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-rw-r--r--src/backends/test/Pooling2dTestImpl.hpp177
1 files changed, 52 insertions, 125 deletions
diff --git a/src/backends/test/Pooling2dTestImpl.hpp b/src/backends/test/Pooling2dTestImpl.hpp
index 7366bcaf44..29263af9bc 100644
--- a/src/backends/test/Pooling2dTestImpl.hpp
+++ b/src/backends/test/Pooling2dTestImpl.hpp
@@ -15,100 +15,44 @@
#include <algorithm>
template<typename T>
-LayerTestResult<T, 4> SimplePooling2dTestImpl(
- armnn::IWorkloadFactory& workloadFactory,
+LayerTestResult<T, 4> SimplePooling2dTestImpl(armnn::IWorkloadFactory& workloadFactory,
armnn::Pooling2dDescriptor descriptor,
float qScale,
int32_t qOffset,
const boost::multi_array<T, 4>& input,
const boost::multi_array<T, 4>& outputExpected)
{
- unsigned int inputHeight = boost::numeric_cast<unsigned int>(input.shape()[2]);
- unsigned int inputWidth = boost::numeric_cast<unsigned int>(input.shape()[3]);
- unsigned int inputChannels = boost::numeric_cast<unsigned int>(input.shape()[1]);
- unsigned int inputBatchSize = boost::numeric_cast<unsigned int>(input.shape()[0]);
-
- unsigned int outputHeight = boost::numeric_cast<unsigned int>(outputExpected.shape()[2]);
- unsigned int outputWidth = boost::numeric_cast<unsigned int>(outputExpected.shape()[3]);
- unsigned int outputChannels = boost::numeric_cast<unsigned int>(outputExpected.shape()[1]);
+ const unsigned int channelsIndex = descriptor.m_DataLayout.GetChannelsIndex();
+ const unsigned int heightIndex = descriptor.m_DataLayout.GetHeightIndex();
+ const unsigned int widthIndex = descriptor.m_DataLayout.GetWidthIndex();
+
+ unsigned int inputHeight = boost::numeric_cast<unsigned int>(input.shape()[heightIndex]);
+ unsigned int inputWidth = boost::numeric_cast<unsigned int>(input.shape()[widthIndex]);
+ unsigned int inputChannels = boost::numeric_cast<unsigned int>(input.shape()[channelsIndex]);
+ unsigned int inputBatchSize = boost::numeric_cast<unsigned int>(input.shape()[0]);
+
+ unsigned int outputHeight = boost::numeric_cast<unsigned int>(outputExpected.shape()[heightIndex]);
+ unsigned int outputWidth = boost::numeric_cast<unsigned int>(outputExpected.shape()[widthIndex]);
+ unsigned int outputChannels = boost::numeric_cast<unsigned int>(outputExpected.shape()[channelsIndex]);
unsigned int outputBatchSize = boost::numeric_cast<unsigned int>(outputExpected.shape()[0]);
- armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
- armnn::GetDataType<T>());
- armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
- armnn::GetDataType<T>());
+ armnn::TensorShape inputTensorShape;
+ armnn::TensorShape outputTensorShape;
- // Set quantization parameters if the requested type is a quantized type.
- if(armnn::IsQuantizedType<T>())
- {
- inputTensorInfo.SetQuantizationScale(qScale);
- inputTensorInfo.SetQuantizationOffset(qOffset);
- outputTensorInfo.SetQuantizationScale(qScale);
- outputTensorInfo.SetQuantizationOffset(qOffset);
- }
-
- LayerTestResult<T, 4> result(outputTensorInfo);
-
- std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
- std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
- armnn::Pooling2dQueueDescriptor queueDescriptor;
- queueDescriptor.m_Parameters = descriptor;
- armnn::WorkloadInfo workloadInfo;
- AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfo, inputHandle.get());
- AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get());
-
- // Don't execute if Pooling is not supported, as an exception will be raised.
- armnn::Compute compute = workloadFactory.GetCompute();
- const size_t reasonIfUnsupportedMaxLen = 255;
- char reasonIfUnsupported[reasonIfUnsupportedMaxLen+1];
- result.supported = armnn::IsPooling2dSupported(compute, inputTensorInfo, outputTensorInfo,
- queueDescriptor.m_Parameters,
- reasonIfUnsupported, reasonIfUnsupportedMaxLen);
- if (!result.supported)
+ switch (descriptor.m_DataLayout.GetDataLayout())
{
- return result;
+ case armnn::DataLayout::NHWC:
+ inputTensorShape = { inputBatchSize, inputHeight, inputWidth, inputChannels };
+ outputTensorShape = { outputBatchSize, outputHeight, outputWidth, outputChannels };
+ break;
+ case armnn::DataLayout::NCHW:
+ default:
+ inputTensorShape = { inputBatchSize, inputChannels, inputHeight, inputWidth };
+ outputTensorShape = { outputBatchSize, outputChannels, outputHeight, outputWidth };
}
- std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePooling2d(queueDescriptor, workloadInfo);
-
- inputHandle->Allocate();
- outputHandle->Allocate();
-
- CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
- workload->Execute();
-
- CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-
- result.outputExpected = outputExpected;
-
- return result;
-}
-
-template<typename T>
-LayerTestResult<T, 4> SimplePooling2dNhwcTestImpl(
- armnn::IWorkloadFactory& workloadFactory,
- armnn::Pooling2dDescriptor descriptor,
- float qScale,
- int32_t qOffset,
- const boost::multi_array<T, 4>& input,
- const boost::multi_array<T, 4>& outputExpected)
-{
- unsigned int inputHeight = boost::numeric_cast<unsigned int>(input.shape()[1]);
- unsigned int inputWidth = boost::numeric_cast<unsigned int>(input.shape()[2]);
- unsigned int inputChannels = boost::numeric_cast<unsigned int>(input.shape()[3]);
- unsigned int inputBatchSize = boost::numeric_cast<unsigned int>(input.shape()[0]);
-
- unsigned int outputHeight = boost::numeric_cast<unsigned int>(outputExpected.shape()[1]);
- unsigned int outputWidth = boost::numeric_cast<unsigned int>(outputExpected.shape()[2]);
- unsigned int outputChannels = boost::numeric_cast<unsigned int>(outputExpected.shape()[3]);
- unsigned int outputBatchSize = boost::numeric_cast<unsigned int>(outputExpected.shape()[0]);
-
- armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputHeight, inputWidth, inputChannels },
- armnn::GetDataType<T>());
- armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputHeight, outputWidth, outputChannels },
- armnn::GetDataType<T>());
+ armnn::TensorInfo inputTensorInfo(inputTensorShape, armnn::GetDataType<T>());
+ armnn::TensorInfo outputTensorInfo(outputTensorShape, armnn::GetDataType<T>());
// Set quantization parameters if the requested type is a quantized type.
if(armnn::IsQuantizedType<T>())
@@ -126,7 +70,7 @@ LayerTestResult<T, 4> SimplePooling2dNhwcTestImpl(
armnn::Pooling2dQueueDescriptor queueDescriptor;
queueDescriptor.m_Parameters = descriptor;
- queueDescriptor.m_Parameters.m_DataLayout = armnn::DataLayout::NHWC;
+ queueDescriptor.m_Parameters.m_DataLayout = descriptor.m_DataLayout;
armnn::WorkloadInfo workloadInfo;
AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfo, inputHandle.get());
@@ -291,6 +235,9 @@ LayerTestResult<T, 4> SimpleMaxPooling2dSize3x3Stride2x4TestCommon(armnn::IWorkl
template<typename T>
LayerTestResult<T, 4> SimpleAveragePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory,
+ const armnn::TensorShape& inputTensorShape,
+ const armnn::TensorShape& outputTensorShape,
+ armnn::DataLayout dataLayout,
float qScale = 1.0f,
int32_t qOffset = 0)
{
@@ -303,9 +250,10 @@ LayerTestResult<T, 4> SimpleAveragePooling2dTestCommon(armnn::IWorkloadFactory&
descriptor.m_PadTop = 1;
descriptor.m_PadBottom = 1;
descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude;
+ descriptor.m_DataLayout = dataLayout;
- armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>());
- armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType<T>());
+ armnn::TensorInfo inputTensorInfo(inputTensorShape, armnn::GetDataType<T>());
+ armnn::TensorInfo outputTensorInfo(outputTensorShape, armnn::GetDataType<T>());
// Set quantization parameters if the requested type is a quantized type.
if(armnn::IsQuantizedType<T>())
@@ -335,54 +283,33 @@ LayerTestResult<T, 4> SimpleAveragePooling2dTestCommon(armnn::IWorkloadFactory&
}
template<typename T>
-LayerTestResult<T, 4> SimpleAveragePooling2dNhwcTestCommon(armnn::IWorkloadFactory& workloadFactory,
- float qScale = 1.0f,
- int32_t qOffset = 0)
+LayerTestResult<T, 4> SimpleAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory,
+ float qScale = 1.0f,
+ int32_t qOffset = 0)
{
- armnn::Pooling2dDescriptor descriptor;
- descriptor.m_PoolType = armnn::PoolingAlgorithm::Average;
- descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2;
- descriptor.m_StrideX = descriptor.m_StrideY = 2;
- descriptor.m_PadLeft = 1;
- descriptor.m_PadRight = 1;
- descriptor.m_PadTop = 1;
- descriptor.m_PadBottom = 1;
- descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude;
-
- armnn::TensorInfo inputTensorInfo({ 1, 4, 4, 1 }, armnn::GetDataType<T>());
- armnn::TensorInfo outputTensorInfo({ 1, 3, 3, 1 }, armnn::GetDataType<T>());
-
- // Set quantization parameters if the requested type is a quantized type.
- if(armnn::IsQuantizedType<T>())
- {
- inputTensorInfo.SetQuantizationScale(qScale);
- inputTensorInfo.SetQuantizationOffset(qOffset);
- outputTensorInfo.SetQuantizationScale(qScale);
- outputTensorInfo.SetQuantizationOffset(qOffset);
- }
+ const armnn::TensorShape inputTensorShape { 1, 1, 4, 4 };
+ const armnn::TensorShape outputTensorShape { 1, 1, 3, 3 };
- auto input = MakeTensor<T, 4>(inputTensorInfo,
- QuantizedVector<T>(qScale, qOffset, {
- 1.0f, 2.0f, 3.0f, 4.0f,
- 1.0f, 2.0f, 3.0f, 4.0f,
- 1.0f, 2.0f, 3.0f, 4.0f,
- 1.0f, 2.0f, 3.0f, 4.0f,
- }));
+ return SimpleAveragePooling2dTestCommon<T>(workloadFactory, inputTensorShape, outputTensorShape,
+ armnn::DataLayout::NCHW, qScale, qOffset);
+}
- auto outputExpected = MakeTensor<T, 4>(outputTensorInfo,
- QuantizedVector<T>(qScale, qOffset, {
- 1.0f, 2.5f, 4.0f,
- 1.0f, 2.5f, 4.0f,
- 1.0f, 2.5f, 4.0f,
- }));
+template<typename T>
+LayerTestResult<T, 4> SimpleAveragePooling2dNhwcTest(armnn::IWorkloadFactory& workloadFactory,
+ float qScale = 1.0f,
+ int32_t qOffset = 0)
+{
+ const armnn::TensorShape inputTensorShape { 1, 4, 4, 1 };
+ const armnn::TensorShape outputTensorShape { 1, 3, 3, 1 };
- return SimplePooling2dNhwcTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected);
+ return SimpleAveragePooling2dTestCommon<T>(workloadFactory, inputTensorShape, outputTensorShape,
+ armnn::DataLayout::NHWC, qScale, qOffset);
}
template<typename T>
LayerTestResult<T, 4> LargeTensorsAveragePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory,
- float qScale = 1.0f,
- int32_t qOffset = 0)
+ float qScale = 1.0f,
+ int32_t qOffset = 0)
{
armnn::Pooling2dDescriptor descriptor;
descriptor.m_PoolType = armnn::PoolingAlgorithm::Average;
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-15 01:21:56 +0000