IVGCVSW-3381 Break up LayerTests.hpp into more manageable files

Signed-off-by: Aron Virginas-Tar <Aron.Virginas-Tar@arm.com>
Change-Id: Icf39434f09fd340ad664cb3b97b8bee6d9da4838

1 files changed, 0 insertions, 307 deletions

diff --git a/src/backends/backendsCommon/test/ConcatTestImpl.hpp b/src/backends/backendsCommon/test/ConcatTestImpl.hpp
deleted file mode 100644
index ded3857282..0000000000
--- a/src/backends/backendsCommon/test/ConcatTestImpl.hpp
+++ /dev/null
@@ -1,307 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#pragma once
-
-#include "CommonTestUtils.hpp"
-
-#include <ResolveType.hpp>
-
-#include <armnn/INetwork.hpp>
-
-#include <boost/test/unit_test.hpp>
-
-#include <vector>
-
-namespace
-{
-
-template<typename armnn::DataType DataType>
-INetworkPtr CreateConcatNetwork(const std::vector<TensorShape>& inputShapes,
-                                const TensorShape &outputShape,
-                                unsigned int concatAxis,
-                                const float qScale = 1.0f,
-                                const int32_t qOffset = 0)
-{
-    using namespace armnn;
-    // Builds up the structure of the network.
-    INetworkPtr net(INetwork::Create());
-
-    OriginsDescriptor descriptor;
-
-    descriptor = CreateDescriptorForConcatenation(inputShapes.begin(),
-                                                  inputShapes.end(),
-                                                  concatAxis);
-    IConnectableLayer* concat = net->AddConcatLayer(descriptor, "concat");
-
-    for (unsigned int i = 0; i < inputShapes.size(); ++i)
-    {
-        TensorInfo inputTensorInfo(inputShapes[i], DataType, qScale, qOffset);
-        IConnectableLayer* input = net->AddInputLayer(boost::numeric_cast<LayerBindingId>(i));
-        Connect(input, concat, inputTensorInfo, 0, i);
-    }
-
-    TensorInfo outputTensorInfo(outputShape, DataType, qScale, qOffset);
-    IConnectableLayer* output = net->AddOutputLayer(0, "output");
-    Connect(concat, output, outputTensorInfo, 0, 0);
-
-    return net;
-}
-
-template<armnn::DataType ArmnnType>
-void ConcatDim0EndToEnd(const std::vector<BackendId>& backends)
-{
-    using namespace armnn;
-    using T = ResolveType<ArmnnType>;
-
-    unsigned int concatAxis = 0;
-    const std::vector<TensorShape> inputShapes{{ 2, 3, 2, 2 }, { 2, 3, 2, 2 }};
-    const TensorShape& outputShape = { 4, 3, 2, 2 };
-
-    // Builds up the structure of the network
-    INetworkPtr net = CreateConcatNetwork<ArmnnType>(inputShapes, outputShape, concatAxis);
-
-    BOOST_TEST_CHECKPOINT("create a network");
-
-    // Creates structures for input & output.
-    std::vector<T> inputData{
-        1, 2,
-        3, 4,
-        5, 6,
-        7, 8,
-        9, 10,
-        11, 12,
-        1, 2,
-        3, 4,
-        5, 6,
-        7, 8,
-        9, 10,
-        11, 12
-    };
-
-    std::vector<T> expectedOutput{
-        1, 2,
-        3, 4,
-        5, 6,
-        7, 8,
-        9, 10,
-        11, 12,
-        1, 2,
-        3, 4,
-        5, 6,
-        7, 8,
-        9, 10,
-        11, 12,
-        1, 2,
-        3, 4,
-        5, 6,
-        7, 8,
-        9, 10,
-        11, 12,
-        1, 2,
-        3, 4,
-        5, 6,
-        7, 8,
-        9, 10,
-        11, 12
-    };
-
-    std::map<int, std::vector<T>> inputTensorData = {{ 0,inputData }, { 1,inputData }};
-    std::map<int, std::vector<T>> expectedOutputData = {{ 0,expectedOutput }};
-
-    EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends);
-}
-
-template<armnn::DataType ArmnnType>
-void ConcatDim1EndToEnd(const std::vector<BackendId>& backends)
-{
-    using namespace armnn;
-    using T = ResolveType<ArmnnType>;
-
-    unsigned int concatAxis = 1;
-    const std::vector<TensorShape> inputShapes{{ 2, 3, 2, 2 }, { 2, 3, 2, 2 }};
-    const TensorShape& outputShape = { 2, 6, 2, 2 };
-
-    // Builds up the structure of the network
-    INetworkPtr net = CreateConcatNetwork<ArmnnType>(inputShapes, outputShape, concatAxis);
-
-    BOOST_TEST_CHECKPOINT("create a network");
-
-    // Creates structures for input & output.
-    std::vector<T> inputData{
-        1, 2,
-        3, 4,
-        5, 6,
-        7, 8,
-        9, 10,
-        11, 12,
-        1, 2,
-        3, 4,
-        5, 6,
-        7, 8,
-        9, 10,
-        11, 12
-    };
-
-    std::vector<T> expectedOutput{
-        1, 2,
-        3, 4,
-        5, 6,
-        7, 8,
-        9, 10,
-        11, 12,
-        1, 2,
-        3, 4,
-        5, 6,
-        7, 8,
-        9, 10,
-        11, 12,
-        1, 2,
-        3, 4,
-        5, 6,
-        7, 8,
-        9, 10,
-        11, 12,
-        1, 2,
-        3, 4,
-        5, 6,
-        7, 8,
-        9, 10,
-        11, 12
-    };
-
-    std::map<int, std::vector<T>> inputTensorData = {{ 0,inputData }, { 1,inputData }};
-    std::map<int, std::vector<T>> expectedOutputData = {{ 0,expectedOutput }};
-
-    EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends);
-}
-
-template<armnn::DataType ArmnnType>
-void ConcatDim2EndToEnd(const std::vector<BackendId>& backends)
-{
-    using namespace armnn;
-    using T = ResolveType<ArmnnType>;
-
-    unsigned int concatAxis = 2;
-    const std::vector<TensorShape> inputShapes{{ 2, 3, 2, 2 }, { 2, 3, 2, 2 }};
-    const TensorShape& outputShape = { 2, 3, 4, 2 };
-
-    // Builds up the structure of the network
-    INetworkPtr net = CreateConcatNetwork<ArmnnType>(inputShapes, outputShape, concatAxis);
-
-    BOOST_TEST_CHECKPOINT("create a network");
-
-    // Creates structures for input & output.
-    std::vector<T> inputData{
-        1, 2,
-        3, 4,
-        5, 6,
-        7, 8,
-        9, 10,
-        11, 12,
-        1, 2,
-        3, 4,
-        5, 6,
-        7, 8,
-        9, 10,
-        11, 12
-    };
-
-    std::vector<T> expectedOutput{
-        1, 2,
-        3, 4,
-        1, 2,
-        3, 4,
-        5, 6,
-        7, 8,
-        5, 6,
-        7, 8,
-        9, 10,
-        11, 12,
-        9, 10,
-        11, 12,
-        1, 2,
-        3, 4,
-        1, 2,
-        3, 4,
-        5, 6,
-        7, 8,
-        5, 6,
-        7, 8,
-        9, 10,
-        11, 12,
-        9, 10,
-        11, 12
-    };
-
-    std::map<int, std::vector<T>> inputTensorData = {{ 0,inputData }, { 1,inputData }};
-    std::map<int, std::vector<T>> expectedOutputData = {{ 0,expectedOutput }};
-
-    EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends);
-}
-
-template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
-void ConcatDim3EndToEnd(const std::vector<BackendId>& backends)
-{
-    using namespace armnn;
-
-    unsigned int concatAxis = 3;
-    const std::vector<TensorShape> inputShapes{{ 2, 3, 2, 2 }, { 2, 3, 2, 2 }};
-    const TensorShape& outputShape = { 2, 3, 2, 4 };
-
-    // Builds up the structure of the network
-    INetworkPtr net = CreateConcatNetwork<ArmnnType>(inputShapes, outputShape, concatAxis);
-
-    BOOST_TEST_CHECKPOINT("create a network");
-
-    // Creates structures for input & output.
-    std::vector<T> inputData{
-        1, 2,
-        3, 4,
-        5, 6,
-        7, 8,
-        9, 10,
-        11, 12,
-        1, 2,
-        3, 4,
-        5, 6,
-        7, 8,
-        9, 10,
-        11, 12
-    };
-
-    std::vector<T> expectedOutput{
-        1, 2,
-        1, 2,
-        3, 4,
-        3, 4,
-        5, 6,
-        5, 6,
-        7, 8,
-        7, 8,
-        9, 10,
-        9, 10,
-        11, 12,
-        11, 12,
-        1, 2,
-        1, 2,
-        3, 4,
-        3, 4,
-        5, 6,
-        5, 6,
-        7, 8,
-        7, 8,
-        9, 10,
-        9, 10,
-        11, 12,
-        11, 12
-    };
-
-    std::map<int, std::vector<T>> inputTensorData = {{ 0,inputData }, { 1,inputData }};
-    std::map<int, std::vector<T>> expectedOutputData = {{ 0,expectedOutput }};
-
-    EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends);
-}
-
-} // anonymous namespace