101 lines
4.1 KiB
C++
101 lines
4.1 KiB
C++
/*
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* Copyright (c) 2018-2019 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#include "SpaceToBatch.h"
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#include "tests/validation/Helpers.h"
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namespace arm_compute
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{
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namespace test
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{
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namespace validation
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{
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namespace reference
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{
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// Space to Batch
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template <typename T>
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SimpleTensor<T> space_to_batch(const SimpleTensor<T> &src, const SimpleTensor<int32_t> &block_shape, const SimpleTensor<int32_t> &paddings, const TensorShape &dst_shape)
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{
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SimpleTensor<T> result(dst_shape, src.data_type(), 1, src.quantization_info());
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const auto width_out = static_cast<int>(dst_shape[0]);
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const auto height_out = static_cast<int>(dst_shape[1]);
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const auto batch_out = static_cast<int>(dst_shape[3]);
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const auto width_in = static_cast<int>(src.shape()[0]);
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const auto height_in = static_cast<int>(src.shape()[1]);
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const auto batch_in = static_cast<int>(src.shape()[3]);
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const auto channel = static_cast<int>(src.shape()[2]);
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const auto block_width = block_shape[0];
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const auto block_height = block_shape[1];
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const auto padding_left = paddings[0];
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const auto padding_top = paddings[2];
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// Pad value must be logic zero
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const auto pad_value = is_data_type_quantized(src.data_type()) ? src.quantization_info().uniform().offset : 0;
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int out_pos = 0;
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for(int outB = 0; outB < batch_out; ++outB)
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{
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unsigned int inB = outB % batch_in;
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int shift_w = (outB / batch_in) % block_width;
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int shift_h = (outB / batch_in) / block_width;
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for(int c = 0; c < channel; ++c)
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{
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for(int outH = 0; outH < height_out; ++outH)
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{
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for(int outW = 0; outW < width_out; ++outW)
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{
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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;
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if(outH * block_height + shift_h < padding_top || outH * block_height + shift_h >= padding_top + height_in || outW * block_width + shift_w < padding_left
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|| outW * block_width + shift_w >= padding_left + width_in)
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{
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result[out_pos] = pad_value;
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}
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else
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{
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result[out_pos] = src[in_pos];
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}
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++out_pos;
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}
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}
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}
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}
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return result;
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}
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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);
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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);
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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);
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} // namespace reference
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} // namespace validation
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} // namespace test
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} // namespace arm_compute
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