解释gt = torch.zeros_like(output).scatter_(1, gt[:, None, :], 1)
时间: 2023-10-22 14:10:06 浏览: 31
这行代码主要是用来将标签(gt)向量转成独热编码的形式,以便于计算交叉熵损失。具体来说,它首先创建了一个和output同样大小的全零张量(gt = torch.zeros_like(output)),然后使用scatter_()函数在第1维上按照标签值(gt[:, None, :])的位置将1填入该张量中,最终得到一个独热编码张量。其中,[:, None, :]表示在第2维上增加了一个维度,以便于scatter_()函数按照标签值的位置进行填充。这个操作相当于将gt中的每个元素转换为一个独热编码向量,大小为(output.shape[1],),并将这些向量按照标签值的位置填入gt张量中。
相关问题
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
```
帮我看看这段代码报错原因: Traceback (most recent call last): File "/home/bder73002/hpy/ConvNextV2_Demo/train+.py", line 274, 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 48, 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/losses.py", line 38, in forward index.scatter_(1, target.data.view(-1, 1).type(torch.LongTensor), 1) RuntimeError: Expected index [128, 1] to be smaller than self [16, 8] apart from dimension 1 部分代码如下: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) 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.scatter_(1, target.data.view(-1, 1).type(torch.LongTensor), 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)
这个错误是由于 `index` 张量的大小与 `output` 张量的大小不匹配导致的。在你的代码中,`index` 张量的大小是 `[batch_size, classes]`,即每个样本的预测标签的 one-hot 编码,而 `output` 张量的大小是 `[batch_size, features]`,即每个样本的特征向量的大小。因此,如果在 `index.scatter_()` 操作中使用了一个大小为 `[batch_size, 1]` 的张量,则会导致上述错误。
要解决这个问题,你可以将 `index` 的大小更改为 `[batch_size, num_classes]`,其中 `num_classes` 是分类数量。你可以在 `LDAMLoss` 的 `__init__` 方法中将 `num_classes` 作为参数并存储在实例变量中,然后在 `forward` 方法中使用它来创建 `index` 张量。例如:
```
class LDAMLoss(nn.Module):
def __init__(self, cls_num_list, max_m=0.5, weight=None, s=30, num_classes=10):
super(LDAMLoss, self).__init__()
self.num_classes = num_classes
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.LongTensor), 1)
index = index[:, :self.num_classes] # 取前 num_classes 列
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)
```
然后在使用 `LDAMLoss` 时,你需要将 `num_classes` 参数传递给它。例如:
```
num_classes = len(train_loader.dataset.classes)
criterion_train = LDAMLoss(cls_num_list=cls_num_list, max_m=0.5, s=30, num_classes=num_classes)
```