ai.onnx.GridSample

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

Description

Samples values from input tensor X at positions defined by a flow-field grid, producing output Y with spatial dimensions taken from grid. Grid coordinates are normalized to [-1, 1] over the input spatial extent; positions outside this range are handled according to padding_mode. Supports spatial (rank-4, NCHW) and volumetric (rank-5, NCDHW) inputs with linear, nearest, or cubic interpolation.

See the ONNX GridSample spec for the reference semantics.

Inputs

Name	Bind key	Logical dtype	Rank	Shape	Description	Presence
`X`	`x`	`T`	—	—	Input tensor of shape `(N, C, D1, ..., Dr)` whose values are sampled.	required
`grid`	`grid`	`T`	—	—	Flow-field of shape `(N, D1_out, ..., Dr_out, r)` with normalized sampling coordinates in `[-1, 1]`.	required

Outputs

Name	Bind key	Logical dtype	Rank	Shape	Description	Presence
`Y`	`y`	`T`	same as `grid`	derived; see description	Output tensor of shape `(N, C, D1_out, ..., Dr_out)` containing the interpolated samples.	required

Attributes

Default values (overridable per request):

Attribute	Default	Description
`mode`	`"linear"`	Interpolation method: `linear` (bilinear or trilinear, depending on rank), `nearest`, or `cubic`. Cubic interpolation is supported for rank-4 (2-D spatial) inputs.
`padding_mode`	`"zeros"`	How out-of-bound grid positions are handled: `zeros` pads with 0, `border` clamps to the border value, or `reflection` reflects coordinates back into the valid range.
`align_corners`	`0`	When 1, extrema values `-1` and `1` map to the center of the corner pixels; when 0 (default) they map to the outer edge of corner pixels, making sampling resolution-agnostic.

Type constraints

Variable	Allowed dtypes
`T`	`float32`, `float16`

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
grid-sample.wgsl.jinja
grid-sample3d.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.GridSample", { version: 1 });
const { y } = await kernel({
  x: { data: xData, shape: [1, 1, 2, 2] },
  grid: { data: gridData, shape: [1, 1, 3, 2] },
});

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kernel

webgpu

wgsl

apache-2.0

WebGPU

Requires WebGPU support. See the compatibility table.