir: Path, + day_from: str, + day_to: str, + *, + force: bool = False, + seed_id: int | None = None, + download: Callable[[str], bytes] = _default_download, + exclude_orgs: set[str] | None = None, +) -> dict[str, int]: + """Import sequences of EVERY organization by scanning the global ID space. + + The listing endpoint is locked to the token's organization, but an admin + token can read any sequence by id and ids are a global autoincrement, + roughly monotone with started_at. Strategy: seed from the store's max id + (or the own-org listing, or --seed-id), probe upward until HEAD_GAP + consecutive 404s (the head), then walk downward importing sequences whose + started_at date falls within [day_from, day_to], stopping after + OLDER_STOP consecutive sequences older than day_from. 404 holes (deleted + sequences) are skipped in both directions. + """ + store_dir.mkdir(parents=True, exist_ok=True) + seed = ( + seed_id or _max_store_id(store_dir) or _max_listed_id(client, day_from, day_to) + ) + if seed is None: + raise SystemExit( + "cannot seed the id scan: store is empty and the account's own-org " + "listing returned nothing — pass --seed-id " + ) + head = _find_head(client, seed) + cameras = _camera_index(client) + org_names = _org_names(client) + imported = skipped = excluded = 0 + misses = older = 0 + sid = head + while sid > 0 and misses < WALK_GAP and older < OLDER_STOP: + seq = client.get_sequence(sid) + sid -= 1 + if seq is None: + misses += 1 + continue + misses = 0 + day = (seq.get("started_at") or "")[:10] + if day > day_to: + # ids are only roughly monotone with started_at: a newer-than-range + # sequence here breaks the "consecutive older" streak too + older = 0 + continue + if day < day_from: + older += 1 + continue + older = 0 + if exclude_orgs and slugify(_org_name(seq, cameras, org_names)) in exclude_orgs: + excluded += 1 + continue + if not force and sequence_exists(store_dir, seq["id"]): + skipped += 1 + continue + try: + _import_one(client, store_dir, seq, cameras, org_names, download) + except requests.RequestException: + logger.exception( + "import failed for sequence %s; will retry next run", seq["id"] + ) + continue + imported += 1 + logger.info( + "scan import done: %d imported, %d skipped, %d excluded", + imported, + skipped, + excluded, + ) + return {"imported": imported, "skipped": skipped, "excluded": excluded} + + +def _max_store_id(store_dir: Path) -> int | None: + ids = [meta.sequence_id for _, meta in iter_metas(store_dir)] + return max(ids) if ids else None + + +def _max_listed_id(client, day_from: str, day_to: str) -> int | None: + ids = [ + s["id"] + for day in _date_range(day_from, day_to) + for s in client.list_sequences_for_date(day) + ] + return max(ids) if ids else None + + +def _find_head(client, seed: int) -> int: + """Highest existing sequence id at or above ``seed``.""" + head = seed + misses = 0 + sid = seed + 1 + while misses < HEAD_GAP: + if client.get_sequence(sid) is None: + misses += 1 + else: + head = sid + misses = 0 + sid += 1 + return head diff --git a/monitor/src/temporal_model/monitor/reconstruct.py b/monitor/src/temporal_model/monitor/reconstruct.py new file mode 100644 index 0000000..f597fc6 --- /dev/null +++ b/monitor/src/temporal_model/monitor/reconstruct.py @@ -0,0 +1,91 @@ +"""Reconstruct the exact /predict call alert-api made for a sequence. + +Line-for-line port of pyro-api's ``validation._sequence_frames_and_roi`` +(pyro-api ``src/app/services/validation.py``) and its bbox parsing +(``src/app/api/api_v1/endpoints/detections.py`` + ``schemas/detections.py``): +distinct bucket_keys oldest first, truncated to the most recent MAX_FRAMES; +ROI = union envelope of the kept detections' PRIMARY bboxes (the first box in +each detection's bbox string), clamped to [0, 1], None when nothing parses or +the envelope is degenerate. Keep this in sync with pyro-api — parity is the +whole point of replay. + +Known, accepted limit (spec): detections that arrived after the last +production scoring can shift the reconstruction; the replay_matches check +makes that visible. +""" + +from __future__ import annotations + +import re +from ast import literal_eval + +from temporal_model.monitor.store import FrameMeta + +# pyro-api src/app/services/temporal.py +MIN_FRAMES = 4 +MAX_FRAMES = 10 + +# pyro-api src/app/schemas/detections.py (verbatim) +FLOAT_PATTERN = r"(0?\.[0-9]{1,3}|0|1)" +BOX_PATTERN = ( + rf"\({FLOAT_PATTERN},{FLOAT_PATTERN},{FLOAT_PATTERN}," + rf"{FLOAT_PATTERN},{FLOAT_PATTERN}\)" +) + + +def extract_bbox_strings(bboxes: str) -> list[str]: + return [match.group(0) for match in re.finditer(BOX_PATTERN, bboxes)] + + +def parse_bbox(bbox_str: str) -> tuple[float, float, float, float, float] | None: + """Parse one '(xmin,ymin,xmax,ymax,conf)' string; None when malformed. + + pyro-api raises HTTP 422 here; the worker catches it and skips the bbox — + returning None reproduces the skip without the exception plumbing. + """ + try: + bbox = literal_eval(bbox_str) + except (SyntaxError, ValueError): + return None + if not isinstance(bbox, tuple) or len(bbox) != 5: + return None + return bbox + + +def frames_and_roi( + frames: list[FrameMeta], last_n: int | None = MAX_FRAMES +) -> tuple[int, list[str], list[float] | None]: + """(total_distinct, kept_frame_keys, roi_xyxyn) for a stored sequence. + + ``frames`` must be ordered by created_at ascending (the store writes them + that way), matching the worker's ``order_by="created_at"`` fetch. + """ + ordered: list[str] = [] + seen: set[str] = set() + corners_by_frame: dict[str, list[tuple[float, float, float, float]]] = {} + for f in frames: + if f.bucket_key not in seen: + seen.add(f.bucket_key) + ordered.append(f.bucket_key) + bbox_strs = extract_bbox_strings(f.bbox) + if bbox_strs: + parsed = parse_bbox(bbox_strs[0]) + if parsed is not None: + xmin, ymin, xmax, ymax, _ = parsed + corners_by_frame.setdefault(f.bucket_key, []).append( + (xmin, ymin, xmax, ymax) + ) + total = len(ordered) + kept = ordered if last_n is None or total <= last_n else ordered[-last_n:] + corners = [c for fr in kept for c in corners_by_frame.get(fr, [])] + if not corners: + return total, kept, None + roi = [ + max(0.0, min(c[0] for c in corners)), + max(0.0, min(c[1] for c in corners)), + min(1.0, max(c[2] for c in corners)), + min(1.0, max(c[3] for c in corners)), + ] + if not (roi[0] < roi[2] and roi[1] < roi[3]): + return total, kept, None + return total, kept, roi diff --git a/monitor/src/temporal_model/monitor/replay.py b/monitor/src/temporal_model/monitor/replay.py new file mode 100644 index 0000000..dcab93f --- /dev/null +++ b/monitor/src/temporal_model/monitor/replay.py @@ -0,0 +1,410 @@ +"""Replay stored sequences through their pinned api release, write reports. + +Flow: group sequences by recorded temporal_api_version -> one compose stack +per group (image tag == version; model.zip is baked into the image) -> verify +/health model_version matches each sequence's recorded one -> upload frames +under their original bucket_keys -> POST /predict?verbose=true& +compute_trigger=true (older releases ignore the unknown params; the trigger +fields are then simply absent) -> compare the replayed probability to the +recorded score -> write one eval-viewer tree per organization. +""" + +from __future__ import annotations + +import logging +import re +import subprocess +from collections.abc import Callable +from pathlib import Path +from typing import Any, Protocol + +import requests + +from temporal_model.monitor import reconstruct +from temporal_model.monitor.report import ( + MODEL_DIR, + OrgReport, + reshape_details, + result_row, + write_report, +) +from temporal_model.monitor.stack import API_URL, BUCKET, ReplayStack, StackError +from temporal_model.monitor.store import SequenceMeta, iter_metas, slugify + +logger = logging.getLogger(__name__) + +# Production and replay run on different hardware; identical inputs reproduce +# to ~1e-6 (observed cross-CPU float noise), while any behavioral difference +# (other frames, other tubes) moves the probability by >=1e-2. +SCORE_TOLERANCE = 1e-5 +STORE_REL = "data/01_raw/sequences" # viewer frame paths are relative to monitor/ +# Docker image tag charset (no slashes, colons or @ — see run_replay's guard). +_TAG_RE = re.compile(r"^[A-Za-z0-9][A-Za-z0-9._-]{0,127}$") + + +class _StackFactory(Protocol): + def __call__( + self, compose_file: Path, version: str, image: str | None = None + ) -> Any: ... + + +def _default_predict(frames: list[str], roi_xyxyn: list[float] | None) -> dict: + body: dict[str, Any] = {"bucket": BUCKET, "frames": frames} + if roi_xyxyn is not None: + body["roi_xyxyn"] = roi_xyxyn + resp = requests.post( + f"{API_URL}/predict", + params={"verbose": "true", "compute_trigger": "true"}, + json=body, + timeout=600, + ) + resp.raise_for_status() + return resp.json() + + +SOURCE_SLUG = "alert-api" + + +def _org_report(reports: dict[str, OrgReport], _meta: SequenceMeta) -> OrgReport: + if SOURCE_SLUG not in reports: + reports[SOURCE_SLUG] = OrgReport(org_slug=SOURCE_SLUG) + return reports[SOURCE_SLUG] + + +def _files_by_key( + seq_dir: Path, meta: SequenceMeta, kept: list[str] +) -> dict[str, Path]: + """First stored file per kept bucket_key (several detections may share one).""" + files: dict[str, Path] = {} + for f in meta.frames: + if f.bucket_key in kept and f.bucket_key not in files: + files[f.bucket_key] = seq_dir / f.file + return files + + +def run_replay( + *, + store_dir: Path, + output_dir: Path, + compose_file: Path, + stack_factory: _StackFactory = ReplayStack, + predict: Callable[[list[str], list[float] | None], dict] = _default_predict, + trigger_image: str | None = None, +) -> dict[str, int]: + reports: dict[str, OrgReport] = {} + groups: dict[str, list[tuple[Path, SequenceMeta]]] = {} + replayed = mismatched = window_drift = dropped = 0 + + for seq_dir, meta in iter_metas(store_dir): + if not meta.temporal_api_version: + _org_report(reports, meta).drop(meta.key, "no_temporal_version") + dropped += 1 + continue + # The version becomes a Docker image tag; a malformed value (slashes, + # "@sha256:", registry prefixes) must never redirect the pull. + if not _TAG_RE.match(meta.temporal_api_version): + logger.warning( + "%s: %r is not a valid image tag", meta.key, meta.temporal_api_version + ) + _org_report(reports, meta).drop(meta.key, "invalid_api_version") + dropped += 1 + continue + groups.setdefault(meta.temporal_api_version, []).append((seq_dir, meta)) + + for version in sorted(groups): + items = groups[version] + logger.info( + "replaying %d sequence(s) against api version %s", len(items), version + ) + stack = stack_factory(compose_file, version) + try: + stack.up() + except subprocess.CalledProcessError: + logger.error("could not start image for api version %s", version) + for _, meta in items: + _org_report(reports, meta).drop(meta.key, "image_pull_failed") + dropped += 1 + continue + try: + try: + health = stack.wait_healthy() + except StackError: + logger.exception("api for version %s never became healthy", version) + for _, meta in items: + _org_report(reports, meta).drop(meta.key, "stack_unhealthy") + dropped += 1 + continue + for seq_dir, meta in items: + outcome = _replay_one(stack, health, seq_dir, meta, reports, predict) + if outcome == "ok": + replayed += 1 + elif outcome == "mismatch": + replayed += 1 + mismatched += 1 + elif outcome == "window_drift": + replayed += 1 + mismatched += 1 + window_drift += 1 + else: + dropped += 1 + finally: + stack.down() + + trigger_enriched = 0 + if trigger_image is not None and any(r.rows for r in reports.values()): + trigger_enriched, _ = _enrich_triggers( + reports, store_dir, compose_file, stack_factory, predict, trigger_image + ) + + for report in reports.values(): + write_report(output_dir, report) + summary = { + "replayed": replayed, + "mismatched": mismatched, + "window_drift": window_drift, + "dropped": dropped, + "trigger_enriched": trigger_enriched, + } + logger.info( + "replay done: %(replayed)d replayed (%(mismatched)d score mismatches, " + "%(window_drift)d window drift), %(dropped)d dropped, " + "%(trigger_enriched)d trigger-enriched", + summary, + ) + return summary + + +def _enrich_triggers( + reports: dict[str, OrgReport], + store_dir: Path, + compose_file: Path, + stack_factory: _StackFactory, + predict: Callable[[list[str], list[float] | None], dict], + trigger_image: str, +) -> tuple[int, int]: + """Second pass with a newer serving build: fill the trigger fields. + + The pinned replay stays authoritative for tubes/probability; the trigger + search just needs serving code >= the compute_trigger feature. Fields are + merged ONLY when the enrichment probability reproduces the pinned replay's + (same window, same model.zip) — otherwise the sequence keeps no trigger. + Returns (enriched, skipped). + """ + # Build a lookup from meta.key -> (seq_dir, meta) for all sequences in the store. + meta_by_key: dict[str, tuple[Path, SequenceMeta]] = { + meta.key: (seq_dir, meta) for seq_dir, meta in iter_metas(store_dir) + } + + # There is only one report (alert-api). + report = next(iter(reports.values())) + rows = report.rows + if not rows: + return 0, 0 + + enriched = skipped = 0 + # Use a placeholder version for labeling; image is what matters. + stack = stack_factory(compose_file, "trigger-enrichment", image=trigger_image) + try: + stack.up() + try: + stack.wait_healthy() + except StackError: + logger.exception("trigger-enrichment stack never became healthy") + return 0, len(rows) + for row in rows: + key = row["key"] + entry = meta_by_key.get(key) + if entry is None: + logger.warning( + "trigger enrichment: meta not found for %s, skipping", key + ) + skipped += 1 + continue + seq_dir, meta = entry + _, kept, roi = reconstruct.frames_and_roi(meta.frames) + files = _files_by_key(seq_dir, meta, kept) + try: + stack.upload_frames(files) + resp = predict(kept, roi) + except Exception: # noqa: BLE001 + logger.warning( + "trigger enrichment: predict failed for %s, skipping", key + ) + skipped += 1 + continue + enr_prob = resp.get("probability") + pinned_prob = row.get("replayed_probability") + if ( + enr_prob is None + or pinned_prob is None + or abs(enr_prob - pinned_prob) > SCORE_TOLERANCE + ): + logger.warning( + "trigger enrichment: probability mismatch for %s " + "(pinned=%.6f, enrichment=%.6f), skipping trigger fields", + key, + pinned_prob if pinned_prob is not None else float("nan"), + enr_prob if enr_prob is not None else float("nan"), + ) + skipped += 1 + continue + # Merge trigger fields into the row and its details. + row["trigger_frame_index"] = resp.get("trigger_frame_index") + enr = reshape_details(resp["details"]) + details = report.details_by_key[key] + details["decision"]["trigger_tube_id"] = enr["decision"]["trigger_tube_id"] + # Build tube_id -> first_crossing_frame from enrichment kept tubes. + fcf_by_id = { + t["tube_id"]: t.get("first_crossing_frame") + for t in enr["tubes"]["kept"] + } + for tube in details["tubes"]["kept"]: + if tube["tube_id"] in fcf_by_id: + tube["first_crossing_frame"] = fcf_by_id[tube["tube_id"]] + # Record which image was used for enrichment (once per report). + if ( + report.model_config is not None + and "trigger_image" not in report.model_config + ): # noqa: E501 + report.model_config["trigger_image"] = trigger_image + enriched += 1 + except subprocess.CalledProcessError: + logger.error("could not start trigger-enrichment stack (%s)", trigger_image) + return 0, len(rows) + finally: + stack.down() + + logger.info("trigger enrichment: %d enriched, %d skipped", enriched, skipped) + return enriched, skipped + + +def _find_matching_window( + stack: Any, + seq_dir: Path, + meta: SequenceMeta, + recorded: float, + predict: Callable[[list[str], list[float] | None], dict], +) -> int | None: + """Distinct-frame count n whose window reproduces the recorded score. + + Production scored "the last <=10 of the first n distinct frames" for some + unknown n (it stops scoring once a sequence validates, while detections + keep arriving). Probe candidates ascending and stop at the first match; + None when no window matches (genuine drift, not window drift). The ROI is + recomputed per candidate from that window's detections, mirroring the + production call. + """ + _, all_keys, _ = reconstruct.frames_and_roi(meta.frames, last_n=None) + # n == len(all_keys) is the main replay's window, already known mismatched. + for n in range(reconstruct.MIN_FRAMES, len(all_keys)): + first_n = set(all_keys[:n]) + subset = [f for f in meta.frames if f.bucket_key in first_n] + _, kept, roi = reconstruct.frames_and_roi(subset) + files = _files_by_key(seq_dir, meta, kept) + try: + stack.upload_frames(files) + probability = predict(kept, roi).get("probability") + except Exception: # noqa: BLE001 — a failed probe ends the search, not the run + logger.exception("window probe failed for %s at n=%d", meta.key, n) + return None + if probability is not None and abs(recorded - probability) <= SCORE_TOLERANCE: + return n + return None + + +def _replay_one( + stack: Any, + health: dict, + seq_dir: Path, + meta: SequenceMeta, + reports: dict[str, OrgReport], + predict: Callable[[list[str], list[float] | None], dict], +) -> str: + report = _org_report(reports, meta) + # version and score are written by the same alert-api UPDATE, but import + # copies them independently — guard against a version without a score, + # which result_row could not compare against the threshold. + if meta.temporal_model_score is None: + report.drop(meta.key, "no_recorded_score") + return "no_recorded_score" + if health.get("model_version") != meta.temporal_model_version: + report.drop(meta.key, "model_version_mismatch") + return "model_version_mismatch" + total, kept, roi = reconstruct.frames_and_roi(meta.frames) + if total < reconstruct.MIN_FRAMES: + report.drop(meta.key, "too_few_frames") + return "too_few_frames" + files = _files_by_key(seq_dir, meta, kept) + if len(files) < len(kept) or not all(p.is_file() for p in files.values()): + report.drop(meta.key, "no_images") + return "no_images" + try: + stack.upload_frames(files) + response = predict(kept, roi) + except Exception: # noqa: BLE001 — one bad sequence must not stop the run + logger.exception("predict failed for %s", meta.key) + report.drop(meta.key, "predict_failed") + return "predict_failed" + + details = reshape_details(response["details"]) + matches = _score_matches(meta.temporal_model_score, response.get("probability")) + matched_window_frames = None + if matches is False: + matched_window_frames = _find_matching_window( + stack, seq_dir, meta, meta.temporal_model_score, predict + ) + if matched_window_frames is not None: + logger.info( + "%s: recorded score reproduced at the first-%d-frame window " + "(window drift, not model drift)", + meta.key, + matched_window_frames, + ) + org = slugify(meta.organization_name) + cam = slugify(meta.camera_name) + # The frames the model actually saw, in request order, as paths relative + # to monitor/ (the viewer resolves them against DATA_ROOT). + frames_rel = [] + for key in kept: + rel = files[key].relative_to(seq_dir) # e.g. images/detection_100.jpg + frames_rel.append( + f"{STORE_REL}/{org}/{cam}/seq_{meta.sequence_id}/{rel.as_posix()}" + ) + view = { + "key": meta.key, + # source matches the single reporting tree under "alert-api" + "source": SOURCE_SLUG, + "label": meta.label, + "organization_name": meta.organization_name, + "camera_name": meta.camera_name, + "started_at": meta.started_at, + "frames": frames_rel, + } + report.add( + row=result_row( + meta=meta, + response=response, + details=details, + replay_matches=matches, + matched_window_frames=matched_window_frames, + ), + details=details, + view=view, + model_config={ + "variant": MODEL_DIR, + "decision": details["decision"], + "model_version": health.get("model_version"), + "api_version": health.get("api_version"), + }, + ) + if matches is None or matches: + return "ok" + return "window_drift" if matched_window_frames is not None else "mismatch" + + +def _score_matches(recorded: float | None, replayed: float | None) -> bool | None: + if recorded is None: + return None + if replayed is None: + return False + return abs(recorded - replayed) <= SCORE_TOLERANCE diff --git a/monitor/src/temporal_model/monitor/report.py b/monitor/src/temporal_model/monitor/report.py new file mode 100644 index 0000000..66e7dd5 --- /dev/null +++ b/monitor/src/temporal_model/monitor/report.py @@ -0,0 +1,165 @@ +"""Writers for the eval-viewer reporting contract, one tree per organization. + +Layout (read by viewer/ with DATA_ROOT=../monitor — see viewer/lib/paths.ts): +``data/08_reporting//vit_dinov2_finetune/{results.json, details/, +sequences/, model_config.json, dropped.json}``. Shapes mirror +``eval/src/temporal_model/eval/evaluate.py`` rows and +``core/src/temporal_model/core/details_schema.py`` details; results rows add +the monitor-only provenance columns (replayed_probability, replayed_decision, +replay_matches, temporal_*_version), which the viewer treats as optional. +""" + +from __future__ import annotations + +import json +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any + +from temporal_model.monitor.geometry import tube_stabilized_window +from temporal_model.monitor.store import SequenceMeta + +MODEL_DIR = "vit_dinov2_finetune" # viewer/lib/paths.ts MODEL_NAME + + +def decision_from_output(is_smoke: bool) -> str: + return "keep" if is_smoke else "discard" + + +def compute_outcome(decision: str, label: str) -> str: + """Copy of eval's outcomes.compute_outcome (same strings, same n/a rule).""" + if label == "smoke": + return "kept-smoke" if decision == "keep" else "discarded-smoke" + if label == "fp": + return "kept-fp" if decision == "keep" else "discarded-fp" + return "n/a" + + +def reshape_details(api_details: dict[str, Any]) -> dict[str, Any]: + """Verbose /predict ``details`` -> eval ``BboxTubeDetails`` shape. + + Differences bridged: the API nests tube counters under preprocessing and + flattens tubes to a list; trigger fields exist only on releases with + compute_trigger (absent -> None); stabilized_window is never in the API + response (derived here). + """ + pre = api_details["preprocessing"] + dec = api_details["decision"] + kept = [ + { + "tube_id": t["tube_id"], + "start_frame": t["start_frame"], + "end_frame": t["end_frame"], + "logit": t["logit"], + "probability": t.get("probability"), + "first_crossing_frame": t.get("first_crossing_frame"), + "entries": t["entries"], + "stabilized_window": tube_stabilized_window(t["entries"]), + } + for t in api_details["tubes"] + ] + return { + "preprocessing": { + "num_frames_input": pre["num_frames_input"], + "num_truncated": pre["num_truncated"], + "padded_frame_indices": pre["padded_frame_indices"], + }, + "tubes": { + "num_candidates": pre.get("num_tube_candidates", 0), + "num_outside_roi": pre.get("num_tubes_outside_roi", 0), + "kept": kept, + }, + "decision": { + "aggregation": dec["aggregation"], + "threshold": dec["threshold"], + "trigger_tube_id": dec.get("trigger_tube_id"), + }, + } + + +def result_row( + *, + meta: SequenceMeta, + response: dict[str, Any], + details: dict[str, Any], + replay_matches: bool | None, + matched_window_frames: int | None = None, +) -> dict[str, Any]: + """One results.json row: eval columns + monitor provenance extras. + + Philosophy: the row verdict is production's — ``decision`` and + ``probability`` reflect what the alert-api recorded (``meta.temporal_model_score`` + compared against the packaged threshold in ``details``). The local replay's + probability and decision are diagnostic only and live in the ``replayed_*`` + extras, visible in the viewer's detail pane. + """ + kept = details["tubes"]["kept"] + # >= mirrors core's decision rule (inference.py: predict_proba >= threshold); + # replay drops unscored sequences before this point, so the score is a float. + decision = ( + "keep" + if meta.temporal_model_score >= details["decision"]["threshold"] + else "discard" + ) + return { + "key": meta.key, + # fixed source slug — all orgs land in one "alert-api" reporting tree + "source": "alert-api", + "label": meta.label, + "decision": decision, + "outcome": compute_outcome(decision, meta.label), + "score": max(t["logit"] for t in kept) if kept else None, + "probability": meta.temporal_model_score, + "num_tubes_kept": len(kept), + "trigger_frame_index": response.get("trigger_frame_index"), + "organization_name": meta.organization_name, + "camera_name": meta.camera_name, + "started_at": meta.started_at, + "replayed_probability": response.get("probability"), + "replayed_decision": decision_from_output(response["is_smoke"]), + "replay_matches": replay_matches, + "matched_window_frames": matched_window_frames, + "temporal_model_version": meta.temporal_model_version, + "temporal_api_version": meta.temporal_api_version, + } + + +@dataclass +class OrgReport: + """Accumulates one organization's rows/details/views before writing.""" + + org_slug: str + rows: list[dict] = field(default_factory=list) + details_by_key: dict[str, dict] = field(default_factory=dict) + views_by_key: dict[str, dict] = field(default_factory=dict) + model_config: dict | None = None + dropped: list[dict] = field(default_factory=list) + + def add(self, *, row: dict, details: dict, view: dict, model_config: dict) -> None: + self.rows.append(row) + self.details_by_key[row["key"]] = details + self.views_by_key[row["key"]] = view + if self.model_config is None: + self.model_config = model_config + + def drop(self, key: str, reason: str) -> None: + # eval's dropped.json field name is sequence_id; keep it for tooling parity + self.dropped.append({"sequence_id": key, "reason": reason}) + + +def write_report(output_dir: Path, report: OrgReport) -> None: + out = output_dir / report.org_slug / MODEL_DIR + out.mkdir(parents=True, exist_ok=True) + (out / "results.json").write_text(json.dumps(report.rows, indent=2)) + (out / "model_config.json").write_text( + json.dumps(report.model_config or {}, indent=2) + ) + (out / "dropped.json").write_text(json.dumps(report.dropped, indent=2)) + details_dir = out / "details" + details_dir.mkdir(exist_ok=True) + for key, details in report.details_by_key.items(): + (details_dir / f"{key}.json").write_text(json.dumps(details, indent=2)) + sequences_dir = out / "sequences" + sequences_dir.mkdir(exist_ok=True) + for key, view in report.views_by_key.items(): + (sequences_dir / f"{key}.json").write_text(json.dumps(view, indent=2)) diff --git a/monitor/src/temporal_model/monitor/stack.py b/monitor/src/temporal_model/monitor/stack.py new file mode 100644 index 0000000..dd83cb6 --- /dev/null +++ b/monitor/src/temporal_model/monitor/stack.py @@ -0,0 +1,93 @@ +"""Docker compose lifecycle for the pinned-release replay stack. + +One ReplayStack per api-version group: ``up()`` starts +``pyronear/temporal-model-api:`` (model.zip baked into the image) + +a throwaway MinIO; ``upload_frames`` puts a sequence's frames under their +ORIGINAL bucket_keys so the request body is byte-identical to production's; +``down()`` removes containers and the MinIO volume. +""" + +from __future__ import annotations + +import os +import subprocess +import time +from pathlib import Path + +import boto3 +import requests + +IMAGE_REPO = "pyronear/temporal-model-api" +COMPOSE_PROJECT = "temporal-monitor-replay" +API_URL = "http://localhost:18000" +MINIO_URL = "http://localhost:19000" +BUCKET = "frames" +_MINIO_CREDS = { + "aws_access_key_id": "minioadmin", + "aws_secret_access_key": "minioadmin", +} + + +class StackError(RuntimeError): + pass + + +class ReplayStack: + def __init__( + self, compose_file: Path, version: str, image: str | None = None + ) -> None: + self.compose_file = compose_file + self.version = version + self.image = image or f"{IMAGE_REPO}:{self.version}" + + def _compose(self, *args: str) -> None: + subprocess.run( + [ + "docker", + "compose", + "-f", + str(self.compose_file), + "-p", + COMPOSE_PROJECT, + *args, + ], + env={**os.environ, "MONITOR_API_IMAGE": self.image}, + check=True, + ) + + def up(self) -> None: + """Start the stack; raises CalledProcessError on pull/start failure.""" + self._compose("up", "-d") + + def down(self) -> None: + self._compose("down", "-v") + + def _fetch_health(self) -> dict: + resp = requests.get(f"{API_URL}/health", timeout=5) + resp.raise_for_status() + return resp.json() + + def wait_healthy(self, timeout_s: float = 300, poll_s: float = 2) -> dict: + """Poll /health until model_loaded; returns the health payload.""" + deadline = time.monotonic() + timeout_s + last: str = "no response" + while time.monotonic() < deadline: + try: + health = self._fetch_health() + except Exception as exc: # noqa: BLE001 — keep polling until deadline + last = repr(exc) + else: + if health.get("model_loaded"): + return health + last = str(health) + time.sleep(poll_s) + raise StackError(f"api at {API_URL} never became healthy: {last}") + + def _s3_client(self): + return boto3.client("s3", endpoint_url=MINIO_URL, **_MINIO_CREDS) + + def upload_frames(self, files_by_key: dict[str, Path]) -> None: + """Upload local frame files under their original S3 bucket_keys.""" + client = self._s3_client() + for key, path in files_by_key.items(): + client.upload_file(str(path), BUCKET, key) diff --git a/monitor/src/temporal_model/monitor/store.py b/monitor/src/temporal_model/monitor/store.py new file mode 100644 index 0000000..d7f5c93 --- /dev/null +++ b/monitor/src/temporal_model/monitor/store.py @@ -0,0 +1,101 @@ +"""Sequence store: data/01_raw/sequences///seq_/{meta.json, images/}. + +Mirrors the vision-rd explorer's store layout, extended with everything replay +needs: the recorded temporal score + version provenance and, per detection, +the original S3 ``bucket_key`` and the verbatim ``bbox`` string (parsed later +by ``reconstruct``). +""" + +from __future__ import annotations + +import re +import unicodedata +from collections.abc import Iterator +from pathlib import Path + +from pydantic import BaseModel + +META_NAME = "meta.json" +IMAGES_DIR = "images" + +# alert-api AnnotationType -> (label, label_detail) +_SMOKE_VALUES = {"wildfire_smoke", "other_smoke"} +_FP_VALUES = {"other"} + + +class FrameMeta(BaseModel): + file: str # relative to the sequence dir, e.g. "images/detection_100.jpg" + detection_id: int + created_at: str + bucket_key: str + bbox: str # verbatim alert-api bbox string, e.g. "[(0.1,0.2,0.3,0.4,0.9)]" + + +class SequenceMeta(BaseModel): + key: str # "alert-api_" — the viewer join key + sequence_id: int + source: str = "alert-api" + label: str # "smoke" | "fp" | "unknown" + label_detail: str | None = None + 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 + temporal_model_score: float | None = None + temporal_model_version: str | None = None + temporal_api_version: str | None = None + frames: list[FrameMeta] = [] + + +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 label_from_is_wildfire(is_wildfire: str | None) -> tuple[str, str | None]: + """Map alert-api's ``is_wildfire`` annotation to (label, label_detail).""" + if is_wildfire in _SMOKE_VALUES: + return "smoke", is_wildfire + if is_wildfire in _FP_VALUES: + return "fp", is_wildfire + return "unknown", is_wildfire + + +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]: + """All store dirs holding this sequence (>1 only after an org/camera rename).""" + 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_metas(store_dir: Path) -> Iterator[tuple[Path, SequenceMeta]]: + """Yield (sequence_dir, meta) for every sequence in the store.""" + for meta_path in sorted(store_dir.glob(f"*/*/seq_*/{META_NAME}")): + yield meta_path.parent, read_meta(meta_path.parent) diff --git a/monitor/tests/test_alert_api.py b/monitor/tests/test_alert_api.py new file mode 100644 index 0000000..a95ddf4 --- /dev/null +++ b/monitor/tests/test_alert_api.py @@ -0,0 +1,133 @@ +import pytest +import requests + +from temporal_model.monitor.alert_api import AlertApiClient, AlertApiConfig + + +class FakeResponse: + def __init__(self, payload, status_code=200): + self.payload = payload + self.status_code = status_code + + def raise_for_status(self): + if self.status_code >= 400: + exc = requests.HTTPError(response=self) + raise exc + + def json(self): + return self.payload + + +class FakeSession: + """Records requests; pops queued responses in FIFO order.""" + + def __init__(self, responses): + self.responses = list(responses) + self.calls = [] + + def post(self, url, data=None, timeout=None): + self.calls.append(("POST", url, data)) + return self.responses.pop(0) + + def get(self, url, headers=None, params=None, timeout=None): + self.calls.append(("GET", url, params)) + return self.responses.pop(0) + + +CONFIG = AlertApiConfig(url="https://api.test", login="user", password="pw") + + +def make_client(responses): + # Each item is either a plain payload (200 OK) or a FakeResponse already built. + wrapped = [r if isinstance(r, FakeResponse) else FakeResponse(r) for r in responses] + session = FakeSession(wrapped) + client = AlertApiClient(CONFIG, session=session) + return client, session + + +def test_login_posts_form_credentials(): + client, session = make_client([{"access_token": "tok"}]) + client.login() + method, url, data = session.calls[0] + assert (method, url) == ("POST", "https://api.test/api/v1/login/creds") + assert data == {"username": "user", "password": "pw"} + assert client.token == "tok" + + +def test_list_sequences_for_date_paginates_until_short_page(): + page1 = [{"id": i} for i in range(100)] + page2 = [{"id": 100}] + client, session = make_client([{"access_token": "t"}, page1, page2]) + client.login() + seqs = client.list_sequences_for_date("2026-06-11") + assert len(seqs) == 101 + # two GET calls with increasing offset + gets = [c for c in session.calls if c[0] == "GET"] + assert gets[0][2]["from_date"] == "2026-06-11" + assert gets[0][2]["offset"] == 0 + assert gets[1][2]["offset"] == 100 + + +def test_list_sequence_detections_paginates_ascending(): + page1 = [{"id": i} for i in range(100)] + page2 = [{"id": 100}] + client, session = make_client([{"access_token": "t"}, page1, page2]) + client.login() + dets = client.list_sequence_detections(42) + assert len(dets) == 101 + gets = [c for c in session.calls if c[0] == "GET"] + assert gets[0][1].endswith("/api/v1/sequences/42/detections") + assert gets[0][2] == {"limit": 100, "offset": 0, "desc": False} + assert gets[1][2]["offset"] == 100 + + +def test_pagination_stops_on_empty_page(): + page1 = [{"id": i} for i in range(100)] + client, _ = make_client([{"access_token": "t"}, page1, []]) + client.login() + assert len(client.list_sequence_detections(42)) == 100 + + +def test_requests_require_login(): + client, _ = make_client([]) + with pytest.raises(RuntimeError, match="login"): + client.list_cameras() + + +def test_list_cameras_and_organizations(): + client, session = make_client( + [{"access_token": "t"}, [{"id": 1, "name": "cam"}], [{"id": 11, "name": "org"}]] + ) + client.login() + assert client.list_cameras() == [{"id": 1, "name": "cam"}] + assert client.list_organizations() == [{"id": 11, "name": "org"}] + gets = [c for c in session.calls if c[0] == "GET"] + assert gets[0][1].endswith("/api/v1/cameras/") + assert gets[0][2] == {"include_non_trustable": True} + + +def test_get_sequence_returns_payload_on_200(): + payload = {"id": 99, "started_at": "2026-06-01T10:00:00"} + client, session = make_client([{"access_token": "t"}, payload]) + client.login() + result = client.get_sequence(99) + assert result == payload + gets = [c for c in session.calls if c[0] == "GET"] + assert gets[0][1].endswith("/api/v1/sequences/99") + + +def test_get_sequence_returns_none_on_404(): + client, session = make_client( + [{"access_token": "t"}, FakeResponse(None, status_code=404)] + ) + client.login() + assert client.get_sequence(999) is None + + +def test_get_sequence_reraises_on_500(): + client, session = make_client( + [{"access_token": "t"}, FakeResponse(None, status_code=500)] + ) + client.login() + with pytest.raises(requests.HTTPError): + client.get_sequence(1) diff --git a/monitor/tests/test_geometry.py b/monitor/tests/test_geometry.py new file mode 100644 index 0000000..6f2ceae --- /dev/null +++ b/monitor/tests/test_geometry.py @@ -0,0 +1,40 @@ +import pytest + +from temporal_model.monitor.geometry import tube_stabilized_window, union_window + + +def entry(bbox, is_gap=False): + return {"frame_idx": 0, "bbox": bbox, "is_gap": is_gap, "confidence": 0.5} + + +def test_union_window_single_box_is_identity(): + assert union_window([(0.5, 0.5, 0.2, 0.1)]) == pytest.approx((0.5, 0.5, 0.2, 0.1)) + + +def test_union_window_encloses(): + # box A spans x [0.1, 0.3], box B spans x [0.4, 0.6] -> union x [0.1, 0.6] + a = (0.2, 0.2, 0.2, 0.2) + b = (0.5, 0.5, 0.2, 0.2) + assert union_window([a, b]) == pytest.approx((0.35, 0.35, 0.5, 0.5)) + + +def test_union_window_empty_raises(): + with pytest.raises(ValueError): + union_window([]) + + +def test_stabilized_window_ignores_gap_boxes(): + observed = (0.2, 0.2, 0.2, 0.2) + gap = (0.8, 0.8, 0.1, 0.1) # interpolated — must not widen the window + win = tube_stabilized_window([entry(list(observed)), entry(list(gap), is_gap=True)]) + assert win == pytest.approx(observed) + + +def test_stabilized_window_falls_back_to_gap_boxes(): + gap = (0.8, 0.8, 0.1, 0.1) + win = tube_stabilized_window([entry(None), entry(list(gap), is_gap=True)]) + assert win == pytest.approx(gap) + + +def test_stabilized_window_none_without_any_box(): + assert tube_stabilized_window([entry(None), entry(None, is_gap=True)]) is None diff --git a/monitor/tests/test_import_alert_api.py b/monitor/tests/test_import_alert_api.py new file mode 100644 index 0000000..686a664 --- /dev/null +++ b/monitor/tests/test_import_alert_api.py @@ -0,0 +1,388 @@ +import pytest +import requests + +import temporal_model.monitor.import_alert_api as _imp_mod +from temporal_model.monitor.import_alert_api import import_alert_api, import_all_orgs +from temporal_model.monitor.store import ( + find_sequence_dirs, + read_meta, + sequence_exists, + slugify, +) + +SEQ = { + "id": 42307, + "camera_id": 122, + "is_wildfire": "wildfire_smoke", + "started_at": "2026-05-15T13:08:18.671072", + "temporal_model_score": 0.9867, + "temporal_model_version": "0.1.0", + "temporal_api_version": "0.3.1", +} +DETS = [ + { + "id": 100, + "created_at": "2026-05-15T13:08:18.671072", + "bucket_key": "cam122/frame-100.jpg", + "bbox": "[(0.1,0.2,0.3,0.4,0.9)]", + "url": "https://s3.test/frame-100.jpg?sig=x", + }, + { + "id": 101, + "created_at": "2026-05-15T13:09:18.671072", + "bucket_key": "cam122/frame-101.jpg", + "bbox": "[(0.11,0.2,0.31,0.4,0.8)]", + "url": "https://s3.test/frame-101.jpg?sig=x", + }, +] + + +class FakeClient: + def __init__(self, sequences_by_id: dict | None = None): + self.detection_calls = 0 + self.sequences_by_id: dict[int, dict | None] = sequences_by_id or {} + + def list_sequences_for_date(self, day): + return [SEQ] if day == "2026-05-15" else [] + + def list_sequence_detections(self, sequence_id): + assert sequence_id == 42307 + self.detection_calls += 1 + return DETS + + def list_cameras(self): + return [{"id": 122, "name": "donon-sarrebourg-01", "organization_id": 11}] + + def list_organizations(self): + return [{"id": 11, "name": "sis-67"}] + + def get_sequence(self, sequence_id: int) -> dict | None: + return self.sequences_by_id.get(sequence_id, None) + + +def fake_download(url: str) -> bytes: + return b"jpegbytes:" + url.encode() + + +def test_import_writes_store(tmp_path): + client = FakeClient() + stats = import_alert_api( + client, tmp_path, "2026-05-15", "2026-05-16", download=fake_download + ) + assert stats == {"imported": 1, "skipped": 0, "excluded": 0} + assert sequence_exists(tmp_path, 42307) + seq_dir = tmp_path / "sis-67" / "donon-sarrebourg-01" / "seq_42307" + meta = read_meta(seq_dir) + assert meta.key == "alert-api_42307" + assert meta.label == "smoke" + assert meta.temporal_api_version == "0.3.1" + assert [f.bucket_key for f in meta.frames] == [ + "cam122/frame-100.jpg", + "cam122/frame-101.jpg", + ] + assert ( + (seq_dir / "images" / "detection_100.jpg") + .read_bytes() + .startswith(b"jpegbytes:") + ) + + +def test_import_is_incremental(tmp_path): + client = FakeClient() + import_alert_api( + client, tmp_path, "2026-05-15", "2026-05-16", download=fake_download + ) + stats = import_alert_api( + client, tmp_path, "2026-05-15", "2026-05-16", download=fake_download + ) + assert stats == {"imported": 0, "skipped": 1, "excluded": 0} + assert client.detection_calls == 1 # second run never re-fetched detections + + +def test_import_force_redownloads(tmp_path): + client = FakeClient() + import_alert_api( + client, tmp_path, "2026-05-15", "2026-05-16", download=fake_download + ) + stats = import_alert_api( + client, + tmp_path, + "2026-05-15", + "2026-05-16", + force=True, + download=fake_download, + ) + assert stats == {"imported": 1, "skipped": 0, "excluded": 0} + assert client.detection_calls == 2 + + +def test_import_handles_missing_org_names(tmp_path): + class NoOrgClient(FakeClient): + def list_organizations(self): + raise PermissionError("403") + + import_alert_api( + NoOrgClient(), tmp_path, "2026-05-15", "2026-05-16", download=fake_download + ) + assert (tmp_path / "org-11").is_dir() # falls back to org- + + +def test_import_creates_store_dir_even_when_empty(tmp_path): + class EmptyClient(FakeClient): + def list_sequences_for_date(self, day): + return [] + + store = tmp_path / "store" + stats = import_alert_api( + EmptyClient(), store, "2026-01-01", "2026-01-01", download=fake_download + ) + assert stats == {"imported": 0, "skipped": 0, "excluded": 0} + assert store.is_dir() + + +# --------------------------------------------------------------------------- +# Helpers for scan-mode tests +# --------------------------------------------------------------------------- + +# Two cameras in two different orgs. +SCAN_CAMERAS = [ + {"id": 10, "name": "cam-alpha", "organization_id": 1}, + {"id": 20, "name": "cam-beta", "organization_id": 2}, +] +SCAN_ORGS = [ + {"id": 1, "name": "sdis-07"}, + {"id": 2, "name": "sdis-77"}, +] + + +def _det(det_id: int, hour: int, letter: str) -> dict: + return { + "id": det_id, + "created_at": f"2026-06-01T{hour:02d}:00:00", + "bucket_key": f"k/{letter}.jpg", + "bbox": "", + "url": f"https://s3.test/{letter}.jpg", + } + + +SCAN_DETS = { + 97: [_det(200, 10, "a")], + 99: [_det(201, 11, "b")], + 101: [_det(202, 12, "c")], + 103: [_det(203, 13, "d")], +} + + +def _seq(sid: int, cam: int, wildfire, started: str) -> dict: + return {"id": sid, "camera_id": cam, "is_wildfire": wildfire, "started_at": started} + + +# IDs 97, 99, 101, 103 are in-range; 98, 100, 102 are 404 holes; 104+ are 404. +# ID 95 is before day_from to trigger OLDER_STOP. +_SCAN_SEQS_BY_ID: dict[int, dict | None] = { + 95: _seq(95, 10, None, "2026-05-31T09:00:00"), + 96: None, + 97: _seq(97, 10, "wildfire_smoke", "2026-06-01T10:00:00"), + 98: None, + 99: _seq(99, 20, "other", "2026-06-01T11:00:00"), + 100: None, + 101: _seq(101, 10, None, "2026-06-01T12:00:00"), + 102: None, + 103: _seq(103, 20, "wildfire_smoke", "2026-06-01T13:00:00"), + # 104, 105, 106 absent → HEAD_GAP stops probing above 103 +} + + +class ScanFakeClient(FakeClient): + """FakeClient wired for the all-orgs scan tests.""" + + def __init__(self, seqs_by_id=None): + super().__init__(sequences_by_id=seqs_by_id or dict(_SCAN_SEQS_BY_ID)) + + def list_sequences_for_date(self, day): + # own-org listing returns only id=103 (the highest in-range id) for seeding + if day == "2026-06-01": + return [self.sequences_by_id[103]] + return [] + + def list_sequence_detections(self, sequence_id): + self.detection_calls += 1 + return SCAN_DETS.get(sequence_id, []) + + def list_cameras(self): + return SCAN_CAMERAS + + def list_organizations(self): + return SCAN_ORGS + + def get_sequence(self, sequence_id: int) -> dict | None: + return self.sequences_by_id.get(sequence_id, None) + + +def test_scan_imports_all_orgs(tmp_path, monkeypatch): + monkeypatch.setattr(_imp_mod, "HEAD_GAP", 3) + monkeypatch.setattr(_imp_mod, "OLDER_STOP", 3) + + client = ScanFakeClient() + stats = import_all_orgs( + client, tmp_path, "2026-06-01", "2026-06-01", download=fake_download + ) + # IDs 97, 99, 101, 103 are in range across two orgs + assert stats == {"imported": 4, "skipped": 0, "excluded": 0} + # Both orgs' dirs exist + assert (tmp_path / "sdis-07").is_dir() + assert (tmp_path / "sdis-77").is_dir() + # Each in-range sequence was stored + for sid in (97, 99, 101, 103): + assert sequence_exists(tmp_path, sid), f"seq_{sid} missing" + # Head discovery: seed is 103 (from own-org listing); scan walks up to 106 + # (3 consecutive 404s) then back down — just verify ids 104+ don't appear + for sid in (104, 105, 106): + assert not sequence_exists(tmp_path, sid) + + +def test_scan_is_idempotent(tmp_path, monkeypatch): + monkeypatch.setattr(_imp_mod, "HEAD_GAP", 3) + monkeypatch.setattr(_imp_mod, "OLDER_STOP", 3) + + client = ScanFakeClient() + import_all_orgs( + client, tmp_path, "2026-06-01", "2026-06-01", download=fake_download + ) + stats = import_all_orgs( + client, tmp_path, "2026-06-01", "2026-06-01", download=fake_download + ) + assert stats == {"imported": 0, "skipped": 4, "excluded": 0} + # No duplicate directories per sequence + for sid in (97, 99, 101, 103): + assert len(find_sequence_dirs(tmp_path, sid)) == 1 + + +def test_force_reimport_after_org_rename_leaves_single_dir(tmp_path, monkeypatch): + monkeypatch.setattr(_imp_mod, "HEAD_GAP", 3) + monkeypatch.setattr(_imp_mod, "OLDER_STOP", 3) + + client = ScanFakeClient() + import_all_orgs( + client, tmp_path, "2026-06-01", "2026-06-01", download=fake_download + ) + + # Rename org 1 from "sdis-07" to "sdis-07-renamed" + renamed_orgs = [ + {"id": 1, "name": "sdis-07-renamed"}, + {"id": 2, "name": "sdis-77"}, + ] + + class RenamedClient(ScanFakeClient): + def list_organizations(self): + return renamed_orgs + + client2 = RenamedClient() + import_all_orgs( + client2, + tmp_path, + "2026-06-01", + "2026-06-01", + force=True, + download=fake_download, + ) + + # Sequences that belonged to sdis-07 (cam 10: ids 97, 101) should now live + # under sdis-07-renamed, and the old sdis-07 dir must not hold them. + for sid in (97, 101): + dirs = find_sequence_dirs(tmp_path, sid) + assert len(dirs) == 1, f"seq_{sid} has {len(dirs)} dirs after rename" + assert "sdis-07-renamed" in str(dirs[0]), f"seq_{sid} not under new org dir" + # Sequences for org 2 (sdis-77: ids 99, 103) are unchanged + for sid in (99, 103): + dirs = find_sequence_dirs(tmp_path, sid) + assert len(dirs) == 1 + + +def test_scan_seed_error(tmp_path, monkeypatch): + monkeypatch.setattr(_imp_mod, "HEAD_GAP", 3) + monkeypatch.setattr(_imp_mod, "OLDER_STOP", 3) + + class EmptyScanClient(ScanFakeClient): + def list_sequences_for_date(self, day): + return [] + + client = EmptyScanClient(seqs_by_id={}) + with pytest.raises(SystemExit, match="--seed-id"): + import_all_orgs( + client, tmp_path, "2026-06-01", "2026-06-01", download=fake_download + ) + + +# --------------------------------------------------------------------------- +# exclude_orgs tests +# --------------------------------------------------------------------------- + + +def test_listing_exclude_org_skips_sequence_and_no_detections(tmp_path): + """Listing mode: excluding sis-67 skips its sequence; detections never fetched.""" + client = FakeClient() + stats = import_alert_api( + client, + tmp_path, + "2026-05-15", + "2026-05-16", + download=fake_download, + exclude_orgs={"sis-67"}, + ) + assert stats == {"imported": 0, "skipped": 0, "excluded": 1} + assert not sequence_exists(tmp_path, 42307) + assert client.detection_calls == 0 + + +def test_listing_exclude_accepts_raw_name_via_slugify(tmp_path): + """Exclusion should work when exclude_orgs contains the slugified raw name.""" + client = FakeClient() + stats = import_alert_api( + client, + tmp_path, + "2026-05-15", + "2026-05-16", + download=fake_download, + exclude_orgs={slugify("SIS 67")}, + ) + assert stats == {"imported": 0, "skipped": 0, "excluded": 1} + assert client.detection_calls == 0 + + +def test_scan_exclude_org_skips_one_org(tmp_path, monkeypatch): + """Scan mode: excluding sdis-77 (ids 99, 103) keeps only sdis-07's dirs.""" + monkeypatch.setattr(_imp_mod, "HEAD_GAP", 3) + monkeypatch.setattr(_imp_mod, "OLDER_STOP", 3) + + client = ScanFakeClient() + stats = import_all_orgs( + client, + tmp_path, + "2026-06-01", + "2026-06-01", + download=fake_download, + exclude_orgs={"sdis-77"}, + ) + # sdis-07 sequences (97, 101) imported; sdis-77 sequences (99, 103) excluded + assert stats == {"imported": 2, "skipped": 0, "excluded": 2} + assert (tmp_path / "sdis-07").is_dir() + assert not (tmp_path / "sdis-77").is_dir() + assert sequence_exists(tmp_path, 97) + assert sequence_exists(tmp_path, 101) + assert not sequence_exists(tmp_path, 99) + assert not sequence_exists(tmp_path, 103) + + +def test_failed_download_skips_sequence_and_continues(tmp_path): + def flaky_download(url: str) -> bytes: + if "frame-100" in url: + raise requests.ConnectionError("s3 timeout") + return b"jpg" + + stats = import_alert_api( + FakeClient(), tmp_path, "2026-05-15", "2026-05-16", download=flaky_download + ) + # the sequence is skipped (no meta.json), so the next run retries it + assert stats["imported"] == 0 + assert not sequence_exists(tmp_path, 42307) diff --git a/monitor/tests/test_reconstruct.py b/monitor/tests/test_reconstruct.py new file mode 100644 index 0000000..9716c67 --- /dev/null +++ b/monitor/tests/test_reconstruct.py @@ -0,0 +1,92 @@ +from temporal_model.monitor.reconstruct import ( + MAX_FRAMES, + MIN_FRAMES, + extract_bbox_strings, + frames_and_roi, + parse_bbox, +) +from temporal_model.monitor.store import FrameMeta + + +def frame(i: int, bucket_key: str, bbox: str) -> FrameMeta: + return FrameMeta( + file=f"images/detection_{i}.jpg", + detection_id=i, + created_at=f"2026-05-15T13:{i:02d}:00", + bucket_key=bucket_key, + bbox=bbox, + ) + + +def test_constants_match_pyro_api(): + assert MIN_FRAMES == 4 + assert MAX_FRAMES == 10 + + +def test_extract_and_parse_bbox(): + strs = extract_bbox_strings("[(0.1,0.2,0.3,0.4,0.9),(0.5,0.5,0.6,0.6,0.2)]") + assert strs == ["(0.1,0.2,0.3,0.4,0.9)", "(0.5,0.5,0.6,0.6,0.2)"] + assert parse_bbox(strs[0]) == (0.1, 0.2, 0.3, 0.4, 0.9) + assert parse_bbox("garbage") is None + assert parse_bbox("(0.1,0.2)") is None + + +def test_frames_distinct_and_ordered(): + frames = [ + frame(0, "k0.jpg", "[(0.1,0.1,0.2,0.2,0.9)]"), + frame(1, "k0.jpg", "[(0.15,0.1,0.25,0.2,0.9)]"), # same frame, 2nd detection + frame(2, "k1.jpg", "[(0.2,0.1,0.3,0.2,0.9)]"), + ] + total, kept, roi = frames_and_roi(frames) + assert total == 2 + assert kept == ["k0.jpg", "k1.jpg"] + # envelope of the three primary bboxes + assert roi == [0.1, 0.1, 0.3, 0.2] + + +def test_truncates_to_last_max_frames_and_roi_covers_kept_only(): + frames = [ + frame(i, f"k{i}.jpg", f"[(0.{i},0.1,0.{i + 1},0.2,0.9)]") for i in range(1, 9) + ] + [ + frame(20, "k20.jpg", "[(0.5,0.5,0.6,0.6,0.9)]"), + frame(21, "k21.jpg", "[(0.5,0.5,0.6,0.6,0.9)]"), + frame(22, "k22.jpg", "[(0.5,0.5,0.6,0.6,0.9)]"), + ] + total, kept, roi = frames_and_roi(frames) + assert total == 11 + assert len(kept) == MAX_FRAMES + assert kept[0] == "k2.jpg" # k1 truncated away + # k1's bbox (xmin 0.1) is truncated away; the kept envelope starts at k2's 0.2 + assert roi[0] == 0.2 + + +def test_roi_none_when_no_bbox_parses(): + frames = [frame(i, f"k{i}.jpg", "") for i in range(5)] + total, kept, roi = frames_and_roi(frames) + assert total == 5 + assert roi is None + + +def test_roi_none_when_degenerate(): + # a single zero-area box -> xmin == xmax -> degenerate -> None + frames = [frame(0, "k0.jpg", "[(0.5,0.5,0.5,0.5,0.9)]")] + _, _, roi = frames_and_roi(frames) + assert roi is None + + +def test_roi_clamped_to_unit_square(): + # parseable floats are already in [0,1] per the regex, but clamping is kept + # for parity with pyro-api (max(0,...), min(1,...)) + frames = [frame(0, "k0.jpg", "[(0,0,1,1,0.9)]")] + _, _, roi = frames_and_roi(frames) + assert roi == [0.0, 0.0, 1.0, 1.0] + + +def test_unparseable_primary_bbox_skipped_not_fatal(): + frames = [ + frame(0, "k0.jpg", "[(0.1,0.1,0.2,0.2,0.9)]"), + frame(1, "k1.jpg", "not-a-bbox"), + ] + total, kept, roi = frames_and_roi(frames) + assert kept == ["k0.jpg", "k1.jpg"] + assert roi == [0.1, 0.1, 0.2, 0.2] diff --git a/monitor/tests/test_replay.py b/monitor/tests/test_replay.py new file mode 100644 index 0000000..5c990cb --- /dev/null +++ b/monitor/tests/test_replay.py @@ -0,0 +1,451 @@ +import json +import subprocess +from pathlib import Path + +from temporal_model.monitor.cli import _parse_args +from temporal_model.monitor.replay import SCORE_TOLERANCE, run_replay +from temporal_model.monitor.stack import StackError +from temporal_model.monitor.store import ( + FrameMeta, + SequenceMeta, + sequence_dir, + write_meta, +) + +VERBOSE_DETAILS = { + "decision": {"aggregation": "logistic", "threshold": 0.52}, + "preprocessing": { + "num_frames_input": 4, + "num_truncated": 0, + "padded_frame_indices": [], + "num_tube_candidates": 1, + "num_tubes_outside_roi": 0, + }, + "tubes": [ + { + "tube_id": 0, + "start_frame": 0, + "end_frame": 3, + "logit": 3.41, + "probability": 0.93, + "entries": [ + { + "frame_idx": 0, + "bbox": [0.2, 0.2, 0.2, 0.2], + "is_gap": False, + "confidence": 0.8, + } + ], + } + ], +} + + +def store_sequence( + store: Path, + sequence_id: int, + *, + api_version: str | None = "0.3.1", + model_version: str | None = "0.1.0", + score: float | None = 0.93, + n_frames: int = 4, + with_images: bool = True, + organization_name: str = "sis-67", +) -> SequenceMeta: + meta = SequenceMeta( + key=f"alert-api_{sequence_id}", + sequence_id=sequence_id, + label="smoke", + label_detail="wildfire_smoke", + camera_name="cam-01", + organization_name=organization_name, + started_at="2026-05-15T13:08:18", + temporal_model_score=score, + temporal_model_version=model_version, + temporal_api_version=api_version, + frames=[ + FrameMeta( + file=f"images/detection_{i}.jpg", + detection_id=i, + created_at=f"2026-05-15T13:{i:02d}:00", + bucket_key=f"cam/seq{sequence_id}-f{i}.jpg", + bbox="[(0.1,0.1,0.3,0.3,0.9)]", + ) + for i in range(n_frames) + ], + ) + seq_dir = sequence_dir(store, meta) + write_meta(seq_dir, meta) + if with_images: + (seq_dir / "images").mkdir(exist_ok=True) + for i in range(n_frames): + (seq_dir / "images" / f"detection_{i}.jpg").write_bytes(b"jpg") + return meta + + +class FakeStack: + instances: list["FakeStack"] = [] + fail_versions: set[str] = set() + + def __init__(self, compose_file, version, image=None): + self.version = version + self.image = image + self.uploaded: list[dict] = [] + self.up_called = self.down_called = False + FakeStack.instances.append(self) + + def up(self): + self.up_called = True + if self.version in FakeStack.fail_versions: + raise subprocess.CalledProcessError(1, ["docker"]) + + def down(self): + self.down_called = True + + def wait_healthy(self, **kwargs): + return {"status": "ok", "model_loaded": True, "model_version": "0.1.0"} + + def upload_frames(self, files_by_key): + self.uploaded.append(dict(files_by_key)) + + +def fake_predict_ok(frames, roi_xyxyn): + return { + "is_smoke": True, + "probability": 0.93, + "version": {"api": "0.3.1", "model": "0.1.0"}, + "details": VERBOSE_DETAILS, + } + + +def run(store, out, predict=fake_predict_ok): + FakeStack.instances = [] + return run_replay( + store_dir=store, + output_dir=out, + compose_file=Path("dc.yml"), + stack_factory=FakeStack, + predict=predict, + ) + + +def test_happy_path_writes_org_tree(tmp_path): + store, out = tmp_path / "store", tmp_path / "out" + store_sequence(store, 1) + summary = run(store, out) + assert summary["replayed"] == 1 + assert summary["mismatched"] == 0 + rows = json.loads( + (out / "alert-api" / "vit_dinov2_finetune" / "results.json").read_text() + ) + assert rows[0]["replay_matches"] is True + assert rows[0]["probability"] == 0.93 + stack = FakeStack.instances[0] + assert stack.up_called and stack.down_called + # 4 distinct keys uploaded once + assert len(stack.uploaded[0]) == 4 + view = json.loads( + ( + out / "alert-api" / "vit_dinov2_finetune" / "sequences" / "alert-api_1.json" + ).read_text() + ) + # viewer frames = the kept (replayed) frames, relative to monitor/ + assert view["frames"] == [ + f"data/01_raw/sequences/sis-67/cam-01/seq_1/images/detection_{i}.jpg" + for i in range(4) + ] + + +def test_groups_by_api_version_one_stack_each(tmp_path): + store, out = tmp_path / "store", tmp_path / "out" + store_sequence(store, 1, api_version="0.3.0") + store_sequence(store, 2, api_version="0.3.1") + store_sequence(store, 3, api_version="0.3.1") + run(store, out) + assert sorted(s.version for s in FakeStack.instances) == ["0.3.0", "0.3.1"] + + +def test_multi_org_sequences_land_in_single_tree(tmp_path): + store, out = tmp_path / "store", tmp_path / "out" + store_sequence(store, 1, organization_name="sis-67") + store_sequence(store, 2, organization_name="sdis-77") + summary = run(store, out) + assert summary["replayed"] == 2 + # Single reporting tree under "alert-api", not per-org dirs + assert not (out / "sis-67").exists() + assert not (out / "sdis-77").exists() + rows = json.loads( + (out / "alert-api" / "vit_dinov2_finetune" / "results.json").read_text() + ) + assert len(rows) == 2 + org_names = {r["organization_name"] for r in rows} + assert org_names == {"sis-67", "sdis-77"} + # All rows carry the unified source slug + assert all(r["source"] == "alert-api" for r in rows) + + +def test_drop_reasons(tmp_path): + store, out = tmp_path / "store", tmp_path / "out" + store_sequence(store, 1, api_version=None) # no_temporal_version + store_sequence(store, 2, n_frames=2) # too_few_frames + store_sequence(store, 3, with_images=False) # no_images + store_sequence(store, 4, model_version="9.9.9") # model_version_mismatch + store_sequence(store, 5) # ok + store_sequence(store, 6, score=None) # no_recorded_score (version set, score null) + store_sequence(store, 7, api_version="evil/img@sha256:0") # invalid_api_version + summary = run(store, out) + assert summary["replayed"] == 1 + dropped = json.loads( + (out / "alert-api" / "vit_dinov2_finetune" / "dropped.json").read_text() + ) + reasons = {d["sequence_id"]: d["reason"] for d in dropped} + assert reasons == { + "alert-api_1": "no_temporal_version", + "alert-api_2": "too_few_frames", + "alert-api_3": "no_images", + "alert-api_4": "model_version_mismatch", + "alert-api_6": "no_recorded_score", + "alert-api_7": "invalid_api_version", + } + + +def test_image_pull_failure_drops_whole_group(tmp_path): + store, out = tmp_path / "store", tmp_path / "out" + store_sequence(store, 1, api_version="0.0.9") + FakeStack.fail_versions = {"0.0.9"} + try: + summary = run(store, out) + finally: + FakeStack.fail_versions = set() + assert summary["replayed"] == 0 + dropped = json.loads( + (out / "alert-api" / "vit_dinov2_finetune" / "dropped.json").read_text() + ) + assert dropped[0]["reason"] == "image_pull_failed" + + +def test_predict_failure_drops_sequence_and_continues(tmp_path): + store, out = tmp_path / "store", tmp_path / "out" + store_sequence(store, 1) + store_sequence(store, 2) + calls = [] + + def flaky_predict(frames, roi_xyxyn): + calls.append(frames) + if len(calls) == 1: + raise RuntimeError("boom") + return fake_predict_ok(frames, roi_xyxyn) + + summary = run(store, out, predict=flaky_predict) + assert summary["replayed"] == 1 + dropped = json.loads( + (out / "alert-api" / "vit_dinov2_finetune" / "dropped.json").read_text() + ) + assert dropped[0]["reason"] == "predict_failed" + + +def test_score_mismatch_flagged(tmp_path): + store, out = tmp_path / "store", tmp_path / "out" + store_sequence(store, 1, score=0.5) # recorded 0.5, replay says 0.93 + summary = run(store, out) + assert summary["mismatched"] == 1 + assert summary["window_drift"] == 0 + rows = json.loads( + (out / "alert-api" / "vit_dinov2_finetune" / "results.json").read_text() + ) + assert rows[0]["replay_matches"] is False + assert rows[0]["matched_window_frames"] is None + assert SCORE_TOLERANCE == 1e-5 + + +def test_cli_replay_defaults_point_at_package_compose_file(): + args = _parse_args(["replay"]) + assert args.compose_file.name == "docker-compose.yml" + assert args.compose_file.parent.name == "monitor" + assert args.compose_file.is_file() + + +def test_unhealthy_stack_drops_group_and_continues(tmp_path): + store, out = tmp_path / "store", tmp_path / "out" + store_sequence(store, 1, api_version="0.0.8") + store_sequence(store, 2, api_version="0.3.1") + + class SickStack(FakeStack): + def wait_healthy(self, **kwargs): + if self.version == "0.0.8": + raise StackError("api never became healthy") + return super().wait_healthy(**kwargs) + + FakeStack.instances = [] + summary = run_replay( + store_dir=store, + output_dir=out, + compose_file=Path("dc.yml"), + stack_factory=SickStack, + predict=fake_predict_ok, + ) + assert summary["replayed"] == 1 # the 0.3.1 group still ran + dropped = json.loads( + (out / "alert-api" / "vit_dinov2_finetune" / "dropped.json").read_text() + ) + assert {d["reason"] for d in dropped} == {"stack_unhealthy"} + # the sick stack was still torn down + sick = next(s for s in FakeStack.instances if s.version == "0.0.8") + assert sick.down_called + + +def test_window_drift_found_by_probing(tmp_path): + store, out = tmp_path / "store", tmp_path / "out" + # 12 distinct frames; production scored at the first-5 window (recorded 0.5) + store_sequence(store, 1, score=0.5, n_frames=12) + + def window_predict(frames, roi_xyxyn): + resp = json.loads(json.dumps(fake_predict_ok(frames, roi_xyxyn))) + # full final window (last 10 of 12) -> 0.93; the first-5 window -> 0.5 + if frames == [f"cam/seq1-f{i}.jpg" for i in range(5)]: + resp["probability"] = 0.5 + return resp + + summary = run(store, out, predict=window_predict) + assert summary == { + "replayed": 1, + "mismatched": 1, + "window_drift": 1, + "dropped": 0, + "trigger_enriched": 0, + } + rows = json.loads( + (out / "alert-api" / "vit_dinov2_finetune" / "results.json").read_text() + ) + assert rows[0]["replay_matches"] is False + assert rows[0]["matched_window_frames"] == 5 + + # probing stopped at n=5: 1 main replay upload + 2 probe uploads (n=4, n=5) + assert len(FakeStack.instances[0].uploaded) == 3 + + +TRIGGER_DETAILS = { + "decision": {"aggregation": "logistic", "threshold": 0.52, "trigger_tube_id": 0}, + "preprocessing": { + "num_frames_input": 4, + "num_truncated": 0, + "padded_frame_indices": [], + "num_tube_candidates": 1, + "num_tubes_outside_roi": 0, + }, + "tubes": [ + { + "tube_id": 0, + "start_frame": 0, + "end_frame": 3, + "logit": 3.41, + "probability": 0.93, + "first_crossing_frame": 1, + "entries": [ + { + "frame_idx": 0, + "bbox": [0.2, 0.2, 0.2, 0.2], + "is_gap": False, + "confidence": 0.8, + } + ], + } + ], +} + + +def test_trigger_enrichment_merges_when_probability_agrees(tmp_path): + store, out = tmp_path / "store", tmp_path / "out" + store_sequence(store, 1) # score 0.93, matches fake_predict_ok + + call_count = [0] + + def predict_with_triggers(frames, roi_xyxyn): + call_count[0] += 1 + resp = { + "is_smoke": True, + "probability": 0.93, + "version": {"api": "0.3.1", "model": "0.1.0"}, + "details": TRIGGER_DETAILS, + "trigger_frame_index": 2, + } + return resp + + FakeStack.instances = [] + summary = run_replay( + store_dir=store, + output_dir=out, + compose_file=Path("dc.yml"), + stack_factory=FakeStack, + predict=predict_with_triggers, + trigger_image="temporal-model-api:dev", + ) + + assert summary["trigger_enriched"] == 1 + + rows = json.loads( + (out / "alert-api" / "vit_dinov2_finetune" / "results.json").read_text() + ) + assert rows[0]["trigger_frame_index"] == 2 + + details = json.loads( + ( + out / "alert-api" / "vit_dinov2_finetune" / "details" / "alert-api_1.json" + ).read_text() + ) + assert details["decision"]["trigger_tube_id"] == 0 + tube = next(t for t in details["tubes"]["kept"] if t["tube_id"] == 0) + assert tube["first_crossing_frame"] == 1 + + # Two stacks created: main pass and enrichment pass + assert len(FakeStack.instances) == 2 + assert FakeStack.instances[1].image == "temporal-model-api:dev" + assert FakeStack.instances[0].down_called + assert FakeStack.instances[1].down_called + + +def test_trigger_enrichment_skipped_on_probability_disagreement(tmp_path): + store, out = tmp_path / "store", tmp_path / "out" + store_sequence(store, 1) # score 0.93 + + # First call (main pass): 0.93, no trigger fields (simulates old v0.3.1). + # Second call (enrichment): 0.5 — probability disagreement → skip. + call_count = [0] + + def predict_disagreeing(frames, roi_xyxyn): + call_count[0] += 1 + if call_count[0] == 1: + # Main pass: old image, no trigger fields + return { + "is_smoke": True, + "probability": 0.93, + "version": {"api": "0.3.1", "model": "0.1.0"}, + "details": VERBOSE_DETAILS, + } + # Enrichment pass: returns different probability -> guard triggers skip + return { + "is_smoke": True, + "probability": 0.5, + "version": {"api": "dev", "model": "0.1.0"}, + "details": TRIGGER_DETAILS, + "trigger_frame_index": 2, + } + + FakeStack.instances = [] + summary = run_replay( + store_dir=store, + output_dir=out, + compose_file=Path("dc.yml"), + stack_factory=FakeStack, + predict=predict_disagreeing, + trigger_image="temporal-model-api:dev", + ) + + assert summary["trigger_enriched"] == 0 + + rows = json.loads( + (out / "alert-api" / "vit_dinov2_finetune" / "results.json").read_text() + ) + assert rows[0]["trigger_frame_index"] is None + # Main pass result is untouched + assert rows[0]["replay_matches"] is True diff --git a/monitor/tests/test_report.py b/monitor/tests/test_report.py new file mode 100644 index 0000000..15b580f --- /dev/null +++ b/monitor/tests/test_report.py @@ -0,0 +1,208 @@ +import json + +from temporal_model.monitor.report import ( + OrgReport, + compute_outcome, + reshape_details, + result_row, + write_report, +) +from temporal_model.monitor.store import FrameMeta, SequenceMeta + +VERBOSE_RESPONSE = { + "is_smoke": True, + "probability": 0.93, + "version": {"api": "0.3.1", "model": "0.1.0"}, + "details": { + "decision": { + "aggregation": "logistic", + "threshold": 0.52, + "threshold_overridden": False, + "packaged_threshold": None, + }, + "preprocessing": { + "num_frames_input": 10, + "num_truncated": 0, + "padded_frame_indices": [], + "num_tube_candidates": 3, + "num_tubes_outside_roi": 1, + }, + "tubes": [ + { + "tube_id": 7, + "start_frame": 2, + "end_frame": 4, + "logit": 3.41, + "probability": 0.93, + "entries": [ + { + "frame_idx": 2, + "bbox": [0.2, 0.2, 0.2, 0.2], + "is_gap": False, + "confidence": 0.81, + }, + { + "frame_idx": 3, + "bbox": [0.5, 0.5, 0.2, 0.2], + "is_gap": False, + "confidence": 0.7, + }, + ], + } + ], + "profiling": None, + }, +} + + +def make_meta() -> SequenceMeta: + return SequenceMeta( + key="alert-api_42307", + sequence_id=42307, + label="smoke", + camera_name="donon-sarrebourg-01", + organization_name="sis-67", + started_at="2026-05-15T13:08:18", + temporal_model_score=0.93, + temporal_model_version="0.1.0", + temporal_api_version="0.3.1", + frames=[ + FrameMeta( + file="images/detection_100.jpg", + detection_id=100, + created_at="2026-05-15T13:08:18", + bucket_key="cam122/frame-100.jpg", + bbox="[(0.1,0.2,0.3,0.4,0.9)]", + ) + ], + ) + + +def test_compute_outcome_matches_eval(): + assert compute_outcome("keep", "smoke") == "kept-smoke" + assert compute_outcome("discard", "smoke") == "discarded-smoke" + assert compute_outcome("keep", "fp") == "kept-fp" + assert compute_outcome("discard", "fp") == "discarded-fp" + assert compute_outcome("keep", "unknown") == "n/a" + + +def test_reshape_details_to_eval_shape(): + details = reshape_details(VERBOSE_RESPONSE["details"]) + assert set(details) == {"preprocessing", "tubes", "decision"} + assert details["preprocessing"] == { + "num_frames_input": 10, + "num_truncated": 0, + "padded_frame_indices": [], + } + assert details["tubes"]["num_candidates"] == 3 + assert details["tubes"]["num_outside_roi"] == 1 + kept = details["tubes"]["kept"] + assert len(kept) == 1 + tube = kept[0] + # absent on <= v0.3.1 -> filled with None / derived + assert tube["first_crossing_frame"] is None + cx, cy, w, h = tube["stabilized_window"] + assert (round(cx, 6), round(cy, 6), round(w, 6), round(h, 6)) == ( + 0.35, + 0.35, + 0.5, + 0.5, + ) + assert details["decision"] == { + "aggregation": "logistic", + "threshold": 0.52, + "trigger_tube_id": None, + } + + +def test_reshape_preserves_trigger_fields_when_present(): + resp = json.loads(json.dumps(VERBOSE_RESPONSE)) # deep copy + resp["details"]["decision"]["trigger_tube_id"] = 7 + resp["details"]["tubes"][0]["first_crossing_frame"] = 3 + details = reshape_details(resp["details"]) + assert details["decision"]["trigger_tube_id"] == 7 + assert details["tubes"]["kept"][0]["first_crossing_frame"] == 3 + + +def test_result_row(): + meta = make_meta() + details = reshape_details(VERBOSE_RESPONSE["details"]) + row = result_row( + meta=meta, + response=VERBOSE_RESPONSE, + details=details, + replay_matches=True, + ) + # decision and probability come from production's recorded score (0.93 > 0.52) + assert row == { + "key": "alert-api_42307", + "source": "alert-api", + "label": "smoke", + "decision": "keep", + "outcome": "kept-smoke", + "score": 3.41, + "probability": 0.93, + "num_tubes_kept": 1, + "trigger_frame_index": None, + "organization_name": "sis-67", + "camera_name": "donon-sarrebourg-01", + "started_at": "2026-05-15T13:08:18", + "replayed_probability": 0.93, + "replayed_decision": "keep", + "replay_matches": True, + "matched_window_frames": None, + "temporal_model_version": "0.1.0", + "temporal_api_version": "0.3.1", + } + + +def test_result_row_source_is_fixed_alert_api(): + meta = make_meta().model_copy(update={"organization_name": "SIS 67"}) + details = reshape_details(VERBOSE_RESPONSE["details"]) + row = result_row( + meta=meta, response=VERBOSE_RESPONSE, details=details, replay_matches=True + ) + # source is always the fixed slug; organization_name carries the raw name + assert row["source"] == "alert-api" + assert row["organization_name"] == "SIS 67" + + +def test_write_report_tree(tmp_path): + meta = make_meta() + details = reshape_details(VERBOSE_RESPONSE["details"]) + row = result_row( + meta=meta, response=VERBOSE_RESPONSE, details=details, replay_matches=True + ) + report = OrgReport(org_slug="sis-67") + report.add( + row=row, + details=details, + view={ + "key": "alert-api_42307", + "source": "sis-67", + "label": "smoke", + "organization_name": "sis-67", + "camera_name": "donon-sarrebourg-01", + "started_at": "2026-05-15T13:08:18", + "frames": [ + "data/01_raw/sequences/sis-67/donon-sarrebourg-01/seq_42307/images/detection_100.jpg" + ], + }, + model_config={"variant": "vit_dinov2_finetune"}, + ) + report.drop("alert-api_999", "no_temporal_version") + write_report(tmp_path, report) + + out = tmp_path / "sis-67" / "vit_dinov2_finetune" + rows = json.loads((out / "results.json").read_text()) + assert rows[0]["key"] == "alert-api_42307" + written = json.loads((out / "details" / "alert-api_42307.json").read_text()) + assert written["tubes"]["kept"][0]["tube_id"] == 7 + view = json.loads((out / "sequences" / "alert-api_42307.json").read_text()) + assert view["frames"][0].startswith("data/01_raw/sequences/") + assert json.loads((out / "model_config.json").read_text()) == { + "variant": "vit_dinov2_finetune" + } + assert json.loads((out / "dropped.json").read_text()) == [ + {"sequence_id": "alert-api_999", "reason": "no_temporal_version"} + ] diff --git a/monitor/tests/test_scaffold.py b/monitor/tests/test_scaffold.py new file mode 100644 index 0000000..369b8bb --- /dev/null +++ b/monitor/tests/test_scaffold.py @@ -0,0 +1,5 @@ +from temporal_model.monitor import __doc__ as pkg_doc + + +def test_package_importable(): + assert "replay" in pkg_doc diff --git a/monitor/tests/test_stack.py b/monitor/tests/test_stack.py new file mode 100644 index 0000000..437c759 --- /dev/null +++ b/monitor/tests/test_stack.py @@ -0,0 +1,95 @@ +from pathlib import Path +from unittest.mock import patch + +import pytest + +from temporal_model.monitor.stack import ( + API_URL, + BUCKET, + ReplayStack, + StackError, +) + + +def test_compose_commands_pin_the_image_tag(): + stack = ReplayStack(Path("dc.yml"), version="0.3.1") + with patch("temporal_model.monitor.stack.subprocess.run") as run: + stack.up() + stack.down() + up_call, down_call = run.call_args_list + assert up_call.args[0] == [ + "docker", + "compose", + "-f", + "dc.yml", + "-p", + "temporal-monitor-replay", + "up", + "-d", + ] + assert ( + up_call.kwargs["env"]["MONITOR_API_IMAGE"] + == "pyronear/temporal-model-api:0.3.1" + ) + assert up_call.kwargs["check"] is True + assert down_call.args[0][-2:] == ["down", "-v"] + + +def test_wait_healthy_polls_until_model_loaded(): + stack = ReplayStack(Path("dc.yml"), version="0.3.1") + responses = iter( + [ + ConnectionError("not up yet"), + {"status": "ok", "model_loaded": False}, + {"status": "ok", "model_loaded": True, "model_version": "0.1.0"}, + ] + ) + + def fake_health(): + item = next(responses) + if isinstance(item, Exception): + raise item + return item + + with patch.object(stack, "_fetch_health", side_effect=fake_health): + health = stack.wait_healthy(timeout_s=5, poll_s=0) + assert health["model_version"] == "0.1.0" + + +def test_wait_healthy_times_out(): + stack = ReplayStack(Path("dc.yml"), version="0.3.1") + with ( + patch.object(stack, "_fetch_health", side_effect=ConnectionError), + pytest.raises(StackError, match="health"), + ): + stack.wait_healthy(timeout_s=0.05, poll_s=0.01) + + +def test_upload_frames_puts_each_key_once(tmp_path): + (tmp_path / "a.jpg").write_bytes(b"a") + (tmp_path / "b.jpg").write_bytes(b"b") + stack = ReplayStack(Path("dc.yml"), version="0.3.1") + with patch.object(stack, "_s3_client") as make_client: + stack.upload_frames( + {"cam/k1.jpg": tmp_path / "a.jpg", "cam/k2.jpg": tmp_path / "b.jpg"} + ) + uploaded = [c.args for c in make_client.return_value.upload_file.call_args_list] + assert sorted(u[2] for u in uploaded) == ["cam/k1.jpg", "cam/k2.jpg"] + assert all(u[1] == BUCKET for u in uploaded) + + +def test_custom_image_overrides_default(): + stack = ReplayStack(Path("dc.yml"), version="0.3.1", image="temporal-model-api:dev") + assert stack.image == "temporal-model-api:dev" + with patch("temporal_model.monitor.stack.subprocess.run") as run: + stack.up() + assert run.call_args.kwargs["env"]["MONITOR_API_IMAGE"] == "temporal-model-api:dev" + + +def test_default_image_uses_image_repo_and_version(): + stack = ReplayStack(Path("dc.yml"), version="0.3.1") + assert stack.image == "pyronear/temporal-model-api:0.3.1" + + +def test_api_url_uses_offset_port(): + assert API_URL == "http://localhost:18000" diff --git a/monitor/tests/test_store.py b/monitor/tests/test_store.py new file mode 100644 index 0000000..9b1878d --- /dev/null +++ b/monitor/tests/test_store.py @@ -0,0 +1,77 @@ +from temporal_model.monitor.store import ( + FrameMeta, + SequenceMeta, + iter_metas, + label_from_is_wildfire, + read_meta, + sequence_dir, + sequence_exists, + slugify, + write_meta, +) + + +def make_meta(sequence_id: int = 42307) -> SequenceMeta: + return SequenceMeta( + key=f"alert-api_{sequence_id}", + sequence_id=sequence_id, + label="smoke", + label_detail="wildfire_smoke", + camera_id=122, + camera_name="donon-sarrebourg-01", + organization_id=11, + organization_name="sis-67", + started_at="2026-05-15T13:08:18.671072", + temporal_model_score=0.9867, + temporal_model_version="0.1.0", + temporal_api_version="0.3.1", + frames=[ + FrameMeta( + file="images/detection_100.jpg", + detection_id=100, + created_at="2026-05-15T13:08:18.671072", + bucket_key="cam122/frame-100.jpg", + bbox="[(0.1,0.2,0.3,0.4,0.9)]", + ) + ], + ) + + +def test_slugify(): + assert slugify("SIS 67") == "sis-67" + assert slugify("Donon/Sarrebourg_01") == "donon-sarrebourg-01" + assert slugify("") == "unknown" + assert slugify(None) == "unknown" + assert slugify("Forêt-d'Orient") == "foret-d-orient" + + +def test_label_from_is_wildfire(): + assert label_from_is_wildfire("wildfire_smoke") == ("smoke", "wildfire_smoke") + assert label_from_is_wildfire("other_smoke") == ("smoke", "other_smoke") + assert label_from_is_wildfire("other") == ("fp", "other") + assert label_from_is_wildfire(None) == ("unknown", None) + + +def test_meta_roundtrip(tmp_path): + meta = make_meta() + seq_dir = sequence_dir(tmp_path, meta) + write_meta(seq_dir, meta) + assert seq_dir == tmp_path / "sis-67" / "donon-sarrebourg-01" / "seq_42307" + assert read_meta(seq_dir) == meta + + +def test_sequence_exists(tmp_path): + meta = make_meta() + assert not sequence_exists(tmp_path, 42307) + write_meta(sequence_dir(tmp_path, meta), meta) + assert sequence_exists(tmp_path, 42307) + assert not sequence_exists(tmp_path, 999) + + +def test_iter_metas(tmp_path): + for sid in 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r.replayed_probability !== undefined), + [sourceRows], + ); + // Monitor rows carry production's verdict — never re-decided locally, so + // the threshold slider is eval-only. const showSlider = + !monitorMode && cfg.decision?.aggregation === "logistic" && sourceRows.some((r) => r.probability != null); const rows = useMemo( @@ -86,7 +94,21 @@ export default function Page() { setPrevFilterSource(source); setFilters(defaultFilters()); } - const cameras = useMemo(() => cameraOptions(sourceRows), [sourceRows]); + const cameras = useMemo( + () => + cameraOptions( + filters.organization === "all" + ? sourceRows + : sourceRows.filter( + (r) => r.organization_name === filters.organization, + ), + ), + [sourceRows, filters.organization], + ); + const organizations = useMemo( + () => organizationOptions(sourceRows), + [sourceRows], + ); const [sort, setSort] = useState(null); const tableRows = useMemo( () => sortRows(applyFilters(rows, filters), sort), @@ -121,14 +143,22 @@ export default function Page() { defaultThreshold={defaultThr} onThreshold={setThreshold} onReset={() => setThreshold(defaultThr)} + monitorMode={monitorMode} + selectedOrganization={filters.organization} + onSelectOrganization={(org) => + // mirror the FilterBar's org change: camera resets with the org + setFilters({ ...filters, organization: org, camera: "all" }) + } />
setSort((cur) => nextSort(cur, col))} + monitorMode={monitorMode} />
diff --git a/viewer/components/ControlRail.tsx b/viewer/components/ControlRail.tsx index 1b80479..258dcd9 100644 --- a/viewer/components/ControlRail.tsx +++ b/viewer/components/ControlRail.tsx @@ -1,4 +1,5 @@ import { ModelConfigPanel } from "@/components/ModelConfigPanel"; +import { MonitorCards } from "@/components/MonitorCards"; import { PerfCards } from "@/components/PerfCards"; import { SourceSelect } from "@/components/SourceSelect"; import { ThresholdSlider } from "@/components/ThresholdSlider"; @@ -15,15 +16,29 @@ export function ControlRail(props: { defaultThreshold: number; onThreshold: (v: number) => void; onReset: () => void; + monitorMode?: boolean; + selectedOrganization?: string; + onSelectOrganization?: (org: string) => void; }) { return (