There are few ways to extend and customize Datumaro behavior, which is supported by plugins. Check our contribution guide for details on plugin implementation. In general, a plugin is a Python module. It must be put into a plugin directory:
<project_dir>/.datumaro/pluginsfor project-specific plugins
<datumaro_dir>/pluginsfor global plugins
Datumaro provides several builtin plugins. Plugins can have dependencies, which need to be installed separately.
The plugin provides support of TensorFlow Detection API format, which includes boxes and masks.
The plugin depends on TensorFlow, which can be installed with
pip install tensorflow
pip install tensorflow-gpu
pip install datumaro[tf]
pip install datumaro[tf-gpu]
This plugin allows to use Accuracy Checker to launch deep learning models from various frameworks (Caffe, MxNet, PyTorch, OpenVINO, …) through Accuracy Checker’s API.
The plugin depends on Accuracy Checker, which can be installed with
pip install 'git+https://github.com/openvinotoolkit/open_model_zoo.git#subdirectory=tools/accuracy_checker'
To execute models with deep learning frameworks, they need to be installed too.
This plugin provides support for model inference with OpenVINO™.
The plugin depends on the OpenVINO™ Toolkit, which can be installed by following these instructions
Dataset reading is supported by Extractors and Importers. An Extractor produces a list of dataset items corresponding to the dataset. An Importer creates a project from the data source location. It is possible to add custom Extractors and Importers. To do this, you need to put an Extractor and Importer implementation scripts to a plugin directory.
Dataset writing is supported by Converters. A Converter produces a dataset of a specific format from dataset items. It is possible to add custom Converters. To do this, you need to put a Converter implementation script to a plugin directory.
Dataset Conversions (“Transforms”)#
A Transform is a function for altering a dataset and producing a new one. It can update dataset items, annotations, classes, and other properties. A list of available transforms for dataset conversions can be extended by adding a Transform implementation script into a plugin directory.
A list of available launchers for model execution can be extended by adding a Launcher implementation script into a plugin directory.