bvandeusen
f2fbe2ae6e
Operator: 10-frame max-pooled tagging on video produces a lot of noisy tags, and the sampling burns time/GPU. Drop the VIDEO_ML_FRAMES default to 6 (still env- overridable). Fewer frames = less per-frame noise into the max-pool and a smaller frame-sampling budget. Quality/perf of the whole video path is being reviewed separately.
446 lines
16 KiB
Python
446 lines
16 KiB
Python
"""ML Celery tasks: per-image inference, backfill discovery, centroid
|
||
recompute, allowlist auto-apply, model self-heal.
|
||
|
||
All run on the ml-worker (queue 'ml') except recompute_centroids and
|
||
apply_allowlist_tags sweeps which are 'maintenance' lane. Sync sessions
|
||
(Celery workers are sync processes), same pattern as FC-2a tasks.
|
||
"""
|
||
|
||
import logging
|
||
from pathlib import Path
|
||
|
||
from celery.exceptions import SoftTimeLimitExceeded
|
||
from sqlalchemy import select
|
||
from sqlalchemy.exc import DBAPIError, OperationalError
|
||
|
||
from ..celery_app import celery
|
||
from ..models import ImageRecord, MLSettings
|
||
from ._sync_engine import sync_session_factory as _sync_session_factory
|
||
|
||
log = logging.getLogger(__name__)
|
||
|
||
IMAGES_ROOT = Path("/images")
|
||
VIDEO_EXTS = {".mp4", ".mov", ".avi", ".mkv", ".webm", ".m4v", ".wmv", ".flv"}
|
||
|
||
|
||
def _is_video(path: Path) -> bool:
|
||
return path.suffix.lower() in VIDEO_EXTS
|
||
|
||
|
||
@celery.task(
|
||
name="backend.app.tasks.ml.tag_and_embed",
|
||
bind=True,
|
||
autoretry_for=(OperationalError, DBAPIError, OSError),
|
||
retry_backoff=5,
|
||
retry_backoff_max=60,
|
||
retry_jitter=True,
|
||
max_retries=3,
|
||
# Sized for the video branch: sample 6 frames, run tagger +
|
||
# embedder on each (≈12 GPU ops vs 2 for an image). A loaded
|
||
# ml-worker can take 5-10 min on a long video; bumped from
|
||
# 5min/7min on 2026-05-28 after operator-flagged image 6288 (a
|
||
# .mp4) hit the recovery sweep at 5 min while still legitimately
|
||
# processing. Image runs return in seconds; the bump doesn't
|
||
# affect their UX.
|
||
soft_time_limit=900, # 15 min
|
||
time_limit=1200, # 20 min hard
|
||
)
|
||
def tag_and_embed(self, image_id: int) -> dict:
|
||
"""Run Camie + SigLIP on one image; store predictions + embedding;
|
||
then enqueue per-image allowlist application.
|
||
|
||
Video: sample frames between 10% and 90% of duration (VIDEO_ML_FRAMES,
|
||
default 6). Max-pool tagger confidences across frames, mean-pool the
|
||
SigLIP embeddings. On no-frames returns status='no_frames' (not an error).
|
||
"""
|
||
import os
|
||
import time
|
||
|
||
from ..services.ml.embedder import get_embedder
|
||
from ..services.ml.tagger import get_tagger
|
||
|
||
# Phase + file context, so a timeout/crash names WHICH file and WHERE it
|
||
# died instead of a bare SoftTimeLimitExceeded() (operator-flagged 2026-06-08:
|
||
# the activity told them nothing about the file or why). `ctx` is enriched
|
||
# once the record is loaded; both feed the worker log AND the re-raised
|
||
# exception message (which becomes the activity's error_message).
|
||
started = time.monotonic()
|
||
phase = "open_session"
|
||
ctx = f"image_id={image_id}"
|
||
|
||
def _elapsed() -> float:
|
||
return time.monotonic() - started
|
||
|
||
try:
|
||
SessionLocal = _sync_session_factory()
|
||
with SessionLocal() as session:
|
||
record = session.get(ImageRecord, image_id)
|
||
if record is None:
|
||
return {"status": "missing", "image_id": image_id}
|
||
settings = session.execute(
|
||
select(MLSettings).where(MLSettings.id == 1)
|
||
).scalar_one()
|
||
|
||
src = Path(record.path)
|
||
is_vid = _is_video(src)
|
||
ctx = (
|
||
f"image_id={image_id} path={record.path} mime={record.mime} "
|
||
f"bytes={record.size_bytes} video={is_vid}"
|
||
)
|
||
log.info("tag_and_embed start: %s", ctx)
|
||
if not src.is_file():
|
||
log.warning("tag_and_embed file missing on disk: %s", ctx)
|
||
return {"status": "file_missing", "image_id": image_id}
|
||
|
||
phase = "load_models"
|
||
tagger = get_tagger()
|
||
embedder = get_embedder()
|
||
|
||
if is_vid:
|
||
# Layer-3 isolation: ffprobe (a separate process) validates
|
||
# the container before we burn ~20 GPU ops sampling frames
|
||
# from it. A corrupt video that would crash the frame
|
||
# decoder is rejected cleanly here instead of taking down
|
||
# the ml-worker. Operator-flagged 2026-05-28.
|
||
phase = "video_probe"
|
||
from ..utils import safe_probe
|
||
vprobe = safe_probe.probe_video(src)
|
||
if not vprobe.ok:
|
||
log.warning(
|
||
"tag_and_embed bad video (%s): %s", vprobe.reason, ctx
|
||
)
|
||
return {
|
||
"status": "bad_video", "image_id": image_id,
|
||
"reason": vprobe.reason,
|
||
}
|
||
phase = "video_sample_frames"
|
||
t0 = time.monotonic()
|
||
frames = _sample_video_frames(
|
||
src, int(os.environ.get("VIDEO_ML_FRAMES", "6"))
|
||
)
|
||
log.info(
|
||
"tag_and_embed sampled %d frame(s) in %.1fs: %s",
|
||
len(frames), time.monotonic() - t0, ctx,
|
||
)
|
||
if not frames:
|
||
return {"status": "no_frames", "image_id": image_id}
|
||
phase = "video_infer"
|
||
import numpy as np
|
||
|
||
preds = _maxpool_predictions([tagger.infer(f) for f in frames])
|
||
embedding = np.mean(
|
||
[embedder.infer(f) for f in frames], axis=0
|
||
).astype("float32")
|
||
for f in frames:
|
||
f.unlink(missing_ok=True)
|
||
else:
|
||
phase = "tag"
|
||
t0 = time.monotonic()
|
||
raw = tagger.infer(src)
|
||
log.info(
|
||
"tag_and_embed tagged in %.1fs (%d tags): %s",
|
||
time.monotonic() - t0, len(raw), ctx,
|
||
)
|
||
preds = {
|
||
name: {"category": p.category, "confidence": p.confidence}
|
||
for name, p in raw.items()
|
||
}
|
||
phase = "embed"
|
||
t0 = time.monotonic()
|
||
embedding = embedder.infer(src)
|
||
log.info(
|
||
"tag_and_embed embedded in %.1fs: %s",
|
||
time.monotonic() - t0, ctx,
|
||
)
|
||
|
||
phase = "persist"
|
||
record.tagger_predictions = preds
|
||
record.tagger_model_version = settings.tagger_model_version
|
||
record.siglip_embedding = embedding.tolist()
|
||
record.siglip_model_version = settings.embedder_model_version
|
||
session.add(record)
|
||
session.commit()
|
||
except SoftTimeLimitExceeded:
|
||
log.error(
|
||
"tag_and_embed TIMED OUT after %.0fs in phase=%s: %s",
|
||
_elapsed(), phase, ctx,
|
||
)
|
||
# Re-raise as SoftTimeLimitExceeded (preserves the 'timeout' status in
|
||
# the task_run signal) but WITH context, so the activity error_message
|
||
# names the file + phase instead of being empty.
|
||
raise SoftTimeLimitExceeded(
|
||
f"timed out in phase={phase} after {_elapsed():.0f}s ({ctx})"
|
||
) from None
|
||
except Exception:
|
||
# OSError/DBAPIError/OperationalError are autoretried — re-raise the
|
||
# ORIGINAL so the type is preserved; just make sure it's logged with
|
||
# context first.
|
||
log.exception(
|
||
"tag_and_embed FAILED in phase=%s after %.0fs: %s",
|
||
phase, _elapsed(), ctx,
|
||
)
|
||
raise
|
||
|
||
log.info(
|
||
"tag_and_embed ok in %.1fs (%d tags): %s", _elapsed(), len(preds), ctx
|
||
)
|
||
apply_allowlist_tags.delay(image_id=image_id)
|
||
return {"status": "ok", "image_id": image_id, "tags": len(preds)}
|
||
|
||
|
||
def _sample_video_frames(src: Path, n: int) -> list[Path]:
|
||
"""Extract n frames evenly between 10% and 90% of duration via ffmpeg.
|
||
Returns temp file paths (caller deletes). Empty list on failure."""
|
||
import json
|
||
import subprocess
|
||
import tempfile
|
||
|
||
try:
|
||
probe = subprocess.run(
|
||
[
|
||
"ffprobe", "-v", "quiet", "-print_format", "json",
|
||
"-show_format", str(src),
|
||
],
|
||
check=True, capture_output=True, timeout=30,
|
||
)
|
||
duration = float(json.loads(probe.stdout)["format"]["duration"])
|
||
except Exception:
|
||
return []
|
||
if duration <= 0:
|
||
return []
|
||
|
||
start, end = duration * 0.10, duration * 0.90
|
||
step = (end - start) / max(n - 1, 1)
|
||
out: list[Path] = []
|
||
tmpdir = Path(tempfile.mkdtemp(prefix="fc_vid_"))
|
||
for i in range(n):
|
||
ts = start + i * step
|
||
dest = tmpdir / f"frame_{i:02d}.jpg"
|
||
try:
|
||
subprocess.run(
|
||
[
|
||
"ffmpeg", "-ss", f"{ts:.2f}", "-i", str(src),
|
||
"-frames:v", "1", "-q:v", "3", "-y", str(dest),
|
||
],
|
||
check=True, capture_output=True, timeout=60,
|
||
)
|
||
if dest.is_file():
|
||
out.append(dest)
|
||
except Exception:
|
||
continue
|
||
return out
|
||
|
||
|
||
def _maxpool_predictions(per_frame: list[dict]) -> dict:
|
||
"""Aggregate per-frame {name: TagPrediction} dicts by max confidence."""
|
||
merged: dict[str, dict] = {}
|
||
for frame_preds in per_frame:
|
||
for name, p in frame_preds.items():
|
||
cur = merged.get(name)
|
||
if cur is None or p.confidence > cur["confidence"]:
|
||
merged[name] = {
|
||
"category": p.category,
|
||
"confidence": p.confidence,
|
||
}
|
||
return merged
|
||
|
||
|
||
@celery.task(name="backend.app.tasks.ml.backfill", bind=True)
|
||
def backfill(self) -> int:
|
||
"""Enqueue tag_and_embed for images missing predictions/embeddings for
|
||
the current model versions. Keyset pagination by id ASC (restart-safe).
|
||
"""
|
||
SessionLocal = _sync_session_factory()
|
||
enqueued = 0
|
||
last_id = 0
|
||
with SessionLocal() as session:
|
||
settings = session.execute(
|
||
select(MLSettings).where(MLSettings.id == 1)
|
||
).scalar_one()
|
||
while True:
|
||
rows = session.execute(
|
||
select(ImageRecord.id)
|
||
.where(ImageRecord.id > last_id)
|
||
.where(
|
||
(ImageRecord.tagger_predictions.is_(None))
|
||
| (
|
||
ImageRecord.tagger_model_version
|
||
!= settings.tagger_model_version
|
||
)
|
||
| (ImageRecord.siglip_embedding.is_(None))
|
||
| (
|
||
ImageRecord.siglip_model_version
|
||
!= settings.embedder_model_version
|
||
)
|
||
)
|
||
.order_by(ImageRecord.id.asc())
|
||
.limit(500)
|
||
).scalars().all()
|
||
if not rows:
|
||
break
|
||
for image_id in rows:
|
||
tag_and_embed.delay(image_id)
|
||
enqueued += 1
|
||
last_id = rows[-1]
|
||
return enqueued
|
||
|
||
|
||
@celery.task(
|
||
name="backend.app.tasks.ml.apply_allowlist_tags",
|
||
bind=True,
|
||
# Audit 2026-06-02 — the full-sweep mode (neither tag_id nor image_id)
|
||
# is O(images × allowlist) and legitimately runs >5 min on large
|
||
# libraries. Cap matches the maintenance queue's recovery threshold.
|
||
soft_time_limit=1800, time_limit=2100,
|
||
)
|
||
def apply_allowlist_tags(self, tag_id: int | None = None,
|
||
image_id: int | None = None) -> int:
|
||
"""Retroactively apply allowlisted tags.
|
||
|
||
Modes:
|
||
- tag_id only : scan all images for this tag.
|
||
- image_id only : scan all allowlisted tags for this image.
|
||
- both : just the (image, tag) pair.
|
||
- neither : full sweep (daily beat).
|
||
|
||
Skips: already-applied, rejected (tag_suggestion_rejection), or
|
||
confidence below the tag's allowlist min_confidence. Applied with
|
||
source='ml_auto'.
|
||
"""
|
||
from sqlalchemy import and_
|
||
from sqlalchemy import select as sa_select
|
||
from sqlalchemy.dialects.postgresql import insert as pg_insert
|
||
|
||
from ..models import TagAllowlist, TagSuggestionRejection
|
||
from ..models.tag import image_tag
|
||
|
||
SessionLocal = _sync_session_factory()
|
||
applied = 0
|
||
with SessionLocal() as session:
|
||
allow_rows = session.execute(
|
||
sa_select(TagAllowlist.tag_id, TagAllowlist.min_confidence)
|
||
if tag_id is None
|
||
else sa_select(
|
||
TagAllowlist.tag_id, TagAllowlist.min_confidence
|
||
).where(TagAllowlist.tag_id == tag_id)
|
||
).all()
|
||
allow = {r[0]: r[1] for r in allow_rows}
|
||
if not allow:
|
||
return 0
|
||
|
||
img_query = sa_select(
|
||
ImageRecord.id, ImageRecord.tagger_predictions
|
||
).where(ImageRecord.tagger_predictions.is_not(None))
|
||
if image_id is not None:
|
||
img_query = img_query.where(ImageRecord.id == image_id)
|
||
|
||
for img_id, preds in session.execute(img_query).all():
|
||
preds = preds or {}
|
||
for a_tag_id, min_conf in allow.items():
|
||
exists = session.execute(
|
||
sa_select(image_tag.c.tag_id).where(
|
||
and_(
|
||
image_tag.c.image_record_id == img_id,
|
||
image_tag.c.tag_id == a_tag_id,
|
||
)
|
||
)
|
||
).scalar_one_or_none()
|
||
if exists is not None:
|
||
continue
|
||
rej = session.get(
|
||
TagSuggestionRejection, (img_id, a_tag_id)
|
||
)
|
||
if rej is not None:
|
||
continue
|
||
from ..models import Tag
|
||
|
||
tag = session.get(Tag, a_tag_id)
|
||
if tag is None:
|
||
continue
|
||
conf = _confidence_for_tag(session, tag, preds)
|
||
if conf is None or conf < min_conf:
|
||
continue
|
||
stmt = pg_insert(image_tag).values(
|
||
image_record_id=img_id,
|
||
tag_id=a_tag_id,
|
||
source="ml_auto",
|
||
)
|
||
stmt = stmt.on_conflict_do_nothing(
|
||
index_elements=["image_record_id", "tag_id"]
|
||
)
|
||
session.execute(stmt)
|
||
applied += 1
|
||
session.commit()
|
||
return applied
|
||
|
||
|
||
def _confidence_for_tag(session, tag, preds: dict) -> float | None:
|
||
"""Highest confidence among predictions that resolve to `tag` —
|
||
either the prediction name equals the tag name, or an alias maps
|
||
(prediction name, category) -> tag.id.
|
||
"""
|
||
from sqlalchemy import select as sa_select
|
||
|
||
from ..models import TagAlias
|
||
|
||
best: float | None = None
|
||
direct = preds.get(tag.name)
|
||
if direct is not None:
|
||
best = float(direct.get("confidence", 0.0))
|
||
alias_rows = session.execute(
|
||
sa_select(TagAlias.alias_string, TagAlias.alias_category).where(
|
||
TagAlias.canonical_tag_id == tag.id
|
||
)
|
||
).all()
|
||
for alias_string, alias_category in alias_rows:
|
||
p = preds.get(alias_string)
|
||
if p is None:
|
||
continue
|
||
if p.get("category") != alias_category:
|
||
continue
|
||
c = float(p.get("confidence", 0.0))
|
||
if best is None or c > best:
|
||
best = c
|
||
return best
|
||
|
||
|
||
@celery.task(name="backend.app.tasks.ml.recompute_centroid", bind=True)
|
||
def recompute_centroid(self, tag_id: int) -> bool:
|
||
import asyncio
|
||
|
||
from ..extensions import get_session
|
||
from ..services.ml.centroids import CentroidService
|
||
|
||
async def _run() -> bool:
|
||
async with get_session() as session:
|
||
svc = CentroidService(session)
|
||
result = await svc.recompute_for_tag(tag_id)
|
||
await session.commit()
|
||
return result
|
||
|
||
return asyncio.run(_run())
|
||
|
||
|
||
@celery.task(
|
||
name="backend.app.tasks.ml.recompute_centroids",
|
||
bind=True,
|
||
# Audit 2026-06-02 — drifted-centroid rebuild over potentially
|
||
# hundreds of tags.
|
||
soft_time_limit=1800, time_limit=2100,
|
||
)
|
||
def recompute_centroids(self) -> int:
|
||
"""Daily: find drifted centroids, enqueue recompute_centroid for each."""
|
||
import asyncio
|
||
|
||
from ..extensions import get_session
|
||
from ..services.ml.centroids import CentroidService
|
||
|
||
async def _list() -> list[int]:
|
||
async with get_session() as session:
|
||
return await CentroidService(session).list_drifted()
|
||
|
||
drifted = asyncio.run(_list())
|
||
for tid in drifted:
|
||
recompute_centroid.delay(tid)
|
||
return len(drifted)
|