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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()
{
float output_activation_min, output_activation_max;
CalculateActivationRangeFloat(_activation, &output_activation_min, &output_activation_max);
// b/80425683, optimized implementation produces incorrect results when the
// number of input elements is the squre of batch_size.
uint32_t batch_size = getSizeOfDimension(_outputShape, 0);
uint32_t input_n_elements = getNumberOfElements(_inputShape);
if (batch_size * batch_size == input_n_elements)
{
::tflite::reference_ops::FullyConnected(
reinterpret_cast<const float *>(_inputData), convertShapeToDims(_inputShape),
reinterpret_cast<const float *>(_weightsData), convertShapeToDims(_weightsShape),
reinterpret_cast<const float *>(_biasData), convertShapeToDims(_biasShape),
output_activation_min, output_activation_max, reinterpret_cast<float *>(_outputData),
convertShapeToDims(_outputShape));
}
else
{
::tflite::optimized_ops::FullyConnected(
reinterpret_cast<const float *>(_inputData), convertShapeToDims(_inputShape),
reinterpret_cast<const float *>(_weightsData), convertShapeToDims(_weightsShape),
reinterpret_cast<const float *>(_biasData), convertShapeToDims(_biasShape),
output_activation_min, output_activation_max, reinterpret_cast<float *>(_outputData),
convertShapeToDims(_outputShape));
}
return true;
}
bool FullyConnectedLayer::fullyConnectedQuant8()
{
int32_t inputOffset = -_inputShape.offset;
int32_t weightsOffset = -_weightsShape.offset;
int32_t outputOffset = _outputShape.offset;
float real_multiplier = 0.0;
int32_t output_multiplier = 0;
int32_t output_shift = 0;
int32_t output_activation_min = 0;
int32_t output_activation_max = 0;
// Caution : 'Convolution' can make misleading. It seems it is just math term.
if (!GetQuantizedConvolutionMultipler(_inputShape, _weightsShape, _biasShape, _outputShape,
&real_multiplier) ||
!QuantizeMultiplierSmallerThanOne(real_multiplier, &output_multiplier, &output_shift))
{
return false;
}
CalculateActivationRangeUint8(_activation, _outputShape, &output_activation_min,
&output_activation_max);
static gemmlowp::GemmContext gemm_context;
// Prevent concurrent executions that access gemm_context.
std::unique_lock<std::mutex> lock(executionMutex);
// Alow gemmlowp automatically decide how many threads to use.
gemm_context.set_max_num_threads(0);
::tflite::optimized_ops::FullyConnected(
_inputData, convertShapeToDims(_inputShape), inputOffset, _weightsData,
convertShapeToDims(_weightsShape), weightsOffset,
reinterpret_cast<const int32_t *>(_biasData), convertShapeToDims(_biasShape), outputOffset,
output_multiplier, output_shift, output_activation_min, output_activation_max, _outputData,
convertShapeToDims(_outputShape), &gemm_context);
return true;
}
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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