(l-onnx-doc-DepthToSpace)=

# DepthToSpace


(l-onnx-op-depthtospace-13)=

## DepthToSpace - 13

### Version

- **name**: [DepthToSpace (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#DepthToSpace)
- **domain**: `main`
- **since_version**: `13`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 13**.

### Summary

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])
```

### Attributes

* **blocksize - INT** (required) :

  Blocks of [blocksize, blocksize] are moved.

* **mode - STRING** (default is `'DCR'`):

  DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.

### Inputs

- **input** (heterogeneous) - **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** (heterogeneous) - **T**:

  Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].

### Type Constraints

* **T** in ( `tensor(bfloat16)`, `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)` ):

  Constrain input and output types to all tensor types.

```{toctree}
text_diff_DepthToSpace_11_13
```

(l-onnx-op-depthtospace-11)=

## DepthToSpace - 11

### Version

- **name**: [DepthToSpace (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#DepthToSpace)
- **domain**: `main`
- **since_version**: `11`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 11**.

### Summary

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])

### Attributes

* **blocksize - INT** (required) :

  Blocks of [blocksize, blocksize] are moved.

* **mode - STRING** (default is `'DCR'`):

  DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.

### Inputs

- **input** (heterogeneous) - **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** (heterogeneous) - **T**:

  Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].

### Type Constraints

* **T** in ( `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)` ):

  Constrain input and output types to all tensor types.

```{toctree}
text_diff_DepthToSpace_1_13
text_diff_DepthToSpace_1_11
```

(l-onnx-op-depthtospace-1)=

## DepthToSpace - 1

### Version

- **name**: [DepthToSpace (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#DepthToSpace)
- **domain**: `main`
- **since_version**: `1`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 1**.

### Summary

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.

### Attributes

* **blocksize - INT** (required) :

  Blocks of [blocksize, blocksize] are moved.

### Inputs

- **input** (heterogeneous) - **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** (heterogeneous) - **T**:

  Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].

### Type Constraints

* **T** in ( `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)` ):

  Constrain input and output types to all tensor types.