trw.utils.batch_pad_minmax

Module Contents

Functions

batch_pad_minmax_numpy(array: trw.basic_typing.NumpyTensorNCX, padding_min: trw.basic_typing.ShapeCX, padding_max: trw.basic_typing.ShapeCX, mode: str = 'edge', constant_value: trw.basic_typing.Numeric = 0) → trw.basic_typing.NumpyTensorNCX

Add padding on a numpy array of samples. This works for an arbitrary number of dimensions

batch_pad_minmax_torch(array: trw.basic_typing.TorchTensorNCX, padding_min: trw.basic_typing.ShapeCX, padding_max: trw.basic_typing.ShapeCX, mode: str = 'edge', constant_value: trw.basic_typing.Numeric = 0) → trw.basic_typing.TorchTensorNCX

Add padding on a numpy array of samples. This works for an arbitrary number of dimensions

batch_pad_minmax(array: trw.basic_typing.TensorNCX, padding_min: trw.basic_typing.ShapeCX, padding_max: trw.basic_typing.ShapeCX, mode: str = 'edge', constant_value: trw.basic_typing.Numeric = 0) → trw.basic_typing.TensorNCX

Add padding on a numpy array of samples. This works for an arbitrary number of dimensions

batch_pad_minmax_joint(arrays: List[trw.basic_typing.TensorNCX], padding_min: trw.basic_typing.ShapeCX, padding_max: trw.basic_typing.ShapeCX, mode: str = 'edge', constant_value: trw.basic_typing.Numeric = 0) → List[trw.basic_typing.TensorNCX]

Add padding on a list of numpy or tensor array of samples. Supports arbitrary number of dimensions
trw.utils.batch_pad_minmax.batch_pad_minmax_numpy(array: trw.basic_typing.NumpyTensorNCX, padding_min: trw.basic_typing.ShapeCX, padding_max: trw.basic_typing.ShapeCX, mode: str = 'edge', constant_value: trw.basic_typing.Numeric = 0) → trw.basic_typing.NumpyTensorNCX

Add padding on a numpy array of samples. This works for an arbitrary number of dimensions

Parameters

  • array – a numpy array. Samples are stored in the first dimension

  • padding_min – a sequence of size len(array.shape)-1 indicating the width of the padding to be added at the beginning of each dimension (except for dimension 0)

  • padding_max – a sequence of size len(array.shape)-1 indicating the width of the padding to be added at the end of each dimension (except for dimension 0)

  • mode – numpy.pad mode

  • constant_value – constant used if mode == constant

Returns

a padded array

trw.utils.batch_pad_minmax.batch_pad_minmax_torch(array: trw.basic_typing.TorchTensorNCX, padding_min: trw.basic_typing.ShapeCX, padding_max: trw.basic_typing.ShapeCX, mode: str = 'edge', constant_value: trw.basic_typing.Numeric = 0) → trw.basic_typing.TorchTensorNCX

Add padding on a numpy array of samples. This works for an arbitrary number of dimensions

This function mimics the API of transform_batch_pad_numpy so they can be easily interchanged.

Parameters

  • array – a Torch array. Samples are stored in the first dimension

  • padding_min – a sequence of size len(array.shape)-1 indicating the width of the padding to be added at the beginning of each dimension (except for dimension 0)

  • padding_max – a sequence of size len(array.shape)-1 indicating the width of the padding to be added at the end of each dimension (except for dimension 0)

  • mode – numpy.pad mode. Currently supported are (‘constant’, ‘edge’, ‘symmetric’)

  • constant_value – constant used if mode == constant

Returns

a padded array

trw.utils.batch_pad_minmax.batch_pad_minmax(array: trw.basic_typing.TensorNCX, padding_min: trw.basic_typing.ShapeCX, padding_max: trw.basic_typing.ShapeCX, mode: str = 'edge', constant_value: trw.basic_typing.Numeric = 0) → trw.basic_typing.TensorNCX

Add padding on a numpy array of samples. This works for an arbitrary number of dimensions

Parameters

  • array – a numpy array. Samples are stored in the first dimension

  • padding_min – a sequence of size len(array.shape)-1 indicating the width of the padding to be added at the beginning of each dimension (except for dimension 0)

  • padding_max – a sequence of size len(array.shape)-1 indicating the width of the padding to be added at the end of each dimension (except for dimension 0)

  • mode – numpy.pad mode

  • constant_value – constant used if mode == constant

Returns

a padded array

trw.utils.batch_pad_minmax.batch_pad_minmax_joint(arrays: List[trw.basic_typing.TensorNCX], padding_min: trw.basic_typing.ShapeCX, padding_max: trw.basic_typing.ShapeCX, mode: str = 'edge', constant_value: trw.basic_typing.Numeric = 0) → List[trw.basic_typing.TensorNCX]

Add padding on a list of numpy or tensor array of samples. Supports arbitrary number of dimensions

Parameters

  • arrays – a numpy array. Samples are stored in the first dimension

  • padding_min – a sequence of size len(array.shape)-1 indicating the width of the padding to be added at the beginning of each dimension (except for dimension 0)

  • padding_max – a sequence of size len(array.shape)-1 indicating the width of the padding to be added at the end of each dimension (except for dimension 0)

  • mode – numpy.pad mode

  • constant_value – constant used if mode == constant

Returns

a list of padded arrays