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Diffstat (limited to 'src/armnn/backends/test/Conv2dTestImpl.hpp')
-rw-r--r--src/armnn/backends/test/Conv2dTestImpl.hpp129
1 files changed, 124 insertions, 5 deletions
diff --git a/src/armnn/backends/test/Conv2dTestImpl.hpp b/src/armnn/backends/test/Conv2dTestImpl.hpp
index 0c0511b234..0c34beaa33 100644
--- a/src/armnn/backends/test/Conv2dTestImpl.hpp
+++ b/src/armnn/backends/test/Conv2dTestImpl.hpp
@@ -60,8 +60,6 @@ void ApplyBias(std::vector<T>& v, float vScale, int32_t vOffset,
}
}
-
-
template<typename T, typename B>
LayerTestResult<T, 4> SimpleConvolution2dTestImpl(armnn::IWorkloadFactory& workloadFactory,
const boost::multi_array<T, 4>& input,
@@ -87,6 +85,8 @@ LayerTestResult<T, 4> SimpleConvolution2dTestImpl(armnn::IWorkloadFactory& workl
unsigned int kernelHeight = boost::numeric_cast<unsigned int>(kernel.shape()[2]);
unsigned int kernelWidth = boost::numeric_cast<unsigned int>(kernel.shape()[3]);
+ unsigned int kernelChannels = boost::numeric_cast<unsigned int>(kernel.shape()[1]);
+ unsigned int kernelDepthMul = boost::numeric_cast<unsigned int>(kernel.shape()[0]);
bool biasEnabled = bias.size() > 0;
@@ -102,7 +102,7 @@ LayerTestResult<T, 4> SimpleConvolution2dTestImpl(armnn::IWorkloadFactory& workl
armnn::TensorInfo inputTensorInfo({2*inputNum, inputChannels, inputHeight, inputWidth}, armnn::GetDataType<T>());
armnn::TensorInfo outputTensorInfo({2*outputNum, outputChannels, outputHeight, outputWidth},
armnn::GetDataType<T>());
- armnn::TensorInfo kernelDesc({outputChannels, inputChannels, kernelHeight, kernelWidth}, armnn::GetDataType<T>());
+ armnn::TensorInfo kernelDesc({kernelDepthMul, kernelChannels, kernelHeight, kernelWidth}, armnn::GetDataType<T>());
armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, armnn::GetDataType<B>());
// Set quantization parameters if the requested type is a quantized type.
@@ -186,6 +186,120 @@ LayerTestResult<T, 4> SimpleConvolution2dTestImpl(armnn::IWorkloadFactory& workl
CopyDataToITensorHandle(inputHandle.get(), &batchedInput[0][0][0][0]);
+ workloadFactory.Finalize();
+ workload->Execute();
+
+ CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
+
+ return ret;
+}
+
+template<typename T, typename B>
+LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestImpl(armnn::IWorkloadFactory& workloadFactory,
+ const boost::multi_array<T, 4>& input,
+ const boost::multi_array<T, 4>& kernel,
+ const boost::multi_array<B, 1>& bias,
+ const boost::multi_array<T, 4>& outputExpected,
+ float qScale,
+ int32_t qOffset,
+ uint32_t padLeft = 0,
+ uint32_t padTop = 0,
+ uint32_t padRight = 0,
+ uint32_t padBottom = 0,
+ uint32_t strideX = 1,
+ uint32_t strideY = 1)
+{
+ unsigned int inputNum = boost::numeric_cast<unsigned int>(input.shape()[0]);
+ unsigned int inputChannels = boost::numeric_cast<unsigned int>(input.shape()[1]);
+ unsigned int inputHeight = boost::numeric_cast<unsigned int>(input.shape()[2]);
+ unsigned int inputWidth = boost::numeric_cast<unsigned int>(input.shape()[3]);
+ unsigned int kernelChanMul = boost::numeric_cast<unsigned int>(kernel.shape()[0]);
+ unsigned int kernelChannels = boost::numeric_cast<unsigned int>(kernel.shape()[1]);
+ unsigned int kernelHeight = boost::numeric_cast<unsigned int>(kernel.shape()[2]);
+ unsigned int kernelWidth = boost::numeric_cast<unsigned int>(kernel.shape()[3]);
+ unsigned int outputNum = boost::numeric_cast<unsigned int>(outputExpected.shape()[0]);
+ unsigned int outputChannels = boost::numeric_cast<unsigned int>(outputExpected.shape()[1]);
+ unsigned int outputHeight = boost::numeric_cast<unsigned int>(outputExpected.shape()[2]);
+ unsigned int outputWidth = boost::numeric_cast<unsigned int>(outputExpected.shape()[3]);
+
+ // If a bias is used, its size must equal the number of output channels
+ bool biasEnabled = bias.size() > 0;
+ BOOST_ASSERT(!biasEnabled || bias.size() == outputChannels);
+
+ // create the tensors
+ armnn::TensorInfo inputTensorInfo({inputNum, inputChannels, inputHeight, inputWidth}, armnn::GetDataType<T>());
+ armnn::TensorInfo outputTensorInfo({outputNum, outputChannels, outputHeight, outputWidth},
+ armnn::GetDataType<T>());
+ armnn::TensorInfo kernelDesc({kernelChanMul, kernelChannels, kernelHeight, kernelWidth}, armnn::GetDataType<T>());
+ armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, armnn::GetDataType<B>());
+
+ // Set quantization parameters if the requested type is a quantized type.
+ if (armnn::IsQuantizedType<T>())
+ {
+ inputTensorInfo.SetQuantizationScale(qScale);
+ inputTensorInfo.SetQuantizationOffset(qOffset);
+ outputTensorInfo.SetQuantizationScale(qScale);
+ outputTensorInfo.SetQuantizationOffset(qOffset);
+ kernelDesc.SetQuantizationScale(qScale);
+ kernelDesc.SetQuantizationOffset(qOffset);
+ biasDesc.SetQuantizationScale(qScale*qScale);
+ biasDesc.SetQuantizationOffset(0);
+ }
+
+ // Construct the input data
+ std::vector<T> inputData;
+ inputData.assign(input.data(), input.data() + inputChannels*inputHeight*inputWidth);
+ auto batchedInput = MakeTensor<T, 4>(inputTensorInfo, inputData);
+
+ // Construct the output data, with bias applied, as appropriate
+ std::vector<T> outputData;
+ outputData.assign(outputExpected.data(), outputExpected.data() + outputChannels*outputHeight*outputWidth);
+ if (biasEnabled)
+ {
+ std::vector<T> biasV;
+ biasV.assign(bias.data(), bias.data() + outputChannels);
+ ApplyBias(outputData, outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(),
+ biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(),
+ outputWidth, outputHeight);
+ }
+
+ LayerTestResult<T, 4> ret(outputTensorInfo);
+ ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData);
+
+ std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+ armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc);
+ AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]);
+
+ armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
+ if (biasEnabled)
+ {
+ AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]);
+ }
+
+ armnn::DepthwiseConvolution2dQueueDescriptor data;
+ data.m_Weight = &weightsTensor;
+ data.m_Bias = &biasTensor; // still set this whether or not bias is enabled - can be a source of bugs
+ data.m_Parameters.m_StrideX = strideX;
+ data.m_Parameters.m_StrideY = strideY;
+ data.m_Parameters.m_PadLeft = padLeft;
+ data.m_Parameters.m_PadRight = padRight;
+ data.m_Parameters.m_PadTop = padTop;
+ data.m_Parameters.m_PadBottom = padBottom;
+ data.m_Parameters.m_BiasEnabled = biasEnabled;
+
+ armnn::WorkloadInfo info;
+ AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
+ AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
+
+ std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDepthwiseConvolution2d(data, info);
+ inputHandle->Allocate();
+ outputHandle->Allocate();
+
+ CopyDataToITensorHandle(inputHandle.get(), &batchedInput[0][0][0][0]);
+
+ workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
@@ -306,6 +420,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dDepthMul1TestImpl(armnn::IWorkloadFa
CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+ workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
@@ -478,6 +593,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(armnn::IWorkloadFactory& wo
CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+ workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
@@ -485,8 +601,6 @@ LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(armnn::IWorkloadFactory& wo
return ret;
}
-
-
template<typename T>
LayerTestResult<T,4> Convolution1dTestImpl(armnn::IWorkloadFactory& workloadFactory,
float qScale,
@@ -595,6 +709,7 @@ LayerTestResult<T,4> Convolution1dTestImpl(armnn::IWorkloadFactory& workloadFact
CopyDataToITensorHandle(inputHandle.get(), inputData.data());
+ workloadFactory.Finalize();
workload->Execute();
// output
@@ -692,7 +807,9 @@ LayerTestResult<T,4> CompareConvolution2dTestImpl(armnn::IWorkloadFactory& workl
CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]);
+ workloadFactory.Finalize();
workload->Execute();
+ refWorkloadFactory.Finalize();
workloadRef->Execute();
CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
@@ -792,7 +909,9 @@ LayerTestResult<T, 4> CompareDepthwiseConvolution2dTestImpl(armnn::IWorkloadFact
CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]);
+ workloadFactory.Finalize();
workload->Execute();
+ refWorkloadFactory.Finalize();
workloadRef->Execute();
CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
generated by cgit v1.2.1 (git 2.23.0) at 2024-06-29 22:16:01 +0000