pre-refactor 041426
This commit is contained in:
rpotter6298
@@ -0,0 +1,170 @@
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"""
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Dispatch a 10×5 rep-CV of logit_mlp_head with --save-checkpoints.
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Results land in v3/results/phase5/logit_mlp_head_ckpt/{rep00..rep09}/binary/ensemble/
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Usage:
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# Dry run
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python -m v3.scripts.main.phase5.dispatch_logit_mlp_ckpt --dry-run
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# Submit to server
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python -m v3.scripts.main.phase5.dispatch_logit_mlp_ckpt \
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--server http://hades:8765 --token hypertower
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# Skip reps already done, re-queue only missing ones:
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python -m v3.scripts.main.phase5.dispatch_logit_mlp_ckpt \
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--server http://hades:8765 --token hypertower
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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 os
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import sys
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from pathlib import Path
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import requests
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sys.path.insert(0, str(Path(__file__).resolve().parents[4]))
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RUN_NAME = "phase5/logit_mlp_head_ckpt"
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MODULE = "v3.scripts.main.run_cv"
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OUTPUT_DIR = "v3/results"
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RESULTS_ROOT = Path(__file__).resolve().parents[4] / "v3" / "results"
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REP_SEED_START = 100
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REP_SEED_STEP = 100
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N_REPS = 10
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RUN_ARGS = [
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"--eval-mode", "binary",
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"--bridge-mode", "fused",
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"--tower-mode", "ensemble",
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"--fused-head",
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"--head-type", "logit_mlp",
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"--epochs", "30",
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"--in-memory-cache",
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"--augment",
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"--tune-binary-threshold",
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"--backbone", "refugelike",
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"--iop-corr-method", "ratio",
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"--iop-drop-raw",
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"--exclude-cols", "Axial_Length",
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"--output-root", "v3/results",
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"--save-checkpoints",
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]
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# ── Completion check ──────────────────────────────────────────────────────────
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def _completed_reps(reps: int) -> list[int]:
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done = []
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for i in range(reps):
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rep_dir = RESULTS_ROOT / RUN_NAME / f"rep{i:02d}" / "binary" / "ensemble"
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if (rep_dir / "summary.json").exists():
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# Also verify at least one checkpoint exists
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if any(rep_dir.glob("fold*/best_single.pt")):
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done.append(i)
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else:
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print(f" [warn] rep{i:02d} has summary.json but no checkpoints — will re-queue")
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return done
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# ── Server API ────────────────────────────────────────────────────────────────
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class _API:
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def __init__(self, base_url: str, token: str):
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self.base_url = base_url.rstrip("/")
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self._h = {"x-token": token}
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def get(self, path: str, **params) -> object:
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r = requests.get(f"{self.base_url}{path}", headers=self._h, params=params, timeout=10)
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r.raise_for_status()
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return r.json()
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def post(self, path: str, body: dict) -> dict:
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r = requests.post(f"{self.base_url}{path}", headers=self._h, json=body, timeout=10)
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r.raise_for_status()
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return r.json()
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def _queued_reps(jobs: list[dict]) -> set[int]:
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active = set()
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for job in jobs:
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if job.get("run_name") != RUN_NAME:
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continue
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if job["state"] not in ("pending", "running"):
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continue
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try:
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args = job["args"] if isinstance(job["args"], list) else json.loads(job["args"])
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if "--rep-index" in args:
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active.add(int(args[args.index("--rep-index") + 1]))
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except Exception:
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pass
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return active
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# ── Main ──────────────────────────────────────────────────────────────────────
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def main():
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ap = argparse.ArgumentParser(description=__doc__,
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formatter_class=argparse.RawDescriptionHelpFormatter)
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ap.add_argument("--server", default=os.environ.get("HT_SERVER", ""))
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ap.add_argument("--token", default=os.environ.get("HT_TOKEN", ""))
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ap.add_argument("--reps", type=int, default=N_REPS)
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ap.add_argument("--dry-run", action="store_true")
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args = ap.parse_args()
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if not args.dry_run:
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if not args.server:
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ap.error("--server required (or set HT_SERVER)")
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if not args.token:
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ap.error("--token required (or set HT_TOKEN)")
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api = _API(args.server, args.token) if (args.server and args.token) else None
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server_jobs: list[dict] = []
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if api:
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try:
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all_jobs = api.get("/jobs")
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server_jobs = [j for j in all_jobs if j["state"] in ("pending", "running")]
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print(f"[server] {len(server_jobs)} job(s) pending/running")
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except Exception as e:
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print(f"[warn] could not fetch queue: {e}")
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done = set(_completed_reps(args.reps))
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queued = _queued_reps(server_jobs)
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missing = [i for i in range(args.reps) if i not in (done | queued)]
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print(f"\nRun: {RUN_NAME}")
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print(f" Done: {sorted(done)}")
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print(f" Queued: {sorted(queued - done)}")
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print(f" Missing: {missing}")
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if not missing:
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print("Nothing to submit.")
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return
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for i in missing:
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seed = REP_SEED_START + i * REP_SEED_STEP
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rep_args = RUN_ARGS + [
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"--run-name", RUN_NAME,
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"--reps", "1",
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"--rep-seed-start", str(seed),
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"--rep-index", str(i),
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]
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body = {
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"run_name": RUN_NAME,
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"module": MODULE,
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"args": rep_args,
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"output_dir": OUTPUT_DIR,
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"priority": 0,
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}
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if args.dry_run:
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print(f" [dry-run] rep{i:02d} seed={seed}")
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else:
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resp = api.post("/jobs", body)
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print(f" queued rep{i:02d} seed={seed} job_id={resp['job_id']}")
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,199 @@
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"""
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Dispatch phase 5 experiment runs to the distributed job server.
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Reads experiment_grid.json, checks which runs already have complete 10x5 results,
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and submits the rest. Skips runs marked needs_implementation.
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Usage:
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python -m v3.scripts.main.phase5.dispatch_phase5 \
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--server http://hades:8765 --token hypertower
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# Dry run (print what would be submitted, don't actually submit):
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python -m v3.scripts.main.phase5.dispatch_phase5 \
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--server http://hades:8765 --token hypertower --dry-run
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# Override number of reps (default 10):
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python -m v3.scripts.main.phase5.dispatch_phase5 \
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--server http://hades:8765 --token hypertower --reps 4
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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 os
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import sys
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from pathlib import Path
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import requests
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# Allow running as `python v3/scripts/main/phase3/dispatch_phase3.py`
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sys.path.insert(0, str(Path(__file__).resolve().parents[4]))
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GRID_PATH = Path(__file__).parent / "experiment_grid.json"
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RESULTS_ROOT = Path(__file__).resolve().parents[4] / "v3" / "results"
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MODULE = "v3.scripts.main.run_cv"
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OUTPUT_DIR = "v3/results"
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REP_SEED_START = 100
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REP_SEED_STEP = 100
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# ── Completion check ──────────────────────────────────────────────────────────
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def _completed_reps(run_name: str, reps: int) -> list[int]:
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"""Return list of rep indices that already have a summary.json (any tower mode)."""
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done = []
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for i in range(reps):
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rep_dir = RESULTS_ROOT / run_name / f"rep{i:02d}" / "binary"
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# Accept any tower mode subdir
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if rep_dir.exists() and any((rep_dir / tm / "summary.json").exists()
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for tm in ("single", "bilateral", "siamese", "ensemble")):
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done.append(i)
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return done
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# ── Server API ────────────────────────────────────────────────────────────────
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class _API:
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def __init__(self, base_url: str, token: str):
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self.base_url = base_url.rstrip("/")
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self._h = {"x-token": token}
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def get(self, path: str, **params) -> object:
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r = requests.get(f"{self.base_url}{path}", headers=self._h, params=params, timeout=10)
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r.raise_for_status()
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return r.json()
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def post(self, path: str, body: dict) -> dict:
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r = requests.post(f"{self.base_url}{path}", headers=self._h, json=body, timeout=10)
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r.raise_for_status()
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return r.json()
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def _queued_reps(jobs: list[dict], run_name: str) -> set[int]:
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"""Return rep indices already pending or running in the server queue."""
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active = set()
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for job in jobs:
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if job["run_name"] != run_name:
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continue
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if job["state"] not in ("pending", "running"):
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continue
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# Extract --rep-index from job args
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try:
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args = job["args"] if isinstance(job["args"], list) else json.loads(job["args"])
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if "--rep-index" in args:
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active.add(int(args[args.index("--rep-index") + 1]))
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except Exception:
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pass
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return active
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def _submit_cv(api: _API, run_name: str, run_args: list[str],
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reps: int, missing: list[int], dry_run: bool):
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"""Submit one job per missing rep."""
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for i in missing:
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seed = REP_SEED_START + i * REP_SEED_STEP
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rep_args = run_args + [
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"--run-name", run_name,
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"--reps", "1",
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"--rep-seed-start", str(seed),
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"--rep-index", str(i),
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]
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body = {
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"run_name": run_name,
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"module": MODULE,
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"args": rep_args,
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"output_dir": OUTPUT_DIR,
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"priority": 0,
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}
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if dry_run:
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print(f" [dry-run] would queue rep{i:02d} seed={seed}")
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else:
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resp = api.post("/jobs", body)
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print(f" queued rep{i:02d} seed={seed} job_id={resp['job_id']}")
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# ── Main ──────────────────────────────────────────────────────────────────────
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def main():
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ap = argparse.ArgumentParser(description=__doc__,
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formatter_class=argparse.RawDescriptionHelpFormatter)
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ap.add_argument("--server", default=os.environ.get("HT_SERVER", ""),
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help="Server URL (or set HT_SERVER)")
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ap.add_argument("--token", default=os.environ.get("HT_TOKEN", ""),
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help="Shared secret (or set HT_TOKEN)")
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ap.add_argument("--reps", type=int, default=10,
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help="Expected number of reps per run (default: 10)")
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ap.add_argument("--grid", type=Path, default=GRID_PATH,
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help="Path to experiment grid JSON (default: experiment_grid.json)")
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ap.add_argument("--dry-run", action="store_true",
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help="Print what would be submitted without actually submitting")
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args = ap.parse_args()
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if not args.dry_run:
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if not args.server:
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ap.error("--server is required (or set HT_SERVER)")
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if not args.token:
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ap.error("--token is required (or set HT_TOKEN)")
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elif not args.server or not args.token:
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print("[dry-run] no --server/--token provided — skipping queue check, showing disk state only")
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grid = json.loads(args.grid.read_text())
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common_args = grid["common_args"]
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api = _API(args.server, args.token) if (args.server and args.token) else None
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# Fetch current server queue once (pending + running)
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server_jobs: list[dict] = []
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if api:
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try:
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all_jobs = api.get("/jobs")
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server_jobs = [j for j in all_jobs if j["state"] in ("pending", "running")]
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print(f"[server] {len(server_jobs)} job(s) currently pending/running in queue")
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except Exception as e:
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print(f"[warn] could not fetch server queue: {e}")
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# Collect all runs: baseline + every group's runs
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all_runs = [grid["baseline"]]
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for group in grid["groups"]:
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if group.get("needs_implementation"):
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print(f"\n[skip] group '{group['name']}' — {group['needs_implementation']}")
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continue
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all_runs.extend(group["runs"])
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submitted_total = 0
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skipped_total = 0
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for run in all_runs:
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run_name = run["run_name"]
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run_args = common_args + run.get("extra_args", [])
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done = set(_completed_reps(run_name, args.reps))
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queued = _queued_reps(server_jobs, run_name)
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accounted = done | queued
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missing = [i for i in range(args.reps) if i not in accounted]
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if not missing:
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if len(done) == args.reps:
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print(f"\n[done] {run_name} ({args.reps}/{args.reps} reps complete)")
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else:
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in_q = sorted(queued - done)
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print(f"\n[skip] {run_name} ({len(done)} done, {len(in_q)} queued: {[f'rep{i:02d}' for i in in_q]})")
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skipped_total += 1
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continue
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parts = []
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if done: parts.append(f"{len(done)} done")
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if queued: parts.append(f"{len(queued - done)} queued")
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status = ", ".join(parts) if parts else "not started"
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print(f"\n[queue] {run_name} ({status}) — submitting {len(missing)} rep(s)")
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_submit_cv(api, run_name, run_args, args.reps, missing, args.dry_run)
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submitted_total += len(missing)
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print(f"\n{'='*50}")
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print(f"Submitted: {submitted_total} jobs | Already accounted for: {skipped_total} runs")
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if grid.get("groups"):
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needs_impl = sum(1 for g in grid["groups"] if g.get("needs_implementation"))
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if needs_impl:
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print(f"Skipped (needs implementation): {needs_impl} group(s)")
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,87 @@
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{
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"_notes": [
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"Phase 5 — Full HyperTower: bilateral + clinical data + aggregation strategy comparison.",
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"Goal: show the effect of a dual CNN + clinical data, and compare ensemble vs fused-head.",
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"Best settings from all prior phases: refugelike, iop_ratio_drop_raw, bcd_p05.",
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"Best bilateral architecture from phase 4 should be used — update tower-mode accordingly.",
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"common_args are prepended to every run's args list.",
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"NOTE: update --tower-mode in groups below once phase 4 winner is known.",
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"Placeholder uses 'bilateral' — change to 'siamese' if that wins phase 4."
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],
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"common_args": [
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"--eval-mode", "binary",
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"--bridge-mode", "fused",
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"--epochs", "30",
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"--in-memory-cache",
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"--augment",
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"--tune-binary-threshold",
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"--backbone", "refugelike",
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"--iop-corr-method", "ratio",
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"--iop-drop-raw",
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"--exclude-cols", "Axial_Length",
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"--output-root", "v3/results"
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],
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"_common_args_implicit_defaults": {
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"--tower-loss-mode": "bcd",
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"--bcd-prob": "0.5",
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"--warmup-cd-epochs": "40",
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"--single-warmup-tower-epochs": "3",
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"--single-warmup-fused-epochs": "3",
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"--bilat-warmup-tower-epochs": "3",
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"--bilat-warmup-fused-epochs": "3"
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},
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"baseline": {
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"run_name": "phase5/single_fused",
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"description": "Single-eye + clinical data — phase 3 best config, re-run as direct comparison baseline for phase 5.",
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"extra_args": ["--tower-mode", "single"]
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},
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"groups": [
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{
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"name": "bilateral_clinical",
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"description": "Add clinical data to bilateral architectures.",
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"runs": [
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{
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"run_name": "phase5/ensemble_fused",
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"description": "Ensemble (independent OD+OS) + clinical data via fused bridge.",
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"extra_args": ["--tower-mode", "ensemble"]
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},
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{
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"run_name": "phase5/bilateral_fused",
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"description": "BilateralHT + clinical data — full canonical HyperTower.",
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"extra_args": ["--tower-mode", "bilateral"]
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},
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{
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"run_name": "phase5/siamese_fused",
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"description": "SiameseHT + clinical data — siamese mean+delta with fused clinical bridge.",
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"extra_args": ["--tower-mode", "siamese"]
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}
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]
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},
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{
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"name": "aggregation",
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"description": "Compare patient-level prediction aggregation strategies on top of ensemble.",
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"runs": [
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{
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"run_name": "phase5/ensemble_fused_head",
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"description": "Ensemble + clinical data + attention scorer head (Linear(C→1) per eye, softmax-weighted average).",
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"extra_args": ["--tower-mode", "ensemble", "--fused-head"]
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||||
},
|
||||
{
|
||||
"run_name": "phase5/logit_mlp_head",
|
||||
"description": "Ensemble + clinical data + logit-level MLP head (cat([logit_od, logit_os]) → FC(64) → FC(C)).",
|
||||
"extra_args": ["--tower-mode", "ensemble", "--fused-head", "--head-type", "logit_mlp"]
|
||||
},
|
||||
{
|
||||
"run_name": "phase5/embedding_mlp_head",
|
||||
"description": "Ensemble + clinical data + embedding-level MLP head (cat([z_od, z_os]) → FC(256) → FC(C)).",
|
||||
"extra_args": ["--tower-mode", "ensemble", "--fused-head", "--head-type", "embedding_mlp"]
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
Reference in New Issue
Block a user