先看官网上给的转化方式: importtorchimporttorchvisionfromtorch.utils.mobile_optimizerimportoptimize_for_mobile model=torchvision.models.mobilenet_v3_small(pretrained=True)model.eval()example=torch.rand(1,3,224,224)traced_script_module=torch.jit.trace(model,example)optimized_traced_model=optimize_for_mobil...
If you have parameters in your model, which should be saved and restored in the state_dict, but not trained by the optimizer, you should register them as buffers.Buffers won’t be returned in model.parameters(), so that the optimizer won’t have a change to update them. 模型中需要保存...
self).__init__()self.linear=nn.Linear(3,1)defforward(self,x):returnself.linear(x)# 实例化模型并移动到设备model=SimpleModel().to(device)# 定义损失函数
to(device) # 定义损失函数和优化器 criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9) # 训练网络 for epoch in range(10): running_loss = 0.0 for i, data in enumerate(trainloader, 0): inputs, labels = data[0].to(device), data[1]....
//在每个训练迭代或推断步骤中，确保将输入数据和模型的参数发送到GPU，并在GPU上执行计算： optimizer....
model.to(device) #使用序号为0的GPU #或model.to(device1) #使用序号为1的GPU 多GPU加速 这里我们介绍单主机多GPUs的情况,单机多GPUs主要采用的DataParallel函数,而不是DistributedParallel,后者一般用于多主机多GPUs,当然也可用于单机多GPU。 使用多卡训练的方式有很多,当然前提是我们的设备中存在两个及以上的GPU...
其中optimizer不需要转换 首先定义 1device = t.device('cuda:0') 将model和criterion to(device) 1#cuda2model =model.to(device)3criterion = criterion.to(device) 再将43行的inputs、target,46行的outputs to(device)到GPU上训练 1deftrain(epoch):2running_loss = 0.03forbatch_idx, datainenumerate(tr...
to(device) cnn torch.nn.CrossEntropyLoss对输出概率介于0和1之间的分类模型进行分类。 训练模型 代码语言:javascript 代码运行次数:0 运行 AI代码解释 # 超参数:Hyper Parameters learning_rate = 0.001 train_losses = [] val_losses = [] # Loss function and Optimizer criterion = nn.CrossEntropyLoss()...
to(device).long() output = model(spec_mag) # 计算损失值 los = loss(output, label) optimizer.zero_grad() los.backward() optimizer.step() # 计算准确率 output = torch.nn.functional.softmax(output) output = output.data.cpu().numpy() output = np.argmax(output, axis=1) label = label...
train_step(inputs, label, model, optimizer, criterion):with torch.autocast(device_type='cuda', dtype=torch.bfloat16):outputs = model(inputs)loss = criterion(outputs, label)optimizer.zero_grad(set_to_none=True)loss.backward()if fp8_type:sync_...