File size: 4,534 Bytes
8511d54 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 | #!/usr/bin/env python
"""Checkpoint selection for the orbit-alpha LoRA.
Fixed 48-edit subset (first 12 eval objects x 4 pairs) at 20 steps, for the
untouched base model AND every saved checkpoint, all through ONE pipeline load
(adapter unload/load between checkpoints). Choice rule per spec: best LPIPS
that does not lose alpha IoU vs the base model.
Usage: python select_ckpt.py --out /out/ckpt_select
Uploads summary + per-edit results to ysharma/gso-orbit-rgba/eval/ckpt_select/.
"""
import argparse, json, os, collections
import numpy as np
import eval_edit as ee
# 20-step selection pass, per spec (ee.run_pairs reads module-level STEPS)
ee.STEPS = 20
MODEL_REPO = 'ysharma/orbit-alpha-lora'
CKPT_DIR = 'checkpoints/steps2000res768/orbit_alpha_lora'
CKPTS = [
('ckpt500', f'{CKPT_DIR}/orbit_alpha_lora_000000500.safetensors'),
('ckpt1000', f'{CKPT_DIR}/orbit_alpha_lora_000001000.safetensors'),
('ckpt1500', f'{CKPT_DIR}/orbit_alpha_lora_000001500.safetensors'),
('ckpt2000', f'{CKPT_DIR}/orbit_alpha_lora.safetensors'),
]
DS_REPO = ee.DS_REPO
OUT_REPO_PATH = 'eval/ckpt_select'
def pick_subset(pairs, n_obj=12):
by_obj = collections.OrderedDict()
for p in pairs:
by_obj.setdefault(p['object_id'], []).append(p)
objs = list(by_obj)[:n_obj]
sel = [p for o in objs for p in by_obj[o]]
assert len(objs) == n_obj and all(len(by_obj[o]) == 4 for o in objs), \
f'expected {n_obj} objects x 4 pairs, got {len(objs)} objs'
return sel
def swap_adapter(pipe, path):
try:
pipe.unload_lora_weights()
except Exception as e:
print(f'[swap] unload_lora_weights failed ({e!r}); continuing with load', flush=True)
pipe.load_lora_weights(path)
print(f'[swap] loaded {path}', flush=True)
def agg_rows(rows, label):
a = float(np.mean([r['alpha_iou'] for r in rows]))
l = float(np.mean([r['lpips'] for r in rows]))
p = float(np.mean([r['psnr'] for r in rows]))
d = float(np.mean([r['dino_sim'] for r in rows]))
s = float(np.mean([r['s_edit'] for r in rows]))
print(f'{label}: n={len(rows)} aIoU={a:.3f} LPIPS={l:.4f} PSNR={p:.2f} DINO={d:.3f} s/edit={s:.1f}', flush=True)
return {'name': label, 'n': len(rows), 'alpha_iou': round(a, 4),
'lpips': round(l, 4), 'psnr': round(p, 2),
'dino_sim': round(d, 4), 's_edit': round(s, 2)}
def upload(local, remote):
from huggingface_hub import HfApi
HfApi().upload_file(path_or_fileobj=local, path_in_repo=remote,
repo_id=DS_REPO, repo_type='dataset',
commit_message=f'ckpt-select: {remote}')
def main():
ap = argparse.ArgumentParser()
ap.add_argument('--out', required=True)
args = ap.parse_args()
os.makedirs(args.out, exist_ok=True)
pairs = pick_subset([json.loads(l) for l in open(ee.get_file('pairs_eval.jsonl'))])
print(f'subset: {len(pairs)} edits, objects={sorted({p["object_id"] for p in pairs})[:3]}...', flush=True)
pipe = ee.load_pipe()
m = ee.make_metrics()
summary = []
# base model at 20 steps first (reference point)
rows = ee.run_pairs(pipe, m, pairs, 'instruction', os.path.join(args.out, 'base20'))
summary.append(agg_rows(rows, 'base20'))
base_aiou = summary[0]['alpha_iou']
for name, path in CKPTS:
local = ee.hf_hub_download(repo_id=MODEL_REPO, repo_type='model', filename=path)
swap_adapter(pipe, local)
rows = ee.run_pairs(pipe, m, pairs, 'instruction', os.path.join(args.out, name))
r = agg_rows(rows, name)
r['kept_aiou'] = bool(r['alpha_iou'] >= base_aiou)
summary.append(r)
# choice rule: best LPIPS among checkpoints that do not lose alpha IoU
ok = [s for s in summary[1:] if s['kept_aiou']]
chosen = min(ok, key=lambda s: s['lpips']) if ok else None
if chosen is None:
chosen = min(summary[1:], key=lambda s: s['lpips'])
print('WARNING: no checkpoint kept base alpha IoU; fell back to best LPIPS overall', flush=True)
summary.append({'chosen': chosen['name']})
with open(os.path.join(args.out, 'summary.json'), 'w') as f:
json.dump(summary, f, indent=2)
print('SUMMARY ' + json.dumps(summary), flush=True)
upload(os.path.join(args.out, 'summary.json'), f'{OUT_REPO_PATH}/summary.json')
for name, _ in [('base20', None)] + CKPTS:
upload(os.path.join(args.out, name, 'results.jsonl'), f'{OUT_REPO_PATH}/{name}_results.jsonl')
print('DONE', flush=True)
if __name__ == '__main__':
main() |