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) # 3、将数据输入mixup_fn生成mixup数据 samples, targets = mixup_fn(data, target) # 4、将上一步生成的数据输入model，输出预测结果，再计算loss output = model(samples) # 5、梯度清零（将loss关于weight的导数变成0） optimizer.zero_grad() # 6、若使用混合精度 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) target = torch.clamp(target, 0, index.size(1) - 1) index.scatter(1, target.data.view(-1, 1).type(torch.int64), 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：RuntimeError: Expected index [112, 1] to be smaller than self [16, 7] apart from dimension 1 帮我看看如何修改源代码

根据你提供的代码，报错是出现在 LDAMLoss 的 forward 函数中。根据报错信息，可以推测是 index 的大小与 x 的大小不匹配导致的。可以在 forward 函数中添加一行代码，对 index 进行裁剪，使其与 x 的大小匹配，代码如下：

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.data.view(-1, 1).type(torch.int64), 1)
    # 添加以下一行代码
    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)

这样应该就能解决报错问题了。