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//
// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "EndToEndTestImpl.hpp"
#include <armnnUtils/QuantizeHelper.hpp>
#include <ResolveType.hpp>
#include <CommonTestUtils.hpp>
#include <armnnTestUtils/DataLayoutUtils.hpp>
#include <map>
#include <vector>
namespace
{
armnn::INetworkPtr CreateDepthwiseConvolution2dNetwork(const armnn::DepthwiseConvolution2dDescriptor& descriptor,
const armnn::TensorInfo& inputInfo,
const armnn::TensorInfo& weightsInfo,
const armnn::TensorInfo& biasInfo,
const armnn::TensorInfo& outputInfo,
const armnn::ConstTensor& weights,
const armnn::ConstTensor& biases)
{
using namespace armnn;
INetworkPtr network(INetwork::Create());
IConnectableLayer* input = network->AddInputLayer(0, "input");
armnn::IConnectableLayer* weightsLayer = network->AddConstantLayer(weights, "Weights");
armnn::IConnectableLayer* biasLayer = network->AddConstantLayer(biases, "Bias");
IConnectableLayer* convolution2d = network->AddDepthwiseConvolution2dLayer(descriptor, "depthwiseConvolution2d");
IConnectableLayer* output = network->AddOutputLayer(0, "output");
Connect(input, convolution2d, inputInfo, 0, 0);
Connect(weightsLayer, convolution2d, weightsInfo, 0, 1);
Connect(biasLayer, convolution2d, biasInfo, 0, 2);
Connect(convolution2d, output, outputInfo, 0, 0);
return network;
}
} // anonymous namespace
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType>
void DepthwiseConvolution2dEndToEnd(const std::vector<armnn::BackendId>& backends,
armnn::DataLayout dataLayout)
{
using namespace armnn;
using T = ResolveType<ArmnnType>;
using BT = ResolveType<ArmnnBType>;
const float qScale = IsQuantizedType<T>() ? 0.25f : 1.0f;
const int32_t qOffset = IsQuantizedType<T>() ? 50 : 0;
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;
TensorInfo inputInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, ArmnnType, qScale, qOffset, true);
TensorInfo outputInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, ArmnnType, qScale, qOffset);
TensorInfo weightsInfo({1, kernelHeight, kernelWidth, outputChannels}, ArmnnType, qScale, qOffset, true);
TensorInfo biasesInfo({outputChannels}, ArmnnBType, qScale * qScale, 0, true);
std::vector<float> inputData =
{
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,
0.0f, 0.0f, 1.0f, 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, 1.0f, 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, 1.0f, 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, 1.0f, 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, 1.0f, 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, 1.0f, 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, 1.0f, 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, 1.0f, 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
};
std::vector<float> weightsData =
{
1.0f, 1.0f, 1.0f,
1.0f, -1.0f, 1.0f,
1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f,
2.0f, 2.0f, 2.0f,
2.0f, 2.0f, 2.0f,
2.0f, 2.0f, 2.0f,
2.0f, 2.0f, 2.0f,
2.0f, 2.0f, 2.0f,
0.0f, 0.0f, 0.0f,
0.0f, -1.0f, 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, 1.0f, 0.0f,
0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 0.0f
};
std::vector<float> biasesData = { 0.0f, 2.0f, 1.0f, -1.0f };
std::vector<float> expectedOutputData =
{
3.0f, 3.0f, 3.0f, 3.0f, 3.0f, 3.0f, 3.0f, 3.0f, 3.0f, 3.0f, 3.0f, 3.0f, 3.0f, 3.0f,
5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.5f, 5.5f, 5.5f, 5.5f, 5.5f, 5.5f, 5.5f,
5.5f, 5.5f, 5.5f, 5.5f, 5.5f, 5.5f, 5.5f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f,
2.5f, 2.5f, 2.5f, 2.5f, 2.5f, 2.5f, 2.5f, 3.5f, 3.5f, 3.5f, 3.5f, 3.5f, 3.5f, 3.5f,
4.5f, 4.5f, 4.5f, 4.5f, 4.5f, 4.5f, 4.5f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f,
6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f,
1.0f, 3.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 2.0f, 4.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
2.0f, 4.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 2.0f, 4.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
2.0f, 4.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 2.0f, 4.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
2.0f, 4.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 3.0f, 5.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
3.0f, 5.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 3.0f, 5.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
3.0f, 5.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 3.0f, 5.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f
};
DepthwiseConvolution2dDescriptor descriptor;
descriptor.m_PadLeft = 0;
descriptor.m_PadRight = 0;
descriptor.m_PadTop = 1;
descriptor.m_PadBottom = 0;
descriptor.m_StrideX = 2;
descriptor.m_StrideY = 1;
descriptor.m_BiasEnabled = true;
descriptor.m_DataLayout = dataLayout;
// Permute input and output if NCDHW.
if (dataLayout == DataLayout::NCHW)
{
PermuteTensorNhwcToNchw(inputInfo, inputData);
PermuteTensorNhwcToNchw(outputInfo, expectedOutputData);
}
// Quantize data
std::vector<T> qInputData = armnnUtils::QuantizedVector<T>(inputData, qScale, qOffset);
std::vector<T> qWeightsData = armnnUtils::QuantizedVector<T>(weightsData, qScale, qOffset);
std::vector<T> qExpectedOutputData = armnnUtils::QuantizedVector<T>(expectedOutputData, qScale, qOffset);
std::vector<BT> qBiasesData = armnnUtils::QuantizedVector<BT>(biasesData, qScale * qScale, 0);
ConstTensor weights(weightsInfo, qWeightsData);
ConstTensor biases(biasesInfo, qBiasesData);
INetworkPtr network = CreateDepthwiseConvolution2dNetwork(descriptor,
inputInfo,
weightsInfo,
biasesInfo,
outputInfo,
weights,
biases);
EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network),
{ { 0, qInputData } },
{ { 0, qExpectedOutputData } },
backends);
}
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