OpenVINO

We use the OpenVINO toolkit to run our YOLO models, such that the model can be used in the OpenVINO inference engine, which runs on the CPU, GPU or our Intel Neural Compute Stick2.

Convert Darknet to OpenVINO models

Convert weights to Tensorflow format

  1. Clone GibHub:tensorflow-yolo-v3 to your PC (no OpenVINO needed).

  2. Checkout commit ed60b90 if you have problems.

  3. Run the command:

    python3 convert_weights_pb.py --class_names <PATH to names.names file> --data_format NHWC --weights_file <PATH_TO_YOUR_CURRENT_YOLO_WEIGHTS_FILE> --tiny
The variables are the following:

  • The required names.names file consists of all class names, with each one in its own line.

  • The <PATH_TO_YOUR_CURRENT_YOLO_WEIGHTS_FILE> represents the Darknet .weights file.

  • The --tiny setting stands for tiny-yolo which is the currently used architecture.

  • The output file will be written to the current folder as a frozen_darknet_yolov3_model.pb file.

Install OpenVINO

You can skip this step, if you have OpenVINO installed or access to a robot.

Otherwise visit the OpenVINO Install Page and follow the instructions.

Tip

If you do not use Ubuntu, e.g. “Arch Linux”, there is also a OpenVINO Docker container provided by Intel.

Convert Tensorflow format to OpenVINO

  1. Source OpenVINO in your shell. Run source /opt/intel/openvino/bin/setupvars.sh.

  2. Create a config like the Example .json config below.

  3. Run:

    python3 /opt/intel/openvino/deployment_tools/model_optimizer/mo_tf.py --input_model <PATH_TO_YOUR_FROZEN_TENSORFLOW_MODEL> --output_dir <YOUR_OUTPUT_DIR> --data_type FP16 --batch 1 --tensorflow_use_custom_operations_config <PATH_TO_YOUR_CONFIG_JSON>
The variables are the following:

  • The tensorflow model path is set in the <PATH_TO_YOUR_FROZEN_TENSORFLOW_MODEL> part.

  • The model output path is saved in <YOUR_OUTPUT_DIR>.

  • The precision is set by replacing FP16 with e.g. FP32.

  • Set the path to your freshly created .json config file (see Example .json config) in <PATH_TO_YOUR_CONFIG_JSON>.

Example .json config

In Darknet, these values are located in the .cfg files.

[
{
    "id": "TFYOLOV3",
    "match_kind": "general",
    "custom_attributes": {
    "classes": 2,
    "anchors": [17, 27, 8,140, 16,124, 37, 56, 18,211, 32,218, 83,105, 66,288, 142,185],
    "coords": 4,
    "num": 9,
    "masks": [[3, 4, 5], [0, 1, 2]],
    "entry_points": ["detector/yolo-v3-tiny/Reshape", "detector/yolo-v3-tiny/Reshape_4"]
    }
}
]