-torch.load(f, map_location):f表示文件得路径,map_location指定存放位置,CPU或者GPU,这个参数挺重要,再使用GPU训练得时候再具体说。 1.2 模型保存与加载得两种方式 pytorch得模型保存有两种方式,一种是保存整个Module,另外一种保存模型得参数。 -保存和加载整个Moudle:torch.save(net,path),torch.load(fpath) ...
torch.load('tensors.pt', map_location=torch.device('cpu')) # Load all tensors onto the CPU, using a function torch.load('tensors.pt', map_location=lambdastorage, loc: storage) # Load all tensors onto GPU 1 torch.load('tensors.pt', map_location=lambdastorage, loc: storage.cuda(1...
checkpoint = torch.load("checkpoint.pth", map_location=torch.device('cpu')) model.load_state_dict(checkpoint["state_dict"]) 按照马佬的建议,此处如果不想用到cpu的话,也可以map_location=rank。具体的写法参考了《pytorch源码》以及《pytorch 分布式训练 distributed parallel 笔记》 # 获取GPU的rank号gpu=...
import cfg # cfg是参数的预定于文件 def load_checkpoint(filepath): checkpoint = torch.load(filepath, map_location='cpu') model = checkpoint['model'] # 提取网络结构 model.load_state_dict(checkpoint['model_state_dict']) # 加载网络权重参数 for parameter in model.parameters(): parameter.require...
torch.load('tensors.pt', map_location=torch.device('cpu')) # Load all tensors onto the CPU, using a function torch.load('tensors.pt', map_location=lambda storage, loc: storage) # Load all tensors onto GPU 1 torch.load('tensors.pt', map_location=lambda storage, loc: storage.cuda...
torch.load('tensors.pt', map_location=torch.device('cpu')) # Load all tensors onto the CPU, using a function torch.load('tensors.pt', map_location=lambda storage, loc: storage) # Load all tensors onto GPU 1 torch.load('tensors.pt', map_location=lambda storage, loc: storage.cuda...
importtorchmodel=torch.jit.load('checkpoint-10000-embedding.torchscript',map_location='cpu')model.eval()x=torch.ones(1,3,224,224)y=model(x) the TorchScript ishere, which is simplya MobileNetv2fine-tuned on GPU. This should raise:
checkpoint = torch.load(args.resume) else: # Map model to be loaded to specified single gpu.loc = "cuda:{}".format(args.gpu) checkpoint = torch.load(args.resume, map_location=loc) args.start_epoch = checkpoint["epoch"] model.load_state_dict(checkpoint["state_dict"]...
checkpoint = torch.load("checkpoint.pth", map_location=torch.device('cpu')) model.load_state_dict(checkpoint["state_dict"]) 按照马佬的建议,此处如果不想用到cpu的话,也可以map_location=rank。具体的写法参考了《pytorch源码》以及《pytorch 分布式训练 distributed parallel 笔记》 # 获取GPU的rank号 gpu...
os.path.exists(checkpoint_path): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") checkpoint = torch.load(checkpoint_path, map_location=device) model.load_state_dict(checkpoint['model_state_dict']) print("Model loaded successfully.") else: print("Checkpoint...