pytorch部分代码如下：train_loss, train_acc = train(model_ft, DEVICE, train_loader, optimizer, epoch,model_ema) for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device, non_blocking=True), Variable(target).to(device,non_blocking=True) samples, targets = mixup_fn(data, target) output = model(samples) optimizer.zero_grad() if use_amp: with torch.cuda.amp.autocast(): loss = torch.nan_to_num(criterion_train(output, targets)) scaler.scale(loss).backward() 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) target = torch.clamp(target, 0, index.size(1) - 1) index.scatter_(1, target.unsqueeze(1).type(torch.int64), 1) index = index[:, :x.size(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： 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 622, in forward index.scatter_(1, target.unsqueeze(1).type(torch.int64), 1) # target.data.view(-1, 1). RuntimeError: Index tensor must have the same number of dimensions as self tensor 帮我看看如何修改源代码

从错误信息来看，`index`张量的维度和`target`张量不匹配。你可以尝试检查以下`target`张量的形状，看是否存在问题。同时，你可以在 `index.scatter_` 操作之前，将 `index` 张量的最后一维进行裁剪，使其与 `output` 张量的最后一维相同，例如：

index = torch.zeros_like(x, dtype=torch.uint8)
target = torch.clamp(target, 0, index.size(1) - 1)
index = index[:, :x.size(-1)]  # 裁剪最后一维
target = target[:, :x.size(-1)]  # 裁剪最后一维
index.scatter_(1, target.unsqueeze(1).type(torch.int64), 1)

另外，你还需要检查 `target` 张量是否位于正确的设备上，可在 `forward` 方法中添加以下代码，将 `target` 张量移动到与 `x` 张量相同的设备上：

target = target.to(x.device)