predict.onnx_model: Predict with an ONNX Model

In ggmlR: 'GGML' Tensor Operations for Machine Learning

Predict with an ONNX Model

Description

Runs inference over x, batching it to the fixed input shape the model was loaded with. This is the keras-compatible entry point: together with the ggml_sequential_model and ggml_functional_model methods it gives ggmlR and ONNX models a single predict() interface.

Usage

## S3 method for class 'onnx_model'
predict(object, x, batch_size = NULL, ...)

Arguments

object	An onnx_model from onnx_load.
x	Input data: an array or matrix whose first dimension indexes samples, or a named list of such arrays for multi-input models.
batch_size	Ignored, with a warning if not NULL. The batch size is fixed when the model is loaded; pass input_shapes to onnx_load to change it. The argument exists so the signature matches the other predict() methods.
...	Ignored.

Details

Unlike onnx_run, which takes a named list of inputs and always returns a named list of outputs, this method takes an array (or a named list for multi-input models) and unwraps a single output. The output keeps the dimensions the model produced: a 2D output comes back as a matrix, a 4D output stays 4D.

An ONNX graph is built for one fixed batch size at onnx_load time and cannot be resized afterwards, so x is split into chunks of that size. A trailing partial chunk is padded to a full batch and the padding is dropped from the result.

Value

For a single-output model, an array with the model's output dimensions and nrow(x) samples in the first one (a matrix when the output is 2D). For a multi-output model, a named list of such arrays.

See Also

onnx_run for the lower-level named-list interface.

ggmlR documentation built on Oct. 23, 2026, 1:07 a.m.