diff options
Diffstat (limited to 'src/armnn/test/CreateWorkload.hpp')
| -rw-r--r-- | src/armnn/test/CreateWorkload.hpp | 46 |
1 files changed, 25 insertions, 21 deletions
diff --git a/src/armnn/test/CreateWorkload.hpp b/src/armnn/test/CreateWorkload.hpp index a33189efeb..f3cf544fa3 100644 --- a/src/armnn/test/CreateWorkload.hpp +++ b/src/armnn/test/CreateWorkload.hpp @@ -397,52 +397,56 @@ std::unique_ptr<Convolution2dWorkload> CreateDirectConvolution2dWorkloadTest(arm return workload; } -template <typename DepthwiseConvolution2dFloat32Workload> +template <typename DepthwiseConvolution2dFloat32Workload, armnn::DataType DataType> std::unique_ptr<DepthwiseConvolution2dFloat32Workload> CreateDepthwiseConvolution2dWorkloadTest( - armnn::IWorkloadFactory& factory, armnn::Graph& graph) + armnn::IWorkloadFactory& factory, armnn::Graph& graph, DataLayout dataLayout = DataLayout::NCHW) { // Creates the layer we're testing. DepthwiseConvolution2dDescriptor layerDesc; - layerDesc.m_PadLeft = 3; - layerDesc.m_PadRight = 3; + layerDesc.m_PadLeft = 1; + layerDesc.m_PadRight = 2; layerDesc.m_PadTop = 1; - layerDesc.m_PadBottom = 1; - layerDesc.m_StrideX = 2; - layerDesc.m_StrideY = 4; - layerDesc.m_BiasEnabled = true; + layerDesc.m_PadBottom = 2; + layerDesc.m_StrideX = 1; + layerDesc.m_StrideY = 1; + layerDesc.m_BiasEnabled = false; + layerDesc.m_DataLayout = dataLayout; DepthwiseConvolution2dLayer* const layer = graph.AddLayer<DepthwiseConvolution2dLayer>(layerDesc, "layer"); - layer->m_Weight = std::make_unique<ScopedCpuTensorHandle>(TensorInfo({3, 3, 5, 3}, DataType::Float32)); - layer->m_Bias = std::make_unique<ScopedCpuTensorHandle>(TensorInfo({9}, DataType::Float32)); + layer->m_Weight = std::make_unique<ScopedCpuTensorHandle>(TensorInfo({1, 4, 4, 2}, DataType)); layer->m_Weight->Allocate(); - layer->m_Bias->Allocate(); // Creates extra layers. Layer* const input = graph.AddLayer<InputLayer>(0, "input"); Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + TensorShape inputShape = (dataLayout == DataLayout::NCHW) ? + TensorShape{ 2, 2, 5, 5 } : TensorShape{ 2, 5, 5, 2 }; + TensorShape outputShape = (dataLayout == DataLayout::NCHW) ? + TensorShape{ 2, 2, 5, 5 } : TensorShape{ 2, 5, 5, 2 }; + // Connects up. - Connect(input, layer, TensorInfo({2, 3, 8, 16}, armnn::DataType::Float32)); - Connect(layer, output, TensorInfo({2, 9, 2, 10}, armnn::DataType::Float32)); + Connect(input, layer, TensorInfo(inputShape, DataType)); + Connect(layer, output, TensorInfo(outputShape, DataType)); CreateTensorHandles(graph, factory); // Makes the workload and checks it. auto workload = MakeAndCheckWorkload<DepthwiseConvolution2dFloat32Workload>(*layer, graph, factory); DepthwiseConvolution2dQueueDescriptor queueDescriptor = workload->GetData(); - BOOST_TEST(queueDescriptor.m_Parameters.m_StrideX == 2); - BOOST_TEST(queueDescriptor.m_Parameters.m_StrideY == 4); - BOOST_TEST(queueDescriptor.m_Parameters.m_PadLeft == 3); - BOOST_TEST(queueDescriptor.m_Parameters.m_PadRight == 3); + BOOST_TEST(queueDescriptor.m_Parameters.m_StrideX == 1); + BOOST_TEST(queueDescriptor.m_Parameters.m_StrideY == 1); + BOOST_TEST(queueDescriptor.m_Parameters.m_PadLeft == 1); + BOOST_TEST(queueDescriptor.m_Parameters.m_PadRight == 2); BOOST_TEST(queueDescriptor.m_Parameters.m_PadTop == 1); - BOOST_TEST(queueDescriptor.m_Parameters.m_PadBottom == 1); - BOOST_TEST(queueDescriptor.m_Parameters.m_BiasEnabled == true); + BOOST_TEST(queueDescriptor.m_Parameters.m_PadBottom == 2); + BOOST_TEST(queueDescriptor.m_Parameters.m_BiasEnabled == false); + BOOST_TEST((queueDescriptor.m_Parameters.m_DataLayout == dataLayout)); BOOST_TEST(queueDescriptor.m_Inputs.size() == 1); BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); - BOOST_TEST((queueDescriptor.m_Weight->GetTensorInfo() == TensorInfo({3, 3, 5, 3}, DataType::Float32))); - BOOST_TEST((queueDescriptor.m_Bias->GetTensorInfo() == TensorInfo({9}, DataType::Float32))); + BOOST_TEST((queueDescriptor.m_Weight->GetTensorInfo() == TensorInfo({1, 4, 4, 2}, DataType))); // Returns so we can do extra, backend-specific tests. return workload; |