RuntimeError: cuda runtime error (710) : device-side assert triggered at

Question

Traning the image classification with pytorch
got following error messageK

RuntimeError Traceback (most recent call last) in 29 print(len(train_loader.dataset),len(valid_loader.dataset)) 30 #break ---> 31 train_loss, train_acc ,model= train(model, device, train_loader, optimizer, criterion) 32 valid_loss, valid_acc,model = evaluate(model, device, valid_loader, criterion) 33

in train(model, device, iterator, optimizer, criterion) 21 acc = calculate_accuracy(fx, y) 22 #print("5.") ---> 23 loss.backward() 24 25 optimizer.step()

~/venv/lib/python3.7/site-packages/torch/tensor.py in backward(self, gradient, retain_graph, create_graph) 164 products. Defaults to False. 165 """ --> 166 torch.autograd.backward(self, gradient, retain_graph, create_graph) 167 168 def register_hook(self, hook):

~/venv/lib/python3.7/site-packages/torch/autograd/init.py in backward(tensors, grad_tensors, retain_graph, create_graph, grad_variables) 97 Variable._execution_engine.run_backward( 98 tensors, grad_tensors, retain_graph, create_graph, ---> 99 allow_unreachable=True) # allow_unreachable flag 100 101

RuntimeError: cuda runtime error (710) : device-side assert triggered at /pytorch/aten/src/THC/generic/THCTensorMath.cu:26

Related code block is here

def train(model, device, iterator, optimizer, criterion):

print('train')
epoch_loss = 0
epoch_acc = 0

model.train()


for (x, y) in iterator:
    #print(x,y)
    x,y = x.cuda(), y.cuda()
    #x = x.to(device)
    #y = y.to(device)
    #print('1')
    optimizer.zero_grad()
    #print('2')
    fx = model(x)
    #print('3')
    loss = criterion(fx, y)
    #print("4.loss->",loss)
    acc = calculate_accuracy(fx, y)
    #print("5.")
    loss.backward()

    optimizer.step()

    epoch_loss += loss.item()
    epoch_acc += acc.item()

return epoch_loss / len(iterator), epoch_acc / len(iterator),model


    EPOCHS = 5
    SAVE_DIR = 'models'
    MODEL_SAVE_PATH = os.path.join(SAVE_DIR, 'please.pt')
    from torch.utils.data import DataLoader
    best_valid_loss = float('inf')

    if not os.path.isdir(f'{SAVE_DIR}'):
        os.makedirs(f'{SAVE_DIR}')
    print("start")
    for epoch in range(EPOCHS):
        print('================================',epoch ,'================================')
        for i , (train_idx, valid_idx) in enumerate(zip(train_indexes, valid_indexes)):
            print(i,train_idx,valid_idx,len(train_idx),len(valid_idx))

            traindf = df_train.iloc[train_index, :].reset_index()
            validdf = df_train.iloc[valid_index, :].reset_index()

            #traindf = df_train
            #validdf = df_train

            train_dataset = TrainDataset(traindf, mode='train', transforms=data_transforms)
            valid_dataset = TrainDataset(validdf, mode='valid', transforms=data_transforms)

            train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
            valid_loader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=False)



            print(len(train_loader.dataset),len(valid_loader.dataset))
            #break
            train_loss, train_acc ,model= train(model, device, train_loader, optimizer, criterion)
            valid_loss, valid_acc,model = evaluate(model, device, valid_loader, criterion)

            if valid_loss < best_valid_loss:
                best_valid_loss = valid_loss
                torch.save(model,MODEL_SAVE_PATH)

            print(f'| Epoch: {epoch+1:02} | Train Loss: {train_loss:.3f} | Train Acc: {train_acc*100:05.2f}% | Val. Loss: {valid_loss:.3f} | Val. Acc: {valid_acc*100:05.2f}% |')

splits = zip(train_indexes, valid_indexes) [ 3692 3696 3703 ... 30733 30734 30735] [ 0 1 2 ... 4028 4041 4046] [ 0 1 2 ... 30733 30734 30735] [3692 3696 3703 ... 7986 7991 8005] [ 0 1 2 ... 30733 30734 30735] [ 7499 7500 7502 ... 11856 11858 11860] [ 0 1 2 ... 30733 30734 30735] [11239 11274 11280 ... 15711 15716 15720] [ 0 1 2 ... 30733 30734 30735] [15045 15051 15053 ... 19448 19460 19474] [
0 1 2 ... 30733 30734 30735] [18919 18920 18926 ... 23392 23400 23402] [ 0 1 2 ... 30733 30734 30735] [22831 22835 22846 ... 27118 27120 27124] [ 0 1 2 ... 27118 27120 27124] [26718 26721 26728 ... 30733 30734 30735]

hoper hoper · Accepted Answer · 2020-07-10T13:16:39

What was your loss function?

I got this error too. My problem was a multi-class classification and I was using a crossEntropy loss.

As it say in the documentations, labels should be in the range [0, C-1] where C is number of classes. But my labels were not in the range and when I used proper values for labels, Everything was ok.

RuntimeError: cuda runtime error (710) : device-side assert triggered at

1 Answers