IVGCVSW-1860 Support NHWC for Pooling2D
Change-Id: I9059a292e47867df82a5efbba5808fd264949ec9
diff --git a/src/backends/test/Pooling2dTestImpl.hpp b/src/backends/test/Pooling2dTestImpl.hpp
index c87548c..7366bca 100644
--- a/src/backends/test/Pooling2dTestImpl.hpp
+++ b/src/backends/test/Pooling2dTestImpl.hpp
@@ -5,25 +5,23 @@
#pragma once
#include <armnn/ArmNN.hpp>
-#include <armnn/Tensor.hpp>
-#include <armnn/TypesUtils.hpp>
#include <test/TensorHelpers.hpp>
#include "QuantizeHelper.hpp"
#include <backends/CpuTensorHandle.hpp>
#include <backends/WorkloadFactory.hpp>
-
+#include <backends/WorkloadInfo.hpp>
#include <algorithm>
template<typename T>
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)
+ 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]);
@@ -88,6 +86,80 @@
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>());
+
+ // 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;
+ queueDescriptor.m_Parameters.m_DataLayout = armnn::DataLayout::NHWC;
+
+ 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)
+ {
+ return result;
+ }
+
+ 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;
+}
+
//
// Tests max pooling with the following parameters:
//
@@ -263,6 +335,51 @@
}
template<typename T>
+LayerTestResult<T, 4> SimpleAveragePooling2dNhwcTestCommon(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);
+ }
+
+ 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,
+ }));
+
+ 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,
+ }));
+
+ return SimplePooling2dNhwcTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected);
+}
+
+template<typename T>
LayerTestResult<T, 4> LargeTensorsAveragePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory,
float qScale = 1.0f,
int32_t qOffset = 0)