Delete raven/benchmark.py

raven/benchmark.py

@@ -1,43 +0,0 @@
-from __future__ import annotations
-import copy
-import time
-import torch
-from .foundation import FoundationEncoder
-from .foundation_runtime import FoundationRuntime
-from .utils import configure_cpu
-
-
-def benchmark_foundation(cfg: dict, accelerator: str = "auto", loops: int = 5) -> dict:
-    cfg = copy.deepcopy(cfg)
-    hc = cfg["hardware"]
-    fc = cfg["foundation"]
-    fc["accelerator"] = accelerator
-    configure_cpu(hc.get("torch_threads", 6), hc.get("interop_threads", 1))
-    enc = FoundationEncoder(
-        fc.get("backend", "dinov2_vits14"),
-        bool(fc.get("allow_fallback", True)),
-        fc.get("fallback_backend", "torchvision_convnext_tiny"),
-    ).eval().to("cpu")
-    rt = FoundationRuntime(fc, enc)
-    b = rt.batch_size
-    x = torch.randn(b, 3, 224, 224)
-    rt.infer(x)
-    loops = max(2, int(loops))
-    t0 = time.perf_counter()
-    for _ in range(loops):
-        rt.infer(x)
-    sec = time.perf_counter() - t0
-    ips = (b * loops) / max(sec, 1e-9)
-    result = {
-        "requested_accelerator": accelerator,
-        "selected_runtime": rt.backend,
-        "foundation_backend": enc.backend,
-        "batch_size": b,
-        "loops": loops,
-        "seconds": sec,
-        "foundation_images_per_second": ips,
-        "foundation_only_estimate_minutes_30000_images": 30000.0 / max(ips, 1e-9) / 60.0,
-        "note": "This measures frozen foundation inference only; JPEG/Parquet decode and forensic branches add extra time.",
-    }
-    rt.release_accelerator_memory()
-    return result