From bd18eab07a8f30492de1e462b1815189014cb8d5 Mon Sep 17 00:00:00 2001
From: Cathal Corbett <cathal.corbett@arm.com>
Date: Tue, 15 Nov 2022 12:56:16 +0000
Subject: IVGCVSW-7345 Add Pooling2d support to TOSA Reference Backend

Signed-off-by: Cathal Corbett <cathal.corbett@arm.com>
Change-Id: I73a47e513fe2d064ef233b121a68ef2edf0396dc
---
 .../test/Pooling2dEndToEndTestImpl.hpp             | 172 +++++++++++++++++++++
 1 file changed, 172 insertions(+)
 create mode 100644 src/backends/backendsCommon/test/Pooling2dEndToEndTestImpl.hpp

(limited to 'src/backends/backendsCommon/test/Pooling2dEndToEndTestImpl.hpp')

diff --git a/src/backends/backendsCommon/test/Pooling2dEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/Pooling2dEndToEndTestImpl.hpp
new file mode 100644
index 0000000000..addd27caca
--- /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
-- 
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