Irina Efode
c9e7e60a84
* Init * try adopt softmax * fix size of expected * Fix cpuFuncTests * Add configure model * Remove extra * fix softmax * Adopt eltwise * Eltwise * Remove old dynamism * fix oss build * Remove In,OutPrecision * Remove incorrect OPENVINO_ASSERT, correct eltwise tests and fix threshold * Apply Maxim's comments * Fix build * ieFuncTests * return in, out, prc * fix tests * Fix tests * Fixes according to new api * skip gpu * try to fix myriad * Fix myriad * cpu fix
201 lines
9.4 KiB
C++
201 lines
9.4 KiB
C++
//// Copyright (C) 2021 Intel Corporation
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//// SPDX-License-Identifier: Apache-2.0
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////
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//
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//#include "test_utils/cpu_test_utils.hpp"
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//
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//#include "ngraph_functions/builders.hpp"
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//#include "ngraph_functions/utils/ngraph_helpers.hpp"
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//
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//using namespace InferenceEngine;
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//using namespace CPUTestUtils;
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//
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//namespace CPULayerTestsDefinitions {
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//typedef std::tuple<
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// std::pair<std::vector<ngraph::PartialShape>, std::vector<std::vector<ngraph::Shape>>>, // input shape
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// std::tuple<float, float, float>, // start, limit, delta
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// Precision // output type
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//> RangeSpecificParams;
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//
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//typedef std::tuple<
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// RangeSpecificParams,
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// InferenceEngine::Precision, // Net precision
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// LayerTestsUtils::TargetDevice // Device name
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//> RangeLayerTestParams;
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//
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//typedef std::tuple<
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// CPULayerTestsDefinitions::RangeLayerTestParams,
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// CPUSpecificParams> RangeLayerCPUTestParamsSet;
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//
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//class RangeLayerCPUTest : public testing::WithParamInterface<RangeLayerCPUTestParamsSet>,
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// virtual public LayerTestsUtils::LayerTestsCommon, public CPUTestsBase {
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// float start = 0;
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// float stop = 0;
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// float step = 0;
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//public:
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// static std::string getTestCaseName(testing::TestParamInfo<RangeLayerCPUTestParamsSet> obj) {
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// CPULayerTestsDefinitions::RangeLayerTestParams basicParamsSet;
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// CPUSpecificParams cpuParams;
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// std::tie(basicParamsSet, cpuParams) = obj.param;
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// std::string td;
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// Precision netPrc = Precision::FP32;
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// std::pair<std::vector<ngraph::PartialShape>, std::vector<std::vector<ngraph::Shape>>> shapes;
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//
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// RangeSpecificParams rangePar;
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// std::tie(rangePar, netPrc, td) = basicParamsSet;
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// std::tuple<float, float, float> rangeInputs;
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// InferenceEngine::Precision outPrc = Precision::FP32;
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// std::tie(shapes, rangeInputs, outPrc) = rangePar;
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// float start = std::get<0>(rangeInputs);
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// float stop = std::get<1>(rangeInputs);
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// float step = std::get<2>(rangeInputs);
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//
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// std::ostringstream result;
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// result << "RangeTest_" << std::to_string(obj.index) << "_";
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// result << "NetPr_" << netPrc.name() << "_";
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// result << "OutPr_" << outPrc.name() << "_";
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// result << "Start_" << start << "_";
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// result << "Stop_" << stop << "_";
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// result << "Step_" << step << "_";
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// result << CPUTestsBase::getTestCaseName(cpuParams);
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// result << CommonTestUtils::vec2str(shapes.second[0]) << "_";
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// return result.str();
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// }
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//protected:
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// void GenerateInputs() override {
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// // for correct work of fill_data_random() method
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// size_t blobFillingRange = (inPrc == Precision::FP32 ? 0 : 1);
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// inputs.clear();
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// const auto& inputsInfo = executableNetwork.GetInputsInfo();
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// const auto& functionParams = function->get_parameters();
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// for (int i = 0; i < functionParams.size(); ++i) {
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// const float scalarVal = (i == 0 ? start : (i == 1 ? stop : step));
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// const auto& param = functionParams[i];
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// const auto infoIt = inputsInfo.find(param->get_friendly_name());
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// GTEST_ASSERT_NE(infoIt, inputsInfo.cend());
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// InferenceEngine::InputInfo::CPtr info = infoIt->second;
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// InferenceEngine::Blob::Ptr blob = nullptr;
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// if (!inputDynamicShapes.empty()) {
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// if (inputDynamicShapes[i].rank() != 0) {
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// InferenceEngine::DataPtr dataNew(
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// new InferenceEngine::Data(infoIt->first, info->getTensorDesc().getPrecision(),
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// targetStaticShapes[index][i],
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// info->getTensorDesc().getLayout()));
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// InferenceEngine::InputInfo infoNew;
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// infoNew.setInputData(dataNew);
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// blob = FuncTestUtils::createAndFillBlob(infoNew.getTensorDesc(), blobFillingRange, scalarVal);
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// }
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// }
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// if (blob == nullptr) {
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// blob = FuncTestUtils::createAndFillBlob((*info).getTensorDesc(), blobFillingRange, scalarVal);
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// }
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// inputs.push_back(blob);
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// }
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// }
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//
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// void SetUp() override {
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// CPULayerTestsDefinitions::RangeLayerTestParams basicParamsSet;
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// CPUSpecificParams cpuParams;
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// std::tie(basicParamsSet, cpuParams) = this->GetParam();
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// std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
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// CPULayerTestsDefinitions::RangeSpecificParams rangeParams;
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// std::pair<std::vector<ngraph::PartialShape>, std::vector<std::vector<ngraph::Shape>>> shapes;
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// std::tie(rangeParams, inPrc, targetDevice) = basicParamsSet;
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// std::tuple<float, float, float> rangeInputs;
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//
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// std::tie(shapes, rangeInputs, outPrc) = rangeParams;
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// targetStaticShapes = shapes.second;
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// inputDynamicShapes = shapes.first;
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//
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// start = std::get<0>(rangeInputs);
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// stop = std::get<1>(rangeInputs);
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// step = std::get<2>(rangeInputs);
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// auto ngOutPr = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(outPrc);
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// auto ngNetPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(inPrc);
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// auto startPar = std::make_shared<ngraph::opset5::Parameter>(ngNetPrc, ngraph::Shape{});
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// auto stopPar = std::make_shared<ngraph::opset5::Parameter>(ngNetPrc, ngraph::Shape{});
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// auto stepPar = std::make_shared<ngraph::opset5::Parameter>(ngNetPrc, ngraph::Shape{});
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// auto range = std::make_shared<ngraph::opset4::Range>(startPar, stopPar, stepPar, ngOutPr);
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// range->get_rt_info() = getCPUInfo();
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// selectedType = std::string("ref_any_") + (inPrc == outPrc ? inPrc.name() : "FP32");
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// startPar->set_friendly_name("start");
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// stopPar->set_friendly_name("stop");
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// stepPar->set_friendly_name("step");
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//
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// const ngraph::ResultVector results{std::make_shared<ngraph::opset3::Result>(range)};
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// function = std::make_shared<ngraph::Function>(results, ngraph::ParameterVector {
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// startPar, stopPar, stepPar}, "Range");
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// functionRefs = ngraph::clone_function(*function);
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// }
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//};
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//
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//TEST_P(RangeLayerCPUTest, CompareWithRefs) {
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// SKIP_IF_CURRENT_TEST_IS_DISABLED()
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// Run();
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// CheckPluginRelatedResults(executableNetwork, "Range");
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//}
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//
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//namespace {
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//
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///* CPU PARAMS */
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//std::vector<CPUSpecificParams> filterCPUInfoForDevice() {
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// return std::vector<CPUSpecificParams> {CPUSpecificParams{{}, {x}, {}, {}}};
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//}
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//
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//const std::vector<InferenceEngine::Precision> netPrecisions = {
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// InferenceEngine::Precision::FP32,
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// InferenceEngine::Precision::I32
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//};
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//const std::vector<InferenceEngine::Precision> outputType = {
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// InferenceEngine::Precision::FP32,
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// InferenceEngine::Precision::I32
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//};
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//
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//std::vector<std::pair<std::vector<ngraph::PartialShape>, std::vector<std::vector<ngraph::Shape>>>> inShapesDynamic = {
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// {{ngraph::PartialShape(), ngraph::PartialShape(), ngraph::PartialShape()},
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// {{ngraph::Shape{}, ngraph::Shape{}, ngraph::Shape{}}, {ngraph::Shape{}, ngraph::Shape{}, ngraph::Shape{}}}}
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//};
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//std::vector<std::pair<std::vector<ngraph::PartialShape>, std::vector<std::vector<ngraph::Shape>>>> inShapesPseudoStatic = {
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// {{}, {{ngraph::Shape{}, ngraph::Shape{}, ngraph::Shape{}}}}
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//};
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//
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//const std::vector<std::tuple<float, float, float>> rangeInputValues = {
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// std::tuple<float, float, float> {1.0, -5.0, -1.0},
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// std::tuple<float, float, float> {1.0, 10.0, 1.2},
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// std::tuple<float, float, float> {1.1, 12.2, 1.1},
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// std::tuple<float, float, float> {1.1, -5.1, -1.1},
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// std::tuple<float, float, float> {1.0, 5.0, 2.0},
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// std::tuple<float, float, float> {10.0, 6.0, -3.0},
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// std::tuple<float, float, float> {5, 35, 5}
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//};
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//const auto rangeParDynamic = ::testing::Combine(
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// ::testing::ValuesIn(inShapesDynamic),
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// ::testing::ValuesIn(rangeInputValues),
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// ::testing::ValuesIn(outputType)
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//);
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//const auto rangeParStatic = ::testing::Combine(
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// ::testing::ValuesIn(inShapesPseudoStatic),
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// ::testing::ValuesIn(rangeInputValues),
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// ::testing::ValuesIn(outputType)
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//);
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//const auto params3dDynamic = ::testing::Combine(
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// ::testing::Combine(
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// rangeParDynamic,
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// ::testing::ValuesIn(netPrecisions),
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// ::testing::Values(CommonTestUtils::DEVICE_CPU)),
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// ::testing::ValuesIn(filterCPUInfoForDevice()));
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//const auto params3dPseudoStatic = ::testing::Combine(
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// ::testing::Combine(
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// rangeParStatic,
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// ::testing::ValuesIn(netPrecisions),
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// ::testing::Values(CommonTestUtils::DEVICE_CPU)),
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// ::testing::ValuesIn(filterCPUInfoForDevice()));
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//// We don't check static case, because of constant folding, but we can use static shape for test infrastructure,
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//// however Range node will be dynamic, since inputs are parameters, not a constants
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//INSTANTIATE_TEST_SUITE_P(smoke_RangePseudoStaticLayoutTest, RangeLayerCPUTest,
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// params3dPseudoStatic, RangeLayerCPUTest::getTestCaseName);
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//INSTANTIATE_TEST_SUITE_P(smoke_RangeDynamicLayoutTest, RangeLayerCPUTest,
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// params3dDynamic, RangeLayerCPUTest::getTestCaseName);
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//} // namespace
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//} // namespace CPULayerTestsDefinitions
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