Datasets:
File size: 12,733 Bytes
9126e0d | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 | """Aggregate only complete executed shards; expression-weighted cluster CIs."""
from pathlib import Path
import json,gzip,collections,csv,math,argparse,hashlib,os
import numpy as np
from scipy.stats import binomtest,norm
ROOT=Path(__file__).resolve().parents[1];from project_paths import legacy_root;OLD=legacy_root(ROOT)
DEST=ROOT/'results/analysis';DEST.mkdir(parents=True,exist_ok=True)
def readlines(p):
with (gzip.open(p,'rt') if str(p).endswith('.gz') else p.open()) as f:return [json.loads(s) for s in f]
def ci_cluster(rows,values,alpha=.05,B=2000):
groups=collections.defaultdict(list)
for r,v in zip(rows,values):groups[(r.get('split','test'),r['scene_id'])].append(float(v))
if not groups:return [None,None]
strata=collections.defaultdict(list)
for (s,k),v in groups.items():strata[s].append((sum(v),len(v)))
rng=np.random.default_rng(77123);sums=np.zeros(B);counts=np.zeros(B)
for v in strata.values():
v=np.array(v);idx=rng.integers(0,len(v),(B,len(v)));sums+=v[idx,0].sum(1);counts+=v[idx,1].sum(1)
return np.quantile(sums/counts,[alpha/2,1-alpha/2]).tolist()
def describe(rows,variant,key='scores'):
s=[r[key][variant] for r in rows];pos=[z for r,z in zip(rows,s) if r['target_count']>0];neg=[z for r,z in zip(rows,s) if r['target_count']==0]
nt=lambda z:z.get('pred_nt',z['empty'])
return dict(n=len(rows),images=len({r['scene_id'] for r in rows}),positive=len(pos),negative=len(neg),gIoU=float(np.mean([z['iou'] for z in s])),cIoU=sum(z['intersection'] for z in s)/max(1,sum(z['union'] for z in s)),positive_mIoU=float(np.mean([z['iou'] for z in pos])) if pos else None,Nacc=float(np.mean([nt(z) for z in neg])) if neg else None,false_empty=float(np.mean([nt(z) for z in pos])) if pos else None,Pr50=float(np.mean([z['iou']>=.5 for z in pos])) if pos else None)
def comparison(rows,v,b='frozen',key='scores',alpha=.05):
d=np.array([r[key][v]['iou']-r[key][b]['iou'] for r in rows]);helped=int((d>1e-12).sum());harmed=int((d< -1e-12).sum());n=helped+harmed
return dict(n=len(rows),images=len({r['scene_id'] for r in rows}),delta=float(d.mean()) if len(d) else None,ci=ci_cluster(rows,d,alpha),helped=helped,harmed=harmed,tied=len(rows)-n,sign_p=binomtest(helped,n,.5).pvalue if n else 1.,paired_sd=float(d.std(ddof=1)) if len(d)>1 else None,approx_iid_mde80=(norm.ppf(.975)+norm.ppf(.8))*float(d.std(ddof=1))/math.sqrt(len(d)) if len(d)>1 else None)
def add_calibration(rows,model):
p=OLD/'results/analysis/calibration.json'
if not p.exists():return
params=json.loads(p.read_text())[model]
if not (OLD/'assets/grefcoco/instances.json').exists():return
coco=json.loads((OLD/'assets/grefcoco/instances.json').read_text());sizes={i['id']:i['width']*i['height'] for i in coco['images']}
for r in rows:
features=np.array(r['features']);features[4]=r['scores']['cacp']['pred_pixels']/sizes[r['image_id']]
for name in ['source_unconstrained_s11','target_unconstrained_s11','constrained_0.01_s11','constrained_0.025_s11','constrained_0.05_s11','constrained_0.1_s11']:
p=params[name];fit=p['fit'];coef=np.array(fit['coef']);x=(features-np.array(fit['mean']))/np.array(fit['std']);logit=coef[-1]+float(x@coef[:-1]);prob=1/(1+np.exp(-np.clip(logit,-50,50)));reject=prob>=p['operating_point']['threshold']
for key in ['scores','official480_scores']:
base=r[key]['cacp'];z=dict(base)
if reject:z.update(iou=float(r['target_count']==0),intersection=0,union=base['gt_pixels'],pred_pixels=0,empty=True,pred_nt=True)
r[key][name]=z
r.setdefault('added_abstention',{})[name]=bool(reject and not r['scores']['cacp']['empty'] and r['target_count']>0)
def natural(model,shards):
paths=[ROOT/'results'/f'natural_full_{model}_{i}of{shards}.jsonl' for i in range(shards)]
if not all(p.with_name(p.stem+'_meta.json').exists() for p in paths):return None
rows=[r for p in paths for r in readlines(p)];assert len(rows)==49492 and len({r['id'] for r in rows})==49492
if model!='rela':add_calibration(rows,model)
variants=list(rows[0]['scores']);out=dict(records=len(rows),images=len({r['scene_id'] for r in rows}),tables=[],comparisons={},coverage={},cost={})
for split in ['val','testA','testB','all']:
subset=[r for r in rows if split=='all' or r['split']==split]
for key in ['scores','official480_scores']:
for v in variants:out['tables'].append(dict(split=split,grid=key,variant=v,**describe(subset,v,key)))
for scope in ['all','old_supported','scope_safe','old_relational','safe_relational','positive','safe_positive']:
s=[r for r in rows if scope=='all' or (scope=='old_supported' and r['flags'].get('supported')) or (scope=='scope_safe' and r['flags'].get('scope_safe')) or (scope=='old_relational' and r['flags'].get('anchor')) or (scope=='safe_relational' and r['flags'].get('anchor') and r['flags'].get('scope_safe')) or (scope=='positive' and r['target_count']>0) or (scope=='safe_positive' and r['target_count']>0 and r['flags'].get('scope_safe'))]
out['coverage'][scope]=dict(records=len(s),images=len({r['scene_id'] for r in s}))
for v in ['anchor_gate','cacp','safe_anchor','source_unconstrained_s11','constrained_0.05_s11']:
if v in variants and s:out['comparisons'][scope+'/'+v]=comparison(s,v,key='official480_scores')
out['scope_reasons']=dict(collections.Counter(r['flags'].get('scope_reason','native') for r in rows))
out['exact_cf']=dict(changed=sum(r['flags'].get('cf_vs_anchor_changed',False) for r in rows),nogate_changed=sum(r['flags'].get('nogate_cf_changed',False) for r in rows))
out['cost']=dict(total_primary_query_requests=sum(r['primary_query_count'] for r in rows),total_cf_query_requests=sum(r['query_count'] for r in rows),average_primary_requests=float(np.mean([r['primary_query_count'] for r in rows])),average_cf_requests=float(np.mean([r['query_count'] for r in rows])))
out['added_abstention']={v:sum(r.get('added_abstention',{}).get(v,False) for r in rows)/sum(r['target_count']>0 for r in rows) for v in variants if 's11' in v}
(DEST/f'natural_{model}.json').write_text(json.dumps(out,indent=2));print('NATURAL',model,out['coverage'],flush=True)
with gzip.open(DEST/f'natural_{model}_evaluated.jsonl.gz','wt') as f:
for r in rows:f.write(json.dumps(r)+'\n')
return out
def rich(model,shards):
paths=[ROOT/'results'/f'rich_synthetic_{model}_{i}of{shards}.jsonl' for i in range(shards)]
if not all(p.with_name(p.stem+'_meta.json').exists() for p in paths):return None
rows=[r for p in paths for r in readlines(p)];assert len(rows)==28800 and len({r['id'] for r in rows})==28800
variants=list(rows[0]['scores']);tables=[];curves=[];comparisons={}
for render in ['flat','rich']:
for family in ['attribute','relational','multi_target','negative_action','empty_target','paraphrase','primary']:
s=[r for r in rows if r['render']==render and (r['mode'] in ['attribute','relational','multi_target','empty_target'] if family=='primary' else r['mode']==family)]
pairs=collections.defaultdict(list)
for r in s:pairs[r['pair_id']].append(r)
assert all(len(v)==2 for v in pairs.values())
for v in variants:
pc=[dict(scene_id=rr[0]['scene_id'],split='test',pc=float(min(r['scores'][v]['iou'] for r in rr)>=.5)) for rr in pairs.values()]
tables.append(dict(render=render,family=family,variant=v,PC50=float(np.mean([x['pc'] for x in pc])),PC50_ci=ci_cluster(pc,[x['pc'] for x in pc]),single_Pr50=float(np.mean([r['scores'][v]['iou']>=.5 for r in s])),**describe(s,v)))
if family=='primary':
for tau in np.arange(.3,.901,.05):curves.append(dict(render=render,variant=v,tau=float(tau),PC=float(np.mean([min(r['scores'][v]['iou'] for r in rr)>=tau for rr in pairs.values()])),Pr=float(np.mean([r['scores'][v]['iou']>=tau for r in s]))))
if family=='primary':
for v in ['anchor_gate','cacp','safe_anchor']:
if v not in variants:
continue
d=[dict(scene_id=rr[0]['scene_id'],split='test',value=float(min(r['scores'][v]['iou'] for r in rr)>=.5)-float(min(r['scores']['frozen']['iou'] for r in rr)>=.5)) for rr in pairs.values()]
comparisons[render+'/'+v]=dict(delta=float(np.mean([x['value'] for x in d])),ci=ci_cluster(d,[x['value'] for x in d]))
out=dict(records=len(rows),scenes=1200,tables=tables,curves=curves,comparisons=comparisons)
(DEST/f'rich_{model}.json').write_text(json.dumps(out,indent=2));print('RICH',model,comparisons,flush=True)
with gzip.open(DEST/f'rich_{model}_evaluated.jsonl.gz','wt') as f:
for r in rows:f.write(json.dumps(r)+'\n')
return out
def audits():
out={}
for model in ['clipseg','groundedsam']:
paths=list((ROOT/'results').glob(f'phase1_audit_{model}_*of4.jsonl'))
if len(paths)!=4:continue
rows=[r for p in paths for r in readlines(p)];out[model]={}
for scope in ['all','controlled_clean_test','natural_eval']:
s=[r for r in rows if scope=='all' or (scope=='controlled_clean_test' and r['domain']=='controlled' and r['split']=='test' and r['corruption']=='clean') or (scope=='natural_eval' and r['domain']=='natural' and r['split'] in ('val','testA','testB'))]
changed=[r for r in s if r['cf_vs_anchor_changed']]
out[model][scope]=dict(anchor_records=len(s),changed=len(changed),helped=sum(r['delta_iou']>1e-12 for r in changed),harmed=sum(r['delta_iou']< -1e-12 for r in changed),tied=sum(abs(r['delta_iou'])<=1e-12 for r in changed),nogate_changed=sum(r['nogate_cf_changed'] for r in s),reasons=dict(collections.Counter(r['cf_reason'] for r in s)))
s=[r for r in rows if r['domain']=='controlled' and r['split']=='test' and r['corruption']=='clean'];pairs=collections.defaultdict(list)
for r in s:pairs[r['pair_id']].append(r)
keys=list(s[0]['sensitivity']) if s else []
out[model]['sensitivity']=[dict(setting=k,PC50=float(np.mean([min(r['sensitivity'][k] for r in rr)>=.5 for rr in pairs.values()])),mIoU=float(np.mean([r['sensitivity'][k] for r in s]))) for k in keys]
(DEST/'phase1_exact_audit.json').write_text(json.dumps(out,indent=2));print('AUDITS',out,flush=True)
def phase1():
out={};curves=[]
for model in ['clipseg','groundedsam']:
p=OLD/'results/analysis'/f'{model}_evaluated.jsonl.gz'
if not p.exists():p=p.with_suffix('')
if not p.exists():continue
rows=readlines(p);s=[r for r in rows if r['domain']=='controlled' and r['split']=='test' and r['corruption']=='clean' and r['mode'] in ['attribute','relation','quantifier','absence']]
pairs=collections.defaultdict(list)
for r in s:pairs[r['pair_id']].append(r)
assert len(pairs)==3200
out[model]={}
for base in ['frozen','global_direction','anchor_gate']:
rr=[dict(scene_id=v[0]['scene_id'],split=str(v[0]['seed']),value=float(min(r['scores']['cacp']['iou'] for r in v)>=.5)-float(min(r['scores'][base]['iou'] for r in v)>=.5)) for v in pairs.values()]
out[model][base]=dict(delta=float(np.mean([r['value'] for r in rr])),pointwise_ci=ci_cluster(rr,[r['value'] for r in rr]),simultaneous_ci=ci_cluster(rr,[r['value'] for r in rr],alpha=.05/6))
for v in ['frozen','global_direction','anchor_gate','cacp']:
for tau in np.arange(.3,.901,.05):curves.append(dict(model=model,variant=v,tau=float(tau),PC=float(np.mean([min(r['scores'][v]['iou'] for r in rr)>=tau for rr in pairs.values()])),Pr=float(np.mean([r['scores'][v]['iou']>=tau for r in s]))))
for scope in ['all_natural','supported_natural']:
ns=[r for r in rows if r['domain']=='natural' and r['split'] in ('val','testA','testB') and (scope=='all_natural' or r['flags']['supported'])]
out[model][scope]=comparison(ns,'cacp')
(DEST/'phase1_multiplicity.json').write_text(json.dumps(out,indent=2));(DEST/'phase1_threshold_curves.json').write_text(json.dumps(curves,indent=2));print('PHASE1_STATS_COMPLETE',flush=True)
if __name__=='__main__':
ap=argparse.ArgumentParser();ap.add_argument('--part',default='all',choices=['all','natural','rich','audit','phase1']);a=ap.parse_args()
if a.part in ('all','natural'):
for m,n in [('clipseg',4),('groundedsam',16),('rela',32)]:natural(m,n)
if a.part in ('all','rich'):
for m,n in [('clipseg',4),('groundedsam',12),('rela',16)]:rich(m,n)
if a.part in ('all','audit'):audits()
if a.part in ('all','phase1'):phase1()
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