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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include <ResolveType.hpp>
#include "WorkloadTestUtils.hpp"
#include <backendsCommon/IBackendInternal.hpp>
LayerTestResult<float, 2> FullyConnectedFloat32Test(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool biasEnabled,
bool transposeWeights)
{
unsigned int inputWidth = 1;
unsigned int inputHeight = 1;
unsigned int inputChannels = 5;
unsigned int inputNum = 2;
unsigned int outputChannels = 3;
unsigned int outputNum = 2;
// Define the tensor descriptors.
armnn::TensorInfo inputTensorInfo;
armnn::TensorInfo outputTensorInfo;
armnn::TensorInfo weightsDesc;
armnn::TensorInfo biasesDesc;
unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth };
unsigned int outputShape[] = { outputNum, outputChannels };
unsigned int weightsShape[] = { inputChannels, outputChannels };
if (transposeWeights)
{
std::swap(weightsShape[0], weightsShape[1]);
}
unsigned int biasShape[] = { outputChannels };
inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
outputTensorInfo = armnn::TensorInfo(2, outputShape, armnn::DataType::Float32);
weightsDesc = armnn::TensorInfo(2, weightsShape, armnn::DataType::Float32);
biasesDesc = armnn::TensorInfo(1, biasShape, armnn::DataType::Float32);
LayerTestResult<float, 2> result(outputTensorInfo);
boost::multi_array<float, 4> input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>(
{
1.0f, 2.0f, 3.0f, 4.0f, 5.0f,
5.0f, 4.0f, 3.0f, 2.0f, 1.0f
})
);
boost::multi_array<float, 2> weights = MakeTensor<float, 2>(weightsDesc, std::vector<float>(
{
.5f, 2.f, .5f,
.5f, 2.f, 1.f,
.5f, 2.f, 2.f,
.5f, 2.f, 3.f,
.5f, 2.f, 4.f
}));
if (transposeWeights)
{
weights = MakeTensor<float, 2>(weightsDesc, std::vector<float>(
{
.5f, .5f, .5f, .5f, .5f,
2.f, 2.f, 2.f, 2.f, 2.f,
.5f, 1.f, 2.f, 3.f, 4.f
}));
}
std::vector<float> biasValues({0.f, 0.f, 0.f});
if (biasEnabled)
{
biasValues = std::vector<float>({10.f, 20.f, 30.f});
}
boost::multi_array<float, 1> bias = MakeTensor<float, 1>(biasesDesc, biasValues);
result = SimpleFullyConnectedTestImpl<float>(
workloadFactory,
memoryManager,
inputTensorInfo, outputTensorInfo,
weightsDesc, biasesDesc,
weights, bias, input,
biasEnabled, transposeWeights
);
result.outputExpected = MakeTensor<float, 2>(outputTensorInfo, std::vector<float>(
{
0.5f + 1.0f + 1.5f + 2.0f + 2.5f + biasValues[0],
2.0f + 4.0f + 6.0f + 8.0f + 10.f + biasValues[1],
0.5f + 2.0f + 6.0f + 12.f + 20.f + biasValues[2],
2.5f + 2.0f + 1.5f + 1.0f + 0.5f + biasValues[0],
10.0f + 8.0f + 6.0f + 4.0f + 2.f + biasValues[1],
2.5f + 4.0f + 6.0f + 6.f + 4.f + biasValues[2]
})
);
return result;
}
//
// ArmNN variant of the AndroidNN fully_connected_float_large test.
//
// Tests the fully connected layer with large values, optionally transposing weights.
// Note this is templated for consistency, but the nature of this tests makes it unlikely to be useful in Uint8 mode.
//
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 2> FullyConnectedLargeTestCommon(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool transposeWeights,
float qScale = 0.0f,
int32_t qOffset = 0)
{
unsigned int inputWidth = 1;
unsigned int inputHeight = 1;
unsigned int inputChannels = 5;
unsigned int inputNum = 1;
unsigned int outputChannels = 1;
unsigned int outputNum = 1;
// Define the tensor descriptors.
armnn::TensorInfo inputTensorInfo;
armnn::TensorInfo outputTensorInfo;
armnn::TensorInfo weightsDesc;
armnn::TensorInfo biasesDesc;
unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth };
unsigned int outputShape[] = { outputNum, outputChannels };
unsigned int weightsShape[] = { inputChannels, outputChannels };
if (transposeWeights)
{
std::swap(weightsShape[0], weightsShape[1]);
}
unsigned int biasShape[] = { outputChannels };
inputTensorInfo = armnn::TensorInfo(4, inputShape, ArmnnType);
outputTensorInfo = armnn::TensorInfo(2, outputShape, ArmnnType);
weightsDesc = armnn::TensorInfo(2, weightsShape, ArmnnType);
biasesDesc = armnn::TensorInfo(1, biasShape, ArmnnType);
// 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);
}
LayerTestResult<T, 2> result(outputTensorInfo);
boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputTensorInfo,
QuantizedVector<T>(qScale, qOffset, {
1.0f, 10.0f, 100.0f, 1000.0f, 10000.0f,
})
);
boost::multi_array<T, 2> weights = MakeTensor<T, 2>(weightsDesc,
QuantizedVector<T>(qScale, qOffset, {
2.0f, 3.0f, 4.0f, 5.0f, 6.0f
})
);
std::vector<T> biasValues({900000.f});
boost::multi_array<T, 1> bias = MakeTensor<T, 1>(biasesDesc, biasValues);
result = SimpleFullyConnectedTestImpl<T>(
workloadFactory,
memoryManager,
inputTensorInfo, outputTensorInfo,
weightsDesc, biasesDesc,
weights, bias, input,
true, transposeWeights
);
result.outputExpected = MakeTensor<T, 2>(outputTensorInfo,
QuantizedVector<T>(qScale, qOffset, {
965432.0f,
})
);
return result;
}
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