IVGCVSW-3234 Add unit test for Concat with different quantization params
Signed-off-by: Narumol Prangnawarat <narumol.prangnawarat@arm.com>
Change-Id: Ia655b1b6c5f57d2b7c0d9dd17be342cd01f36c09
diff --git a/src/backends/backendsCommon/test/LayerTests.hpp b/src/backends/backendsCommon/test/LayerTests.hpp
index f3d707c..058d694 100644
--- a/src/backends/backendsCommon/test/LayerTests.hpp
+++ b/src/backends/backendsCommon/test/LayerTests.hpp
@@ -3023,3 +3023,138 @@
return MeanTestHelper<ArmnnType, T, 4, 3>(
workloadFactory, memoryManager, inputShape, input, { 2 }, false, outputShape, output);
}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 3> ConcatDifferentInputOutputQParamTest(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+ bool useSubtensor)
+{
+ // Defines the tensor descriptors.
+ armnn::TensorInfo outputTensorInfo({ 3, 6, 3 }, ArmnnType);
+ armnn::TensorInfo inputTensorInfo1({ 3, 6, 2 }, ArmnnType);
+ armnn::TensorInfo inputTensorInfo2({ 3, 6, 1 }, ArmnnType);
+
+ std::vector<armnn::TensorShape> inputTensorShapes({inputTensorInfo1.GetShape(), inputTensorInfo2.GetShape()});
+
+ // Quantized input1 tensor.
+ const float inputScale1 = 0.5f;
+ const int32_t inputOffset1 = 5;
+
+ auto input1 = MakeTensor<T, 3>(inputTensorInfo1, std::vector<T>(
+ {
+ 1, 2, 3,
+ 4, 5, 6,
+ 7, 8, 9,
+ 10, 11, 12,
+ 13, 14, 15,
+ 16, 17, 18,
+
+ 19, 20, 21,
+ 22, 23, 24,
+ 25, 26, 27,
+ 28, 29, 30,
+ 31, 32, 33,
+ 34, 35, 36
+ }));
+
+ // Quatized input2 tensor.
+ const float inputScale2 = 0.2f;
+ const int32_t inputOffset2 = 10;
+
+ auto input2 = MakeTensor<T, 3>(inputTensorInfo2, std::vector<T>(
+ {
+ 37, 38, 39,
+ 40, 41, 42,
+ 43, 44, 45,
+ 46, 47, 48,
+ 49, 50, 51,
+ 52, 53, 54
+ }));
+
+ // Quantized output tensor.
+ const float outputScale = 0.1f;
+ const int32_t outputOffset = 20;
+
+ LayerTestResult<T, 3> ret(outputTensorInfo);
+
+ ret.outputExpected = MakeTensor<T, 3>(outputTensorInfo, std::vector<T>(
+ {
+ 0, 5, 74,
+ 10, 15, 76,
+ 20, 25, 78,
+ 30, 35, 80,
+ 40, 45, 82,
+ 50, 55, 84,
+
+ 60, 65, 86,
+ 70, 75, 88,
+ 80, 85, 90,
+ 90, 95, 92,
+ 100, 105, 94,
+ 110, 115, 96,
+
+ 120, 125, 98,
+ 130, 135, 100,
+ 140, 145, 102,
+ 150, 155, 104,
+ 160, 165, 106,
+ 170, 175, 108
+ }));
+
+ outputTensorInfo.SetQuantizationScale(outputScale);
+ outputTensorInfo.SetQuantizationOffset(outputOffset);
+ inputTensorInfo1.SetQuantizationScale(inputScale1);
+ inputTensorInfo1.SetQuantizationOffset(inputOffset1);
+ inputTensorInfo2.SetQuantizationScale(inputScale2);
+ inputTensorInfo2.SetQuantizationOffset(inputOffset2);
+
+ std::vector<unsigned int> wOrigin1 = { 0, 0, 0 }; //Extent of the window is defined by size of input[0].
+ armnn::ConcatQueueDescriptor::ViewOrigin window1(wOrigin1);
+
+ std::vector<unsigned int> wOrigin2 = { 0, 0, 2 }; //Extent of the window is defined by size of input[1].
+ armnn::ConcatQueueDescriptor::ViewOrigin window2(wOrigin2);
+
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ bool subTensorsSupported = useSubtensor && workloadFactory.SupportsSubTensors();
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle1 =
+ subTensorsSupported ?
+ workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) :
+ workloadFactory.CreateTensorHandle(inputTensorInfo1);
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle2 =
+ subTensorsSupported ?
+ workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) :
+ workloadFactory.CreateTensorHandle(inputTensorInfo2);
+
+ armnn::ConcatQueueDescriptor data;
+ armnn::OriginsDescriptor desc = armnn::CreateDescriptorForConcatenation(
+ inputTensorShapes.begin(),inputTensorShapes.end(), 2);
+ data.m_Parameters = desc;
+
+ armnn::WorkloadInfo info;
+ AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
+ AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());
+ AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
+
+ data.m_ViewOrigins.push_back(window1);
+ data.m_ViewOrigins.push_back(window2);
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConcat(data, info);
+
+ inputHandle1->Allocate();
+ inputHandle2->Allocate();
+ outputHandle->Allocate();
+
+ CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]);
+ CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]);
+
+ workload->PostAllocationConfigure();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get());
+
+ return ret;
+}