diff options
Diffstat (limited to 'src/armnn/test/OptimizerTests.cpp')
| -rw-r--r-- | src/armnn/test/OptimizerTests.cpp | 558 |
1 files changed, 0 insertions, 558 deletions
diff --git a/src/armnn/test/OptimizerTests.cpp b/src/armnn/test/OptimizerTests.cpp index b06403c8d6..c0ad9c8927 100644 --- a/src/armnn/test/OptimizerTests.cpp +++ b/src/armnn/test/OptimizerTests.cpp @@ -17,45 +17,6 @@ using namespace armnn; namespace { -template <typename LayerT> -bool IsLayerOfType(const armnn::Layer* const layer) -{ - return (layer->GetType() == armnn::LayerEnumOf<LayerT>()); -} - -bool CheckSequence(const armnn::Graph::ConstIterator first, const armnn::Graph::ConstIterator last) -{ - return (first == last); -} - -/// Checks each unary function in Us evaluates true for each correspondent layer in the sequence [first, last). -template <typename U, typename... Us> -bool CheckSequence(const armnn::Graph::ConstIterator first, - const armnn::Graph::ConstIterator last, - U&& u, - Us&&... us) -{ - return u(*first) && CheckSequence(std::next(first), last, us...); -} - -template <typename LayerT> -bool CheckRelatedLayers(armnn::Graph& graph, const std::list<std::string>& testRelatedLayers) -{ - for (auto& layer : graph) - { - if (layer->GetType() == armnn::LayerEnumOf<LayerT>()) - { - auto& relatedLayers = layer->GetRelatedLayerNames(); - if(!std::equal(relatedLayers.begin(), relatedLayers.end(), - testRelatedLayers.begin(), testRelatedLayers.end())) - { - return false; - } - } - } - - return true; -} void CreateLSTMLayerHelper(Graph &graph, bool CifgEnabled) { @@ -168,38 +129,6 @@ void CreateLSTMLayerHelper(Graph &graph, bool CifgEnabled) BOOST_AUTO_TEST_SUITE(Optimizer) using namespace armnn::optimizations; -BOOST_AUTO_TEST_CASE(OptimizeInversePermutesTest) -{ - armnn::Graph graph; - - auto output = graph.AddLayer<armnn::OutputLayer>(0, "output"); - - graph.InsertNewLayer<armnn::InputLayer>(output->GetInputSlot(0), 0, "input"); - - // Inserts two permutes, one the inverse of the other. - graph.InsertNewLayer<armnn::PermuteLayer>(output->GetInputSlot(0), - armnn::PermuteDescriptor({0, 2, 3, 1}), - "perm0231"); - graph.InsertNewLayer<armnn::PermuteLayer>(output->GetInputSlot(0), - armnn::PermuteDescriptor({0, 3, 1, 2}), - "perm0312"); - - BOOST_TEST(CheckSequence(graph.cbegin(), - graph.cend(), - &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::PermuteLayer>, - &IsLayerOfType<armnn::PermuteLayer>, - &IsLayerOfType<armnn::OutputLayer>)); - - armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(OptimizeInversePermutes())); - - // The permutes are removed. - BOOST_TEST(CheckSequence(graph.cbegin(), - graph.cend(), - &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::OutputLayer>)); -} - BOOST_AUTO_TEST_CASE(LSTMValidateTensorShapesFromInputsCIFGDisabledTest) { Graph graph; @@ -222,421 +151,6 @@ BOOST_AUTO_TEST_CASE(LSTMValidateTensorShapesFromInputsCIFGEnabledTest) BOOST_CHECK_NO_THROW(graph.InferTensorInfos()); } -BOOST_AUTO_TEST_CASE(MovePermuteUpTest) -{ - const armnn::TensorInfo info({ 1, 5, 2, 3 }, armnn::DataType::Float32); - const armnn::TensorInfo permuted({ 1, 3, 5, 2 }, armnn::DataType::Float32); - - armnn::Graph graph; - - armnn::LayerBindingId inputId = 0; - - armnn::Layer* head = graph.AddLayer<armnn::OutputLayer>(0, "output"); - - std::string permuteLayerName = "original_permute"; - - // Insert permute - head = graph.InsertNewLayer<armnn::PermuteLayer>(head->GetInputSlot(0), - armnn::PermuteDescriptor({ 0, 2, 3, 1 }), - permuteLayerName.c_str()); - - head->GetOutputHandler().SetTensorInfo(permuted); - - // Inserts layers that don't care about data format. - head = graph.InsertNewLayer<armnn::ActivationLayer>(head->GetInputSlot(0), - armnn::ActivationDescriptor{}, ""); - head->GetOutputHandler().SetTensorInfo(info); - - head = graph.InsertNewLayer<armnn::AdditionLayer>(head->GetInputSlot(0), ""); - head->GetOutputHandler().SetTensorInfo(info); - - // Inserts input for 2nd input of Addition. - graph.InsertNewLayer<armnn::InputLayer>(head->GetInputSlot(1), inputId++, "") - ->GetOutputHandler().SetTensorInfo(info); - - head = graph.InsertNewLayer<armnn::FakeQuantizationLayer>(head->GetInputSlot(0), - armnn::FakeQuantizationDescriptor{}, ""); - head->GetOutputHandler().SetTensorInfo(info); - - head = graph.InsertNewLayer<armnn::FloorLayer>(head->GetInputSlot(0), ""); - head->GetOutputHandler().SetTensorInfo(info); - - head = graph.InsertNewLayer<armnn::MemCopyLayer>(head->GetInputSlot(0), ""); - head->GetOutputHandler().SetTensorInfo(info); - - head = graph.InsertNewLayer<armnn::MultiplicationLayer>(head->GetInputSlot(0), ""); - head->GetOutputHandler().SetTensorInfo(info); - - // Inserts input for 2nd input of Multiplication. - graph.InsertNewLayer<armnn::InputLayer>(head->GetInputSlot(1), inputId++, "") - ->GetOutputHandler().SetTensorInfo(info); - - // Inserts input. - graph.InsertNewLayer<armnn::InputLayer>(head->GetInputSlot(0), inputId++, "") - ->GetOutputHandler().SetTensorInfo(info); - - BOOST_TEST(CheckSequence(graph.cbegin(), - graph.cend(), - &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::MultiplicationLayer>, - &IsLayerOfType<armnn::MemCopyLayer>, - &IsLayerOfType<armnn::FloorLayer>, - &IsLayerOfType<armnn::FakeQuantizationLayer>, - &IsLayerOfType<armnn::AdditionLayer>, - &IsLayerOfType<armnn::ActivationLayer>, - &IsLayerOfType<armnn::PermuteLayer>, - &IsLayerOfType<armnn::OutputLayer>)); - - armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(MovePermuteUp())); - - // The permute is moved to the top. New permutes for layers with multiple inputs. - BOOST_TEST(CheckSequence(graph.cbegin(), - graph.cend(), - &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::PermuteLayer>, - &IsLayerOfType<armnn::PermuteLayer>, - &IsLayerOfType<armnn::PermuteLayer>, - &IsLayerOfType<armnn::MultiplicationLayer>, - &IsLayerOfType<armnn::MemCopyLayer>, - &IsLayerOfType<armnn::FloorLayer>, - &IsLayerOfType<armnn::FakeQuantizationLayer>, - &IsLayerOfType<armnn::AdditionLayer>, - &IsLayerOfType<armnn::ActivationLayer>, - &IsLayerOfType<armnn::OutputLayer>)); - - std::list<std::string> testRelatedLayers = { permuteLayerName }; - - BOOST_TEST(CheckRelatedLayers<armnn::PermuteLayer>(graph, testRelatedLayers)); -} - -BOOST_AUTO_TEST_CASE(PermuteAsReshapeTest) -{ - armnn::Graph graph; - - std::string permuteLayerName = "permute"; - - const armnn::TensorInfo infoIn({ 1, 2, 3, 1 }, armnn::DataType::Float32); - const armnn::TensorInfo infoOut({ 1, 1, 2, 3 }, armnn::DataType::Float32); - - auto output = graph.AddLayer<armnn::OutputLayer>(0, "output"); - - graph.InsertNewLayer<armnn::InputLayer>(output->GetInputSlot(0), 0, "input") - ->GetOutputHandler().SetTensorInfo(infoIn); - - // Inserts permute. - graph.InsertNewLayer<armnn::PermuteLayer>(output->GetInputSlot(0), - armnn::PermuteDescriptor({ 0, 2, 3, 1 }), permuteLayerName.c_str()) - ->GetOutputHandler().SetTensorInfo(infoOut); - - BOOST_TEST(CheckSequence(graph.cbegin(), - graph.cend(), - &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::PermuteLayer>, - &IsLayerOfType<armnn::OutputLayer>)); - - armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(PermuteAsReshape())); - - // The permute is replaced by an equivalent reshape. - - auto checkReshape = [&infoOut](const armnn::Layer* const layer) -> bool - { - const auto reshapeLayer = static_cast<const armnn::ReshapeLayer*>(layer); - return IsLayerOfType<armnn::ReshapeLayer>(layer) && - (reshapeLayer->GetParameters().m_TargetShape == infoOut.GetShape()) && - (reshapeLayer->GetOutputHandler().GetTensorInfo().GetShape() == infoOut.GetShape()); - }; - - BOOST_TEST(CheckSequence(graph.cbegin(), - graph.cend(), - &IsLayerOfType<armnn::InputLayer>, - checkReshape, - &IsLayerOfType<armnn::OutputLayer>)); - - - std::list<std::string> testRelatedLayers = { permuteLayerName }; - BOOST_TEST(CheckRelatedLayers<armnn::ReshapeLayer>(graph, testRelatedLayers)); -} - -BOOST_AUTO_TEST_CASE(OptimizeConsecutiveReshapesTest) -{ - armnn::Graph graph; - - const armnn::TensorInfo info0({ 1, 2, 3, 5 }, armnn::DataType::Float32); - - auto output = graph.AddLayer<armnn::OutputLayer>(0, "output"); - auto input = graph.InsertNewLayer<armnn::InputLayer>(output->GetInputSlot(0), 0, "input"); - - input->GetOutputHandler().SetTensorInfo(info0); - - { - // Inserts two reshapes. - const armnn::TensorInfo info1({1, 30, 1, 1}, armnn::DataType::Float32); - const armnn::TensorInfo info2({1, 2, 1, 15}, armnn::DataType::Float32); - - std::string reshape1Name = "reshape1"; - std::string reshape2Name = "reshape2"; - - auto reshape1 = graph.InsertNewLayer<armnn::ReshapeLayer>(output->GetInputSlot(0), - armnn::ReshapeDescriptor{ info1.GetShape() }, - reshape1Name.c_str()); - auto reshape2 = graph.InsertNewLayer<armnn::ReshapeLayer>(output->GetInputSlot(0), - armnn::ReshapeDescriptor{ info2.GetShape() }, - reshape2Name.c_str()); - - reshape1->GetOutputHandler().SetTensorInfo(info1); - reshape2->GetOutputHandler().SetTensorInfo(info2); - - BOOST_TEST(CheckSequence(graph.cbegin(), - graph.cend(), - &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::ReshapeLayer>, - &IsLayerOfType<armnn::ReshapeLayer>, - &IsLayerOfType<armnn::OutputLayer>)); - - armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(OptimizeConsecutiveReshapes())); - - auto checkReshape = [&info2](const armnn::Layer* const layer) -> bool - { - const auto reshapeLayer = static_cast<const armnn::ReshapeLayer*>(layer); - return IsLayerOfType<armnn::ReshapeLayer>(layer) && - (reshapeLayer->GetParameters().m_TargetShape == info2.GetShape()) && - (reshapeLayer->GetOutputHandler().GetTensorInfo().GetShape() == info2.GetShape()); - }; - - // The two reshapes are replaced by a single equivalent reshape. - BOOST_TEST(CheckSequence(graph.cbegin(), - graph.cend(), - &IsLayerOfType<armnn::InputLayer>, - checkReshape, - &IsLayerOfType<armnn::OutputLayer>)); - - // Check the new reshape layer has the other two reshapes as related layers - std::list<std::string> testRelatedLayers = { reshape2Name, reshape1Name }; - - BOOST_TEST(CheckRelatedLayers<armnn::ReshapeLayer>(graph, testRelatedLayers)); - } - - { - // Inserts a reshape to the input shape. - auto reshapeToIn = graph.InsertNewLayer<armnn::ReshapeLayer>(output->GetInputSlot(0), - armnn::ReshapeDescriptor{ info0.GetShape() }, - "reshapeToIn"); - - reshapeToIn->GetOutputHandler().SetTensorInfo(info0); - - armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(OptimizeConsecutiveReshapes())); - - // The two reshapes are removed. - BOOST_TEST(CheckSequence(graph.cbegin(), - graph.cend(), - &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::OutputLayer>)); - } -} - -BOOST_AUTO_TEST_CASE(SquashEqualSiblingsTest) -{ - armnn::Graph graph; - - armnn::LayerBindingId outputId = 0; - - const armnn::TensorInfo info({ 1, 2, 3, 5 }, armnn::DataType::Float32); - const armnn::TensorInfo permuted({ 1, 5, 2, 3 }, armnn::DataType::Float32); - - auto input = graph.AddLayer<armnn::InputLayer>(0, "input"); - input->GetOutputSlot().SetTensorInfo(info); - - // Inserts equal permutes, equal reshapes and something else. - const armnn::PermuteDescriptor permDesc({ 0, 2, 3, 1 }); - const armnn::ReshapeDescriptor reshapeDesc{ { 1, 3, 1, 5 } }; - - armnn::Layer* layer; - - layer = graph.AddLayer<armnn::PermuteLayer>(permDesc, ""); - layer->GetOutputSlot().SetTensorInfo(permuted); - layer->GetOutputSlot().Connect(graph.AddLayer<armnn::OutputLayer>(outputId++, "")->GetInputSlot(0)); - input->GetOutputSlot().Connect(layer->GetInputSlot(0)); - - layer = graph.AddLayer<armnn::ReshapeLayer>(reshapeDesc, ""); - layer->GetOutputSlot().Connect(graph.AddLayer<armnn::OutputLayer>(outputId++, "")->GetInputSlot(0)); - input->GetOutputSlot().Connect(layer->GetInputSlot(0)); - - layer = graph.AddLayer<armnn::FloorLayer>(""); - layer->GetOutputSlot().Connect(graph.AddLayer<armnn::OutputLayer>(outputId++, "")->GetInputSlot(0)); - input->GetOutputSlot().Connect(layer->GetInputSlot(0)); - - layer = graph.AddLayer<armnn::ReshapeLayer>(reshapeDesc, ""); - layer->GetOutputSlot().Connect(graph.AddLayer<armnn::OutputLayer>(outputId++, "")->GetInputSlot(0)); - input->GetOutputSlot().Connect(layer->GetInputSlot(0)); - - layer = graph.AddLayer<armnn::PermuteLayer>(permDesc, ""); - layer->GetOutputSlot().SetTensorInfo(permuted); - layer->GetOutputSlot().Connect(graph.AddLayer<armnn::OutputLayer>(outputId++, "")->GetInputSlot(0)); - input->GetOutputSlot().Connect(layer->GetInputSlot(0)); - - BOOST_TEST(CheckSequence(graph.cbegin(), - graph.cend(), - &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::PermuteLayer>, - &IsLayerOfType<armnn::ReshapeLayer>, - &IsLayerOfType<armnn::FloorLayer>, - &IsLayerOfType<armnn::ReshapeLayer>, - &IsLayerOfType<armnn::PermuteLayer>, - &IsLayerOfType<armnn::OutputLayer>, - &IsLayerOfType<armnn::OutputLayer>, - &IsLayerOfType<armnn::OutputLayer>, - &IsLayerOfType<armnn::OutputLayer>, - &IsLayerOfType<armnn::OutputLayer>)); - - armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(SquashEqualPermuteSiblings(), - SquashEqualReshapeSiblings())); - - // The permutes and reshapes are squashed. - - BOOST_TEST(CheckSequence(graph.cbegin(), - graph.cend(), - &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::PermuteLayer>, - &IsLayerOfType<armnn::ReshapeLayer>, - &IsLayerOfType<armnn::FloorLayer>, - &IsLayerOfType<armnn::OutputLayer>, - &IsLayerOfType<armnn::OutputLayer>, - &IsLayerOfType<armnn::OutputLayer>, - &IsLayerOfType<armnn::OutputLayer>, - &IsLayerOfType<armnn::OutputLayer>)); -} - -BOOST_AUTO_TEST_CASE(ConvertConstantsHalfToFloatTest) -{ - armnn::Graph graph; - - const armnn::TensorInfo info({ 1,1,1,2 }, armnn::DataType::Float32); - - // Create the half precision input data - unsigned int dims[] = { 4,1,1,1 }; - std::vector<float> convWeightsData{1.f, 2.f, 3.f, 4.f}; - std::vector<uint16_t> halfWeights(4); - armnnUtils::FloatingPointConverter::ConvertFloat32To16(convWeightsData.data(), - convWeightsData.size(), - halfWeights.data()); - armnn::ConstTensor weights(armnn::TensorInfo(4, dims, armnn::DataType::Float16), halfWeights); - - //Create the simple test network - auto input = graph.AddLayer<armnn::InputLayer>(0, "input"); - input->GetOutputSlot().SetTensorInfo(info); - - auto fc = graph.AddLayer<armnn::FullyConnectedLayer>(armnn::FullyConnectedDescriptor(), "fc"); - fc->m_Weight = std::make_unique<armnn::ScopedCpuTensorHandle>(weights); - fc->GetOutputSlot().SetTensorInfo(info); - - auto output = graph.AddLayer<armnn::OutputLayer>(1, "output"); - - //Connect up the layers - input->GetOutputSlot().Connect(fc->GetInputSlot(0)); - fc->GetOutputSlot().Connect(output->GetInputSlot(0)); - - //Test the tensor info is correct. - BOOST_CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::Float16); - - // Run the optimizer - armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(ConvertConstantsHalfToFloat())); - - //Test the tensor info is correct. - BOOST_CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::Float32); - - // Now test the data matches float32 data - float* data = fc->m_Weight->GetTensor<float>(); - BOOST_CHECK(1.0f == data[0]); - BOOST_CHECK(2.0f == data[1]); - BOOST_CHECK(3.0f == data[2]); - BOOST_CHECK(4.0f == data[3]); -} - -BOOST_AUTO_TEST_CASE(ConvertConstantsFloatToHalfTest) -{ - armnn::Graph graph; - - const armnn::TensorInfo info({ 1, 1, 1, 2 }, armnn::DataType::Float16); - - // Create const tensor from fp32 data - unsigned int dims[] = { 4, 1, 1, 1 }; - std::vector<float> floatWeights{ 1.0f, 2.0f, 3.0f, 4.0f }; - armnn::ConstTensor weights(armnn::TensorInfo(4, dims, armnn::DataType::Float32), floatWeights); - - // Create simple test network - auto input = graph.AddLayer<armnn::InputLayer>(0, "input"); - input->GetOutputSlot().SetTensorInfo(info); - - auto fc = graph.AddLayer<armnn::FullyConnectedLayer>(armnn::FullyConnectedDescriptor(), "fc"); - fc->m_Weight = std::make_unique<armnn::ScopedCpuTensorHandle>(weights); - fc->GetOutputSlot().SetTensorInfo(info); - - auto output = graph.AddLayer<armnn::OutputLayer>(1, "output"); - - // Connect up the layers - input->GetOutputSlot().Connect(fc->GetInputSlot(0)); - fc->GetOutputSlot().Connect(output->GetInputSlot(0)); - - // Check tensor data type before conversion - BOOST_CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::Float32); - - // Run the optimizer - armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(ConvertConstantsFloatToHalf())); - - // Check tensor data type after conversion - BOOST_CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::Float16); - - // Check whether data matches expected fp16 data - Half* data = fc->m_Weight->GetTensor<Half>(); - BOOST_CHECK(data[0] == Half(1.0f)); - BOOST_CHECK(data[1] == Half(2.0f)); - BOOST_CHECK(data[2] == Half(3.0f)); - BOOST_CHECK(data[3] == Half(4.0f)); -} - -BOOST_AUTO_TEST_CASE(OptimizeInverseConversionsTest) -{ - armnn::Graph graph; - - auto output = graph.AddLayer<armnn::OutputLayer>(0, "output"); - - graph.InsertNewLayer<armnn::InputLayer>(output->GetInputSlot(0), 0, "input"); - - // Fp32ToFp16 conversion followed by an inverse Fp16ToFp32 conversion - graph.InsertNewLayer<armnn::ConvertFp32ToFp16Layer>(output->GetInputSlot(0), "convert1"); - graph.InsertNewLayer<armnn::ConvertFp16ToFp32Layer>(output->GetInputSlot(0), "convert2"); - - graph.InsertNewLayer<armnn::Convolution2dLayer>(output->GetInputSlot(0), Convolution2dDescriptor(), "conv"); - - // Fp16ToFp32 conversion followed by an inverse Fp32ToFp16 conversion - graph.InsertNewLayer<armnn::ConvertFp16ToFp32Layer>(output->GetInputSlot(0), "convert3"); - graph.InsertNewLayer<armnn::ConvertFp32ToFp16Layer>(output->GetInputSlot(0), "convert4"); - - BOOST_TEST(CheckSequence(graph.cbegin(), - graph.cend(), - &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::ConvertFp32ToFp16Layer>, - &IsLayerOfType<armnn::ConvertFp16ToFp32Layer>, - &IsLayerOfType<armnn::Convolution2dLayer>, - &IsLayerOfType<armnn::ConvertFp16ToFp32Layer>, - &IsLayerOfType<armnn::ConvertFp32ToFp16Layer>, - &IsLayerOfType<armnn::OutputLayer>)); - - armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(OptimizeInverseConversionsFp16(), - OptimizeInverseConversionsFp32())); - - // Check that all consecutive inverse conversions are removed - BOOST_TEST(CheckSequence(graph.cbegin(), - graph.cend(), - &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::Convolution2dLayer>, - &IsLayerOfType<armnn::OutputLayer>)); -} - BOOST_AUTO_TEST_CASE(InsertConvertersTest) { const armnn::TensorInfo info({ 1, 5, 2, 3 }, armnn::DataType::Float16); @@ -728,79 +242,7 @@ BOOST_AUTO_TEST_CASE(InsertConvertersTest) &IsLayerOfType<armnn::OutputLayer>)); } -BOOST_AUTO_TEST_CASE(Fp32NetworkToFp16OptimizationTest) -{ - armnn::Graph graph; - - const armnn::TensorInfo infoFP32({ 2,2,1,3 }, armnn::DataType::Float32); - - // Create the simple test network - auto input = graph.AddLayer<armnn::InputLayer>(0, "input"); - input->GetOutputSlot().SetTensorInfo(infoFP32); - - auto floor = graph.AddLayer<armnn::FloorLayer>("floor"); - floor->GetOutputSlot().SetTensorInfo(infoFP32); - - auto output = graph.AddLayer<armnn::OutputLayer>(1, "output"); - - // Connect up the layers - input->GetOutputSlot().Connect(floor->GetInputSlot(0)); - floor->GetOutputSlot().Connect(output->GetInputSlot(0)); - - BOOST_TEST(CheckSequence(graph.cbegin(), - graph.cend(), - &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::FloorLayer>, - &IsLayerOfType<armnn::OutputLayer>)); - - // Run the optimizer - armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(Fp32NetworkToFp16Converter())); - - BOOST_TEST(CheckSequence(graph.cbegin(), - graph.cend(), - &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::ConvertFp32ToFp16Layer>, - &IsLayerOfType<armnn::FloorLayer>, - &IsLayerOfType<armnn::ConvertFp16ToFp32Layer>, - &IsLayerOfType<armnn::OutputLayer>)); -} - -BOOST_AUTO_TEST_CASE(InsertDebugOptimizationTest) -{ - armnn::Graph graph; - - const armnn::TensorInfo info({ 2,2,1,3 }, armnn::DataType::Float32); - - // Create the simple test network - auto input = graph.AddLayer<armnn::InputLayer>(0, "input"); - input->GetOutputSlot().SetTensorInfo(info); - auto floor = graph.AddLayer<armnn::FloorLayer>("floor"); - floor->GetOutputSlot().SetTensorInfo(info); - - auto output = graph.AddLayer<armnn::OutputLayer>(1, "output"); - - // Connect up the layers - input->GetOutputSlot().Connect(floor->GetInputSlot(0)); - floor->GetOutputSlot().Connect(output->GetInputSlot(0)); - - BOOST_TEST(CheckSequence(graph.cbegin(), - graph.cend(), - &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::FloorLayer>, - &IsLayerOfType<armnn::OutputLayer>)); - - // Run the optimizer - armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(InsertDebugLayer())); - - BOOST_TEST(CheckSequence(graph.cbegin(), - graph.cend(), - &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::DebugLayer>, - &IsLayerOfType<armnn::FloorLayer>, - &IsLayerOfType<armnn::DebugLayer>, - &IsLayerOfType<armnn::OutputLayer>)); -} void CreateConvolution2dGraph(Graph &graph, const unsigned int* inputShape, const unsigned int* weightsShape, const unsigned int* outputShape, |