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/*
* Copyright (c) 2017-2019 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 "DepthConcatenateLayer.h"
#include "tests/validation/Helpers.h"
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace reference
{
template <typename T>
SimpleTensor<T> depthconcatenate_layer(const std::vector<SimpleTensor<T>> &srcs, SimpleTensor<T> &dst)
{
// Create reference
std::vector<TensorShape> shapes;
for(const auto &src : srcs)
{
shapes.emplace_back(src.shape());
}
// Compute reference
int depth_offset = 0;
const int width_out = dst.shape().x();
const int height_out = dst.shape().y();
const int depth_out = dst.shape().z();
const int out_stride_z = width_out * height_out;
const int batches = dst.shape().total_size_upper(3);
if(srcs[0].data_type() == DataType::QASYMM8 && srcs[0].quantization_info() != dst.quantization_info())
{
for(int b = 0; b < batches; ++b)
{
// input tensors can have smaller width and height than the output, so for each output's slice we need to requantize 0 (as this is the value
// used in NEFillBorderKernel by NEDepthConcatenateLayer) using the corresponding quantization info for that particular slice/input tensor.
int slice = 0;
for(const auto &src : srcs)
{
auto ptr_slice = static_cast<T *>(dst(Coordinates(0, 0, slice, b)));
const auto num_elems_in_slice((dst.num_elements() / depth_out) * src.shape().z());
std::transform(ptr_slice, ptr_slice + num_elems_in_slice, ptr_slice, [src, dst](T t)
{
return dst.quantization_info().quantize(src.quantization_info().dequantize(0), RoundingPolicy::TO_NEAREST_UP);
});
slice += src.shape().z();
}
}
}
else
{
std::fill_n(dst.data(), dst.num_elements(), 0);
}
for(const auto &src : srcs)
{
ARM_COMPUTE_ERROR_ON(depth_offset >= depth_out);
ARM_COMPUTE_ERROR_ON(batches != static_cast<int>(src.shape().total_size_upper(3)));
const int width = src.shape().x();
const int height = src.shape().y();
const int depth = src.shape().z();
const int x_diff = (width_out - width) / 2;
const int y_diff = (height_out - height) / 2;
const T *src_ptr = src.data();
for(int b = 0; b < batches; ++b)
{
const size_t offset_to_first_element = b * out_stride_z * depth_out + depth_offset * out_stride_z + y_diff * width_out + x_diff;
for(int d = 0; d < depth; ++d)
{
for(int r = 0; r < height; ++r)
{
if(src.data_type() == DataType::QASYMM8 && src.quantization_info() != dst.quantization_info())
{
std::transform(src_ptr, src_ptr + width, dst.data() + offset_to_first_element + d * out_stride_z + r * width_out, [src, dst](T t)
{
const float dequantized_input = src.quantization_info().dequantize(t);
return dst.quantization_info().quantize(dequantized_input, RoundingPolicy::TO_NEAREST_UP);
});
src_ptr += width;
}
else
{
std::copy(src_ptr, src_ptr + width, dst.data() + offset_to_first_element + d * out_stride_z + r * width_out);
src_ptr += width;
}
}
}
}
depth_offset += depth;
}
return dst;
}
template SimpleTensor<uint8_t> depthconcatenate_layer(const std::vector<SimpleTensor<uint8_t>> &srcs, SimpleTensor<uint8_t> &dst);
template SimpleTensor<float> depthconcatenate_layer(const std::vector<SimpleTensor<float>> &srcs, SimpleTensor<float> &dst);
template SimpleTensor<half> depthconcatenate_layer(const std::vector<SimpleTensor<half>> &srcs, SimpleTensor<half> &dst);
} // namespace reference
} // namespace validation
} // namespace test
} // namespace arm_compute
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