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).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，报错信息显示 index 的形状是 [112, 1]，而 x 的形状是 [16, 7]，因此 index 与 x 的形状不匹配，导致了这个错误。

为了解决这个问题，你可以尝试将 LDAMLoss 类的 forward 函数中的 index 的形状改为 [x.size(0), x.size(1)]，这样就能够与 x 的形状匹配。同时，由于 target.data 已经被弃用，建议改为 target。修改后的代码如下：

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.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)

另外，你的 init 方法也存在一些错误。super 函数应该在 init 方法的第一行调用，而不是在函数体内。因此，init 方法应该改为：

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