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

• downloader.py (model downloader) downloads model files from online sources and, if necessary, patches them to make them more usable with Model Optimizer;
• converter.py (model converter) converts the models that are not in the Inference Engine IR format into that format using Model Optimizer.
• quantizer.py (model quantizer) quantizes full-precision models in the IR format into low-precision versions using Post-Training Optimization Toolkit.
• info_dumper.py (model information dumper) prints information about the models in a stable machine-readable format.

Please use these tools instead of attempting to parse the configuration files directly. Their format is undocumented and may change in incompatible ways in future releases.

TIP: You also can work with the Model Downloader inside the OpenVINO™ Deep Learning Workbench (DL Workbench). DL Workbench is a platform built upon OpenVINO™ and provides a web-based graphical environment that enables you to optimize, fine-tune, analyze, visualize, and compare performance of deep learning models on various Intel® architecture configurations. In the DL Workbench, you can use most of OpenVINO™ toolkit components.
Proceed to an easy installation from Docker to get started.

## Prerequisites

1. Install Python (version 3.5.2 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.

If you using models from PyTorch or Caffe2 framework, you will also need to use intermediate conversion to ONNX format. To use automatic conversion install additional dependencies.

For models from PyTorch:

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

For models from Caffe2:

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

When running the model downloader with Python 3.5.x on macOS, you may encounter an error similar to the following:

requests.exceptions.SSLError: [...] (Caused by SSLError(SSLError(1, '[SSL: TLSV1_ALERT_PROTOCOL_VERSION]

You can work around this by installing additional packages:

python3 -mpip install --user 'requests[security]'

Alternatively, upgrade to Python 3.6 or a later version.

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.

By default, the script will download models into a directory tree rooted in the current directory. To download into a different directory, use the -o/--output_dir option:

You may use --precisions flag to specify comma separated precisions of weights to be downloaded.

By default, the script will attempt to download each file only once. You can use the --num_attempts option to change that and increase the robustness of the download process:

You can use the --cache_dir option to 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.

By default, the script outputs progress information as unstructured, human-readable text. If you want to consume progress information programmatically, use the --progress_format option:

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.

The script can download files for multiple models concurrently. To enable this, use the -j/--jobs option:

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. The following event types are currently defined: • model_download_begin Additional members: 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 Additional members: 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 Additional members: 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 Additional members: 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 Additional members: 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 Additional members: 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 Additional members: 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 usage 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. The current directory must be the root of a download tree created by the model downloader. To specify a different download tree path, use the -d/--download_dir option: ./converter.py --all --download_dir my/download/directory By default, the converted models are placed into the download tree. To place them into a different directory tree, use the -o/--output_dir option: ./converter.py --all --output_dir my/output/directory >Note: models in intermediate format are placed to this directory too. By default, the script will produce models in every precision that is supported for conversion. To only produce models in a specific precision, use the --precisions option: ./converter.py --all --precisions=FP16 If the specified precision is not supported for a model, that model will be skipped. The script will attempt to locate Model Optimizer using the environment variables set by the OpenVINO™ toolkit's setupvars.sh/setupvars.bat script. You can override this heuristic with the --mo option: ./converter.py --all --mo my/openvino/path/model_optimizer/mo.py You can add extra Model Optimizer arguments to the ones specified in the model configuration by using the --add_mo_arg option. The option can be repeated to add multiple arguments: ./converter.py --name=caffenet --add_mo_arg=--reverse_input_channels --add_mo_arg=--silent By default, the script will run Model Optimizer using the same Python executable that was used to run the script itself. To use a different Python executable, use the -p/--python option: ./converter.py --all --python my/python The script can run multiple conversion commands concurrently. To enable this, use the -j/--jobs option: ./converter.py --all -j8 # run up to 8 commands at a time 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. The script can print the conversion commands without actually running them. To do this, use the --dry_run option: ./converter.py --all --dry_run See the "Shared options" section for information on other options accepted by the script. ## Model Quantizer Usage 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 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. The current directory must be the root of a tree of model files create by the model converter. To specify a different model tree path, use the --model_dir option: ./quantizer.py --all --dataset_dir <DATASET_DIR> --model_dir my/model/directory By default, the quantized models are placed into the same model tree. To place them into a different directory tree, use the -o/--output_dir option: ./quantizer.py --all --dataset_dir <DATASET_DIR> --output_dir my/output/directory By default, the script will produce models in every precision that is supported as a quantization output. To only produce models in a specific precision, use the --precisions option: ./quantizer.py --all --dataset_dir <DATASET_DIR> --precisions=FP16-INT8 The script will attempt to locate Post-Training Optimization Toolkit using the environment variables set by the OpenVINO™ toolkit's setupvars.sh/setupvars.bat script. You can override this heuristic with the --pot option: ./quantizer.py --all --dataset_dir <DATASET_DIR> --pot my/openvino/path/post_training_optimization_toolkit/main.py By default, the script will run Post-Training Optimization Toolkit using the same Python executable that was used to run the script itself. To use a different Python executable, use the -p/--python option: ./quantizer.py --all --dataset_dir <DATASET_DIR> --python my/python It's possible to specify a target device for Post-Training Optimization Toolkit to optimize for, by using the --target_device option: ./quantizer.py --all --dataset_dir <DATASET_DIR> --target_device VPU 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. The script can print the quantization commands without actually running them. To do this, use the --dry_run option: ./quantizer.py --all --dataset_dir <DATASET_DIR> --dry_run With this option specified, the configuration file for Post-Training Optimization Toolkit will still be created, so that you can inspect it. See the "Shared options" section for information on other options accepted by the script. ## Model information dumper usage 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: • name: the identifier of the model, as accepted by the --name option. • 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: an URL for the license that the model is distributed under. • 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. • subdirectory: the subdirectory of the output tree into which the downloaded or converted files will be placed by the downloader or the converter, respectively. • task_type: a string identifying the type of task that the model performs. Current possible values are: • action_recognition • classification • detection • face_recognition • feature_extraction • head_pose_estimation • human_pose_estimation • image_inpainting • image_processing • instance_segmentation • machine_translation • monocular_depth_estimation • object_attributes • optical_character_recognition • question_answering • semantic_segmentation • sound_classification • speech_recognition • style_transfer • 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: • --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. ./TOOL.py --name 'mtcnn-p,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. ./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-p 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
emotions-recognition-retail-0003
face-detection-retail-0004
face-detection-retail-0005
[...]

Either --print_all or one of the filter options must be specified.

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