IVGCVSW-2316 Add reference implementation and unit tests for Debug

Change-Id: Ib2e5de2a057da57ef77a9f5c4367d699d4773294

1 files changed, 272 insertions, 0 deletions

diff --git a/src/backends/backendsCommon/test/DebugTestImpl.hpp b/src/backends/backendsCommon/test/DebugTestImpl.hpp
new file mode 100644
index 0000000000..e0f8a35d0a
--- /dev/null
+++ b/src/backends/backendsCommon/test/DebugTestImpl.hpp
@@ -0,0 +1,272 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+#pragma once
+
+#include "WorkloadTestUtils.hpp"
+
+#include <armnn/ArmNN.hpp>
+#include <armnn/Tensor.hpp>
+#include <armnn/TypesUtils.hpp>
+
+#include <backendsCommon/CpuTensorHandle.hpp>
+#include <backendsCommon/IBackendInternal.hpp>
+#include <backendsCommon/WorkloadFactory.hpp>
+
+#include <test/TensorHelpers.hpp>
+
+namespace
+{
+
+template<typename T, std::size_t Dim>
+LayerTestResult<T, Dim> DebugTestImpl(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    armnn::TensorInfo& inputTensorInfo,
+    armnn::TensorInfo& outputTensorInfo,
+    std::vector<float>& inputData,
+    std::vector<float>& outputExpectedData,
+    armnn::DebugQueueDescriptor descriptor,
+    const std::string expectedStringOutput,
+    const float qScale = 1.0f,
+    const int32_t qOffset = 0)
+{
+    if(armnn::IsQuantizedType<T>())
+    {
+        inputTensorInfo.SetQuantizationScale(qScale);
+        inputTensorInfo.SetQuantizationOffset(qOffset);
+
+        outputTensorInfo.SetQuantizationScale(qScale);
+        outputTensorInfo.SetQuantizationOffset(qOffset);
+    }
+
+    boost::multi_array<T, Dim> input =
+        MakeTensor<T, Dim>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData));
+
+    LayerTestResult<T, Dim> ret(outputTensorInfo);
+    ret.outputExpected =
+        MakeTensor<T, Dim>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, outputExpectedData));
+
+    std::unique_ptr<armnn::ITensorHandle> inputHandle =
+        workloadFactory.CreateTensorHandle(inputTensorInfo);
+
+    std::unique_ptr<armnn::ITensorHandle> outputHandle =
+        workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+    armnn::WorkloadInfo info;
+    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
+    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
+
+    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDebug(descriptor, info);
+
+    inputHandle->Allocate();
+    outputHandle->Allocate();
+
+    CopyDataToITensorHandle(inputHandle.get(), input.data());
+  
+    std::ostringstream oss;
+    std::streambuf* coutStreambuf = std::cout.rdbuf();
+    std::cout.rdbuf(oss.rdbuf());
+
+    ExecuteWorkload(*workload, memoryManager);
+  
+    std::cout.rdbuf(coutStreambuf);
+    
+    BOOST_TEST(oss.str() == expectedStringOutput);
+
+    CopyDataFromITensorHandle(ret.output.data(), outputHandle.get());
+
+    return ret;
+}
+
+template <typename T>
+LayerTestResult<T, 4> Debug4DTest(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    armnn::TensorInfo inputTensorInfo;
+    armnn::TensorInfo outputTensorInfo;
+
+    unsigned int inputShape[]  = {1, 2, 2, 3};
+    unsigned int outputShape[] = {1, 2, 2, 3};
+
+    armnn::DebugQueueDescriptor desc;
+    desc.m_Parameters.m_LayerName = "TestOutput";
+    desc.m_Parameters.m_SlotIndex = 1;
+
+    inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::GetDataType<T>());
+    outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::GetDataType<T>());
+
+    std::vector<float> input = std::vector<float>(
+    {
+        1.0f,   2.0f,  3.0f,
+        4.0f,   5.0f,  6.0f,
+        7.0f,   8.0f,  9.0f,
+        10.0f, 11.0f, 12.0f,
+    });
+
+    std::vector<float> outputExpected = std::vector<float>(
+    {
+        1.0f,   2.0f,  3.0f,
+        4.0f,   5.0f,  6.0f,
+        7.0f,   8.0f,  9.0f,
+        10.0f, 11.0f, 12.0f,
+    });
+
+    const std::string expectedStringOutput =
+        "{ \"layer\": \"TestOutput\","
+        " \"outputSlot\": 1,"
+        " \"shape\": [1, 2, 2, 3],"
+        " \"min\": 1, \"max\": 12,"
+        " \"data\": [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]] }\n";
+
+    return DebugTestImpl<T, 4>(workloadFactory,
+                               memoryManager,
+                               inputTensorInfo,
+                               outputTensorInfo,
+                               input,
+                               outputExpected,
+                               desc,
+                               expectedStringOutput);
+}
+
+template <typename T>
+LayerTestResult<T, 3> Debug3DTest(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    armnn::TensorInfo inputTensorInfo;
+    armnn::TensorInfo outputTensorInfo;
+
+    unsigned int inputShape[]  = {3, 3, 1};
+    unsigned int outputShape[] = {3, 3, 1};
+
+    armnn::DebugQueueDescriptor desc;
+    desc.m_Parameters.m_LayerName = "TestOutput";
+
+    inputTensorInfo = armnn::TensorInfo(3, inputShape, armnn::GetDataType<T>());
+    outputTensorInfo = armnn::TensorInfo(3, outputShape, armnn::GetDataType<T>());
+
+    std::vector<float> input = std::vector<float>(
+    {
+        1.0f, 2.0f, 3.0f,
+        4.0f, 5.0f, 6.0f,
+        7.0f, 8.0f, 9.0f,
+    });
+
+    std::vector<float> outputExpected = std::vector<float>(
+    {
+        1.0f, 2.0f, 3.0f,
+        4.0f, 5.0f, 6.0f,
+        7.0f, 8.0f, 9.0f,
+    });
+
+    const std::string expectedStringOutput =
+        "{ \"layer\": \"TestOutput\","
+        " \"outputSlot\": 0,"
+        " \"shape\": [3, 3, 1],"
+        " \"min\": 1, \"max\": 9,"
+        " \"data\": [[[1], [2], [3]], [[4], [5], [6]], [[7], [8], [9]]] }\n";
+
+    return DebugTestImpl<T, 3>(workloadFactory,
+                               memoryManager,
+                               inputTensorInfo,
+                               outputTensorInfo,
+                               input,
+                               outputExpected,
+                               desc,
+                               expectedStringOutput);
+}
+
+template <typename T>
+LayerTestResult<T, 2> Debug2DTest(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    armnn::TensorInfo inputTensorInfo;
+    armnn::TensorInfo outputTensorInfo;
+
+    unsigned int inputShape[]  = {2, 2};
+    unsigned int outputShape[] = {2, 2};
+
+    armnn::DebugQueueDescriptor desc;
+    desc.m_Parameters.m_LayerName = "TestOutput";
+
+    inputTensorInfo = armnn::TensorInfo(2, inputShape, armnn::GetDataType<T>());
+    outputTensorInfo = armnn::TensorInfo(2, outputShape, armnn::GetDataType<T>());
+
+    std::vector<float> input = std::vector<float>(
+    {
+        1.0f, 2.0f,
+        3.0f, 4.0f,
+    });
+
+    std::vector<float> outputExpected = std::vector<float>(
+    {
+        1.0f, 2.0f,
+        3.0f, 4.0f,
+    });
+
+    const std::string expectedStringOutput =
+        "{ \"layer\": \"TestOutput\","
+        " \"outputSlot\": 0,"
+        " \"shape\": [2, 2],"
+        " \"min\": 1, \"max\": 4,"
+        " \"data\": [[1, 2], [3, 4]] }\n";
+
+    return DebugTestImpl<T, 2>(workloadFactory,
+                               memoryManager,
+                               inputTensorInfo,
+                               outputTensorInfo,
+                               input,
+                               outputExpected,
+                               desc,
+                               expectedStringOutput);
+}
+
+template <typename T>
+LayerTestResult<T, 1> Debug1DTest(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    armnn::TensorInfo inputTensorInfo;
+    armnn::TensorInfo outputTensorInfo;
+
+    unsigned int inputShape[]  = {4};
+    unsigned int outputShape[] = {4};
+
+    armnn::DebugQueueDescriptor desc;
+    desc.m_Parameters.m_LayerName = "TestOutput";
+
+    inputTensorInfo = armnn::TensorInfo(1, inputShape, armnn::GetDataType<T>());
+    outputTensorInfo = armnn::TensorInfo(1, outputShape, armnn::GetDataType<T>());
+
+    std::vector<float> input = std::vector<float>(
+    {
+        1.0f, 2.0f, 3.0f, 4.0f,
+    });
+
+    std::vector<float> outputExpected = std::vector<float>(
+    {
+        1.0f, 2.0f, 3.0f, 4.0f,
+    });
+
+    const std::string expectedStringOutput =
+        "{ \"layer\": \"TestOutput\","
+        " \"outputSlot\": 0,"
+        " \"shape\": [4],"
+        " \"min\": 1, \"max\": 4,"
+        " \"data\": [1, 2, 3, 4] }\n";
+
+    return DebugTestImpl<T, 1>(workloadFactory,
+                               memoryManager,
+                               inputTensorInfo,
+                               outputTensorInfo,
+                               input,
+                               outputExpected,
+                               desc,
+                               expectedStringOutput);
+}
+
+} // anonymous namespace