https://discuss.pytorch.org/t/is-map-location-in-torch-load-and-model-load-state-dict-independent-from-device-in-to/99983 我的问题和参考资料中的一样,在使用torch.load的时候有一个map_location参数,此时可以将checkpoint等加载到对应的device上。但是,如果接下来初始化一个model,并且使用model.load_state...
torch.load('tensors.pt', map_location={'cuda:1':'cuda:0'}) # Load tensor from io.BytesIO object with open('tensor.pt') as f: buffer = io.BytesIO(f.read()) torch.load(buffer) 3 torch.nn.Module.load_state_dict(state_dict) [source] 使用state_dict 反序列化模型参数字典。用来加载...
from torch.hub import load_state_dict_from_url load_state_dict_from_url(url, model_dir=None, map_location=None, progress=True, check_hash=False, file_name=None) 具体参数: url(string) -要下载的对象的 URL; model_dir(string,可选) -保存对象的目录; map_location(可选) -指定如何重新映射存...
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') model.load_state_dict(checkpoi...
这种情况是最简单的,可不使用map_location参数,也可不适用model.to(device)。 # 保存模型 torch.save(model.state_dict(), PATH) device = torch.device('cpu') #加载模型 model = resnet34(num_classes=5) # load model weights weights_path = "./resNet34.pth" ...
wqrf.load_state_dict(torch.load(model_path,map_location=torch.device('cpu')))# 评估模式(关闭dropout和batch normalization的训练时行为) wqrf.eval()# 假设new_data是一个包含新数据的列表,每个元素是两个特征向量的列表 new_data_tensor=torch.tensor(new_data,dtype=torch.float32)# 如果在GPU上训练,...
torch.load('', map_location={'cuda:1':'cuda:0'}) # Load tensor from io.BytesIO object with open('tensor.pt') as f: buffer = io.BytesIO(f.read()) torch.load(buffer) 3 torch.nn.Module.load_state_dict(state_dict) [source] ...
net=Net()#我的电脑没有GPU,他的参数是GPU训练的cudatensor,于是要下面这样转换一下 dict_trained=torch.load("mobilenet_sgd_rmsprop_69.526.tar",map_location=lambda storage,loc:storage)["state_dict"]dict_new=net.state_dict().copy()new_list=list(net.state_dict().keys())trained_list=list(dict...
eval() torch_ckpt = torch.load('./pretrained/rdn-liif.pth', map_location=torch.device('cpu')) m = torch_ckpt['model'] sd = m['sd'] paddle_sd = {} for k, v in sd.items(): if torch.is_tensor(v): if 'imnet.layers' in k and 'weight' in k: print(k) print(v) ...
#saving jit_sample = (batch_x['input_ids'].int().to(device), batch_x['attention_mask'].int().to(device)) model.eval() model.to(device) module = torch.jit.trace(model, jit_sample) torch.jit.save('model_jit.pt') #loading model = torch.jit.load('model_jit.pt', map_location=...