trw.transforms.resize

Module Contents

Functions

resize_torch(array: trw.basic_typing.TorchTensorNCX, size: trw.basic_typing.ShapeX, mode: typing_extensions.Literal[nearest, linear]) → trw.basic_typing.TorchTensorNCX

resize_numpy(array: trw.basic_typing.NumpyTensorNCX, size: trw.basic_typing.ShapeX, mode: typing_extensions.Literal[nearest, linear]) → trw.basic_typing.NumpyTensorNCX

resize(array: trw.basic_typing.TensorNCX, size: trw.basic_typing.ShapeX, mode: typing_extensions.Literal[nearest, linear] = 'linear') → trw.basic_typing.TensorNCX

Resize the array
trw.transforms.resize.resize_torch(array: trw.basic_typing.TorchTensorNCX, size: trw.basic_typing.ShapeX, mode: typing_extensions.Literal[nearest, linear]) → trw.basic_typing.TorchTensorNCX
trw.transforms.resize.resize_numpy(array: trw.basic_typing.NumpyTensorNCX, size: trw.basic_typing.ShapeX, mode: typing_extensions.Literal[nearest, linear]) → trw.basic_typing.NumpyTensorNCX
trw.transforms.resize.resize(array: trw.basic_typing.TensorNCX, size: trw.basic_typing.ShapeX, mode: typing_extensions.Literal[nearest, linear] = 'linear') → trw.basic_typing.TensorNCX

Resize the array

Parameters

  • array – a N-dimensional tensor, representing 1D to 3D data (3 to 5 dimensional data with dim 0 for the samples and dim 1 for filters)

  • size – a (N-2) list to which the array will be upsampled or downsampled

  • mode – string among (‘nearest’, ‘linear’) specifying the resampling method

Returns

a resized N-dimensional tensor