-保存模型参数:torch.save(net.state_dict(),path),net.load_state_dict(torch.load(path)) 第一种方法比较懒,保存整个得模型架构,比较费时占内存,第二种方法是只保留模型上得可学习参数,等建立一个新得网络结构,然后放上这些参数即可,所以推荐使用第二种。下面通过代码看看怎么使用。 这里先建立一个网络模型...
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=lambdastorage, loc: storage) # Load all tensors onto GPU 1 torch.load('tensors.pt', map_location=lambdastorage, loc: storage.cuda(1...
model=torchvision.models.vgg16() pthfile =r'./checkpoint-epoch100.pth' loaded_model = torch.load(pthfile, map_location='cpu') # try: # loaded_model.eval() # except AttributeError as error: # print(error) #model.load_state_dict(loaded_model['state_dict']) # model = model.to(device...
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 fil...
torch.load('tensors.pt') # Load all tensors onto the CPU 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) ...
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.requires_grad = False ...
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("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》 ...
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》 ...