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Diffstat (limited to 'src/armnn/backends/test/Conv2dTestImpl.hpp')
-rw-r--r-- | src/armnn/backends/test/Conv2dTestImpl.hpp | 921 |
1 files changed, 0 insertions, 921 deletions
diff --git a/src/armnn/backends/test/Conv2dTestImpl.hpp b/src/armnn/backends/test/Conv2dTestImpl.hpp deleted file mode 100644 index eb7165bf09..0000000000 --- a/src/armnn/backends/test/Conv2dTestImpl.hpp +++ /dev/null @@ -1,921 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#pragma once - -#include <armnn/ArmNN.hpp> -#include <armnn/Tensor.hpp> -#include <armnn/TypesUtils.hpp> -#include <backends/WorkloadInfo.hpp> - -#include "test/TensorHelpers.hpp" -#include "QuantizeHelper.hpp" - -#include "backends/CpuTensorHandle.hpp" -#include "backends/WorkloadFactory.hpp" - -// Mapping from input type to bias type for fully connected layers. -// float => float, uint8_t => int32_t -template<typename T> -struct FullyConnectedBiasTypeForInputType; - -template<> -struct FullyConnectedBiasTypeForInputType<float> -{ - using Type = float; -}; - -template<> -struct FullyConnectedBiasTypeForInputType<uint8_t> -{ - using Type = int32_t; -}; - -// Modifies a std::vector in-place using a specified bias. -template<typename T, typename B> -void ApplyBias(std::vector<T>& v, float vScale, int32_t vOffset, - const std::vector<B>& bias, float bScale, int32_t bOffset, uint32_t w, uint32_t h) -{ - BOOST_ASSERT_MSG((armnn::IsQuantizedType<T>() && vScale != 0.0f) || (!armnn::IsQuantizedType<T>()), - "Invalid type and parameter combination."); - BOOST_ASSERT_MSG((armnn::IsQuantizedType<B>() && bScale != 0.0f) || (!armnn::IsQuantizedType<B>()), - "Invalid type and parameter combination."); - - // Note we need to dequantize and re-quantize the image value and the bias. - for (uint32_t i = 0; i < bias.size(); ++i) - { - float dBias = SelectiveDequantize(bias[i], bScale, bOffset); - for (uint32_t y = 0; y < h; ++y) - { - for (uint32_t x = 0; x < w; ++x) - { - uint32_t offset = (i * h + y) * w + x; - BOOST_ASSERT(offset < v.size()); - T& outRef = v[offset]; - float dOutput = SelectiveDequantize(outRef, vScale, vOffset); - outRef = SelectiveQuantize<T>(dOutput + dBias, vScale, vOffset); - } - } - } -} - -template<typename T, typename B> -LayerTestResult<T, 4> SimpleConvolution2dTestImpl(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) -{ - unsigned int inputHeight = boost::numeric_cast<unsigned int>(input.shape()[2]); - unsigned int inputWidth = boost::numeric_cast<unsigned int>(input.shape()[3]); - unsigned int inputChannels = boost::numeric_cast<unsigned int>(input.shape()[1]); - unsigned int inputNum = boost::numeric_cast<unsigned int>(input.shape()[0]); - - unsigned int outputHeight = boost::numeric_cast<unsigned int>(outputExpected.shape()[2]); - unsigned int outputWidth = boost::numeric_cast<unsigned int>(outputExpected.shape()[3]); - unsigned int outputChannels = boost::numeric_cast<unsigned int>(outputExpected.shape()[1]); - unsigned int outputNum = boost::numeric_cast<unsigned int>(outputExpected.shape()[0]); - - 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; - - // 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({2*inputNum, inputChannels, inputHeight, inputWidth}, armnn::GetDataType<T>()); - armnn::TensorInfo outputTensorInfo({2*outputNum, outputChannels, outputHeight, outputWidth}, - 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. - 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); - } - - LayerTestResult<T, 4> ret(outputTensorInfo); - - // Construct input data - two batches of the same input image. - std::vector<T> inputImage; - inputImage.assign(input.data(), input.data() + 1*inputChannels*inputHeight*inputWidth); - std::vector<T> inputData; - inputData.insert(inputData.end(), inputImage.begin(), inputImage.end()); - inputData.insert(inputData.end(), inputImage.begin(), inputImage.end()); - auto batchedInput = MakeTensor<T, 4>(inputTensorInfo, inputData); - - std::vector<T> outputImage; - outputImage.assign(outputExpected.data(), outputExpected.data() + outputChannels*outputHeight*outputWidth); - - // Apply bias to output image if it is enabled. - if(biasEnabled) - { - std::vector<T> biasV; - biasV.assign(bias.data(), bias.data() + outputChannels); - ApplyBias(outputImage, outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), - biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(), - outputWidth, outputHeight); - } - - // Construct expected output data - two identical images. - std::vector<T> outputData; - outputData.insert(outputData.end(), outputImage.begin(), outputImage.end()); - outputData.insert(outputData.end(), outputImage.begin(), outputImage.end()); - - ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData); - - // Todo: nontrivial padding and strides. - uint32_t strideX = 1; - uint32_t strideY = 1; - - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::Convolution2dQueueDescriptor data; - armnn::WorkloadInfo info; - armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc); - armnn::ScopedCpuTensorHandle biasTensor(biasDesc); - - AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]); - - if(biasEnabled) - { - AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]); - } - - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - 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; - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConvolution2d(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()); - - 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); - - // Creates 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 - it 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()); - - return ret; -} - -template<typename T, typename B> -LayerTestResult<T, 4> DepthwiseConvolution2dDepthMul1TestImpl(armnn::IWorkloadFactory& workloadFactory, - float qScale, - int32_t qOffset, - bool biasEnabled) -{ - unsigned int inputHeight = 3; - unsigned int inputWidth = 3; - unsigned int inputChannels = 2; - unsigned int inputNum = 1; - - unsigned int kernelHeight = 3; - unsigned int kernelWidth = 3; - unsigned int kernelChannels = inputChannels; - - unsigned int outputHeight = 1; - unsigned int outputWidth = 1; - unsigned int outputChannels = kernelChannels; - unsigned int outputNum = inputNum; - - armnn::TensorInfo inputTensorInfo({ inputNum, inputChannels, inputHeight, inputWidth }, armnn::GetDataType<T>()); - armnn::TensorInfo outputTensorInfo({ outputNum, outputChannels, outputHeight, outputWidth }, - armnn::GetDataType<T>()); - armnn::TensorInfo kernelDesc({ 1, outputChannels, kernelHeight, kernelWidth }, armnn::GetDataType<T>()); - armnn::TensorInfo biasDesc({ outputChannels }, 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); - } - - auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>( - QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), { - 1.f, 2.f, 1.f, - 2.f, 1.f, 2.f, - 1.f, 2.f, 1.f, - - 1.f, 2.f, 1.f, - 2.f, 1.f, 2.f, - 1.f, 2.f, 1.f, - }))); - - std::vector<B> biasV(QuantizedVector<B>(biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(), - {0, 2})); - auto bias = MakeTensor<B, 1>(biasDesc, biasV); - - auto kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>( - QuantizedVector<T>(kernelDesc.GetQuantizationScale(), kernelDesc.GetQuantizationOffset(), { - 1.f, 0.f, 1.f, - 0.f, 0.f, 0.f, - -1.f, 0.f, -1.f, - - 1.f, 0.f, 1.f, - 0.f, 0.f, 0.f, - -1.f, 0.f, -1.f, - }))); - - // Manually calculated. - std::vector<T> outputImage( - QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), - outputTensorInfo.GetQuantizationOffset(), - {0.f, 0.f}) - ); - - // Optionally apply bias to output image. - if(biasEnabled) - { - ApplyBias(outputImage, outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), - biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(), - outputWidth, outputHeight); - } - - LayerTestResult<T, 4> ret(outputTensorInfo); - ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputImage); - - 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); - - AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]); - AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]); - - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - data.m_Weight = &weightsTensor; - data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled. - data.m_Parameters.m_StrideX = 1; - data.m_Parameters.m_StrideY = 1; - data.m_Parameters.m_PadLeft = 0; - data.m_Parameters.m_PadRight = 0; - data.m_Parameters.m_PadTop = 0; - data.m_Parameters.m_PadBottom = 0; - data.m_Parameters.m_BiasEnabled = biasEnabled; - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDepthwiseConvolution2d(data, info); - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[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> DepthwiseConvolution2dTestImpl(armnn::IWorkloadFactory& workloadFactory, - float qScale, - int32_t qOffset, - bool biasEnabled) -{ - unsigned int depthMultiplier = 2; - - unsigned int inputHeight = 8; - unsigned int inputWidth = 16; - unsigned int inputChannels = 2; - unsigned int inputBatchSize = 1; - - unsigned int kernelHeight = 5; - unsigned int kernelWidth = 3; - - unsigned int outputHeight = inputHeight - kernelHeight + 1 + 2; - unsigned int outputWidth = (inputWidth - kernelWidth + 1)/2; - unsigned int outputChannels = inputChannels * depthMultiplier; - unsigned int outputBatchSize = inputBatchSize; - - armnn::TensorInfo inputTensorInfo({inputBatchSize, inputChannels, inputHeight, inputWidth}, - armnn::GetDataType<T>()); - armnn::TensorInfo outputTensorInfo({outputBatchSize, outputChannels, outputHeight, outputWidth}, - armnn::GetDataType<T>()); - armnn::TensorInfo kernelDesc({depthMultiplier, inputChannels, kernelHeight, kernelWidth}, armnn::GetDataType<T>()); - armnn::TensorInfo biasDesc({outputChannels}, 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); - } - - auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>( - QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), { - 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, - 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, - 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, - 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, - 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, - 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 - }))); - - std::vector<B> biasV(QuantizedVector<B>(biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(), - {0, 2, 1, -1})); - auto bias = MakeTensor<B, 1>(biasDesc, biasV); - - auto kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>( - QuantizedVector<T>(kernelDesc.GetQuantizationScale(), kernelDesc.GetQuantizationOffset(), { - 1, 1, 1, - 1, -1, 1, - 1, 1, 1, - 1, 1, 1, - 1, 1, 1, - - 2, 2, 2, - 2, 2, 2, - 2, 2, 2, - 2, 2, 2, - 2, 2, 2, - - 0, 0, 0, - 0, -1, 0, - 0, 0, 0, - 0, 0, 0, - 0, 0, 0, - - 0, 0, 0, - 0, 0, 0, - 0, 1, 0, - 0, 0, 0, - 0, 0, 0 - }))); - - // Manually calculated. - std::vector<T> outputImage = std::vector<T>( - QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), { - 3.5f, 3.5f, 3.5f, 3.5f, 3.5f, 3.5f, 3.5f, - 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, - 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, - 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, - 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, - 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, - - -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, - -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, - -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, - -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, - - 8.0f, 8.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 10.0f, 10.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 10.0f, 10.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 10.0f, 10.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 10.0f, 10.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 8.0f, 8.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - - 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f - })); - - // Optionally apply bias to output image. - if(biasEnabled) - { - ApplyBias(outputImage, outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), - biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(), - outputWidth, outputHeight); - } - - LayerTestResult<T, 4> ret(outputTensorInfo); - ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputImage); - - 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); - - AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]); - AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]); - - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - data.m_Weight = &weightsTensor; - data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled. - data.m_Parameters.m_StrideX = 2; - data.m_Parameters.m_StrideY = 1; - data.m_Parameters.m_PadLeft = 0; - data.m_Parameters.m_PadRight = 0; - data.m_Parameters.m_PadTop = 1; - data.m_Parameters.m_PadBottom = 1; - data.m_Parameters.m_BiasEnabled = biasEnabled; - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDepthwiseConvolution2d(data, info); - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - - return ret; -} - -template<typename T> -LayerTestResult<T,4> Convolution1dTestImpl(armnn::IWorkloadFactory& workloadFactory, - float qScale, - int32_t qOffset, - bool biasEnabled) -{ - using B = typename FullyConnectedBiasTypeForInputType<T>::Type; - - // Until we have a specialist 1D convolution layer, we can fake one using - // 2D convolution with the final dimension set to 1. - // I don't anticipate this being particularly slow, given that convolution is implemented - // as a matrix multiplication, at which point dimension doesn't matter. - - unsigned int batchSize = 1; - unsigned int inputChannels = 2; - unsigned int outputChannels = 3; - unsigned int inputSize = 5; // The 1D size (could view as 'width' or 'height'). - unsigned int kernelSize = 3; - unsigned int padSize = 2; - unsigned int stride = 1; - unsigned int outputSize = 7; // (inputSize + 2 * padSize - kernelSize + 1) / stride. - - armnn::TensorInfo inputInfo({batchSize, inputChannels, inputSize, 1}, armnn::GetDataType<T>()); - armnn::TensorInfo outputInfo({batchSize, outputChannels, outputSize, 1}, armnn::GetDataType<T>()); - armnn::TensorInfo kernelInfo({outputChannels, inputChannels, kernelSize, 1}, armnn::GetDataType<T>()); - armnn::TensorInfo biasInfo({outputChannels}, armnn::GetDataType<B>()); - - // Set quantization parameters if the requested type is a quantized type. - if(armnn::IsQuantizedType<T>()) - { - inputInfo.SetQuantizationScale(qScale); - inputInfo.SetQuantizationOffset(qOffset); - outputInfo.SetQuantizationScale(qScale); - outputInfo.SetQuantizationOffset(qOffset); - kernelInfo.SetQuantizationScale(qScale); - kernelInfo.SetQuantizationOffset(qOffset); - biasInfo.SetQuantizationScale(inputInfo.GetQuantizationScale()*kernelInfo.GetQuantizationScale()); - biasInfo.SetQuantizationOffset(0); - } - - std::vector<T> inputData( - QuantizedVector<T>(inputInfo.GetQuantizationScale(), inputInfo.GetQuantizationOffset(), { - 5.0f, -2.0f, 2.5f, 0.0f, 1.0f, - -3.0f, 3.2f, 5.0f, 2.0f, 3.0f, - })); - - std::vector<T> kernelData( - QuantizedVector<T>(kernelInfo.GetQuantizationScale(), kernelInfo.GetQuantizationOffset(), { - 1.0f, 0.0f, 0.0f, - 0.0f, 2.0f, -1.5f, - - 0.0f, 0.0f, 0.0f, - 0.2f, 0.2f, 0.2f, - - 0.5f, 0.0f, 0.5f, - 0.0f, -1.0f, 0.0f - })); - - std::vector<B> biasData( - QuantizedVector<B>(biasInfo.GetQuantizationScale(), biasInfo.GetQuantizationOffset(), { - 1.0f, 0.0f, 0.0f - })); - - std::vector<T> outputData( - QuantizedVector<T>(outputInfo.GetQuantizationScale(), outputInfo.GetQuantizationOffset(), { - 4.5f, -10.8f, 5.0f + 6.4f - 7.5f, -2.0f + 10.0f -3.0f, 2.5f + 4.0f - 4.5f, 6.0f, 1.0f, - -0.6f, -0.6f + 0.64f, -0.6f + 0.64f + 1.0f, 0.64f + 1.0f + 0.4f, 1.0f + 0.4f + 0.6f, 0.4f + 0.6f, 0.6f, - 2.5f, -1.0f + 3.0f, 1.25f - 3.2f + 2.5f, -1.0f - 5.0f, 1.25f + 0.5f - 2.0f, -3.0f, 0.5f - })); - - // Optionally apply bias to output image. - if(biasEnabled) - { - ApplyBias(outputData, outputInfo.GetQuantizationScale(), outputInfo.GetQuantizationOffset(), - biasData, biasInfo.GetQuantizationScale(), biasInfo.GetQuantizationOffset(), - 1, outputSize); - } - - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputInfo); - - armnn::Convolution2dQueueDescriptor data; - armnn::WorkloadInfo info; - armnn::ScopedCpuTensorHandle weightsTensor(kernelInfo); - armnn::ScopedCpuTensorHandle biasTensor(biasInfo); - - AllocateAndCopyDataToITensorHandle(&weightsTensor, kernelData.data()); - AllocateAndCopyDataToITensorHandle(&biasTensor, biasData.data()); - - AddInputToWorkload(data, info, inputInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputInfo, outputHandle.get()); - - data.m_Weight = &weightsTensor; - data.m_Bias = &biasTensor; - data.m_Parameters.m_StrideX = 1; - data.m_Parameters.m_StrideY = stride; - data.m_Parameters.m_PadLeft = 0; - data.m_Parameters.m_PadRight = 0; - data.m_Parameters.m_PadTop = padSize; - data.m_Parameters.m_PadBottom = padSize; - data.m_Parameters.m_BiasEnabled = biasEnabled; - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConvolution2d(data, info); - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), inputData.data()); - - workloadFactory.Finalize(); - workload->Execute(); - - // Output - LayerTestResult<T,4> ret(outputInfo); - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - ret.outputExpected = MakeTensor<T, 4>(outputInfo, outputData); - return ret; -} - - - -template<typename T> -LayerTestResult<T,4> CompareConvolution2dTestImpl(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory) -{ - unsigned int inputHeight = 8; - unsigned int inputWidth = 16; - unsigned int inputChannels = 3; - unsigned int inputNum = 5; - - unsigned int kernelHeight = 3; - unsigned int kernelWidth = 3; - - unsigned int strideX = 2; - unsigned int strideY = 3; - unsigned int padX = 1; - unsigned int padY = 1; - - unsigned int outputNum = inputNum; - unsigned int outputChannels = 2; - unsigned int outputHeight = (inputHeight + 2 * padY - kernelHeight + strideY) / strideY; - unsigned int outputWidth = (inputWidth + 2 * padX - kernelWidth + strideX) / strideX; - - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - armnn::TensorInfo kernelDesc; - armnn::TensorInfo biasDesc; - - unsigned int inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth}; - unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth}; - unsigned int kernelShape[] = {outputChannels, inputChannels, kernelHeight, kernelWidth}; - unsigned int biasShape[] = {outputChannels}; - - inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::GetDataType<T>()); - outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::GetDataType<T>()); - kernelDesc = armnn::TensorInfo(4, kernelShape, armnn::GetDataType<T>()); - biasDesc = armnn::TensorInfo(1, biasShape, armnn::GetDataType<T>()); - - LayerTestResult<T,4> ret(outputTensorInfo); - - auto input = MakeRandomTensor<T, 4>(inputTensorInfo, 124908); - auto kernel = MakeRandomTensor<T, 4>(kernelDesc, 891234); - auto bias = MakeRandomTensor<T, 1>(biasDesc, 1028); - - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::Convolution2dQueueDescriptor data; - armnn::WorkloadInfo info; - armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc); - armnn::ScopedCpuTensorHandle biasTensor(biasDesc); - - AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]); - AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]); - - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - data.m_Weight = &weightsTensor; - data.m_Bias = &biasTensor; - data.m_Parameters.m_StrideX = strideX; - data.m_Parameters.m_StrideY = strideY; - data.m_Parameters.m_PadLeft = padX; - data.m_Parameters.m_PadRight = padX; - data.m_Parameters.m_PadTop = padY; - data.m_Parameters.m_PadBottom = padY; - data.m_Parameters.m_BiasEnabled = true; - - std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); - - armnn::Convolution2dQueueDescriptor refData = data; - armnn::WorkloadInfo refInfo = info; - SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); - SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConvolution2d(data, info); - std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateConvolution2d(refData, refInfo); - - outputHandleRef->Allocate(); - inputHandleRef->Allocate(); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - 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()); - CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get()); - - return ret; -} - -template<typename T> -LayerTestResult<T, 4> CompareDepthwiseConvolution2dTestImpl(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory) -{ - unsigned int inputHeight = 8; - unsigned int inputWidth = 16; - unsigned int inputChannels = 3; - unsigned int inputNum = 5; - - unsigned int kernelHeight = 3; - unsigned int kernelWidth = 3; - unsigned int channelMultiplier = 1; - - unsigned int strideX = 2; - unsigned int strideY = 3; - unsigned int padX = 1; - unsigned int padY = 1; - - unsigned int outputNum = inputNum; - unsigned int outputChannels = inputChannels * channelMultiplier; - unsigned int outputHeight = (inputHeight + 2 * padY - kernelHeight + strideY) / strideY; - unsigned int outputWidth = (inputWidth + 2 * padX - kernelWidth + strideX) / strideX; - - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - armnn::TensorInfo kernelDesc; - armnn::TensorInfo biasDesc; - - unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth }; - unsigned int outputShape[] = { outputNum, outputChannels, outputHeight, outputWidth }; - unsigned int kernelShape[] = { channelMultiplier, inputChannels, kernelHeight, kernelWidth }; - unsigned int biasShape[] = { outputChannels }; - - float inputsQScale = armnn::IsQuantizedType<T>() ? 1.0f : 0; - float outputQScale = armnn::IsQuantizedType<T>() ? 2.0f : 0; - int32_t qOffset = 0; - - inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::GetDataType<T>(), inputsQScale, qOffset); - outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::GetDataType<T>(), outputQScale, qOffset); - kernelDesc = armnn::TensorInfo(4, kernelShape, armnn::GetDataType<T>(), inputsQScale, qOffset); - biasDesc = armnn::TensorInfo(1, biasShape, armnn::GetBiasDataType(armnn::GetDataType<T>()), inputsQScale, qOffset); - - LayerTestResult<T, 4> ret(outputTensorInfo); - - auto input = MakeRandomTensor<T, 4>(inputTensorInfo, 124908, 0.0f, 255.0f); - auto kernel = MakeRandomTensor<T, 4>(kernelDesc, 891234, 0.0f, 255.0f); - auto bias = MakeRandomTensor<typename FullyConnectedBiasTypeForInputType<T>::Type, 1>(biasDesc, 1028, 0.0f, 255.0f); - - 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); - - AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]); - AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]); - - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - data.m_Weight = &weightsTensor; - data.m_Bias = &biasTensor; - data.m_Parameters.m_StrideX = strideX; - data.m_Parameters.m_StrideY = strideY; - data.m_Parameters.m_PadLeft = padX; - data.m_Parameters.m_PadRight = padX; - data.m_Parameters.m_PadTop = padY; - data.m_Parameters.m_PadBottom = padY; - data.m_Parameters.m_BiasEnabled = true; - - std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); - - armnn::DepthwiseConvolution2dQueueDescriptor refData = data; - armnn::WorkloadInfo refInfo = info; - SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); - SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDepthwiseConvolution2d(data, info); - std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateDepthwiseConvolution2d(refData, refInfo); - - outputHandleRef->Allocate(); - inputHandleRef->Allocate(); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - 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()); - CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get()); - - return ret; -} |