| """Trainer for strict-local-wrist Stereo decoder variants.""" |
| from __future__ import annotations |
| import argparse, json, glob |
| from collections import Counter |
| from pathlib import Path |
| import torch |
| from torch.utils.data import DataLoader |
| from train_act import EpisodeBlockBatchSampler, _stats, _trajectories, seed_everything |
| from train_stereo_act import WristRGBDACTDataset, DEPTH_MM_TO_M |
| from five_task_contract import hierarchical_item_weights |
| from stereo_decoder_variants import StereoFFNMoE, StereoARCA |
|
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|
| def one_loss(model, rgb, depth, qpos, actions, mask, beta, router_aux_weight): |
| rgb = rgb.float().div_(255) |
| with torch.autocast('cuda', dtype=torch.bfloat16): |
| pred, mu, logvar, aux = model(rgb, depth, qpos, actions) |
| mse = ((pred-actions).square().mean(-1)*mask).sum()/mask.sum().clamp_min(1) |
| kl = -.5*(1+logvar-mu.square()-logvar.exp()).sum(-1).mean() |
| total = mse + beta*kl + router_aux_weight*(aux-1.0) |
| return total, mse, kl, aux |
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|
| def main(): |
| p=argparse.ArgumentParser() |
| p.add_argument('--variant', choices=('ffn_moe','arca'), required=True) |
| p.add_argument('--data', required=True); p.add_argument('--shared-arms',default='0,1,2,3'); p.add_argument('--output',required=True) |
| p.add_argument('--batch-size',type=int,default=32); p.add_argument('--workers',type=int,default=8); p.add_argument('--updates',type=int,default=80000) |
| p.add_argument('--cache-episodes',type=int,default=0); p.add_argument('--episode-block-updates',type=int,default=64) |
| p.add_argument('--save-updates',default='40000,80000'); p.add_argument('--lr',type=float,default=2e-4); p.add_argument('--beta',type=float,default=1e-3) |
| p.add_argument('--router-aux-weight',type=float,default=1e-2); p.add_argument('--experts',type=int,default=4); p.add_argument('--role-rank',type=int,default=32) |
| p.add_argument('--task-balanced',action='store_true'); p.add_argument('--seed',type=int,default=20260726) |
| a=p.parse_args(); arms=tuple(int(x) for x in a.shared_arms.split(',')) |
| paths=sorted({path for pat in a.data.split(',') for path in glob.glob(pat)}) |
| trajectories=_trajectories(paths,arms) |
| if len(trajectories)<10: raise ValueError('need at least 10 successful RGB-D demonstrations') |
| seed_everything(a.seed); torch.backends.cudnn.benchmark=True |
| lazy_cache=a.cache_episodes>0 |
| if lazy_cache and a.workers: raise ValueError('bounded RGB-D cache requires --workers 0') |
| stats=_stats(trajectories,arms); train=WristRGBDACTDataset(trajectories,100,stats,True,preload=not lazy_cache,cache_limit=a.cache_episodes) |
| counts=Counter(train.item_tasks); sampler=None |
| if lazy_cache: |
| sampler=EpisodeBlockBatchSampler(train,a.batch_size,a.updates,a.episode_block_updates,a.seed,a.task_balanced) |
| loader=DataLoader(train,batch_sampler=sampler,num_workers=0,pin_memory=True) |
| elif a.task_balanced and len(counts)>1: |
| weights=torch.as_tensor(train.item_weights,dtype=torch.double) |
| sampler=torch.utils.data.WeightedRandomSampler(weights,num_samples=len(weights),replacement=True) |
| if not lazy_cache: |
| loader=DataLoader(train,batch_size=a.batch_size,shuffle=sampler is None,sampler=sampler,drop_last=True,num_workers=a.workers,pin_memory=True,persistent_workers=a.workers>0) |
| sample=train[0]; kwargs=dict(horizon=100,d_model=384,enc_layers=4,dec_layers=7) |
| if a.variant=='ffn_moe': model=StereoFFNMoE(len(sample[2]),len(sample[3][0]),experts=a.experts,**kwargs) |
| else: model=StereoARCA(len(sample[2]),len(sample[3][0]),roles=a.experts,role_rank=a.role_rank,**kwargs) |
| device=torch.device('cuda:0'); model=model.to(device) |
| opt=torch.optim.AdamW((x for x in model.parameters() if x.requires_grad),lr=a.lr,weight_decay=1e-4) |
| sched=torch.optim.lr_scheduler.CosineAnnealingLR(opt,a.updates) |
| out=Path(a.output);out.mkdir(parents=True,exist_ok=True) |
| cfg=vars(a)|{'horizon':100,'enc_layers':4,'dec_layers':7,'d_model':384,'vision_backbone':'stereo_act_cross_relbias','dino_model':'facebook/dinov3-vitb16-pretrain-lvd1689m','defm_model':model.defm_model_name,'camera_width':640,'camera_height':480,'patch_grid':[30,40],'fusion_layers':2,'depth_storage_unit':'millimeters','depth_to_meters_scale':DEPTH_MM_TO_M,'arms':arms,'state_dim':len(sample[2]),'action_dim':len(sample[3][0]),'files':paths,'episodes':len(trajectories),'train_task_item_counts':dict(counts),'policy_variant':'stereo_'+a.variant,'strict_policy_input':'current local panda_hand wrist RGB-D and local qpos only; no task/agent ID, peer/global/right-camera/language input'} |
| (out/'config.json').write_text(json.dumps(cfg,indent=2)); torch.save({'stats':stats},out/'normalization.pt') |
| milestones={int(x) for x in a.save_updates.split(',') if x}; update=0; totals={'loss':0.,'mse':0.,'kl':0.,'router_aux':0.,'n':0} |
| while update<a.updates: |
| model.train() |
| for rgb,depth,qpos,actions,mask in loader: |
| rgb,depth,qpos,actions,mask=(x.to(device,non_blocking=True) for x in (rgb,depth,qpos,actions,mask)) |
| opt.zero_grad(set_to_none=True); total,mse,kl,aux=one_loss(model,rgb,depth,qpos,actions,mask,a.beta,a.router_aux_weight) |
| total.backward(); torch.nn.utils.clip_grad_norm_(model.parameters(),1.0);opt.step();sched.step();update+=1 |
| n=len(rgb) |
| for name,val in (('loss',total),('mse',mse),('kl',kl),('router_aux',aux)): totals[name]+=float(val.detach())*n |
| totals['n']+=n |
| if update in milestones: torch.save({'model':model.state_dict(),'stats':stats,'config':cfg,'update':update},out/f'checkpoint_{update:06d}.pt') |
| if update%100==0: print(json.dumps({'update':update,**{k:v/totals['n'] for k,v in totals.items() if k!='n'}}),flush=True) |
| if update>=a.updates: break |
|
|
| if __name__=='__main__': main() |
|
|
