| """Reviewer-requested cache replay; no model fitting or additional language queries. |
| |
| The R2 rule remains primary. All sweeps are post-review diagnostics. GT is used |
| only by score(), never by projection(). Each threshold sweep has a matched |
| native comparator. Rejected expressions contribute exactly zero paired delta. |
| """ |
| from pathlib import Path |
| import argparse, collections, hashlib, json, os, time |
| import numpy as np |
| from PIL import Image |
| from scipy import ndimage |
| import phase1_repair as p1 |
| from r2_repair import scope_safe |
|
|
| ROOT=Path(os.getenv('REFSEG_ROOT',Path(__file__).resolve().parents[1])) |
| BASE=dict(anchor_margin=.05,target_margin=.05,geometry=.03,size_factor=1.,threshold_offset=0.) |
| CONFIGS={'primary':BASE.copy()} |
| for key,values in [('anchor_margin',[.02,.10]),('target_margin',[.02,.10]),('geometry',[.01,.05]),('size_factor',[.5,2.]),('threshold_offset',[-.05,.05])]: |
| for v in values:CONFIGS[key+'_'+str(v)]={**BASE,key:v} |
|
|
| def components(prob,t,factor): |
| lab,n=ndimage.label(prob>=t,structure=np.ones((3,3),int));h,w=prob.shape |
| sizes=np.bincount(lab.ravel());out=[];minimum=max(1,int(max(16,int(.0002*h*w))*factor)) |
| for i,sl in enumerate(ndimage.find_objects(lab),1): |
| if sl is None or sizes[i]<minimum:continue |
| mask=lab==i;ys,xs=np.nonzero(mask) |
| out.append(dict(mask=mask,area=int(sizes[i]),x=float(xs.mean()/w),y=float(ys.mean()/h),confidence=float(prob[mask].mean()))) |
| return sorted(out,key=lambda c:c['confidence'],reverse=True) |
|
|
| def projection(q,cs,shape,cfg): |
| base=p1.union(cs[q.original],shape);empty=np.zeros(shape,bool) |
| trace=dict(reason='fallback',plural=q.plural,relation=q.relation,anchor_margin=None,target_margin=None,boundary_distance=None) |
| def finish(m,why):trace['reason']=why;return m,trace |
| if q.negate:return finish(empty,'action') |
| ts=cs.get(q.target,cs[q.original]);trace['target_components']=len(ts) |
| if not ts:return finish(base,'target_missing') |
| if q.relation.endswith('most'):return finish(p1.global_component(ts,q.relation)['mask'],'extreme') |
| if not q.anchor: |
| if q.plural:return finish(p1.union(ts,shape),'plural') |
| tm=ts[0]['confidence']-(ts[1]['confidence'] if len(ts)>1 else 0);trace['target_margin']=tm |
| return finish(base,'target_ambiguous') if tm<cfg['target_margin'] else finish(ts[0]['mask'],'singular') |
| ac=cs[q.anchor];trace['anchor_components']=len(ac) |
| if not ac:return finish(base,'anchor_missing') |
| am=ac[0]['confidence']-(ac[1]['confidence'] if len(ac)>1 else 0);trace['anchor_margin']=am |
| if am<cfg['anchor_margin']:return finish(base,'anchor_ambiguous') |
| key='x' if q.relation in ('left','right') else 'y';sign=1 if q.relation in ('right','below') else -1 |
| distances=[sign*(c[key]-ac[0][key]) for c in ts] |
| trace['boundary_distance']=min(abs(d-cfg['geometry']) for d in distances) |
| allowed=[c for c,d in zip(ts,distances) if d>=cfg['geometry']];trace['eligible_components']=len(allowed) |
| if not allowed:return finish(base,'relation_unsatisfied') |
| tm=allowed[0]['confidence']-(allowed[1]['confidence'] if len(allowed)>1 else 0);trace['target_margin']=tm |
| if not q.plural and tm<cfg['target_margin']:return finish(base,'assignment_ambiguous') |
| return finish(p1.union(allowed if q.plural else [allowed[0]],shape),'projected') |
|
|
| def score(pred,gt,is_nt): |
| op=np.asarray(Image.fromarray(pred).resize((480,480),Image.Resampling.NEAREST),bool) |
| inter=int((op>).sum());union=int((op|gt).sum());nt=not pred.any() |
| return dict(iou=float(nt) if is_nt else inter/union if union and not nt else 0.,intersection=inter if not is_nt else 0,union=0 if is_nt and nt else union,pred_nt=bool(nt)) |
|
|
| def main(): |
| ap=argparse.ArgumentParser();ap.add_argument('--model',choices=['clipseg','groundedsam'],required=True);ap.add_argument('--shard',type=int,default=0);ap.add_argument('--shards',type=int,default=16);ap.add_argument('--limit',type=int,default=0);a=ap.parse_args() |
| rows=[json.loads(s) for s in (ROOT/'data/natural_full.jsonl').read_text().splitlines()] |
| groups=collections.defaultdict(list) |
| for r in rows:groups[r['image_path']].append(r) |
| paths=[p for p in sorted(groups) if any(scope_safe(r['query'])[0] for r in groups[p])][a.shard::a.shards] |
| if a.limit:paths=paths[:a.limit] |
| dest=ROOT/'results/r3';dest.mkdir(parents=True,exist_ok=True) |
| name=f'{a.model}_{a.shard}of{a.shards}'+('_pilot' if a.limit else '') |
| output=dest/(name+'.jsonl');count=0;t0=time.time();native_t=.5 if a.model=='clipseg' else .25 |
| with output.open('w') as out: |
| for idx,path in enumerate(paths): |
| group=groups[path];selected=[r for r in group if scope_safe(r['query'])[0]] |
| texts=list(dict.fromkeys(q for r in group for q in p1.query_plan(r['query'])[1])) |
| needed=set(q for r in selected for q in p1.query_plan(r['query'])[1]);maps={} |
| for start in range(0,len(texts),12): |
| batch=texts[start:start+12] |
| if not needed.intersection(batch):continue |
| digest=hashlib.sha256((path+'\n'+'\n'.join(batch)).encode()).hexdigest()[:24] |
| cp=ROOT/'cache'/a.model/(digest+'.npz') |
| with np.load(cp) as z: |
| assert z['texts'].tolist()==batch,(cp,'query mismatch') |
| maps.update({q:z[f'p{i}'].astype(np.float32) for i,q in enumerate(batch) if q in needed}) |
| cc={};gtcache={} |
| for r in selected: |
| q=p1.parse_query(r['query']);keys=list(dict.fromkeys([q.original,q.target]+([q.anchor] if q.anchor else []))) |
| if r['ref_id'] not in gtcache:gtcache[r['ref_id']]=np.asarray(Image.open(ROOT/'data/natural/masks_official480'/f"ref_{r['ref_id']}.png"))>0 |
| gt=gtcache[r['ref_id']];allres={};tr=None;primary_mask=None |
| for nameconf,cfg in CONFIGS.items(): |
| threshold=native_t+cfg['threshold_offset'];cs={} |
| for key in keys: |
| ck=(key,threshold,cfg['size_factor']) |
| if ck not in cc:cc[ck]=components(maps[key],threshold,cfg['size_factor']) |
| cs[key]=cc[ck] |
| shape=maps[q.original].shape;base=p1.union(cs[q.original],shape) |
| pred,trace=projection(q,cs,shape,cfg) |
| allres[nameconf]=dict(frozen=score(base,gt,r['target_count']==0),sfap=score(pred,gt,r['target_count']==0),changed=bool((base!=pred).any())) |
| if nameconf=='primary':tr=trace;primary_mask=pred.copy() |
| else:allres[nameconf]['different_from_primary']=bool((primary_mask!=pred).any()) |
| row={k:r[k] for k in ['id','scene_id','split','query','target_count']} |
| row.update(model=a.model,trace=tr,configs=allres) |
| out.write(json.dumps(row)+'\n');count+=1 |
| out.flush() |
| if idx%10==0:print(json.dumps(dict(image=idx,images=len(paths),records=count,seconds=time.time()-t0)),flush=True) |
| meta=dict(status='COMPLETE',records=count,images=len(paths),seconds=time.time()-t0,args=vars(a),configs=CONFIGS,job_id=os.getenv('SLURM_JOB_ID')) |
| output.with_suffix('.meta.json').write_text(json.dumps(meta,indent=2));print(json.dumps(meta),flush=True) |
| if __name__=='__main__':main() |
|
|
