chore: remove dead object/SigLIP references after pipeline removal

- cache.py: drop siglip MODEL_VERSIONS entry
- log_config.py: drop ultralytics/transformers/torch from noise silencers
- scheduler.py: drop HF_HOME and HuggingFace model check
- scripts/benchmark.py: drop bench_siglip and make_random_image

scheduler.py

@@ -16,7 +16,6 @@ except ImportError:
 from winnow.cli import main
 
 SCHEDULE = os.environ["CRON_SCHEDULE"]
-MODELS_DIR = os.environ.get("HF_HOME", "/models/huggingface")
 INSIGHTFACE_HOME = os.environ.get("INSIGHTFACE_HOME", "/models/.insightface")
 
 logger = logging.getLogger(__name__)
@@ -24,11 +23,8 @@ logger = logging.getLogger(__name__)
 
 def check_models() -> None:
     buffalo = Path(INSIGHTFACE_HOME) / "models" / "buffalo_l"
-    hf_hub = Path(MODELS_DIR) / "hub"
     if not buffalo.exists():
         print("  InsightFace Buffalo_L not found — will download on first run", flush=True)
-    if not (hf_hub.exists() and any(hf_hub.iterdir())):
-        print("  HuggingFace models not found — will download on first run", flush=True)
 
 
 NOW = time.time()

scripts/benchmark.py

@@ -2,8 +2,8 @@
 """
 winnow inference benchmark: GPU vs CPU throughput.
 
-Measures InsightFace (face mode) and SigLIP (object mode) latency and
+Measures InsightFace (ArcFace) latency and throughput.
-throughput. Run with FORCE_CPU=true for CPU-only baseline.
+Run with FORCE_CPU=true for CPU-only baseline.
 
 Usage inside container:
     # GPU mode:
@@ -47,11 +47,6 @@ def make_face_image(size: int = 640) -> Image.Image:
     return img
 
 
-def make_random_image(width: int = 224, height: int = 224) -> Image.Image:
-    rng = np.random.default_rng(42)
-    return Image.fromarray(rng.integers(0, 256, (height, width, 3), dtype=np.uint8), "RGB")
-
-
 def _stats(times_s: list[float]) -> dict:
     arr = np.array(times_s) * 1000  # ms
     return {
@@ -119,61 +114,6 @@ def bench_insightface(n_warmup: int = 5, n_runs: int = 30) -> None:
     print(f"  320×320 median  : {s2['median_ms']:.1f} ms  ({s2['ips']:.1f} img/s)")
 
 
-def bench_siglip(
-    n_warmup: int = 3,
-    n_runs: int = 20,
-    batch_sizes: tuple = (1, 4, 8, 16, 32),
-) -> None:
-    import torch
-
-    import winnow.embeddings as emb_mod
-    emb_mod._siglip_model = None
-    emb_mod._siglip_processor = None
-    emb_mod._siglip_loaded = False
-
-    print("  Loading model...")
-    t_load = time.perf_counter()
-    model, processor = emb_mod.get_siglip_model()
-    load_s = time.perf_counter() - t_load
-
-    if model is None:
-        print("  SKIP: SigLIP failed to load")
-        return
-
-    device = next(model.parameters()).device
-    print(f"  Model load time : {load_s:.2f} s  (device: {device})")
-
-    print(f"  {'Batch':>5}  {'ms/batch':>10}  {'ms/img':>8}  {'img/s':>8}  {'p95/img':>9}")
-    for bs in batch_sizes:
-        imgs = [make_random_image(224, 224) for _ in range(bs)]
-        inputs = processor(images=imgs, return_tensors="pt")
-        inputs = {k: v.to(device) for k, v in inputs.items()}
-
-        # Warmup
-        for _ in range(n_warmup):
-            with torch.no_grad():
-                model(**inputs)
-        if str(device) != "cpu":
-            torch.cuda.synchronize()
-
-        times: list[float] = []
-        for _ in range(n_runs):
-            if str(device) != "cpu":
-                torch.cuda.synchronize()
-            t0 = time.perf_counter()
-            with torch.no_grad():
-                model(**inputs)
-            if str(device) != "cpu":
-                torch.cuda.synchronize()
-            times.append(time.perf_counter() - t0)
-
-        s = _stats(times)
-        print(
-            f"  {bs:>5}  {s['median_ms']:>10.1f}  {s['median_ms']/bs:>8.2f}"
-            f"  {bs * 1000 / s['median_ms']:>8.1f}  {s['p95_ms']/bs:>9.2f}"
-        )
-
-
 def main() -> None:
     print("=" * 56)
     print("  winnow inference benchmark")
@@ -185,10 +125,6 @@ def main() -> None:
     bench_insightface()
     print()
 
-    print("── SigLIP google/siglip-base-patch16-224  (objects) ───")
-    bench_siglip()
-    print()
-
 
 if __name__ == "__main__":
     # Add winnow to path when run directly inside container

winnow/cache.py

@@ -15,7 +15,6 @@ logger = logging.getLogger(__name__)
 # Model versions — bump these when the upstream model changes
 MODEL_VERSIONS = {
     "insightface": "buffalo_l_v1",
-    "siglip": "siglip-base-patch16-224_v1",
     "immich": "immich_buffalo_l_v1",
 }
 

winnow/log_config.py

@@ -13,12 +13,9 @@ console = Console()
 NOISY_LOGGERS = (
     "urllib3",
     "PIL",
-    "ultralytics",
     "insightface",
     "onnxruntime",
     "matplotlib",
-    "transformers",
-    "torch",
 )
 
 
@@ -51,7 +48,6 @@ def setup_logging(verbose: bool = False) -> logging.Logger:
 
     # Suppress Python warnings from ML libraries
     warnings.filterwarnings("ignore", category=UserWarning, module="onnxruntime")
-    warnings.filterwarnings("ignore", category=FutureWarning, module="transformers")
 
     return root