运行时错误 - 张量的元素 0 不需要 grad 并且没有 grad_fn

Posted 2023-03-25

【中文标题】运行时错误 - 张量的元素 0 不需要 grad 并且没有 grad_fn

【英文标题】：Runtime Error - element 0 of tensors does not require grad and does not have a grad_fn

【发布时间】：2020-10-23 05:27:48

【问题描述】：

我正在使用 Unet 模型进行语义分割 - 我有一个 .png 格式的图像及其掩码的自定义数据集。我在网上论坛上找过东西，也试过，但效果不大？ 有关如何解决错误或改进代码的任何建议都会有所帮助。

model.eval()
with torch.no_grad():
    for xb, yb in val_dl:
        yb_pred = model(xb.to(device))
        # yb_pred = yb_pred["out"].cpu()
        print(yb_pred.shape)
        yb_pred = torch.argmax(yb_pred,axis = 1)     
        break
    
    print(yb_pred.shape)
    

criteron = nn.CrossEntropyLoss(reduction = 'sum')
opt = optim.Adam(model.parameters(), lr = 3e-4)

def loss_batch(loss_func, output, target, opt = None):
    loss = loss_func(output, target)
    
    if opt is not None:
        opt.zero_grad()
        loss.backward()
        opt.step()        
    return loss.item(), None

lr_scheduler = ReduceLROnPlateau(opt, mode = 'min', factor = 0.5, patience= 20, verbose = 1)

def get_lr(opt):
    for param_group in opt.param_groups:
        return param_group['lr']
    
current_lr = get_lr(opt)
print('current_lr = '.format(current_lr))


def loss_epoch(model, loss_func, dataset_dl, sanity_check = False, opt = None):
    running_loss = 0.0
    len_data = len(dataset_dl.dataset)
    
    for xb, yb in dataset_dl:
        xb = xb.to(device)
        yb = yb.to(device)
        
        # xb = torch.tensor(xbh, requires_grad=True)
                
        output = model(xb)
        
        loss_b, metric_b = loss_batch(loss_func, output, yb, opt)
        running_loss += loss_b
        if sanity_check is True:
            break
    loss = running_loss/float(len_data) 
    return loss, None

def train_val(model, params):
    num_epochs = params["num_epochs"]
    loss_func = params["loss_func"]
    opt = params["optimizer"]
    train_dl = params["train_dl"]
    val_dl = params["val_dl"]
    sanity_check = params["sanity_check"]
    lr_scheduler = params["lr_scheduler"]
    path2weights = params["path2weights"]
    
    loss_history = "train": [],
                    "val": []
    best_model_wts = copy.deepcopy(model.state_dict())
    best_loss = float('inf')
    
    for epoch in range(num_epochs):
        current_lr = get_lr(opt)
        print('Epoch /, current_lr = '.format(epoch, num_epochs - 1, current_lr))
        
        with torch.enable_grad():
            model.train()
            train_loss, _ = loss_epoch(model, loss_func, train_dl, sanity_check, opt)
        loss_history["train"].append(train_loss)
        model.eval()
        
        with torch.no_grad():
            val_loss, _ = loss_epoch(model, loss_func, val_dl, sanity_check, opt)
        loss_history["val"].append(val_loss)
        
        if val_loss < best_loss:
            best_loss = val_loss
            best_model_wts = copy.deepcopy(model.state_dict())
            torch.save(model.state_dict(), path2weights)
            print("copied best model weights!!")
    
        lr_scheduler.step(val_loss)
        if current_lr != get_lr(opt):
            print("Loading best model weights!!")
            model.load_state_dict(best_model_wts)
            print("train Loss: %.6f" %(train_loss))
            print("val_loss: %.6f" %(val_loss))
            print("-"*20)
            
        model.load_state_dict(best_model_wts)
        return model, loss_history, metric_history
    

path2models = "./models/"
if not os.path.exists(path2models):
    os.mkdir(path2models)
    
param_train = 
    "num_epochs": 10,
    "loss_func": criteron,
    "optimizer": opt,
    "train_dl": train_dl,
    "val_dl": val_dl,
    "sanity_check": False,
    "lr_scheduler": lr_scheduler,
    "path2weights": path2models + "weights.pt"
model, loss_hist, _ = train_val(model, param_train)

错误消息看起来像 - 文件“”，第 10 行，在 模型, loss_hist, _ = train_val(model, param_train)

文件“”，第 27 行，在 train_val 中 val_loss, _ = loss_epoch(model, loss_func, val_dl, sanity_check, opt)

文件“”，第 13 行，在 loss_epoch 中 loss_b, metric_b = loss_batch(loss_func, output, yb, opt)

文件“”，第 6 行，在 loss_batch 中 loss.backward()

文件“C:\Users\W540\anaconda3\lib\site-packages\torch\tensor.py”，第 198 行，向后 torch.autograd.backward(self, gradient, retain_graph, create_graph)

文件“C:\Users\W540\anaconda3\lib\site-packages\torch\autograd_init_.py”，第 100 行，向后 allow_unreachable=True) #allow_unreachable 标志

RuntimeError：张量的元素 0 不需要 grad 并且没有 grad_fn

我不确定将哪个变量设置为 require_grad = True 或者我应该在哪里启用 grad...

【参考方案1】：

你可以在loss.backward()之前试试这个：

loss = Variable(loss, requires_grad = True)

或者，由于变量已从 PyTorch 中删除（仍然存在但已弃用），您只需使用以下代码即可完成相同的操作：

loss.requires_grad = True

【讨论】：

我的代码中有同样的错误，这个答案没有帮助。

@CrackedStone 你能解决这个问题吗？对我来说是一样的

【参考方案2】：

对于我来说，在.backward() 解决了here 所述问题之前，我打电话给.retain_grad()