import torchvision.models as models
model_ft = models.resnet18(weights='IMAGENET1K_V1')
for param in model_ft.parameters():
    param.requires_grad = False

for param in model_ft.fc.parameters():
    param.requires_grad = True

num_ftrs = model_ft.fc.in_features
fc_layer = nn.Linear(in_features=num_ftrs, out_features=1)
model_ft.fc = fc_layer
    

model_ft = model_ft.to(device)
criterion = nn.MSELoss()
optimizer_ft = optim.Adam(model_ft.parameters(), lr=0.001)
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
model_ft = simpleTrainingLoop(model_ft,
                       train_data_loader,
                       criterion,
                       optimizer_ft,
                       exp_lr_scheduler)

def simpleTrainingLoop(model,
                dataloader_train,
                criterion, 
                optimizer, 
                scheduler, 
                num_epoch = 1):
        since = time.time()
        for e in range(num_epoch):
                running_loss = 0
                for inputs, labels in dataloader_train:
                        inputs = inputs.to(device)
                        labels = labels.to(device)
                        
                        optimizer.zero_grad()
                        outputs = model(inputs)
                        \_,preds = torch.max(outputs,1)
                        loss = criterion(preds,labels)
                        # if we can know which layer has the problem will be better 
                        loss.backward()
                        optimizer.step()
                        running_loss += loss.item() \* inputs.size(0)
                scheduler.step()
                train_loss = running_loss/train_size
                print(f' training loss: {train_loss}')
        time_elapsed = time.time() - since
        print(f'training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')

运行时出错：Tensor的元素0不需要grad，也没有grad_fn，它在loss.backward（）时发生。我尝试使用resnet 18进行迁移学习，并使resnet 18中的所有参数都要求grad为false，并启用fc层grad。但这让我得到了运行时错误：
这个问题和GPU有关吗？因为我做输入和模型到设备，但我的cuda无法访问我的gpu。

c:\Users\LangZheZR\Desktop\projects\eye_on_ground_challenge\test_CNN_image_tabular_concat.ipynb Cell 11 line 2
     21 optimizer_ft = optim.Adam(model_ft.parameters(), lr=0.001)
     22 exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
---> 23 model_ft = simpleTrainingLoop(model_ft,
     24                        train_data_loader,
     25                        criterion,
     26                        optimizer_ft,
     27                        exp_lr_scheduler)

c:\Users\LangZheZR\Desktop\projects\eye_on_ground_challenge\test_CNN_image_tabular_concat.ipynb Cell 11 line 2
     18 loss = criterion(preds,labels)
     19 # if we can know which layer has the problem will be better 
---> 20 loss.backward()
     21 optimizer.step()
     22 running_loss += loss.item() * inputs.size(0)

File c:\Python311\Lib\site-packages\torch\_tensor.py:492, in Tensor.backward(self, gradient, retain_graph, create_graph, inputs)
    482 if has_torch_function_unary(self):
    483     return handle_torch_function(
    484         Tensor.backward,
    485         (self,),
   (...)
    490         inputs=inputs,
...
    257     allow_unreachable=True,
    258     accumulate_grad=True,
    259 )

RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn