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12ecf8e 482faf4 12ecf8e 482faf4 12ecf8e 34b649d 482faf4 34b649d 482faf4 34b649d 482faf4 34b649d 12ecf8e 482faf4 12ecf8e 482faf4 12ecf8e 482faf4 12ecf8e | 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 | #!/usr/bin/env python
"""Final comparison: chosen checkpoint (steps2000) on the full 160-edit
held-out set at 40 steps, run twice: with the transparency suffix (deployed
usage, matches training captions) and without (apples-to-apples with the
baselines, which ran bare instructions). Reuses eval_edit.py's pipeline,
metrics and seeding so numbers are directly comparable to the baseline run.
Self-contained for job bootstrap: fetches its sibling eval_edit.py from the
dataset repo, resolves the LoRA checkpoint from the model repo via
hf_hub_download, and uploads results to the dataset repo at the end.
Usage:
python final_eval.py --lora checkpoints/steps2000res768/orbit_alpha_lora/orbit_alpha_lora.safetensors --out /out/final
"""
import argparse, json, os, sys
_here = os.path.dirname(os.path.abspath(__file__))
if _here not in sys.path:
sys.path.insert(0, _here)
# The job bootstrap only runs this file; pull eval_edit.py (pipeline/metrics/
# seeding shared with the baseline run) from the dataset repo.
if not os.path.exists(os.path.join(_here, 'eval_edit.py')):
from huggingface_hub import hf_hub_download
import shutil
p = hf_hub_download(repo_id='ysharma/gso-orbit-rgba', repo_type='dataset',
filename='eval_edit.py')
shutil.copy(p, os.path.join(_here, 'eval_edit.py'))
print(f'fetched eval_edit.py -> {os.path.join(_here, "eval_edit.py")}', flush=True)
import eval_edit as E
def main():
ap = argparse.ArgumentParser()
ap.add_argument('--lora', required=True)
ap.add_argument('--out', required=True)
args = ap.parse_args()
# LoRA: local path if it exists, else a file path inside ysharma/orbit-alpha-lora.
lora_path = args.lora
if not os.path.exists(lora_path):
from huggingface_hub import hf_hub_download
lora_path = hf_hub_download(repo_id='ysharma/orbit-alpha-lora',
filename=args.lora)
print(f'using lora: {lora_path}', flush=True)
pairs = [json.loads(l) for l in open(E.get_file('pairs_eval.jsonl'))]
assert len(pairs) == 160, f'expected 160 eval pairs, got {len(pairs)}'
pipe = E.load_pipe()
m = E.make_metrics()
pipe.load_lora_weights(lora_path)
for variant, suffix in (('no_suffix', ''), ('suffix', ' ' + E.ALPHA_SUFFIX)):
pv = [dict(p, instruction=p['instruction'] + suffix) for p in pairs]
r = E.run_pairs(pipe, m, pv, 'instruction', os.path.join(args.out, variant))
E.agg(r, f'FINAL/{variant}')
# side-by-side contact sheet: pred-with-suffix vs GT for 12 objects
try:
from PIL import Image
sheet_pairs = pairs[::13][:12]
tiles = []
for p in sheet_pairs:
pred = Image.open(os.path.join(args.out, 'suffix', p['pair_id'] + '.png'))
gt = Image.open(E.get_file(p['target_file'])).convert('RGBA')
for im in (pred, gt):
bg = Image.new('RGB', im.size, (128, 128, 128))
bg.paste(im, mask=im.split()[3])
tiles.append(bg.resize((256, 256)))
sheet = Image.new('RGB', (256 * 4, 256 * 6), (30, 30, 30))
for i, t in enumerate(tiles):
sheet.paste(t, ((i % 4) * 256, (i // 4) * 256))
sheet.save(os.path.join(args.out, 'final_sheet.png'))
print('sheet written', flush=True)
except Exception as e:
print(f'sheet failed (non-fatal): {e}', flush=True)
# results survive the container only if pushed from inside the job
from huggingface_hub import HfApi
HfApi().upload_folder(folder_path=args.out, repo_id='ysharma/gso-orbit-rgba',
repo_type='dataset', path_in_repo='eval/final')
print('uploaded results -> ysharma/gso-orbit-rgba/eval/final', flush=True)
print('DONE', flush=True)
if __name__ == '__main__':
main() |