| """Paired warmed latency, same images/GPU, balanced plan order, no GT use.""" |
| from pathlib import Path |
| import sys,json,time,os |
| import numpy as np |
| from PIL import Image |
| ROOT=Path(__file__).resolve().parents[1];from project_paths import legacy_root;OLD=legacy_root(ROOT);sys.path.insert(0,str(OLD/'code')) |
| from infer import ClipSeg,GroundedSam |
| import phase1_repair as p |
| from r2_repair import primary_plan,primary_mask,repair |
| import torch |
| torch.set_num_threads(4) |
| rows=[json.loads(s) for s in (ROOT/'data/rich_synthetic.jsonl').read_text().splitlines()] |
| chosen=[r for r in rows if r['render']=='rich' and r['mode']=='relational' and r['endpoint']==0][:12] |
| output=[] |
| for model,cls in [('clipseg',ClipSeg),('groundedsam',GroundedSam)]: |
| engine=cls() |
| image=Image.open(ROOT/chosen[0]['image_path']).convert('RGB');q=p.parse_query(chosen[0]['query']);engine.predict(image,p.query_plan(chosen[0]['query'])[1]);torch.cuda.synchronize() |
| for i,r in enumerate(chosen): |
| image=Image.open(ROOT/r['image_path']).convert('RGB');q=p.parse_query(r['query']) |
| plans={'frozen':[q.original],'sfap':primary_plan(r['query']),'cacp':p.query_plan(r['query'])[1]} |
| for rep in range(3): |
| order=['frozen','sfap','cacp'];shift=(i+rep)%3;order=order[shift:]+order[:shift] |
| for variant in order: |
| torch.cuda.synchronize();start=time.perf_counter();maps,stats=engine.predict(image,plans[variant]);torch.cuda.synchronize();forward=time.perf_counter()-start |
| maps={k:v.astype(np.float16).astype(np.float32) for k,v in maps.items()} |
| start=time.perf_counter() |
| if variant=='frozen':mask=p.native(maps[q.original],engine.threshold) |
| elif variant=='sfap':mask=primary_mask(r['query'],maps,engine.threshold) |
| else:mask=repair(r['query'],maps,engine.threshold)[0]['cacp'] |
| cpu=time.perf_counter()-start |
| if variant=='cacp':assert np.array_equal(primary_mask(r['query'],maps,engine.threshold),repair(r['query'],maps,engine.threshold)[0]['safe_anchor']) |
| output.append(dict(model=model,image=r['image_path'],scene_id=r['scene_id'],repeat=rep,order=order,variant=variant,queries=len(plans[variant]),forward_seconds=forward,postprocess_seconds=cpu,total_seconds=forward+cpu)) |
| print('TIMING',model,i+1,len(chosen),flush=True) |
| del engine;torch.cuda.empty_cache() |
| dest=ROOT/'results/paired_timing.json';dest.write_text(json.dumps(dict(gpu=torch.cuda.get_device_name(0),slurm_job_id=os.getenv('SLURM_JOB_ID'),records=output,warmup='One complete four-query plan per model before timing',cache='Disabled',repeats=3,selection='First twelve rich relational scene records, text/ID selection; no label or score selection'),indent=2)) |
| print('PAIRED_TIMING_COMPLETE',flush=True) |
|
|
