IVGCVSW-2872 Unit tests for Quantize layer and reference workload

Change-Id: I291c08cb6e359453978b398255cf8ff051ed2686
Signed-off-by: Nattapat Chaimanowong <nattapat.chaimanowong@arm.com>

1 files changed, 126 insertions, 0 deletions

diff --git a/src/backends/backendsCommon/test/QuantizeTestImpl.hpp b/src/backends/backendsCommon/test/QuantizeTestImpl.hpp
new file mode 100644
index 0000000000..fee68f073e
--- /dev/null
+++ b/src/backends/backendsCommon/test/QuantizeTestImpl.hpp
@@ -0,0 +1,126 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+#pragma once
+
+#include "WorkloadTestUtils.hpp"
+
+#include <test/TensorHelpers.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>
+
+
+namespace
+{
+
+template<typename T, std::size_t Dim>
+LayerTestResult<T, Dim> QuantizeTestImpl(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    const armnn::TensorInfo& inputTensorInfo,
+    const armnn::TensorInfo& outputTensorInfo,
+    const std::vector<float>& inputData,
+    const std::vector<T>& expectedOutputData,
+    armnn::QuantizeQueueDescriptor descriptor)
+{
+    boost::multi_array<float, Dim> input = MakeTensor<float, Dim>(inputTensorInfo, inputData);
+
+    LayerTestResult<T, Dim> ret(outputTensorInfo);
+    ret.outputExpected = MakeTensor<T, Dim>(outputTensorInfo, expectedOutputData);
+
+    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.CreateQuantize(descriptor, info);
+
+    inputHandle->Allocate();
+    outputHandle->Allocate();
+
+    CopyDataToITensorHandle(inputHandle.get(), input.data());
+
+    ExecuteWorkload(*workload, memoryManager);
+
+    CopyDataFromITensorHandle(ret.output.data(), outputHandle.get());
+
+    return ret;
+}
+
+template <armnn::DataType ArmnnOutputType, typename T = armnn::ResolveType<ArmnnOutputType>>
+LayerTestResult<T, 4> QuantizeSimpleTest(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    armnn::QuantizeQueueDescriptor desc;
+
+    const armnn::TensorInfo inputTensorInfo({1, 2, 2, 3}, armnn::DataType::Float32);
+    const armnn::TensorInfo outputTensorInfo({1, 2, 2, 3}, ArmnnOutputType, 0.5f, 1);
+
+    std::vector<float> inputData = 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<T> expectedOutputData = std::vector<T>(
+    {
+         3,  5,  7,
+         9, 11, 13,
+        15, 17, 19,
+        21, 23, 25,
+    });
+
+    return QuantizeTestImpl<T, 4>(workloadFactory,
+                                  memoryManager,
+                                  inputTensorInfo,
+                                  outputTensorInfo,
+                                  inputData,
+                                  expectedOutputData,
+                                  desc);
+}
+
+template <armnn::DataType ArmnnOutputType, typename T = armnn::ResolveType<ArmnnOutputType>>
+LayerTestResult<T, 4> QuantizeClampTest(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    armnn::QuantizeQueueDescriptor desc;
+
+    const armnn::TensorInfo inputTensorInfo({1, 1, 2, 1}, armnn::DataType::Float32);
+    const armnn::TensorInfo outputTensorInfo({1, 1, 2, 1}, ArmnnOutputType, 0.0001f, 0);
+
+    const T max = std::numeric_limits<T>::max();
+    const T min = std::numeric_limits<T>::lowest();
+
+    std::vector<float> inputData = std::vector<float>(
+    {
+        -100.0f, 100.0f
+    });
+
+    std::vector<T> expectedOutputData = std::vector<T>(
+    {
+        min, max
+    });
+
+    return QuantizeTestImpl<T, 4>(workloadFactory,
+                                  memoryManager,
+                                  inputTensorInfo,
+                                  outputTensorInfo,
+                                  inputData,
+                                  expectedOutputData,
+                                  desc);
+}
+
+} // anonymous namespace