ModelPreparing

Model preparing

Intel® Distribution of OpenVINO™ Toolkit

To prepare models and data for benchmarking, please, follow instructions.

1. Create <working_dir> directory which will contain models and datasets.

   mkdir <working_dir>

2. Download models using the OpenVINO Model Downloader tool to the <working_dir> directory:

   omz_downloader --all --output_dir <working_dir> --cache_dir <cache_dir>

3. Convert models using the OpenVINO Model Converter tool to the <working_dir> directory:

   omz_converter --output_dir <working_dir> --download_dir <working_dir>

4. (Optional) Convert models to the INT8 precision:

   1. Prepare configuration files in accordance with src/configs/quantization_configuration_file_template.xml. Please, use GUI application (src/config_maker).

   2. Quantize models to the INT8 precision using the script src/quantization/quantization.py in accordiance with src/quantization/README.md.

      python3 ~/dl-benchmark/src/quantization/quantization.py -c <config_path>

Intel® Optimization for Caffe

[TBD]

Intel® Optimizations for TensorFlow

[TBD]

ONNX Runtime

omz_converter supports exporting of PyTorch-models to ONNX-format. For more info see Exporting a PyTorch Model to ONNX Format

1. Create <working_dir> directory which will contain models and datasets.

   mkdir <working_dir>

2. Download models using the OpenVINO Model Downloader tool to the <working_dir> directory:

   omz_downloader --all --output_dir <working_dir> --cache_dir <cache_dir>

   or

   omz_downloader --name <model_name> --output_dir <working_dir> --cache_dir <cache_dir>

3. Convert models using the OpenVINO Model Converter tool to the <working_dir> directory:

   omz_converter --output_dir <working_dir> --download_dir <working_dir>

   output_dir will contain model converted to ONNX-format.

TensorFlow Lite

[TBD]

MXNet

To infer MXNet-models it is required to install GluonCV python package.

pip install gluoncv[full]