dipas.backends module

class dipas.backends.Backend(*args, **kwds)

Bases: Generic[_TensorType, _ParameterType]

ModuleType: ClassVar[Type]

ParameterType: ClassVar[Type]

TensorType: ClassVar[Type]

as_float64(x: _TensorType) → _TensorType

Convert the given tensor’s data representation to float64.

as_parameter(x: _TensorType) → _ParameterType

Transform the given tensor to a parameter.

concatenate(xs: Union[List[_TensorType], Tuple[_TensorType, ...]], *, dim: int = 0) → _TensorType

Concatenate multiple tensors along one of their dimensions.

from_numbers(value: Union[float, Sequence[Union[float, Sequence[Union[float, Sequence[float]]]]]]) → _TensorType

Create a new tensor from the given number(s).

from_numpy(array: <MagicMock name='mock.ndarray' id='139745953275856'>) → _TensorType

Create a new tensor from the given numpy array.

functions: _Functions[_TensorType]

ignore_gradient: Callable[[], AbstractContextManager]

linalg: _Linalg[_TensorType]

make_id_matrix(n: int) → _TensorType

Create a new tensor representing the nxn identity matrix.

make_zeros(*shape: int) → _TensorType

Create a new tensor of the given shape, filled with zeros.

random: _Random[_TensorType]

requires_grad(x: _TensorType) → bool

Check whether the given tensor requires gradient tracking.

stack(xs: Union[List[_TensorType], Tuple[_TensorType, ...]], *, dim: int = 0) → _TensorType

Stack multiple tensors along a new dimension.

to_number(x: _TensorType) → float

Convert the given 0-dim tensor to a float object.

to_numpy(x: _TensorType) → <MagicMock name='mock.ndarray' id='139745953275856'>

Convert the given tensor to a numpy array.

transpose(x: _TensorType) → _TensorType

Transpose the given tensor by swapping the (0,1) dimensions.

update_tensor_data(x: _TensorType, new_value: _TensorType) → None

Update the underlying tensor data while ignoring any gradient tracking.

zeros_like(x: _TensorType) → _TensorType

Create a new tensor of same shape as the given tensor, filled will zeros.

class dipas.backends.Numpy

Bases: Backend[<MagicMock name=’mock.ndarray’ id=’139745953275856’>, <MagicMock name=’mock.ndarray’ id=’139745953275856’>]

ModuleType

alias of ModuleProxy

ParameterType: ClassVar[Type] = <MagicMock name='mock.ndarray' id='139745953275856'>

TensorType: ClassVar[Type] = <MagicMock name='mock.ndarray' id='139745953275856'>

as_float64(x: <MagicMock name='mock.ndarray' id='139745953275856'>) → <MagicMock name='mock.ndarray' id='139745953275856'>

Convert the given tensor’s data representation to float64.

as_parameter(x: <MagicMock name='mock.ndarray' id='139745953275856'>) → <MagicMock name='mock.ndarray' id='139745953275856'>

Transform the given tensor to a parameter.

concatenate(xs: ~typing.Union[~typing.List[<MagicMock name='mock.ndarray' id='139745953275856'>], ~typing.Tuple[<MagicMock name='mock.ndarray' id='139745953275856'>, ...]], *, dim: int = 0) → <MagicMock name='mock.ndarray' id='139745953275856'>

Concatenate multiple tensors along one of their dimensions.

from_numbers(value: Union[float, Sequence[Union[float, Sequence[Union[float, Sequence[float]]]]]]) → <MagicMock name='mock.ndarray' id='139745953275856'>

Create a new tensor from the given number(s).

from_numpy(array: <MagicMock name='mock.ndarray' id='139745953275856'>) → <MagicMock name='mock.ndarray' id='139745953275856'>

Create a new tensor from the given numpy array.

functions: _Functions[_TensorType]

ignore_gradient: Callable[[], AbstractContextManager]

linalg: _Linalg[_TensorType]

make_id_matrix(n: int) → <MagicMock name='mock.ndarray' id='139745953275856'>

Create a new tensor representing the nxn identity matrix.

make_zeros(*shape: int) → <MagicMock name='mock.ndarray' id='139745953275856'>

Create a new tensor of the given shape, filled with zeros.

random: _Random[_TensorType]

requires_grad(x: <MagicMock name='mock.ndarray' id='139745953275856'>) → bool

Check whether the given tensor requires gradient tracking.

stack(xs: ~typing.Union[~typing.List[<MagicMock name='mock.ndarray' id='139745953275856'>], ~typing.Tuple[<MagicMock name='mock.ndarray' id='139745953275856'>, ...]], *, dim: int = 0) → <MagicMock name='mock.ndarray' id='139745953275856'>

Stack multiple tensors along a new dimension.

to_number(x: <MagicMock name='mock.ndarray' id='139745953275856'>) → float

Convert the given 0-dim tensor to a float object.

to_numpy(x: <MagicMock name='mock.ndarray' id='139745953275856'>) → <MagicMock name='mock.ndarray' id='139745953275856'>

Convert the given tensor to a numpy array.

transpose(x: <MagicMock name='mock.ndarray' id='139745953275856'>) → <MagicMock name='mock.ndarray' id='139745953275856'>

Transpose the given tensor by swapping the (0,1) dimensions.

update_tensor_data(x: <MagicMock name='mock.ndarray' id='139745953275856'>, new_value: <MagicMock name='mock.ndarray' id='139745953275856'>) → None

Update the underlying tensor data while ignoring any gradient tracking.

zeros_like(x: <MagicMock name='mock.ndarray' id='139745953275856'>) → <MagicMock name='mock.ndarray' id='139745953275856'>

Create a new tensor of same shape as the given tensor, filled will zeros.

class dipas.backends.PyTorch

Bases: Backend[<MagicMock name=’mock.Tensor’ id=’139745955357904’>, <MagicMock name=’mock.nn.Parameter’ id=’139745955712976’>]

ModuleType

alias of object

ParameterType: ClassVar[Type] = <MagicMock name='mock.nn.Parameter' id='139745955712976'>

TensorType: ClassVar[Type] = <MagicMock name='mock.Tensor' id='139745955357904'>

as_float64(x: <MagicMock name='mock.Tensor' id='139745955357904'>) → <MagicMock name='mock.Tensor' id='139745955357904'>

Convert the given tensor’s data representation to float64.

as_parameter(x: <MagicMock name='mock.Tensor' id='139745955357904'>) → <MagicMock name='mock.nn.Parameter' id='139745955712976'>

Transform the given tensor to a parameter.

concatenate(xs: ~typing.Union[~typing.List[<MagicMock name='mock.Tensor' id='139745955357904'>], ~typing.Tuple[<MagicMock name='mock.Tensor' id='139745955357904'>, ...]], *, dim: int = 0) → <MagicMock name='mock.Tensor' id='139745955357904'>

Concatenate multiple tensors along one of their dimensions.

from_numbers(value: Union[float, Sequence[Union[float, Sequence[Union[float, Sequence[float]]]]]]) → <MagicMock name='mock.Tensor' id='139745955357904'>

Create a new tensor from the given number(s).

from_numpy(array: <MagicMock name='mock.ndarray' id='139745953275856'>) → <MagicMock name='mock.Tensor' id='139745955357904'>

Create a new tensor from the given numpy array.

functions: _Functions[_TensorType]

ignore_gradient: Callable[[], AbstractContextManager]

linalg: _Linalg[_TensorType]

make_id_matrix(n: int) → <MagicMock name='mock.Tensor' id='139745955357904'>

Create a new tensor representing the nxn identity matrix.

make_zeros(*shape: int) → <MagicMock name='mock.Tensor' id='139745955357904'>

Create a new tensor of the given shape, filled with zeros.

random: _Random[_TensorType]

requires_grad(x: <MagicMock name='mock.Tensor' id='139745955357904'>) → bool

Check whether the given tensor requires gradient tracking.

stack(xs: ~typing.Union[~typing.List[<MagicMock name='mock.Tensor' id='139745955357904'>], ~typing.Tuple[<MagicMock name='mock.Tensor' id='139745955357904'>, ...]], *, dim: int = 0) → <MagicMock name='mock.Tensor' id='139745955357904'>

Stack multiple tensors along a new dimension.

to_number(x: <MagicMock name='mock.Tensor' id='139745955357904'>) → float

Convert the given 0-dim tensor to a float object.

to_numpy(x: <MagicMock name='mock.Tensor' id='139745955357904'>) → <MagicMock name='mock.ndarray' id='139745953275856'>

Convert the given tensor to a numpy array.

transpose(x: <MagicMock name='mock.Tensor' id='139745955357904'>) → <MagicMock name='mock.Tensor' id='139745955357904'>

Transpose the given tensor by swapping the (0,1) dimensions.

update_tensor_data(x: <MagicMock name='mock.Tensor' id='139745955357904'>, new_value: <MagicMock name='mock.Tensor' id='139745955357904'>) → None

Update the underlying tensor data while ignoring any gradient tracking.

zeros_like(x: <MagicMock name='mock.Tensor' id='139745955357904'>) → <MagicMock name='mock.Tensor' id='139745955357904'>

Create a new tensor of same shape as the given tensor, filled will zeros.