解释一下这段代码PREVIOUS_X = enum.auto()
时间: 2024-05-24 16:13:11 浏览: 10
这段代码定义了一个枚举类型并为枚举类型定义了一个成员。其中,PREVIOUS_X为该成员的名称,而enum.auto()是用来自动分配枚举值的语法。这意味着该枚举类型的每个成员都将具有独特的枚举值,但我们无需显式地为它们赋值。而在这个例子中,PREVIOUS_X是第一个被定义的成员,它的枚举值将自动设为0。
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简化java代码: List<RegionManageVo> addressList = new ArrayList<>() ; if( dataType!= MyEnums.REGION_MANAGE_VO___DATA_TYPE_ENUM.镇乡档案.toCode() ){ addressList = regionManageService.gainTownCountryAddress(query.getWorkflowOrderId()) ; }else if( dataType!= MyEnums.REGION_MANAGE_VO___DATA_TYPE_ENUM.村庄档案.toCode() ){ addressList = regionManageService.gainVillageAddress(query.getWorkflowOrderId()) ; } if( addressList != null && addressList.size() > 0 ){ if( addressList.size() == 1 ){ RegionManageVo vo = addressList.get(0) ; if( vo!=null ){ String address = vo.getDetailAddress() ; List<RegionManageVo> ifHasAgreedList = new ArrayList<>() ; if( dataType!= MyEnums.REGION_MANAGE_VO___DATA_TYPE_ENUM.镇乡档案.toCode() ){ ifHasAgreedList = regionManageService.gainTownCountryListAgreed( address , MyEnums.REGION_MANAGE_VO_VERIFY_STATUS_ENUM.通过.toCode(), dataType ) ; }else if( dataType!= MyEnums.REGION_MANAGE_VO___DATA_TYPE_ENUM.村庄档案.toCode() ){ ifHasAgreedList = regionManageService.gainVillageListAgreed( address , MyEnums.REGION_MANAGE_VO_VERIFY_STATUS_ENUM.通过.toCode(), dataType ) ; } if(ifHasAgreedList!=null && ifHasAgreedList.size() >0 ){ return ResultVoUtil.success(true) ; } } }else{ log.error("checkIfHasSamePassedRegion -- > should not has this data twice . 工作流订单ID:{} ",query.getWorkflowOrderId()) ; return ResultVoUtil.success(true) ; } }
List<RegionManageVo> addressList = new ArrayList<>();
if(dataType != MyEnums.REGION_MANAGE_VO___DATA_TYPE_ENUM.镇乡档案.toCode()) {
addressList = regionManageService.gainTownCountryAddress(query.getWorkflowOrderId());
} else if(dataType != MyEnums.REGION_MANAGE_VO___DATA_TYPE_ENUM.村庄档案.toCode()) {
addressList = regionManageService.gainVillageAddress(query.getWorkflowOrderId());
}
if(!addressList.isEmpty()) {
RegionManageVo vo = addressList.get(0);
if(vo != null) {
String address = vo.getDetailAddress();
List<RegionManageVo> ifHasAgreedList = new ArrayList<>();
if(dataType != MyEnums.REGION_MANAGE_VO___DATA_TYPE_ENUM.镇乡档案.toCode()) {
ifHasAgreedList = regionManageService.gainTownCountryListAgreed(address, MyEnums.REGION_MANAGE_VO_VERIFY_STATUS_ENUM.通过.toCode(), dataType);
} else if(dataType != MyEnums.REGION_MANAGE_VO___DATA_TYPE_ENUM.村庄档案.toCode()) {
ifHasAgreedList = regionManageService.gainVillageListAgreed(address, MyEnums.REGION_MANAGE_VO_VERIFY_STATUS_ENUM.通过.toCode(), dataType);
}
if(!ifHasAgreedList.isEmpty()) {
return ResultVoUtil.success(true);
}
} else {
log.error("checkIfHasSamePassedRegion -- > should not has this data twice . 工作流订单ID:{} ", query.getWorkflowOrderId());
return ResultVoUtil.success(true);
}
}
pytorch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(1,0)) # 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) classes=7, 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) 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() 报错:File "/home/adminis/hpy/ConvNextV2_Demo/models/losses.py", line 53, in forward return F.cross_entropy(logit, target, weight=self.weight) File "/home/adminis/anaconda3/envs/wln/lib/python3.9/site-packages/torch/nn/functional.py", line 2824, in cross_entropy return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index) RuntimeError: multi-target not supported at /pytorch/aten/src/THCUNN/generic/ClassNLLCriterion.cu:15
这个报错是因为 `target` 中包含了多个标签,而 `F.cross_entropy` 函数只支持单标签的情况。你可以将 `target` 转换成一个 1D 的 Tensor,其中每个元素表示一个样本的类别标签。可以使用 `torch.flatten()` 或者 `torch.view()` 来实现这个转换。修改后的代码如下:
```python
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(1,0)) # 0,1
batch_m = batch_m.view((-1, 1)) # size=(batch_size, 1)
x_m = x - batch_m
output = torch.where(index, x_m, x)
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)
```