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.
TIP: You also can work with the Accuracy Checker 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.
Install prerequisites first:
accuracy checker uses Python 3. Install it first:
Python setuptools and python package manager (pip) install packages into system directory by default. Installation of accuracy checker tested only via virtual environment.
In order to use virtual environment you should install it first:
Before starting to work inside virtual environment, it should be activated:
Virtual environment can be deactivated using command
The next step is installing backend frameworks for Accuracy Checker.
In order to evaluate some models required frameworks have to be installed. Accuracy-Checker supports these frameworks:
You can use any of them or several at a time. For correct work, Accuracy Checker requires at least one. You are able postpone installation of other frameworks and install them when they will be necessary.
If all prerequisite are installed, then you are ready to install accuracy checker:
Accuracy Checker is modular tool and have some task-specific dependencies, all specific required modules can be found in
requirements.in file. You can install only core part of the tool without additional dependencies and manage them by your-self using following command instead of standard installation:
When previous version of the tool already installed in the environment, in some cases, it can broke new installation. If you see the error about directory/file not found, please try remove manually old tool version from your environment or install the tool with following command (in accuracy checker directory, instead of setup.py install):
Accuracy Checker tool has entry point for running in CLI, however majority of popular code editors or IDE expects scripts as starting point of application. Sometimes it can be useful to have opportunity to run the tool as script for debugging or enabling new models. For usage Accuracy Checker inside the IDE, you need to create a script in accuracy_checker root directory (e.g.
<open_model_zoo>/tools/accuracy_checker/main.py) with following code:
Now, you can use this script for running in IDE.
You may test your installation and get familiar with accuracy checker by running sample.
Each Open Model Zoo model can be evaluated using a configuration file. Please refer to How to use predefined configuration files guide.
Once you installed accuracy checker you can evaluate your configurations with:
You may refer to
-h, --help to full list of command line options. Some arguments are:
-c, --configpath to configuration file.
-m, --modelsspecifies directory in which models and weights declared in config file will be searched. You also can 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, --sourcespecifies directory in which input images will be searched.
-a, --annotationsspecifies directory in which annotation and meta files will be searched.
-d, --definitionspath to the global configuration file.
-e, --extensionsdirectory with InferenceEngine extensions.
-b, --bitstreamsdirectory with bitstream (for Inference Engine with fpga plugin).
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 support it. -–num_requests
number requests for async execution. Allows override provided in config info. Default isAUTO
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 isTrue
enables intermediate metrics results printing. Default isFalse
number of iteration 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 environment variables for path prefixing. Supported following list of variables:
DEFINITIONS_FILE- equivalent of
DATA_DIR- equivalent of
MODELS_DIR- equivalent of
EXTENSIONS- equivalent of
ANNOTATIONS_DIR- equivalent of
BITSTREAMS_DIR- equivalent of
MODEL_ATTRIBUTES_DIR- equivalent of
There is config file which declares validation process. Every validated model has to have its entry in
models list with distinct
name and other properties described below.
There is also definitions file, which declares global options shared across all models. Config file has priority over definitions file.
Optionally you can use global configuration. It can be useful for avoiding duplication if you have several models which should be run on the same dataset. Example of global definitions file can be found here. Global definitions will be merged with evaluation config in the runtime by dataset name. Parameters of global configuration can be overwritten by local config (e.g. 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 path to global definitions directly in the model config or via command line arguments (
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. Please view:
Dataset entry describes 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 (definition file). This way in your local configuration file you can provide only
name and all required steps will be picked from global one. To pass 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. More detailed information about dataset preparation you can find 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.
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
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 converter for your dataset or write your own.
dataset_meta: path to metadata file (generated by converter). More detailed information about annotation conversion you can find in Annotation Conversion Guide.
example of dataset definition:
Each entry of preprocessing, metrics, postprocessing must have a
type field, with other options are specific to the type. If you do not provide any other option, then it will be picked from the definitions file.
You can find useful following instructions:
You may optionally provide
reference field for metric, if you want the calculated metric tested against a specific value (i.e. reported in canonical paper).
Some metrics support providing vector results ( e. g. mAP is able to return average precision for each detection class). You can change view mode for metric results using
Typical workflow for testing a new model includes:
Standard Accuracy Checker validation pipeline: Annotation Reading -> Data Reading -> Preprocessing -> Inference -> Postprocessing -> Metrics. In some cases it can be unsuitable (e.g. if you have sequence of models). You are able to customize validation pipeline using own evaluator. More details about custom evaluations can be found in the related section.