IVGCVSW-7345 Add Pooling2d support to TOSA Reference Backend
Signed-off-by: Cathal Corbett <cathal.corbett@arm.com>
Change-Id: I73a47e513fe2d064ef233b121a68ef2edf0396dc
diff --git a/src/backends/backendsCommon/test/Pooling2dEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/Pooling2dEndToEndTestImpl.hpp
new file mode 100644
index 0000000..addd27c
--- /dev/null
+++ b/src/backends/backendsCommon/test/Pooling2dEndToEndTestImpl.hpp
@@ -0,0 +1,172 @@
+//
+// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+#pragma once
+
+#include <armnn/INetwork.hpp>
+#include <armnn/Types.hpp>
+
+#include <CommonTestUtils.hpp>
+#include <ResolveType.hpp>
+
+#include <doctest/doctest.h>
+
+namespace
+{
+
+using namespace armnn;
+
+template<typename armnn::DataType DataType>
+armnn::INetworkPtr CreatePooling2dNetwork(const armnn::TensorShape& inputShape,
+ const armnn::TensorShape& outputShape,
+ PaddingMethod padMethod = PaddingMethod::Exclude,
+ PoolingAlgorithm poolAlg = PoolingAlgorithm::Max,
+ const float qScale = 1.0f,
+ const int32_t qOffset = 0)
+{
+ INetworkPtr network(INetwork::Create());
+
+ TensorInfo inputTensorInfo(inputShape, DataType, qScale, qOffset, true);
+ TensorInfo outputTensorInfo(outputShape, DataType, qScale, qOffset, true);
+
+ Pooling2dDescriptor descriptor;
+ descriptor.m_PoolType = poolAlg;
+ descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3;
+ descriptor.m_StrideX = descriptor.m_StrideY = 1;
+ descriptor.m_PadLeft = 1;
+ descriptor.m_PadRight = 1;
+ descriptor.m_PadTop = 1;
+ descriptor.m_PadBottom = 1;
+ descriptor.m_PaddingMethod = padMethod;
+ descriptor.m_DataLayout = DataLayout::NHWC;
+
+ IConnectableLayer* pool = network->AddPooling2dLayer(descriptor, "pool");
+ IConnectableLayer* input = network->AddInputLayer(0, "input");
+ IConnectableLayer* output = network->AddOutputLayer(0, "output");
+
+ Connect(input, pool, inputTensorInfo, 0, 0);
+ Connect(pool, output, outputTensorInfo, 0, 0);
+
+ return network;
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+void MaxPool2dEndToEnd(const std::vector<armnn::BackendId>& backends,
+ PaddingMethod padMethod = PaddingMethod::Exclude)
+{
+ const TensorShape& inputShape = { 1, 3, 3, 1 };
+ const TensorShape& outputShape = { 1, 3, 3, 1 };
+
+ INetworkPtr network = CreatePooling2dNetwork<ArmnnType>(inputShape, outputShape, padMethod);
+
+ CHECK(network);
+
+ std::vector<T> inputData{ 1, 2, 3,
+ 4, 5, 6,
+ 7, 8, 9 };
+ std::vector<T> expectedOutput{ 5, 6, 6,
+ 8, 9, 9,
+ 8, 9, 9 };
+
+ std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } };
+ std::map<int, std::vector<T>> expectedOutputData = { { 0, expectedOutput } };
+
+ EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), inputTensorData, expectedOutputData, backends);
+}
+
+template<armnn::DataType ArmnnType>
+void MaxPool2dEndToEndFloat16(const std::vector<armnn::BackendId>& backends)
+{
+ using namespace half_float::literal;
+ using Half = half_float::half;
+
+ const TensorShape& inputShape = { 1, 3, 3, 1 };
+ const TensorShape& outputShape = { 1, 3, 3, 1 };
+
+ INetworkPtr network = CreatePooling2dNetwork<ArmnnType>(inputShape, outputShape);
+ CHECK(network);
+
+ std::vector<Half> inputData{ 1._h, 2._h, 3._h,
+ 4._h, 5._h, 6._h,
+ 7._h, 8._h, 9._h };
+ std::vector<Half> expectedOutput{ 5._h, 6._h, 6._h,
+ 8._h, 9._h, 9._h,
+ 8._h, 9._h, 9._h };
+
+ std::map<int, std::vector<Half>> inputTensorData = { { 0, inputData } };
+ std::map<int, std::vector<Half>> expectedOutputData = { { 0, expectedOutput } };
+
+ EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), inputTensorData, expectedOutputData, backends);
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+void AvgPool2dEndToEnd(const std::vector<armnn::BackendId>& backends,
+ PaddingMethod padMethod = PaddingMethod::Exclude)
+{
+ const TensorShape& inputShape = { 1, 3, 3, 1 };
+ const TensorShape& outputShape = { 1, 3, 3, 1 };
+
+ INetworkPtr network = CreatePooling2dNetwork<ArmnnType>(
+ inputShape, outputShape, padMethod, PoolingAlgorithm::Average);
+ CHECK(network);
+
+ std::vector<T> inputData{ 1, 2, 3,
+ 4, 5, 6,
+ 7, 8, 9 };
+ std::vector<T> expectedOutput;
+ if (padMethod == PaddingMethod::Exclude)
+ {
+ expectedOutput = { 3 , 3.5 , 4 ,
+ 4.5, 5 , 5.5,
+ 6 , 6.5, 7 };
+ }
+ else
+ {
+ expectedOutput = { 1.33333, 2.33333, 1.77778,
+ 3 , 5 , 3.66667,
+ 2.66667, 4.33333, 3.11111 };
+ }
+
+ std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } };
+ std::map<int, std::vector<T>> expectedOutputData = { { 0, expectedOutput } };
+
+ EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network),
+ inputTensorData,
+ expectedOutputData,
+ backends,
+ 0.00001f);
+}
+
+template<armnn::DataType ArmnnType>
+void AvgPool2dEndToEndFloat16(const std::vector<armnn::BackendId>& backends,
+ PaddingMethod padMethod = PaddingMethod::IgnoreValue)
+{
+ using namespace half_float::literal;
+ using Half = half_float::half;
+
+ const TensorShape& inputShape = { 1, 3, 3, 1 };
+ const TensorShape& outputShape = { 1, 3, 3, 1 };
+
+ INetworkPtr network = CreatePooling2dNetwork<ArmnnType>(
+ inputShape, outputShape, padMethod, PoolingAlgorithm::Average);
+ CHECK(network);
+
+ std::vector<Half> inputData{ 1._h, 2._h, 3._h,
+ 4._h, 5._h, 6._h,
+ 7._h, 8._h, 9._h };
+ std::vector<Half> expectedOutput{ 1.33333_h, 2.33333_h, 1.77778_h,
+ 3._h , 5._h , 3.66667_h,
+ 2.66667_h, 4.33333_h, 3.11111_h };
+
+ std::map<int, std::vector<Half>> inputTensorData = { { 0, inputData } };
+ std::map<int, std::vector<Half>> expectedOutputData = { { 0, expectedOutput } };
+
+ EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network),
+ inputTensorData,
+ expectedOutputData,
+ backends,
+ 0.00001f);
+}
+
+} // anonymous namespace