modelconv 0.3.0

Converter for neural models into various formats.

ModelConverter - Compilation Library

Convert your ONNX models to a format compatible with any generation of Luxonis camera using the Model Compilation Library.

Status

Package	Test	Deploy
RVC2
RVC3
RVC4
Hailo

Table of Contents

• ModelConverter - Compilation Library
  • Status
  • Table of Contents
  • Installation
    • System Requirements
    • Before You Begin
    • Instructions
    • GPU Support
  • Running ModelConverter
    • Encoding Configuration Flags
      • YAML Configuration File
      • NN Archive Configuration File
    • Sharing Files
    • Usage
    • Examples
  • Multi-Stage Conversion
  • Interactive Mode
  • Calibration Data
  • Inference
    • Inference Example
  • Benchmarking

Installation

System Requirements

ModelConverter requires docker to be installed on your system. It is recommended to use Ubuntu OS for the best compatibility. On Windows or MacOS, it is recommended to install docker using the Docker Desktop. Otherwise follow the installation instructions for your OS from the official website.

Before You Begin

ModelConverter is in an experimental public beta stage. Some parts might change in the future.

To build the images, you need to download additional packages depending on the selected target and the desired version of the underlying conversion tools.

RVC2

Requires openvino-<version>.tar.gz to be present in docker/extra_packages/.

• Version 2023.2.0 archive can be downloaded from here.

• Version 2021.4.0 archive can be downloaded from here

You only need to rename the archive to either openvino-2023.2.0.tar.gz or openvino-2021.4.0.tar.gz and place it in the docker/extra_packages directory.

RVC3

Only the version 2023.2.0 of OpenVino is supported for RVC3. Follow the instructions for RVC2 to use the correct archive.

RVC4

Requires snpe-<version>.zip archive to be present in docker/extra_packages. You can download version 2.23.0 from here. You only need to rename it to snpe-2.23.0.zip and place it in the docker/extra_packages directory.

HAILO

Requires hailo_ai_sw_suite_<version>:1 docker image to be present on the system. You can obtain the image by following the instructions on Hailo website.

After you obtain the image, you need to rename it to hailo_ai_sw_suite_<version>:1 using docker tag <old_name> hailo_ai_sw_suite_<version>:1.

Furthermore, you need to use the docker/hailo/Dockerfile.public file to build the image. The docker/hailo/Dockerfile is for internal use only.

Instructions

1. Build the docker image:

   docker build -f docker/<package>/Dockerfile -t luxonis/modelconverter-<package>:latest .
   

2. For easier use, you can install the ModelConverter CLI. You can install it from PyPI using the following command:

   pip install modelconv
   

   For usage instructions, see modelconverter --help.

GPU Support

To enable GPU acceleration for hailo conversion, install the Nvidia Container Toolkit.

Running ModelConverter

There are two main ways to execute configure the conversion process:

1. YAML Config File (Primary Method): The primary way to configure the conversion is through a YAML configuration file. For reference, you can check defaults.yaml and other examples located in the shared_with_container/configs directory.
2. NN Archive: Alternatively, you can use an NN Archive as input. An NN Archive includes a model in one of the supported formats—ONNX (.onnx), OpenVINO IR (.xml and .bin), or TensorFlow Lite (.tflite)—alongside a config.json file. The config.json file follows a specific configuration format as described in the NN Archive Configuration Guide.

Modifying Settings with Command-Line Arguments: In addition to these two configuration methods, you have the flexibility to override specific settings directly via command-line arguments. By supplying key-value pairs in the CLI, you can adjust particular settings without explicitly altering the config files (YAML or NN Archive). For further details, refer to the Examples section.

Encoding Configuration Flags

In the conversion process, you have options to control the color encoding format in both the YAML configuration file and the NN Archive configuration. Here’s a breakdown of each available flag:

YAML Configuration File

The encoding flag in the YAML configuration file allows you to specify color encoding as follows:

• Single-Value encoding: Setting encoding to a single value, such as "RGB", "BGR", "GRAY", or "NONE", will automatically apply this setting to both encoding.from and encoding.to. For example, encoding: RGB sets both encoding.from and encoding.to to "RGB" internally.
• Multi-Value encoding.from and encoding.to: Alternatively, you can explicitly set encoding.from and encoding.to to different values. For example:

  encoding:
    from: RGB
    to: BGR
  

  This configuration specifies that the input data is in RGB format and will be converted to BGR format during processing.

[!NOTE] If the encoding is not specified in the YAML configuration, the default values are set to encoding.from=RGB and encoding.to=BGR.

[!NOTE] Certain options can be set globally, applying to all inputs of the model, or per input. If specified per input, these settings will override the global configuration for that input alone. The options that support this flexibility include scale_values, mean_values, encoding, data_type, shape, and layout.

NN Archive Configuration File

In the NN Archive configuration, there are two flags related to color encoding control:

• dai_type: Provides a more comprehensive control over the input type compatible with the DAI backend. It is read by DepthAI to automatically configure the processing pipeline, including any necessary modifications to the input image format.
• reverse_channels (Deprecated): Determines the input color format of the model: when set to True, the input is considered to be "RGB", and when set to False, it is treated as "BGR". This flag is deprecated and will be replaced by the dai_type flag in future versions.

[!NOTE] If neither dai_type nor reverse_channels the input to the model is considered to be "RGB".

[!NOTE] If both dai_type and reverse_channels are provided, the converter will give priority to dai_type.

[!IMPORTANT] Provide mean/scale values in the original color format used during model training (e.g., RGB or BGR). Any necessary channel permutation is handled internally—do not reorder values manually.

Sharing Files

When using the supplied docker-compose.yaml, the shared_with_container directory facilitates file sharing between the host and container. This directory is mounted as /app/shared_with_container/ inside the container. You can place your models, calibration data, and config files here. The directory structure is:

shared_with_container/
│
├── calibration_data/
│ └── <calibration data will be downloaded here>
│
├── configs/
│ ├── resnet18.yaml
│ └── <configs will be downloaded here>
│
├── models/
│ ├── resnet18.onnx
│ └── <models will be downloaded here>
│
└── outputs/
  └── <output_dir>
    ├── resnet18.onnx
    ├── resnet18.dlc
    ├── logs.txt
    ├── config.yaml
    └── intermediate_outputs/
      └── <intermediate files generated during the conversion>

While adhering to this structure is not mandatory as long as the files are visible inside the container, it is advised to keep the files organized.

The converter first searches for files exactly at the provided path. If not found, it searches relative to /app/shared_with_container/.

The output_dir can be specified using the --output-dir CLI argument. If such a directory already exists, the output_dir_name will be appended with the current date and time. If not specified, the output_dir_name will be autogenerated in the following format: <model_name>_to_<target>_<date>_<time>.

Usage

You can run the built image either manually using the docker run command or using the modelconverter CLI.

1. Set your credentials as environment variables (if required):

   export AWS_SECRET_ACCESS_KEY=<your_aws_secret_access_key>
   export AWS_ACCESS_KEY_ID=<your_aws_access_key_id>
   export AWS_S3_ENDPOINT_URL=<your_aws_s3_endpoint_url>
   

2. If shared_with_container directory doesn't exist on your host, create it.

3. Without remote files, place the model, config, and calibration data in the respective directories (refer Sharing Files).

4. Execute the conversion:

• If using the docker run command:

  docker run --rm -it \
    -v $(pwd)/shared_with_container:/app/shared_with_container/ \
    -e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \
    -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \
    -e AWS_S3_ENDPOINT_URL=$AWS_S3_ENDPOINT_URL \
    luxonis/modelconverter-<package>:latest \
    convert <target> \
    --path <s3_url_or_path> [ config overrides ]
  

• If using the modelconverter CLI:

  modelconverter convert <target> --path <s3_url_or_path> [ config overrides ]
  

• If using docker-compose:

  docker compose run <target> convert <target> ...
  

Examples

Use resnet18.yaml config, but override calibration.path:

modelconverter convert rvc4 --path configs/resnet18.yaml \
                        calibration.path s3://path/to/calibration_data

Override inputs and outputs with command line arguments:

modelconverter convert rvc3 --path configs/resnet18.yaml \
                        inputs.0.name input_1 \
                        inputs.0.shape "[1,3,256,256]" \
                        outputs.0.name output_0

Specify all options via the command line without a config file:

modelconverter convert rvc2 input_model models/yolov6n.onnx \
                        scale_values "[255,255,255]" \
                        inputs.0.encoding.from RGB \
                        inputs.0.encoding.to BGR \
                        shape "[1,3,256,256]" \
                        outputs.0.name out_0 \
                        outputs.1.name out_1 \
                        outputs.2.name out_2

[!WARNING] If you modify the default stages names (stages.stage_name) in the configuration file (config.yaml), you need to provide the full path to each stage in the command-line arguments. For instance, if a stage name is changed to stage1, use stages.stage1.inputs.0.name instead of inputs.0.name.

Multi-Stage Conversion

The converter supports multi-stage conversion. This means conversion of multiple models where the output of one model is the input to another model. For mulit-stage conversion you must specify the stages section in the config file, see defaults.yaml and multistage.yaml for reference.

The output directory structure would be (assuming RVC4 conversion):

output_path/
├── config.yaml
├── modelconverter.log
├── stage_name1
│   ├── config.yaml
│   ├── intermediate_outputs/
│   ├── model1.onnx
│   └── model1.dlc
└── stage_name2
    ├── config.yaml
    ├── intermediate_outputs/
    ├── model2.onnx
    └── model2.dlc

Interactive Mode

Run the container interactively without any post-target arguments:

modelconverter shell rvc4

Inside, you'll find all the necessary tools for manual conversion. The modelconverter CLI is available inside the container as well.

Calibration Data

Calibration data can be a mix of images (.jpg, .png, .jpeg) and .npy, .raw files. Image files will be loaded and converted to the format specified in the config. No conversion is performed for .npy or .raw files, the files are used as provided. NOTE for RVC4: RVC4 expects images to be provided in NHWC layout. If you provide the calibration data in a form of .npy or .raw format, you need to make sure they have the correct layout.

Inference

A basic support for inference. To run the inference, use modelconverter infer <target> <args>. For usage instructions, see modelconverter infer --help.

The input files must be provided in a specific directory structure.

input_path/
├── <name of first input node>
│   ├── 0.npy
│   ├── 1.npy
│   └── ...
├── <name of second input node>
│   ├── 0.npy
│   ├── 1.npy
│   └── ...
├── ...
└── <name of last input node>
    ├── 0.npy
    ├── 1.npy
    └── ...

Note: The numpy files are sent to the model with no preprocessing, so they must be provided in the correct format and shape.

The output files are then saved in a similar structure.

Inference Example

For yolov6n model, the input directory structure would be:

input_path/
└── images
    ├── 0.npy
    ├── 1.npy
    └── ...

To run the inference, use:

modelconverter infer rvc4 \
  --model_path <path_to_model.dlc> \
  --output-dir <output_dir_name> \
  --input_path <input_path>
  --path <path_to_config.yaml>

The output directory structure would be:

output_path/
├── output1_yolov6r2
│   ├── 0.npy
│   ├── 1.npy
│   └── ...
├── output2_yolov6r2
│   └── <outputs>
└── output3_yolov6r2
    └── <outputs>

Benchmarking

The ModelConverter additionally supports benchmarking of converted models.

To install the package with the benchmarking dependencies, use:

pip install modelconv[bench]

To run the benchmark, use modelconverter benchmark <target> <args>.

For usage instructions, see modelconverter benchmark --help.

Example:

modelconverter benchmark rvc3 --model-path <path_to_model.xml>

The command prints a table with the benchmark results to the console and optionally saves the results to a .csv file.