Plugin Testing

Inference Engine (IE) tests infrastructure provides a predefined set of functional tests and utilities. They are used to verify a plugin using the Inference Engine public API. All the tests are written in the Google Test C++ framework.

Inference Engine Plugin tests are included in the IE::funcSharedTests CMake target which is built within the OpenVINO repository (see Build Plugin Using CMake guide). This library contains tests definitions (the tests bodies) which can be parametrized and instantiated in plugins depending on whether a plugin supports a particular feature, specific sets of parameters for test on supported operation set and so on.

Test definitions are split into tests class declaration (see inference_engine/tests/functional/plugin/shared/include) and tests class implementation (see inference_engine/tests/functional/plugin/shared/src) and include the following scopes of plugin conformance tests:

  1. Behavior tests (behavior sub-folder), which are a separate test group to check that a plugin satisfies basic Inference Engine concepts: plugin creation, multiple executable networks support, multiple synchronous and asynchronous inference requests support, and so on. See the next section with details how to instantiate the tests definition class with plugin-specific parameters.

  2. Single layer tests (single_layer_tests sub-folder). This groups of tests checks that a particular single layer can be inferenced on a device. An example of test instantiation based on test definition from IE::funcSharedTests library:

    • From the declaration of convolution test class we can see that it’s a parametrized GoogleTest based class with the convLayerTestParamsSet tuple of parameters:

      typedef std::tuple<
              InferenceEngine::SizeVector,    // Kernel size
              InferenceEngine::SizeVector,    // Strides
              std::vector<ptrdiff_t>,         // Pad begin
              std::vector<ptrdiff_t>,         // Pad end
              InferenceEngine::SizeVector,    // Dilation
              size_t,                         // Num out channels
              ngraph::op::PadType             // Padding type
      > convSpecificParams;
      typedef std::tuple<
              InferenceEngine::Precision,     // Net precision
              InferenceEngine::Precision,     // Input precision
              InferenceEngine::Precision,     // Output precision
              InferenceEngine::Layout,        // Input layout
              InferenceEngine::Layout,        // Output layout
              InferenceEngine::SizeVector,    // Input shapes
              LayerTestsUtils::TargetDevice   // Device name
      > convLayerTestParamsSet;
      class ConvolutionLayerTest : public testing::WithParamInterface<convLayerTestParamsSet>,
                                   virtual public LayerTestsUtils::LayerTestsCommon {
          static std::string getTestCaseName(const testing::TestParamInfo<convLayerTestParamsSet>& obj);
          void SetUp() override;
    • Based on that, define a set of parameters for Template plugin functional test instantiation:

      const std::vector<InferenceEngine::Precision> netPrecisions = {
      /\* ============= 2D Convolution ============= \*/
      const std::vector<std::vector<size_t >> kernels = {{3, 3},
                                                         {3, 5}};
      const std::vector<std::vector<size_t >> strides = {{1, 1},
                                                         {1, 3}};
      const std::vector<std::vector<ptrdiff_t>> padBegins = {{0, 0},
                                                             {0, 3}};
      const std::vector<std::vector<ptrdiff_t>> padEnds = {{0, 0},
                                                           {0, 3}};
      const std::vector<std::vector<size_t >> dilations = {{1, 1},
                                                           {3, 1}};
      const std::vector<size_t> numOutChannels = {1, 5};
      const std::vector<ngraph::op::PadType> padTypes = {
      const auto conv2DParams_ExplicitPadding = ::testing::Combine(
    • Instantiate the test itself using standard GoogleTest macro INSTANTIATE_TEST_SUITE_P :

      INSTANTIATE_TEST_SUITE_P(Convolution2D_ExplicitPadding, ConvolutionLayerTest,
                                       ::testing::Values(std::vector<size_t >({1, 3, 30, 30})),
  3. Sub-graph tests (subgraph_tests sub-folder). This group of tests is designed to tests small patterns or combination of layers. E.g. when a particular topology is being enabled in a plugin e.g. TF ResNet-50, there is no need to add the whole topology to test tests. In opposite way, a particular repetitive subgraph or pattern can be extracted from ResNet-50 and added to the tests. The instantiation of the sub-graph tests is done in the same way as for single layer tests. Note, such sub-graphs or patterns for sub-graph tests should be added to IE::ngraphFunctions library first (this library is a pre-defined set of small ov::Model) and re-used in sub-graph tests after.

  4. HETERO tests (subgraph_tests sub-folder) contains tests for HETERO scenario (manual or automatic affinities settings, tests for QueryNetwork).

  5. Other tests, which contain tests for other scenarios and has the following types of tests:

    • Tests for execution graph

    • Etc.

To use these tests for your own plugin development, link the IE::funcSharedTests library to your test binary and instantiate required test cases with desired parameters values.


A plugin may contain its own tests for use cases that are specific to hardware or need to be extensively tested.

To build test binaries together with other build artifacts, use the make all command. For details, see Build Plugin Using CMake*.

How to Extend Inference Engine Plugin Tests

Inference Engine Plugin tests are open for contribution. Add common test case definitions applicable for all plugins to the IE::funcSharedTests target within the OpenVINO repository. Then, any other plugin supporting corresponding functionality can instantiate the new test.

All Inference Engine per-layer tests check test layers functionality. They are developed using ov::Model. as input graphs used by tests. In this case, to test a new layer with layer tests, extend the IE::ngraphFunctions library, which is also included in the Inference Engine Developer package, with a new model. including the corresponding operation.


When implementing a new subgraph test, add new single-layer tests for each operation of the subgraph if such test does not exist.