问如何从内存不足的pytroch CUDA中恢复？

EN

try:
    output = model(Variable(torch.LongTensor(np.array(x))).to(device),Variable(torch.LongTensor(np.array(pos))).to(device),Variable(torch.LongTensor(np.array(m))).to(device))
    loss = criterion(output, Variable(torch.LongTensor(y)).to(device))#lb.transform(y)
    loss.backward()
    optimizer.step()
    losses.append(loss.data.mean())
except:
    half = int(len(x) / 2)
    x1 = x[:half]
    x2 = x[half:]
    pos1 = pos[:half]
    pos2 = pos[half:]
    m1 = m[:half]
    m2 = m[half:]
    y1 = y[:half]
    y2 = y[half:]
    optimizer.zero_grad()
    output = model(Variable(torch.LongTensor(np.array(x1))).to(device),Variable(torch.LongTensor(np.array(pos1))).to(device),Variable(torch.LongTensor(np.array(m1))).to(device))
    loss = criterion(output, Variable(torch.LongTensor(y1)).to(device))#lb.transform(y)
    loss.backward()
    optimizer.step()
    losses.append(loss.data.mean())
    output = model(Variable(torch.LongTensor(np.array(x2))).to(device),Variable(torch.LongTensor(np.array(pos2))).to(device),Variable(torch.LongTensor(np.array(m2))).to(device))
    loss = criterion(output, Variable(torch.LongTensor(y2)).to(device))#lb.transform(y)
    loss.backward()
    optimizer.step()
    losses.append(loss.data.mean())