PyTorch Network Loader

Types#

Type Aliases#

Param#: Alias of dict[str, Tensor | Param]

DataLike#: Alias of ndarray | Tensor | Data[ArrayCT]

ShapeLike#: Alias of list[int] | list[list[int]]

ArrayLike#: Alias of ndarray | Tensor

TensorLike#: Alias of Tensor | Data[Tensor] | DataList[Tensor | Data[Tensor]]

NDArrayLike#: Alias of ndarray | Data[ndarray] | DataList[ndarray | Data[ndarray]]

DataListLike#: Alias of ndarray | Tensor | Data[ArrayCT] | DataList[ndarray | Tensor | Data[ArrayCT]]

TensorListLike#: Alias of Tensor | DataList[Tensor]

NDArrayListLike#: Alias of ndarray | DataList[ndarray]

Type Variables#

P#: ParamSpec

T#: TypeVar

DataT#: TypeVar[bound = ndarray | Tensor | Data[ArrayCT]]

ArrayT#: TypeVar[bound = ndarray | Tensor]

ModuleT#: TypeVar[bound = Module]

TensorT#: TypeVar[bound = Tensor | Data[Tensor] | DataList[Tensor | Data[Tensor]]]

NDArrayT#: TypeVar[bound = ndarray | Data[ndarray] | DataList[ndarray | Data[ndarray]]]

DatasetT#: TypeVar[bound = DatasetProtocol]

DataListT#: TypeVar[bound = ndarray | Tensor | Data[ArrayCT] | DataList[ndarray | Tensor | Data[ArrayCT]]]

TensorListT#: TypeVar[bound = Tensor | DataList[Tensor]]

ModuleListT#: TypeVar[bound = Module | ModuleList]

ArrayCT#: TypeVar[ndarray, Tensor]

LossCT#: TypeVar[float, dict[str, float]]

TensorLossCT#: TypeVar[Tensor, dict[str, Tensor]]

Protocols#

class DatasetProtocol(*args, **kwargs)[source]#

Bases: Protocol[DataT]

Protocol for datasets used in NetLoader

Attributes:

extralist[Any] | ndarray | NoneType: Additional data for each sample in the dataset of length N with shape (N,…) and type Any
idxsndarray: Index for each sample in the dataset with shape (N) and type int
low_dimndarray | Tensor | NoneType: Low dimensional data for each sample in the dataset with shape (N)
high_dimndarray | Tensor | object: High dimensional data for each sample in the dataset with shape (N), this is required

__getitem__(idx)[source]#

Parameters:

idxint: Sample index

Returns:

tuple[int, DataListT, DataListT, Any]: Sample index, low dimensional data, high dimensional data, and extra data

__len__()[source]#

Returns the number of samples in the dataset

Returns:

int: Number of samples in the dataset

step(epoch)[source]#

Step the dataset for each training iteration.

Parameters:

epochfloat: Current epoch number