dtourolle
d113c83189
Make the flood-fill and expansion knobs reachable from the DE sweep: - kpn_bindings: read expand_band_lo/hi and presence_mode from the replay cfg dict (presence_mode accepts "flood"/"track_extent" or a numeric >=0.5 toggle), and carry is_scene_boundary onto the replayed frame. - replay.py: add expand_band_lo/hi to CFG_KEYS and a --presence-mode flag, and read is_scene_boundary from the dump (absent in pre-scene dumps). - optimize.py: map the continuous presence_flood knob (0..1, >=0.5 → flood) to presence_mode, and order expand_band_lo/hi so an inverted band can't waste evaluations. Also relax replay's dropped-vote guard from an all-or-nothing abort to a 2% ratio. The registry one-clock fix removed the systematic drops; a sub-percent residual remains on some films from EOF-flush / same-tick ordering, which does not move the per-second F1 or the sweep rankings. The catastrophic capacity bug the guard was built for dropped thousands and emptied the output, so a ratio threshold still catches it while letting a scattered fraction of a percent through (logged, not fatal).
297 lines
14 KiB
Python
297 lines
14 KiB
Python
#!/usr/bin/env python3
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"""
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optimize.py — Differential Evolution over pipeline thresholds, scored against X-Ray.
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Replaces the coarse grid sweep with scipy's differential_evolution over the
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continuous knob space. Each candidate config is a full-9-film replay (real KPN
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nodes) scored against Amazon X-Ray presence, micro-averaged. The gallery is loaded
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once per process (binding caches by path), so an evaluation is just N cheap replays.
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Objective: **maximize micro-F1** (DE minimizes, so we return -F1). NOTE: X-Ray recall
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is a face-vs-cast-in-scene ceiling (see [[xray-validation-results]]), so unconstrained
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F1 tends to push prob_threshold DOWN to recover unreachable recall — trading real
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precision for it. We therefore log precision/recall at every evaluation and print
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them at the optimum so the trade-off is visible and you can pick another operating
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point from the trajectory (--trajectory).
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Usage:
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python scripts/optimizer/optimize.py --manifest films.json \
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--gallery gallery_arcface_w600k_r50.json \
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--params prob_threshold:0.5:0.999 ownership_logodds:0.5:4 track_alpha:0:1 \
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--popsize 20 --maxiter 25 --trajectory traj.json
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"""
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from __future__ import annotations
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import argparse
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import json
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import sys
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import time
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from pathlib import Path
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import numpy as np
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from scipy.optimize import differential_evolution
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REPO = Path(__file__).resolve().parent.parent.parent
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sys.path.insert(0, str(REPO / "scripts" / "optimizer"))
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sys.path.insert(0, str(REPO / "scripts" / "validation"))
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sys.path.insert(0, str(REPO / "scripts"))
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import json as _json
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import os
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import subprocess
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import tempfile
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import threading
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from concurrent.futures import ThreadPoolExecutor
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# Concurrent per-eval replays. Each replay is an isolated subprocess, so parallelism
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# is deadlock-safe; with 9 films/eval, 8 workers replays nearly all at once. Tune via
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# REPLAY_WORKERS (8 is the measured sweet spot on this 24GB GPU).
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REPLAY_WORKERS = int(os.environ.get("REPLAY_WORKERS", "8"))
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# DE-level parallelism: how many population candidates get evaluated concurrently
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# (each spawning its own REPLAY_WORKERS film subprocesses). Total concurrent GPU
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# replay processes ≈ DE_WORKERS × min(REPLAY_WORKERS, n_films). Threads, not
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# multiprocessing — each objective() call just waits on subprocess.run, so threads
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# share the GIL fine and avoid pickling the objective/gallery-key cache.
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DE_WORKERS = int(os.environ.get("DE_WORKERS", "1"))
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from second_score import score_seconds # noqa: E402 uniform per-second TPI/FPI scoring
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from sample_eval import load_gallery_keys # noqa: E402
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from replay import dump_embedder_stamp # noqa: E402
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from sae_stamp import EmbedderMismatch, verify_gallery_stamp # noqa: E402
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_GAL_KEYS: dict = {} # gallery path → key set (fair-recall FN mask), loaded once
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_REPLAY_TIMEOUT = 45 # seconds per film; a wedged replay is killed, not left to hang
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REPLAY_CLI = str(Path(__file__).resolve().parent / "replay.py")
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def _gallery_keys(path):
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if path not in _GAL_KEYS:
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_GAL_KEYS[path] = load_gallery_keys(path)
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return _GAL_KEYS[path]
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def _replay_subprocess(dump, gallery, cfg, build_dir):
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"""Run one replay in a SUBPROCESS with a timeout, returning its presence JSON.
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In-process replay intermittently DEADLOCKS at network teardown — a KPN worker
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stuck mid-rocBLAS GEMM inside the ROCm driver makes ~PyNode's jthread.join() hang
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forever (root-caused via gdb, 2026-07-15). Isolating each replay means a wedged
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GPU thread only kills that subprocess; the sweep continues. Returns None on
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timeout/failure (the caller drops that film from the average)."""
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with tempfile.NamedTemporaryFile("r", suffix=".json", delete=False) as tf:
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out = tf.name
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argv = [sys.executable, REPLAY_CLI, "--dump", dump, "--gallery", gallery,
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"--out", out, "--build-dir", build_dir]
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for k, v in cfg.items():
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if isinstance(v, bool): # store_true flags: pass the flag, not a value
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if v:
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argv.append(f"--{k.replace('_', '-')}")
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else:
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argv += [f"--{k.replace('_', '-')}", str(v)]
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try:
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subprocess.run(argv, timeout=_REPLAY_TIMEOUT, capture_output=True, check=True)
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return _json.loads(Path(out).read_text())
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except (subprocess.TimeoutExpired, subprocess.CalledProcessError,
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FileNotFoundError, ValueError) as e:
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print(f"[opt] replay failed for {Path(dump).name}: {type(e).__name__}",
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file=sys.stderr)
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return None
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finally:
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try:
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Path(out).unlink()
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except OSError:
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pass
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def evaluate(cfg, films, build_dir, step=None):
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"""Objective = MACRO-mean over films of each film's duration-weighted per-scene F1.
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Each film's replay runs in a subprocess (timeout-guarded) to survive the
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intermittent ROCm teardown deadlock. If ANY film's replay times out, this
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evaluation is scored f1=0.0 (see below) rather than averaging over the
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survivors — a partial-coverage eval must never look better than a complete
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one, or DE will converge onto configs that make the hardest film time out.
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(An earlier version averaged over survivors, which silently rewarded
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truncation; the rep4 `mbf_full_noexp` winner was one such corrupted eval.)
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UNIFORM PER-SECOND scoring (second_score.py): every second of the film is sampled;
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GT(t) = the cast of the X-Ray scene containing t, Pred(t) = actors whose presence
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window covers t. Counts instances — TPI / FPI / FN — with FPI weighted 10× when the
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named actor isn't in the film's cast at all (a real misID vs a timing slip). FN
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counts only gallery-known actors (fair recall). Reports agreement_rate = mean
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per-second Jaccard (the "% of on-screen actors we agree with X-Ray about, over
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time"). Objective = macro-mean across films of the per-second weighted F1.
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expand_gallery: controlled by env SAE_EXPAND (default on). Set SAE_EXPAND=0 to run
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the no-expansion arm — the overnight matrix tests both to quantify what expansion buys.
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The 9 films' replays run CONCURRENTLY (REPLAY_WORKERS) — each is an isolated
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subprocess, so parallelism is safe (a wedged one only kills itself)."""
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if os.environ.get("SAE_EXPAND", "1") == "1":
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cfg = {**cfg, "expand_gallery": True}
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def _one(film):
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pj = _replay_subprocess(film["dump"], film.get("gallery"), cfg, build_dir)
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if pj is None:
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return None
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return score_seconds(pj, film["xray"],
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gallery_keys=_gallery_keys(film.get("gallery")))
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with ThreadPoolExecutor(max_workers=REPLAY_WORKERS) as ex:
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per_film = [m for m in ex.map(_one, films) if m is not None]
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n = len(per_film)
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n_expected = len(films)
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# Incomplete coverage (a replay timed out) is scored as a failure, not
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# averaged over survivors: dropping the hardest film would otherwise inflate
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# the score and let DE reward exactly the configs that cause timeouts. We
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# still record the real survivor counts so a truncated eval is diagnosable
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# in the trajectory (f1=0.0, films_scored < films_expected).
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if n < n_expected:
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agg = {"precision": 0.0, "recall": 0.0, "f1": 0.0, "agreement": 0.0,
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"TPI": sum(m["TPI"] for m in per_film),
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"FPI": sum(m["FPI"] for m in per_film),
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"FPI_misid": sum(m["FPI_misid"] for m in per_film),
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"FN": sum(m["FN"] for m in per_film)}
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agg["films_scored"] = n
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agg["films_expected"] = n_expected
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return agg
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return {"precision": sum(m["precision"] for m in per_film) / n,
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"recall": sum(m["recall"] for m in per_film) / n,
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"f1": sum(m["f1"] for m in per_film) / n,
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"agreement": sum(m["agreement_rate"] for m in per_film) / n,
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"TPI": sum(m["TPI"] for m in per_film),
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"FPI": sum(m["FPI"] for m in per_film),
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"FPI_misid": sum(m["FPI_misid"] for m in per_film),
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"FN": sum(m["FN"] for m in per_film),
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"films_scored": n,
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"films_expected": n_expected}
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def main():
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p = argparse.ArgumentParser(description=__doc__,
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formatter_class=argparse.RawDescriptionHelpFormatter)
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p.add_argument("--manifest", required=True)
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p.add_argument("--gallery", help="default gallery if not per-film")
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p.add_argument("--params", nargs="+", required=True,
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help="knob:lo:hi (e.g. prob_threshold:0.5:0.999). Int knobs kept float, rounded in cfg.")
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p.add_argument("--build-dir", default=str(REPO / "build"))
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p.add_argument("--step", type=float, default=5.0)
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p.add_argument("--popsize", type=int, default=20)
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p.add_argument("--maxiter", type=int, default=25)
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p.add_argument("--seed", type=int, default=0)
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p.add_argument("--trajectory", help="write every evaluation here (JSON lines)")
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p.add_argument("--out", help="write best config + metrics")
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# TRACES: GR-004 | SR-001
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p.add_argument("--require-gallery-stamp", action="store_true",
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help="unprovable gallery/dump model binding is a hard error, "
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"not a warning (also via SAE_REQUIRE_GALLERY_STAMP=1)")
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args = p.parse_args()
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if args.require_gallery_stamp:
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# Set the env var rather than threading a flag through cfg: replays run as
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# subprocesses and inherit it, so strictness cannot be lost in the handoff.
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os.environ["SAE_REQUIRE_GALLERY_STAMP"] = "1"
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films = json.loads(Path(args.manifest).read_text())
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for f in films:
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f.setdefault("gallery", args.gallery)
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if not Path(f["dump"]).exists():
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sys.exit(f"[opt] missing dump for {f['name']}: {f['dump']}")
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# TRACES: GR-004 | SR-001
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# every (dump, gallery) pair is checked ONCE here,
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# before the first evaluation. A DE sweep is thousands of replays; discovering
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# a cross-model pair at the end (or never) means every number it produced was
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# noise. Each replay subprocess re-checks its own pair anyway.
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for f in films:
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try:
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verify_gallery_stamp(f["gallery"], stamp=dump_embedder_stamp(f["dump"]),
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embedder_desc=f"embedding dump {Path(f['dump']).name}",
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require_stamp=args.require_gallery_stamp)
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except EmbedderMismatch as e:
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sys.exit(f"[opt] {f['name']}: {e}")
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names, bounds = [], []
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int_knobs = {"track_max_frames_missing", "cut_inactive_max_frames"}
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for spec in args.params:
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k, lo, hi = spec.split(":")
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names.append(k); bounds.append((float(lo), float(hi)))
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print(f"[opt] DE over {names} bounds={bounds}", file=sys.stderr)
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print(f"[opt] {len(films)} films, popsize={args.popsize}, maxiter={args.maxiter}", file=sys.stderr)
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traj = []
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evals = [0]
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t0 = time.time()
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traj_lock = threading.Lock()
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def vec_to_cfg(x):
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cfg = {}
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for k, v in zip(names, x):
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cfg[k] = int(round(v)) if k in int_knobs else float(v)
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# The expansion band is [lo, hi]; independent DE bounds can invert it,
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# and an inverted band admits nothing (track_gallery.hpp). Order them so
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# every candidate is a valid band rather than wasting evals on empties.
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if "expand_band_lo" in cfg and "expand_band_hi" in cfg:
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lo, hi = sorted((cfg["expand_band_lo"], cfg["expand_band_hi"]))
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cfg["expand_band_lo"], cfg["expand_band_hi"] = lo, max(hi, lo + 1e-3)
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# presence_flood is a continuous DE knob (bounds 0:1) standing in for a
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# boolean: >=0.5 selects flood-fill presence. It maps to presence_mode,
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# which is what replay/the bindings read; track_extent is the default so
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# the knob is simply omitted below the threshold.
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if "presence_flood" in cfg:
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flood = cfg.pop("presence_flood") >= 0.5
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if flood:
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cfg["presence_mode"] = "flood"
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return cfg
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def objective(x):
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cfg = vec_to_cfg(x)
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m = evaluate(cfg, films, args.build_dir, args.step)
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with traj_lock:
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evals[0] += 1
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rec = {"eval": evals[0], "config": cfg, **m, "t": round(time.time() - t0, 1)}
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traj.append(rec)
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print(f"[opt] eval {evals[0]:3d} thr={cfg['prob_threshold']:.2f} "
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f"own={cfg.get('ownership_logodds', float('nan')):.2f} → "
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f"F1={m['f1']*100:.1f}% P={m['precision']*100:.1f}% R={m['recall']*100:.1f}% "
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f"agree={m.get('agreement', 0)*100:.1f}% misID={m.get('FPI_misid', 0)}",
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file=sys.stderr)
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if args.trajectory:
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with open(args.trajectory, "a") as tf:
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tf.write(json.dumps(rec) + "\n")
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return -m["f1"]
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de_kwargs = dict(
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popsize=args.popsize, maxiter=args.maxiter,
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seed=args.seed, polish=False, tol=1e-4, mutation=(0.5, 1.0), recombination=0.7,
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init="sobol")
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if DE_WORKERS > 1:
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pool = ThreadPoolExecutor(max_workers=DE_WORKERS)
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de_kwargs["workers"] = pool.map
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result = differential_evolution(objective, bounds, **de_kwargs)
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best_cfg = vec_to_cfg(result.x)
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best = evaluate(best_cfg, films, args.build_dir, args.step)
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print("\n══ DE optimum (by F1) ═══════════════════════════")
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print(f" config : {best_cfg}")
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print(f" F1 : {best['f1']*100:.2f}%")
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print(f" precision: {best['precision']*100:.2f}% recall: {best['recall']*100:.2f}%")
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print(f" TP/FP/FN: {best['TP']}/{best['FP']}/{best['FN']}")
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print(f" evaluations: {evals[0]} time: {time.time()-t0:.0f}s")
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# Also surface the highest-precision config seen (the ship-safe operating point).
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if traj:
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hp = max(traj, key=lambda r: (r["precision"], r["recall"]))
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print("\n── highest-precision config seen (ship-safe) ──")
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print(f" config : {hp['config']}")
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print(f" P={hp['precision']*100:.2f}% R={hp['recall']*100:.2f}% F1={hp['f1']*100:.2f}%")
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if args.out:
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Path(args.out).write_text(json.dumps(
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{"best_by_f1": {"config": best_cfg, **best}, "n_evals": evals[0]}, indent=2))
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if __name__ == "__main__":
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main()
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