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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 "nnsuite/conv/RandomModel.h"
#include <nncc/core/ADT/kernel/NCHWLayout.h>
#include <random>
using namespace nncc::core::ADT;
namespace nnsuite
{
namespace conv
{
RandomModel::RandomModel(int32_t seed)
: _ifm_shape{1, 8, 8}, _ifm_name{"ifm"}, _ofm_name{"ofm"}, _ofm_shape{2, 6, 6},
_ker_buffer{kernel::Shape{2, 1, 3, 3}, kernel::NCHWLayout{}}
{
std::default_random_engine gen{static_cast<uint32_t>(seed)};
std::normal_distribution<float> dist{0.0f, 1.0f};
const uint32_t N = _ker_buffer.shape().count();
const uint32_t C = _ker_buffer.shape().depth();
const uint32_t H = _ker_buffer.shape().height();
const uint32_t W = _ker_buffer.shape().width();
for (uint32_t n = 0; n < N; ++n)
{
for (uint32_t ch = 0; ch < C; ++ch)
{
for (uint32_t row = 0; row < H; ++row)
{
for (uint32_t col = 0; col < W; ++col)
{
_ker_buffer.at(n, ch, row, col) = dist(gen);
}
}
}
}
}
} // namespace conv
} // namespace nnsuite
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