Deep Learning accuracy validation framework

The Accuracy Checker is an extensible, flexible and configurable Deep Learning accuracy validation framework. The tool has a modular structure and allows to reproduce validation pipeline and collect aggregated quality indicators for popular datasets both for networks in source frameworks and in the OpenVINO™ supported formats.

Installation

Tip

You can quick start with the Accuracy Checker inside the OpenVINO™ Deep Learning Workbench (DL Workbench). DL Workbench is an OpenVINO™ 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. Python

Accuracy Checker uses Python 3. Install it first:

sudo apt-get install python3 python3-dev python3-setuptools python3-pip

Python* setuptools and Python* package manager (pip) install packages into system directory by default. Installation of Accuracy Checker is tested only via virtual environment.

Install the virtual environment:

python3 -m pip install virtualenv
python3 -m virtualenv -p `which python3` <directory_for_environment>

Activate the virtual environment:

source <directory_for_environment>/bin/activate

Virtual environment can be deactivated using the following command:

deactivate

2. Frameworks

The next step is installing backend frameworks for Accuracy Checker.

To evaluate some models, you need to install the required frameworks. Accuracy Checker supports the following frameworks:

You can use any of them or several at a time. For correct work, Accuracy Checker requires at least one. You can postpone installation of other frameworks and install them when they will be necessary.

Install Accuracy Checker

If all prerequisites are installed, you are ready to install Accuracy Checker :

python3 setup.py install

Accuracy Checker is a modular tool and have some task-specific dependencies, all specific required modules can be found in requirements.in file. Instead of the standard installation, you can install only the core part of the tool without additional dependencies and manage them by yourself using the following command:

python setup.py install_core

Installation Troubleshooting

When previous version of the tool is already installed in the environment, in some cases, it can broke the new installation. If you get a directory/file not found error, try manually removing the previous tool version from your environment or install the tool using following command in Accuracy Checker directory instead of setup.py install:

pip install --upgrade --force-reinstall .

Running the Tool inside IDE for Development Purposes

Accuracy Checker tool has an entry point for running in CLI, however, the majority of popular code editors or integrated development environments (IDEs) expect scripts as the starting point of application. Sometimes it can be useful to run the tool as a script for debugging or enabling new models. To use Accuracy Checker inside the IDE, you need to create a script in accuracy_checker root directory, for example, <open_model_zoo>/tools/accuracy_checker/main.py, with the following code:

from accuracy_checker.main import main

if __name__ == '__main__':
    main()

Now, you can use this script for running Accuracy Checker in IDE.

Usage

You may test your installation and get familiar with Accuracy Checker by running a sample.

Each Open Model Zoo model can be evaluated using a configuration file. To learn more, refer to How to use predefined configuration files guide.

Once you installed accuracy checker, you can evaluate your configurations using:

accuracy_check -c path/to/configuration_file -m /path/to/models -s /path/to/source/data -a /path/to/annotation

Use -h, --help to get the full list of command-line options. Some arguments are described below:

  • -c, --config path to configuration file.

  • -m, --models specifies directory in which models and weights declared in config file will be searched. You can also specify space-separated list of directories if you want to run the same configuration several times with models located in different directories or if you have the pipeline with several models.

  • -s, --source specifies directory in which input images will be searched.

  • -a, --annotations specifies directory in which annotation and meta files will be searched.

  • -d, --definitions path to the global configuration file.

  • -e, --extensions directory with InferenceEngine extensions.

  • -b, --bitstreams directory with bitstream (for Inference Engine with fpga plugin).

  • -C, --converted_models directory to store Model Optimizer converted models (used for DLSDK launcher only).

  • -tf, --target_framework framework for infer.

  • -td, --target_devices devices for infer. You can specify several devices using space as a delimiter.

  • --async_mode allows run the tool in async mode if launcher supports it.

  • --num_requests number requests for async execution. Allows override provided in config info. Default is AUTO

  • --model_attributes directory with additional models attributes.

  • --subsample_size dataset subsample size.

  • --shuffle allows shuffle annotation during creation a subset if subsample_size argument is provided. Default is True.

  • --intermediate_metrics_results enables intermediate metrics results printing. Default is False

  • --metrics_interval number of iterations for updated metrics result printing if --intermediate_metrics_results flag enabled. Default is 1000.

You are also able to replace some command-line arguments with the environment variables for path prefixing. Supported list of variables includes:

  • DEFINITIONS_FILE - equivalent of -d, -definitions.

  • DATA_DIR - equivalent of -s, --source.

  • MODELS_DIR - equivalent of -m, --models.

  • EXTENSIONS - equivalent of -e, --extensions.

  • ANNOTATIONS_DIR - equivalent of -a, --annotations.

  • BITSTREAMS_DIR - equivalent of -b, --bitstreams.

  • MODEL_ATTRIBUTES_DIR - equivalent of --model_attributes.

Configuration

There is a config file, which declares the validation process. Every validated model has to have its entry in the models list with distinct name and other properties described below.

There is also a definitions file, which declares global options shared across all models. Config file has priority over definitions file.

Example:

models:
- name: model_name
  launchers:
    - framework: caffe
      model:   bvlc_alexnet.prototxt
      weights: bvlc_alexnet.caffemodel
      adapter: classification
      batch: 128
  datasets:
    - name: dataset_name

Optionally you can use global configuration. It may be useful for avoiding duplication if you have several models which need to be run on the same dataset. Example of global definitions file can be found in the Github repository. Global definitions will be merged with evaluation config in the runtime by dataset name. Parameters of global configuration can be overwritten by local config. For example, if in definitions specified resize with destination size 224 and in the local config used resize with size 227, the value in config 227 will be used as resize parameter. You can use field global_definitions for specifying the path to global definitions directly in the model config or via command-line arguments (-d, --definitions).

Launchers

Launcher is a description of how your model should be executed. Each launcher configuration starts with setting framework name. Currently caffe, dlsdk, mxnet, tf, tf2, tf_lite, opencv, onnx_runtime, pytorch, paddlepaddle supported. Launcher description can have differences.

Datasets

Dataset entry describes the data on which model should be evaluated, all required preprocessing and postprocessing/filtering steps, and metrics that will be used for evaluation.

If your dataset data is a well-known competition problem (COCO, Pascal VOC, and others) and/or can be potentially reused for other models, it is reasonable to declare it in some global configuration file (<omz_dir>/tools/accuracy_checker/dataset_definitions.yml). This way in your local configuration file you can provide only name, and all required steps will be picked from the global one. To pass the path to this global configuration use --definition argument of CLI.

If you want to evaluate models using prepared config files and well-known datasets, you need to organize folders with validation datasets in a certain way. Find more detailed information about dataset preparation in Dataset Preparation Guide.

Each dataset must have:

  • name - unique identifier of your model/topology.

  • data_source - path to directory where input data is stored.

  • metrics - list of metrics that should be computed.

And optionally:

  • preprocessing - list of preprocessing steps applied to input data. If you want calculated metrics to match reported, you must reproduce preprocessing from canonical paper of your topology or ask topology author about required steps.

  • postprocessing - list of postprocessing steps.

  • reader - approach for data reading. Default reader is opencv_imread.

  • segmentation_masks_source - path to directory where gt masks for semantic segmentation task stored.

Also it must contain data related to annotation. You can convert annotation in-place using:

  • annotation_conversion - parameters for annotation conversion

or use existing annotation file and dataset meta:

  • annotation - path to annotation file, you must convert annotation to representation of dataset problem first, you may choose one of the converters from annotation-converters if there is already a converter for your dataset or write your own.

  • dataset_meta - path to metadata file (generated by converter). Find more detailed information about annotation conversion in Annotation Conversion Guide.

Example of dataset definition:

- name: dataset_name
  annotation: annotation.pickle
  data_source: images_folder

  preprocessing:
    - type: resize
      dst_width: 256
      dst_height: 256

    - type: normalization
      mean: imagenet

    - type: crop
      dst_width: 227
      dst_height: 227

  metrics:
    - type: accuracy

Preprocessing, Metrics, Postprocessing

Each entry of preprocessing, metrics, postprocessing must have a type field with other options specific to the type. If you do not provide any other option, it will be picked from the definitions file.

You can use the following instructions:

You may optionally provide reference field for metric, if you want the calculated metric tested against a specific value (reported in canonical paper).

Some metrics support providing vector results, for example, mAP is able to return average precision for each detection class. You can change view mode for metric results using presenter (for example, print_vector, print_scalar).

Example:

metrics:
- type: accuracy
  top_k: 5
  reference: 86.43
  threshold: 0.005

Testing New Models

Typical workflow for testing a new model includes:

  1. Convert annotation of your dataset. Use one of the converters from annotation-converters, or write your own if there is no converter for your dataset. You can find detailed instruction on how to use converters in Annotation Conversion Guide.

  2. Choose one of the adapters or write your own. Adapter converts raw output produced by the framework to high-level problem-specific representation (for example, ClassificationPrediction, DetectionPrediction, and others).

  3. Reproduce preprocessing, metrics and postprocessing from canonical paper.

  4. Create entry in config file and execute.

Customizing Evaluation

Standard Accuracy Checker validation pipeline: Annotation Reading -> Data Reading -> Preprocessing -> Inference -> Postprocessing -> Metrics. In some cases, this validation pipeline can be unsuitable, for example, when you have a sequence of models. You can customize validation pipeline using your own evaluator. Find more details about custom evaluations in the related section.