17 template<armnn::DataType ArmnnType,
typename T = armnn::ResolveType<ArmnnType>>
24 const std::vector<float>& inputData,
25 const std::vector<float>& outputData,
26 const std::vector<int32_t> vAxis,
28 bool keepDims =
false)
31 auto inputTensor = ConvertToDataType<ArmnnType>(inputData, inputTensorInfo);
33 std::vector<float> actualOutput(outputTensorInfo.
GetNumElements());
35 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.
CreateTensorHandle(inputTensorInfo);
36 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.
CreateTensorHandle(outputTensorInfo);
39 std::vector<uint32_t> updated_idx;
40 uint32_t resolvedAxis = 0;
41 for (uint32_t i = 0; i < vAxis.size(); ++i)
45 resolvedAxis = inputTensorInfo.
GetNumDimensions() +
static_cast<uint32_t
>(vAxis[i]);
48 resolvedAxis =
static_cast<uint32_t
>(vAxis[i]);
51 updated_idx.push_back(resolvedAxis);
59 AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
60 AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
62 std::unique_ptr<armnn::IWorkload> workload = workloadFactory.
CreateReduce(descriptor, info);
64 inputHandle->Allocate();
65 outputHandle->Allocate();
75 outputHandle->GetShape(),
81 template<armnn::DataType ArmnnType,
typename T>
92 if (armnn::IsQuantizedType<T>())
100 std::vector<float> inputValues({ 5.0f, 2.0f, 8.0f, 10.0f, 9.0f });
101 std::vector<float> outputValues({ 34.0f });
103 return ReduceTestCommon<ArmnnType>(workloadFactory,
114 template<armnn::DataType ArmnnType,
typename T>
125 if (armnn::IsQuantizedType<T>())
133 std::vector<float> inputValues({ 1.0f, 2.0f, 3.0f, 4.0f,
134 5.0f, 6.0f, 7.0f, 8.0f,
136 10.0f, 20.0f, 30.0f, 40.0f,
137 50.0f, 60.0f, 70.0f, 80.0f,
139 100.0f, 200.0f, 300.0f, 400.0f,
140 500.0f, 600.0f, 700.0f, 800.0f });
141 std::vector<float> outputValues({ 111.0f, 222.0f, 333.0f, 444.0f,
142 555.0f, 666.0f, 777.0f, 888.0f });
144 return ReduceTestCommon<ArmnnType>(workloadFactory,
155 template<armnn::DataType ArmnnType,
typename T>
166 if (armnn::IsQuantizedType<T>())
174 std::vector<float> inputValues( {7, 8, 6, 1,
199 std::vector<float> outputValues({ 28.0f, 35.0f, 30.0f, 27.0f,
200 27.0f, 31.0f, 31.0f, 24.0f,
201 35.0f, 32.0f, 29.0f, 44.0f});
203 return ReduceTestCommon<ArmnnType>(workloadFactory,
214 template<armnn::DataType ArmnnType,
typename T>
225 if (armnn::IsQuantizedType<T>())
233 std::vector<float> inputValues( {7, 8, 6, 1,
258 std::vector<float> outputValues({ 22.0f, 17.0f, 24.0f,
265 13.0f, 17.0f, 23.0f});
267 return ReduceTestCommon<ArmnnType>(workloadFactory,
279 template<armnn::DataType ArmnnType,
typename T>
290 if (armnn::IsQuantizedType<T>())
298 std::vector<float> inputValues({ 1.0f, 2.0f, 3.0f, 4.0f,
299 5.0f, 6.0f, 7.0f, 8.0f,
301 10.0f, 20.0f, 30.0f, 40.0f,
302 50.0f, 60.0f, 70.0f, 80.0f,
304 100.0f, 200.0f, 300.0f, 400.0f,
305 500.0f, 600.0f, 700.0f, 800.0f });
306 std::vector<float> outputValues({ 666.0f, 888.0f, 1110.0f, 1332.0f });
308 return ReduceTestCommon<ArmnnType>(workloadFactory,
322 ReduceSumSimpleTest<armnn::DataType::Float32>(
328 ReduceSumSingleAxisTest1<armnn::DataType::Float32>(
334 ReduceSumSingleAxisTest2<armnn::DataType::Float32>(
340 ReduceSumSingleAxisTest3<armnn::DataType::Float32>(
346 ReduceSumMultipleAxisTest<armnn::DataType::Float32>(
const TensorShape & GetShape() const
LayerTestResult< float, 4 > ReduceSumMultipleAxisTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)
LayerTestResult< float, 4 > ReduceSumSimpleTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)
LayerTestResult< float, 4 > ReduceSumSingleAxisTest1(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)
bool m_KeepDims
if true then output shape has no change.
ReduceOperation m_ReduceOperation
Specifies the reduction operation to execute.
void IgnoreUnused(Ts &&...)
LayerDescriptor m_Parameters
std::shared_ptr< IMemoryManager > IMemoryManagerSharedPtr
virtual std::unique_ptr< IWorkload > CreateReduce(const ReduceQueueDescriptor &descriptor, const WorkloadInfo &info) const
void SetQuantizationScale(float scale)
LayerTestResult< float, 4 > ReduceSumSingleAxisTest3(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)
void CopyDataFromITensorHandle(void *memory, const armnn::ITensorHandle *tensorHandle)
std::vector< uint32_t > m_vAxis
The indices of the dimensions to reduce.
Contains information about TensorInfos of a layer.
void SetQuantizationOffset(int32_t offset)
unsigned int GetNumDimensions() const
virtual std::unique_ptr< ITensorHandle > CreateTensorHandle(const TensorInfo &tensorInfo) const =0
LayerTestResult< float, 4 > ReduceSumSingleAxisTest2(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)
unsigned int GetNumElements() const
void CopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)