loss = criterion(output, y_train.cuda())时出现Expected input batch_size (250) to match target batch_size (1000).

这个错误提示表明你的输出张量和目标张量的批次大小不匹配。在你的情况下，输出张量的批次大小为250，而目标张量的批次大小为1000。这可能是因为你的训练集和标签集的大小不匹配。你可以检查一下你的数据集和标签集的大小是否一致，然后进行相应的更改。另外，你也可以尝试使用批次大小相同的数据集和标签集来训练模型。

相关问题

pytorch部分代码如下：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 if weight is not None: weight = torch.FloatTensor(weight).cuda() self.weight = weight self.cls_num_list = cls_num_list def forward(self, x, target): index = torch.zeros_like(x, dtype=torch.uint8) 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)) # size=(batch_size, 1) (-1,1) x_m = x - batch_m output = torch.where(index, x_m, x) if self.weight is not None: output = output * self.weight[None, :] logit = output * self.s return F.cross_entropy(logit, target, weight=self.weight) train_loader = torch.utils.data.DataLoader(dataset_train, batch_size=BATCH_SIZE, shuffle=True,drop_last=True) test_loader = torch.utils.data.DataLoader(dataset_test, batch_size=BATCH_SIZE, shuffle=True) 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) criterion_val = LDAMLoss(cls_num_list=cls_num_list, max_m=0.5, s=30) mixup_fn = Mixup( mixup_alpha=0.8, cutmix_alpha=1.0, cutmix_minmax=None, prob=0.1, switch_prob=0.5, mode='batch', label_smoothing=0.1, num_classes=classes) 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) targets = torch.tensor(targets).to(torch.long) # 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) # 梯度裁剪，防止梯度爆炸 scaler.step(optimizer) # 更新下一次迭代的scaler scaler.update() # 否则，直接反向传播求梯度 else: loss = criterion_train(output, targets) loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), CLIP_GRAD) optimizer.step() 报错：RuntimeError: Expected index [112, 1] to be smaller than self [16, 7] apart from dimension 1

这个报错是因为在计算batch_m时，index_float的维度是反过来的，应该将index_float.transpose(0,1)改为index_float.transpose(1,0)，即将第0维和第1维交换。修改后的代码如下：

...
batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(1,0))
...

你可以尝试修改后再运行看看是否还会出现报错。

pytorch部分代码如下：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 if weight is not None: weight = torch.FloatTensor(weight).cuda() self.weight = weight self.cls_num_list = cls_num_list def forward(self, x, target): index = torch.zeros_like(x, dtype=torch.uint8) index_float = index.type(torch.cuda.FloatTensor) batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(0,1)) # 0,1 batch_m = batch_m.view((x.size(0), 1)) # size=(batch_size, 1) (-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) if self.weight is not None: output = output * self.weight[None, :] target = torch.flatten(target) # 将 target 转换成 1D Tensor logit = output * self.s return F.cross_entropy(logit, target, weight=self.weight) 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() loss = criterion_train(output, targets) # 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, target_a, target_b, lam)) # 计算loss # loss = lam * criterion_train(output, target_a) + (1 - lam) * criterion_train(output, target_b) # 计算 mixup 后的损失函数 scaler.scale(loss).backward() # 梯度放大 torch.nn.utils.clip_grad_norm_(model.parameters(), CLIP_GRAD) # 梯度裁剪，防止梯度爆炸 scaler.step(optimizer) # 更新下一次迭代的scaler scaler.update() # 否则，直接反向传播求梯度 else: # loss = criterion_train(output, targets) loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), CLIP_GRAD) optimizer.step() 报错：) File "/home/adminis/hpy/ConvNextV2_Demo/models/losses.py", line 48, in forward output = torch.where(index, x_m, x) RuntimeError: expected scalar type float but found c10::Half

这个错误通常是由于在定义 `index` 和 `index_float` 时，没有指定数据类型，导致数据类型不匹配。可以尝试将 `index` 和 `index_float` 的数据类型都指定为 `torch.cuda.FloatTensor`。修改代码如下：

index = torch.zeros_like(x, dtype=torch.uint8).cuda()
index_float = index.type(torch.cuda.FloatTensor)

这样就可以保证 `index` 和 `index_float` 的数据类型都是 `torch.cuda.FloatTensor`，与其他计算中使用的数据类型匹配。