ai.onnx.DepthToSpace

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

Description

Rearranges data from the depth dimension into spatial blocks, expanding height and width by blocksize while reducing channels by blocksize * blocksize. The inverse of SpaceToDepth; supports DCR (depth-column-row) and CRD (column-row-depth) element orderings.

See the ONNX DepthToSpace spec for the reference semantics.

Inputs

Outputs

Attributes

Attributes and default values (overridable per request):

Type constraints

Files

• metadata.json — kernel metadata (id, digests, per-variant templates, provenance)
• manifest.json — the op contract (source of truth)
• test.json — correctness cases
• bench.json — benchmark + tuning cases
• depth-to-space-nchw-coarsened4.wgsl.jinja
• space-depth-permute.wgsl.jinja

Use with @huggingface/kernels

npm install --save-exact @huggingface/kernels@0.0.1-preview.2

Required output shapes and logical data types are inferred from the supplied inputs and attributes; result tensors are allocated automatically.

The version: 1 option selects the published kernel contract; it is independent of any operator opset, contrib since_version, or model version. It follows the v1 branch as fixes land. To pin exact artifact bytes, pass a 40-character commit revision instead of 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.DepthToSpace", { version: 1 });
const { output } = await kernel({ input: { data: inputData, shape: [1, 8, 1, 1] } }, {
  attrs: { blocksize: 2 },
});