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/*
* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
*
* 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 "SoftMaxLayer.h"
#include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h"
#include "kernel/cpu/OperationUtils.h"
namespace neurun
{
namespace kernel
{
namespace cpu
{
SoftMaxLayer::SoftMaxLayer()
: _inputData(nullptr), _outputData(nullptr), _beta(0.0), _inputShape(), _outputShape(),
_inputType(OperandType::SCALAR_FLOAT32)
{
// DO NOTHING
}
bool SoftMaxLayer::softmaxFloat32()
{
::tflite::Dims<4> dim;
if (getNumberOfDimensions(_inputShape) == 2)
{
uint32_t batch_size = getSizeOfDimension(_inputShape, 0);
uint32_t input_size = getNumberOfElements(_inputShape) / batch_size;
Shape shapeIn4D;
shapeIn4D.dimensions = {batch_size, 1, 1, input_size};
dim = convertShapeToDims(shapeIn4D);
}
else if (getNumberOfDimensions(_inputShape) == 4)
{
dim = convertShapeToDims(_inputShape);
}
else
{
std::cout << "only 2D and 4D tensors supported" << std::endl;
return false;
}
::tflite::optimized_ops::Softmax(reinterpret_cast<const float *>(_inputData), dim, _beta,
reinterpret_cast<float *>(_outputData), dim);
return true;
}
bool SoftMaxLayer::softmaxQuant8()
{
::tflite::Dims<4> dim;
if (getNumberOfDimensions(_inputShape) == 2)
{
uint32_t batch_size = getSizeOfDimension(_inputShape, 0);
uint32_t input_size = getNumberOfElements(_inputShape) / batch_size;
Shape shapeIn4D;
shapeIn4D.dimensions = {batch_size, 1, 1, input_size};
dim = convertShapeToDims(shapeIn4D);
}
else if (getNumberOfDimensions(_inputShape) == 4)
{
dim = convertShapeToDims(_inputShape);
}
else
{
std::cout << "only 2D and 4D tensors supported" << std::endl;
return false;
}
if (_outputShape.offset != 0 || _outputShape.scale != 1.f / 256)
{
std::cout << "incorrect scale / offset for output" << std::endl;
return false;
}
static const int32_t kScaledDiffIntegerBits = 5;
const double input_beta_real_multiplier = std::min(
1.0 * _beta * _inputShape.scale * (1 << (31 - kScaledDiffIntegerBits)), (1ll << 31) - 1.0);
int32_t input_multiplier = 0;
int32_t input_left_shift = 0;
if (!QuantizeMultiplierGreaterThanOne(input_beta_real_multiplier, &input_multiplier,
&input_left_shift))
{
return false;
}
float diff_min = -1.0f * CalculateInputRadius(kScaledDiffIntegerBits, input_left_shift);
::tflite::optimized_ops::Softmax(_inputData, dim, input_multiplier, input_left_shift, diff_min,
_outputData, dim);
return true;
}
void SoftMaxLayer::configure(uint8_t *inputData, const Shape &inputShape, const float beta,
uint8_t *outputData, const Shape &outputShape)
{
_inputData = inputData;
_inputShape = inputShape;
_inputType = inputShape.type;
_outputData = outputData;
_outputShape = outputShape;
_beta = beta;
}
void SoftMaxLayer::run()
{
if (_inputType == OperandType::TENSOR_FLOAT32)
{
softmaxFloat32();
}
else if (_inputType == OperandType::TENSOR_QUANT8_ASYMM)
{
throw std::runtime_error{"SoftMaxLayer : Not tested for TENSOR_QUANT8_ASYMM"};
// softmaxQuant8();
}
}
} // namespace cpu
} // namespace kernel
} // namespace neurun
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