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fix+logging: model load logging, CPU fallback, log level audit

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Bugs fixed (from high-effort review):
- InsightFace CPU fallback now passes providers=['CPUExecutionProvider'] and
  wraps in _suppress_output() so broken GPU drivers don't cause fallback to
  try the same broken provider again, and C-extension noise stays suppressed
- scheduler.py: BaseException → Exception (KeyboardInterrupt already re-raised;
  winnow has no sys.exit() calls, so SystemExit would not occur, but Exception
  is the correct scope)
- compose.yml: fix inverted AUTO_MODE comment (docker run -it enables
  interactive mode via TTY, not non-interactive)

Model loading logging (embeddings.py):
- InsightFace: disk cache check, "not cached — downloading now (~300 MB)",
  "loading into memory on GPU/CPU...", "ready on GPU/CPU (Xs)"
- SigLIP: same treatment; cache path derived dynamically from model_name
  via HuggingFace slug convention (models--org--model) so it stays correct
  if the model variant ever changes

Logging level audit (INFO/DEBUG/WARNING/ERROR):
- diversity.py: internal algo steps (clustering, medoids, adaptive threshold,
  auto-stop decision) → DEBUG; final selection summaries stay INFO
- immich_api.py: "Fetching assets" and "Retained N assets" → DEBUG (callers
  already print this to the console via rprint)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
flan
2026-06-12 15:23:44 +00:00
co-authored by Claude Sonnet 4.6
parent af8bae1c45
commit 3db52c2141
5 changed files with 51 additions and 21 deletions
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winnow/embeddings.py
+41 -11
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@@ -9,8 +9,10 @@ Unified embedding interface for faces and objects.
import importlib
import logging
import os
import time
import warnings
from contextlib import contextmanager
from pathlib import Path
import cv2
import numpy as np
@@ -90,9 +92,16 @@ def get_insightface_app():
import onnxruntime as ort
from insightface.app import FaceAnalysis
# Disk cache check — lets the user know whether a download is coming
buffalo_path = Path(insightface_home) / "models" / "buffalo_l"
if buffalo_path.exists() and any(buffalo_path.iterdir()):
logger.info("InsightFace Buffalo_L: found in model cache")
else:
logger.info("InsightFace Buffalo_L: not cached — downloading now (~300 MB)")
# Get providers, excluding TensorRT to avoid noisy errors
providers = [p for p in ort.get_available_providers() if p != "TensorrtExecutionProvider"]
logger.info(f"Available ONNX providers: {providers}")
logger.debug(f"ONNX providers available: {providers}")
gpu_providers = {
"CUDAExecutionProvider",
@@ -111,12 +120,14 @@ def get_insightface_app():
)
device_str = "GPU" if ctx_id >= 0 else "CPU"
logger.info(f"Loading InsightFace Buffalo_L on {device_str} (ctx_id={ctx_id})...")
logger.info(f"InsightFace Buffalo_L: loading into memory on {device_str}...")
t0 = time.time()
with _suppress_output():
_insightface_app = FaceAnalysis(name="buffalo_l", root=insightface_home, providers=providers)
_insightface_app.prepare(ctx_id=ctx_id, det_size=(640, 640))
logger.info(f"InsightFace Buffalo_L: ready on {device_str} ({time.time() - t0:.1f}s)")
return _insightface_app
except ImportError:
@@ -125,15 +136,22 @@ def get_insightface_app():
except Exception as e:
logger.error(f"Failed to load InsightFace: {e}")
if ctx_id == 0:
logger.warning("Retrying InsightFace on CPU...")
logger.warning("InsightFace GPU load failed — retrying on CPU...")
try:
from insightface.app import FaceAnalysis
_insightface_app = FaceAnalysis(name="buffalo_l", root=insightface_home)
_insightface_app.prepare(ctx_id=-1, det_size=(640, 640))
t0 = time.time()
with _suppress_output():
_insightface_app = FaceAnalysis(
name="buffalo_l",
root=insightface_home,
providers=["CPUExecutionProvider"],
)
_insightface_app.prepare(ctx_id=-1, det_size=(640, 640))
logger.info(f"InsightFace Buffalo_L: ready on CPU (fallback, {time.time() - t0:.1f}s)")
return _insightface_app
except Exception as ex:
logger.error(f"CPU fallback failed: {ex}")
logger.error(f"InsightFace CPU fallback failed: {ex}")
return None
@@ -182,7 +200,18 @@ def get_siglip_model():
from transformers import AutoImageProcessor, SiglipVisionModel
model_name = "google/siglip-base-patch16-224"
logger.info(f"Loading SigLIP model ({model_name})...")
# Disk cache check — path derived from model_name using HuggingFace's slug convention
hf_home = os.environ.get("HF_HOME", os.path.join(os.path.expanduser("~"), ".cache", "huggingface"))
cache_slug = "models--" + model_name.replace("/", "--")
model_cache = Path(hf_home) / "hub" / cache_slug
if model_cache.exists() and any(model_cache.iterdir()):
logger.info(f"SigLIP {model_name}: found in model cache")
else:
logger.info(f"SigLIP {model_name}: not cached — downloading now (~380 MB)")
logger.info(f"SigLIP {model_name}: loading into memory...")
t0 = time.time()
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=FutureWarning)
@@ -196,15 +225,16 @@ def get_siglip_model():
if not _is_force_cpu():
if torch.cuda.is_available():
_siglip_model = _siglip_model.cuda()
logger.info("SigLIP running on CUDA GPU")
device_name = "CUDA GPU"
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
_siglip_model = _siglip_model.to("mps")
logger.info("SigLIP running on Apple MPS")
device_name = "Apple MPS"
else:
logger.info("SigLIP running on CPU")
device_name = "CPU"
else:
logger.info("FORCE_CPU set. SigLIP running on CPU")
device_name = "CPU (FORCE_CPU)"
logger.info(f"SigLIP {model_name}: ready on {device_name} ({time.time() - t0:.1f}s)")
return _siglip_model, _siglip_processor
except ImportError as e: