model = TheModelClass(*args, **kwargs) optimizer = TheOptimizerClass(*args, **kwargs) checkpoint = torch.load(PATH) model.load_state_dict(checkpoint["model_state_dict"]) optimizer.load_state_dict(checkpoint["optimizer_state_dict"]) epoch = checkpoint["epoch"] loss = checkpoint["loss"] ...
即可以通过调用consume_prefix_in_state_dict_if_present()函数来消除该前缀。 方法二 上述的函数从功能上来说是很容易自己实现的 defremove_prefix(checkpoint:str):state_dict=torch.load(checkpoint,map_location='cpu')['state_dict']state_dict_new=OrderedDict()forkeyinstate_dict.keys():ifkey.startswith...
checkpoint = torch.load(model_save_path, map_location=device) model.load_state_dict(checkpoint['model']) else: save_path = 'initial_weights.pth' if opts.local_rank == 0: torch.save(model.state_dict(), save_path) dist.barrier() # 这里注意,一定要指定map_location参数,否则会导致第一块GP...
model.load_state_dict(load_weights_dict, strict=False) # 如果是多卡训练,加载weights后要设置DDP模式,其后先定义一下optimizer和scheduler,之后再加载断点中保存的optimizer和scheduler以及设置epoch, optimizer.load_state_dict(load_ckpt['optimizer']) # 加载优化器状态 scheduler.load_state_dict(load_ckpt['sch...
model.load_state_dict(checkpoint['model']) model=DDP(model,device_ids=[gpu]) returnmodel 1. 2. 3. 4. 5. 6. 但是此时往往会遇到多进程在GPU0上占用过多显存的问题: 使用nvidia-smi命令: 上图中,在所有使用GPU0的进程中,除了PID为62250的进程外,还存在其他三个进程,而这三个进程还分别使用GPU1\...
model.load_state_dict(torch.load(checkpoint_path, map_location=device))如果需要冻结模型权重,和单GPU基本没有差别。如果不需要冻结权重,可以选择是否同步BN层。然后再把模型包装成DDP模型,就可以方便进程之间的通信了。多GPU和单GPU的优化器设置没有差别,这里不...
CHECKPOINT_PATH="./model.checkpoint"ifrank ==0: torch.save(ddp_model.state_dict(), CHECKPOINT_PATH)#barrier()其他保证rank 0保存完成dist.barrier() map_location= {"cuda:0": f"cuda:{local_rank}"} model.load_state_dict(torch.load(CHECKPOINT_PATH, map_location=map_location))#后面正常训练代...
--load $CHECKPOINT_PATH \ --data-path ${DATASET} \ --vocab-file $VOCAB_FILE \ --data-impl mmap \ --split 949,50,1 \ --lr 0.0001 \ --lr-decay-style linear \ --min-lr 1.0e-5 \ --lr-decay-iters 990000 \ --weight-decay 1e-2 \ ...
Even with model_test = CoolSystem(hyperparams_test).load_from_checkpoint('checkpoints/try_ckpt_epoch_1.ckpt'), PyTorch Lightning is still complaining that 'dict' object has no attribute 'data_dir' Am I doing something wrong here?Contributor william...
ifargs.checkpointisnotNone: map_location= {'cuda:%d'%0:'cuda:%d'%local_rank} ddp_model.load_state_dict(torch.load(args.checkpoint,map_location=map_location)) # distribute batch size (mini-batch)batch_size=args.batch_sizebatch_size_per_gpu=batch_size//size ...