Revert "MLCE-418 Reduce layer does not support multiple axes"

This reverts commit d905decd256558bbee165e636ce4242ac3b9c917.

Reason for revert: LargeGraph_TENSOR_FLOAT32/FLOAT16 CTS tests failures

Change-Id: Ie69826549e73775825f45134375b5b2c41aebd01

1 files changed, 0 insertions, 288 deletions

diff --git a/src/armnn/test/optimizations/ReduceMultipleAxesTests.cpp b/src/armnn/test/optimizations/ReduceMultipleAxesTests.cpp
deleted file mode 100644
index 28c0ea1466..0000000000
--- a/src/armnn/test/optimizations/ReduceMultipleAxesTests.cpp
+++ /dev/null
@@ -1,288 +0,0 @@
-//
-// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include "../GraphUtils.hpp"
-#include "../TestUtils.hpp"
-
-#include <armnn/INetwork.hpp>
-
-#include <boost/test/unit_test.hpp>
-
-using namespace armnn;
-
-BOOST_AUTO_TEST_SUITE(Optimizer)
-
-INetworkPtr CreateSimpleReduceNetwork(ReduceDescriptor reduceDescriptor,
-                                      TensorShape& inputShape,
-                                      TensorShape& outputShape)
-{
-    // Create a network
-    INetworkPtr network = INetwork::Create();
-
-    const std::string layerName("reduce_layer");
-    const TensorInfo inputInfo (inputShape, DataType::Float32);
-    const TensorInfo outputInfo(outputShape, DataType::Float32);
-
-    IConnectableLayer* const inputLayer  = network->AddInputLayer(0);
-    IConnectableLayer* const reduceLayer = network->AddReduceLayer(reduceDescriptor, layerName.c_str());
-    IConnectableLayer* const outputLayer = network->AddOutputLayer(0);
-
-    inputLayer->GetOutputSlot(0).SetTensorInfo(inputInfo);
-    reduceLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
-
-    inputLayer->GetOutputSlot(0).Connect(reduceLayer->GetInputSlot(0));
-    reduceLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
-
-    return network;
-}
-
-void ReduceWithMultipleAxesTest(INetworkPtr& network,
-                                const TensorShape& outputShape,
-                                const std::vector<float>& inputData,
-                                const std::vector<float>& expectedOutput,
-                                const size_t numOfAxes,
-                                Compute backendId)
-{
-    // Create ArmNN runtime
-    IRuntimePtr run = IRuntime::Create(IRuntime::CreationOptions());
-
-    // Optimise ArmNN network
-    IOptimizedNetworkPtr optNet = Optimize(*network, {backendId}, run->GetDeviceSpec());
-
-    Graph& graph = GetGraphForTesting(optNet.get());
-    if (numOfAxes == 2)
-    {
-        BOOST_CHECK(graph.GetNumLayers() == 4);
-        BOOST_TEST(CheckSequence(graph.cbegin(),
-                                 graph.cend(),
-                                 &IsLayerOfType<InputLayer>,
-                                 &IsLayerOfType<ReduceLayer>,
-                                 &IsLayerOfType<ReduceLayer>,
-                                 &IsLayerOfType<OutputLayer>));
-    }
-    else
-    {
-        BOOST_CHECK(graph.GetNumLayers() == 5);
-        BOOST_TEST(CheckSequence(graph.cbegin(),
-                                 graph.cend(),
-                                 &IsLayerOfType<InputLayer>,
-                                 &IsLayerOfType<ReduceLayer>,
-                                 &IsLayerOfType<ReduceLayer>,
-                                 &IsLayerOfType<ReduceLayer>,
-                                 &IsLayerOfType<OutputLayer>));
-    }
-
-    // Get last layer in new chain, layers name follow 0, 1, 2 pattern
-    std::string layerName = "reduce_layer_" + std::to_string(numOfAxes - 1);
-    Layer* const reduceLayer = GetFirstLayerWithName(graph, layerName);
-    BOOST_TEST(reduceLayer);
-    auto reduceTensorInfo = reduceLayer->GetOutputSlot().GetTensorInfo();
-
-    // Tensorshape and the data type are correct
-    BOOST_TEST((reduceTensorInfo.GetShape() == outputShape));
-    BOOST_TEST((reduceTensorInfo.GetDataType() == DataType::Float32));
-
-    // Load network into runtime
-    NetworkId networkIdentifier;
-    run->LoadNetwork(networkIdentifier, std::move(optNet));
-
-    // Create input and output tensors
-    std::vector<float> outputData(expectedOutput.size());
-    InputTensors inputTensors
-    {
-        { 0, armnn::ConstTensor(run->GetInputTensorInfo(networkIdentifier, 0), inputData.data()) }
-    };
-    OutputTensors outputTensors
-    {
-        { 0, armnn::Tensor(run->GetOutputTensorInfo(networkIdentifier, 0), outputData.data()) }
-    };
-
-    // Run inference
-    run->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);
-
-    // Checks the results
-    BOOST_TEST(outputData == expectedOutput);
-}
-
-void ReduceSumWithTwoAxesKeepDimsTest(Compute backendId)
-{
-    armnn::ReduceDescriptor reduceDescriptor;
-    reduceDescriptor.m_vAxis = { 1, 2 };
-    reduceDescriptor.m_KeepDims = true;
-    reduceDescriptor.m_ReduceOperation = armnn::ReduceOperation::Sum;
-
-    TensorShape inputShape  = { 1, 3, 2, 4 };
-    TensorShape outputShape = { 1, 1, 1, 4 };
-
-    // Construct ArmNN network
-    INetworkPtr network = CreateSimpleReduceNetwork(reduceDescriptor, inputShape, outputShape);
-
-    // Creates structures for input & output.
-    const std::vector<float> inputData({  1.0f,   2.0f,   3.0f,   4.0f,
-                                          5.0f,   6.0f,   7.0f,   8.0f,
-
-                                          10.0f,  20.0f,  30.0f,  40.0f,
-                                          50.0f,  60.0f,  70.0f,  80.0f,
-
-                                          100.0f, 200.0f, 300.0f, 400.0f,
-                                          500.0f, 600.0f, 700.0f, 800.0f });
-    const std::vector<float> expectedOutput({ 666.0f, 888.0f, 1110.0f, 1332.0f });
-
-    ReduceWithMultipleAxesTest(network,
-                               outputShape,
-                               inputData,
-                               expectedOutput,
-                               reduceDescriptor.m_vAxis.size(),
-                               backendId);
-}
-
-void ReduceSumWithTwoAxesTest(Compute backendId)
-{
-    armnn::ReduceDescriptor reduceDescriptor;
-    reduceDescriptor.m_vAxis = { 1, 2 };
-    reduceDescriptor.m_KeepDims = false;
-    reduceDescriptor.m_ReduceOperation = armnn::ReduceOperation::Sum;
-
-    TensorShape inputShape  = { 1, 3, 2, 4 };
-    TensorShape outputShape = { 1, 4 };
-
-    // Construct ArmNN network
-    INetworkPtr network = CreateSimpleReduceNetwork(reduceDescriptor, inputShape, outputShape);
-
-    // Creates structures for input & output.
-    const std::vector<float> inputData({  1.0f,   2.0f,   3.0f,   4.0f,
-                                          5.0f,   6.0f,   7.0f,   8.0f,
-
-                                          10.0f,  20.0f,  30.0f,  40.0f,
-                                          50.0f,  60.0f,  70.0f,  80.0f,
-
-                                          100.0f, 200.0f, 300.0f, 400.0f,
-                                          500.0f, 600.0f, 700.0f, 800.0f });
-    const std::vector<float> expectedOutput({ 666.0f, 888.0f, 1110.0f, 1332.0f });
-
-    ReduceWithMultipleAxesTest(network,
-                               outputShape,
-                               inputData,
-                               expectedOutput,
-                               reduceDescriptor.m_vAxis.size(),
-                               backendId);
-}
-
-void ReduceSumWithThreeAxesKeepDimsTest(Compute backendId)
-{
-    armnn::ReduceDescriptor reduceDescriptor;
-    reduceDescriptor.m_vAxis = { 0, 2, 3 };
-    reduceDescriptor.m_KeepDims = true;
-    reduceDescriptor.m_ReduceOperation = armnn::ReduceOperation::Sum;
-
-    TensorShape inputShape  = { 2, 2, 2, 2 };
-    TensorShape outputShape = { 1, 2, 1, 1 };
-
-    // Construct ArmNN network
-    INetworkPtr network = CreateSimpleReduceNetwork(reduceDescriptor, inputShape, outputShape);
-
-    // Creates structures for input & output.
-    const std::vector<float> inputData({  1.0f,   2.0f,
-                                          3.0f,   4.0f,
-
-                                          5.0f,   6.0f,
-                                          7.0f,   8.0f,
-
-                                          10.0f,  20.0f,
-                                          30.0f,  40.0f,
-
-                                          50.0f,  60.0f,
-                                          70.0f,  80.0f });
-    const std::vector<float> expectedOutput({ 110.0f, 286.0f });
-
-    ReduceWithMultipleAxesTest(network,
-                               outputShape,
-                               inputData,
-                               expectedOutput,
-                               reduceDescriptor.m_vAxis.size(),
-                               backendId);
-}
-
-void ReduceSumWithThreeAxesTest(Compute backendId)
-{
-    armnn::ReduceDescriptor reduceDescriptor;
-    reduceDescriptor.m_vAxis = { 0, 2, 3 };
-    reduceDescriptor.m_KeepDims = false;
-    reduceDescriptor.m_ReduceOperation = armnn::ReduceOperation::Sum;
-
-    TensorShape inputShape  = { 2, 2, 2, 2 };
-    TensorShape outputShape = { 2 };
-
-    // Construct ArmNN network
-    INetworkPtr network = CreateSimpleReduceNetwork(reduceDescriptor, inputShape, outputShape);
-
-    // Creates structures for input & output.
-    const std::vector<float> inputData({  1.0f,   2.0f,
-                                          3.0f,   4.0f,
-
-                                          5.0f,   6.0f,
-                                          7.0f,   8.0f,
-
-                                          10.0f,  20.0f,
-                                          30.0f,  40.0f,
-
-                                          50.0f,  60.0f,
-                                          70.0f,  80.0f });
-    const std::vector<float> expectedOutput({ 110.0f, 286.0f });
-
-    ReduceWithMultipleAxesTest(network,
-                               outputShape,
-                               inputData,
-                               expectedOutput,
-                               reduceDescriptor.m_vAxis.size(),
-                               backendId);
-}
-
-using namespace armnn;
-#if defined(ARMCOMPUTENEON_ENABLED)
-BOOST_AUTO_TEST_CASE(ReduceSumWithTwoAxesKeepDimsCpuAccTest)
-{
-    ReduceSumWithTwoAxesKeepDimsTest(Compute::CpuAcc);
-}
-
-BOOST_AUTO_TEST_CASE(ReduceSumWithTwoAxesCpuAccTest)
-{
-    ReduceSumWithTwoAxesTest(Compute::CpuAcc);
-}
-
-BOOST_AUTO_TEST_CASE(ReduceSumWithThreeAxesKeepDimsCpuAccTest)
-{
-    ReduceSumWithThreeAxesKeepDimsTest(Compute::CpuAcc);
-}
-
-BOOST_AUTO_TEST_CASE(ReduceSumWithThreeAxesCpuAccTest)
-{
-    ReduceSumWithThreeAxesTest(Compute::CpuAcc);
-}
-#endif
-
-#if defined(ARMCOMPUTECL_ENABLED)
-BOOST_AUTO_TEST_CASE(ReduceSumWithTwoAxesKeepDimsGpuAccTest)
-{
-    ReduceSumWithTwoAxesKeepDimsTest(Compute::GpuAcc);
-}
-
-BOOST_AUTO_TEST_CASE(ReduceSumWithTwoAxesGpuAccTest)
-{
-    ReduceSumWithTwoAxesTest(Compute::GpuAcc);
-}
-
-BOOST_AUTO_TEST_CASE(ReduceSumWithThreeAxesKeepDimsGpuAccTest)
-{
-    ReduceSumWithThreeAxesKeepDimsTest(Compute::GpuAcc);
-}
-
-BOOST_AUTO_TEST_CASE(ReduceSumWithThreeAxesGpuAccTest)
-{
-    ReduceSumWithThreeAxesTest(Compute::GpuAcc);
-}
-#endif
-
-BOOST_AUTO_TEST_SUITE_END()
\ No newline at end of file