cpu -> gpu 1 torch.load('modelparameters.pth', map_location=lambda storage, loc: storage.cuda(1)) 1. gpu 1 -> gpu 0 torch.load('modelparameters.pth', map_location={'cuda:1':'cuda:0'}) 1. gpu -> cpu torch.load('modelparameters.pth', map_location=lambda storage, loc: storage)...
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))#后面正常训练代码optimizer =xxxforepoch:fordatainDataloader: model(...
torch.save主要参数:obj:对象 、f:输出路径 torch.load 主要参数 :f:文件路径 、map_location:指定存放位置、 cpu or gpu 模型的保存的两种方法: 1、保存整个Module torch.save(net,path) 2、保存模型参数 state_dict = net.state_dict()torch.save(state_dict ...
map_location=device)# 简单对比每层的权重参数个数是否一致load_weights_dict = {k: v for k, v in weights_dict.items()if model.state_dict()[k].numel() == v.numel()}model.load_state_dict(load_weights_dict, strict=False)else:checkpoint_path...
torch.load("0.9472_0048.weights",map_location='cpu') 就可以解决问题了。 方便查阅,整理: 假设我们只保存了模型的参数(model.state_dict())到文件名为modelparameters.pth, model = Net() 1. cpu -> cpu或者gpu -> gpu: checkpoint = torch.load('modelparameters.pth') ...
load(pretrained, map_location="cpu") model.load_state_dict(state_dict) return model def qint8edsr(block=QuantizableResBlock, pretrained=None, quantize=False): model = QuantizableEDSR(block=block) _replace_relu(model) if quantize: backend = 'fbgemm' quantize_model(model, backend) else: ...
load('tensors.pt', map_location=torch.device('cpu')) torch.load('tensors.pt', map_location=lambda storage, loc: storage) torch.load('tensors.pt', map_location=lambda storage, loc: storage.cuda(1)) torch.load('tensors.pt', map_location={'cuda:1': 'cuda:0'}) with open('tensor...
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,这里不再赘述。
2019-10-20 15:04 − #[深度学习] Pytorch(三)——多/单GPU、CPU,训练保存、加载预测模型问题 ###上一篇实践学习中,遇到了在多/单个GPU、GPU与CPU的不同环境下训练保存、加载使用使用模型的问题,如果保存、加载的上述三类环境不同,加载时会出错。就去研究了一下,做了实验,得出以下结论: **多... 长颈...
map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) progress (bool, optional): whether or not to display a progress bar to stderr Example: >>> state_dict = torch.hub.load_state_dict_from_url('https://s3.amazonaws.com/pytorch/models...