From 1f58f03d82c482626b1b4673b6c0e25da4338fb5 Mon Sep 17 00:00:00 2001 From: James Conroy Date: Tue, 27 Apr 2021 17:13:27 +0100 Subject: IVGCVSW-5815 Generalise ConstCpuTensorHandle * Generalises ConstCpuTensorHandle and inherited classes by removing 'Cpu' from aliases. * New renamed classes: ConstTensorHandle, TensorHandle, ScopedTensorHandle, PassthroughTensorHandle, ConstPassthroughTensorHandle. Signed-off-by: James Conroy Change-Id: I1824e0e134202735fb77051f20a7252f161dfe16 --- .../test/LayerReleaseConstantDataTest.cpp | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) (limited to 'src/backends/backendsCommon/test/LayerReleaseConstantDataTest.cpp') diff --git a/src/backends/backendsCommon/test/LayerReleaseConstantDataTest.cpp b/src/backends/backendsCommon/test/LayerReleaseConstantDataTest.cpp index 817cdeed79..0ca4b0a7f9 100644 --- a/src/backends/backendsCommon/test/LayerReleaseConstantDataTest.cpp +++ b/src/backends/backendsCommon/test/LayerReleaseConstantDataTest.cpp @@ -7,7 +7,7 @@ #include -#include +#include #include #include @@ -35,10 +35,10 @@ BOOST_AUTO_TEST_CASE(ReleaseBatchNormalizationLayerConstantDataTest) BatchNormalizationLayer* const layer = graph.AddLayer(layerDesc, "layer"); armnn::TensorInfo weightInfo({3}, armnn::DataType::Float32); - layer->m_Mean = std::make_unique(weightInfo); - layer->m_Variance = std::make_unique(weightInfo); - layer->m_Beta = std::make_unique(weightInfo); - layer->m_Gamma = std::make_unique(weightInfo); + layer->m_Mean = std::make_unique(weightInfo); + layer->m_Variance = std::make_unique(weightInfo); + layer->m_Beta = std::make_unique(weightInfo); + layer->m_Gamma = std::make_unique(weightInfo); layer->m_Mean->Allocate(); layer->m_Variance->Allocate(); layer->m_Beta->Allocate(); @@ -87,9 +87,9 @@ BOOST_AUTO_TEST_CASE(ReleaseBatchNormalizationLayerConstantDataTest) Convolution2dLayer* const layer = graph.AddLayer(layerDesc, "layer"); - layer->m_Weight = std::make_unique(TensorInfo({2, 3, 5, 3}, + layer->m_Weight = std::make_unique(TensorInfo({2, 3, 5, 3}, armnn::DataType::Float32)); - layer->m_Bias = std::make_unique + layer->m_Bias = std::make_unique (TensorInfo({2}, GetBiasDataType(armnn::DataType::Float32))); layer->m_Weight->Allocate(); @@ -131,8 +131,8 @@ BOOST_AUTO_TEST_CASE(ReleaseDepthwiseConvolution2dLayerConstantDataTest) DepthwiseConvolution2dLayer* const layer = graph.AddLayer(layerDesc, "layer"); - layer->m_Weight = std::make_unique(TensorInfo({3, 3, 5, 3}, DataType::Float32)); - layer->m_Bias = std::make_unique(TensorInfo({9}, DataType::Float32)); + layer->m_Weight = std::make_unique(TensorInfo({3, 3, 5, 3}, DataType::Float32)); + layer->m_Bias = std::make_unique(TensorInfo({9}, DataType::Float32)); layer->m_Weight->Allocate(); layer->m_Bias->Allocate(); @@ -170,9 +170,9 @@ BOOST_AUTO_TEST_CASE(ReleaseFullyConnectedLayerConstantDataTest) float inputsQScale = 1.0f; float outputQScale = 2.0f; - layer->m_Weight = std::make_unique(TensorInfo({7, 20}, + layer->m_Weight = std::make_unique(TensorInfo({7, 20}, DataType::QAsymmU8, inputsQScale, 0)); - layer->m_Bias = std::make_unique(TensorInfo({7}, + layer->m_Bias = std::make_unique(TensorInfo({7}, GetBiasDataType(DataType::QAsymmU8), inputsQScale)); layer->m_Weight->Allocate(); layer->m_Bias->Allocate(); -- cgit v1.2.1