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chore: remove remaining dead-mode references from executor, jobs, compose, Dockerfile

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After removing the object pipeline, several dead 'mode' artifacts remained:

- executor.py: unpack `config` from job even though it was no longer read
- jobs.py: set `"mode": "face"` in both configure paths (key never consumed)
- compose.yml: TRAINING_MODE=face env, OBJECT_CLASS comment, HF_HOME, stale MAX_AUTO_IMAGES default note
- Dockerfile: "and object classification" label, /models/huggingface mkdir, HF_HOME ENV
This commit is contained in:
flan
2026-06-14 17:42:27 +00:00
parent 2d7b52470e
commit 274d50ee99
5 changed files with 27 additions and 1569 deletions
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winnow/executor.py
+20 -22
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@@ -173,11 +173,11 @@ def execute_jobs(jobs: list[dict]) -> None:
use_full_res = Config.USE_FULL_RESOLUTION
# Load InsightFace app for landmark-based crop alignment (face mode only).
# Load InsightFace app for landmark-based crop alignment.
# The model is already resident from the diversity/embedding phase, so this
# is just a singleton lookup — no load cost.
insightface_app = None
if any(j["config"].get("mode", "face") == "face" for j in jobs) and Config.ENABLE_FACE_ALIGNMENT:
if Config.ENABLE_FACE_ALIGNMENT:
try:
from .embeddings import get_insightface_app
@@ -196,8 +196,8 @@ def execute_jobs(jobs: list[dict]) -> None:
overall_task = progress.add_task("[green]Overall Progress", total=grand_total)
for job in jobs:
person, assets, config = job["person"], job["assets"], job["config"]
name, mode = person["name"], config.get("mode", "face")
person, assets = job["person"], job["assets"]
name = person["name"]
job_task = progress.add_task(f"Processing {name}...", total=len(assets))
try:
@@ -206,8 +206,7 @@ def execute_jobs(jobs: list[dict]) -> None:
logger.error(str(e))
continue
# Face crops are transient (uploaded then discarded); wipe before each run.
# Object crops are the deliverable; preserve them across runs.
if mode == "face" and os.path.isdir(person_dir):
if os.path.isdir(person_dir):
shutil.rmtree(person_dir)
os.makedirs(person_dir, exist_ok=True)
@@ -219,22 +218,21 @@ def execute_jobs(jobs: list[dict]) -> None:
count = 0
for asset in assets:
try:
# For face mode, enrich the asset with face bounding box data
# from the Immich faces API (not included in search/metadata results)
if mode == "face":
asset = _enrich_asset_with_face_data(asset, person)
# Skip download if detection confidence already disqualifies
# the asset — avoids fetching a large image we'll discard.
conf = asset.get("face_confidence")
if conf is not None and conf < Config.MIN_CONFIDENCE:
progress.console.print(
f"[yellow]Skipped {asset['id']}"
f" (detection confidence {conf:.2f} < {Config.MIN_CONFIDENCE})[/yellow]"
)
mark_rejected(asset["id"], person_name=name)
progress.advance(job_task)
progress.advance(overall_task)
continue
# Enrich the asset with face bounding box data from the Immich
# faces API (not included in search/metadata results).
asset = _enrich_asset_with_face_data(asset, person)
# Skip download if detection confidence already disqualifies
# the asset — avoids fetching a large image we'll discard.
conf = asset.get("face_confidence")
if conf is not None and conf < Config.MIN_CONFIDENCE:
progress.console.print(
f"[yellow]Skipped {asset['id']}"
f" (detection confidence {conf:.2f} < {Config.MIN_CONFIDENCE})[/yellow]"
)
mark_rejected(asset["id"], person_name=name)
progress.advance(job_task)
progress.advance(overall_task)
continue
# Use full-resolution for final output when configured
if use_full_res: