class Dataset(Generic[T_co]): # Generic is an Abstract base class for generic types. def __getitem__(self, index) -> T_co: raise NotImplementedError def __add__(self, other: 'Dataset[T_co]') -> 'ConcatDataset[T_co]': return ConcatDataset([self, other]) 1. 2. 3. 4. 5. 6....
ConcatDataset类源码见下一期博文 二、相应的python语法补充 2.1 泛型编程 相当于C++中的template<typename T>,即定义类模板时不显示指定参数类比,而是在实际实例化时再进行指定 T_co是由API:typing.TypeVar()实例化的对象,其定义如下: AI检测代码解析 T_co = TypeVar('T_co', covariant=True) 1. 其中'T_co'...
class DistributedSampler(Sampler[T_co]): def __iter__(self) -> Iterator[T_co]: if self.shuffle: # 如果需要shuffle,则会基于epoch和seed进行处理 # deterministically shuffle based on epoch and seed g = torch.Generator() g.manual_seed(self.seed + self.epoch) indices = torch.randperm(len(...
classDataset(Generic[T_co]):# Generic is an Abstract baseclassforgeneric types.def__getitem__(self,index)->T_co:raise NotImplementedError def__add__(self,other:'Dataset[T_co]')->'ConcatDataset[T_co]':returnConcatDataset([self,other])# No`def __len__(self)`default?# SeeNOTE[LackofDe...
def__init__(self, dataset: Dataset[T_co], indices:Sequence[int]) ->None: self.dataset = dataset self.indices = indicesdef__getitem__(self, idx):ifisinstance(idx,list):returnself.dataset[[self.indices[i]foriinidx]]returnself.dataset[self.indices[idx]]def__len__(self):returnlen(self...
class DataLoader(Generic[T_co]): def __init__(self, dataset: Dataset[T_co], batch_size: Optional[int] = 1, shuffle: Optional[bool] = None, sampler: Union[Sampler, Iterable, None] = None, batch_sampler: Union[Sampler[Sequence], Iterable[Sequence], None] = None, num_workers: int ...
classDistributedSampler(Sampler[T_co]):def__init__(self, dataset: Dataset, num_replicas:Optional[int] =None, rank:Optional[int] =None, shuffle:bool=True, seed:int=0, drop_last:bool=False) ->None: self.dataset = dataset self.num_replicas = num_replicas ...
图片由作者使用AI(https://copilot.microsoft.com/images/create)协助创建 简介 多年来,我一直使用PyTorch来构建和训练深度学习模型。尽管我已经熟悉了它的语法和规则,但总有一些东西让我感到好奇:在这些操作内部到底发生了什么?这一切是如何运作的? 如果你已经看到了这里,你可能也有同样的疑问。如果你问我如何在PyTo...
class DataLoader(Generic[T_co]): def __init__(self, dataset: Dataset[T_co], batch_size: Optional[int] = 1, shuffle: Optional[bool] = None, sampler: Union[Sampler, Iterable, None] = None, batch_sampler: Union[Sampler[Sequence], Iterable[Sequence], None] = None, num_workers: int ...
Intrinsic model of coregionalization(ICM) ICM(共区域化的内在模型)方法通过引入核心区域化矩阵 (B) 来推广独立多输出高斯过程,该矩阵模型化任务之间的相关性。ICM方法中的协方差函数定义如下: 其中k_input是在输入空间上定义的协方差函数(例如,平方指数核),而 B ∈ R^{T×T}是捕捉任务特定协方差的核心区域化...