SpaceToDepth#
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 (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.
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")
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 (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.