tensorplay.functional.hsplit

tensorplay.functional.hsplit(input, indices_or_sections) → List of Tensors[source]

Splits input, a tensor with one or more dimensions, into multiple tensors horizontally according to indices_or_sections. Each split is a view of input.

If input is one dimensional this is equivalent to calling tensorplay.tensor_split(input, indices_or_sections, dim=0) (the split dimension is zero), and if input has two or more dimensions it’s equivalent to calling tensorplay.tensor_split(input, indices_or_sections, dim=1) (the split dimension is 1), except that if indices_or_sections is an integer it must evenly divide the split dimension or a runtime error will be thrown.

This function is based on NumPy’s numpy.hsplit().

Parameters:

• input (Tensor) – tensor to split.

• indices_or_sections (int or list or tuple of ints) – See argument in tensorplay.tensor_split().

Example:

>>> t = tensorplay.arange(16.0).reshape(4,4)
>>> t
tensor([[ 0.,  1.,  2.,  3.],
        [ 4.,  5.,  6.,  7.],
        [ 8.,  9., 10., 11.],
        [12., 13., 14., 15.]])
>>> tensorplay.hsplit(t, 2)
(tensor([[ 0.,  1.],
         [ 4.,  5.],
         [ 8.,  9.],
         [12., 13.]]),
 tensor([[ 2.,  3.],
         [ 6.,  7.],
         [10., 11.],
         [14., 15.]]))
>>> tensorplay.hsplit(t, [3, 6])
(tensor([[ 0.,  1.,  2.],
         [ 4.,  5.,  6.],
         [ 8.,  9., 10.],
         [12., 13., 14.]]),
 tensor([[ 3.],
         [ 7.],
         [11.],
         [15.]]),
 tensor([], size=(4, 0)))