# ONNX to QNN for Linux Host

This guide will teach you how to convert your ONNX model into an executable that can be run on a target device’s processors using **Qualcomm AI Engine Direct (aka the QNN SDK)**.

In order to do that, you will learn how to:

1. Convert your Open Neural Net eXchange (ONNX) model to a Qualcomm Neural Net (QNN) Model.
2. Build that model for a specific target device operating system. (Ex. Android)
3. Transfer and use the model to make inferences on the desired processing unit. (Ex. GPU)

![Model Workflow](data:image/png;base64,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)

## Part 1: Tutorial Setup

### Install the QNN SDK

1. Follow the instructions in [Setup](https://docs.qualcomm.com/doc/80-63442-10/topic/general_setup.html) to install the QNN SDK.
1. Make sure to install the optional ONNX dependencies as this tutorial will use an ONNX model. (See Step 3 in Setup for more instructions).

> 
> 
> Note
> 
> 
> Using the same terminal for the Setup and these steps will speed up the process as some necessary setup steps only affect the terminal’s environment variables.
2. - Check that `QNN_SDK_ROOT` is set to the folder just **inside** `qairt` by running `$QNN_SDK_ROOT`.
    - 1. You should see the path to the folder name inside `qairt` (Ex. `.../qairt/2.22.6.240515`)

    2. If `QNN_SDK_ROOT` is not set:
1. Navigate to `qairt/<QNN_SDK_ROOT_LOCATION>/bin`

> 
> 
> 2. - Run `source ./envsetup.sh` to set the environment variable.
>     - 1. Note: These changes will only apply to the current terminal instance.
3. Ensure you are in the proper virtual environment for Python.
1. If you are not in a `venv`, see Step 2 of Setup to install / activate your environment.

### Set Up An Example ONNX Model

> 
> 
> - **Step 1:** Enter the models directory
> 
> 
> cd ${QNN_SDK_ROOT}/examples/Models
>         Copy to clipboard
> - **Step 2:** Install numpy, onnx, aimet\_onnx, onnxsim, and pandas.
> 
> 
> pip3 install numpy onnx aimet_onnx onnxsim pandas
>         Copy to clipboard
> - **Step 3:** Obtain an ONNX Model
> 
> 
>     You can use whichever model you want, but as an example this guide uses [EfficientNet Lite](https://github.com/onnx/models/tree/main/validated/vision/classification/efficientnet-lite4/model). You’ll likely want a packaged model (like `.tar.gz` or `.zip`) to have access to both the model files and sample input data.
> 
>     - **Step 3.1:** Grab the download link for [EfficientNet Lite](https://github.com/onnx/models/tree/main/validated/vision/classification/efficientnet-lite4/model)
> 
>         1. Navigate to [EfficientNet Lite](https://github.com/onnx/models/tree/main/validated/vision/classification/efficientnet-lite4/model) in your web browser.
>         2. Left-click `efficientnet-lite4-11.tar.gz`.
>         3. Right-click “Raw” in the top-right and click “Copy link address”.
>     - **Step 3.2:** Download the model using wget.
> 
> 
> wget https://github.com/onnx/models/raw/refs/heads/main/validated/vision/classification/efficientnet-lite4/model/efficientnet-lite4-11.tar.gz
>             Copy to clipboard
>     - **Step 3.3:** Extract the model package.
> 
> 
> tar -xf *.tar.gz
>             Copy to clipboard
> - **Step 4:** Save model path to an environment variable
> 
> 
>     In this step we want to save the model path to an environment variable for future use. This is the file that ends in `.onnx`.
> 
> 
> export MODEL_PATH="${QNN_SDK_ROOT}/examples/Models/efficientnet-lite4/efficientnet-lite4.onnx"
>         Copy to clipboard
> - **Step 5:** Get model dimensions and name
> 
>     - **Step 5.1:** Retrieve model dimensions and name
> 
> 
>         Run this command to get the input name and dimensions
> 
> 
> python3 -c "import os, onnx, onnxruntime; \
>             f = os.environ['MODEL_PATH']; \
>             m = onnx.load(f); \
>             s = onnxruntime.InferenceSession(f); \
>             lines = [f'ONNX Input: name={i.name}, shape={[d.dim_value for d in i.type.tensor_type.shape.dim]}\n' for i in m.graph.input]
>             print(''.join(lines), end=''); \
>             open('input_name_and_dim.txt', 'w').writelines(lines)"
>             Copy to clipboard
> 
> 
>         You can access these values later by looking at `input_name_and_dim.txt`
>     - **Step 5.2:** Save model dimensions and name to environment variables
> 
> 
> eval $(sed -n 's/ONNX Input: name=\([^,]*\), shape=\[\(.*\)\]/export ONNX_INPUT_NAME="\1"; export ONNX_INPUT_DIMENSIONS="\2"/p' ${MODEL_PATH%/*}/input_name_and_dim.txt)
>             Copy to clipboard
> - **Step 6:** Create input\_list.txt
> 
> 
>     For running the model and performing quantized conversions we need input data. The QNN tools expect this data to be in a raw format, and the paths defined in a text file.
> 
>     - **Step 6.1:** Convert inputs to raw
> 
> 
>         If your input data is in Protobuf format (`*.pb`) it’s going to need to be converted.
> 
> 
> Note
> 
> 
> This command assumes data in a path of `${MODEL_PATH%/*}/test_data_set_*/input_0.pb`, edit it to suit your path if needed.
> 
> 
> python3 -c '
>             import onnx, numpy as np, struct, glob, os
>             onnx_model_path = os.environ["MODEL_PATH"]
>             base_dir = os.path.dirname(onnx_model_path)
>             pattern = os.path.join(base_dir, "test_data_set_*/input_0.pb")
>             
>             for pb in glob.glob(pattern):
>                 tensor = onnx.TensorProto()
>                 with open(pb, "rb") as f:
>                     tensor.ParseFromString(f.read())
>                 arr = onnx.numpy_helper.to_array(tensor).astype(np.float32)
>                 raw_path = os.path.splitext(pb)[0] + ".raw"
>                 arr.tofile(raw_path)
>                 print("Wrote", raw_path, arr.shape, arr.nbytes, "bytes")
>             '
>             Copy to clipboard
> 
> 
>         You should see the following output:
> 
> 
> Wrote /home/qnn/qairt/2.36.0.250627/examples/Models/efficientnet-lite4/test_data_set_0/input_0.raw (1, 224, 224, 3) 602112 bytes
>             Wrote /home/qnn/qairt/2.36.0.250627/examples/Models/efficientnet-lite4/test_data_set_2/input_0.raw (1, 224, 224, 3) 602112 bytes
>             Wrote /home/qnn/qairt/2.36.0.250627/examples/Models/efficientnet-lite4/test_data_set_1/input_0.raw (1, 224, 224, 3) 602112 bytes
>             Copy to clipboard
>     - **Step 6.2:** Create a file containing every input path
> 
> 
> ls -la "${MODEL_PATH%/*}" | grep '^d' | awk '{print $9}' | grep -vE '^\.\.?$' | awk -v dir="${MODEL_PATH%/*}" '{print dir "/" $0 "/input_0.raw"}' > "${MODEL_PATH%/*}/input_list.txt"
>             Copy to clipboard
>     - **Step 6.3:** Save `input_list.txt` to an environment variable
> 
> 
> export QNN_INPUT_LIST="${MODEL_PATH%/*}/input_list.txt"
>             Copy to clipboard

## Part 2: Converting the ONNX model into a QNN model

Converting models into QNN format allows them to be built for specific target device operating systems and processors.

This tutorial is using an ONNX model, so we can convert by running the `qnn-onnx-converter` tool. If you are using another type of model, you can look at the [Tools page](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-10/general_tools.html) for a table of potential scripts to help convert them into QNN format. They will have a similar `qnn-model-type-converter` naming convention.

You can use the QNN SDK to convert either full precision models or quantized models by following the below steps.

Warning

HTP and DSP target devices MUST use quantized models with the `--input_list` param.

### Full Precision Model Conversion

> 
> 
> - **Step 1:** Convert the model
> 
> 
> ${QNN_SDK_ROOT}/bin/x86_64-linux-clang/qnn-onnx-converter \
>           --input_network "${MODEL_PATH}" \
>           -d "${ONNX_INPUT_NAME}" "${ONNX_INPUT_DIMENSIONS}" \
>           -l "${ONNX_INPUT_NAME}" NHWC \
>           --output_path "${MODEL_PATH%.*}_qnn_model.cpp"
>         Copy to clipboard
> - **Step 2:** Save path to model for later use
> 
> 
> Note
> 
> 
> We don’t want to save the file extension as we’ll be using the variable to reference both the `.bin` and `.cpp` files.
> 
> 
> export CONVERTED_MODEL_PATH="${MODEL_PATH%.*}_qnn_model"
>         Copy to clipboard

### Quantized Model Conversion

To use a quantized model instead of a floating point model, you will need to pass in the `--input_list` flag to specify the input.

> 
> 
> - **Step 1:** Run the quantized conversion
> 
> 
> ${QNN_SDK_ROOT}/bin/x86_64-linux-clang/qnn-onnx-converter \
>           --input_network "${MODEL_PATH}" \
>           --input_list "${MODEL_PATH%/*}/input_list.txt" \
>           -d "${INPUT_NAME}" "${INPUT_DIMENSIONS}" \
>           --weights_bitwidth 8 \
>           --act_bitwidth 8 \
>           --output_path "${MODEL_PATH%.*}_qnn_quantized_model.cpp" \
>           --float_bitwidth 16
>         Copy to clipboard
> - **Step 2:** Save path to model for later use
> 
> 
>     We don’t want to save the file extension as we’ll be using the variable to reference both the `.bin` and `.cpp` files.
> 
> 
> export CONVERTED_MODEL_PATH="${MODEL_PATH%.*}_qnn_quantized_model"
>         Copy to clipboard

## Part 3: Build the Model for a Target Device

- [Convert to QNN for Linux Host on Linux / Android / QNX Target](https://docs.qualcomm.com/doc/80-63442-10/topic/qnn_tutorial_linux_host_linux_target.html)
- [CNN to QNN for Linux Host on Windows Target](https://docs.qualcomm.com/doc/80-63442-10/topic/qnn_tutorial_linux_host_windows_target.html)

Last Published: Jun 04, 2026

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