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/*
* Copyright (c) 2019 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 "TransposeConvLayer.h"
#include "OperationUtils.h"
#include "srcn/srcn_conv.h"
namespace neurun
{
namespace backend
{
namespace srcn
{
namespace kernel
{
TransposeConvLayer::TransposeConvLayer()
: _inputData(), _kernelData(), _outputData(), _inputShape(), _kernelShape(), _outputShape(),
_paddingType(0), _paddingLeft(0), _paddingTop(0), _paddingRight(0), _paddingBottom(0),
_strideWidth(0), _strideHeight(0), _inputType(OperandType::FLOAT32)
{
// DO NOTHING
}
void TransposeConvLayer::convFloat32()
{
nnfw::srcn::convMat_t in_mat, out_mat, kernel_mat;
nnfw::srcn::convParams_t in_param;
const int batches = MatchingDim(_inputShape, 0, _outputShape, 0);
const int input_height = _inputShape.dimensions[1];
const int input_width = _inputShape.dimensions[2];
const int input_depth = MatchingDim(_inputShape, 3, _kernelShape, 3);
in_mat.c = input_depth;
in_mat.w = input_width;
in_mat.h = input_height;
in_mat.n = batches;
in_mat.data = _inputData.f;
const int output_height = _outputShape.dimensions[1];
const int output_width = _outputShape.dimensions[2];
const int output_depth = MatchingDim(_kernelShape, 0, _outputShape, 3);
out_mat.c = output_depth;
out_mat.w = output_width;
out_mat.h = output_height;
out_mat.n = batches;
out_mat.data = _outputData.f;
const int ker_height = _kernelShape.dimensions[1];
const int ker_width = _kernelShape.dimensions[2];
kernel_mat.c = output_depth;
kernel_mat.w = ker_width;
kernel_mat.h = ker_height;
kernel_mat.n = input_depth;
kernel_mat.data = _kernelData.f;
in_param.kernel_w = ker_width;
in_param.kernel_h = ker_height;
in_param.stride_w = _strideWidth;
in_param.stride_h = _strideHeight;
in_param.padding = _paddingType;
in_param.pad_w = _paddingLeft;
in_param.pad_h = _paddingTop;
in_param.dilation_w = 1;
in_param.dilation_h = 1;
nnfw::srcn::srcn_deconvolution2D(in_mat, kernel_mat, out_mat, in_param, 4, nnfw::srcn::col_major);
}
void TransposeConvLayer::configure(uint8_t *inputData, const Shape inputShape, uint8_t *kernelData,
const Shape kernelShape, const uint32_t paddingType,
const uint32_t paddingLeft, const uint32_t paddingRight,
const uint32_t paddingTop, const uint32_t paddingBottom,
const uint32_t strideWidth, const uint32_t strideHeight,
uint8_t *outputData, const Shape outputShape)
{
_inputData.u8 = inputData;
_inputShape = inputShape;
_inputType = inputShape.type;
_kernelData.u8 = kernelData;
_kernelShape = kernelShape;
_paddingType = paddingType;
_paddingLeft = paddingLeft;
_paddingRight = paddingRight;
_paddingTop = paddingTop;
_paddingBottom = paddingBottom;
_strideWidth = strideWidth;
_strideHeight = strideHeight;
_outputData.u8 = outputData;
_outputShape = outputShape;
}
void TransposeConvLayer::run()
{
if (_inputType == OperandType::FLOAT32)
{
convFloat32();
}
else if (_inputType == OperandType::QUANT8_ASYMM)
{
throw std::runtime_error("NYI");
}
}
} // namespace kernel
} // namespace srcn
} // namespace backend
} // namespace neurun
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