DepthToSpace#

DepthToSpace - 13#

Version

  • name: DepthToSpace (GitHub)

  • 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 (required): Blocks of [blocksize, blocksize] are moved.

  • mode: 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.

Examples

_default_mode_example

import numpy as np
import onnx

node = onnx.helper.make_node(
    "DepthToSpace", inputs=["x"], outputs=["y"], blocksize=2, mode="DCR"
)

# (1, 8, 2, 3) input tensor
x = np.array(
    [
        [
            [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]],
            [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0]],
            [[18.0, 19.0, 20.0], [21.0, 22.0, 23.0]],
            [[27.0, 28.0, 29.0], [30.0, 31.0, 32.0]],
            [[36.0, 37.0, 38.0], [39.0, 40.0, 41.0]],
            [[45.0, 46.0, 47.0], [48.0, 49.0, 50.0]],
            [[54.0, 55.0, 56.0], [57.0, 58.0, 59.0]],
            [[63.0, 64.0, 65.0], [66.0, 67.0, 68.0]],
        ]
    ]
).astype(np.float32)

# (1, 2, 4, 6) output tensor
y = np.array(
    [
        [
            [
                [0.0, 18.0, 1.0, 19.0, 2.0, 20.0],
                [36.0, 54.0, 37.0, 55.0, 38.0, 56.0],
                [3.0, 21.0, 4.0, 22.0, 5.0, 23.0],
                [39.0, 57.0, 40.0, 58.0, 41.0, 59.0],
            ],
            [
                [9.0, 27.0, 10.0, 28.0, 11.0, 29.0],
                [45.0, 63.0, 46.0, 64.0, 47.0, 65.0],
                [12.0, 30.0, 13.0, 31.0, 14.0, 32.0],
                [48.0, 66.0, 49.0, 67.0, 50.0, 68.0],
            ],
        ]
    ]
).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_depthtospace_example")

_crd_mode_example

import numpy as np
import onnx

node = onnx.helper.make_node(
    "DepthToSpace", inputs=["x"], outputs=["y"], blocksize=2, mode="CRD"
)

# (1, 8, 2, 3) input tensor
x = np.array(
    [
        [
            [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]],
            [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0]],
            [[18.0, 19.0, 20.0], [21.0, 22.0, 23.0]],
            [[27.0, 28.0, 29.0], [30.0, 31.0, 32.0]],
            [[36.0, 37.0, 38.0], [39.0, 40.0, 41.0]],
            [[45.0, 46.0, 47.0], [48.0, 49.0, 50.0]],
            [[54.0, 55.0, 56.0], [57.0, 58.0, 59.0]],
            [[63.0, 64.0, 65.0], [66.0, 67.0, 68.0]],
        ]
    ]
).astype(np.float32)

# (1, 2, 4, 6) output tensor
y = np.array(
    [
        [
            [
                [0.0, 9.0, 1.0, 10.0, 2.0, 11.0],
                [18.0, 27.0, 19.0, 28.0, 20.0, 29.0],
                [3.0, 12.0, 4.0, 13.0, 5.0, 14.0],
                [21.0, 30.0, 22.0, 31.0, 23.0, 32.0],
            ],
            [
                [36.0, 45.0, 37.0, 46.0, 38.0, 47.0],
                [54.0, 63.0, 55.0, 64.0, 56.0, 65.0],
                [39.0, 48.0, 40.0, 49.0, 41.0, 50.0],
                [57.0, 66.0, 58.0, 67.0, 59.0, 68.0],
            ],
        ]
    ]
).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_depthtospace_crd_mode_example")

DepthToSpace - 11#

Version

  • name: DepthToSpace (GitHub)

  • 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 (required): Blocks of [blocksize, blocksize] are moved.

  • mode: 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.

DepthToSpace - 1#

Version

  • name: DepthToSpace (GitHub)

  • 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 (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.