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android / platform / external / armnn / 711fa31d / . / src / backends / test / LayerTests.cpp
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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "LayerTests.hpp"
#include "test/TensorHelpers.hpp"
#include "TensorCopyUtils.hpp"
#include "Permute.hpp"
#include <boost/test/unit_test.hpp>
#include <boost/assert.hpp>
#include <armnn/LayerSupport.hpp>
#include <backends/CpuTensorHandle.hpp>
#include <backends/WorkloadFactory.hpp>
#ifdef ARMCOMPUTECL_ENABLED
#include <backends/ClTensorHandle.hpp>
#include <backends/aclCommon/ArmComputeTensorUtils.hpp>
#endif
#include <algorithm>
#include <boost/cast.hpp>
#include "WorkloadTestUtils.hpp"
#include "Conv2dTestImpl.hpp"
#include "BatchNormTestImpl.hpp"
#include "ActivationTestImpl.hpp"
#include "Pooling2dTestImpl.hpp"
#include "ReshapeTestImpl.hpp"
#include "FullyConnectedTestImpl.hpp"
#include "SplitterTestImpl.hpp"
#include "SoftmaxTestImpl.hpp"
#include "NormTestImpl.hpp"
#include "PermuteTestImpl.hpp"
#include "LstmTestImpl.hpp"
#include "ConvertFp16ToFp32TestImpl.hpp"
#include "ConvertFp32ToFp16TestImpl.hpp"
// 3-channel 16x8 image used as common input data for a number of Conv2d tests.
static std::vector<float> ConvInput3x8x16({
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,
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1
});
// 2-channel bias used by a number of Conv2d tests.
static std::vector<float> Bias2({0, 2});
// Helper function that returns either Bias2 or an empty vector depending on whether bias is enabled.
template<typename T>
boost::multi_array<T, 1> GetBias2(bool biasEnabled, float qScale, int32_t qOffset)
{
if(biasEnabled)
{
armnn::TensorInfo biasDesc({static_cast<unsigned int>(Bias2.size())}, armnn::GetDataType<T>());
boost::multi_array<T, 1> bias = MakeTensor<T, 1>(biasDesc, QuantizedVector<T>(qScale, qOffset, Bias2));
return bias;
}
else
{
return boost::multi_array<T, 1>();
}
}
template<typename T>
LayerTestResult<T, 4> SimpleConvolution2d3x5TestCommon(armnn::IWorkloadFactory& workloadFactory,
float qScale,
int32_t qOffset,
bool biasEnabled)
{
// Use common single-batch 3-channel 16x8 image.
armnn::TensorInfo inputDesc({1, 3, 8, 16}, armnn::GetDataType<T>());
boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, QuantizedVector<T>(qScale, qOffset, ConvInput3x8x16));
// Use a 2-element batch with 3-channel 3x5 kernels.
armnn::TensorInfo kernelDesc({2, 3, 5, 3}, armnn::GetDataType<T>());
boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>(
QuantizedVector<T>(qScale, qOffset, {
1, 1, 1,
1, -1, 1,
1, 1, 1,
1, 1, 1,
1, 1, 1,
0, 0, 0,
0, 0, 0,
0, 0, 0,
0, 0, 0,
0, 0, 0,
2, 2, 2,
2, 2, 2,
2, 2, 2,
2, 2, 2,
2, 2, 2,
0, 0, 0,
0, 0, 0,
0, 0, 0,
0, 0, 0,
0, 0, 0,
1, 1, 1,
1, 1, 1,
1, 1, 1,
1, 1, 1,
1, 1, 1,
0, 0, 0,
0, 0, 0,
0, 0, 0,
0, 0, 0,
0, 0, 0
})));
// Expected output is 2 batch elements of a 1-channel 14x4 image.
armnn::TensorInfo outputDesc({1, 2, 4, 14}, armnn::GetDataType<T>());
boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>(
QuantizedVector<T>(qScale, qOffset, {
-24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24,
-25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25,
-23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f,
-23.5f, -23.5f, -23.5f,
-23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f,
-23.5f, -23.5f, -23.5f,
5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
})));
return SimpleConvolution2dTestImpl<T>(workloadFactory,
input,
kernel,
GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(biasEnabled, qScale, qOffset),
expectedOutput,
qScale,
qOffset);
}
template<typename T>
LayerTestResult<T, 4> SimpleConvolution2d3x3TestCommon(armnn::IWorkloadFactory& workloadFactory,
float qScale,
int32_t qOffset,
bool biasEnabled)
{
// Use a 3x3 kernel, which exercises ArmCompute's direct convolution path.
// Use common single-batch 3-channel 16x8 image.
armnn::TensorInfo inputDesc({1, 3, 8, 16}, armnn::GetDataType<T>());
boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, QuantizedVector<T>(qScale, qOffset, ConvInput3x8x16));
// Use a 2-element batch of 3-channel 3x3 kernels.
armnn::TensorInfo kernelDesc({2, 3, 3, 3}, armnn::GetDataType<T>());
boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>(
QuantizedVector<T>(qScale, qOffset, {
1, 1, 1,
1, -1, 1,
1, 1, 1,
0, 0, 0,
0, 0, 0,
0, 0, 0,
2, 2, 2,
2, 2, 2,
2, 2, 2,
0, 0, 0,
0, 0, 0,
0, 0, 0,
1, 1, 1,
1, 1, 1,
1, 1, 1,
0, 0, 0,
0, 0, 0,
0, 0, 0
})));
// Expected output is 1 batch of a 2-channel 14x6 image.
armnn::TensorInfo outputDesc({1, 2, 6, 14}, armnn::GetDataType<T>());
boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>(
QuantizedVector<T>(qScale, qOffset, {
-15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15,
-16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16,
-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,
-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,
-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,
-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,
3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
})));
return SimpleConvolution2dTestImpl<T>(workloadFactory,
input,
kernel,
GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(biasEnabled, qScale, qOffset),
expectedOutput,
qScale,
qOffset);
}
LayerTestResult<float, 4> SimpleConvolution2d3x5Test(armnn::IWorkloadFactory& workloadFactory,
bool biasEnabled)
{
return SimpleConvolution2d3x5TestCommon<float>(workloadFactory, 0.f, 0, biasEnabled);
}
LayerTestResult<uint8_t, 4> SimpleConvolution2d3x5Uint8Test(armnn::IWorkloadFactory& workloadFactory,
bool biasEnabled)
{
return SimpleConvolution2d3x5TestCommon<uint8_t>(workloadFactory, 0.5f, 50, biasEnabled);
}
LayerTestResult<float, 4> SimpleConvolution2d3x3Test(armnn::IWorkloadFactory& workloadFactory,
bool biasEnabled)
{
return SimpleConvolution2d3x3TestCommon<float>(workloadFactory, 0.f, 0, biasEnabled);
}
LayerTestResult<uint8_t, 4> SimpleConvolution2d3x3Uint8Test(armnn::IWorkloadFactory& workloadFactory,
bool biasEnabled)
{
return SimpleConvolution2d3x3TestCommon<uint8_t>(workloadFactory, 0.5f, 50, biasEnabled);
}
template<typename T>
LayerTestResult<T, 4> Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon(
armnn::IWorkloadFactory& workloadFactory,
float qScale,
int32_t qOffset)
{
// Use a single-batch 1-channel 3x3 image as input.
armnn::TensorInfo inputDesc({1, 1, 3, 3}, armnn::GetDataType<T>());
boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, std::vector<T>(
QuantizedVector<T>(qScale, qOffset, {
11,21,31,
12,22,32,
13,23,33
})));
// Use 1 batch of a 1-channel 2x2 kernel.
armnn::TensorInfo kernelDesc({1, 1, 2, 2}, armnn::GetDataType<T>());
boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>(
QuantizedVector<T>(qScale, qOffset, {
-11,-21,
-12,-22,
})));
// Expected output is 1 batch of a 1-channel 6x8 image.
// Manually calculated like this:
//[-11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ..]
//[-11*0 -21*0 -12*0 -22*11 ; -11*0 -21*0 -12*11 -22*21 ; -11*0 -21*0 -12*21 -22*31 ; -11*0 -21*0 -12*31 -22*0 ..]
//[-11*0 -21*11 -12*0 -22*12 ; -11*11 -21*21 -12*12 -22*22 ; -11*21 -21*31 -12*22 -22*32 ; -11*31 -21*0 -12*32 -22*0 ..]
//[-11*0 -21*12 -12*0 -22*13 ; -11*12 -21*22 -12*13 -22*23 ; -11*22 -21*32 -12*23 -22*33 ; -11*32 -21*0 -12*33 -22*0 ..]
//[-11*0 -21*13 -12*0 -22*0 ; -11*13 -21*23 -12*0 -22*0 ; -11*23 -21*33 -12*0 -22*0 ; -11*33 -21*0 -12*0 -22*0 ..]
//[-11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ..]
//[..... ..... ..... ..... ; ..... ..... ..... ..... ; ..... ..... ..... ..... ; ..... ..... ..... ..... ..]
armnn::TensorInfo outputDesc({1, 1, 8, 6}, armnn::GetDataType<T>());
boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>(
QuantizedVector<T>(qScale, qOffset, {
0, 0, 0, 0, 0, 0,
-242, -594, -934, -372, 0, 0,
-495, -1190, -1850, -725, 0, 0,
-538, -1256, -1916, -748, 0, 0,
-273, -626, -946, -363, 0, 0,
0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0
})));
return SimpleConvolution2dTestImpl<T>(workloadFactory,
input,
kernel,
GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(false, qScale, qOffset),
expectedOutput,
qScale,
qOffset,
1, // Padding left.
2, // Padding top.
3, // Padding right.
4); // Padding bottom.
}
template<typename T>
LayerTestResult<T, 4> SimpleConvolution2dAsymmetricPaddingTestCommon(armnn::IWorkloadFactory& workloadFactory,
float qScale,
int32_t qOffset)
{
// Use a single-batch 1-channel 5x5 image as input.
armnn::TensorInfo inputDesc({ 1, 1, 5, 5 }, armnn::GetDataType<T>());
boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, std::vector<T>(
QuantizedVector<T>(qScale, qOffset, {
11,21,31,41,51,
12,22,32,42,52,
13,23,33,43,53,
14,24,34,44,54,
15,25,35,45,55,
})));
// Use 1 batch of a 1-channel 4x4 kernel.
armnn::TensorInfo kernelDesc({ 1, 1, 4, 4 }, armnn::GetDataType<T>());
boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>(
QuantizedVector<T>(qScale, qOffset, {
-11,-21,-31,-41,
-12,-22,-32,-42,
-13,-23,-33,-43,
-14,-24,-34,-44,
})));
// Expected output is 1 batch of a 1-channel 5x5 image.
armnn::TensorInfo outputDesc({ 1, 1, 5, 5 }, armnn::GetDataType<T>());
std::vector<T> myVec(outputDesc.GetNumElements(), 0);
boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>(
QuantizedVector<T>(qScale, qOffset, {
-7140, -10580, -13940, -9300, -5230,
-9590, -14120, -18520, -12290, -6860,
-9980, -14560, -18960, -12560, -7000,
-7518, -10904, -14144, -9318, -5152,
-5032, -7256, -9376, -6142, -3368,
})));
return SimpleConvolution2dTestImpl<T>(workloadFactory,
input,
kernel,
GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(false, qScale, qOffset),
expectedOutput,
qScale,
qOffset,
1, // Padding left.
1, // Padding top.
2, // Padding right.
2); // Padding bottom.
}
template<typename T>
LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestCommon(armnn::IWorkloadFactory& workloadFactory,
float qScale,
int32_t qOffset,
bool biasEnabled)
{
// Use a single-batch 2-channel 5x5 image as input.
armnn::TensorInfo inputTensorInfo({ 1, 2, 5, 5 }, armnn::GetDataType<T>());
auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>(
QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), {
0, 1, 2, 3, 4,
5, 6, 7, 8, 9,
10, 11, 12, 13, 14,
15, 16, 17, 18, 19,
20, 21, 22, 23, 24,
25, 26, 27, 28, 29,
30, 31, 32, 33, 34,
35, 36, 37, 38, 39,
40, 41, 42, 43, 44,
45, 46, 47, 48, 49
})));
// Use a depth multiplier of 1 on a 2-channel 4x4 kernel.
armnn::TensorInfo kernelTensorInfo({ 1, 2, 4, 4 }, armnn::GetDataType<T>());
auto kernel = MakeTensor<T, 4>(kernelTensorInfo, std::vector<T>(
QuantizedVector<T>(kernelTensorInfo.GetQuantizationScale(), kernelTensorInfo.GetQuantizationOffset(), {
32, 31, 30, 29,
28, 27, 26, 25,
24, 23, 22, 21,
20, 19, 18, 17,
16, 15, 14, 13,
12, 11, 10, 9,
8, 7, 6, 5,
4, 3, 2, 1
})));
// Expected output is 1 batch of a 2-channel 5x5 image.
// Calculated using the python tensorflow library with strideX=1, strideY=1.
armnn::TensorInfo outputTensorInfo({ 1, 2, 5, 5 }, armnn::GetDataType<T>());
boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputTensorInfo, std::vector<T>(
QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), {
1062, 1580, 1850, 1530, 1117,
2140, 3108, 3500, 2842, 2042,
3580, 5068, 5460, 4342, 3062,
3618, 5072, 5390, 4248, 2971,
3074, 4282, 4510, 3533, 2457,
1550, 2284, 2362, 1955, 1428,
2910, 4206, 4342, 3528, 2536,
3390, 4886, 5022, 4068, 2916,
3566, 5056, 5182, 4133, 2922,
3100, 4352, 4452, 3517, 2465
})));
return DepthwiseConvolution2dAsymmetricTestImpl<T>(workloadFactory,
input,
kernel,
GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(biasEnabled, qScale, qOffset),
expectedOutput,
qScale,
qOffset,
1, // Padding left.
1, // Padding top.
2, // Padding right.
2, // Padding bottom.
1, // strideX
1); // strideY
}
LayerTestResult<float, 4>
Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest(armnn::IWorkloadFactory& workloadFactory)
{
return Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon<float>(workloadFactory, 0.0f, 0);
}
LayerTestResult<float, 4> Convolution2dAsymmetricPaddingTest(armnn::IWorkloadFactory& workloadFactory)
{
return SimpleConvolution2dAsymmetricPaddingTestCommon<float>(workloadFactory, 0.0f, 0);
}
LayerTestResult<float, 4> DepthwiseConvolution2dTest(armnn::IWorkloadFactory& workloadFactory,
bool biasEnabled)
{
return DepthwiseConvolution2dTestImpl<float, float>(workloadFactory, 0.0f, 0, biasEnabled);
}
LayerTestResult<float, 4> DepthwiseConvolution2dDepthMul1Test(armnn::IWorkloadFactory& workloadFactory,
bool biasEnabled)
{
return DepthwiseConvolution2dDepthMul1TestImpl<float, float>(workloadFactory, 0.0f, 0, biasEnabled);
}
LayerTestResult<float, 4> DepthwiseConvolution2dAsymmetricTest(armnn::IWorkloadFactory& workloadFactory,
bool biasEnabled)
{
return DepthwiseConvolution2dAsymmetricTestCommon<float>(workloadFactory, 0.0f, 0, biasEnabled);
}
LayerTestResult<uint8_t, 4> DepthwiseConvolution2dUint8Test(armnn::IWorkloadFactory& workloadFactory,
bool biasEnabled)
{
return DepthwiseConvolution2dTestImpl<uint8_t, int32_t>(workloadFactory, 0.5f, 50, biasEnabled);
}
LayerTestResult<uint8_t, 4> DepthwiseConvolution2dDepthMul1Uint8Test(armnn::IWorkloadFactory& workloadFactory,
bool biasEnabled)
{
return DepthwiseConvolution2dDepthMul1TestImpl<uint8_t, int32_t>(workloadFactory, 0.5f, 50, biasEnabled);
}
LayerTestResult<float, 4> Convolution1dTest(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled)
{
return Convolution1dTestImpl<float>(workloadFactory, 0.0f, 0, biasEnabled);
}
LayerTestResult<uint8_t, 4> Convolution1dUint8Test(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled)
{
return Convolution1dTestImpl<uint8_t>(workloadFactory, 0.1f, 128, biasEnabled);
}
LayerTestResult<float,4> CompareConvolution2dTest(armnn::IWorkloadFactory& workloadFactory,
armnn::IWorkloadFactory& refWorkloadFactory)
{
return CompareConvolution2dTestImpl<float>(workloadFactory, refWorkloadFactory);
}
template<typename T>
LayerTestResult<T,4> CompareDepthwiseConvolution2dTest(armnn::IWorkloadFactory& workloadFactory,
armnn::IWorkloadFactory& refWorkloadFactory)
{
return CompareDepthwiseConvolution2dTestImpl<T>(workloadFactory, refWorkloadFactory);
}
template LayerTestResult<float, 4> CompareDepthwiseConvolution2dTest<float>(
armnn::IWorkloadFactory&, armnn::IWorkloadFactory&);
template LayerTestResult<uint8_t, 4> CompareDepthwiseConvolution2dTest<uint8_t>(
armnn::IWorkloadFactory&, armnn::IWorkloadFactory&);
LayerTestResult<float,4> SimpleNormalizationAcrossTest(armnn::IWorkloadFactory& workloadFactory)
{
auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness;
auto normChannel = armnn::NormalizationAlgorithmChannel::Across;
return SimpleNormalizationTestImpl(workloadFactory, normChannel, normMethod);
}
LayerTestResult<float,4> SimpleNormalizationWithinTest(armnn::IWorkloadFactory& workloadFactory)
{
auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness;
auto normChannel = armnn::NormalizationAlgorithmChannel::Within;
return SimpleNormalizationTestImpl(workloadFactory, normChannel, normMethod);
}
LayerTestResult<float,2> SimpleSoftmaxTest(armnn::IWorkloadFactory& workloadFactory, float beta)
{
return SimpleSoftmaxTestImpl<float>(workloadFactory, beta);
}
LayerTestResult<uint8_t,2> SimpleSoftmaxUint8Test(armnn::IWorkloadFactory& workloadFactory, float beta)
{
return SimpleSoftmaxTestImpl<uint8_t>(workloadFactory, beta);
}
LayerTestResult<float,4> CompareNormalizationTest(armnn::IWorkloadFactory& workloadFactory,
armnn::IWorkloadFactory& refWorkloadFactory,
armnn::NormalizationAlgorithmChannel normChannel,
armnn::NormalizationAlgorithmMethod normMethod)
{
return CompareNormalizationTestImpl(workloadFactory, refWorkloadFactory, normChannel, normMethod);
}
LayerTestResult<float,2> CompareSoftmaxTest(armnn::IWorkloadFactory& workloadFactory,
armnn::IWorkloadFactory& refWorkloadFactory,
float beta)
{
return CompareSoftmaxTestImpl<float>(workloadFactory, refWorkloadFactory, beta);
}
LayerTestResult<uint8_t,2> CompareSoftmaxUint8Test(armnn::IWorkloadFactory& workloadFactory,
armnn::IWorkloadFactory& refWorkloadFactory,
float beta)
{
return CompareSoftmaxTestImpl<uint8_t>(workloadFactory, refWorkloadFactory, beta);
}
std::vector<LayerTestResult<float,3>> SplitterTest(armnn::IWorkloadFactory& workloadFactory)
{
return SplitterTestCommon<float>(workloadFactory);
}
std::vector<LayerTestResult<uint8_t,3>> SplitterUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return SplitterTestCommon<uint8_t>(workloadFactory, 1.0f, 0);
}
LayerTestResult<float, 3> CopyViaSplitterTest(armnn::IWorkloadFactory& workloadFactory)
{
return CopyViaSplitterTestImpl<float>(workloadFactory, 0.0f, 0);
}
LayerTestResult<uint8_t, 3> CopyViaSplitterUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return CopyViaSplitterTestImpl<uint8_t>(workloadFactory, 1.0f, 0);
}
LayerTestResult<float, 2> LstmLayerFloat32WithCifgWithPeepholeNoProjectionTest(
armnn::IWorkloadFactory& workloadFactory)
{
armnn::TensorInfo inputDesc({ 2, 2 }, armnn::GetDataType<float>());
boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>(
{ 2., 3., 3., 4. }));
armnn::TensorInfo outputDesc({ 2, 4 }, armnn::GetDataType<float>());
boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(outputDesc, std::vector<float>(
{-0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f,
-0.42734814f, -0.00478661f, 0.13455015f, -0.03560682f}));
return LstmLayerWithCifgWithPeepholeNoProjectionTestImpl(workloadFactory, input, expectedOutput);
}
LayerTestResult<float, 2> LstmLayerFloat32NoCifgWithPeepholeWithProjectionTest(
armnn::IWorkloadFactory& workloadFactory)
{
armnn::TensorInfo inputDesc({ 2, 5 }, armnn::GetDataType<float>());
boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>(
{0.787926f, 0.151646f, 0.071352f, 0.118426f, 0.458058f,
0.295743f, 0.544053f, 0.690064f, 0.858138f, 0.497181f}));
armnn::TensorInfo outputDesc({ 2, 16 }, armnn::GetDataType<float>());
boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(outputDesc, std::vector<float>(
{-0.00396806f, 0.029352f, -0.00279226f, 0.0159977f, -0.00835576f,
-0.0211779f, 0.0283512f, -0.0114597f, 0.00907307f, -0.0244004f,
-0.0152191f, -0.0259063f, 0.00914318f, 0.00415118f, 0.017147f,
0.0134203f, -0.013869f, 0.0287268f, -0.00334693f, 0.00733398f, -0.0287926f,
-0.0186926f, 0.0193662f, -0.0115437f, 0.00422612f, -0.0345232f,
0.00223253f, -0.00957321f, 0.0210624f, 0.013331f, 0.0150954f,
0.02168f}));
return LstmLayerFloat32NoCifgWithPeepholeWithProjectionTestImpl(workloadFactory, input, expectedOutput);
}
LayerTestResult<float, 2> LstmLayerFloat32NoCifgNoPeepholeNoProjectionTest(armnn::IWorkloadFactory& workloadFactory)
{
armnn::TensorInfo inputDesc({2, 2}, armnn::GetDataType<float>());
boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>(
{2., 3., 3., 4.}));
armnn::TensorInfo outputDesc({2, 4}, armnn::GetDataType<float>());
boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(outputDesc, std::vector<float>(
{{-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f,
-0.0185422f, 0.11281417f, 0.24466537f, -0.1826292f}}));
return LstmNoCifgNoPeepholeNoProjectionTestImpl(workloadFactory, input, expectedOutput);
}
LayerTestResult<float,3> MergerTest(armnn::IWorkloadFactory& workloadFactory)
{
unsigned int outputWidth = 3;
unsigned int outputHeight = 6;
unsigned int outputChannels = 3;
unsigned int inputWidth1 = 3;
unsigned int inputHeight1 = 6;
unsigned int inputChannels1 = 2;
unsigned int inputWidth2 = 3;
unsigned int inputHeight2 = 6;
unsigned int inputChannels2 = 1;
// Define the tensor descriptors.
armnn::TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, armnn::DataType::Float32);
armnn::TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, armnn::DataType::Float32);
armnn::TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, armnn::DataType::Float32);
LayerTestResult<float,3> ret(outputTensorInfo);
ret.outputExpected = MakeTensor<float, 3>(outputTensorInfo, std::vector<float>(
{
1.0f, 2.0f, 3.0f,
4.0f, 5.0f, 6.0f,
7.0f, 8.0f, 9.0f,
10.0f, 11.0f, 12.0f,
13.0f, 14.0f, 15.0f,
16.0f, 17.0f, 18.0f,
19.0f, 20.0f, 21.0f,
22.0f, 23.0f, 24.0f,
25.0f, 26.0f, 27.0f,
28.0f, 29.0f, 30.0f,
31.0f, 32.0f, 33.0f,
34.0f, 35.0f, 36.0f,
37.0f, 38.0f, 39.0f,
40.0f, 41.0f, 42.0f,
43.0f, 44.0f, 45.0f,
46.0f, 47.0f, 48.0f,
49.0f, 50.0f, 51.0f,
52.0f, 53.0f, 54.0f,
})
);
auto input1 = MakeTensor<float, 3>(inputTensorInfo1, std::vector<float>(
{
1.0f, 2.0f, 3.0f,
4.0f, 5.0f, 6.0f,
7.0f, 8.0f, 9.0f,
10.0f, 11.0f, 12.0f,
13.0f, 14.0f, 15.0f,
16.0f, 17.0f, 18.0f,
19.0f, 20.0f, 21.0f,
22.0f, 23.0f, 24.0f,
25.0f, 26.0f, 27.0f,
28.0f, 29.0f, 30.0f,
31.0f, 32.0f, 33.0f,
34.0f, 35.0f, 36.0f,
})
);
auto input2 = MakeTensor<float, 3>(inputTensorInfo2, std::vector<float>(
{
37.0f, 38.0f, 39.0f,
40.0f, 41.0f, 42.0f,
43.0f, 44.0f, 45.0f,
46.0f, 47.0f, 48.0f,
49.0f, 50.0f, 51.0f,
52.0f, 53.0f, 54.0f,
})
);
std::vector<unsigned int> wOrigin1 = {0, 0, 0}; //Extent of the window is defined by size of input[0].
armnn::MergerQueueDescriptor::ViewOrigin window1(wOrigin1);
std::vector<unsigned int> wOrigin2 = {2, 0, 0}; //Extent of the window is defined by size of input[1].
armnn::MergerQueueDescriptor::ViewOrigin window2(wOrigin2);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
bool subTensorsSupported = workloadFactory.SupportsSubTensors();
std::unique_ptr<armnn::ITensorHandle> inputHandle1 =
subTensorsSupported ?
workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) :
workloadFactory.CreateTensorHandle(inputTensorInfo1);
std::unique_ptr<armnn::ITensorHandle> inputHandle2 =
subTensorsSupported ?
workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) :
workloadFactory.CreateTensorHandle(inputTensorInfo2);
armnn::MergerQueueDescriptor data;
armnn::WorkloadInfo info;
AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
data.m_ViewOrigins.push_back(window1);
data.m_ViewOrigins.push_back(window2);
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMerger(data, info);
inputHandle1->Allocate();
inputHandle2->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]);
CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]);
workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get());
return ret;
}
LayerTestResult<float,4> AdditionTest(armnn::IWorkloadFactory& workloadFactory)
{
unsigned int batchSize = 2;
unsigned int channels = 2;
unsigned int height = 2;
unsigned int width = 3;
armnn::TensorInfo inputTensorInfo1, inputTensorInfo2;
armnn::TensorInfo outputTensorInfo;
unsigned int shape[] = {batchSize, channels, height, width};
inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
inputTensorInfo2 = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
auto input1 = MakeTensor<float, 4>(inputTensorInfo1, std::vector<float>(
{
0.0f, 2.0f, 1.0f,
0.2f, 1.0f, 2.0f,
1.0f, 2.0f, 1.0f,
0.2f, 1.0f, 2.0f,
0.0f, 2.0f, 1.0f,
4.2f, 1.0f, 2.0f,
0.0f, 0.0f, 1.0f,
0.2f, 1.0f, 2.0f,
}));
auto input2 = MakeTensor<float, 4>(inputTensorInfo2, std::vector<float>(
{
1.0f, 2.0f, 1.0f,
0.0f, 1.0f, 2.0f,
1.0f, 2.0f, -2.0f,
0.2f, 1.0f, 2.0f,
0.0f, 2.0f, 1.0f,
4.2f, 0.0f, -3.0f,
0.0f, 0.0f, 1.0f,
0.7f, 1.0f, 5.0f,
}));
LayerTestResult<float,4> ret(outputTensorInfo);
ret.outputExpected = MakeTensor<float, 4>(outputTensorInfo, std::vector<float>(
{
1.0f, 4.0f, 2.0f,
0.2f, 2.0f, 4.0f,
2.0f, 4.0f, -1.0f,
0.4f, 2.0f, 4.0f,
0.0f, 4.0f, 2.0f,
8.4f, 1.0f, -1.0f,
0.0f, 0.0f, 2.0f,
0.9f, 2.0f, 7.0f,
}));
std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::AdditionQueueDescriptor data;
armnn::WorkloadInfo info;
AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info);
inputHandle1->Allocate();
inputHandle2->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
CopyDataToITensorHandle(inputHandle2.get(), &input2[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> AdditionBroadcastTestImpl(armnn::IWorkloadFactory& workloadFactory,
float qScale,
int32_t qOffset)
{
armnn::TensorInfo inputTensorInfo1 = armnn::TensorInfo({1, 3, 2, 1}, armnn::GetDataType<T>());
armnn::TensorInfo inputTensorInfo2 = armnn::TensorInfo({1, 1, 2, 3}, armnn::GetDataType<T>());
armnn::TensorInfo outputTensorInfo = armnn::TensorInfo({1, 3, 2, 3}, armnn::GetDataType<T>());
if (armnn::IsQuantizedType<T>())
{
inputTensorInfo1.SetQuantizationScale(qScale);
inputTensorInfo1.SetQuantizationOffset(qOffset);
inputTensorInfo2.SetQuantizationScale(qScale);
inputTensorInfo2.SetQuantizationOffset(qOffset);
outputTensorInfo.SetQuantizationScale(qScale);
outputTensorInfo.SetQuantizationOffset(qOffset);
}
auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(qScale, qOffset,
{
0.0f,
1.0f,
2.0f,
3.0f,
4.0f,
5.0f,
}));
auto input2 = MakeTensor<T, 4>(inputTensorInfo2, QuantizedVector<T>(qScale, qOffset,
{
0.5f, 1.5f, 2.5f,
3.5f, 4.5f, 5.5f,
}));
LayerTestResult<T,4> ret(outputTensorInfo);
ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset,
{
0.5f, 1.5f, 2.5f,
4.5f, 5.5f, 6.5f,
2.5f, 3.5f, 4.5f,
6.5f, 7.5f, 8.5f,
4.5f, 5.5f, 6.5f,
8.5f, 9.5f, 10.5f,
}));
std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::AdditionQueueDescriptor data;
armnn::WorkloadInfo info;
AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info);
inputHandle1->Allocate();
inputHandle2->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
CopyDataToITensorHandle(inputHandle2.get(), &input2[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> AdditionBroadcast1ElementTestImpl(armnn::IWorkloadFactory& workloadFactory,
float qScale,
int32_t qOffset)
{
armnn::TensorInfo inputTensorInfo1 = armnn::TensorInfo({1, 3, 2, 3}, armnn::GetDataType<T>());
armnn::TensorInfo inputTensorInfo2 = armnn::TensorInfo({1, 1, 1, 1}, armnn::GetDataType<T>());
armnn::TensorInfo outputTensorInfo = armnn::TensorInfo({1, 3, 2, 3}, armnn::GetDataType<T>());
if (armnn::IsQuantizedType<T>())
{
inputTensorInfo1.SetQuantizationScale(qScale);
inputTensorInfo1.SetQuantizationOffset(qOffset);
inputTensorInfo2.SetQuantizationScale(qScale);
inputTensorInfo2.SetQuantizationOffset(qOffset);
outputTensorInfo.SetQuantizationScale(qScale);
outputTensorInfo.SetQuantizationOffset(qOffset);
}
auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(qScale, qOffset,
{
0.0f, 1.0f, 2.0f,
3.0f, 4.0f, 5.0f,
6.0f, 7.0f, 8.0f,
9.0f, 10.0f, 11.0f,
12.0f, 13.0f, 14.0f,
15.0f, 16.0f, 17.0f,
}));
auto input2 = MakeTensor<T, 4>(inputTensorInfo2, QuantizedVector<T>(qScale, qOffset,
{
0.5f,
}));
LayerTestResult<T,4> ret(outputTensorInfo);
ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset,
{
0.5f, 1.5f, 2.5f,
3.5f, 4.5f, 5.5f,
6.5f, 7.5f, 8.5f,
9.5f, 10.5f, 11.5f,
12.5f, 13.5f, 14.5f,
15.5f, 16.5f, 17.5f,
}));
std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::AdditionQueueDescriptor data;
armnn::WorkloadInfo info;
AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info);
inputHandle1->Allocate();
inputHandle2->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]);
workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
return ret;
}
LayerTestResult<float, 4> AdditionBroadcastTest(armnn::IWorkloadFactory& workloadFactory)
{
return AdditionBroadcastTestImpl<float>(workloadFactory, 0.0f, 0);
}
LayerTestResult<uint8_t, 4> AdditionBroadcastUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return AdditionBroadcastTestImpl<uint8_t>(workloadFactory, 2.f, 0);
}
LayerTestResult<float, 4> AdditionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory)
{
return AdditionBroadcast1ElementTestImpl<float>(workloadFactory, 0.0f, 0);
}
LayerTestResult<uint8_t, 4> AdditionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return AdditionBroadcast1ElementTestImpl<uint8_t>(workloadFactory, 0.1333333f, 128);
}
LayerTestResult<float,4> CompareAdditionTest(armnn::IWorkloadFactory& workloadFactory,
armnn::IWorkloadFactory& refWorkloadFactory)
{
unsigned int batchSize = 4;
unsigned int channels = 1;
unsigned int height = 2;
unsigned int width = 3;
armnn::TensorInfo inputTensorInfo1, inputTensorInfo2;
armnn::TensorInfo outputTensorInfo;
unsigned int shape[] = {batchSize, channels, height, width};
inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
inputTensorInfo2 = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
auto input1 = MakeRandomTensor<float, 4>(inputTensorInfo1, 1232);
auto input2 = MakeRandomTensor<float, 4>(inputTensorInfo2, 456);
LayerTestResult<float,4> ret(outputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> inputHandle1Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo1);
std::unique_ptr<armnn::ITensorHandle> inputHandle2Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo2);
std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::AdditionQueueDescriptor data;
armnn::WorkloadInfo info;
AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
armnn::AdditionQueueDescriptor refData = data;
armnn::WorkloadInfo refInfo = info;
SetWorkloadInput(refData, refInfo, 0, inputTensorInfo1, inputHandle1Ref.get());
SetWorkloadInput(refData, refInfo, 1, inputTensorInfo2, inputHandle2Ref.get());
SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info);
std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateAddition(refData, refInfo);
inputHandle1->Allocate();
inputHandle2->Allocate();
outputHandle->Allocate();
inputHandle1Ref->Allocate();
inputHandle2Ref->Allocate();
outputHandleRef->Allocate();
CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]);
CopyDataToITensorHandle(inputHandle1Ref.get(), &input1[0][0][0][0]);
CopyDataToITensorHandle(inputHandle2Ref.get(), &input2[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;
}
namespace {
template <typename T>
LayerTestResult<T, 4> DivisionTestHelper(armnn::IWorkloadFactory& workloadFactory,
const unsigned int shape0[4],
const std::vector<T>& values0,
float scale0,
int32_t offset0,
const unsigned int shape1[4],
const std::vector<T> & values1,
float scale1,
int32_t offset1,
const unsigned int outShape[4],
const std::vector<T> & outValues,
float outScale,
int32_t outOffset)
{
auto dataType = (std::is_same<T, uint8_t>::value ?
armnn::DataType::QuantisedAsymm8 :
armnn::DataType::Float32);
armnn::TensorInfo inputTensorInfo0(4, shape0, dataType);
armnn::TensorInfo inputTensorInfo1(4, shape1, dataType);
armnn::TensorInfo outputTensorInfo(4, outShape, dataType);
inputTensorInfo0.SetQuantizationScale(scale0);
inputTensorInfo0.SetQuantizationOffset(offset0);
inputTensorInfo1.SetQuantizationScale(scale1);
inputTensorInfo1.SetQuantizationOffset(offset1);
outputTensorInfo.SetQuantizationScale(outScale);
outputTensorInfo.SetQuantizationOffset(outOffset);
auto input0 = MakeTensor<T, 4>(inputTensorInfo0, values0);
auto input1 = MakeTensor<T, 4>(inputTensorInfo1, values1);
LayerTestResult<T, 4> result(outputTensorInfo);
result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outValues);
std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0);
std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::DivisionQueueDescriptor data;
armnn::WorkloadInfo info;
AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get());
AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDivision(data, info);
inputHandle0->Allocate();
inputHandle1->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]);
CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
return result;
}
} // anonymous namespace
LayerTestResult<float,4> DivisionByZeroTest(armnn::IWorkloadFactory& workloadFactory)
{
const unsigned int width = 2;
const unsigned int height = 2;
const unsigned int channelCount = 2;
const unsigned int batchSize = 2;
unsigned int shape[] = { batchSize, channelCount, height, width };
std::vector<float> input0({
1.f, 1.f, 1.f, 1.f, 0.f, 0.f, 0.f, 0.f,
-1.f, -1.f, -1.f, -1.f, 5.f, 5.f, 5.f, 5.f });
std::vector<float> input1({
0.f, 0.f, -0.f, -0.f, 0.f, 0.f, -0.f, -0.f,
0.f, 0.f, -0.f, -0.f, 5.f, 5.f, 5.f, 5.f });
std::vector<float> output({
INFINITY, INFINITY, -INFINITY, -INFINITY, NAN, NAN, -NAN, -NAN,
-INFINITY, -INFINITY, INFINITY, INFINITY, 1, 1, 1, 1 });
return DivisionTestHelper<float>(workloadFactory,
shape, input0, 1.0f, 0,
shape, input1, 1.0f, 0,
shape, output, 1.0f, 0);
}
LayerTestResult<float,4> DivisionTest(armnn::IWorkloadFactory& workloadFactory)
{
const unsigned int width = 2;
const unsigned int height = 2;
const unsigned int channelCount = 2;
const unsigned int batchSize = 2;
unsigned int shape[] = { batchSize, channelCount, height, width };
std::vector<float> input0({
2, 2, 2, 2, 3, 3, 3, 3,
4, 4, 4, 4, 5, 5, 5, 5 });
std::vector<float> input1({
1, 1, 1, 1, 2, 2, 2, 2,
4, 4, 4, 4, 4, 4, 4, 4 });
std::vector<float> output({
2, 2, 2, 2, 1.5, 1.5, 1.5, 1.5,
1, 1, 1, 1, 1.25, 1.25, 1.25, 1.25 });
return DivisionTestHelper<float>(workloadFactory,
shape, input0, 1.0f, 0,
shape, input1, 1.0f, 0,
shape, output, 1.0f, 0);
}
LayerTestResult<float, 4> DivisionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory)
{
unsigned int shape0[] = { 1, 2, 2, 2 };
std::vector<float> input0({ 2, 4, 6, 8, 10, 12, 14, 16});
unsigned int shape1[] = { 1, 1, 1, 1 };
std::vector<float> input1({ 2 });
std::vector<float> output({ 1, 2, 3, 4, 5, 6, 7, 8});
return DivisionTestHelper<float>(workloadFactory,
shape0, input0, 1.0f, 0,
shape1, input1, 1.0f, 0,
shape0, output, 1.0f, 0);
}
LayerTestResult<float, 4> DivisionBroadcast1DVectorTest(armnn::IWorkloadFactory& workloadFactory)
{
unsigned int shape0[] = { 1, 3, 3, 2 };
std::vector<float> input0({
1, 4, 3, 8, 5, 12,
7, 16, 9, 20, 11, 24,
13, 28, 15, 32, 17, 36});
unsigned int shape1[] = { 1, 1, 1, 2 };
std::vector<float> input1({ 1, 2 });
std::vector<float> output({
1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18});
return DivisionTestHelper<float>(workloadFactory,
shape0, input0, 1.0f, 0,
shape1, input1, 1.0f, 0,
shape0, output, 1.0f, 0);
}
LayerTestResult<uint8_t,4> DivisionUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
const unsigned int width = 2;
const unsigned int height = 2;
const unsigned int channelCount = 2;
const unsigned int batchSize = 2;
unsigned int shape[] = { batchSize, channelCount, height, width };
std::vector<uint8_t> input0({2, 2, 2, 2, 3, 3, 3, 3,
4, 4, 4, 4, 5, 5, 5, 5 });
std::vector<uint8_t> input1({1, 1, 1, 1, 2, 2, 2, 2,
4, 4, 4, 4, 4, 4, 4, 4 });
std::vector<uint8_t> output({8, 8, 8, 8, 6, 6, 6, 6,
4, 4, 4, 4, 5, 5, 5, 5});
return DivisionTestHelper<uint8_t>(workloadFactory,
shape, input0, 1.0f, 0,
shape, input1, 1.0f, 0,
shape, output, 0.25f, 0);
}
LayerTestResult<uint8_t, 4> DivisionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
unsigned int shape0[] = { 1, 2, 2, 2 };
std::vector<uint8_t> input0({ 2, 4, 6, 8, 10, 12, 14, 16});
unsigned int shape1[] = { 1, 1, 1, 1 };
std::vector<uint8_t> input1({ 2 });
std::vector<uint8_t> output({ 1, 2, 3, 4, 5, 6, 7, 8});
return DivisionTestHelper<uint8_t>(workloadFactory,
shape0, input0, 1.0f, 0,
shape1, input1, 1.0f, 0,
shape0, output, 1.0f, 0);
}
LayerTestResult<uint8_t, 4> DivisionBroadcast1DVectorUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
unsigned int shape0[] = { 1, 3, 3, 2 };
std::vector<uint8_t> input0({1, 4, 3, 8, 5, 12,
7, 16, 9, 20, 11, 24,
13, 28, 15, 32, 17, 36});
unsigned int shape1[] = { 1, 1, 1, 2 };
std::vector<uint8_t> input1({ 1, 2 });
std::vector<uint8_t> output({1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18});
return DivisionTestHelper<uint8_t>(workloadFactory,
shape0, input0, 1.0f, 0,
shape1, input1, 1.0f, 0,
shape0, output, 1.0f, 0);
}
namespace {
LayerTestResult<float,4> MultiplicationTestHelper(armnn::IWorkloadFactory& workloadFactory,
const unsigned int shape0[4],
const std::vector<float> & values0,
const unsigned int shape1[4],
const std::vector<float> & values1,
const unsigned int outShape[4],
const std::vector<float> & outValues)
{
const size_t dimensionCount = 4;
armnn::TensorInfo inputTensorInfo0{dimensionCount, shape0, armnn::DataType::Float32};
armnn::TensorInfo inputTensorInfo1{dimensionCount, shape1, armnn::DataType::Float32};
armnn::TensorInfo outputTensorInfo{dimensionCount, outShape, armnn::DataType::Float32};
auto input0 = MakeTensor<float, 4>(inputTensorInfo0, values0);
auto input1 = MakeTensor<float, 4>(inputTensorInfo1, values1);
LayerTestResult<float,4> ret(outputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0);
std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::MultiplicationQueueDescriptor data;
armnn::WorkloadInfo info;
AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get());
AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMultiplication(data, info);
inputHandle0->Allocate();
inputHandle1->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]);
CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
ret.outputExpected = MakeTensor<float, 4>(outputTensorInfo, outValues);
return ret;
}
} // anonymous namespace
LayerTestResult<float,4> MultiplicationTest(armnn::IWorkloadFactory& workloadFactory)
{
const unsigned int width = 2;
const unsigned int height = 2;
const unsigned int channelCount = 2;
const unsigned int batchSize = 2;
unsigned int shape[] = { batchSize, channelCount, height, width };
std::vector<float> input0({
1, 1, 1, 1, 2, 2, 2, 2,
3, 3, 3, 3, 4, 4, 4, 4 });
std::vector<float> input1({
2, 2, 2, 2, 3, 3, 3, 3,
4, 4, 4, 4, 5, 5, 5, 5 });
std::vector<float> output({
2, 2, 2, 2, 6, 6, 6, 6,
12, 12, 12, 12, 20, 20, 20, 20 });
return MultiplicationTestHelper(workloadFactory,
shape,
input0,
shape,
input1,
shape,
output);
}
LayerTestResult<float, 4> MultiplicationBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory)
{
unsigned int shape0[] = { 1, 2, 2, 2 };
std::vector<float> input0({ 1, 2, 3, 4, 5, 6, 7, 8});
unsigned int shape1[] = { 1, 1, 1, 1 };
std::vector<float> input1({ 2 });
std::vector<float> output({ 2, 4, 6, 8, 10, 12, 14, 16});
return MultiplicationTestHelper(workloadFactory,
shape0,
input0,
shape1,
input1,
shape0,
output);
}
LayerTestResult<float, 4> MultiplicationBroadcast1DVectorTest(armnn::IWorkloadFactory& workloadFactory)
{
unsigned int shape0[] = { 1, 3, 3, 2 };
std::vector<float> input0({
1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18});
unsigned int shape1[] = { 1, 1, 1, 2 };
std::vector<float> input1({ 1, 2 });
std::vector<float> output({
1, 4, 3, 8, 5, 12,
7, 16, 9, 20, 11, 24,
13, 28, 15, 32, 17, 36});
return MultiplicationTestHelper(workloadFactory,
shape0,
input0,
shape1,
input1,
shape0,
output);
}
LayerTestResult<float,4> CompareMultiplicationTest(armnn::IWorkloadFactory& workloadFactory,
armnn::IWorkloadFactory& refWorkloadFactory)
{
const unsigned int width = 16;
const unsigned int height = 32;
const unsigned int channelCount = 2;
const unsigned int batchSize = 5;
armnn::TensorInfo inputTensorInfo0;
armnn::TensorInfo inputTensorInfo1;
armnn::TensorInfo outputTensorInfo;
constexpr unsigned int shape[] = { batchSize, channelCount, height, width };
inputTensorInfo0 = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
LayerTestResult<float,4> comparisonResult(outputTensorInfo);
auto input0 = MakeRandomTensor<float, 4>(inputTensorInfo0, 803506992);
auto input1 = MakeRandomTensor<float, 4>(inputTensorInfo1, 54902257);
std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0);
std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> inputHandle0Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo0);
std::unique_ptr<armnn::ITensorHandle> inputHandle1Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo1);
std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::MultiplicationQueueDescriptor data;
armnn::WorkloadInfo info;
AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get());
AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
armnn::MultiplicationQueueDescriptor refData = data;
armnn::WorkloadInfo refInfo = info;
SetWorkloadInput(refData, refInfo, 0, inputTensorInfo0, inputHandle0Ref.get());
SetWorkloadInput(refData, refInfo, 1, inputTensorInfo1, inputHandle1Ref.get());
SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMultiplication(data, info);
std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateMultiplication(refData, refInfo);
inputHandle0->Allocate();
inputHandle1->Allocate();
outputHandle->Allocate();
inputHandle0Ref->Allocate();
inputHandle1Ref->Allocate();
outputHandleRef->Allocate();
CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]);
CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
CopyDataToITensorHandle(inputHandle0Ref.get(), &input0[0][0][0][0]);
CopyDataToITensorHandle(inputHandle1Ref.get(), &input1[0][0][0][0]);
workloadFactory.Finalize();
workload->Execute();
refWorkloadFactory.Finalize();
workloadRef->Execute();
CopyDataFromITensorHandle(&comparisonResult.output[0][0][0][0], outputHandle.get());
CopyDataFromITensorHandle(&comparisonResult.outputExpected[0][0][0][0], outputHandleRef.get());
return comparisonResult;
}
LayerTestResult<float,4> CompareBatchNormTest(armnn::IWorkloadFactory& workloadFactory,
armnn::IWorkloadFactory& refWorkloadFactory)
{
const unsigned int width = 2;
const unsigned int height = 3;
const unsigned int channels = 5;
const unsigned int batchSize = 3;
armnn::TensorInfo inputTensorInfo;
armnn::TensorInfo outputTensorInfo;
armnn::TensorInfo tensorInfo;
constexpr unsigned int shape[] = {batchSize, channels, height, width};
constexpr unsigned int tensorShape[] = {channels};
inputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
tensorInfo = armnn::TensorInfo(1, tensorShape, armnn::DataType::Float32);
auto input = MakeRandomTensor<float, 4>(inputTensorInfo, 21312);
auto mean = MakeRandomTensor<float, 1>(tensorInfo, 123);
auto variance = MakeRandomTensor<float, 1>(tensorInfo, 234, 0.0f);
auto beta = MakeRandomTensor<float, 1>(tensorInfo, 123);
auto gamma = MakeRandomTensor<float, 1>(tensorInfo, 345);
LayerTestResult<float,4> ret(outputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::BatchNormalizationQueueDescriptor data;
armnn::WorkloadInfo info;
armnn::ScopedCpuTensorHandle meanTensor(tensorInfo);
armnn::ScopedCpuTensorHandle varianceTensor(tensorInfo);
armnn::ScopedCpuTensorHandle betaTensor(tensorInfo);
armnn::ScopedCpuTensorHandle gammaTensor(tensorInfo);
AllocateAndCopyDataToITensorHandle(&meanTensor, &mean[0]);
AllocateAndCopyDataToITensorHandle(&varianceTensor, &variance[0]);
AllocateAndCopyDataToITensorHandle(&betaTensor, &beta[0]);
AllocateAndCopyDataToITensorHandle(&gammaTensor, &gamma[0]);
AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
data.m_Mean = &meanTensor;
data.m_Variance = &varianceTensor;
data.m_Beta = &betaTensor;
data.m_Gamma = &gammaTensor;
data.m_Parameters.m_Eps = 0.01f;
armnn::BatchNormalizationQueueDescriptor 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.CreateBatchNormalization(data, info);
std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateBatchNormalization(refData, refInfo);
inputHandle->Allocate();
outputHandle->Allocate();
inputHandleRef->Allocate();
outputHandleRef->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>
void PermuteTensorData(
armnn::IWorkloadFactory& workloadFactory,
const armnn::PermutationVector& mappings,
armnn::TensorInfo & inputTensorInfo,
const T * inputData,
std::vector<T>& outputData)
{
BOOST_ASSERT_MSG(inputData != nullptr, "inputData must not be null");
if (inputData == nullptr)
{
// Nullptr is an error in the test. By returning without doing the concatenation
// I expect the caller to fail the test. It still makes sense to report this as
// an assert for Debug builds.
return;
}
armnn::TensorInfo outputTensorInfo = armnnUtils::Permuted(inputTensorInfo, mappings);
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::PermuteQueueDescriptor queueDescriptor;
queueDescriptor.m_Parameters = armnn::PermuteDescriptor{mappings};
armnn::WorkloadInfo workloadInfo;
AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePermute(queueDescriptor, workloadInfo);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), inputData);
workload->Execute();
outputData.resize(outputTensorInfo.GetNumElements());
CopyDataFromITensorHandle(&outputData[0], outputHandle.get());
inputTensorInfo = outputTensorInfo;
}
armnn::OriginsDescriptor CreateMergerDescriptorForConcatenation(
const std::vector<armnn::TensorInfo> & inputTensorInfos,
unsigned int concatDim)
{
std::vector<armnn::TensorShape> shapes;
shapes.reserve(inputTensorInfos.size());
for (const armnn::TensorInfo& it: inputTensorInfos)
{
shapes.push_back(it.GetShape());
}
return armnn::CreateMergerDescriptorForConcatenation(shapes.begin(),
shapes.end(),
concatDim);
}
//
// Concatenation is only supported for N and C dimensions for NCHW. In case of
// <4 dimensions we need to make sure that the concat dimensions are at least
// the 3rd slowest iterating one.
//
bool NeedPermuteForConcat(
const std::vector<armnn::TensorInfo> & inputTensorInfos,
unsigned int concatDim)
{
// See note above. Additionally we expect the input shapes to have the
// same number of dimensions.
unsigned int nDimensions = 0;
// Determine the number of dimensions as well as sanity check them
// agains test implementation issues.
for (auto && tensorInfo : inputTensorInfos)
{
if (!nDimensions)
{
nDimensions = tensorInfo.GetShape().GetNumDimensions();
}
else
{
BOOST_ASSERT_MSG(nDimensions == tensorInfo.GetShape().GetNumDimensions(),
"Input shapes must have the same number of dimensions");
}
}
return (nDimensions-concatDim) < 3;
}
armnn::TensorShape ExpandTensorShapeTo3dForPermute(const armnn::TensorShape & inputShape)
{
unsigned int numDims = inputShape.GetNumDimensions();
if (numDims >= 3)
{
// Nothing to do if the inputShape has at least 3 dimensions.
return inputShape;
}
std::vector<unsigned int> newDims(size_t(3), 1u);
unsigned int expandedBy = 3 - numDims;
for (unsigned int i=0; i<numDims; ++i)
{
newDims[expandedBy+i] = inputShape[i];
}
return armnn::TensorShape(3u, &newDims[0]);
}
void Generate3dPermuteVectorForConcat(
unsigned int numDimensions,
unsigned int & concatDim,
std::pair<armnn::PermutationVector, armnn::PermutationVector> & permutations)
{
BOOST_ASSERT_MSG(numDimensions <= 3,
"Only dimensions 1,2 and 3 are supported by this helper");
unsigned int expandedBy = 3 - numDimensions;
unsigned int expandedConcatAxis = concatDim + expandedBy;
if (expandedConcatAxis == 2)
{
concatDim = 0;
armnn::PermutationVector forwardPermutation({1, 2, 0});
armnn::PermutationVector reversePermutation({2, 0, 1});
permutations = std::make_pair(forwardPermutation, reversePermutation);
}
else if (expandedConcatAxis == 1)
{
concatDim = 0;
armnn::PermutationVector forwardPermutation({2, 0, 1});
armnn::PermutationVector reversePermutation({1, 2, 0});
permutations = std::make_pair(forwardPermutation, reversePermutation);
}
else
{
BOOST_ASSERT(expandedConcatAxis == 0);
concatDim = 0;
}
}
//
// Permute the input tensors so we can do a supported concatenation.
// Also treat lower than 3d tensors as 3d by adding dummy 1 dimensions
// at the front. Finally this function tells what the output shape
// of the permuted concatenated tensor is going to be.
//
template <typename T>
void PermuteInputsForConcat(
armnn::IWorkloadFactory& workloadFactory,
std::vector<armnn::TensorInfo> & inputTensorInfos,
std::vector<T *> & inputData,
std::vector<std::vector<T>> & inputDataStorage,
armnn::PermutationVector & permuteVector,
unsigned int & concatDim,
armnn::TensorInfo & outputTensorInfo)
{
BOOST_ASSERT_MSG(inputTensorInfos.size() > 1,
"Expecting more than one tensor to be concatenated here");
unsigned int numDims = 0;
unsigned int nthInput = 0;
const armnn::PermutationVector identity({0, 1, 2});
std::pair<armnn::PermutationVector, armnn::PermutationVector> permutations =
std::make_pair(identity, identity);
inputDataStorage.resize(inputData.size());
for (auto && tensorInfo : inputTensorInfos)
{
if (numDims == 0)
{
numDims = tensorInfo.GetShape().GetNumDimensions();
Generate3dPermuteVectorForConcat(numDims, concatDim, permutations);
// Store the reverese permutation.
permuteVector = permutations.second;
BOOST_ASSERT_MSG(!permuteVector.IsEqual(identity),
"Test logic error, we don't need permutation, so we shouldn't arrive here");
}
else
{
BOOST_ASSERT_MSG(numDims == tensorInfo.GetShape().GetNumDimensions(),
"All inputs must have the same number of dimensions");
}
armnn::TensorInfo newTensorInfo = tensorInfo;
newTensorInfo.SetShape(ExpandTensorShapeTo3dForPermute(tensorInfo.GetShape()));
PermuteTensorData<T>(workloadFactory,
permutations.first,
newTensorInfo,
inputData[nthInput],
inputDataStorage[nthInput]);
inputData[nthInput] = inputDataStorage[nthInput].data();
inputTensorInfos[nthInput] = newTensorInfo;
++nthInput;
}
outputTensorInfo.SetShape(
armnnUtils::Permuted(
ExpandTensorShapeTo3dForPermute(outputTensorInfo.GetShape()),
permutations.first));
}
//
// This is the pair of PermuteInputsForConcat(...) which permutes back
// the output of the concatenation so we can check it against an expected
// output.
//
template <typename T>
void PermuteOutputForConcat(
armnn::IWorkloadFactory& workloadFactory,
const armnn::TensorInfo & tensorInfo,
const armnn::PermutationVector & permuteVector,
std::unique_ptr<armnn::ITensorHandle> && inputDataHandle,
T * data)
{
BOOST_ASSERT_MSG(data != nullptr, "data must not be null");
if (data == nullptr)
{
// Nullptr is an error in the test. By returning without doing the permutation
// I expect the caller to fail the test. It still makes sense to report this as
// an assert for Debug builds.
return;
}
armnn::TensorInfo resultTensorInfo = tensorInfo;
std::vector<T> inputData(tensorInfo.GetNumElements());
std::vector<T> outputData;
CopyDataFromITensorHandle(&inputData[0], inputDataHandle.get());
PermuteTensorData<T>(workloadFactory,
permuteVector,
resultTensorInfo,
&inputData[0],
outputData);
::memcpy(data, &outputData[0], sizeof(T)*outputData.size());
}
template <typename T>
void Concatenate(armnn::IWorkloadFactory& workloadFactory,
std::initializer_list<const armnn::TensorInfo> inputTensorInfosOrig,
std::initializer_list<T *> inputsOrig,
const armnn::TensorInfo& outputTensorInfoOrig,
T * output,
unsigned int concatDim)
{
BOOST_ASSERT_MSG(output != nullptr, "output must not be null");
if (output == nullptr)
{
// Nullptr is an error in the test. By returning without doing the permutation
// I expect the caller to fail the test. It still makes sense to report this as
// an assert for Debug builds.
return;
}
armnn::MergerQueueDescriptor queueDescriptor;
// Saves a copy of the parameters which we might need to change.
std::vector<armnn::TensorInfo> inputTensorInfos(inputTensorInfosOrig.begin(), inputTensorInfosOrig.end());
std::vector<T *> inputs = inputsOrig;
armnn::TensorInfo outputTensorInfo = outputTensorInfoOrig;
armnn::PermutationVector permuteVector{0, 1, 2};
// Holds and automatically releases memory for the reshaped input data.
std::vector<std::vector<T>> tmpInputDataStorage;
const size_t inputCount = inputTensorInfos.size();
bool needPermuteForConcat = NeedPermuteForConcat(inputTensorInfos, concatDim);
if (needPermuteForConcat)
{
//
// We need to permute the inputs, because concatenation along
// the requested axis is not supported.
//
PermuteInputsForConcat<T>(workloadFactory,
inputTensorInfos,
inputs,
tmpInputDataStorage,
permuteVector,
concatDim,
outputTensorInfo);
}
armnn::OriginsDescriptor viewsDescriptor = CreateMergerDescriptorForConcatenation(inputTensorInfos, concatDim);
queueDescriptor.m_ViewOrigins.reserve(viewsDescriptor.GetNumViews());
for (unsigned int i = 0; i < viewsDescriptor.GetNumViews(); ++i)
{
queueDescriptor.m_ViewOrigins.emplace_back(std::vector<unsigned int>(viewsDescriptor.GetViewOrigin(i),
viewsDescriptor.GetViewOrigin(i) + viewsDescriptor.GetNumDimensions()));
}
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
std::vector<std::unique_ptr<armnn::ITensorHandle>> inputHandles;
inputHandles.reserve(inputCount);
const bool subTensorsSupported = workloadFactory.SupportsSubTensors();
for (unsigned int i = 0; i < inputCount; ++i)
{
const armnn::TensorInfo& inputTensorInfo = inputTensorInfos[i];
std::unique_ptr<armnn::ITensorHandle> inputHandle = subTensorsSupported ?
workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo.GetShape(),
queueDescriptor.m_ViewOrigins[i].m_Origin.data())
: workloadFactory.CreateTensorHandle(inputTensorInfo);
inputHandles.emplace_back(std::move(inputHandle));
}
armnn::WorkloadInfo workloadInfo;
for (unsigned int i = 0; i < inputCount; ++i)
{
AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfos[i], inputHandles[i].get());
}
AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMerger(queueDescriptor, workloadInfo);
for (auto& inputHandle : inputHandles)
{
inputHandle->Allocate();
}
outputHandle->Allocate();
unsigned int nextInputId = 0;
for (auto& inputHandle : inputHandles)
{
CopyDataToITensorHandle(inputHandle.get(), inputs[nextInputId]);
++nextInputId;
}
workloadFactory.Finalize();
workload->Execute();
if (needPermuteForConcat)
{
PermuteOutputForConcat<T>(workloadFactory,
outputTensorInfo,
permuteVector,
std::move(outputHandle),
output);
}
else
{
CopyDataFromITensorHandle(output, outputHandle.get());
}
}
template <typename T>
LayerTestResult<T, 1> Concatenation1dTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset)
{
armnn::TensorInfo inputTensorInfo({ 3 }, armnn::GetDataType<T>());
auto input0 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { 1.0f, 2.0f, 3.0f }));
auto input1 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { 4.0f, 5.0f, 6.0f }));
auto input2 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { 7.0f, 8.0f, 9.0f }));
armnn::TensorInfo outputTensorInfo({ 9 }, armnn::GetDataType<T>());
LayerTestResult<T, 1> result(outputTensorInfo);
std::vector<T> output;
output.resize(outputTensorInfo.GetNumElements());
Concatenate<T>(workloadFactory,
{ inputTensorInfo, inputTensorInfo, inputTensorInfo },
{ input0.data(), input1.data(), input2.data() },
outputTensorInfo,
output.data(),
0);
result.output = MakeTensor<T, 1>(outputTensorInfo, output);
result.outputExpected = MakeTensor<T, 1>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f
}));
return result;
}
LayerTestResult<float, 1> Concatenation1dTest(armnn::IWorkloadFactory& workloadFactory)
{
return Concatenation1dTestImpl<float>(workloadFactory, 0.0f, 0);
}
template <typename T>
LayerTestResult<T, 2> Concatenation2dTestImpl(armnn::IWorkloadFactory& workloadFactory,
const armnn::TensorInfo& outputTensorInfo,
unsigned int dimension,
const float qScale,
const int32_t qOffset)
{
armnn::TensorInfo inputTensorInfo({ 2, 3 }, armnn::GetDataType<T>());
auto input0 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0
1.0f, 2.0f, 3.0f,
// Batch 1
10.0f, 11.0f, 12.0f,
}));
auto input1 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0
4.0f, 5.0f, 6.0f,
// Batch 1
13.0f, 14.0f, 15.0f,
}));
auto input2 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0
7.0f, 8.0f, 9.0f,
// Batch 1
16.0f, 17.0f, 18.0f,
}));
LayerTestResult<T, 2> result(outputTensorInfo);
std::vector<T> output;
output.resize(outputTensorInfo.GetNumElements());
Concatenate<T>(workloadFactory,
{ inputTensorInfo, inputTensorInfo, inputTensorInfo },
{ input0.data(), input1.data(), input2.data() },
outputTensorInfo,
output.data(),
dimension);
result.output = MakeTensor<T, 2>(outputTensorInfo, output);
return result;
}
template <typename T>
LayerTestResult<T, 2> Concatenation2dDim0TestImpl(armnn::IWorkloadFactory& workloadFactory,
float qScale, int32_t qOffset)
{
armnn::TensorInfo outputTensorInfo({ 6, 3 }, armnn::GetDataType<T>());
LayerTestResult<T, 2> result = Concatenation2dTestImpl<T>(workloadFactory, outputTensorInfo, 0, qScale, qOffset);
result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0
1.0f, 2.0f, 3.0f,
// Batch 1
10.0f, 11.0f, 12.0f,
// Batch 2
4.0f, 5.0f, 6.0f,
// Batch 3
13.0f, 14.0f, 15.0f,
// Batch 4
7.0f, 8.0f, 9.0f,
// Batch 5
16.0f, 17.0f, 18.0f,
}));
return result;
}
LayerTestResult<float, 2> Concatenation2dDim0Test(armnn::IWorkloadFactory& workloadFactory)
{
return Concatenation2dDim0TestImpl<float>(workloadFactory, 0.0f, 0);
}
template <typename T>
LayerTestResult<T, 2> Concatenation2dDim1TestImpl(armnn::IWorkloadFactory& workloadFactory,
float qScale, int32_t qOffset)
{
armnn::TensorInfo outputTensorInfo({ 2, 9 }, armnn::GetDataType<T>());
LayerTestResult<T, 2> result = Concatenation2dTestImpl<T>(workloadFactory, outputTensorInfo, 1, qScale, qOffset);
result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0
1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f,
// Batch 1
10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f
}));
return result;
}
LayerTestResult<float, 2> Concatenation2dDim1Test(armnn::IWorkloadFactory& workloadFactory)
{
return Concatenation2dDim1TestImpl<float>(workloadFactory, 0.0f, 0);
}
template <typename T>
LayerTestResult<T, 2> Concatenation2dDim0DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale,
int32_t qOffset)
{
armnn::TensorInfo input0TensorInfo({ 2, 3 }, armnn::GetDataType<T>());
auto input0 = MakeTensor<T, 2>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0
1.0f, 2.0f, 3.0f,
// Batch 1
10.0f, 11.0f, 12.0f,
}));
armnn::TensorInfo input1TensorInfo({ 3, 3 }, armnn::GetDataType<T>());
auto input1 = MakeTensor<T, 2>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0
4.0f, 5.0f, 6.0f,
// Batch 1
13.0f, 14.0f, 15.0f,
// Batch 0
7.0f, 8.0f, 9.0f,
}));
armnn::TensorInfo input2TensorInfo({ 1, 3 }, armnn::GetDataType<T>());
auto input2 = MakeTensor<T, 2>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 1
16.0f, 17.0f, 18.0f,
}));
armnn::TensorInfo outputTensorInfo({ 6, 3 }, armnn::GetDataType<T>());
LayerTestResult<T, 2> result(outputTensorInfo);
std::vector<T> output;
output.resize(outputTensorInfo.GetNumElements());
Concatenate<T>(workloadFactory,
{ input0TensorInfo, input1TensorInfo, input2TensorInfo },
{ input0.data(), input1.data(), input2.data() },
outputTensorInfo,
output.data(),
0);
result.output = MakeTensor<T, 2>(outputTensorInfo, output);
result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0
1.0f, 2.0f, 3.0f,
// Batch 1
10.0f, 11.0f, 12.0f,
// Batch 2
4.0f, 5.0f, 6.0f,
// Batch 3
13.0f, 14.0f, 15.0f,
// Batch 4
7.0f, 8.0f, 9.0f,
// Batch 5
16.0f, 17.0f, 18.0f,
}));
return result;
}
LayerTestResult<float, 2> Concatenation2dDim0DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory)
{
return Concatenation2dDim0DiffInputDimsTestImpl<float>(workloadFactory, 0.0f, 0);
}
template <typename T>
LayerTestResult<T, 2> Concatenation2dDim1DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale,
int32_t qOffset)
{
armnn::TensorInfo input0TensorInfo({ 2, 3 }, armnn::GetDataType<T>());
auto input0 = MakeTensor<T, 2>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0
1.0f, 2.0f, 3.0f,
// Batch 1
10.0f, 11.0f, 12.0f,
}));
armnn::TensorInfo input1TensorInfo({ 2, 5 }, armnn::GetDataType<T>());
auto input1 = MakeTensor<T, 2>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0
4.0f, 5.0f, 6.0f, 7.0f, 8.0f,
// Batch 1
13.0f, 14.0f, 15.0f, 16.0f, 17.0f,
}));
armnn::TensorInfo input2TensorInfo({ 2, 1 }, armnn::GetDataType<T>());
auto input2 = MakeTensor<T, 2>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0
9.0f,
// Batch 1
18.0f
}));
armnn::TensorInfo outputTensorInfo({ 2, 9 }, armnn::GetDataType<T>());
LayerTestResult<T, 2> result(outputTensorInfo);
std::vector<T> output;
output.resize(outputTensorInfo.GetNumElements());
Concatenate<T>(workloadFactory,
{ input0TensorInfo, input1TensorInfo, input2TensorInfo },
{ input0.data(), input1.data(), input2.data() },
outputTensorInfo,
output.data(),
1);
result.output = MakeTensor<T, 2>(outputTensorInfo, output);
result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0
1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f,
// Batch 1
10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f,
}));
return result;
}
LayerTestResult<float, 2> Concatenation2dDim1DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory)
{
return Concatenation2dDim1DiffInputDimsTestImpl<float>(workloadFactory, 0.0f, 0);
}
template <typename T>
LayerTestResult<T, 3> Concatenation3dTestImpl(armnn::IWorkloadFactory& workloadFactory,
const armnn::TensorInfo& outputTensorInfo,
unsigned int dimension,
float qScale,
int32_t qOffset)
{
armnn::TensorInfo inputTensorInfo({ 2, 3, 2 }, armnn::GetDataType<T>());
auto input0 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0, Channel 0
1.0f, 2.0f,
// Batch 0, Channel 1
3.0f, 4.0f,
// Batch 0, Channel 2
5.0f, 6.0f,
// Batch 1, Channel 0
19.0f, 20.0f,
// Batch 1, Channel 1
21.0f, 22.0f,
// Batch 1, Channel 2
23.0f, 24.0f
}));
auto input1 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0, Channel 0
7.0f, 8.0f,
// Batch 0, Channel 1
9.0f, 10.0f,
// Batch 0, Channel 2
11.0f, 12.0f,
// Batch 1, Channel 0
25.0f, 26.0f,
// Batch 1, Channel 1
27.0f, 28.0f,
// Batch 1, Channel 2
29.0f, 30.0f
}));
auto input2 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0, Channel 0
13.0f, 14.0f,
// Batch 0, Channel 1
15.0f, 16.0f,
// Batch 0, Channel 2
17.0f, 18.0f,
// Batch 1, Channel 0
31.0f, 32.0f,
// Batch 1, Channel 1
33.0f, 34.0f,
// Batch 1, Channel 2
35.0f, 36.0f
}));
LayerTestResult<T, 3> result(outputTensorInfo);
std::vector<T> output;
output.resize(outputTensorInfo.GetNumElements());
Concatenate<T>(workloadFactory,
{ inputTensorInfo, inputTensorInfo, inputTensorInfo },
{ input0.data(), input1.data(), input2.data() },
outputTensorInfo,
output.data(),
dimension);
result.output = MakeTensor<T, 3>(outputTensorInfo, output);
return result;
}
template <typename T>
LayerTestResult<T, 3> Concatenation3dDim0TestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale,
int32_t qOffset)
{
armnn::TensorInfo outputTensorInfo({ 6, 3, 2 }, armnn::GetDataType<T>());
LayerTestResult<T, 3> result = Concatenation3dTestImpl<T>(workloadFactory, outputTensorInfo, 0,
qScale, qOffset);
result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0, Channel 0
1.0f, 2.0f,
// Batch 0, Channel 1
3.0f, 4.0f,
// Batch 0, Channel 2
5.0f, 6.0f,
// Batch 1, Channel 0
19.0f, 20.0f,
// Batch 1, Channel 1
21.0f, 22.0f,
// Batch 1, Channel 2
23.0f, 24.0f,
// Batch 2, Channel 0
7.0f, 8.0f,
// Batch 2, Channel 1
9.0f, 10.0f,
// Batch 2, Channel 2
11.0f, 12.0f,
// Batch 3, Channel 0
25.0f, 26.0f,
// Batch 3, Channel 1
27.0f, 28.0f,
// Batch 3, Channel 2
29.0f, 30.0f,
// Batch 4, Channel 0
13.0f, 14.0f,
// Batch 4, Channel 1
15.0f, 16.0f,
// Batch 4, Channel 2
17.0f, 18.0f,
// Batch 5, Channel 0
31.0f, 32.0f,
// Batch 5, Channel 1
33.0f, 34.0f,
// Batch 5, Channel 2
35.0f, 36.0f
}));
return result;
}
LayerTestResult<float, 3> Concatenation3dDim0Test(armnn::IWorkloadFactory& workloadFactory)
{
return Concatenation3dDim0TestImpl<float>(workloadFactory, 0.0f, 0);
}
template <typename T>
LayerTestResult<T, 3> Concatenation3dDim1TestImpl(armnn::IWorkloadFactory& workloadFactory,
float qScale, int32_t qOffset)
{
armnn::TensorInfo outputTensorInfo({ 2, 9, 2 }, armnn::GetDataType<T>());
LayerTestResult<T, 3> result = Concatenation3dTestImpl<T>(workloadFactory, outputTensorInfo, 1, qScale, qOffset);
result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0, Channel 0
1.0f, 2.0f,
// Batch 0, Channel 1
3.0f, 4.0f,
// Batch 0, Channel 2
5.0f, 6.0f,
// Batch 0, Channel 3
7.0f, 8.0f,
// Batch 0, Channel 4
9.0f, 10.0f,
// Batch 0, Channel 5
11.0f, 12.0f,
// Batch 0, Channel 6
13.0f, 14.0f,
// Batch 0, Channel 7
15.0f, 16.0f,
// Batch 0, Channel 8
17.0f, 18.0f,
// Batch 1, Channel 0
19.0f, 20.0f,
// Batch 1, Channel 1
21.0f, 22.0f,
// Batch 1, Channel 2
23.0f, 24.0f,
// Batch 1, Channel 3
25.0f, 26.0f,
// Batch 1, Channel 4
27.0f, 28.0f,
// Batch 1, Channel 5
29.0f, 30.0f,
// Batch 1, Channel 6
31.0f, 32.0f,
// Batch 1, Channel 7
33.0f, 34.0f,
// Batch 1, Channel 8
35.0f, 36.0f
}));
return result;
}
LayerTestResult<float, 3> Concatenation3dDim1Test(armnn::IWorkloadFactory& workloadFactory)
{
return Concatenation3dDim1TestImpl<float>(workloadFactory, 0.0f, 0);
}
template <typename T>
LayerTestResult<T, 3> Concatenation3dDim2TestImpl(armnn::IWorkloadFactory& workloadFactory,
float qScale, int32_t qOffset)
{
armnn::TensorInfo outputTensorInfo({ 2, 3, 6 }, armnn::GetDataType<T>());
LayerTestResult<T, 3> result = Concatenation3dTestImpl<T>(workloadFactory, outputTensorInfo, 2, qScale, qOffset);
result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0, Channel 0
1.0f, 2.0f, 7.0f, 8.0f, 13.0f, 14.0f,
// Batch 0, Channel 1
3.0f, 4.0f, 9.0f, 10.0f, 15.0f, 16.0f,
// Batch 0, Channel 2
5.0f, 6.0f, 11.0f, 12.0f, 17.0f, 18.0f,
// Batch 1, Channel 0
19.0f, 20.0f, 25.0f, 26.0f, 31.0f, 32.0f,
// Batch 1, Channel 1
21.0f, 22.0f, 27.0f, 28.0f, 33.0f, 34.0f,
// Batch 1, Channel 2
23.0f, 24.0f, 29.0f, 30.0f, 35.0f, 36.0f,
}));
return result;
}
LayerTestResult<float, 3> Concatenation3dDim2Test(armnn::IWorkloadFactory& workloadFactory)
{
return Concatenation3dDim2TestImpl<float>(workloadFactory, 0.0f, 0);
}
template <typename T>
LayerTestResult<T, 3> Concatenation3dDim0DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale,
int32_t qOffset)
{
armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, armnn::GetDataType<T>());
auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0, Channel 0
1.0f, 2.0f,
// Batch 0, Channel 1
3.0f, 4.0f,
// Batch 0, Channel 2
5.0f, 6.0f,
// Batch 1, Channel 0
19.0f, 20.0f,
// Batch 1, Channel 1
21.0f, 22.0f,
// Batch 1, Channel 2
23.0f, 24.0f
}));
armnn::TensorInfo input1TensorInfo({ 1, 3, 2 }, armnn::GetDataType<T>());
auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0, Channel 0
7.0f, 8.0f,
// Batch 0, Channel 1
9.0f, 10.0f,
// Batch 0, Channel 2
11.0f, 12.0f,
}));
armnn::TensorInfo input2TensorInfo({ 3, 3, 2 }, armnn::GetDataType<T>());
auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0, Channel 0
25.0f, 26.0f,
// Batch 0, Channel 1
27.0f, 28.0f,
// Batch 0, Channel 2
29.0f, 30.0f,
// Batch 1, Channel 0
13.0f, 14.0f,
// Batch 1, Channel 1
15.0f, 16.0f,
// Batch 1, Channel 2
17.0f, 18.0f,
// Batch 2, Channel 0
31.0f, 32.0f,
// Batch 2, Channel 1
33.0f, 34.0f,
// Batch 2, Channel 2
35.0f, 36.0f
}));
armnn::TensorInfo outputTensorInfo({ 6, 3, 2 }, armnn::GetDataType<T>());
LayerTestResult<T, 3> result(outputTensorInfo);
std::vector<T> output;
output.resize(outputTensorInfo.GetNumElements());
Concatenate<T>(workloadFactory,
{ input0TensorInfo, input1TensorInfo, input2TensorInfo },
{ input0.data(), input1.data(), input2.data() },
outputTensorInfo,
output.data(),
0);
result.output = MakeTensor<T, 3>(outputTensorInfo, output);
result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0, Channel 0
1.0f, 2.0f,
// Batch 0, Channel 1
3.0f, 4.0f,
// Batch 0, Channel 2
5.0f, 6.0f,
// Batch 1, Channel 0
19.0f, 20.0f,
// Batch 1, Channel 1
21.0f, 22.0f,
// Batch 1, Channel 2
23.0f, 24.0f,
// Batch 2, Channel 0
7.0f, 8.0f,
// Batch 2, Channel 1
9.0f, 10.0f,
// Batch 2, Channel 2
11.0f, 12.0f,
// Batch 3, Channel 0
25.0f, 26.0f,
// Batch 3, Channel 1
27.0f, 28.0f,
// Batch 3, Channel 2
29.0f, 30.0f,
// Batch 4, Channel 0
13.0f, 14.0f,
// Batch 4, Channel 1
15.0f, 16.0f,
// Batch 4, Channel 2
17.0f, 18.0f,
// Batch 5, Channel 0
31.0f, 32.0f,
// Batch 5, Channel 1
33.0f, 34.0f,
// Batch 5, Channel 2
35.0f, 36.0f
}));
return result;
}
LayerTestResult<float, 3> Concatenation3dDim0DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory)
{
return Concatenation3dDim0DiffInputDimsTestImpl<float>(workloadFactory, 0.0f, 0);
}
template <typename T>
LayerTestResult<T, 3> Concatenation3dDim1DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale,
int32_t qOffset)
{
armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, armnn::GetDataType<T>());
auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0, Channel 0
1.0f, 2.0f,
// Batch 0, Channel 1
3.0f, 4.0f,
// Batch 0, Channel 2
5.0f, 6.0f,
// Batch 1, Channel 0
19.0f, 20.0f,
// Batch 1, Channel 1
21.0f, 22.0f,
// Batch 1, Channel 2
23.0f, 24.0f
}));
armnn::TensorInfo input1TensorInfo({ 2, 4, 2 }, armnn::GetDataType<T>());
auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0, Channel 0
7.0f, 8.0f,
// Batch 0, Channel 1
9.0f, 10.0f,
// Batch 0, Channel 2
11.0f, 12.0f,
// Batch 0, Channel 3
25.0f, 26.0f,
// Batch 1, Channel 0
27.0f, 28.0f,
// Batch 1, Channel 1
29.0f, 30.0f,
// Batch 1, Channel 2
13.0f, 14.0f,
// Batch 1, Channel 3
15.0f, 16.0f,
}));
armnn::TensorInfo input2TensorInfo({ 2, 1, 2 }, armnn::GetDataType<T>());
auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0, Channel 0
17.0f, 18.0f,
// Batch 1, Channel 0
31.0f, 32.0f,
}));
armnn::TensorInfo outputTensorInfo({ 2, 8, 2 }, armnn::GetDataType<T>());
LayerTestResult<T, 3> result(outputTensorInfo);
std::vector<T> output;
output.resize(outputTensorInfo.GetNumElements());
Concatenate<T>(workloadFactory,
{ input0TensorInfo, input1TensorInfo, input2TensorInfo },
{ input0.data(), input1.data(), input2.data() },
outputTensorInfo,
output.data(),
1);
result.output = MakeTensor<T, 3>(outputTensorInfo, output);
result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0, Channel 0
1.0f, 2.0f,
// Batch 0, Channel 1
3.0f, 4.0f,
// Batch 0, Channel 2
5.0f, 6.0f,
// Batch 0, Channel 3
7.0f, 8.0f,
// Batch 0, Channel 4
9.0f, 10.0f,
// Batch 0, Channel 5
11.0f, 12.0f,
// Batch 0, Channel 6
25.0f, 26.0f,
// Batch 0, Channel 7
17.0f, 18.0f,
// Batch 1, Channel 0
19.0f, 20.0f,
// Batch 1, Channel 1
21.0f, 22.0f,
// Batch 1, Channel 2
23.0f, 24.0f,
// Batch 1, Channel 3
27.0f, 28.0f,
// Batch 1, Channel 4
29.0f, 30.0f,
// Batch 1, Channel 5
13.0f, 14.0f,
// Batch 1, Channel 6
15.0f, 16.0f,
// Batch 1, Channel 7
31.0f, 32.0f,
}));
return result;
}
LayerTestResult<float, 3> Concatenation3dDim1DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory)
{
return Concatenation3dDim1DiffInputDimsTestImpl<float>(workloadFactory, 0.0f, 0);
}
template <typename T>
LayerTestResult<T, 3> Concatenation3dDim2DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale,
int32_t qOffset)
{
armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, armnn::GetDataType<T>());
auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0, Channel 0
1.0f, 2.0f,
// Batch 0, Channel 1
3.0f, 4.0f,
// Batch 0, Channel 2
5.0f, 6.0f,
// Batch 1, Channel 0
19.0f, 20.0f,
// Batch 1, Channel 1
21.0f, 22.0f,
// Batch 1, Channel 2
23.0f, 24.0f
}));
armnn::TensorInfo input1TensorInfo({ 2, 3, 1 }, armnn::GetDataType<T>());
auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0, Channel 0
7.0f,
// Batch 0, Channel 1
9.0f,
// Batch 0, Channel 2
11.0f,
// Batch 1, Channel 0
25.0f,
// Batch 1, Channel 1
27.0f,
// Batch 1, Channel 2
29.0f
}));
armnn::TensorInfo input2TensorInfo({ 2, 3, 3 }, armnn::GetDataType<T>());
auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0, Channel 0
13.0f, 14.0f, 50.0f,
// Batch 0, Channel 1
15.0f, 16.0f, 51.0f,
// Batch 0, Channel 2
17.0f, 18.0f, 52.0f,
// Batch 1, Channel 0
31.0f, 32.0f, 53.0f,
// Batch 1, Channel 1
33.0f, 34.0f, 54.0f,
// Batch 1, Channel 2
35.0f, 36.0f, 55.0f,
}));
armnn::TensorInfo outputTensorInfo({ 2, 3, 6 }, armnn::GetDataType<T>());
LayerTestResult<T, 3> result(outputTensorInfo);
std::vector<T> output;
output.resize(outputTensorInfo.GetNumElements());
Concatenate<T>(workloadFactory,
{ input0TensorInfo, input1TensorInfo, input2TensorInfo },
{ input0.data(), input1.data(), input2.data() },
outputTensorInfo,
output.data(),
2);
result.output = MakeTensor<T, 3>(outputTensorInfo, output);
result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
// Batch 0, Channel 0
1.0f, 2.0f, 7.0f, 13.0f, 14.0f, 50.0f,
// Batch 0, Channel 1
3.0f, 4.0f, 9.0f, 15.0f, 16.0f, 51.0f,
// Batch 0, Channel 2
5.0f, 6.0f, 11.0f, 17.0f, 18.0f, 52.0f,
// Batch 1, Channel 0
19.0f, 20.0f, 25.0f, 31.0f, 32.0f, 53.0f,
// Batch 1, Channel 1
21.0f, 22.0f, 27.0f, 33.0f, 34.0f, 54.0f,
// Batch 1, Channel 2
23.0f, 24.0f, 29.0f, 35.0f, 36.0f, 55.0f,
}));
return result;
}
LayerTestResult<float, 3> Concatenation3dDim2DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory)
{
return Concatenation3dDim2DiffInputDimsTestImpl<float>(workloadFactory, 0.0f, 0);
}
LayerTestResult<float, 4> ResizeBilinearNopTest(armnn::IWorkloadFactory& workloadFactory)
{
constexpr unsigned int inputWidth = 4;
constexpr unsigned int inputHeight = 4;
constexpr unsigned int inputChannels = 1;
constexpr unsigned int inputBatchSize = 1;
constexpr unsigned int outputWidth = inputWidth;
constexpr unsigned int outputHeight = inputHeight;
constexpr unsigned int outputChannels = inputChannels;
constexpr unsigned int outputBatchSize = inputBatchSize;
const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
armnn::DataType::Float32);
const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
armnn::DataType::Float32);
auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({
1.0f, 2.0f, 3.0f, 4.0f,
2.0f, 3.0f, 4.0f, 5.0f,
3.0f, 4.0f, 5.0f, 6.0f,
4.0f, 5.0f, 6.0f, 7.0f
}));
LayerTestResult<float, 4> result(outputTensorInfo);
result.outputExpected = input;
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::ResizeBilinearQueueDescriptor descriptor;
armnn::WorkloadInfo info;
AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
return result;
}
LayerTestResult<float, 4> SimpleResizeBilinearTest(armnn::IWorkloadFactory& workloadFactory)
{
constexpr unsigned int inputWidth = 2;
constexpr unsigned int inputHeight = 2;
constexpr unsigned int inputChannels = 1;
constexpr unsigned int inputBatchSize = 1;
constexpr unsigned int outputWidth = inputWidth / 2;
constexpr unsigned int outputHeight = inputHeight / 2;
constexpr unsigned int outputChannels = inputChannels;
constexpr unsigned int outputBatchSize = inputBatchSize;
const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
armnn::DataType::Float32);
const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
armnn::DataType::Float32);
auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({
1.0f, 255.0f,
200.0f, 250.f,
}));
// The 'resize bilinear' operation projects the top-left corner of output texels into the input image,
// then figures out the interpolants and weights. Note this is different to projecting the centre of the
// output texel - and thus we'll expect the output 1x1 matrix to contain, as its single element, the value
// that was at position (0,0) of the input matrix (rather than an average, which we would expect if projecting
// the centre).
LayerTestResult<float, 4> result(outputTensorInfo);
result.outputExpected = MakeTensor<float, 4>(outputTensorInfo, std::vector<float>({
1.0f
}));
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::ResizeBilinearQueueDescriptor descriptor;
armnn::WorkloadInfo info;
AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
return result;
}
LayerTestResult<float, 4> ResizeBilinearSqMinTest(armnn::IWorkloadFactory& workloadFactory)
{
constexpr unsigned int inputWidth = 4;
constexpr unsigned int inputHeight = 4;
constexpr unsigned int inputChannels = 1;
constexpr unsigned int inputBatchSize = 1;
constexpr unsigned int outputWidth = inputWidth / 2;
constexpr unsigned int outputHeight = inputHeight / 2;
constexpr unsigned int outputChannels = inputChannels;
constexpr unsigned int outputBatchSize = inputBatchSize;
const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
armnn::DataType::Float32);
const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
armnn::DataType::Float32);
auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({
1.0f, 2.0f, 3.0f, 4.0f,
2.0f, 3.0f, 4.0f, 5.0f,
3.0f, 4.0f, 5.0f, 6.0f,
4.0f, 5.0f, 6.0f, 7.0f
}));
LayerTestResult<float, 4> result(outputTensorInfo);
result.outputExpected = MakeTensor<float, 4>(outputTensorInfo, std::vector<float>({
1.f, 3.f,
3.f, 5.f
}));
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::ResizeBilinearQueueDescriptor descriptor;
armnn::WorkloadInfo info;
AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
return result;
}
LayerTestResult<float, 4> ResizeBilinearMinTest(armnn::IWorkloadFactory& workloadFactory)
{
constexpr unsigned int inputWidth = 5;
constexpr unsigned int inputHeight = 3;
constexpr unsigned int inputChannels = 1;
constexpr unsigned int inputBatchSize = 1;
constexpr unsigned int outputWidth = 3;
constexpr unsigned int outputHeight = 2;
constexpr unsigned int outputChannels = inputChannels;
constexpr unsigned int outputBatchSize = inputBatchSize;
const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
armnn::DataType::Float32);
const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
armnn::DataType::Float32);
auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({
1.0f, 2.0f, 3.0f, 5.0f, 8.0f,
13.0f, 21.0f, 34.0f, 55.0f, 89.0f,
144.0f, 233.0f, 377.0f, 610.0f, 987.0f
}));
LayerTestResult<float, 4> result(outputTensorInfo);
result.outputExpected = MakeTensor<float, 4>(outputTensorInfo, std::vector<float>({
1.0f, 2.6666f, 6.0f,
78.5f, 179.3333f, 401.f
}));
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::ResizeBilinearQueueDescriptor descriptor;
armnn::WorkloadInfo info;
AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
return result;
}
LayerTestResult<float, 4> ResizeBilinearMagTest(armnn::IWorkloadFactory& workloadFactory)
{
constexpr unsigned int inputWidth = 2;
constexpr unsigned int inputHeight = 3;
constexpr unsigned int inputChannels = 1;
constexpr unsigned int inputBatchSize = 1;
constexpr unsigned int outputWidth = 5;
constexpr unsigned int outputHeight = 3;
constexpr unsigned int outputChannels = inputChannels;
constexpr unsigned int outputBatchSize = inputBatchSize;
const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
armnn::DataType::Float32);
const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
armnn::DataType::Float32);
auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({
1.0f, 2.0f,
13.0f, 21.0f,
144.0f, 233.0f
}));
LayerTestResult<float, 4> result(outputTensorInfo);
result.outputExpected = MakeTensor<float, 4>(outputTensorInfo, std::vector<float>({
1.0f, 1.4f, 1.8f, 2.f, 2.f,
13.f, 16.2f, 19.4f, 21.f, 21.f,
144.f, 179.6f, 215.2f, 233.f, 233.f
}));
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::ResizeBilinearQueueDescriptor descriptor;
armnn::WorkloadInfo info;
AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
return result;
}
LayerTestResult<float, 2> FakeQuantizationTest(armnn::IWorkloadFactory& workloadFactory)
{
constexpr unsigned int width = 2;
constexpr unsigned int height = 3;
const armnn::TensorInfo tensorInfo({height, width },
armnn::DataType::Float32);
auto input = MakeTensor<float, 2>(tensorInfo, std::vector<float>({
-10.0f, -5.0f,
0.0f, 5.0f,
10.0f, 10.0f
}));
LayerTestResult<float, 2> ret(tensorInfo);
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(tensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(tensorInfo);
armnn::FakeQuantizationQueueDescriptor data;
armnn::WorkloadInfo info;
AddInputToWorkload(data, info, tensorInfo, inputHandle.get());
AddOutputToWorkload(data, info, tensorInfo, outputHandle.get());
float min = -10.f;
float max = 10.f;
data.m_Parameters.m_Min = min;
data.m_Parameters.m_Max = max;
armnn::PassthroughCpuTensorHandle refHandle(tensorInfo, &ret.outputExpected[0][0]);
armnn::FakeQuantizationQueueDescriptor refData = data;
armnn::WorkloadInfo refInfo = info;
SetWorkloadOutput(refData, refInfo, 0, tensorInfo, &refHandle);
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateFakeQuantization(data, info);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), &input[0][0]);
workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get());
ret.outputExpected = MakeTensor<float, 2>(tensorInfo, std::vector<float>({
0.0f, 63.0f,
128.0f, 191.0f,
255.0f, 255.0f
}));
return ret;
}
LayerTestResult<float, 4> L2Normalization1dTest(armnn::IWorkloadFactory& workloadFactory)
{
constexpr unsigned int inputWidth = 1;
constexpr unsigned int inputHeight = 1;
constexpr unsigned int inputChannels = 10;
constexpr unsigned int inputBatchSize = 1;
constexpr unsigned int outputWidth = inputWidth;
constexpr unsigned int outputHeight = inputHeight;
constexpr unsigned int outputChannels = inputChannels;
constexpr unsigned int outputBatchSize = inputBatchSize;
const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
armnn::DataType::Float32);
const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
armnn::DataType::Float32);
auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({
1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f
}));
const float approxInvL2Norm = 0.050964719f;
LayerTestResult<float, 4> result(outputTensorInfo);
result.outputExpected = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({
1.0f * approxInvL2Norm,
2.0f * approxInvL2Norm,
3.0f * approxInvL2Norm,
4.0f * approxInvL2Norm,
5.0f * approxInvL2Norm,
6.0f * approxInvL2Norm,
7.0f * approxInvL2Norm,
8.0f * approxInvL2Norm,
9.0f * approxInvL2Norm,
10.0f * approxInvL2Norm
}));
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::L2NormalizationQueueDescriptor descriptor;
armnn::WorkloadInfo info;
AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateL2Normalization(descriptor, info);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
return result;
}
namespace
{
float CalcInvL2Norm(std::initializer_list<float> elements)
{
const float reduction = std::accumulate(elements.begin(), elements.end(), 0.0f,
[](float acc, float element) { return acc + element * element; });
return 1.0f / sqrtf(reduction);
}
}
LayerTestResult<float, 4> L2Normalization2dTest(armnn::IWorkloadFactory& workloadFactory)
{
constexpr unsigned int inputWidth = 5;
constexpr unsigned int inputHeight = 1;
constexpr unsigned int inputChannels = 2;
constexpr unsigned int inputBatchSize = 1;
constexpr unsigned int outputWidth = inputWidth;
constexpr unsigned int outputHeight = inputHeight;
constexpr unsigned int outputChannels = inputChannels;
constexpr unsigned int outputBatchSize = inputBatchSize;
const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
armnn::DataType::Float32);
const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
armnn::DataType::Float32);
auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({
1.0f, 3.0f, 5.0f, 7.0f, 9.0f,
2.0f, 4.0f, 6.0f, 8.0f, 10.0f
}));
LayerTestResult<float, 4> result(outputTensorInfo);
result.outputExpected = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({
1.0f * CalcInvL2Norm({ 1.0f, 2.0f }),
3.0f * CalcInvL2Norm({ 3.0f, 4.0f }),
5.0f * CalcInvL2Norm({ 5.0f, 6.0f }),
7.0f * CalcInvL2Norm({ 7.0f, 8.0f }),
9.0f * CalcInvL2Norm({ 9.0f, 10.0f }),
2.0f * CalcInvL2Norm({ 1.0f, 2.0f }),
4.0f * CalcInvL2Norm({ 3.0f, 4.0f }),
6.0f * CalcInvL2Norm({ 5.0f, 6.0f }),
8.0f * CalcInvL2Norm({ 7.0f, 8.0f }),
10.0f * CalcInvL2Norm({ 9.0f, 10.0f })
}));
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::L2NormalizationQueueDescriptor descriptor;
armnn::WorkloadInfo info;
AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateL2Normalization(descriptor, info);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
return result;
}
LayerTestResult<float, 4> L2Normalization3dTest(armnn::IWorkloadFactory& workloadFactory)
{
constexpr unsigned int inputWidth = 3;
constexpr unsigned int inputHeight = 4;
constexpr unsigned int inputChannels = 2;
constexpr unsigned int inputBatchSize = 1;
constexpr unsigned int outputWidth = inputWidth;
constexpr unsigned int outputHeight = inputHeight;
constexpr unsigned int outputChannels = inputChannels;
constexpr unsigned int outputBatchSize = inputBatchSize;
const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
armnn::DataType::Float32);
const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
armnn::DataType::Float32);
auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({
// Channel 0
119.0f, 21.0f, 150.0f,
149.0f, 32.0f, 179.0f,
15.0f, 227.0f, 141.0f,
147.0f, 199.0f, 220.0f,
// Channel 1
110.0f, 140.0f, 73.0f,
211.0f, 212.0f, 89.0f,
24.0f, 138.0f, 188.0f,
162.0f, 12.0f, 161.0f,
}));
LayerTestResult<float, 4> result(outputTensorInfo);
result.outputExpected = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({
119.0f * CalcInvL2Norm({ 119.0f, 110.0f }),
21.0f * CalcInvL2Norm({ 21.0f, 140.0f }),
150.0f * CalcInvL2Norm({ 150.0f, 73.0f }),
149.0f * CalcInvL2Norm({ 149.0f, 211.0f }),
32.0f * CalcInvL2Norm({ 32.0f, 212.0f }),
179.0f * CalcInvL2Norm({ 179.0f, 89.0f }),
15.0f * CalcInvL2Norm({ 15.0f, 24.0f }),
227.0f * CalcInvL2Norm({ 227.0f, 138.0f }),
141.0f * CalcInvL2Norm({ 141.0f, 188.0f }),
147.0f * CalcInvL2Norm({ 147.0f, 162.0f }),
199.0f * CalcInvL2Norm({ 199.0f, 12.0f }),
220.0f * CalcInvL2Norm({ 220.0f, 161.0f }),
110.0f * CalcInvL2Norm({ 119.0f, 110.0f }),
140.0f * CalcInvL2Norm({ 21.0f, 140.0f }),
73.0f * CalcInvL2Norm({ 150.0f, 73.0f }),
211.0f * CalcInvL2Norm({ 149.0f, 211.0f }),
212.0f * CalcInvL2Norm({ 32.0f, 212.0f }),
89.0f * CalcInvL2Norm({ 179.0f, 89.0f }),
24.0f * CalcInvL2Norm({ 15.0f, 24.0f }),
138.0f * CalcInvL2Norm({ 227.0f, 138.0f }),
188.0f * CalcInvL2Norm({ 141.0f, 188.0f }),
162.0f * CalcInvL2Norm({ 147.0f, 162.0f }),
12.0f * CalcInvL2Norm({ 199.0f, 12.0f }),
161.0f * CalcInvL2Norm({ 220.0f, 161.0f }),
}));
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::L2NormalizationQueueDescriptor descriptor;
armnn::WorkloadInfo info;
AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateL2Normalization(descriptor, info);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
return result;
}
LayerTestResult<float, 4> L2Normalization4dTest(armnn::IWorkloadFactory& workloadFactory)
{
constexpr unsigned int inputWidth = 3;
constexpr unsigned int inputHeight = 4;
constexpr unsigned int inputChannels = 3;
constexpr unsigned int inputBatchSize = 2;
constexpr unsigned int outputWidth = inputWidth;
constexpr unsigned int outputHeight = inputHeight;
constexpr unsigned int outputChannels = inputChannels;
constexpr unsigned int outputBatchSize = inputBatchSize;
const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
armnn::DataType::Float32);
const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
armnn::DataType::Float32);
auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({
// Batch 0, Channel 0
235.0f, 46.0f, 178.0f,
100.0f, 123.0f, 19.0f,
172.0f, 74.0f, 250.0f,
6.0f, 195.0f, 80.0f,
// Batch 0, Channel 1
113.0f, 95.0f, 202.0f,
77.0f, 114.0f, 71.0f,
122.0f, 246.0f, 166.0f,
82.0f, 28.0f, 37.0f,
// Batch 0, Channel 2
56.0f, 170.0f, 162.0f,
194.0f, 89.0f, 254.0f,
12.0f, 209.0f, 200.0f,
1.0f, 64.0f, 54.0f,
// Batch 1, Channel 0
67.0f, 90.0f, 49.0f,
7.0f, 163.0f, 18.0f,
25.0f, 117.0f, 103.0f,
247.0f, 59.0f, 189.0f,
// Batch 1, Channel 1
239.0f, 104.0f, 199.0f,
17.0f, 124.0f, 153.0f,
222.0f, 217.0f, 75.0f,
32.0f, 126.0f, 21.0f,
// Batch 1, Channel 2
97.0f, 145.0f, 215.0f,
115.0f, 116.0f, 238.0f,
226.0f, 16.0f, 132.0f,
92.0f, 125.0f, 88.0f,
}));
LayerTestResult<float, 4> result(outputTensorInfo);
result.outputExpected = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({
// Batch 0, Channel 0
235.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }),
46.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }),
178.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }),
100.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }),
123.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }),
19.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }),
172.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }),
74.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }),
250.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }),
6.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }),
195.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }),
80.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }),
// Batch 0, Channel 1
113.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }),
95.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }),
202.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }),
77.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }),
114.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }),
71.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }),
122.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }),
246.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }),
166.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }),
82.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }),
28.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }),
37.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }),
// Batch 0, Channel 2
56.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }),
170.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }),
162.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }),
194.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }),
89.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }),
254.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }),
12.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }),
209.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }),
200.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }),
1.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }),
64.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }),
54.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }),
// Batch 1, Channel 0
67.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }),
90.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }),
49.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }),
7.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }),
163.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }),
18.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }),
25.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }),
117.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }),
103.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }),
247.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }),
59.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }),
189.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }),
// Batch 1, Channel 1
239.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }),
104.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }),
199.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }),
17.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }),
124.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }),
153.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }),
222.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }),
217.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }),
75.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }),
32.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }),
126.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }),
21.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }),
// Batch 1, Channel 2
97.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }),
145.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }),
215.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }),
115.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }),
116.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }),
238.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }),
226.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }),
16.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }),
132.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }),
92.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }),
125.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }),
88.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }),
}));
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::L2NormalizationQueueDescriptor descriptor;
armnn::WorkloadInfo info;
AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateL2Normalization(descriptor, info);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
return result;
}
template <typename T>
LayerTestResult<T, 4> ConstantTestImpl(armnn::IWorkloadFactory& workloadFactory,
float qScale,
int32_t qOffset)
{
constexpr unsigned int inputWidth = 3;
constexpr unsigned int inputHeight = 4;
constexpr unsigned int inputChannels = 3;
constexpr unsigned int inputBatchSize = 2;
constexpr unsigned int outputWidth = inputWidth;
constexpr unsigned int outputHeight = inputHeight;
constexpr unsigned int outputChannels = inputChannels;
constexpr unsigned int outputBatchSize = inputBatchSize;
armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
armnn::GetDataType<T>());
armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
armnn::GetDataType<T>());
// 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);
}
auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>(
QuantizedVector<T>(qScale, qOffset, {
// Batch 0, Channel 0
235.0f, 46.0f, 178.0f,
100.0f, 123.0f, 19.0f,
172.0f, 74.0f, 250.0f,
6.0f, 195.0f, 80.0f,
// Batch 0, Channel 1
113.0f, 95.0f, 202.0f,
77.0f, 114.0f, 71.0f,
122.0f, 246.0f, 166.0f,
82.0f, 28.0f, 37.0f,
// Batch 0, Channel 2
56.0f, 170.0f, 162.0f,
194.0f, 89.0f, 254.0f,
12.0f, 209.0f, 200.0f,
1.0f, 64.0f, 54.0f,
// Batch 1, Channel 0
67.0f, 90.0f, 49.0f,
7.0f, 163.0f, 18.0f,
25.0f, 117.0f, 103.0f,
247.0f, 59.0f, 189.0f,
// Batch 1, Channel 1
239.0f, 104.0f, 199.0f,
17.0f, 124.0f, 153.0f,
222.0f, 217.0f, 75.0f,
32.0f, 126.0f, 21.0f,
// Batch 1, Channel 2
97.0f, 145.0f, 215.0f,
115.0f, 116.0f, 238.0f,
226.0f, 16.0f, 132.0f,
92.0f, 125.0f, 88.0f,
})));
LayerTestResult<T, 4> result(outputTensorInfo);
result.outputExpected = input;
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::ScopedCpuTensorHandle constantTensor(inputTensorInfo);
AllocateAndCopyDataToITensorHandle(&constantTensor, &input[0][0][0][0]);
armnn::ConstantQueueDescriptor descriptor;
descriptor.m_LayerOutput = &constantTensor;
armnn::WorkloadInfo info;
AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConstant(descriptor, info);
outputHandle->Allocate();
workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
return result;
}
LayerTestResult<float, 4> ConstantTest(armnn::IWorkloadFactory& workloadFactory)
{
return ConstantTestImpl<float>(workloadFactory, 0.0f, 0);
}
LayerTestResult<uint8_t, 4> ConstantTestUint8(armnn::IWorkloadFactory& workloadFactory)
{
return ConstantTestImpl<uint8_t>(workloadFactory, 1.0f, 0);
}
LayerTestResult<uint8_t, 3> MergerUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
unsigned int outputWidth = 3;
unsigned int outputHeight = 6;
unsigned int outputChannels = 3;
unsigned int inputWidth1 = 3;
unsigned int inputHeight1 = 6;
unsigned int inputChannels1 = 2;
unsigned int inputWidth2 = 3;
unsigned int inputHeight2 = 6;
unsigned int inputChannels2 = 1;
// Defines the tensor descriptors.
armnn::TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, armnn::DataType::QuantisedAsymm8);
armnn::TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, armnn::DataType::QuantisedAsymm8);
armnn::TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, armnn::DataType::QuantisedAsymm8);
// Arbitrary scale and offsets. They don't really matter as the merger operator doesn't dequantize/quantize them.
const float scale = 0.13497836f;
const int32_t offset = -7;
outputTensorInfo.SetQuantizationScale(scale);
outputTensorInfo.SetQuantizationOffset(offset);
inputTensorInfo1.SetQuantizationScale(scale);
inputTensorInfo1.SetQuantizationOffset(offset);
inputTensorInfo2.SetQuantizationScale(scale);
inputTensorInfo2.SetQuantizationOffset(offset);
LayerTestResult<uint8_t, 3> ret(outputTensorInfo);
ret.outputExpected = MakeTensor<uint8_t, 3>(outputTensorInfo, std::vector<uint8_t>(
{
1, 2, 3,
4, 5, 6,
7, 8, 9,
10, 11, 12,
13, 14, 15,
16, 17, 18,
19, 20, 21,
22, 23, 24,
25, 26, 27,
28, 29, 30,
31, 32, 33,
34, 35, 36,
37, 38, 39,
40, 41, 42,
43, 44, 45,
46, 47, 48,
49, 50, 51,
52, 53, 54,
})
);
auto input1 = MakeTensor<uint8_t, 3>(inputTensorInfo1, std::vector<uint8_t>(
{
1, 2, 3,
4, 5, 6,
7, 8, 9,
10, 11, 12,
13, 14, 15,
16, 17, 18,
19, 20, 21,
22, 23, 24,
25, 26, 27,
28, 29, 30,
31, 32, 33,
34, 35, 36,
})
);
auto input2 = MakeTensor<uint8_t, 3>(inputTensorInfo2, std::vector<uint8_t>(
{
37, 38, 39,
40, 41, 42,
43, 44, 45,
46, 47, 48,
49, 50, 51,
52, 53, 54,
})
);
std::vector<unsigned int> wOrigin1 = { 0, 0, 0 }; //Extent of the window is defined by size of input[0].
armnn::MergerQueueDescriptor::ViewOrigin window1(wOrigin1);
std::vector<unsigned int> wOrigin2 = { 2, 0, 0 }; //Extent of the window is defined by size of input[1].
armnn::MergerQueueDescriptor::ViewOrigin window2(wOrigin2);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
bool subTensorsSupported = workloadFactory.SupportsSubTensors();
std::unique_ptr<armnn::ITensorHandle> inputHandle1 =
subTensorsSupported ?
workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) :
workloadFactory.CreateTensorHandle(inputTensorInfo1);
std::unique_ptr<armnn::ITensorHandle> inputHandle2 =
subTensorsSupported ?
workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) :
workloadFactory.CreateTensorHandle(inputTensorInfo2);
armnn::MergerQueueDescriptor data;
armnn::WorkloadInfo info;
AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
data.m_ViewOrigins.push_back(window1);
data.m_ViewOrigins.push_back(window2);
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMerger(data, info);
inputHandle1->Allocate();
inputHandle2->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]);
CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]);
workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get());
return ret;
}
LayerTestResult<uint8_t, 4> AdditionUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
unsigned int batchSize = 1;
unsigned int channels = 2;
unsigned int height = 2;
unsigned int width = 3;
const float scale = 7.0f;
const int32_t offset = 3;
armnn::TensorInfo inputTensorInfo1, inputTensorInfo2;
armnn::TensorInfo outputTensorInfo;
const unsigned int shape[] = { batchSize, channels, height, width };
inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::QuantisedAsymm8);
inputTensorInfo1.SetQuantizationScale(scale);
inputTensorInfo1.SetQuantizationOffset(offset);
inputTensorInfo2 = armnn::TensorInfo(4, shape, armnn::DataType::QuantisedAsymm8);
inputTensorInfo2.SetQuantizationScale(scale);
inputTensorInfo2.SetQuantizationOffset(offset);
outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::QuantisedAsymm8);
outputTensorInfo.SetQuantizationScale(scale);
outputTensorInfo.SetQuantizationOffset(offset);
// See dequantized values to the right.
auto input1 = MakeTensor<uint8_t, 4>(inputTensorInfo1, std::vector<uint8_t>(
{
63, 35, 77, 70, 56, 112, // 420, 224, 518, 469, 371, 763
203, 28, 252, 168, 245, 91 // 1400, 175, 1743, 1155, 1694, 616
}));
// See dequantized values to the right.
auto input2 = MakeTensor<uint8_t, 4>(inputTensorInfo1, std::vector<uint8_t>(
{
21, 7, 175, 231, 175, 210, // 126, 28, 1204, 1596, 1204, 1449
126, 161, 63, 21, 105, 126 // 861, 1106, 420, 126, 714, 861
}));
// See dequantized values to the right.
LayerTestResult<uint8_t, 4> result(outputTensorInfo);
result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, std::vector<uint8_t>(
{
81, 39, 249, 255, 228, 255, // 546, 252, 1722, 2065(clamped), 1575, 2212(clamped)
255, 186, 255, 186, 255, 214, // 2261(clamped), 1281, 2163(clamped), 1281, 2408(clamped), 1477
}));
std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::AdditionQueueDescriptor data;
armnn::WorkloadInfo info;
AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info);
inputHandle1->Allocate();
inputHandle2->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]);
workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
return result;
}
namespace
{
LayerTestResult<uint8_t, 4> MultiplicationUint8TestHelper(armnn::IWorkloadFactory& workloadFactory,
const unsigned int shape0[4],
const std::vector<uint8_t> & values0,
float scale0,
int32_t offset0,
const unsigned int shape1[4],
const std::vector<uint8_t> & values1,
float scale1,
int32_t offset1,
const unsigned int outShape[4],
const std::vector<uint8_t> & outValues,
float outScale,
int32_t outOffset)
{
armnn::TensorInfo inputTensorInfo0(4, shape0, armnn::DataType::QuantisedAsymm8);
armnn::TensorInfo inputTensorInfo1(4, shape1, armnn::DataType::QuantisedAsymm8);
armnn::TensorInfo outputTensorInfo(4, outShape, armnn::DataType::QuantisedAsymm8);
inputTensorInfo0.SetQuantizationScale(scale0);
inputTensorInfo0.SetQuantizationOffset(offset0);
inputTensorInfo1.SetQuantizationScale(scale1);
inputTensorInfo1.SetQuantizationOffset(offset1);
outputTensorInfo.SetQuantizationScale(outScale);
outputTensorInfo.SetQuantizationOffset(outOffset);
auto input0 = MakeTensor<uint8_t, 4>(inputTensorInfo0, values0);
auto input1 = MakeTensor<uint8_t, 4>(inputTensorInfo1, values1);
LayerTestResult<uint8_t, 4> result(outputTensorInfo);
result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, outValues);
std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0);
std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::MultiplicationQueueDescriptor data;
armnn::WorkloadInfo info;
AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get());
AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMultiplication(data, info);
inputHandle0->Allocate();
inputHandle1->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]);
CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
return result;
}
} // anonymous namespace
LayerTestResult<uint8_t, 4> MultiplicationUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
unsigned int batchSize = 1;
unsigned int channels = 2;
unsigned int height = 2;
unsigned int width = 3;
const unsigned int shape[] = { batchSize, channels, height, width };
// See dequantized values to the right.
std::vector<uint8_t> input0({
62, 37, 3, 172, 13, 111, // 244, 144, 8, 684, 48, 440,
188, 20, 73, 31, 23, 31 // 748, 76, 288, 120, 88, 120
});
// See dequantized values to the right.
std::vector<uint8_t> input1({
126, 240, 252, 183, 121, 247, // 384, 726, 762, 555, 369, 747,
48, 115, 151, 79, 78, 97 // 150, 351, 459, 243, 240, 297
});
// See dequantized values to the right.
std::vector<uint8_t> output(
{
64, 72, 0, 255, 8, 236, // 93696, 104544, 6096(clamped), 379620(clamped), 17712, 328680,
77, 15, 92, 16, 10, 21, // 112200, 26676, 132192, 29160, 21120, 35640
});
return MultiplicationUint8TestHelper(workloadFactory,
shape,
input0,
4.0f,
1,
shape,
input1,
3.0f,
-2,
shape,
output,
1366.255f, // Scale/offset chosen to have output values out of range.
-5);
}
LayerTestResult<uint8_t, 4> MultiplicationBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
const unsigned int shape0[] = { 1, 2, 2, 3 };
const unsigned int shape1[] = { 1, 1, 1, 1 };
std::vector<uint8_t> input0({
1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12
});
std::vector<uint8_t> input1({2});
std::vector<uint8_t> output({
2, 4, 6, 8, 10, 12,
14, 16, 18, 20, 22, 24
});
return MultiplicationUint8TestHelper(workloadFactory,
shape0,
input0,
1.0f,
0,
shape1,
input1,
1.0f,
0,
shape0,
output,
1.0f,
0);
}
LayerTestResult<uint8_t, 4> MultiplicationBroadcast1DVectorUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
const unsigned int shape0[] = { 1, 2, 2, 3 };
const unsigned int shape1[] = { 1, 1, 1, 3 };
std::vector<uint8_t> input0({
1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12
});
std::vector<uint8_t> input1({1, 2, 3});
std::vector<uint8_t> output({
1, 4, 9, 4, 10, 18,
7, 16, 27, 10, 22, 36
});
return MultiplicationUint8TestHelper(workloadFactory,
shape0,
input0,
1.0f,
0,
shape1,
input1,
1.0f,
0,
shape0,
output,
1.0f,
0);
}
namespace
{
template <typename T>
LayerTestResult<T, 4> SubtractionTestHelper(armnn::IWorkloadFactory& workloadFactory,
const unsigned int shape0[4],
const std::vector<T>& values0,
float scale0,
int32_t offset0,
const unsigned int shape1[4],
const std::vector<T> & values1,
float scale1,
int32_t offset1,
const unsigned int outShape[4],
const std::vector<T> & outValues,
float outScale,
int32_t outOffset)
{
auto dataType = (std::is_same<T, uint8_t>::value ?
armnn::DataType::QuantisedAsymm8 :
armnn::DataType::Float32);
armnn::TensorInfo inputTensorInfo0(4, shape0, dataType);
armnn::TensorInfo inputTensorInfo1(4, shape1, dataType);
armnn::TensorInfo outputTensorInfo(4, outShape, dataType);
inputTensorInfo0.SetQuantizationScale(scale0);
inputTensorInfo0.SetQuantizationOffset(offset0);
inputTensorInfo1.SetQuantizationScale(scale1);
inputTensorInfo1.SetQuantizationOffset(offset1);
outputTensorInfo.SetQuantizationScale(outScale);
outputTensorInfo.SetQuantizationOffset(outOffset);
auto input0 = MakeTensor<T, 4>(inputTensorInfo0, values0);
auto input1 = MakeTensor<T, 4>(inputTensorInfo1, values1);
LayerTestResult<T, 4> result(outputTensorInfo);
result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outValues);
std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0);
std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::SubtractionQueueDescriptor data;
armnn::WorkloadInfo info;
AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get());
AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateSubtraction(data, info);
inputHandle0->Allocate();
inputHandle1->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]);
CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
return result;
}
} // anonymous namespace
LayerTestResult<uint8_t, 4> SubtractionUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
const unsigned int shape0[] = { 1, 1, 2, 2 };
const unsigned int shape1[] = { 1, 1, 2, 2 };
std::vector<uint8_t> input0({ 10, 12, 14, 16 });
std::vector<uint8_t> input1({ 1, 2, 1, 2 });
std::vector<uint8_t> output({ 3, 3, 5, 5 });
return SubtractionTestHelper(workloadFactory,
shape0, input0, 0.5f, 2,
shape1, input1, 1.0f, 0,
shape0, output, 1.0f, 0);
}
LayerTestResult<uint8_t, 4> SubtractionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
const unsigned int shape0[] = { 1, 1, 2, 2 };
const unsigned int shape1[] = { 1, 1, 1, 1 };
std::vector<uint8_t> input0({ 10, 12, 14, 16 });
std::vector<uint8_t> input1({ 2 });
std::vector<uint8_t> output({ 5, 6, 7, 8 });
return SubtractionTestHelper(workloadFactory,
shape0, input0, 0.5f, 2,
shape1, input1, 1.0f, 0,
shape0, output, 1.0f, 3);
}
LayerTestResult<uint8_t, 4> SubtractionBroadcastUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
const unsigned int shape0[] = { 1, 1, 2, 2 };
const unsigned int shape1[] = { 1, 1, 2, 1 };
std::vector<uint8_t> input0({ 10, 12, 14, 16 });
std::vector<uint8_t> input1({ 2, 1 });
std::vector<uint8_t> output({ 8, 11, 12, 15 });
return SubtractionTestHelper(workloadFactory,
shape0, input0, 1.0f, 0,
shape1, input1, 1.0f, 0,
shape0, output, 1.0f, 0);
}
LayerTestResult<float, 4> SubtractionTest(armnn::IWorkloadFactory& workloadFactory)
{
const unsigned int shape0[] = { 1, 1, 2, 2 };
const unsigned int shape1[] = { 1, 1, 2, 2 };
std::vector<float> input0({ 1, 2, 3, 4 });
std::vector<float> input1({ 1, -1, 0, 2 });
std::vector<float> output({ 0, 3, 3, 2 });
return SubtractionTestHelper(workloadFactory,
shape0, input0, 1.0f, 0,
shape1, input1, 1.0f, 0,
shape0, output, 1.0f, 0);
}
LayerTestResult<float, 4> SubtractionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory)
{
const unsigned int shape0[] = { 1, 1, 2, 2 };
const unsigned int shape1[] = { 1, 1, 1, 1 };
std::vector<float> input0({ 1, 2, 3, 4 });
std::vector<float> input1({ 10 });
std::vector<float> output({ -9, -8, -7, -6 });
return SubtractionTestHelper(workloadFactory,
shape0, input0, 1.0f, 0,
shape1, input1, 1.0f, 0,
shape0, output, 1.0f, 0);
}
LayerTestResult<float, 4> SubtractionBroadcastTest(armnn::IWorkloadFactory& workloadFactory)
{
const unsigned int shape0[] = { 1, 1, 2, 2 };
const unsigned int shape1[] = { 1, 1, 1, 2 };
std::vector<float> input0({ 1, 2, 3, 4 });
std::vector<float> input1({ 10, -5 });
std::vector<float> output({ -9, 7, -7, 9 });
return SubtractionTestHelper(workloadFactory,
shape0, input0, 1.0f, 0,
shape1, input1, 1.0f, 0,
shape0, output, 1.0f, 0);
}
LayerTestResult<uint8_t, 4> ResizeBilinearNopUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
constexpr unsigned int inputWidth = 4;
constexpr unsigned int inputHeight = 4;
constexpr unsigned int inputChannels = 1;
constexpr unsigned int inputBatchSize = 1;
constexpr unsigned int outputWidth = inputWidth;
constexpr unsigned int outputHeight = inputHeight;
constexpr unsigned int outputChannels = inputChannels;
constexpr unsigned int outputBatchSize = inputBatchSize;
armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
armnn::DataType::QuantisedAsymm8);
inputTensorInfo.SetQuantizationScale(1.5f);
inputTensorInfo.SetQuantizationOffset(-3);
armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
armnn::DataType::QuantisedAsymm8);
outputTensorInfo.SetQuantizationScale(1.5f);
outputTensorInfo.SetQuantizationOffset(-3);
auto input = MakeTensor<uint8_t, 4>(inputTensorInfo, std::vector<uint8_t>({
1, 2, 3, 4,
2, 3, 4, 5,
3, 4, 5, 6,
4, 5, 6, 7
}));
LayerTestResult<uint8_t, 4> result(outputTensorInfo);
result.outputExpected = input;
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::ResizeBilinearQueueDescriptor descriptor;
armnn::WorkloadInfo info;
AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
return result;
}
LayerTestResult<uint8_t, 4> SimpleResizeBilinearUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
constexpr unsigned int inputWidth = 2;
constexpr unsigned int inputHeight = 2;
constexpr unsigned int inputChannels = 1;
constexpr unsigned int inputBatchSize = 1;
constexpr unsigned int outputWidth = inputWidth / 2;
constexpr unsigned int outputHeight = inputHeight / 2;
constexpr unsigned int outputChannels = inputChannels;
constexpr unsigned int outputBatchSize = inputBatchSize;
armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
armnn::DataType::QuantisedAsymm8);
inputTensorInfo.SetQuantizationScale(0.1567f);
inputTensorInfo.SetQuantizationOffset(1);
armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
armnn::DataType::QuantisedAsymm8);
outputTensorInfo.SetQuantizationScale(0.1567f);
outputTensorInfo.SetQuantizationOffset(1);
auto input = MakeTensor<uint8_t, 4>(inputTensorInfo, std::vector<uint8_t>({
1, 255,
200, 250
}));
// The 'resize bilinear' operation projects the top-left corner of output texels into the input image,
// then figures out the interpolants and weights. Note this is different to projecting the centre of the
// output texel - and thus we'll expect the output 1x1 matrix to contain, as its single element, the value
// that was at position (0,0) of the input matrix (rather than an average, which we would expect if projecting
// the centre).
LayerTestResult<uint8_t, 4> result(outputTensorInfo);
result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, std::vector<uint8_t>({
1
}));
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::ResizeBilinearQueueDescriptor descriptor;
armnn::WorkloadInfo info;
AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
return result;
}
LayerTestResult<uint8_t, 4> ResizeBilinearSqMinUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
constexpr unsigned int inputWidth = 4;
constexpr unsigned int inputHeight = 4;
constexpr unsigned int inputChannels = 1;
constexpr unsigned int inputBatchSize = 1;
constexpr unsigned int outputWidth = inputWidth / 2;
constexpr unsigned int outputHeight = inputHeight / 2;
constexpr unsigned int outputChannels = inputChannels;
constexpr unsigned int outputBatchSize = inputBatchSize;
armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
armnn::DataType::QuantisedAsymm8);
inputTensorInfo.SetQuantizationScale(3.141592f);
inputTensorInfo.SetQuantizationOffset(3);
armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
armnn::DataType::QuantisedAsymm8);
outputTensorInfo.SetQuantizationScale(3.141592f);
outputTensorInfo.SetQuantizationOffset(3);
auto input = MakeTensor<uint8_t, 4>(inputTensorInfo, std::vector<uint8_t>({
1, 2, 3, 4,
2, 3, 4, 5,
3, 4, 5, 6,
4, 5, 6, 7
}));
LayerTestResult<uint8_t, 4> result(outputTensorInfo);
result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, std::vector<uint8_t>({
1, 3,
3, 5
}));
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::ResizeBilinearQueueDescriptor descriptor;
armnn::WorkloadInfo info;
AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
return result;
}
LayerTestResult<uint8_t, 4> ResizeBilinearMinUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
constexpr unsigned int inputWidth = 3;
constexpr unsigned int inputHeight = 2;
constexpr unsigned int inputChannels = 1;
constexpr unsigned int inputBatchSize = 1;
constexpr unsigned int outputWidth = 2;
constexpr unsigned int outputHeight = 1;
constexpr unsigned int outputChannels = inputChannels;
constexpr unsigned int outputBatchSize = inputBatchSize;
armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
armnn::DataType::QuantisedAsymm8);
inputTensorInfo.SetQuantizationScale(1.5f);
inputTensorInfo.SetQuantizationOffset(-1);
armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
armnn::DataType::QuantisedAsymm8);
outputTensorInfo.SetQuantizationScale(1.5f);
outputTensorInfo.SetQuantizationOffset(-1);
auto input = MakeTensor<uint8_t, 4>(inputTensorInfo, std::vector<uint8_t>({
1, 2, 3, // 3.0, 4.5, 6.0
5, 8, 13 // 9.0, 13.5, 21.0
}));
LayerTestResult<uint8_t, 4> result(outputTensorInfo);
result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, std::vector<uint8_t>({
1, 3 // 3.0, 5.25
}));
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::ResizeBilinearQueueDescriptor descriptor;
armnn::WorkloadInfo info;
AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
return result;
}
LayerTestResult<uint8_t, 4> ResizeBilinearMagUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
constexpr unsigned int inputWidth = 2;
constexpr unsigned int inputHeight = 3;
constexpr unsigned int inputChannels = 1;
constexpr unsigned int inputBatchSize = 1;
constexpr unsigned int outputWidth = 5;
constexpr unsigned int outputHeight = 3;
constexpr unsigned int outputChannels = inputChannels;
constexpr unsigned int outputBatchSize = inputBatchSize;
armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
armnn::DataType::QuantisedAsymm8);
inputTensorInfo.SetQuantizationScale(0.010765f);
inputTensorInfo.SetQuantizationOffset(7);
armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
armnn::DataType::QuantisedAsymm8);
outputTensorInfo.SetQuantizationScale(0.010132f);
outputTensorInfo.SetQuantizationOffset(-18);
auto input = MakeTensor<uint8_t, 4>(inputTensorInfo, std::vector<uint8_t>({
24, 228, // 0.183005, 2.379065,
105, 128, // 1.05497, 1.302565
230, 71 // 2.400595, 0.68896
}));
LayerTestResult<uint8_t, 4> result(outputTensorInfo);
result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, std::vector<uint8_t>({
0, 87, 173, 217, 217, // 0.18300501, 1.06142902, 1.93985295, 2.37906504, 2.37906504
86, 96, 106, 111, 111, // 1.05497003, 1.15400803, 1.25304604, 1.30256498, 1.30256498
219, 151, 84, 50, 50 // 2.40059495, 1.71594095, 1.03128707, 0.68896002, 0.68896002
}));
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::ResizeBilinearQueueDescriptor descriptor;
armnn::WorkloadInfo info;
AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
return result;
}
LayerTestResult<float, 4> BatchNormTest(armnn::IWorkloadFactory& workloadFactory)
{
auto ret = BatchNormTestImpl<float>(workloadFactory, 0.f, 0);
return ret;
}
LayerTestResult<uint8_t, 4> BatchNormUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
auto ret = BatchNormTestImpl<uint8_t>(workloadFactory, 1.f/20.f, 50);
return ret;
}
LayerTestResult<uint8_t, 4> ConstantUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return ConstantTestImpl<uint8_t>(workloadFactory, 2e-6f, 1);
}
LayerTestResult<uint8_t, 1> Concatenation1dUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return Concatenation1dTestImpl<uint8_t>(workloadFactory, 0.5f, -1);
}
LayerTestResult<uint8_t, 2> Concatenation2dDim0Uint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return Concatenation2dDim0TestImpl<uint8_t>(workloadFactory, 0.5f, -1);
}
LayerTestResult<uint8_t, 2> Concatenation2dDim1Uint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return Concatenation2dDim1TestImpl<uint8_t>(workloadFactory, 0.5f, -1);
}
LayerTestResult<uint8_t, 2> Concatenation2dDim0DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return Concatenation2dDim0DiffInputDimsTestImpl<uint8_t>(workloadFactory, 0.5f, -1);
}
LayerTestResult<uint8_t, 2> Concatenation2dDim1DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return Concatenation2dDim1DiffInputDimsTestImpl<uint8_t>(workloadFactory, 0.5f, -1);
}
LayerTestResult<uint8_t, 3> Concatenation3dDim0Uint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return Concatenation3dDim0TestImpl<uint8_t>(workloadFactory, 0.5f, -1);
}
LayerTestResult<uint8_t, 3> Concatenation3dDim1Uint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return Concatenation3dDim1TestImpl<uint8_t>(workloadFactory, 0.5f, -1);
}
LayerTestResult<uint8_t, 3> Concatenation3dDim2Uint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return Concatenation3dDim2TestImpl<uint8_t>(workloadFactory, 0.5f, -1);
}
LayerTestResult<uint8_t, 3> Concatenation3dDim0DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return Concatenation3dDim0TestImpl<uint8_t>(workloadFactory, 0.5f, -1);
}
LayerTestResult<uint8_t, 3> Concatenation3dDim1DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return Concatenation3dDim1DiffInputDimsTestImpl<uint8_t>(workloadFactory, 0.5f, -1);
}
LayerTestResult<uint8_t, 3> Concatenation3dDim2DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return Concatenation3dDim2DiffInputDimsTestImpl<uint8_t>(workloadFactory, 0.5f, -1);
}
LayerTestResult<float, 4> SimpleMaxPooling2dSize2x2Stride2x2Test(armnn::IWorkloadFactory& workloadFactory,
bool forceNoPadding)
{
return SimpleMaxPooling2dSize2x2Stride2x2TestCommon<float>(workloadFactory, forceNoPadding);
}
LayerTestResult<uint8_t, 4> SimpleMaxPooling2dSize2x2Stride2x2Uint8Test(armnn::IWorkloadFactory& workloadFactory,
bool forceNoPadding)
{
return SimpleMaxPooling2dSize2x2Stride2x2TestCommon<uint8_t>(workloadFactory, forceNoPadding, 3.0f, -5);
}
LayerTestResult<float, 4> SimpleMaxPooling2dSize3x3Stride2x4Test(armnn::IWorkloadFactory& workloadFactory,
bool forceNoPadding)
{
return SimpleMaxPooling2dSize3x3Stride2x4TestCommon<float>(workloadFactory, forceNoPadding);
}
LayerTestResult<uint8_t, 4> SimpleMaxPooling2dSize3x3Stride2x4Uint8Test(armnn::IWorkloadFactory& workloadFactory,
bool forceNoPadding)
{
return SimpleMaxPooling2dSize3x3Stride2x4TestCommon<uint8_t>(workloadFactory, forceNoPadding, 0.1f, 128);
}
LayerTestResult<float, 4> SimpleAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory)
{
return SimpleAveragePooling2dTestCommon<float>(workloadFactory);
}
LayerTestResult<uint8_t, 4> SimpleAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return SimpleAveragePooling2dTestCommon<uint8_t>(workloadFactory, 0.5, -1);
}
LayerTestResult<float, 4> IgnorePaddingAveragePooling2dSize3x2Stride2x2Test(armnn::IWorkloadFactory& workloadFactory,
bool forceNoPadding)
{
return IgnorePaddingAveragePooling2dSize3x2Stride2x2TestCommon<float>(workloadFactory, forceNoPadding);
}
LayerTestResult<float, 4> LargeTensorsAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory)
{
return LargeTensorsAveragePooling2dTestCommon<float>(workloadFactory);
}
LayerTestResult<uint8_t, 4> LargeTensorsAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return LargeTensorsAveragePooling2dTestCommon<uint8_t>(workloadFactory, 0.5, -1);
}
LayerTestResult<float, 4> SimpleL2Pooling2dTest(armnn::IWorkloadFactory& workloadFactory)
{
return SimpleL2Pooling2dTestCommon<float>(workloadFactory);
}
LayerTestResult<uint8_t, 4> SimpleL2Pooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return SimpleL2Pooling2dTestCommon<uint8_t>(workloadFactory);
}
LayerTestResult<float, 4> L2Pooling2dSize3Stride1Test(armnn::IWorkloadFactory& workloadFactory)
{
return L2Pooling2dSize3Stride1TestCommon<float>(workloadFactory);
}
LayerTestResult<uint8_t, 4> L2Pooling2dSize3Stride1Uint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return L2Pooling2dSize3Stride1TestCommon<uint8_t>(workloadFactory);
}
LayerTestResult<float, 4> L2Pooling2dSize3Stride3Test(armnn::IWorkloadFactory& workloadFactory)
{
return L2Pooling2dSize3Stride3TestCommon<float>(workloadFactory);
}
LayerTestResult<uint8_t, 4> L2Pooling2dSize3Stride3Uint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return L2Pooling2dSize3Stride3TestCommon<uint8_t>(workloadFactory);
}
LayerTestResult<float, 4> L2Pooling2dSize3Stride4Test(armnn::IWorkloadFactory& workloadFactory)
{
return L2Pooling2dSize3Stride4TestCommon<float>(workloadFactory);
}
LayerTestResult<uint8_t, 4> L2Pooling2dSize3Stride4Uint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return L2Pooling2dSize3Stride4TestCommon<uint8_t>(workloadFactory);
}
LayerTestResult<float, 4> L2Pooling2dSize7Test(armnn::IWorkloadFactory& workloadFactory)
{
return L2Pooling2dSize7TestCommon<float>(workloadFactory);
}
LayerTestResult<uint8_t, 4> L2Pooling2dSize7Uint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return L2Pooling2dSize7TestCommon<uint8_t>(workloadFactory);
}
LayerTestResult<float, 4> L2Pooling2dSize9Test(armnn::IWorkloadFactory& workloadFactory)
{
return L2Pooling2dSize9TestCommon<float>(workloadFactory);
}
LayerTestResult<uint8_t, 4> L2Pooling2dSize9Uint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return L2Pooling2dSize9TestCommon<uint8_t>(workloadFactory);
}
LayerTestResult<float, 4> AsymmetricNonSquarePooling2dTest(armnn::IWorkloadFactory& workloadFactory)
{
return AsymmetricNonSquarePooling2dTestCommon<float>(workloadFactory);
}
LayerTestResult<uint8_t, 4> AsymmetricNonSquarePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return AsymmetricNonSquarePooling2dTestCommon<uint8_t>(workloadFactory);
}
LayerTestResult<float, 4> ComparePooling2dTest(armnn::IWorkloadFactory& workloadFactory,
armnn::IWorkloadFactory& refWorkloadFactory,
armnn::PoolingAlgorithm poolingType)
{
return ComparePooling2dTestCommon<float>(workloadFactory, refWorkloadFactory, poolingType);
}
LayerTestResult<uint8_t, 4> ComparePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory,
armnn::IWorkloadFactory& refWorkloadFactory,
armnn::PoolingAlgorithm poolingType)
{
return ComparePooling2dTestCommon<uint8_t>(workloadFactory, refWorkloadFactory, poolingType, 0.1f, 128);
}
LayerTestResult<float, 2> FullyConnectedLargeTest(armnn::IWorkloadFactory& workloadFactory,
bool transposeWeights)
{
return FullyConnectedLargeTestCommon<float>(workloadFactory, transposeWeights);
}
LayerTestResult<float, 4> IgnorePaddingSimpleMaxPooling2dTest(armnn::IWorkloadFactory& workloadFactory)
{
return IgnorePaddingSimpleMaxPooling2dTestCommon<float>(workloadFactory);
}
LayerTestResult<uint8_t, 4> IgnorePaddingSimpleMaxPooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return IgnorePaddingSimpleMaxPooling2dTestCommon<uint8_t>(workloadFactory, 1.0f, -5);
}
LayerTestResult<float, 4> IgnorePaddingMaxPooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory)
{
return IgnorePaddingMaxPooling2dSize3TestCommon<float>(workloadFactory);
}
LayerTestResult<uint8_t, 4> IgnorePaddingMaxPooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return IgnorePaddingMaxPooling2dSize3TestCommon<uint8_t>(workloadFactory, 1.0f, -5);
}
LayerTestResult<float, 4> IgnorePaddingSimpleAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory)
{
return IgnorePaddingSimpleAveragePooling2dTestCommon<float>(workloadFactory);
}
LayerTestResult<uint8_t, 4> IgnorePaddingSimpleAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return IgnorePaddingSimpleAveragePooling2dTestCommon<uint8_t>(workloadFactory);
}
LayerTestResult<float, 4> IgnorePaddingSimpleAveragePooling2dNoPaddingTest(armnn::IWorkloadFactory& workloadFactory)
{
return IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon<float>(workloadFactory);
}
LayerTestResult<uint8_t, 4> IgnorePaddingSimpleAveragePooling2dNoPaddingUint8Test(
armnn::IWorkloadFactory& workloadFactory)
{
return IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon<uint8_t>(workloadFactory);
}
LayerTestResult<float, 4> IgnorePaddingAveragePooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory)
{
return IgnorePaddingAveragePooling2dSize3TestCommon<float>(workloadFactory);
}
LayerTestResult<uint8_t, 4> IgnorePaddingAveragePooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return IgnorePaddingAveragePooling2dSize3TestCommon<uint8_t>(workloadFactory);
}
LayerTestResult<float, 4> IgnorePaddingSimpleL2Pooling2dTest(armnn::IWorkloadFactory& workloadFactory)
{
return IgnorePaddingSimpleL2Pooling2dTestCommon<float>(workloadFactory);
}
LayerTestResult<uint8_t, 4> IgnorePaddingSimpleL2Pooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return IgnorePaddingSimpleL2Pooling2dTestCommon<uint8_t>(workloadFactory);
}
LayerTestResult<float, 4> IgnorePaddingL2Pooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory)
{
return IgnorePaddingL2Pooling2dSize3TestCommon<float>(workloadFactory);
}
LayerTestResult<uint8_t, 4> IgnorePaddingL2Pooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return IgnorePaddingL2Pooling2dSize3TestCommon<uint8_t>(workloadFactory);
}
LayerTestResult<float, 4> SimplePermuteFloat32Test(armnn::IWorkloadFactory& workloadFactory)
{
return SimplePermuteFloat32TestCommon(workloadFactory);
};
LayerTestResult<uint8_t, 4> SimplePermuteUint8Test(armnn::IWorkloadFactory& workloadFactory)
{
return SimplePermuteUint8TestCommon(workloadFactory);
};
LayerTestResult<float, 4> PermuteFloat32ValueSet1Test(armnn::IWorkloadFactory& workloadFactory)
{
return PermuteFloat32ValueSet1TestCommon(workloadFactory);
};
LayerTestResult<float, 4> PermuteFloat32ValueSet2Test(armnn::IWorkloadFactory& workloadFactory)
{
return PermuteFloat32ValueSet2TestCommon(workloadFactory);
};
LayerTestResult<float, 4> PermuteFloat32ValueSet3Test(armnn::IWorkloadFactory& workloadFactory)
{
return PermuteFloat32ValueSet3TestCommon(workloadFactory);
};