S_batch = self.S_cmm[index_, :][:, index_].cuda() IndexError: index 104 is out of bounds for dimension 0 with size 1这个问题怎么解决

这个错误提示表明，你正在尝试访问一个维度为0，但是索引值为104的元素，但是这个维度的大小只有1，因此会出现索引超出范围的错误。

你需要检查一下 `index_` 的范围是否正确，以及 `S_cmm` 的维度是否与 `index_` 的范围相匹配。如果 `S_cmm` 的维度不够大，无法满足你想要的索引范围，你需要重新调整 `S_cmm` 的大小或者重新选择合适的索引范围。

另外，你还需要确认你的代码是否正确地将 `S_cmm` 从CPU移动到了GPU上，如果没有，你需要使用 `to()` 或者 `cuda()` 方法将其移动到GPU上。

相关问题

代码如下：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) 报错：Traceback (most recent call last): File "/home/bder73002/hpy/ConvNextV2_Demo/train+.py", line 280, 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 46, 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/utils.py", line 182, in forward ldam_loss = self.ldam_loss(x, target) 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/utils.py", line 148, in forward index.scatter_(1, target.data.view(-1, 1), 1) IndexError: scatter_(): Expected dtype int64 for index.

根据报错信息，你需要将 index 的数据类型转换为 int64。可以在 index 定义时指定它的数据类型为 int64：

index = torch.zeros_like(x, dtype=torch.int64)

或者可以在 scatter_() 操作前，将 index 的数据类型转换为 int64：

index = index.type(torch.int64)
index.scatter_(1, target.data.view(-1, 1), 1)

这样应该就能解决这个错误了。

pytorch部分代码如下：train_loss, train_acc = train(model_ft, DEVICE, train_loader, optimizer, epoch,model_ema) if use_amp: with torch.cuda.amp.autocast(): # 开启混合精度 loss = torch.nan_to_num(criterion_train(output, targets)) # 计算loss scaler.scale(loss).backward() # 梯度放大 torch.nn.utils.clip_grad_norm_(model.parameters(), CLIP_GRAD) if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks or _global_forward_hooks or _global_forward_pre_hooks): return forward_call(*input, **kwargs) 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) 报错：Traceback (most recent call last): File "/home/adminis/hpy/ConvNextV2_Demo/train+ca.py", line 279, in <module> train_loss, train_acc = train(model_ft, DEVICE, train_loader, optimizer, epoch,model_ema) File "/home/adminis/hpy/ConvNextV2_Demo/train+ca.py", line 46, in train loss = torch.nan_to_num(criterion_train(output, targets)) # 计算loss File "/home/adminis/anaconda3/envs/wln/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl return forward_call(*input, **kwargs) File "/home/adminis/hpy/ConvNextV2_Demo/models/utils.py", line 621, in forward index.scatter_(1, target.data.view(-1, 1), 1) IndexError: scatter_(): Expected dtype int64 for index.

看起来问题出在 `LDAMLoss` 中的 `scatter_()` 函数上。根据报错信息，`scatter_()` 期望的 `dtype` 是 `int64`，但是 `target` 的 `dtype` 不是，导致了错误。你可以尝试在 `LDAMLoss` 中将 `target` 的 `dtype` 转换为 `torch.int64`，例如：

index.scatter_(1, target.data.view(-1, 1).type(torch.int64), 1)

这样应该可以解决这个问题。