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
metadata.json— kernel metadata (id, digests, provenance)manifest.json— the op contract (source of truth)test.json— correctness casesbench.json— benchmark + tuning casesspace-depth-permute.wgsl.jinja
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.