This directory contains scripts that automate certain model-related tasks based on configuration files in the models’ directories.

• Model Converter: converter.py converts the models that are not in the Inference Engine IR format into that format using Model Optimizer.

• Model Quantizer: quantizer.py quantizes full-precision models in the IR format into low-precision versions using Post-Training Optimization Toolkit.

• Model Information Dumper: info_dumper.py prints information about the models in a stable machine-readable format.

Tip

You can quick start with the Model Downloader inside the OpenVINO™ Deep Learning Workbench (DL Workbench). DL Workbench is the OpenVINO™ toolkit UI that enables you to import a model, analyze its performance and accuracy, visualize the outputs, optimize and prepare the model for deployment on various Intel® platforms.

## Prerequisites¶

1. Install Python (version 3.6 or higher)

2. Install the tools’ dependencies with the following command:

python3 -mpip install --user -r ./requirements.in

For the model converter, you will also need to install the OpenVINO toolkit and the prerequisite libraries for Model Optimizer. See the OpenVINO toolkit documentation for details.

To convert models from certain frameworks, you will also need to install additional dependencies.

python3 -mpip install --user -r ./requirements-caffe2.in
python3 -mpip install --user -r ./requirements-pytorch.in
python3 -mpip install --user -r ./requirements-tensorflow.in

The basic usage is to run the script like this:

This will download all models. The --all option can be replaced with other filter options to download only a subset of models. See the “Shared options” section.

Parameter

Explanation

Example

-o/--output_dir

By default, the script will download models into a directory tree rooted in the current directory. Use this parameter to download into a different directory.

--precisions

--num_attempts

By default, the script will attempt to download each file only once. Use this parameter to change that and increase the robustness of the download process

--cache_dir

Make the script use the specified directory as a cache. The script will place a copy of each downloaded file in the cache, or, if it is already there, retrieve it from the cache instead of downloading it again. The cache format is intended to remain compatible in future Open Model Zoo versions, so you can use a cache to avoid redownloading most files when updating Open Model Zoo.

-j/--jobs

--progress_format

By default, the script outputs progress information as unstructured, human-readable text. Use this option, if you want to consume progress information programmatically.

When this option is set to json, the script’s standard output is replaced by a machine-readable progress report, whose format is documented in the “JSON progress report format” section. This option does not affect errors and warnings, which will still be printed to the standard error stream in a human-readable format.

You can also set this option to text to explicitly request the default text format.

See the “Shared options” section for information on other options accepted by the script.

### JSON progress report format¶

This section documents the format of the progress report produced by the script when the --progress_format=json option is specified.

The report consists of a sequence of events, where each event is represented by a line containing a JSON-encoded object. Each event has a member with the name $type whose value determines the type of the event, as well as which additional members it contains. Event type Additional members Explanation model_download_begin model (string), num_files (integer) The script started downloading the model named by model. num_files is the number of files that will be downloaded for this model. This event will always be followed by a corresponding model_download_end event. model_download_end model (string), successful (boolean) The script stopped downloading the model named by model. successful is true if every file was downloaded successfully. model_file_download_begin model (string), model_file (string), size (integer) The script started downloading the file named by model_file of the model named by model. size is the size of the file in bytes. This event will always occur between model_download_begin and model_download_end events for the model, and will always be followed by a corresponding model_file_download_end event. model_file_download_end model (string), model_file (string), successful (boolean) The script stopped downloading the file named by model_file of the model named by model. successful is true if the file was downloaded successfully. model_file_download_progress model (string), model_file (string), size (integer) The script downloaded size bytes of the file named by model_file of the model named by model so far. Note that size can decrease in a subsequent event if the download is interrupted and retried. This event will always occur between model_file_download_begin and model_file_download_end events for the file. model_postprocessing_begin model The script started post-download processing on the model named by model. This event will always be followed by a corresponding model_postprocessing_end event. model_postprocessing_end model The script stopped post-download processing on the model named by model. Additional event types and members may be added in the future. Tools parsing the machine-readable format should avoid relying on undocumented details. In particular: • Tools should not assume that any given event will occur for a given model/file (unless specified otherwise above) or will only occur once. • Tools should not assume that events will occur in a certain order beyond the ordering constraints specified above. In particular, when the --jobs option is set to a value greater than 1, event sequences for different files or models may get interleaved. ## Model Converter¶ The basic usage is to run the script like this: ./converter.py --all This will convert all models into the Inference Engine IR format. Models that were originally in that format are ignored. Models in PyTorch and Caffe2 formats will be converted in ONNX format first. The --all option can be replaced with other filter options to convert only a subset of models. See the “Shared options” section. ### Model Converter Starting Parameters¶ Parameter Explanation Example -d/--download_dir The current directory must be the root of a download tree created by the model downloader. Use this parameter to specify a different download tree path. ./converter.py --all --download_dir my/download/directory -o/--output_dir By default, the script will download models into a directory tree rooted in the current directory. Use this parameter to download into a different directory. Note: models in intermediate format are placed to this directory too. ./converter.py --all --output_dir my/output/directory --precisions By default, the script will produce models in every precision that is supported for conversion. Use this parameter to only produce models in a specific precision. If the specified precision is not supported for a model, that model will be skipped. ./converter.py --all --precisions=FP16 --add_mo_arg Add extra Model Optimizer arguments to the ones specified in the model configuration. The option can be repeated to add multiple arguments ./converter.py --name=caffenet --add_mo_arg=--reverse_input_channels --add_mo_arg=--silent -j/--jobs Run multiple conversion commands concurrently. The argument to the option must be either a maximum number of concurrently executed commands, or “auto”, in which case the number of CPUs in the system is used. By default, all commands are run sequentially. ./converter.py --all -j8 # run up to 8 commands at a time --dry_run Print the conversion commands without actually running them.. ./converter.py --all --dry_run -p/--python By default, the script will run Model Optimizer using the same Python executable that was used to run the script itself. Apply this parameter to use a different Python executable. ./converter.py --all --python my/python The Python script will attempt to locate Model Optimizer using several methods: 1. If the --mo option was specified, then its value will be used as the path to the script to run: ./converter.py --all --mo my/openvino/path/model_optimizer/mo.py 2. Otherwise, if the selected Python executable can import the mo package, then that package will be used. 3. Otherwise, if the OpenVINO toolkit’s setupvars.sh / setupvars.bat script has been executed, the environment variables set by that script will be used to locate Model Optimizer within the toolkit. 4. Otherwise, the script will fail. See the “Shared options” section for information on other options accepted by the script. ## Model Quantizer¶ Before you run the model quantizer, you must prepare a directory with the datasets required for the quantization process. This directory will be referred to as <DATASET_DIR> below. You can find more detailed information about dataset preparation in the ../../data/datasets.md “Dataset Preparation Guide”. The basic usage is to run the script like this: ./quantizer.py --all --dataset_dir <DATASET_DIR> This will quantize all models for which quantization is supported. Other models are ignored. The --all option can be replaced with other filter options to quantize only a subset of models. See the “Shared options” section. ### Model Quantizer Starting Parameters¶ Parameter Explanation Example --model_dir The current directory must be the root of a tree of model files create by the model converter. Use this parameter to specify a different model tree path ./quantizer.py --all --dataset_dir <DATASET_DIR> --model_dir my/model/directory -o/--output_dir By default, the script will download models into a directory tree rooted in the current directory. Use this parameter to download into a different directory. Note: models in intermediate format are placed to this directory too. ./quantizer.py --all --dataset_dir <DATASET_DIR> --output_dir my/output/directory --precisions By default, the script will produce models in every precision that is supported as a quantization output. Use this parameter to only produce models in a specific precision. ./quantizer.py --all --dataset_dir <DATASET_DIR> --precisions=FP16-INT8 --target_device It’s possible to specify a target device for Post-Training Optimization Toolkitto optimize for. The supported values are those accepted by the “target_device” option in Post-Training Optimization Toolkit’s config files. If this option is unspecified, Post-Training Optimization Toolkit’s default is used. ../quantizer.py --all --dataset_dir <DATASET_DIR> --target_device VPU --dry_run The script can print the quantization commands without actually running them. With this option specified, the configuration file for Post-Training Optimization Toolkit will still be created, so that you can inspect it. ./quantizer.py --all --dataset_dir <DATASET_DIR> --dry_run -p/--python By default, the script will run Model Optimizer using the same Python executable that was used to run the script itself. Apply this parameter to use a different Python executable. ./quantizer.py --all --dataset_dir <DATASET_DIR> --python my/python The script will attempt to locate Post-Training Optimization Toolkit using several methods: 1. If the --pot option was specified, then its value will be used as the path to the script to run: ./quantizer.py --all --dataset_dir <DATASET_DIR> --pot my/openvino/path/post_training_optimization_toolkit/main.py 2. Otherwise, if the selected Python executable can import the pot package, then that package will be used. 3. Otherwise, if the OpenVINO toolkit’s setupvars.sh / setupvars.bat script has been executed, the environment variables set by that script will be used to locate Post-Training Optimization Toolkit within the OpenVINO toolkit. 4. Otherwise, the script will fail. See the “Shared options” section for information on other options accepted by the script. ## Model Information Dumper¶ The basic usage is to run the script like this: ./info_dumper.py --all This will print to standard output information about all models. The only options accepted by the script are those described in the “Shared options” section. The script’s output is a JSON array, each element of which is a JSON object describing a single model. Each such object has the following keys: Parameter Explanation name the identifier of the model, as accepted by the --name option. composite_model_name the identifier of the composite model name, if the model is a part of composition of several models (e.g. encoder-decoder), otherwise - null description text describing the model. Paragraphs are separated by line feed characters. framework a string identifying the framework whose format the model is downloaded in. Current possible values are dldt (Inference Engine IR), caffe, caffe2, mxnet, onnx, pytorch and tf (TensorFlow). Additional possible values might be added in the future. license_url a URL for the license that the model is distributed under. quantization_output_precisions the list of precisions that the model can be quantized to by the model quantizer. Current possible values are FP16-INT8 and FP32-INT8; additional possible values might be added in the future. quantization_output_precisions the list of precisions that the model can be quantized to by the model quantizer. Current possible values are FP16-INT8 and FP32-INT8; additional possible values might be added in the future. subdirectory the subdirectory of the output tree into which the downloaded or converted files will be placed by the downloader or the converter, respectively. • precisions : the list of precisions that the model has IR files for. For models downloaded in a format other than the Inference Engine IR format, these are the precisions that the model converter can produce IR files in. Current possible values are: • FP16 • FP16-INT1 • FP16-INT8 • FP32 • FP32-INT1 • FP32-INT8 Additional possible values might be added in the future. • task_type : a string identifying the type of task that the model performs. are: • action_recognition • classification • colorization • detection • face_recognition • feature_extraction • head_pose_estimation • human_pose_estimation • image_inpainting • image_processing • image_translation • instance_segmentation • machine_translation • monocular_depth_estimation • named_entity_recognition • noise_suppression • object_attributes • optical_character_recognition • place_recognition • question_answering • salient_object_detection • semantic_segmentation • sound_classification • speech_recognition • style_transfer • text_to_speech • time_series • token_recognition Additional possible values might be added in the future. ## Shared options¶ The are certain options that all tools accept. -h / --help can be used to print a help message: ./TOOL.py --help There are several mutually exclusive filter options that select the models the tool will process: Parameter Explanation Example --all Selects all models ./TOOL.py --all --name takes a comma-separated list of patterns and selects models that match at least one of these patterns. The patterns may contain shell-style wildcards. For composite models, the name of composite model is accepted, as well as the names of individual models it consists of. ./TOOL.py --name 'mtcnn,densenet-*' See https://docs.python.org/3/library/fnmatch.html for a full description of the pattern syntax. --list takes a path to a file that must contain a list of patterns and selects models that match at least one of those patterns. For composite models, the name of composite model is accepted, as well as the names of individual models it consists of. ./TOOL.py --list my.lst The file must contain one pattern per line. The pattern syntax is the same as for the --name option. Blank lines and comments starting with # are ignored. For example: mtcnn # get all three models: mtcnn-o, mtcnn-p, mtcnn-r densenet-* # get all DenseNet variants To see the available models, you can use the --print_all option. When this option is specified, the tool will print all model names defined in the configuration file and exit:$ ./TOOL.py --print_all
action-recognition-0001-decoder
action-recognition-0001-encoder
age-gender-recognition-retail-0013