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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 "ConvolutionLayer.h"
#include <cker/operation/Conv.h>
#include "OperationUtils.h"
namespace neurun
{
namespace backend
{
namespace cpu
{
namespace kernel
{
ConvolutionLayer::ConvolutionLayer()
: _inputData(), _kernelData(), _outputData(), _biasData(), _inputShape(), _kernelShape(),
_outputShape(), _biasShape(), _paddingLeft(0), _paddingTop(0), _paddingRight(0),
_paddingBottom(0), _strideWidth(0), _strideHeight(0), _activation(model::Activation::NONE),
_inputType(OperandType::FLOAT32)
{
// DO NOTHING
}
void ConvolutionLayer::convFloat32()
{
float output_activation_min, output_activation_max;
CalculateActivationRangeFloat(_activation, &output_activation_min, &output_activation_max);
nnfw::cker::ConvParams op_params;
op_params.padding_values.width = _paddingLeft;
op_params.padding_values.height = _paddingTop;
op_params.stride_width = _strideWidth;
op_params.stride_height = _strideHeight;
op_params.dilation_width_factor = 1;
op_params.dilation_height_factor = 1;
op_params.float_activation_min = output_activation_min;
op_params.float_activation_max = output_activation_max;
nnfw::cker::Conv(op_params, convertShapeToCkerShape(_inputShape), _inputData.f,
convertShapeToCkerShape(_kernelShape), _kernelData.f,
convertShapeToCkerShape(_biasShape), _biasData.f,
convertShapeToCkerShape(_outputShape), _outputData.f);
}
void ConvolutionLayer::convQuant8()
{
int32_t output_activation_min = 0;
int32_t output_activation_max = 0;
CalculateActivationRangeUint8(_activation, _outputShape, &output_activation_min,
&output_activation_max);
float real_multiplier = 0.0;
int32_t output_multiplier = 0;
int32_t output_shift = 0;
GetQuantizedConvolutionMultiplier(_inputShape, _kernelShape, _biasShape, _outputShape,
&real_multiplier);
QuantizeMultiplier(real_multiplier, &output_multiplier, &output_shift);
nnfw::cker::ConvParams op_params;
op_params.stride_width = _strideWidth;
op_params.stride_height = _strideHeight;
op_params.dilation_width_factor = 1;
op_params.dilation_height_factor = 1;
op_params.padding_values.width = _paddingLeft;
op_params.padding_values.height = _paddingTop;
op_params.input_offset = -_inputShape.offset;
op_params.weights_offset = -_kernelShape.offset;
op_params.output_offset = _outputShape.offset;
op_params.output_multiplier = output_multiplier;
op_params.output_shift = output_shift;
op_params.quantized_activation_min = output_activation_min;
op_params.quantized_activation_max = output_activation_max;
nnfw::cker::Conv(op_params, convertShapeToCkerShape(_inputShape), _inputData.u8,
convertShapeToCkerShape(_kernelShape), _kernelData.u8,
convertShapeToCkerShape(_biasShape), _biasData.i32,
convertShapeToCkerShape(_outputShape), _outputData.u8);
}
void ConvolutionLayer::configure(uint8_t *inputData, const Shape inputShape, uint8_t *kernelData,
const Shape kernelShape, uint8_t *biasData, const Shape biasShape,
const uint32_t paddingLeft, const uint32_t paddingRight,
const uint32_t paddingTop, const uint32_t paddingBottom,
const uint32_t strideWidth, const uint32_t strideHeight,
const model::Activation activation, uint8_t *outputData,
const Shape outputShape)
{
_inputData.u8 = inputData;
_inputShape = inputShape;
_inputType = inputShape.type;
_kernelData.u8 = kernelData;
_kernelShape = kernelShape;
_biasData.u8 = biasData;
_biasShape = biasShape;
_paddingLeft = paddingLeft;
_paddingRight = paddingRight;
_paddingTop = paddingTop;
_paddingBottom = paddingBottom;
_strideWidth = strideWidth;
_strideHeight = strideHeight;
_activation = activation;
_outputData.u8 = outputData;
_outputShape = outputShape;
}
void ConvolutionLayer::run()
{
if (_inputType == OperandType::FLOAT32)
{
convFloat32();
}
else if (_inputType == OperandType::QUANT8_ASYMM)
{
convQuant8();
}
}
#undef ANDROID_NN_CONV_PARAMETERS
} // namespace kernel
} // namespace cpu
} // namespace backend
} // namespace neurun
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