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// Copyright (C) 2018 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
//#include <gtest/gtest.h>
//#include "mkldnn_plugin/mkldnn_layers.h"
//
//using namespace std;
//
//class MKLDNNLayersTests : public ::testing::Test {
//protected:
// virtual void TearDown() override{
// }
//
// virtual void SetUp() override{
// }
//
//};
//
//TEST_F(MKLDNNLayersTests, canCreateContext) {
// std::vector<float> sd;
// std::vector<float> dd;
// std::vector<size_t> ds;
// unique_ptr<MKLDNNPlugin::Context> dl ( new MKLDNNPlugin::Context({}, mkldnn::engine(mkldnn::engine::cpu, 0), &sd, &dd, &ds));
//
// ASSERT_NE(nullptr, dynamic_cast<MKLDNNPlugin::Context*>(dl.get()));
//}
//
//TEST_F(MKLDNNLayersTests, canCreateConvLayer) {
// std::vector<float> sd;
// std::vector<float> dd;
// std::vector<size_t> ds;
// InferenceEngine::TBlob<float>::Ptr blobPtr(new InferenceEngine::TBlob<float>());
// unique_ptr<MKLDNNPlugin::Context> ctx ( new MKLDNNPlugin::Context(blobPtr, mkldnn::engine(mkldnn::engine::cpu, 0), &sd, &dd, &ds));
// ASSERT_NE(nullptr, dynamic_cast<MKLDNNPlugin::Context*>(ctx.get()));
//
// InferenceEngine::ConvolutionLayer convLayer({});
// InferenceEngine::DataPtr dPtr(new InferenceEngine::Data("testData"));
// dPtr->dims = {0, 0, 0, 0};
//
// convLayer.insData.push_back(dPtr);
// convLayer.outData.push_back(dPtr);
// unique_ptr<MKLDNNPlugin::Layer> dl ( MKLDNNPlugin::LayerRegistry::CreateLayer(&convLayer, nullptr, dynamic_cast<MKLDNNPlugin::Context*>(ctx.get())));
//
// ASSERT_NE(nullptr, dynamic_cast<MKLDNNPlugin::ConvLayer*>(dl.get()));
//}
//
//TEST_F(MKLDNNLayersTests, canCreateLRNLayer) {
// std::vector<float> sd;
// std::vector<float> dd;
// std::vector<size_t> ds;
// unique_ptr<MKLDNNPlugin::Context> ctx ( new MKLDNNPlugin::Context({}, mkldnn::engine(mkldnn::engine::cpu, 0), &sd, &dd, &ds));
// ASSERT_NE(nullptr, dynamic_cast<MKLDNNPlugin::Context*>(ctx.get()));
//
// InferenceEngine::NormLayer normLayer({});
// InferenceEngine::DataPtr dPtr(new InferenceEngine::Data("testData"));
// dPtr->dims = {1, 1, 27, 27};
//
// normLayer.insData.push_back(dPtr);
// normLayer.outData.push_back(dPtr);
// unique_ptr<MKLDNNPlugin::Layer> dl ( MKLDNNPlugin::LayerRegistry::CreateLayer(&normLayer, nullptr, dynamic_cast<MKLDNNPlugin::Context*>(ctx.get())));
//
// ASSERT_NE(nullptr, dynamic_cast<MKLDNNPlugin::LRNLayer*>(dl.get()));
//}
//
//TEST_F(MKLDNNLayersTests, canCreatePoolingLayer) {
// std::vector<float> sd;
// std::vector<float> dd;
// std::vector<size_t> ds;
// unique_ptr<MKLDNNPlugin::Context> ctx ( new MKLDNNPlugin::Context({}, mkldnn::engine(mkldnn::engine::cpu, 0), &sd, &dd, &ds));
// ASSERT_NE(nullptr, dynamic_cast<MKLDNNPlugin::Context*>(ctx.get()));
//
// InferenceEngine::PoolingLayer poolingLayer({});
// InferenceEngine::DataPtr dPtr(new InferenceEngine::Data("testData"));
// dPtr->dims = {1, 1, 27, 27};
//
// poolingLayer.insData.push_back(dPtr);
// poolingLayer.outData.push_back(dPtr);
// unique_ptr<MKLDNNPlugin::Layer> dl ( MKLDNNPlugin::LayerRegistry::CreateLayer(&poolingLayer, nullptr, dynamic_cast<MKLDNNPlugin::Context*>(ctx.get())));
//
// ASSERT_NE(nullptr, dynamic_cast<MKLDNNPlugin::PoolingLayer*>(dl.get()));
//}
//
//TEST_F(MKLDNNLayersTests, canCreateSplitLayer) {
// std::vector<float> sd;
// std::vector<float> dd;
// std::vector<size_t> ds;
// unique_ptr<MKLDNNPlugin::Context> ctx ( new MKLDNNPlugin::Context({}, mkldnn::engine(mkldnn::engine::cpu, 0), &sd, &dd, &ds));
// ASSERT_NE(nullptr, dynamic_cast<MKLDNNPlugin::Context*>(ctx.get()));
//
// InferenceEngine::SplitLayer splitLayer({});
// unique_ptr<MKLDNNPlugin::Layer> dl ( MKLDNNPlugin::LayerRegistry::CreateLayer(&splitLayer, nullptr, dynamic_cast<MKLDNNPlugin::Context*>(ctx.get())));
//
// ASSERT_NE(nullptr, dynamic_cast<MKLDNNPlugin::SplitLayer*>(dl.get()));
//}
//
//TEST_F(MKLDNNLayersTests, canCreateConcatLayer) {
// std::vector<float> sd;
// std::vector<float> dd;
// std::vector<size_t> ds;
// unique_ptr<MKLDNNPlugin::Context> ctx ( new MKLDNNPlugin::Context({}, mkldnn::engine(mkldnn::engine::cpu, 0), &sd, &dd, &ds));
// ASSERT_NE(nullptr, dynamic_cast<MKLDNNPlugin::Context*>(ctx.get()));
//
// InferenceEngine::ConcatLayer concatLayer({});
// unique_ptr<MKLDNNPlugin::Layer> dl ( MKLDNNPlugin::LayerRegistry::CreateLayer(&concatLayer, nullptr, dynamic_cast<MKLDNNPlugin::Context*>(ctx.get())));
//
// ASSERT_NE(nullptr, dynamic_cast<MKLDNNPlugin::ConcatLayer*>(dl.get()));
//}
//
//TEST_F(MKLDNNLayersTests, canCreateFullyConnectedLayer) {
// std::vector<float> sd;
// std::vector<float> dd;
// std::vector<size_t> ds;
// InferenceEngine::TBlob<float>::Ptr blobPtr(new InferenceEngine::TBlob<float>());
// unique_ptr<MKLDNNPlugin::Context> ctx ( new MKLDNNPlugin::Context(blobPtr, mkldnn::engine(mkldnn::engine::cpu, 0), &sd, &dd, &ds));
// ASSERT_NE(nullptr, dynamic_cast<MKLDNNPlugin::Context*>(ctx.get()));
//
// InferenceEngine::FullyConnectedLayer fcLayer({});
// InferenceEngine::DataPtr dPtr(new InferenceEngine::Data("testData"));
// dPtr->dims = {0, 0, 0, 0};
// InferenceEngine::DataPtr dPtr2(new InferenceEngine::Data("testData2"));
// dPtr2->dims = {0, 0};
//
// fcLayer.insData.push_back(dPtr);
// fcLayer.outData.push_back(dPtr2);
// unique_ptr<MKLDNNPlugin::Layer> dl ( MKLDNNPlugin::LayerRegistry::CreateLayer(&fcLayer, nullptr, dynamic_cast<MKLDNNPlugin::Context*>(ctx.get())));
//
// ASSERT_NE(nullptr, dynamic_cast<MKLDNNPlugin::FullyConnectedLayer*>(dl.get()));
//}
//
//TEST_F(MKLDNNLayersTests, canCreateSoftMaxLayer) {
// std::vector<float> sd;
// std::vector<float> dd;
// std::vector<size_t> ds;
// unique_ptr<MKLDNNPlugin::Context> ctx ( new MKLDNNPlugin::Context({}, mkldnn::engine(mkldnn::engine::cpu, 0), &sd, &dd, &ds));
// ASSERT_NE(nullptr, dynamic_cast<MKLDNNPlugin::Context*>(ctx.get()));
//
// InferenceEngine::SoftMaxLayer softmaxLayer({});
// InferenceEngine::DataPtr dPtr(new InferenceEngine::Data("testData"));
// dPtr->dims = {0, 0, 0, 0};
// InferenceEngine::DataPtr dPtr2(new InferenceEngine::Data("testData2"));
// dPtr2->dims = {0, 0};
//
// softmaxLayer.insData.push_back(dPtr);
// softmaxLayer.outData.push_back(dPtr2);
// unique_ptr<MKLDNNPlugin::Layer> dl ( MKLDNNPlugin::LayerRegistry::CreateLayer(&softmaxLayer, nullptr, dynamic_cast<MKLDNNPlugin::Context*>(ctx.get())));
//
// ASSERT_NE(nullptr, dynamic_cast<MKLDNNPlugin::SoftMaxLayer*>(dl.get()));
//}
//
//TEST_F(MKLDNNLayersTests, canCreateReLULayer) {
// std::vector<float> sd;
// std::vector<float> dd;
// std::vector<size_t> ds;
// unique_ptr<MKLDNNPlugin::Context> ctx ( new MKLDNNPlugin::Context({}, mkldnn::engine(mkldnn::engine::cpu, 0), &sd, &dd, &ds));
// ASSERT_NE(nullptr, dynamic_cast<MKLDNNPlugin::Context*>(ctx.get()));
//
// InferenceEngine::ReLULayer reLULayer({});
// InferenceEngine::DataPtr dPtr(new InferenceEngine::Data("testData"));
// dPtr->dims = {1, 1, 27, 27};
//
// reLULayer.insData.push_back(dPtr);
// reLULayer.outData.push_back(dPtr);
// unique_ptr<MKLDNNPlugin::Layer> dl ( MKLDNNPlugin::LayerRegistry::CreateLayer(&reLULayer, nullptr, dynamic_cast<MKLDNNPlugin::Context*>(ctx.get())));
//
// ASSERT_NE(nullptr, dynamic_cast<MKLDNNPlugin::ReLULayer*>(dl.get()));
//}
//
//TEST_F(MKLDNNLayersTests, canNotCreateCNNLayer) {
// std::vector<float> sd;
// std::vector<float> dd;
// std::vector<size_t> ds;
// unique_ptr<MKLDNNPlugin::Context> ctx ( new MKLDNNPlugin::Context({}, mkldnn::engine(mkldnn::engine::cpu, 0), &sd, &dd, &ds));
// ASSERT_NE(nullptr, dynamic_cast<MKLDNNPlugin::Context*>(ctx.get()));
//
// InferenceEngine::CNNLayer cnnLayer({});
// EXPECT_THROW(MKLDNNPlugin::LayerRegistry::CreateLayer(&cnnLayer, nullptr, dynamic_cast<MKLDNNPlugin::Context*>(ctx.get())) , InferenceEngine::details::InferenceEngineException);
//}
//
//TEST_F(MKLDNNLayersTests, canNotCreateLayerWithoutContext) {
// InferenceEngine::ConvolutionLayer convLayer({});
// EXPECT_THROW(MKLDNNPlugin::LayerRegistry::CreateLayer(&convLayer, nullptr, nullptr), InferenceEngine::details::InferenceEngineException);
//}
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