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/*
* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright (C) 2017 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "FullyConnectedLayer.h"
#include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h"
#include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h"
#include "kernel/cpu/OperationUtils.h"
#include <mutex>
namespace neurun
{
namespace kernel
{
namespace cpu
{
FullyConnectedLayer::FullyConnectedLayer()
: _inputData(nullptr), _weightsData(nullptr), _biasData(nullptr), _outputData(nullptr),
_inputShape(), _weightsShape(), _biasShape(), _outputShape(),
_activation(ANEURALNETWORKS_FUSED_NONE), _inputType(OperandType::SCALAR_FLOAT32)
{
// DO NOTHING
}
// executionMutex is used to protect concurrent access of non-threadsafe resources
// like gemmlowp::GemmContext.
// std::mutex is safe for pthreads on Android.
static std::mutex executionMutex;
bool FullyConnectedLayer::fullyConnectedFloat32()
{
int total_input_size = 1;
for (int i = 0; i < _inputShape.dimensions.size(); i++)
{
total_input_size *= _inputShape.dimensions[i];
}
int input_size = _weightsShape.dimensions[1];
const int batch_size = total_input_size / input_size;
const int num_units = _weightsShape.dimensions[0];
TfLiteFusedActivation act = convertFusedActivation(_activation);
::tflite::tensor_utils::VectorBatchVectorAssign(reinterpret_cast<const float *>(_biasData),
num_units, batch_size,
reinterpret_cast<float *>(_outputData));
// Compute output += weight * input
::tflite::tensor_utils::MatrixBatchVectorMultiplyAccumulate(
reinterpret_cast<const float *>(_weightsData), num_units, input_size,
reinterpret_cast<const float *>(_inputData), batch_size,
reinterpret_cast<float *>(_outputData), /*result_stride=*/1);
// Apply activation function
::tflite::tensor_utils::ApplyActivationToVector(reinterpret_cast<float *>(_outputData),
batch_size * num_units, act,
reinterpret_cast<float *>(_outputData));
return true;
}
bool FullyConnectedLayer::fullyConnectedQuant8()
{
throw std::runtime_error{"FullyConnectedLayer : Not tested for TENSOR_QUANT8_ASYMM"};
}
void FullyConnectedLayer::configure(uint8_t *inputData, const Shape inputShape,
uint8_t *weightsData, const Shape weightsShape,
uint8_t *biasData, const Shape biasShape, FuseCode activation,
uint8_t *outputData, const Shape outputShape)
{
_inputData = inputData;
_inputShape = inputShape;
_inputType = inputShape.type;
_weightsData = weightsData;
_weightsShape = weightsShape;
_biasData = biasData;
_biasShape = biasShape;
_activation = activation;
_outputData = outputData;
_outputShape = outputShape;
}
void FullyConnectedLayer::run()
{
if (_inputType == OperandType::TENSOR_FLOAT32)
{
fullyConnectedFloat32();
}
else if (_inputType == OperandType::TENSOR_QUANT8_ASYMM)
{
throw std::runtime_error{"FullyConnectedLayer : Not tested for TENSOR_QUANT8_ASYMM"};
// fullyConnectedQuant8();
}
}
} // namespace cpu
} // namespace kernel
} // namespace neurun
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