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Model Downloader and other automation tools

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](@ref workbench_docs_Workbench_DG_Introduction) (DL Workbench). [DL Workbench](@ref workbench_docs_Workbench_DG_Introduction) 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](@ref workbench_docs_Workbench_DG_Run_Locally) to get started.

Installation

Model Downloader and other automation tools can be installed as a part of OpenVINO™ toolkit or from source. Installation from source is as follows:

  1. Install Python (version 3.6 or higher), setuptools:

  2. Install the tools with the following command:

python setup.py install

NOTE: On Linux and macOS, you may need to type python3 instead of python. You may also need to install pip. For example, on Ubuntu execute the following command to get pip installed: sudo apt install python3-pip.

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.

For models from Caffe2:

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

For models from PyTorch:

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

For models from TensorFlow:

python -mpip install --user -r ./requirements-tensorflow.in

Model downloader usage

The basic usage is to run the script like this:

omz_downloader --all

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:

omz_downloader --all --output_dir my/download/directory

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

omz_downloader --name face-detection-retail-0004 --precisions FP16,FP16-INT8

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:

omz_downloader --all --num_attempts 5 # attempt each download five times

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.

omz_downloader --all --cache_dir my/cache/directory

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:

omz_downloader --all --progress_format=json

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:

omz_downloader --all -j8 # download up to 8 models at a time

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:

omz_converter --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:

omz_converter --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:

omz_converter --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:

omz_converter --all --precisions=FP16

If the specified precision is not supported for a model, that model will be skipped.

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:

omz_converter --all --python my/python

The 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:

    omz_converter --all --mo my/openvino/path/model_optimizer/mo.py

  2. Otherwise, if the selected Python executable can find the mo entrypoint, then it 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.

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:

omz_converter --name=caffenet --add_mo_arg=--reverse_input_channels --add_mo_arg=--silent

The script can run multiple conversion commands concurrently. To enable this, use the -j/--jobs option:

omz_converter --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:

omz_converter --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:

omz_quantizer --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:

omz_quantizer --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:

omz_quantizer --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:

omz_quantizer --all --dataset_dir <DATASET_DIR> --precisions=FP16-INT8

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:

omz_quantizer --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:

    omz_quantizer --all --dataset_dir <DATASET_DIR> --pot my/openvino/path/post_training_optimization_toolkit/main.py

  2. Otherwise, if the selected Python executable can find the pot entrypoint, then it 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.

It's possible to specify a target device for Post-Training Optimization Toolkit to optimize for, by using the --target_device option:

omz_quantizer --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:

omz_quantizer --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:

omz_info_dumper --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.

  • 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: 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.

  • 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.

  • task_type: a string identifying the type of task that the model performs. Current possible values 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_prediction
    • 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:

omz_TOOL --help

There are several mutually exclusive filter options that select the models the tool will process:

  • --all selects all models.

    omz_TOOL --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.

    omz_TOOL --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.

    omz_TOOL --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:

$ omz_TOOL --print_all
action-recognition-0001-decoder
action-recognition-0001-encoder
age-gender-recognition-retail-0013
driver-action-recognition-adas-0002-decoder
driver-action-recognition-adas-0002-encoder
emotions-recognition-retail-0003
face-detection-adas-0001
face-detection-retail-0004
face-detection-retail-0005
[...]

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

OpenVINO is a trademark of Intel Corporation or its subsidiaries in the U.S. and/or other countries.

Copyright © 2018-2019 Intel Corporation

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

 http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.