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/*
* Copyright (c) 2020 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#include "MaxUnpoolingLayer.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "tests/validation/Helpers.h"
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace reference
{
using namespace arm_compute::misc::shape_calculator;
template <typename T>
SimpleTensor<T> max_unpooling_layer_internal(const SimpleTensor<T> &src, const PoolingLayerInfo &info,
const QuantizationInfo &output_qinfo, SimpleTensor<uint32_t> &indices,
TensorShape output_shape, DataLayout data_layout)
{
ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_UNUSED(output_qinfo);
ARM_COMPUTE_UNUSED(data_layout);
// Create reference
SimpleTensor<T> dst{ output_shape, src.data_type(), 1 };
ARM_COMPUTE_ERROR_ON(indices.shape().total_size() == 0);
std::fill_n(dst.data(), dst.num_elements(), 0);
const auto w_indices = static_cast<int>(indices.shape()[0]);
const auto h_indices = static_cast<int>(indices.shape()[1]);
const auto z_indices = static_cast<int>(indices.shape()[2]);
const auto b_indices = static_cast<int>(indices.shape()[3]);
const auto w_dst = static_cast<int>(dst.shape()[0]);
const auto h_dst = static_cast<int>(dst.shape()[1]);
const auto z_dst = static_cast<int>(dst.shape()[2]);
for(int b = 0; b < b_indices; ++b)
{
for(int r = 0; r < z_indices; ++r)
{
for(int h = 0; h < h_indices; ++h)
{
for(int w = 0; w < w_indices; ++w)
{
const uint32_t index_into_dst = indices[b * z_indices * h_indices * w_indices + r * h_indices * w_indices + h * w_indices + w];
const auto input_val = src[b * z_indices * h_indices * w_indices + r * h_indices * w_indices + h * w_indices + w];
auto *ptr = &dst[b * z_dst * h_dst * w_dst];
ptr[index_into_dst] = input_val;
}
}
}
}
return dst;
}
template <>
SimpleTensor<uint8_t> max_unpooling_layer<uint8_t>(
const SimpleTensor<uint8_t> &src, const PoolingLayerInfo &info,
const QuantizationInfo &output_qinfo, SimpleTensor<uint32_t> &indices,
TensorShape output_shape, DataLayout data_layout)
{
SimpleTensor<float> src_tmp = convert_from_asymmetric(src);
SimpleTensor<float> dst_tmp = max_unpooling_layer_internal<float>(src_tmp, info, output_qinfo, indices, output_shape, data_layout);
SimpleTensor<uint8_t> dst = convert_to_asymmetric<uint8_t>(dst_tmp, output_qinfo);
return dst;
}
template <typename T>
SimpleTensor<T> max_unpooling_layer(const SimpleTensor<T> &src, const PoolingLayerInfo &info,
const QuantizationInfo &output_qinfo, SimpleTensor<uint32_t> &indices,
TensorShape output_shape, DataLayout data_layout)
{
return max_unpooling_layer_internal<T>(src, info, output_qinfo, indices, output_shape, data_layout);
}
template SimpleTensor<float> max_unpooling_layer(const SimpleTensor<float> &src, const PoolingLayerInfo &info,
const QuantizationInfo &output_qinfo, SimpleTensor<uint32_t> &indices,
TensorShape output_shape, DataLayout data_layout);
} // namespace reference
} // namespace validation
} // namespace test
} // namespace arm_compute
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