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/*
* Copyright (c) 2018-2019 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 "SpaceToBatch.h"
#include "tests/validation/Helpers.h"
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace reference
{
// Space to Batch
template <typename T>
SimpleTensor<T> space_to_batch(const SimpleTensor<T> &src, const SimpleTensor<int32_t> &block_shape, const SimpleTensor<int32_t> &paddings, const TensorShape &dst_shape)
{
SimpleTensor<T> result(dst_shape, src.data_type(), 1, src.quantization_info());
const auto width_out = static_cast<int>(dst_shape[0]);
const auto height_out = static_cast<int>(dst_shape[1]);
const auto batch_out = static_cast<int>(dst_shape[3]);
const auto width_in = static_cast<int>(src.shape()[0]);
const auto height_in = static_cast<int>(src.shape()[1]);
const auto batch_in = static_cast<int>(src.shape()[3]);
const auto channel = static_cast<int>(src.shape()[2]);
const auto block_width = block_shape[0];
const auto block_height = block_shape[1];
const auto padding_left = paddings[0];
const auto padding_top = paddings[2];
// Pad value must be logic zero
const auto pad_value = is_data_type_quantized(src.data_type()) ? src.quantization_info().uniform().offset : 0;
int out_pos = 0;
for(int outB = 0; outB < batch_out; ++outB)
{
unsigned int inB = outB % batch_in;
int shift_w = (outB / batch_in) % block_width;
int shift_h = (outB / batch_in) / block_width;
for(int c = 0; c < channel; ++c)
{
for(int outH = 0; outH < height_out; ++outH)
{
for(int outW = 0; outW < width_out; ++outW)
{
const auto in_pos = ((inB * channel + c) * height_in + ((outH * block_height + shift_h) - padding_top)) * width_in + (outW * block_width + shift_w) - padding_left;
if(outH * block_height + shift_h < padding_top || outH * block_height + shift_h >= padding_top + height_in || outW * block_width + shift_w < padding_left
|| outW * block_width + shift_w >= padding_left + width_in)
{
result[out_pos] = pad_value;
}
else
{
result[out_pos] = src[in_pos];
}
++out_pos;
}
}
}
}
return result;
}
template SimpleTensor<float> space_to_batch(const SimpleTensor<float> &src, const SimpleTensor<int32_t> &block_shape, const SimpleTensor<int32_t> &paddings, const TensorShape &dst_shape);
template SimpleTensor<half> space_to_batch(const SimpleTensor<half> &src, const SimpleTensor<int32_t> &block_shape, const SimpleTensor<int32_t> &paddings, const TensorShape &dst_shape);
template SimpleTensor<uint8_t> space_to_batch(const SimpleTensor<uint8_t> &src, const SimpleTensor<int32_t> &block_shape, const SimpleTensor<int32_t> &paddings, const TensorShape &dst_shape);
} // namespace reference
} // namespace validation
} // namespace test
} // namespace arm_compute
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