`trw.basic_typing`¶

Module Contents¶

Classes¶

ModuleCreator

Base class for protocol classes. Protocol classes are defined as:

Attributes¶

`Numeric`	Generic Shape
`Shape`	Shape expressed as [N, C, D, H, W, ...] components
`ShapeNCX`	Shape expressed as [C, D, H, W, ...] components
`ShapeCX`	Shape expressed as [D, H, W, ...] components
`ShapeX`	Shape expressed as [N, D, H, W, ...] components (the component C is removed)
`ShapeNX`	Generic Tensor as numpy or torch
`Tensor`	Generic Tensor as numpy or torch. Must be shaped as [N, C, D, H, W, ...]
`TensorNCX`	Generic Tensor as numpy or torch. Must be shaped as [C, D, H, W, ...]
`TensorCX`	Generic Tensor as numpy or torch. Must be shaped as 2D array [N, X]
`TensorNX`	Generic Tensor with th N and C components removed
`TensorX`	Generic Tensor with N component only
`TensorN`	Torch Tensor. Must be shaped as [N, C, D, H, W, ...]
`TorchTensorNCX`	Torch Tensor. Must be shaped as 2D array [N, X]
`TorchTensorNX`	Torch Tensor with th N and C components removed
`TorchTensorX`	Generic Tensor with N component only
`TorchTensorN`	Numpy Tensor. Must be shaped as [N, C, D, H, W, ...]
`NumpyTensorNCX`	Numpy Tensor. Must be shaped as 2D array [N, X]
`NumpyTensorNX`	Numpy Tensor with th N and C components removed
`NumpyTensorX`	Represent a dictionary of (key, value)
`Batch`	Length shaped as D, H, W, ...
`Length`	Represent a data split, a dictionary of any value
`Split`	Represent a dataset which is composed of named data splits
`Dataset`
`DatasetInfo`	Represent a collection of datasets
`Datasets`
`DatasetsInfo`
`HistoryStep`
`History`
`Activation`
`IntTupleList`
`IntListList`
`ConvKernels`
`ConvStrides`
`PoolingSizes`
`Stride`
`KernelSize`
`Padding`
`Paddings`

trw.basic_typing.Numeric¶: Generic Shape

trw.basic_typing.Shape¶: Shape expressed as [N, C, D, H, W, …] components

trw.basic_typing.ShapeNCX¶: Shape expressed as [C, D, H, W, …] components

trw.basic_typing.ShapeCX¶: Shape expressed as [D, H, W, …] components

trw.basic_typing.ShapeX¶: Shape expressed as [N, D, H, W, …] components (the component C is removed)

trw.basic_typing.ShapeNX¶: Generic Tensor as numpy or torch

trw.basic_typing.Tensor¶: Generic Tensor as numpy or torch. Must be shaped as [N, C, D, H, W, …]

trw.basic_typing.TensorNCX¶: Generic Tensor as numpy or torch. Must be shaped as [C, D, H, W, …]

trw.basic_typing.TensorCX¶: Generic Tensor as numpy or torch. Must be shaped as 2D array [N, X]

trw.basic_typing.TensorNX¶: Generic Tensor with th N and C components removed

trw.basic_typing.TensorX¶: Generic Tensor with N component only

trw.basic_typing.TensorN¶: Torch Tensor. Must be shaped as [N, C, D, H, W, …]

trw.basic_typing.TorchTensorNCX¶: Torch Tensor. Must be shaped as 2D array [N, X]

trw.basic_typing.TorchTensorNX¶: Torch Tensor with th N and C components removed

trw.basic_typing.TorchTensorX¶: Generic Tensor with N component only

trw.basic_typing.TorchTensorN¶: Numpy Tensor. Must be shaped as [N, C, D, H, W, …]

trw.basic_typing.NumpyTensorNCX¶: Numpy Tensor. Must be shaped as 2D array [N, X]

trw.basic_typing.NumpyTensorNX¶: Numpy Tensor with th N and C components removed

trw.basic_typing.NumpyTensorX¶: Represent a dictionary of (key, value)

trw.basic_typing.Batch¶: Length shaped as D, H, W, …

trw.basic_typing.Length¶: Represent a data split, a dictionary of any value

trw.basic_typing.Split¶: Represent a dataset which is composed of named data splits

trw.basic_typing.Dataset¶

trw.basic_typing.DatasetInfo¶: Represent a collection of datasets

trw.basic_typing.Datasets¶

trw.basic_typing.DatasetsInfo¶

trw.basic_typing.HistoryStep¶

trw.basic_typing.History¶

trw.basic_typing.Activation¶

trw.basic_typing.IntTupleList¶

trw.basic_typing.IntListList¶

trw.basic_typing.ConvKernels¶

trw.basic_typing.ConvStrides¶

trw.basic_typing.PoolingSizes¶

trw.basic_typing.Stride¶

trw.basic_typing.KernelSize¶

trw.basic_typing.Padding¶

trw.basic_typing.Paddings¶

class trw.basic_typing.ModuleCreator¶

Bases: typing_extensions.Protocol

Base class for protocol classes. Protocol classes are defined as:

class Proto(Protocol):
    def meth(self) -> int:
        ...

Such classes are primarily used with static type checkers that recognize structural subtyping (static duck-typing), for example:

class C:
    def meth(self) -> int:
        return 0

def func(x: Proto) -> int:
    return x.meth()

func(C())  # Passes static type check

See PEP 544 for details. Protocol classes decorated with @typing_extensions.runtime act as simple-minded runtime protocol that checks only the presence of given attributes, ignoring their type signatures.

Protocol classes can be generic, they are defined as:

class GenProto(Protocol[T]):
    def meth(self) -> T:
        ...

__call__(self, *args, **kwargs) → torch.nn.Module¶

trw.basic_typing¶

Module Contents¶

Classes¶

Attributes¶

`trw.basic_typing`¶