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# torch.dsplit

torch.dsplit(input, indices_or_sections) → List of Tensors

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

  This is equivalent to calling torch.tensor_split(input, indices_or_sections, dim=2) (the split dimension is 2), 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.dsplit()`.

  Parameters:

    - input (Tensor) – tensor to split.

    - indices_or_sections (int or list or tuple of ints) – See argument in `torch.tensor_split()`.

  Example::

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

        >>> torch.dsplit(t, [3, 6])
        (tensor([[[ 0.,  1.,  2.],
                  [ 4.,  5.,  6.]],
                 [[ 8.,  9., 10.],
                  [12., 13., 14.]]]),
         tensor([[[ 3.],
                  [ 7.]],
                 [[11.],
                  [15.]]]),
         tensor([], size=(2, 2, 0)))

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