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Diffstat (limited to 'src/backends/backendsCommon/test/layerTests/Conv2dTestImpl.cpp')
-rw-r--r--src/backends/backendsCommon/test/layerTests/Conv2dTestImpl.cpp53
1 files changed, 25 insertions, 28 deletions
diff --git a/src/backends/backendsCommon/test/layerTests/Conv2dTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/Conv2dTestImpl.cpp
index 8a2d8c8696..69a04df769 100644
--- a/src/backends/backendsCommon/test/layerTests/Conv2dTestImpl.cpp
+++ b/src/backends/backendsCommon/test/layerTests/Conv2dTestImpl.cpp
@@ -246,7 +246,9 @@ LayerTestResult<T, 4> SimpleConvolution2dTestImpl(
armnnUtils::GetTensorInfo(2*outputNum, outputChannels, outputHeight, outputWidth, layout, ArmnnType);
armnn::TensorInfo kernelDesc =
armnnUtils::GetTensorInfo(kernelDepthMul, kernelChannels, kernelHeight, kernelWidth, layout, ArmnnType);
+ kernelDesc.SetConstant(true);
armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, ArmnnBType);
+ biasDesc.SetConstant(true);
// Set quantization parameters if the requested type is a quantized type.
if(armnn::IsQuantizedType<T>())
@@ -412,10 +414,11 @@ LayerTestResult<O, 4> SimpleConvolution2dNhwcTestImpl(
// Creates the tensors.
armnn::TensorInfo inputTensorInfo({inputNum, inputHeight, inputWidth, inputChannels}, ArmnnType);
- armnn::TensorInfo outputTensorInfo({outputNum, outputHeight, outputWidth, outputChannels},
- OutType);
+ armnn::TensorInfo outputTensorInfo({outputNum, outputHeight, outputWidth, outputChannels}, OutType);
armnn::TensorInfo kernelDesc({kernelChanMul, kernelHeight, kernelWidth, kernelChannels}, ArmnnType);
+ kernelDesc.SetConstant(true);
armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, ArmnnBType);
+ biasDesc.SetConstant(true);
// Construct the input data.
std::vector<T> inputData;
@@ -432,11 +435,6 @@ LayerTestResult<O, 4> SimpleConvolution2dNhwcTestImpl(
std::unique_ptr<armnn::ITensorHandle> weightsHandle = tensorHandleFactory.CreateTensorHandle(kernelDesc);
std::unique_ptr<armnn::ITensorHandle> biasHandle = nullptr;
-// armnn::ScopedTensorHandle weightsTensor(kernelDesc);
-// AllocateAndCopyDataToITensorHandle(&weightsTensor, kernel.data());
-
-// armnn::ScopedTensorHandle biasTensor(biasDesc);
-
armnn::Convolution2dQueueDescriptor data;
data.m_Parameters.m_StrideX = strideX;
@@ -512,7 +510,9 @@ LayerTestResult<T,4> Convolution1dTestImpl(
armnn::TensorInfo inputInfo({batchSize, inputChannels, inputSize, 1}, ArmnnType);
armnn::TensorInfo outputInfo({batchSize, outputChannels, outputSize, 1}, ArmnnType);
armnn::TensorInfo kernelInfo({outputChannels, inputChannels, kernelSize, 1}, ArmnnType);
+ kernelInfo.SetConstant(true);
armnn::TensorInfo biasInfo({outputChannels}, ArmnnBType);
+ biasInfo.SetConstant(true);
// Set quantization parameters if the requested type is a quantized type.
if(armnn::IsQuantizedType<T>())
@@ -578,11 +578,6 @@ LayerTestResult<T,4> Convolution1dTestImpl(
armnn::Convolution2dQueueDescriptor data;
armnn::WorkloadInfo info;
-// armnn::ScopedTensorHandle weightsTensor(kernelInfo);
-// armnn::ScopedTensorHandle biasTensor(biasInfo);
-//
-// AllocateAndCopyDataToITensorHandle(&weightsTensor, kernelData.data());
-// AllocateAndCopyDataToITensorHandle(&biasTensor, biasData.data());
AddInputToWorkload(data, info, inputInfo, inputHandle.get());
AddInputToWorkload(data, info, kernelInfo, weightsHandle.get());
@@ -1390,7 +1385,9 @@ LayerTestResult<T,4> CompareConvolution2dTestImpl(
inputTensorInfo = armnn::TensorInfo(4, inputShape, ArmnnType);
outputTensorInfo = armnn::TensorInfo(4, outputShape, ArmnnType);
kernelDesc = armnn::TensorInfo(4, kernelShape, ArmnnType);
+ kernelDesc.SetConstant(true);
biasDesc = armnn::TensorInfo(1, biasShape, ArmnnType);
+ biasDesc.SetConstant(true);
auto input = MakeRandomTensor<T>(inputTensorInfo, 124908);
auto kernel = MakeRandomTensor<T>(kernelDesc, 891234);
@@ -1730,7 +1727,9 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestImpl(
armnn::TensorInfo outputTensorInfo =
armnnUtils::GetTensorInfo(outputNum, outputChannels, outputHeight, outputWidth, layout, ArmnnType);
armnn::TensorInfo kernelDesc({1, kernelHeight, kernelWidth, kernelChannels}, ArmnnType);
+ kernelDesc.SetConstant(true);
armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, ArmnnBType);
+ biasDesc.SetConstant(true);
// Set quantization parameters if the requested type is a quantized type.
if (armnn::IsQuantizedType<T>())
@@ -1874,9 +1873,10 @@ LayerTestResult<T, 4> DepthwiseConvolution2dDepthMul1TestImpl(
armnnUtils::GetTensorInfo(inputNum, inputChannels, inputHeight, inputWidth, layout, ArmnnType);
armnn::TensorInfo outputTensorInfo =
armnnUtils::GetTensorInfo(outputNum, outputChannels, outputHeight, outputWidth, layout, ArmnnType);
- armnn::TensorInfo kernelDesc({1, kernelHeight, kernelWidth, outputChannels},
- ArmnnType);
+ armnn::TensorInfo kernelDesc({1, kernelHeight, kernelWidth, outputChannels}, ArmnnType);
+ kernelDesc.SetConstant(true);
armnn::TensorInfo biasDesc({ outputChannels }, ArmnnBType);
+ biasDesc.SetConstant(true);
// Set quantization parameters if the requested type is a quantized type.
if(armnn::IsQuantizedType<T>())
@@ -2047,9 +2047,10 @@ LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(
inputBatchSize, inputChannels, inputHeight, inputWidth, layout, ArmnnType);
armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo(
outputBatchSize, outputChannels, outputHeight, outputWidth, layout, ArmnnType);
- armnn::TensorInfo kernelDesc({1, kernelHeight, kernelWidth, outputChannels},
- ArmnnType);
+ armnn::TensorInfo kernelDesc({1, kernelHeight, kernelWidth, outputChannels}, ArmnnType);
+ kernelDesc.SetConstant(true);
armnn::TensorInfo biasDesc({outputChannels}, ArmnnBType);
+ biasDesc.SetConstant(true);
// Set quantization parameters if the requested type is a quantized type.
if(armnn::IsQuantizedType<T>())
@@ -2291,8 +2292,9 @@ LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(
// Kernel must be NCHW layout always, independently of the layout of the input and output for depthwise convolution.
armnn::TensorInfo kernelDesc({1, kernelHeight, kernelWidth, kernelChannels}, ArmnnType);
-
+ kernelDesc.SetConstant(true);
armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, ArmnnBType);
+ biasDesc.SetConstant(true);
// Set quantization parameters if the requested type is a quantized type.
if(armnn::IsQuantizedType<T>())
@@ -3084,8 +3086,8 @@ LayerTestResult<T, 4> CompareDepthwiseConvolution2dTestImpl(
inputTensorInfo = armnn::TensorInfo(4, inputShape.data(), ArmnnType, inputsQScale, qOffset);
outputTensorInfo = armnn::TensorInfo(4, outputShape.data(), ArmnnType, outputQScale, qOffset);
- kernelDesc = armnn::TensorInfo(4, kernelShape.data(), ArmnnType, inputsQScale, qOffset);
- biasDesc = armnn::TensorInfo(1, biasShape.data(), armnn::GetBiasDataType(ArmnnType), inputsQScale, qOffset);
+ kernelDesc = armnn::TensorInfo(4, kernelShape.data(), ArmnnType, inputsQScale, qOffset, true);
+ biasDesc = armnn::TensorInfo(1, biasShape.data(), armnn::GetBiasDataType(ArmnnType), inputsQScale, qOffset, true);
auto input = MakeRandomTensor<T>(inputTensorInfo, 124908, 0.0f, 255.0f);
auto kernel = MakeRandomTensor<T>(kernelDesc, 891234, 0.0f, 255.0f);
@@ -3575,10 +3577,10 @@ LayerTestResult<uint8_t, 4> Convolution2dPerAxisQuantTest(
const std::vector<float> quantScales{ 0.5f, 0.75f, 1.0f };
constexpr unsigned int quantDimension = 0;
- TensorInfo kernelInfo({ 3, 1, 1, 2 }, kernelType, quantScales, quantDimension);
+ TensorInfo kernelInfo({ 3, 1, 1, 2 }, kernelType, quantScales, quantDimension, true);
const std::vector<float> biasQuantScales{ 0.25f, 0.375f, 0.5f };
- TensorInfo biasInfo({ 3 }, biasType, biasQuantScales, quantDimension);
+ TensorInfo biasInfo({ 3 }, biasType, biasQuantScales, quantDimension, true);
std::vector<uint8_t> inputData =
{
@@ -3625,11 +3627,6 @@ LayerTestResult<uint8_t, 4> Convolution2dPerAxisQuantTest(
std::unique_ptr<armnn::ITensorHandle> biasHandle = nullptr;
WorkloadInfo workloadInfo;
-// ScopedTensorHandle weightTensor(kernelInfo);
-// ScopedTensorHandle biasTensor(biasInfo);
-//
-// AllocateAndCopyDataToITensorHandle(&weightTensor, kernelData.data());
-// AllocateAndCopyDataToITensorHandle(&biasTensor, biasData.data());
Convolution2dQueueDescriptor queueDescriptor;
queueDescriptor.m_Parameters = descriptor;
@@ -3843,11 +3840,11 @@ LayerTestResult<uint8_t, 4> DepthwiseConvolution2dPerAxisQuantTest(
const std::vector<float> quantScales{ 1.0f, 0.5f, 1.0f, 0.5f };
const unsigned int quantDimension = 3;
- TensorInfo kernelInfo({ 1, 2, 2, 4 }, kernelType, quantScales, quantDimension); // [1, H, W, I*M]
+ TensorInfo kernelInfo({ 1, 2, 2, 4 }, kernelType, quantScales, quantDimension, true); // [1, H, W, I*M]
const std::vector<float> biasQuantScales{ 0.5f, 0.25f, 0.5f, 0.25f };
constexpr unsigned int biasQuantDimension = 0;
- TensorInfo biasInfo({ 4 }, biasType, biasQuantScales, biasQuantDimension);
+ TensorInfo biasInfo({ 4 }, biasType, biasQuantScales, biasQuantDimension, true);
std::vector<uint8_t> inputData =
{
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-31 23:55:29 +0000