SpaceToDepth

SpaceToDepth - 28

Version

  • name: SpaceToDepth (GitHub)

  • domain: main

  • since_version: 28

  • function: True

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 28.

Summary

SpaceToDepth rearranges blocks of spatial data into depth. More specifically, this op outputs a copy of the input tensor where values from the height and width dimensions are moved to the depth dimension. mode determines whether blocks are ordered depth-column-row (DCR, the default) or column-row-depth (CRD).

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.

Examples

default

import numpy as np
import onnx

b, c, h, w = shape = (2, 2, 6, 6)
blocksize = 2
node = onnx.helper.make_node(
    "SpaceToDepth",
    inputs=["x"],
    outputs=["y"],
    blocksize=blocksize,
)
x = np.random.random_sample(shape).astype(np.float32)
tmp = np.reshape(
    x, [b, c, h // blocksize, blocksize, w // blocksize, blocksize]
)
tmp = np.transpose(tmp, [0, 3, 5, 1, 2, 4])
y = np.reshape(tmp, [b, c * (blocksize**2), h // blocksize, w // blocksize])
expect(node, inputs=[x], outputs=[y], name="test_spacetodepth")

_example

import numpy as np
import onnx

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

# (1, 1, 4, 6) input tensor
x = np.array(
    [
        [
            [
                [0, 6, 1, 7, 2, 8],
                [12, 18, 13, 19, 14, 20],
                [3, 9, 4, 10, 5, 11],
                [15, 21, 16, 22, 17, 23],
            ]
        ]
    ]
).astype(np.float32)

# (1, 4, 2, 3) output tensor
y = np.array(
    [
        [
            [[0, 1, 2], [3, 4, 5]],
            [[6, 7, 8], [9, 10, 11]],
            [[12, 13, 14], [15, 16, 17]],
            [[18, 19, 20], [21, 22, 23]],
        ]
    ]
).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_spacetodepth_example")

_dcr_mode_example

import numpy as np
import onnx

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

# (1, 2, 4, 6) input tensor
x = 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)

# (1, 8, 2, 3) output tensor
y = 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)
expect(node, inputs=[x], outputs=[y], name="test_spacetodepth_dcr_mode_example")

_crd_mode_example

import numpy as np
import onnx

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

# (1, 2, 4, 6) input tensor
x = 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)

# (1, 8, 2, 3) output tensor
y = 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)
expect(node, inputs=[x], outputs=[y], name="test_spacetodepth_crd_mode_example")

SpaceToDepth - 13

Version

  • name: SpaceToDepth (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

SpaceToDepth rearranges blocks of spatial data into depth. More specifically, this op outputs a copy of the input tensor where values from the height and width dimensions are moved to the depth dimension.

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(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.

SpaceToDepth - 1

Version

  • name: SpaceToDepth (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

SpaceToDepth rearranges blocks of spatial data into depth. More specifically, this op outputs a copy of the input tensor where values from the height and width dimensions are moved to the depth dimension.

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.