:nosearch:

.. _op_ai_onnx_DepthToSpace-11:

DepthToSpace - version 11
=========================

This page documents version **11** of operator **DepthToSpace**. See :doc:`DepthToSpace` for the latest version (since version 13).

- **Domain**: ``ai.onnx``
- **Since version**: 11

DepthToSpace rearranges (permutes) data from depth into blocks of spatial data.
This is the reverse transformation of SpaceToDepth. More specifically, this op outputs a copy of
the input tensor where values from the depth dimension are moved in spatial blocks to the height
and width dimensions. By default, ``mode`` = ``DCR``.
In the DCR mode, elements along the depth dimension from the input tensor are rearranged in the
following order: depth, column, and then row. The output y is computed from the input x as below:

b, c, h, w = x.shape

tmp = np.reshape(x, [b, blocksize, blocksize, c // (blocksize**2), h, w])

tmp = np.transpose(tmp, [0, 3, 4, 1, 5, 2])

y = np.reshape(tmp, [b, c // (blocksize**2), h \* blocksize, w \* blocksize])


In the CRD mode, elements along the depth dimension from the input tensor are rearranged in the
following order: column, row, and the depth. The output y is computed from the input x as below:

b, c, h, w = x.shape

tmp = np.reshape(x, [b, c // (blocksize ** 2), blocksize, blocksize, h, w])

tmp = np.transpose(tmp, [0, 1, 4, 2, 5, 3])

y = np.reshape(tmp, [b, c // (blocksize ** 2), h \* blocksize, w \* blocksize])

**Inputs**

- **input** (*T*): Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.

**Outputs**

- **output** (*T*): Output tensor of [N, C/(blocksize \* blocksize), H \* blocksize, W \* blocksize].

**Attributes**

- **blocksize** (*int*): Blocks of [blocksize, blocksize] are moved.
- **mode** (*string*): DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.

**Type Constraints**

- **T**: Constrain input and output types to all tensor types.
  Allowed types: tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8).

Differences with previous version (1)
-------------------------------------

**SchemaDiff**: ``DepthToSpace`` (domain ``'ai.onnx'``)

* old version: 1
* new version: 11
* breaking: no

**Attributes:**

* added 'mode': type=STRING; required=False; default=DCR

**Documentation:**

* line similarity: 0.19 (+24/-1 lines)

.. code-block:: diff

    --- DepthToSpace v1
    +++ DepthToSpace v11
    @@ -1,4 +1,27 @@
     DepthToSpace rearranges (permutes) data from depth into blocks of spatial data.
     This is the reverse transformation of SpaceToDepth. More specifically, this op outputs a copy of
     the input tensor where values from the depth dimension are moved in spatial blocks to the height
    -and width dimensions.
    +and width dimensions. By default, `mode` = `DCR`.
    +In the DCR mode, elements along the depth dimension from the input tensor are rearranged in the
    +following order: depth, column, and then row. The output y is computed from the input x as below:
    +
    +b, c, h, w = x.shape
    +
    +tmp = np.reshape(x, [b, blocksize, blocksize, c // (blocksize**2), h, w])
    +
    +tmp = np.transpose(tmp, [0, 3, 4, 1, 5, 2])
    +
    +y = np.reshape(tmp, [b, c // (blocksize**2), h * blocksize, w * blocksize])
    +
    +
    +In the CRD mode, elements along the depth dimension from the input tensor are rearranged in the
    +following order: column, row, and the depth. The output y is computed from the input x as below:
    +
    +b, c, h, w = x.shape
    +
    +tmp = np.reshape(x, [b, c // (blocksize ** 2), blocksize, blocksize, h, w])
    +
    +tmp = np.transpose(tmp, [0, 1, 4, 2, 5, 3])
    +
    +y = np.reshape(tmp, [b, c // (blocksize ** 2), h * blocksize, w * blocksize])
    +