for (int i = 0; i < word_num * 4; i++) { if (data[SITE][i] != 0){ rdata[SITE] = 1; } } 修改代码,if生效时,跳出for
时间: 2024-02-02 14:02:32 浏览: 27
你可以使用 `break` 语句来跳出 `for` 循环。修改后的代码如下所示:
```c++
for (int i = 0; i < word_num * 4; i++) {
if (data[SITE][i] != 0) {
rdata[SITE] = 1;
break; // 如果 if 生效,跳出 for 循环
}
}
```
这样,当 `if` 语句中的条件成立时,就会执行 `break` 语句,跳出 `for` 循环,不再继续执行循环体中的语句。
相关问题
帮我看看这段代码报错原因:Traceback (most recent call last): File "/home/bder73002/hpy/ConvNextV2_Demo/train+.py", line 272, in <module> train_loss, train_acc = train(model_ft, DEVICE, train_loader, optimizer, epoch,model_ema) File "/home/bder73002/hpy/ConvNextV2_Demo/train+.py", line 48, in train loss = torch.nan_to_num(criterion_train(output, targets)) # 计算loss File "/home/bder73002/anaconda3/envs/python3.9.2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl result = self.forward(*input, **kwargs) File "/home/bder73002/hpy/ConvNextV2_Demo/models/losses.py", line 37, in forward index.scatter_(1, target.data.view(-1, 1), 1) IndexError: scatter_(): Expected dtype int64 for index. 部分代码如下:cls_num_list = np.zeros(classes) for _, label in train_loader.dataset: cls_num_list[label] += 1 criterion_train = LDAMLoss(cls_num_list=cls_num_list, max_m=0.5, s=30) class LDAMLoss(nn.Module): def __init__(self, cls_num_list, max_m=0.5, weight=None, s=30): super(LDAMLoss, self).__init__() m_list = 1.0 / np.sqrt(np.sqrt(cls_num_list)) m_list = m_list * (max_m / np.max(m_list)) m_list = torch.cuda.FloatTensor(m_list) self.m_list = m_list assert s > 0 self.s = s self.weight = weight def forward(self, x, target): index = torch.zeros_like(x, dtype=torch.uint8) index.scatter_(1, target.data.view(-1, 1), 1) index_float = index.type(torch.cuda.FloatTensor) batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(0,1)) batch_m = batch_m.view((-1, 1)) x_m = x - batch_m output = torch.where(index, x_m, x) return F.cross_entropy(self.s*output, target, weight=self.weight)
根据报错信息,可以看出问题出在这一行代码上:`index.scatter_(1, target.data.view(-1, 1), 1)`,报错信息中指出 `scatter_()` 函数期望得到的是 int64 类型的索引,但是目前传入的 `target.data.view(-1, 1)` 返回的类型不是 int64。
可以尝试修改这一行代码,将 `target.data.view(-1, 1)` 的类型转换为 int64 类型,如下所示:
```
index.scatter_(1, target.data.view(-1, 1).type(torch.LongTensor), 1)
```
这里使用了 `torch.LongTensor` 将类型转换为 int64。
TypeError Traceback (most recent call last) Input In [37], in <cell line: 57>() 52 return num_correct / len(test_loader.dataset) 56 # 转换数据格式 ---> 57 train_dataset = TensorDataset(train_norm_vec, y_train) 58 val_dataset = TensorDataset(valid_norm_vec, y_valid) 59 test_dataset = TensorDataset(test_norm_vec, y_test) File ~\AppData\Roaming\Python\Python38\site-packages\torch\utils\data\dataset.py:189, in TensorDataset.__init__(self, *tensors) 188 def __init__(self, *tensors: Tensor) -> None: --> 189 assert all(tensors[0].size(0) == tensor.size(0) for tensor in tensors), "Size mismatch between tensors" 190 self.tensors = tensors File ~\AppData\Roaming\Python\Python38\site-packages\torch\utils\data\dataset.py:189, in <genexpr>(.0) 188 def __init__(self, *tensors: Tensor) -> None: --> 189 assert all(tensors[0].size(0) == tensor.size(0) for tensor in tensors), "Size mismatch between tensors" 190 self.tensors = tensors TypeError: 'int' object is not callable
这个错误通常是因为你尝试将整数当作函数来调用。根据你提供的代码,可能是因为在构建 `TensorDataset` 对象时,传入的参数不是 `Tensor` 类型,而是整数类型。你可以检查一下输入的 `train_norm_vec`、`y_train`、`valid_norm_vec`、`y_valid`、`test_norm_vec`、`y_test` 是否都是 `Tensor` 类型的数据,或者是否有其他的类型错误。
另外,你的 `train_loader`、`val_loader` 和 `test_loader` 都是使用 `DataLoader` 构建的,但是在 `test` 函数中,你却直接使用 `test_dataset` 进行预测。这可能会导致数据类型不一致的问题。建议在 `test` 函数中也使用 `DataLoader` 对象来进行预测,例如:
```python
def test(model, test_loader):
model.eval()
num_correct = 0
for inputs, labels in test_loader:
inputs, labels = inputs.to(device), labels.to(device)
outputs = model(inputs)
_, predicted = torch.max(outputs.data, 1)
num_correct += (predicted == labels).sum().item()
return num_correct / len(test_loader.dataset)
test_loader = DataLoader(test_dataset, batch_size=16, shuffle=False)
test_acc = test(model, test_loader)
```