`zetta_utils.tensor_ops`¶

Handling tensor operations. Each opperation must support both np.ndarray and torch.Tensor input types.

`zetta_utils.tensor_ops.convert`¶

zetta_utils.tensor_ops.convert.to_np(data)[source]¶

Convert the given tensor to numpy.ndarray.

Parameters:: data (Union[Tensor, ndarray[tuple[int, ...], dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]]) – Input tensor_ops.
Return type:: ndarray[tuple[int, ...], dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]
Returns:: Input tensor in numpy.ndarray format.

zetta_utils.tensor_ops.convert.to_torch(data, device=None)[source]¶

Convert the given tensor to torch.Tensor.

Parameters:

data (Union[Tensor, ndarray[tuple[int, ...], dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]]) – Input tensor_ops.
device (Union[device, str, int, None]) – Device name on which the torch tensor will reside.

Return type:

Tensor

Returns:

Input tensor in torch.Tensor format.

zetta_utils.tensor_ops.convert.astype(data, reference, cast=False)[source]¶

Convert the given tensor to np.ndarray or torch.Tensor depending on the type of reference tensor_ops.

Parameters:

data (Union[Tensor, ndarray[tuple[int, ...], dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]]) – Input tensor_ops.
reference (TypeVar(TensorTypeVar, Tensor, ndarray[tuple[int, ...], dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]], Union[Tensor, ndarray[tuple[int, ...], dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]])) – Reference type tensor_ops.
cast (bool) – If True, cast data to the type of reference.

Return type:

TypeVar(TensorTypeVar, Tensor, ndarray[tuple[int, ...], dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]], Union[Tensor, ndarray[tuple[int, ...], dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]])

Returns:

Input tensor converted to the reference type.