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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "FullyConnected.hpp"
#include "RefWorkloadUtils.hpp"
namespace armnn
{
void FullyConnected(const TensorShape& rInputShape,
Decoder<float>& rInputDecoder,
const TensorShape& rOutputShape,
Encoder<float>& rOutputEncoder,
const TensorShape& rWeightsShape,
Decoder<float>& rWeightDecoder,
Decoder<float>& rBiasDecoder,
const bool biasEnabled,
const unsigned int K,
const bool transposeWeights)
{
// Perform FullyConnected implementation
unsigned int outputSize = rOutputShape[1];
const std::vector<float> decodedInputs = rInputDecoder.DecodeTensor(rInputShape);
const std::vector<float> decodedWeights = rWeightDecoder.DecodeTensor(rWeightsShape);
const TensorShape biasShape{outputSize};
const std::vector<float> decodedBiases = biasEnabled ? rBiasDecoder.DecodeTensor(biasShape) : std::vector<float>();
for (unsigned int n = 0; n < rInputShape[0]; n++)
{
for (unsigned int channelOutput = 0; channelOutput < outputSize; channelOutput++)
{
float outval = 0.f;
for (unsigned int channelInput = 0; channelInput < K; channelInput++)
{
float weight;
if (transposeWeights)
{
weight = decodedWeights[channelOutput * K + channelInput];
}
else
{
weight = decodedWeights[channelInput * outputSize + channelOutput];
}
outval += weight * decodedInputs[n * K + channelInput];
}
if (biasEnabled)
{
outval += decodedBiases[channelOutput];
}
rOutputEncoder[n * outputSize + channelOutput];
rOutputEncoder.Set(outval);
}
}
}
} //namespace armnn
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