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[PP GAPI] Extended plug-ins shared precision conversion tests to use (#2677)

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`GetBlob()` as well

- test were extended to cover case when input tensors are copied into
Blob return by `InferRequest::GetBlob`
- channel number of input tensor is made a test parameter
This commit is contained in:
Anton Potapov 2020-10-19 12:35:59 +03:00 committed by GitHub
parent e1428ecf1d
commit 8715b60d88
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 60 additions and 5 deletions
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13
docs/template_plugin/tests/functional/shared_tests_instances/behavior/preprocessing.cpp
View File

@ -19,9 +19,20 @@ const std::vector<std::map<std::string, std::string>> configs = {
{}
};
INSTANTIATE_TEST_CASE_P(PreprocessingPrecisionConvertTests, PreprocessingPrecisionConvertTest,
INSTANTIATE_TEST_CASE_P(PreprocessingPrecisionConvertTestsViaSetInput, PreprocessingPrecisionConvertTest,
::testing::Combine(
::testing::ValuesIn(inputPrecisions),
::testing::Values(1, 2, 3, 4, 5), // Number of input tensor channels
::testing::Values(true), // Use SetInput
::testing::Values("TEMPLATE"),
::testing::ValuesIn(configs)),
PreprocessingPrecisionConvertTest::getTestCaseName);
INSTANTIATE_TEST_CASE_P(PreprocessingPrecisionConvertTestsViaGetBlob, PreprocessingPrecisionConvertTest,
::testing::Combine(
::testing::ValuesIn(inputPrecisions),
::testing::Values(4, 5), // Number of input tensor channels (blob_copy only supports 4d and 5d tensors)
::testing::Values(false), // use GetBlob
::testing::Values("TEMPLATE"),
::testing::ValuesIn(configs)),
PreprocessingPrecisionConvertTest::getTestCaseName);

12
inference-engine/tests/functional/plugin/cpu/shared_tests_instances/behavior/preprocessing.cpp
View File

@ -19,11 +19,21 @@ const std::vector<std::map<std::string, std::string>> configs = {
{}
};
INSTANTIATE_TEST_CASE_P(smoke_BehaviourPreprocessingTests, PreprocessingPrecisionConvertTest,
INSTANTIATE_TEST_CASE_P(BehaviourPreprocessingTestsViaSetInput, PreprocessingPrecisionConvertTest,
::testing::Combine(
::testing::ValuesIn(inputPrecisions),
::testing::Values(1, 2, 3, 4, 5), // Number of input tensor channels
::testing::Values(true), // Use SetInput
::testing::Values(CommonTestUtils::DEVICE_CPU),
::testing::ValuesIn(configs)),
PreprocessingPrecisionConvertTest::getTestCaseName);
INSTANTIATE_TEST_CASE_P(BehaviourPreprocessingTestsViaGetBlob, PreprocessingPrecisionConvertTest,
::testing::Combine(
::testing::ValuesIn(inputPrecisions),
::testing::Values(4, 5), // Number of input tensor channels (blob_copy only supports 4d and 5d tensors)
::testing::Values(false), // use GetBlob
::testing::Values(CommonTestUtils::DEVICE_CPU),
::testing::ValuesIn(configs)),
PreprocessingPrecisionConvertTest::getTestCaseName);
} // namespace

40
inference-engine/tests/functional/plugin/shared/include/behavior/preprocessing.hpp
View File

@ -27,6 +27,8 @@ namespace BehaviorTestsDefinitions {
using PreprocessingPrecisionConvertParams = std::tuple<
InferenceEngine::Precision, // Input precision
unsigned, // channels number
bool, // Use normal (i.e. SetInput() or unusal i.e. GetBlob()) inut method
std::string, // Device name
std::map<std::string, std::string> // Config
>;
@ -37,11 +39,15 @@ struct PreprocessingPrecisionConvertTest :
public:
static std::string getTestCaseName(testing::TestParamInfo<PreprocessingPrecisionConvertParams> obj) {
InferenceEngine::Precision inPrc;
bool useSetInput;
unsigned channels;
std::string targetDevice;
std::map<std::string, std::string> configuration;
std::tie(inPrc, targetDevice, configuration) = obj.param;
std::tie(inPrc, channels, useSetInput, targetDevice, configuration) = obj.param;
std::ostringstream result;
result << "inPRC=" << inPrc.name() << "_";
result << channels << "Ch" << "_";
result << (useSetInput ? "SetInput" : "GetBlob") << "_";
result << "targetDevice=" << targetDevice;
if (!configuration.empty()) {
for (auto& configItem : configuration) {
@ -51,6 +57,32 @@ public:
return result.str();
}
// Need to override Infer() due to usage of GetBlob() as input method.
// Mostly a copy of LayerTestsCommon::Infer()
void Infer() override {
inferRequest = executableNetwork.CreateInferRequest();
inputs.clear();
for (const auto &input : executableNetwork.GetInputsInfo()) {
const auto &info = input.second;
auto blob = GenerateInput(*info);
if (!use_set_input) {
InferenceEngine::Blob::Ptr input = inferRequest.GetBlob(info->name());
blob_copy(blob, input);
} else {
inferRequest.SetBlob(info->name(), blob);
}
inputs.push_back(blob);
}
if (configuration.count(InferenceEngine::PluginConfigParams::KEY_DYN_BATCH_ENABLED) &&
configuration.count(InferenceEngine::PluginConfigParams::YES)) {
auto batchSize = executableNetwork.GetInputsInfo().begin()->second->getTensorDesc().getDims()[0] / 2;
inferRequest.SetBatch(batchSize);
}
inferRequest.Infer();
}
void SetUp() override {
// This test:
// - Strive to test the plugin internal preprocessing (precision conversion) only.
@ -60,11 +92,11 @@ public:
SetRefMode(LayerTestsUtils::RefMode::INTERPRETER);
std::tie(inPrc, targetDevice, configuration) = this->GetParam();
std::tie(inPrc, channels, use_set_input, targetDevice, configuration) = this->GetParam();
bool specialZero = true;
std::vector<size_t> inputShape {4, 4};
std::vector<size_t> inputShape(channels, 4);
auto make_ngraph = [&](bool with_extra_conv) {
auto in_prec = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(with_extra_conv ? inPrc : decltype(inPrc)(InferenceEngine::Precision::FP32));
@ -95,6 +127,8 @@ public:
public:
std::shared_ptr<InferenceEngine::Core> ie = PluginCache::get().ie();
std::shared_ptr<ngraph::Function> reference_function;
bool use_set_input = true;
unsigned channels = 0;
};