| """Reload the selected checkpoint and evaluate the held-out trajectories.""" |
| import hashlib |
| import json |
| import sys |
| import time |
| from pathlib import Path |
| sys.path.insert(0,str(Path(__file__).resolve().parents[1])) |
| import numpy as np |
| import torch |
| from model.vrba import VRBADeepONet |
| from common import arguments,config,path,accelerator,load_data,metrics,write_json |
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|
|
| def run(cfg,checkpoint=None): |
| torch.set_num_threads(1) |
| device=accelerator(cfg['training']['device']) |
| checkpoint=path(checkpoint or cfg['paths']['best_checkpoint']) |
| ck=torch.load(checkpoint,map_location='cpu',weights_only=False) |
| model=VRBADeepONet(**ck['model_config']).to(device) |
| model.load_state_dict(ck['model_state_dict'],strict=True); model.eval() |
| if cfg['data']['sensors']!=ck['model_config']['sensors']:raise ValueError('Pressure sensor count mismatch.') |
| data=load_data(cfg,stats=ck['normalization']) |
| s=data['splits']['test']; x=torch.as_tensor(data['x'][s],device=device) |
| t=torch.as_tensor(data['time'],device=device) |
| begin=time.perf_counter(); torch.cuda.synchronize() |
| with torch.inference_mode(): |
| pred=torch.cat([model(x[i:i+50],t) for i in range(0,len(x),50)]).cpu().numpy() |
| torch.cuda.synchronize(); duration=time.perf_counter()-begin |
| truth=data['radius'][s]; pred_physical=pred.astype(np.float64)*ck['normalization']['radius_std']+ck['normalization']['radius_mean'] |
| per_case=np.linalg.norm(pred_physical-truth,axis=1)/np.linalg.norm(truth,axis=1) |
| normalized=metrics(pred,data['y'][s]); physical=metrics(pred_physical,truth) |
| result=path(cfg['paths']['results']);result.mkdir(parents=True,exist_ok=True) |
| np.savez_compressed(result/'predictions.npz',prediction=pred_physical,target=truth, |
| prediction_normalized=pred,target_normalized=data['y'][s],time=data['output_t'], |
| pressure=data['pressure'][s],pressure_time=data['pressure_t'], |
| original_indices=np.arange(s.start,s.stop),per_trajectory_relative_l2=per_case) |
| report=dict(checkpoint_name=checkpoint.name,checkpoint_sha256=hashlib.sha256(checkpoint.read_bytes()).hexdigest(), |
| checkpoint_epoch=ck['epoch'],best_validation=ck['best_validation'],test_count=len(x), |
| inference_seconds=duration,physical=physical,normalized=normalized, |
| paper_reference=dict(relative_l2=0.00516,residual_variance=1.94e-5,residual_linf=0.0822), |
| comparability='Different reproducible split; authors original indices unknown. Report both physical and train-normalized metrics; table aggregation is not fully specified.', |
| worst_original_index=int(s.start+np.argmax(per_case))) |
| write_json(result/'metrics.json',report) |
| print('TEST_METRICS '+json.dumps(report),flush=True) |
| return report |
|
|
|
|
| if __name__=='__main__': |
| p=arguments(__doc__);p.add_argument('--checkpoint',default=None) |
| a=p.parse_args();run(config(a.config),a.checkpoint) |
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|
