torchoutil.nn.functional.transform module¶

as_tensor( data: Sequence[Never], dtype: Literal[None] = None, device: device | None | Literal['default', 'cuda_if_available'] | str | int = None, ) → Tensor1D[source]¶

as_tensor( data: Sequence[Sequence[Never]], dtype: Literal[None] = None, device: device | None | Literal['default', 'cuda_if_available'] | str | int = None, ) → Tensor2D

as_tensor( data: Sequence[Sequence[Sequence[Never]]], dtype: Literal[None] = None, device: device | None | Literal['default', 'cuda_if_available'] | str | int = None, ) → Tensor3D

as_tensor( data: bool, dtype: Literal[None, 'bool'] = None, device: device | None | Literal['default', 'cuda_if_available'] | str | int = None, ) → BoolTensor0D

as_tensor( data: Sequence[bool], dtype: Literal[None, 'bool'] = None, device: device | None | Literal['default', 'cuda_if_available'] | str | int = None, ) → BoolTensor1D

as_tensor( data: Sequence[Sequence[bool]], dtype: Literal[None, 'bool'] = None, device: device | None | Literal['default', 'cuda_if_available'] | str | int = None, ) → BoolTensor2D

as_tensor( data: Sequence[Sequence[Sequence[bool]]], dtype: Literal[None, 'bool'] = None, device: device | None | Literal['default', 'cuda_if_available'] | str | int = None, ) → BoolTensor3D

as_tensor( data: int, dtype: Literal[None, 'int64', 'long'] = None, device: device | None | Literal['default', 'cuda_if_available'] | str | int = None, ) → LongTensor0D

as_tensor( data: Sequence[int], dtype: Literal[None, 'int64', 'long'] = None, device: device | None | Literal['default', 'cuda_if_available'] | str | int = None, ) → LongTensor1D

as_tensor( data: Sequence[Sequence[int]], dtype: Literal[None, 'int64', 'long'] = None, device: device | None | Literal['default', 'cuda_if_available'] | str | int = None, ) → LongTensor2D

as_tensor( data: Sequence[Sequence[Sequence[int]]], dtype: Literal[None, 'int64', 'long'] = None, device: device | None | Literal['default', 'cuda_if_available'] | str | int = None, ) → LongTensor3D

as_tensor( data: float, dtype: Literal[None, 'float32', 'float'] = None, device: device | None | Literal['default', 'cuda_if_available'] | str | int = None, ) → FloatTensor0D

as_tensor( data: Sequence[float], dtype: Literal[None, 'float32', 'float'] = None, device: device | None | Literal['default', 'cuda_if_available'] | str | int = None, ) → FloatTensor1D

as_tensor( data: Sequence[Sequence[float]], dtype: Literal[None, 'float32', 'float'] = None, device: device | None | Literal['default', 'cuda_if_available'] | str | int = None, ) → FloatTensor2D

as_tensor( data: Sequence[Sequence[Sequence[float]]], dtype: Literal[None, 'float32', 'float'] = None, device: device | None | Literal['default', 'cuda_if_available'] | str | int = None, ) → FloatTensor3D

as_tensor( data: complex, dtype: Literal[None, 'complex64', 'cfloat'] = None, device: device | None | Literal['default', 'cuda_if_available'] | str | int = None, ) → CFloatTensor0D

as_tensor( data: Sequence[complex], dtype: Literal[None, 'complex64', 'cfloat'] = None, device: device | None | Literal['default', 'cuda_if_available'] | str | int = None, ) → CFloatTensor1D

as_tensor( data: Sequence[Sequence[complex]], dtype: Literal[None, 'complex64', 'cfloat'] = None, device: device | None | Literal['default', 'cuda_if_available'] | str | int = None, ) → CFloatTensor2D

as_tensor( data: Sequence[Sequence[Sequence[complex]]], dtype: Literal[None, 'complex64', 'cfloat'] = None, device: device | None | Literal['default', 'cuda_if_available'] | str | int = None, ) → CFloatTensor3D

Convert arbitrary data to tensor.

Unlike torch.as_tensor, it works recursively and stack sequences like List[Tensor]. It also accept python generator objects.

Args:: data: Data to convert to tensor. Can be Tensor, np.ndarray, list, tuple or any number-like object. dtype: Target torch dtype. defaults to None. device: Target torch device. defaults to None.
Returns:: PyTorch tensor created from data.

flatten( x: Tensor, start_dim: int = 0, end_dim: int | None = None, ) → Tensor1D[source]¶
flatten( x: ndarray | generic, start_dim: int = 0, end_dim: int | None = None, ) → ndarray
flatten( x: T_BuiltinScalar, start_dim: int = 0, end_dim: int | None = None, ) → List[T_BuiltinScalar]
flatten( x: Iterable[T_BuiltinScalar], start_dim: int = 0, end_dim: int | None = None, ) → List[T_BuiltinScalar]

move_to_rec(

x: Mapping[T, U],

predicate: Callable[[Tensor | Module], bool] | None = None,

**kwargs,

) → Dict[T, U][source]¶
move_to_rec(

x: T,

predicate: Callable[[Tensor | Module], bool] | None = None,

**kwargs,

) → T: Move all modules and tensors recursively to a specific dtype or device.

pad_and_crop_dim( x: Tensor, target_length: int, *, align: Literal['left', 'right', 'center', 'random'] = 'left', pad_value: int | float | bool | Callable[[Tensor], int | float | bool] = 0.0, dim: int = -1, mode: Literal['constant', 'reflect', 'replicate', 'circular'] = 'constant', generator: Generator | None | Literal['default'] | int = None, ) → Tensor[source]¶: Pad and crop along the specified dimension.

repeat_interleave_nd( x: Tensor, repeats: int, dim: int = 0, ) → Tensor[source]¶

Generalized version of torch.repeat_interleave for N >= 1 dimensions. The output size will be (…, D*repeats, …), where D is the size of the dimension of the dim argument.

Args:: x: Any tensor of shape (…, D, …) with at least 1 dim. repeats: Number of repeats. dim: The dimension to repeat. defaults to 0.

Examples::¶

>>> x = torch.as_tensor([[0, 1, 2, 3], [4, 5, 6, 7]])
>>> repeat_interleave_nd(x, n=2, dim=0)
tensor([[0, 1, 2, 3],
        [0, 1, 2, 3],
        [4, 5, 6, 7],
        [4, 5, 6, 7]])

resample_nearest_freqs(x: ~torch.Tensor, orig_freq: int, new_freq: int, *, dims: int | ~typing.Iterable[int] = -1, round_fn: ~typing.Callable[[~torch.Tensor], ~torch.Tensor] = <built-in method floor of type object>) → Tensor[source]¶

Nearest neigbour resampling using tensor slices.

Args:: x: Input tensor. orig_freq: Source sampling rate. new_freq: Target sampling rate. dims: Dimensions to apply resampling. defaults to -1. round_fn: Rounding function to compute sub-indices. defaults to torch.floor.

resample_nearest_rates(x: ~torch.Tensor, rates: float | ~typing.Iterable[float], *, dims: int | ~typing.Iterable[int] = -1, round_fn: ~typing.Callable[[~torch.Tensor], ~torch.Tensor] = <built-in method floor of type object>) → Tensor[source]¶

Nearest neigbour resampling using tensor slices.

Args:: x: Input tensor. rate: The reduction factor of each axis, e.g. a factor of 0.5 will divide the input axes by 2. dims: Dimensions to apply resampling. defaults to -1. round_fn: Rounding function to compute sub-indices. defaults to torch.floor.

resample_nearest_steps(x: ~torch.Tensor, steps: float | ~typing.Iterable[float], *, dims: int | ~typing.Iterable[int] = -1, round_fn: ~typing.Callable[[~torch.Tensor], ~torch.Tensor] = <built-in method floor of type object>) → Tensor[source]¶

Nearest neigbour resampling using tensor slices.

Args:: x: Input tensor. steps: Floating step for resampling each value. dims: Dimensions to apply resampling. defaults to -1. round_fn: Rounding function to compute sub-indices. defaults to torch.floor.

shuffled( x: Tensor, dims: int | Iterable[int] = -1, generator: Generator | None | Literal['default'] | int = None, ) → Tensor[source]¶: Returns a shuffled version of the input tensor along specific dimension(s).

squeeze( x: T_TensorOrArray, dim: None | int | Iterable[int] = None, mode: Literal['view_if_possible', 'view', 'copy', 'inplace'] = 'view_if_possible', ) → T_TensorOrArray[source]¶

squeeze_( x: Tensor, dim: None | int | Iterable[int] = None, ) → Tensor[source]¶

squeeze_copy( x: T_TensorOrArray, dim: None | int | Iterable[int] = None, ) → T_TensorOrArray[source]¶

to_item( x: T_BuiltinScalar, ) → T_BuiltinScalar[source]¶
to_item( x: Tensor | ndarray | SupportsIterLen, ) → bool | int | float | complex | None | str | bytes: Convert scalar value to the closest built-in type.

Convert arbitrary data to tensor.

Unlike torch.as_tensor, it works recursively and stack sequences like List[Tensor]. It also accept python generator objects.

Args:: data: Data to convert to tensor. Can be Tensor, np.ndarray, list, tuple or any number-like object. dtype: Target torch dtype. defaults to None. device: Target torch device. defaults to None.
Returns:: PyTorch tensor created from data.

top_k( x: T_Tensor, k: int, dim: int = -1, largest: bool = True, sorted: bool = True, *, return_values: bool = True, return_indices: bool = True, ) → T_Tensor | LongTensor | topk[source]¶

top_p( x: Tensor, p: float, dim: int = -1, largest: bool = True, *, return_values: Literal[True] = True, return_indices: Literal[True] = True, ) → top_p[source]¶
top_p( x: T_Tensor, p: float, dim: int = -1, largest: bool = True, *, return_values: Literal[True] = True, return_indices: Literal[False], ) → T_Tensor
top_p( x: Tensor, p: float, dim: int = -1, largest: bool = True, *, return_values: Literal[False], return_indices: Literal[True] = True, ) → LongTensor
top_p( x: T_Tensor, p: float, dim: int = -1, largest: bool = True, *, return_values: bool = True, return_indices: bool = True, ) → T_Tensor | LongTensor | top_p

topk( x: Tensor, k: int, dim: int = -1, largest: bool = True, sorted: bool = True, *, return_values: Literal[True] = True, return_indices: Literal[True] = True, ) → topk[source]¶
topk( x: T_Tensor, k: int, dim: int = -1, largest: bool = True, sorted: bool = True, *, return_values: Literal[True] = True, return_indices: Literal[False], ) → T_Tensor
topk( x: Tensor, k: int, dim: int = -1, largest: bool = True, sorted: bool = True, *, return_values: Literal[False], return_indices: Literal[True] = True, ) → LongTensor
topk( x: T_Tensor, k: int, dim: int = -1, largest: bool = True, sorted: bool = True, *, return_values: bool = True, return_indices: bool = True, ) → T_Tensor | LongTensor | topk

transform_drop( transform: Callable[[T], T], x: T, p: float, *, generator: Generator | None | Literal['default'] | int = None, ) → T[source]¶

Apply a transform on a tensor with a probability of p.

Args:: transform: Transform to apply. x: Argument of the transform. p: Probability p to apply the transform. Cannot be negative.

If > 1, it will apply the transform floor(p) times and apply a last time with a probability of p - floor(p).

unsqueeze( x: T_TensorOrArray, dim: int | Iterable[int], mode: Literal['view_if_possible', 'view', 'copy', 'inplace'] = 'view_if_possible', ) → T_TensorOrArray[source]¶

unsqueeze_( x: Tensor, dim: int | Iterable[int], ) → Tensor[source]¶

unsqueeze_copy( x: T_TensorOrArray, dim: int | Iterable[int], ) → T_TensorOrArray[source]¶

view_as_complex( x: Tensor, ) → ComplexFloatingTensor[source]¶
view_as_complex( x: ndarray, ) → ndarray
view_as_complex( x: Tuple[float, float], ) → complex: Convert floating-point input to complex-valued object.

view_as_real( x: Tensor, ) → Tensor[source]¶
view_as_real( x: ndarray, ) → ndarray
view_as_real( x: complex, ) → Tuple[float, float]: Convert complex-valued input to floating-point object.