ai.onnx.Col2Im / README.md

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library_name: kernels
license: apache-2.0
tags:
  - kernel
  - webgpu
  - wgsl

ai.onnx.Col2Im

ai.onnx · standard ONNX operator · ONNX opset ≥ 18

Description

Rearranges column blocks back into a batched multidimensional image. Takes a 3-D input of shape [N, C * product(block_shape), L] (where L is the number of blocks) and accumulates overlapping block contributions into the output image using the specified block_shape, strides, pads, and dilations.

See the ONNX Col2Im spec for the reference semantics.

Inputs

Name	Bind key	Logical dtype	WebGPU storage	Rank	Shape	Description	Presence
`input`	`input`	`T`	same as logical dtype	`3`	—	Column-block data tensor of shape `[N, C * product(block_shape), L]` to be folded back into an image.	required
`image_shape`	`image_shape`	`I`	`uint32`	`1`	—	Logical int64 metadata tensor specifying the output spatial dimensions (for example, `[H, W]` for 2-D); non-negative values use uint32 WebGPU storage.	required
`block_shape`	`block_shape`	`I`	`uint32`	`1`	—	Logical int64 metadata tensor specifying the positive block size on each spatial axis (for example, `[H_block, W_block]` for 2-D); values use uint32 WebGPU storage.	required

Outputs

Name	Bind key	Logical dtype	Rank	Shape	Description	Presence
`output`	`output`	`T`	derived	—	Output image tensor produced by accumulating rearranged column blocks. Its rank is two greater than the length of `image_shape`.	required

Attributes

Attributes and default values (overridable per request):

Attribute	Default	Description
`dilations`	—	Dilation factors for each spatial axis; defaults to one on every axis.
`pads`	—	Padding at the beginning of every spatial axis followed by padding at the end of every spatial axis; defaults to zeros.
`strides`	—	Stride factors for each spatial axis; defaults to one on every axis.

Type constraints

Variable	Allowed dtypes
`T`	`float32`
`I`	`int64`

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
col2im-nd.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:

output

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.Col2Im", { version: 1 });
// Explicit destinations request optional results or supply metadata that cannot be inferred.
const { output } = await kernel({
  input: { data: inputData, shape: [1, 9, 1] },
  image_shape: { data: image_shapeData, shape: [2] },
  block_shape: { data: block_shapeData, shape: [2] },
}, {
  outputs: { output: { shape: [1, 1, 3, 3], dtype: "float32" } },
});