ai.onnx.SpaceToDepth

ai.onnx · standard ONNX operator · ONNX opset ≥ 13

Description

Rearranges blocks of spatial data into depth by moving values from the height and width dimensions into the channel dimension. An NCHW input of shape [N, C, H, W] produces an output of shape [N, C * blocksize * blocksize, H / blocksize, W / blocksize].

See the ONNX SpaceToDepth spec for the reference semantics.

Inputs

Name Bind key Logical dtype Rank Shape Description Presence
input input T 4 4-D input tensor of shape [N, C, H, W]. required

Outputs

Name Bind key Logical dtype Rank Shape Description Presence
output output T 4 derived; see description 4-D output tensor of shape [N, C * blocksize * blocksize, H / blocksize, W / blocksize]. required

Attributes

Attributes and default values (overridable per request):

Attribute Default Description
blocksize Size of the spatial block to collapse into depth; each blocksize x blocksize patch of pixels becomes additional channels.

Type constraints

Variable Allowed dtypes
T float32, float16, int32, int16, int8, uint32, uint8, bool

Files

Use with @huggingface/kernels

The loader derives every required output's shape and logical dtype from the manifest contract and this call. It then allocates the result tensors automatically.

The version: 1 option selects the published kernel contract; it is independent of any operator opset, contrib since_version, or model version.

Replace each *Data placeholder with a typed array containing the corresponding input data.

import { getKernel } from "@huggingface/kernels";

const kernel = await getKernel("webgpu-kernels/ai.onnx.SpaceToDepth", { version: 1 });
const { output } = await kernel({ input: { data: inputData, shape: [1, 1, 2, 4] } }, {
  attrs: { blocksize: 2 },
});
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Requires WebGPU support. See the compatibility table.