| from pathlib import Path |
| import sys,os,numpy as np,torch;import torch.distributed as dist |
| from torch.nn.parallel import DistributedDataParallel as DDP |
| R=Path(__file__).resolve().parents[1];sys.path.insert(0,str(R));from model.climatenet import * |
| c=cfg(R);rank=int(os.getenv('RANK',0));world=int(os.getenv('WORLD_SIZE',1));ddp=world>1 |
| if ddp:dist.init_process_group('gloo') |
| d=np.load(R/c['data']['path']);ids=np.where(d['split']==0)[0];base=ClimateNetDeepLab(**c['model']);m=DDP(base) if ddp else base;opt=torch.optim.Adam(m.parameters(),lr=c['train']['learning_rate']);w=torch.tensor(c['train']['class_weights']).float();ls=[] |
| for _ in range(c['train']['epochs']): |
| for i in ids[rank::world]:loss=F.cross_entropy(m(torch.tensor(d['input'][i:i+1])),torch.tensor(d['target'][i:i+1]),weight=w);opt.zero_grad();loss.backward();opt.step();ls.append(float(loss)) |
| v=torch.tensor([sum(ls),len(ls)],dtype=torch.float64) |
| if ddp:dist.all_reduce(v) |
| p=R/c['paths']['checkpoint'] |
| if rank==0:p.parent.mkdir(parents=True,exist_ok=True);torch.save({'model':base.state_dict(),'model_config':c['model']},p);write(R/c['paths']['training_metrics'],{'weighted_cross_entropy':float(v[0]/v[1]),'world_size':world});print(p) |
| if ddp:dist.destroy_process_group() |
|
|
