IVGCVSW-5318 'Create a Neon/CL Workload Unit Test fast_math option enabled'
* Unit test implemented to make sure it returns WINOGRAD
* Updated the enable-fast-math option in ExecuteNetwork to be consistent
Signed-off-by: Sadik Armagan <sadik.armagan@arm.com>
Change-Id: Id64f114ae47966def69a9eef0770a4251ee56a41
diff --git a/src/armnn/test/CreateWorkload.hpp b/src/armnn/test/CreateWorkload.hpp
index 3f3cdc3..60beb51 100644
--- a/src/armnn/test/CreateWorkload.hpp
+++ b/src/armnn/test/CreateWorkload.hpp
@@ -279,6 +279,64 @@
return workload;
}
+template <typename Convolution2dWorkload, armnn::DataType DataType>
+std::unique_ptr<Convolution2dWorkload> CreateConvolution2dWorkloadFastMathTest(armnn::IWorkloadFactory& factory,
+ armnn::Graph& graph,
+ DataLayout dataLayout = DataLayout::NCHW,
+ const ModelOptions& modelOptions = {})
+{
+ // Creates the layer we're testing.
+ Convolution2dDescriptor layerDesc;
+ layerDesc.m_PadLeft = 0;
+ layerDesc.m_PadRight = 0;
+ layerDesc.m_PadTop = 0;
+ layerDesc.m_PadBottom = 0;
+ layerDesc.m_StrideX = 1;
+ layerDesc.m_StrideY = 1;
+ layerDesc.m_BiasEnabled = false;
+ layerDesc.m_DataLayout = dataLayout;
+
+ Convolution2dLayer* const layer = graph.AddLayer<Convolution2dLayer>(layerDesc, "layer");
+
+ TensorShape weightShape = TensorShape{32, 32, 3, 3};
+ TensorShape inputShape = TensorShape{1, 32, 149, 149};
+ TensorShape outputShape = TensorShape{1, 32, 147, 147};
+
+ layer->m_Weight = std::make_unique<ScopedCpuTensorHandle>(TensorInfo(weightShape, DataType));
+ layer->m_Bias = std::make_unique<ScopedCpuTensorHandle>(TensorInfo({2}, GetBiasDataType(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");
+
+ // Connects up.
+ Connect(input, layer, TensorInfo(inputShape, DataType));
+ Connect(layer, output, TensorInfo(outputShape, DataType));
+ CreateTensorHandles(graph, factory);
+
+ // Makes the workload and checks it.
+ auto workload = MakeAndCheckWorkload<Convolution2dWorkload>(*layer, factory, modelOptions);
+
+ Convolution2dQueueDescriptor queueDescriptor = workload->GetData();
+ BOOST_TEST(queueDescriptor.m_Parameters.m_StrideX == 1);
+ BOOST_TEST(queueDescriptor.m_Parameters.m_StrideY == 1);
+ BOOST_TEST(queueDescriptor.m_Parameters.m_PadLeft == 0);
+ BOOST_TEST(queueDescriptor.m_Parameters.m_PadRight == 0);
+ BOOST_TEST(queueDescriptor.m_Parameters.m_PadTop == 0);
+ BOOST_TEST(queueDescriptor.m_Parameters.m_PadBottom == 0);
+ 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(weightShape, DataType)));
+
+ // Returns so we can do extra, backend-specific tests.
+ return workload;
+}
+
template <typename LstmWorkload>
std::unique_ptr<LstmWorkload> CreateLstmWorkloadTest(armnn::IWorkloadFactory& factory, armnn::Graph& graph)
{
diff --git a/src/backends/cl/test/ClCreateWorkloadTests.cpp b/src/backends/cl/test/ClCreateWorkloadTests.cpp
index fc5ccfe..4bd3d3a 100644
--- a/src/backends/cl/test/ClCreateWorkloadTests.cpp
+++ b/src/backends/cl/test/ClCreateWorkloadTests.cpp
@@ -322,7 +322,7 @@
ClWorkloadFactoryHelper::GetFactory(ClWorkloadFactoryHelper::GetMemoryManager(), modelOptions);
auto workload =
- CreateConvolution2dWorkloadTest<ClConvolution2dWorkload, armnn::DataType::Float32>(factory,
+ CreateConvolution2dWorkloadFastMathTest<ClConvolution2dWorkload, armnn::DataType::Float32>(factory,
graph,
DataLayout::NCHW,
modelOptions);
@@ -331,8 +331,7 @@
auto conv2dWorkload = PolymorphicDowncast<ClConvolution2dWorkload*>(workload.get());
IgnoreUnused(conv2dWorkload);
ARMNN_ASSERT(conv2dWorkload != nullptr);
- // fast_math enabled but configuration does not match with WINOGRAD
- ARMNN_ASSERT(conv2dWorkload->GetConvolutionMethod() == arm_compute::ConvolutionMethod::GEMM);
+ ARMNN_ASSERT(conv2dWorkload->GetConvolutionMethod() == arm_compute::ConvolutionMethod::WINOGRAD);
}
template <typename DepthwiseConvolutionWorkloadType, typename armnn::DataType DataType>
diff --git a/src/backends/neon/test/NeonCreateWorkloadTests.cpp b/src/backends/neon/test/NeonCreateWorkloadTests.cpp
index 99ff9ae..c994bfe 100644
--- a/src/backends/neon/test/NeonCreateWorkloadTests.cpp
+++ b/src/backends/neon/test/NeonCreateWorkloadTests.cpp
@@ -292,7 +292,7 @@
NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager(), modelOptions);
auto workload =
- CreateConvolution2dWorkloadTest<NeonConvolution2dWorkload, armnn::DataType::Float32>(factory,
+ CreateConvolution2dWorkloadFastMathTest<NeonConvolution2dWorkload, armnn::DataType::Float32>(factory,
graph,
DataLayout::NCHW,
modelOptions);
@@ -301,8 +301,7 @@
auto conv2dWorkload = PolymorphicDowncast<NeonConvolution2dWorkload*>(workload.get());
IgnoreUnused(conv2dWorkload);
ARMNN_ASSERT(conv2dWorkload != nullptr);
- // fast_math enabled but configuration does not match with WINOGRAD
- ARMNN_ASSERT(conv2dWorkload->GetConvolutionMethod() == arm_compute::ConvolutionMethod::GEMM);
+ ARMNN_ASSERT(conv2dWorkload->GetConvolutionMethod() == arm_compute::ConvolutionMethod::WINOGRAD);
}
template <typename armnn::DataType DataType>