ai.onnx.AffineGrid

ai.onnx · standard ONNX operator · ONNX opset ≥ 20

Description

Generates a 2-D or 3-D flow field (sampling grid) from a batch of affine matrices theta. Each affine matrix is applied to the target output's normalized coordinate grid in [-1, 1], producing sample coordinates at every output position.

See the ONNX AffineGrid spec for the reference semantics.

Inputs

Outputs

Attributes

Default values (overridable per request):

Type constraints

Files

• metadata.json — kernel metadata (id, digests, provenance)
• manifest.json — the op contract (source of truth)
• test.json — correctness cases
• bench.json — benchmark + tuning cases
• affine-grid.wgsl.jinja
• affine-grid3d-w4.wgsl.jinja

Use with @huggingface/kernels

The loader automatically allocates outputs whose metadata it can derive from the manifest contract and this call.

The explicit outputs entries provide shape and logical dtype metadata for the results listed below:

• grid

Each entry either requests an optional result or supplies metadata that cannot be inferred from the inputs.

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.AffineGrid", { version: 1 });
// Explicit destinations request optional results or supply metadata that cannot be inferred.
const { grid } = await kernel({ theta: { data: thetaData, shape: [1, 2, 3] } }, {
  outputs: { grid: { shape: [1, 3, 2, 2], dtype: "float32" } },
});