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ai.onnx.Resize

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ai.onnx.Resize

ai.onnx · standard ONNX operator · ONNX opset ≥ 19

Description

Resizes the input tensor by sampling neighboring input values. Output dimensions are determined by per-axis scale factors or explicit target sizes. Supports nearest, linear, and cubic interpolation with configurable coordinate transformation modes.

See the ONNX Resize spec for the reference semantics.

Inputs

Name Upstream name Logical dtype Rank Shape Description Presence
x X T — — N-D input tensor to be resized. required

Outputs

Name Upstream name Logical dtype Rank Shape Description Presence
y Y T same as x — N-D output tensor after resizing. required

Attributes

Attributes and default values (overridable per request):

Attribute Default Description
antialias 0 When set to 1, stretches the resampling filter during downscaling so that more input pixels contribute to each output pixel, reducing aliasing.
axes — Optional axes to resize. When omitted, the scale values or requested output size apply to every input axis.
coordinate_transformation_mode "half_pixel" How to map a coordinate in the output tensor back to the input tensor; supported modes include "half_pixel", "pytorch_half_pixel", "align_corners", "asymmetric", and "tf_crop_and_resize".
cubic_coeff_a -0.75 Coefficient a in the cubic interpolation filter; the default is -0.75. Valid only when mode is "cubic".
exclude_outside 0 When set to 1, assigns zero weight to sampling locations outside the input tensor and renormalizes the remaining weights to sum to 1.
extrapolation_value 0 Fill value used for out-of-bounds samples when coordinate_transformation_mode is "tf_crop_and_resize".
keep_aspect_ratio_policy "stretch" How explicit sizes preserve aspect ratio. This package supports only the default "stretch" policy; "not_larger" and "not_smaller" are unsupported.
mode "nearest" Interpolation mode: "nearest" (default), "linear" (bilinear/N-linear), or "cubic" (bicubic/N-cubic).
nearest_mode "round_prefer_floor" Rounding strategy used when mode is "nearest": "round_prefer_floor" (default), "round_prefer_ceil", "floor", or "ceil".
roi [] Values of the optional roi tensor, supplied as [starts..., ends...] with one pair per input axis or per axes entry; used only by "tf_crop_and_resize".
scales [] Values of the optional scales tensor. Supply one positive value per input axis, or one per axes entry when that attribute is present; omit it when the output shape represents the exact sizes input.

Type constraints

Variable Allowed dtypes
T float32, float16, uint8, int8

Files

Use with @huggingface/kernels 

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

Outputs with inferable metadata are allocated automatically. Explicit outputs entries request optional results or provide metadata that cannot be inferred from the supplied inputs and attributes.

This example supplies explicit metadata for:

  • y

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.Resize", { version: 1 });
// Explicit destinations request optional results or supply metadata that cannot be inferred.
const { y } = await kernel({ x: { data: xData, shape: [1, 1, 2, 4] } }, {
  outputs: { y: { shape: [1, 1, 1, 2], dtype: "float32" } },
});
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Requires WebGPU support. See the compatibility table.