#加载整个state_dict, 还需要将它放到一个模型中,这样才算完成一个模型的重新加载,所以通常需要再重新构建一个模型,这个模型里面的参数可以不用管,可以通过load_state_dict()这个方法将加载进来的state_dict_load字典放到新的网络中,这样这个网络就与之前保存下来的网络一样了 net_new = LeNet2(classes =
model = Model(args) ckpt = torch.load(args.pretrained_model, map_location='cpu') state = ckpt['state_dict'] net.load_state_dict(state) 注意map_location的参数,如果在gpu上进行加载,则声明map_location='cuda:0'。如果不声明,可能会报错,input和weight的类型不一致。 多卡训练,单卡加载部署。举例:...
# load the last checkpoint with the best model net.load_state_dict(torch.load(p_list+'checkpoint.pt')) return net, train_loss, valid_loss def test_model(netopt, ec, er, N, allow_short_selling=True): w_hat = netopt(ec.to(torch.float32)) weigh0 = 1/(~torch.isnan(er)).sum(ax...
"\t", net.state_dict()[param_tensor].size())print()# Print optimizer's state_dictprint("Optimizer's state_dict:")forvar_nameinoptimizer.state_dict():print(var_name,"\t", optimizer.state_dict()[var_name])
net.load_state_dict(torch.load('best.mdl')) with torch.no_grad(): out = net(image) #确定分类 class_cl =out.argmax(dim=1) class_num = class_cl.numpy() if class_num == 0: print('这张照片是蚂蚁') else: print('这张照片是蜜蜂') ...
torch.save(net.state_dict(), './cifar_net.pth') 在这段代码中,我们使用torch.save函数,将训练好的模型参数(通过net.state_dict()获得)保存到文件中。 当我们需要加载模型时,首先需要创建一个新的模型实例,然后使用load_state_dict方法将参数加载到模型中。
if__name__=='__main__':a=Net()torch.save(a.state_dict(),'12.pt')c=Net()c.load_state_dict(torch.load('12.pt')) 运行该方法,发现计算器没有被打开。 总结 加载模型时,尽可能不要加载整个模型,否则存在反序列化风险,加载模型参数则不存在风险。
load_state_dict(torch.load('net_params.pkl')) data_X = data_X.reshape(-1, 1, 2) #reshape中,-1使元素变为一行,然后输出为1列,每列2个子元素 data_X = torch.from_numpy(data_X) #torch.from_numpy(): numpy中的ndarray转化成pytorch中的tensor(张量) var_data = Variable(data_X) #转为...
docker run -ti --net host nvcr.io/nvidia/tritonserver:<xx.yy>-py3-sdk /bin/bash In the client container, clone the Python backend repository.git clone https://github.com/triton-inference-server/python_backend -b r<xx.yy> Run the example client....
states = [ State(name='solid'), 'liquid', { 'name': 'gas'} ] machine = Machine(lump, states) # This alternative example illustrates more explicit # addition of states and state callbacks, but the net # result is identical to the above. machine = Machine(lump) solid = State('solid...