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ai.onnx.ConvTranspose

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ai.onnx.ConvTranspose

ai.onnx · standard ONNX operator · ONNX opset ≥ 11

Description

Computes the transpose of a convolution, also known as a fractionally strided convolution or deconvolution, from input tensor X, filter weights W, and an optional bias B. Output spatial dimensions follow the stride, dilation, padding, and optional output_padding attributes. Supports grouped convolution through group.

See the ONNX ConvTranspose spec for the reference semantics.

Inputs

Name Bind key Logical dtype Rank Shape Description Presence
X x T — — Input data tensor of shape (N x C x D1 x ... x Dn), where N is batch size and C is the number of input channels. required
W w T — — Filter weight tensor of shape (C x M/group x k1 x ... x kn), where M is the number of output feature maps. required
B bias T 1 — Optional 1-D bias of length M added to each output channel. optional

Outputs

Name Bind key Logical dtype Rank Shape Description Presence
Y y T same as X derived; see description Output tensor whose spatial dimensions are computed from the input size, kernel shape, strides, dilations, and padding. required

Attributes

Attributes and default values (overridable per request):

Attribute Default Description
auto_pad "NOTSET" Padding mode: NOTSET uses explicit pads; SAME_UPPER and SAME_LOWER make output spatial size equal input size times stride, with any odd extra padding added at the end or beginning respectively; VALID applies no padding.
group 1 Number of groups that input and output channels are divided into for grouped (depthwise) convolution.
dilations — Dilation factors for each spatial axis; defaults to one on every axis.
kernel_shape — Kernel dimensions for each spatial axis. When omitted, they are inferred from the spatial dimensions of W.
output_padding — Additional size on the high-index end of each output spatial axis; each value must be smaller than the corresponding stride or dilation.
output_shape — Requested output spatial dimensions. When present, it must match the declared spatial shape of Y.
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, float16

Device requirements

Some implementation variants require subgroup-matrix, shader-f16, and subgroups. These are route-specific capabilities, not package-wide requirements; availability also depends on the request shape and dtype.

Files

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.ConvTranspose", { version: 1 });
const { y } = await kernel({
  x: { data: xData, shape: [1, 1, 3] },
  w: { data: wData, shape: [1, 2, 2] },
});
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Requires WebGPU support. See the compatibility table.