| """Rebuild primary paper metrics from shipped per-record scores, without TRUBA.""" |
| from pathlib import Path |
| import csv,gzip,json,math,collections |
| ROOT=Path(__file__).resolve().parents[1] |
| def describe(rows): |
| pos=[r for r in rows if int(r['target_count'])>0];neg=[r for r in rows if int(r['target_count'])==0] |
| mean=lambda xs:sum(xs)/len(xs) if xs else None |
| return dict(n=len(rows),gIoU=mean([float(r['iou']) for r in rows]),cIoU=sum(int(r['intersection']) for r in rows)/max(1,sum(int(r['union']) for r in rows)),positive_mIoU=mean([float(r['iou']) for r in pos]),Nacc=mean([int(r['pred_nt']) for r in neg]),false_empty=mean([int(r['pred_nt']) for r in pos]),Pr50=mean([float(r['iou'])>=.5 for r in pos])) |
| def main(): |
| expected=json.loads((ROOT/'results/analysis/paper_results.json').read_text());reports=[];maxerr=0. |
| for dataset in ['natural','rich']: |
| for model in ['clipseg','groundedsam','rela']: |
| p=ROOT/'results/r3/portable'/f'{dataset}_{model}_primary.csv.gz' |
| with gzip.open(p,'rt') as f:allrows=list(csv.DictReader(f)) |
| for e in expected[dataset][model]['tables']: |
| if e['variant'] not in ['frozen','safe_anchor']:continue |
| variant='anchor_gate' if dataset=='rich' and e['variant']=='safe_anchor' else e['variant'] |
| rows=[r for r in allrows if r['variant']==variant] |
| if dataset=='natural':rows=[r for r in rows if e['split']=='all' or r['split']==e['split']] |
| else:rows=[r for r in rows if r['corruption']==e['render'] and r['mode'] in ['attribute','relational','multi_target','empty_target']] |
| assert len(rows)==e['n'],(dataset,model,variant,len(rows),e['n']) |
| z=describe(rows) |
| if dataset=='rich': |
| pairs=collections.defaultdict(list) |
| for r in rows:pairs[r['pair_id']].append(float(r['iou'])) |
| assert all(len(v)==2 for v in pairs.values()) |
| z['PC50']=sum(all(x>=.5 for x in v) for v in pairs.values())/len(pairs) |
| for k,v in z.items(): |
| if k in e and v is not None: |
| err=abs(v-e[k]);maxerr=max(maxerr,err);assert err<1e-10,(dataset,model,variant,k,v,e[k]) |
| reports.append(dict(dataset=dataset,model=model,variant=e['variant'],split=e.get('split',e.get('render')),**z)) |
| |
| summaries=json.loads((ROOT/'results/r3/review_results.json').read_text()) |
| for model in ['clipseg','groundedsam']: |
| with gzip.open(ROOT/'results/r3'/f'{model}_replay.jsonl.gz','rt') as f:rows=[json.loads(s) for s in f] |
| assert len(rows)==1557 |
| for e in summaries[model]['sensitivity']: |
| d=[r['configs'][e['config']]['sfap']['iou']-r['configs'][e['config']]['frozen']['iou'] for r in rows] |
| assert abs(sum(d)/49492-e['delta'])<1e-12 |
| assert sum(v< -1e-12 for v in d)==e['harmed'] |
| report=dict(status='PASS',metric_rows=len(reports),max_absolute_error=maxerr,primary_tables=reports,sensitivity_settings_replayed=22) |
| out=ROOT/'results/r3/portable_verification.json';out.write_text(json.dumps(report,indent=2)) |
| print(json.dumps({k:v for k,v in report.items() if k!='primary_tables'},indent=2)) |
| if __name__=='__main__':main() |
|
|
