IVGCVSW-1982 - add create workload test for 2D Pooling (NHWC data layout)
Change-Id: Ief0c91ba9abc2578944860ddbd3c19e2bad465bd
diff --git a/src/armnn/test/CreateWorkload.hpp b/src/armnn/test/CreateWorkload.hpp
index f2c8b5a..b63e95d 100644
--- a/src/armnn/test/CreateWorkload.hpp
+++ b/src/armnn/test/CreateWorkload.hpp
@@ -162,7 +162,6 @@
// Makes the workload and checks it.
auto workload = MakeAndCheckWorkload<BatchNormalizationFloat32Workload>(*layer, graph, factory);
-
BatchNormalizationQueueDescriptor queueDescriptor = workload->GetData();
BOOST_TEST(queueDescriptor.m_Parameters.m_Eps == 0.05f);
BOOST_TEST(queueDescriptor.m_Inputs.size() == 1);
@@ -532,7 +531,8 @@
template <typename Pooling2dWorkload, armnn::DataType DataType>
std::unique_ptr<Pooling2dWorkload> CreatePooling2dWorkloadTest(armnn::IWorkloadFactory& factory,
- armnn::Graph& graph)
+ armnn::Graph& graph,
+ DataLayout dataLayout = DataLayout::NCHW)
{
// Creates the layer we're testing.
Pooling2dDescriptor layerDesc;
@@ -546,6 +546,7 @@
layerDesc.m_StrideX = 2;
layerDesc.m_StrideY = 3;
layerDesc.m_OutputShapeRounding = OutputShapeRounding::Floor;
+ layerDesc.m_DataLayout = dataLayout;
Pooling2dLayer* const layer = graph.AddLayer<Pooling2dLayer>(layerDesc, "layer");
@@ -553,9 +554,12 @@
Layer* const input = graph.AddLayer<InputLayer>(0, "input");
Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
+ TensorShape inputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{3, 2, 5, 5} : TensorShape{3, 5, 5, 2};
+ TensorShape outputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{3, 2, 2, 4} : TensorShape{3, 2, 4, 2};
+
// Connect up
- Connect(input, layer, TensorInfo({3, 2, 5, 5}, DataType));
- Connect(layer, output, TensorInfo({3, 2, 2, 4}, DataType));
+ Connect(input, layer, TensorInfo(inputShape, DataType));
+ Connect(layer, output, TensorInfo(outputShape, DataType));
CreateTensorHandles(graph, factory);
// Make the workload and checks it
diff --git a/src/backends/test/CreateWorkloadCl.cpp b/src/backends/test/CreateWorkloadCl.cpp
index e7e39b0..0314f6d 100644
--- a/src/backends/test/CreateWorkloadCl.cpp
+++ b/src/backends/test/CreateWorkloadCl.cpp
@@ -320,30 +320,45 @@
}
template <typename Pooling2dWorkloadType, typename armnn::DataType DataType>
-static void ClPooling2dWorkloadTest()
+static void ClPooling2dWorkloadTest(DataLayout dataLayout)
{
Graph graph;
ClWorkloadFactory factory;
- auto workload = CreatePooling2dWorkloadTest<Pooling2dWorkloadType, DataType>(factory, graph);
+ auto workload = CreatePooling2dWorkloadTest<Pooling2dWorkloadType, DataType>(factory, graph, dataLayout);
+
+ std::initializer_list<unsigned int> inputShape = (dataLayout == DataLayout::NCHW) ?
+ std::initializer_list<unsigned int>({3, 2, 5, 5}) : std::initializer_list<unsigned int>({3, 5, 5, 2});
+ std::initializer_list<unsigned int> outputShape = (dataLayout == DataLayout::NCHW) ?
+ std::initializer_list<unsigned int>({3, 2, 2, 4}) : std::initializer_list<unsigned int>({3, 2, 4, 2});
// Check that inputs/outputs are as we expect them (see definition of CreatePooling2dWorkloadTest).
Pooling2dQueueDescriptor queueDescriptor = workload->GetData();
auto inputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Inputs[0]);
auto outputHandle = boost::polymorphic_downcast<IClTensorHandle*>(queueDescriptor.m_Outputs[0]);
- BOOST_TEST(CompareIClTensorHandleShape(inputHandle, {3, 2, 5, 5}));
- BOOST_TEST(CompareIClTensorHandleShape(outputHandle, {3, 2, 2, 4}));
+ BOOST_TEST(CompareIClTensorHandleShape(inputHandle, inputShape));
+ BOOST_TEST(CompareIClTensorHandleShape(outputHandle, outputShape));
}
-BOOST_AUTO_TEST_CASE(CreatePooling2dFloatWorkload)
+BOOST_AUTO_TEST_CASE(CreatePooling2dFloatNchwWorkload)
{
- ClPooling2dWorkloadTest<ClPooling2dFloatWorkload, armnn::DataType::Float32>();
+ ClPooling2dWorkloadTest<ClPooling2dFloatWorkload, armnn::DataType::Float32>(DataLayout::NCHW);
}
-BOOST_AUTO_TEST_CASE(CreatePooling2dFloat16Workload)
+BOOST_AUTO_TEST_CASE(CreatePooling2dFloatNhwcWorkload)
{
- ClPooling2dWorkloadTest<ClPooling2dFloatWorkload, armnn::DataType::Float16>();
+ ClPooling2dWorkloadTest<ClPooling2dFloatWorkload, armnn::DataType::Float32>(DataLayout::NHWC);
+}
+
+BOOST_AUTO_TEST_CASE(CreatePooling2dFloat16NchwWorkload)
+{
+ ClPooling2dWorkloadTest<ClPooling2dFloatWorkload, armnn::DataType::Float16>(DataLayout::NCHW);
+}
+
+BOOST_AUTO_TEST_CASE(CreatePooling2dFloat16NhwcWorkload)
+{
+ ClPooling2dWorkloadTest<ClPooling2dFloatWorkload, armnn::DataType::Float16>(DataLayout::NHWC);
}
template <typename ReshapeWorkloadType, typename armnn::DataType DataType>
diff --git a/src/backends/test/CreateWorkloadNeon.cpp b/src/backends/test/CreateWorkloadNeon.cpp
index a6f3540..a67e68d 100644
--- a/src/backends/test/CreateWorkloadNeon.cpp
+++ b/src/backends/test/CreateWorkloadNeon.cpp
@@ -273,19 +273,22 @@
template <typename Pooling2dWorkloadType, typename armnn::DataType DataType>
-static void NeonCreatePooling2dWorkloadTest()
+static void NeonCreatePooling2dWorkloadTest(DataLayout dataLayout = DataLayout::NCHW)
{
Graph graph;
NeonWorkloadFactory factory;
auto workload = CreatePooling2dWorkloadTest<Pooling2dWorkloadType, DataType>
- (factory, graph);
+ (factory, graph, dataLayout);
+
+ TensorShape inputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{3, 2, 5, 5} : TensorShape{3, 5, 5, 2};
+ TensorShape outputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{3, 2, 2, 4} : TensorShape{3, 2, 4, 2};
// Checks that outputs and inputs are as we expect them (see definition of CreatePooling2dWorkloadTest).
Pooling2dQueueDescriptor queueDescriptor = workload->GetData();
auto inputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Inputs[0]);
auto outputHandle = boost::polymorphic_downcast<INeonTensorHandle*>(queueDescriptor.m_Outputs[0]);
- BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({3, 2, 5, 5}, DataType)));
- BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({3, 2, 2, 4}, DataType)));
+ BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo(inputShape, DataType)));
+ BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo(outputShape, DataType)));
}
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
@@ -295,14 +298,24 @@
}
#endif
-BOOST_AUTO_TEST_CASE(CreatePooling2dFloatWorkload)
+BOOST_AUTO_TEST_CASE(CreatePooling2dFloatNchwWorkload)
{
- NeonCreatePooling2dWorkloadTest<NeonPooling2dFloatWorkload, DataType::Float32>();
+ NeonCreatePooling2dWorkloadTest<NeonPooling2dFloatWorkload, DataType::Float32>(DataLayout::NCHW);
}
-BOOST_AUTO_TEST_CASE(CreatePooling2dUint8Workload)
+BOOST_AUTO_TEST_CASE(CreatePooling2dFloatNhwcWorkload)
{
- NeonCreatePooling2dWorkloadTest<NeonPooling2dUint8Workload, DataType::QuantisedAsymm8>();
+ NeonCreatePooling2dWorkloadTest<NeonPooling2dFloatWorkload, DataType::Float32>(DataLayout::NHWC);
+}
+
+BOOST_AUTO_TEST_CASE(CreatePooling2dUint8NchwWorkload)
+{
+ NeonCreatePooling2dWorkloadTest<NeonPooling2dUint8Workload, DataType::QuantisedAsymm8>(DataLayout::NCHW);
+}
+
+BOOST_AUTO_TEST_CASE(CreatePooling2dUint8NhwcWorkload)
+{
+ NeonCreatePooling2dWorkloadTest<NeonPooling2dUint8Workload, DataType::QuantisedAsymm8>(DataLayout::NHWC);
}
template <typename ReshapeWorkloadType, typename armnn::DataType DataType>