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/*
* Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright 2017 The TensorFlow Authors. 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.
*/
#ifndef __NNFW_CKER_CONV_H__
#define __NNFW_CKER_CONV_H__
#include "cker/Types.h"
#include "cker/Shape.h"
#include "cker/Utils.h"
#include "cker/operation/reference/Conv.h"
#include "cker/operation/optimized/Conv.h"
#include <vector>
namespace nnfw
{
namespace cker
{
namespace
{
// Naive implementation of transpose for floats. Could be optimized to be more
// cache friendly, but for now it's a one-time cost on first run, and we would
// prefer to remove the need to do this at all eventually.
inline void TransposeFloatTensor(const float *input_data, const nnfw::cker::Shape &output_shape,
float *output_data)
{
const int rows = output_shape.Dims(1);
const int cols = output_shape.Dims(0);
for (int i = 0; i < rows; ++i)
{
for (int j = 0; j < cols; ++j)
{
const float in_value = input_data[i * cols + j];
output_data[j * rows + i] = in_value;
}
}
}
} // namespace
class Conv
{
public:
Conv()
: _modified_filter_data(), _im2col_data(), _im2col_shape(4), _need_im2col(false),
_prepared(false)
{
}
void prepare(const Shape &filter_shape, const float *filter_data, PaddingType padding_type,
bool &is_replaced_weights)
{
(void)filter_shape;
(void)filter_data;
(void)padding_type;
(void)is_replaced_weights;
if (!_prepared)
{
if (padding_type != PaddingType::kNone && std::thread::hardware_concurrency() > 1)
{
const auto output_depth = filter_shape.Dims(0);
const Shape hwcn_filter_shape{filter_shape.FlatSize() / output_depth, output_depth};
_modified_filter_data.resize(hwcn_filter_shape.FlatSize());
TransposeFloatTensor(filter_data, hwcn_filter_shape, &_modified_filter_data[0]);
is_replaced_weights = true;
}
_prepared = true;
}
}
void prepareQuant(const Shape &input_shape, const Shape &kernel_shape, const Shape &output_shape,
uint32_t stride_width, uint32_t stride_height)
{
_need_im2col = stride_width != 1 || stride_height != 1 || kernel_shape.Dims(1) != 1 ||
kernel_shape.Dims(2) != 1;
if (!_prepared && _need_im2col)
{
_im2col_shape.SetDim(0, output_shape.Dims(0));
_im2col_shape.SetDim(1, output_shape.Dims(1));
_im2col_shape.SetDim(2, output_shape.Dims(2));
_im2col_shape.SetDim(3, input_shape.Dims(3) * kernel_shape.Dims(1) * kernel_shape.Dims(2));
_im2col_data.resize(_im2col_shape.FlatSize());
}
_prepared = true;
}
void operator()(const ConvParams ¶ms, const Shape &input_shape, const float *input_data,
const Shape &filter_shape, const float *filter_data, const Shape &bias_shape,
const float *bias_data, const Shape &output_shape, float *output_data)
{
if (params.padding_type != PaddingType::kNone && std::thread::hardware_concurrency() > 1)
{
if (!_prepared)
{
bool not_used_condition = false;
prepare(filter_shape, filter_data, params.padding_type, not_used_condition);
_prepared = true;
}
multithreaded::Conv(params, input_shape, input_data, filter_shape, &_modified_filter_data[0],
bias_shape, bias_data, output_shape, output_data);
}
else
{
// TODO Support optimized kernel
reference::Conv(params, input_shape, input_data, filter_shape, filter_data, bias_shape,
bias_data, output_shape, output_data);
}
}
void operator()(const ConvParams ¶ms, const Shape &input_shape, const uint8_t *input_data,
const Shape &filter_shape, const uint8_t *filter_data, const Shape &bias_shape,
const int32_t *bias_data, const Shape &output_shape, uint8_t *output_data)
{
if (_prepared)
{
uint8_t *im2col_raw_data = _im2col_data.data();
optimized::Conv(params, input_shape, input_data, filter_shape, filter_data, bias_shape,
bias_data, output_shape, output_data, _im2col_shape, im2col_raw_data);
}
else
{
reference::Conv(params, input_shape, input_data, filter_shape, filter_data, bias_shape,
bias_data, output_shape, output_data);
}
}
private:
std::vector<float> _modified_filter_data;
std::vector<uint8_t> _im2col_data;
Shape _im2col_shape;
bool _need_im2col;
bool _prepared;
};
} // namespace cker
} // namespace nnfw
#endif // __NNFW_CKER_CONCATENATION_H_
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