cpu -> cpu或者gpu -> gpu: checkpoint = torch.load('modelparameters.pth') model.load_state_dict(checkpoint) 1. 2. 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=...
-torch.save(obj,f):obj表示对象,也就是我们保存得数据,可以是模型,张量,dict等等,f表示输出得路径 -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...
net_data=torch.load("/home/chenyang/PycharmProjects/mobilenetv3/mbv3_small.pth.tar",map_location="cpu") data=torch.randn((1,3,224,224)) for one_data in net_data["state_dict"]: print(one_data[7:]) dic[one_data[7:]]=net_data["state_dict"][one_data] net.load_state_dict(dic...
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_trained.keys() ) print("new_state_dict size: {} traine...
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] ...
map_location =lambda storage, loc: storage # 把所有的张量加载到GPU 1中 #torch.load('tensors.pt', map_location=lambda storage, loc: storage.cuda(1)) #也可以写成: #device = torch.device('cpu') #netd.load_state_dict(t.load(opt.netd_path, map_location=device)) ...
### 跨设备保存加载模型 ### 保存在GPU上,在CPU上加载 **Save:** torch.save(model.state_dict(),"GPU.pth") **Load:** device=torch.device("cpu") model=TheModelClass() model.load_state_dict(torch.load("GPU.pth",map_location=device))...
weights_dict = torch.load(weights_path, 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...
model.load_state_dict(torch.load('model,pth', map_location='cpu')) 数据准备、特征提取与微调 得到视频数据基本信息 import cv2 video = cv2.VideoCapture(mp4_path) height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)) width = int(video.get(cv2.CAP_PROP_FR...
model_fp.load_state_dict(torch.load("./float_model.pth", map_location=device)) # Copy model to qunatize model_to_quantize = copy.deepcopy(model_fp).to(device) model_to_quantize.eval() qconfig_mapping = QConfigMapping().set_global(torch.ao.quantization.default_dynamic_qconfig) ...