| import argparse |
| import csv |
| import hashlib |
| import json |
| import shutil |
| from pathlib import Path |
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| ROOT = Path(__file__).resolve().parents[3] |
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| def main(): |
| ap = argparse.ArgumentParser(description='Register new canonical patch cells from a staging folder.') |
| ap.add_argument('--cells', required=True, help='csv with dataset, encoder, scale, top1_bacc_rule, best_top_p') |
| ap.add_argument('--staging', required=True, help='folder holding top1-out/{native,multiscale}-build/packets, topp-out/pooled, text-out and best-top-p-hpc.csv') |
| ap.add_argument('--tag', required=True, help='suffix of the new top-1 folders, for example 261028') |
| a = ap.parse_args() |
| staging = Path(a.staging) |
| rows = list(csv.DictReader(open(a.cells))) |
| tsv, best_csv, cells_json = ROOT / 'embedding-acquire/hpc-run/jobs/cells.tsv', ROOT / 'experiments/ex-042-top-p/results/best-top-p.csv', ROOT / 'experiments/miscellaneous/shared/cells.json' |
| have = {tuple(l.split('\t')[:2]) for l in tsv.read_text().splitlines()[1:]} |
| hpc = {(r['dataset'], r['encoder'], r['scale']): r for r in csv.DictReader(open(staging / 'best-top-p-hpc.csv'))} |
| cfg = json.loads(cells_json.read_text()) |
| with open(tsv, 'a') as t, open(best_csv, 'a', newline='') as b: |
| bw = csv.writer(b) |
| for r in rows: |
| d, e, s, p = r['dataset'], r['encoder'], r['scale'], int(r['best_top_p']) |
| if (d, e) in have: |
| print(f'skip {d} {e}: already a canonical cell') |
| continue |
| kind = 'native' if s == 'native' else 'multiscale' |
| name = f'{d}--{e}--{s}--short--filtered-class.pt' |
| top1 = ROOT / f'data/packets/patch/top1/{kind}-{a.tag}/packets/{name}' |
| top1.parent.mkdir(parents=True, exist_ok=True) |
| shutil.copy2(staging / f'top1-out/{kind}-build/packets/{name}', top1) |
| shutil.copy2(staging / f'text-out/{d}--{e}.pt', ROOT / f'data/packets/text/{d}--{e}.pt') |
| if p > 1: |
| shutil.copy2(staging / f'topp-out/pooled/{d}--{e}--{s}--P{p}.pt', ROOT / f'data/packets/patch/pooled/{d}--{e}--{s}--P{p}.pt') |
| rel = str(top1.relative_to(ROOT)) |
| t.write(f'{d}\t{e}\t{s}\tpatch\t{float(r["top1_bacc_rule"]):.2f}\t{rel}\t{top1.stat().st_size}\t{hashlib.sha256(top1.read_bytes()).hexdigest()}\n') |
| h = hpc[(d, e, s)] |
| if int(h['P']) != p: |
| raise ValueError(f'{d} {e} {s}: best Top-P {h["P"]} in the job output differs from {p} in the cell list') |
| bw.writerow([h[k] for k in ('dataset', 'encoder', 'scale', 'P', 'f1_weighted', 'balanced_accuracy', 'auc', 'slides', 'slides_out_of_patches')]) |
| for setting, top_p in (('top1', 1), ('best', p)): |
| cfg[setting][f'{d}--{e}'] = dict(dataset=d, encoder=e, scale=s, packet=rel, top_p=top_p) |
| print(f'added {d} {e} {s} P{p}') |
| cells_json.write_text(json.dumps(cfg, indent=1) + '\n') |
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| if __name__ == '__main__': |
| main() |
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