IVGCVSW-4018 Move QuantizeHelper.hpp to armnnUtils
* Moved QuntizeHelper.hpp to armnnUtils
* Reordered parameters for QuantizedVector and added default
values for qScale and qOffset to make life easier when
using the function for non-quantized types such as Float16
Signed-off-by: Aron Virginas-Tar <Aron.Virginas-Tar@arm.com>
Change-Id: I28c263dfa425f1316feccb4116839a84f5d568e5
diff --git a/src/backends/backendsCommon/test/layerTests/BatchNormalizationTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/BatchNormalizationTestImpl.cpp
index ef43088..68cda7c 100644
--- a/src/backends/backendsCommon/test/layerTests/BatchNormalizationTestImpl.cpp
+++ b/src/backends/backendsCommon/test/layerTests/BatchNormalizationTestImpl.cpp
@@ -6,6 +6,7 @@
#include "BatchNormalizationTestImpl.hpp"
#include <DataLayoutIndexed.hpp>
+#include <QuantizeHelper.hpp>
#include <ResolveType.hpp>
#include <armnn/ArmNN.hpp>
@@ -14,7 +15,6 @@
#include <backendsCommon/IBackendInternal.hpp>
#include <backendsCommon/WorkloadFactory.hpp>
-#include <backendsCommon/test/QuantizeHelper.hpp>
#include <backendsCommon/test/TensorCopyUtils.hpp>
#include <backendsCommon/test/WorkloadTestUtils.hpp>
@@ -23,6 +23,8 @@
namespace
{
+using namespace armnnUtils;
+
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 4> BatchNormTestImpl(
armnn::IWorkloadFactory& workloadFactory,
@@ -53,19 +55,18 @@
tensorInfo.SetQuantizationOffset(qOffset);
}
- auto inputTensor = MakeTensor<T, 4>(inputTensorInfo,
- QuantizedVector<T>(qScale, qOffset, inputValues));
+ auto inputTensor = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(inputValues, qScale, qOffset));
// These values are per-channel of the input.
- auto mean = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {3, -2}));
- auto variance = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {4, 9}));
- auto beta = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {3, 2}));
- auto gamma = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {2, 1}));
+ auto mean = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>({ 3, -2 }, qScale, qOffset));
+ auto variance = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>({ 4, 9 }, qScale, qOffset));
+ auto beta = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>({ 3, 2 }, qScale, qOffset));
+ auto gamma = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>({ 2, 1 }, qScale, qOffset));
LayerTestResult<T, 4> result(outputTensorInfo);
result.outputExpected = MakeTensor<T, 4>(inputTensorInfo,
- QuantizedVector<T>(qScale, qOffset, expectedOutputValues));
+ QuantizedVector<T>(expectedOutputValues, qScale, qOffset));
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
@@ -134,17 +135,18 @@
}
auto input = MakeTensor<T, 4>(inputTensorInfo,
- QuantizedVector<T>(qScale, qOffset,
+ QuantizedVector<T>(
{
1.f, 1.f, 4.f, 1.f,
4.f, 4.f, 2.f, 1.f,
1.f, -2.f, 6.f, 4.f
- }));
+ },
+ qScale, qOffset));
// These values are per-channel of the input.
- auto mean = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {3, -2}));
- auto variance = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {4, 9}));
- auto beta = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {3, 2}));
- auto gamma = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {2, 1}));
+ auto mean = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>({ 3, -2 }, qScale, qOffset));
+ auto variance = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>({ 4, 9 }, qScale, qOffset));
+ auto beta = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>({ 3, 2 }, qScale, qOffset));
+ auto gamma = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>({ 2, 1 }, qScale, qOffset));
LayerTestResult<T,4> ret(outputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
@@ -175,12 +177,13 @@
// substract mean, divide by standard deviation (with an epsilon to avoid div by 0),
// multiply by gamma and add beta
ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo,
- QuantizedVector<T>(qScale, qOffset,
+ QuantizedVector<T>(
{
1.f, 3.f, 4.f, 3.f,
4.f, 4.f, 2.f, 3.f,
1.f, 2.f, 6.f, 4.f
- }));
+ },
+ qScale, qOffset));
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateBatchNormalization(data, info);