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-rw-r--r--src/backends/backendsCommon/test/Conv2dTestImpl.hpp131
1 files changed, 94 insertions, 37 deletions
diff --git a/src/backends/backendsCommon/test/Conv2dTestImpl.hpp b/src/backends/backendsCommon/test/Conv2dTestImpl.hpp
index 5f66d2ec85..98e5090e27 100644
--- a/src/backends/backendsCommon/test/Conv2dTestImpl.hpp
+++ b/src/backends/backendsCommon/test/Conv2dTestImpl.hpp
@@ -816,16 +816,17 @@ LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(
}
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType,
- typename T = armnn::ResolveType<ArmnnType>, typename B = armnn::ResolveType<ArmnnBType>>
-LayerTestResult<T, 4> DepthwiseConvolution2dNhwcTestImpl(
+ typename T = armnn::ResolveType<ArmnnType>, typename B = armnn::ResolveType<ArmnnBType>>
+LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
- const boost::multi_array<T, 4>& input,
- const boost::multi_array<T, 4>& kernel,
+ const boost::multi_array<T, 4>& originalInput,
+ const boost::multi_array<T, 4>& originalKernel,
const boost::multi_array<B, 1>& bias,
- const boost::multi_array<T, 4>& outputExpected,
+ const boost::multi_array<T, 4>& originalOutputExpected,
float qScale,
int32_t qOffset,
+ const armnn::DataLayout layout = armnn::DataLayout::NCHW,
uint32_t padLeft = 0,
uint32_t padTop = 0,
uint32_t padRight = 0,
@@ -835,30 +836,44 @@ LayerTestResult<T, 4> DepthwiseConvolution2dNhwcTestImpl(
uint32_t dilationX = 1,
uint32_t dilationY = 1)
{
- unsigned int inputNum = boost::numeric_cast<unsigned int>(input.shape()[0]);
- unsigned int inputChannels = boost::numeric_cast<unsigned int>(input.shape()[3]);
- unsigned int inputHeight = boost::numeric_cast<unsigned int>(input.shape()[1]);
- unsigned int inputWidth = boost::numeric_cast<unsigned int>(input.shape()[2]);
+ unsigned int inputHeight = boost::numeric_cast<unsigned int>(originalInput.shape()[2]);
+ unsigned int inputWidth = boost::numeric_cast<unsigned int>(originalInput.shape()[3]);
+ unsigned int inputChannels = boost::numeric_cast<unsigned int>(originalInput.shape()[1]);
+ unsigned int inputNum = boost::numeric_cast<unsigned int>(originalInput.shape()[0]);
- 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 outputHeight = boost::numeric_cast<unsigned int>(originalOutputExpected.shape()[2]);
+ unsigned int outputWidth = boost::numeric_cast<unsigned int>(originalOutputExpected.shape()[3]);
+ unsigned int outputChannels = boost::numeric_cast<unsigned int>(originalOutputExpected.shape()[1]);
+ unsigned int outputNum = boost::numeric_cast<unsigned int>(originalOutputExpected.shape()[0]);
- unsigned int outputNum = boost::numeric_cast<unsigned int>(outputExpected.shape()[0]);
- unsigned int outputChannels = boost::numeric_cast<unsigned int>(outputExpected.shape()[3]);
- unsigned int outputHeight = boost::numeric_cast<unsigned int>(outputExpected.shape()[1]);
- unsigned int outputWidth = boost::numeric_cast<unsigned int>(outputExpected.shape()[2]);
+ unsigned int kernelHeight = boost::numeric_cast<unsigned int>(originalKernel.shape()[2]);
+ unsigned int kernelWidth = boost::numeric_cast<unsigned int>(originalKernel.shape()[3]);
+ unsigned int kernelChannels = boost::numeric_cast<unsigned int>(originalKernel.shape()[1]);
+ unsigned int kernelDepthMul = boost::numeric_cast<unsigned int>(originalKernel.shape()[0]);
+
+ bool biasEnabled = bias.size() > 0;
+
+ // This function currently assumes 1 batch of input/output (and duplicates this into 2 batches).
+ BOOST_ASSERT(inputNum == 1);
+ BOOST_ASSERT(outputNum == 1);
+
+ // If a bias is used, its size must equal the number of output channels.
+ BOOST_ASSERT(!biasEnabled || bias.size() == outputChannels);
+
+
+ // Note these tensors will use two (identical) batches.
+ armnn::TensorInfo inputTensorInfo =
+ armnnUtils::GetTensorInfo(2*inputNum, inputChannels, inputHeight, inputWidth, layout, ArmnnType);
+ armnn::TensorInfo outputTensorInfo =
+ armnnUtils::GetTensorInfo(2*outputNum, outputChannels, outputHeight, outputWidth, layout, ArmnnType);
+
+ // Kernel must be NCHW layout always, independently of the layout of the input and output for depthwise convolution.
+ armnn::TensorInfo kernelDesc({kernelDepthMul, kernelChannels, kernelHeight, kernelWidth}, ArmnnType);
- // Creates the tensors.
- armnn::TensorInfo inputTensorInfo({inputNum, inputHeight, inputWidth, inputChannels}, ArmnnType);
- armnn::TensorInfo outputTensorInfo({outputNum, outputHeight, outputWidth, outputChannels},
- ArmnnType);
- armnn::TensorInfo kernelDesc({kernelChanMul, kernelChannels, kernelHeight, kernelWidth}, ArmnnType);
armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, ArmnnBType);
// Set quantization parameters if the requested type is a quantized type.
- if (armnn::IsQuantizedType<T>())
+ if(armnn::IsQuantizedType<T>())
{
inputTensorInfo.SetQuantizationScale(qScale);
inputTensorInfo.SetQuantizationOffset(qOffset);
@@ -870,45 +885,87 @@ LayerTestResult<T, 4> DepthwiseConvolution2dNhwcTestImpl(
biasDesc.SetQuantizationOffset(0);
}
- // Construct the input data.
+ LayerTestResult<T, 4> ret(outputTensorInfo);
+
+ // Construct input data
+ std::vector<T> input;
+ input.assign(originalInput.data(), originalInput.data() + 1*inputChannels*inputHeight*inputWidth);
std::vector<T> inputData;
- inputData.assign(input.data(), input.data() + inputHeight*inputWidth*inputChannels);
+ inputData.insert(inputData.end(), input.begin(), input.end());
+ inputData.insert(inputData.end(), input.begin(), input.end());
+
+ // at this point if we require it permute the input data
+ const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
+ if (layout == armnn::DataLayout::NHWC)
+ {
+ std::vector<T> tmp(inputData.size());
+ armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(T));
+ inputData = tmp;
+ }
+
auto batchedInput = MakeTensor<T, 4>(inputTensorInfo, inputData);
- // Construct the output data, with bias applied, as appropriate.
+ std::vector<T> output;
+ output.assign(originalOutputExpected.data(),
+ originalOutputExpected.data() + outputChannels*outputHeight*outputWidth);
+
+ // Apply bias to output data if it is enabled.
+ if(biasEnabled)
+ {
+ std::vector<T> biasV;
+ biasV.assign(bias.data(), bias.data() + outputChannels);
+ ApplyBias(output, outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(),
+ biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(),
+ outputWidth, outputHeight);
+ }
+
+ // Construct expected output data
std::vector<T> outputData;
- outputData.assign(outputExpected.data(), outputExpected.data() + outputHeight*outputWidth*outputChannels);
+ outputData.insert(outputData.end(), output.begin(), output.end());
+ outputData.insert(outputData.end(), output.begin(), output.end());
- LayerTestResult<T, 4> ret(outputTensorInfo);
+ // at this point if we require it permute the expected output
+ if (layout == armnn::DataLayout::NHWC)
+ {
+ std::vector<T> tmp(outputData.size());
+ armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp.data(), sizeof(T));
+ outputData = tmp;
+ }
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::DepthwiseConvolution2dQueueDescriptor data;
+ armnn::WorkloadInfo info;
armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc);
+ armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
+
+ boost::multi_array<T, 4> kernel = boost::multi_array<T, 4>(originalKernel);
AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]);
- armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
+ if(biasEnabled)
+ {
+ AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]);
+ }
+
+ AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
+ AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
- armnn::DepthwiseConvolution2dQueueDescriptor data;
data.m_Weight = &weightsTensor;
- data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled - it can be a source of bugs.
+ 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_DataLayout = armnn::DataLayout::NHWC;
+ data.m_Parameters.m_BiasEnabled = biasEnabled;
+ data.m_Parameters.m_DataLayout = layout;
data.m_Parameters.m_DilationX = dilationX;
data.m_Parameters.m_DilationY = dilationY;
- 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();
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-17 14:25:44 +0000