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/*
* Copyright (c) 2017-2018 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 "arm_compute/core/NEON/kernels/NEDepthConcatenateLayerKernel.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/IAccessWindow.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/NEON/NEFixedPoint.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/Window.h"
#include <arm_neon.h>
#include <cstdint>
using namespace arm_compute;
namespace
{
// Overloads of 128-bit vector loads
uint16x8_t loadq(const uint16_t *ptr)
{
return vld1q_u16(ptr);
}
uint32x4_t loadq(const uint32_t *ptr)
{
return vld1q_u32(ptr);
}
// Overloads of 128-bit vector stores
void storeq(uint16_t *ptr, uint16x8_t val)
{
return vst1q_u16(ptr, val);
}
void storeq(uint32_t *ptr, uint32x4_t val)
{
return vst1q_u32(ptr, val);
}
template <typename T>
void depth_concat(const ITensor *in, ITensor *out, std::pair<int, int> start_xy, int depth_offset, const Window &window)
{
const int start_x = start_xy.first;
const int start_y = start_xy.second;
// Offset input
const int input_offset_to_first_elements_in_bytes = in->info()->offset_first_element_in_bytes() - start_x * in->info()->strides_in_bytes()[0] - start_y * in->info()->strides_in_bytes()[1];
uint8_t *input_ptr = in->buffer() + input_offset_to_first_elements_in_bytes;
// Offset output
const unsigned int output_offset_to_first_elements_in_bytes = out->info()->offset_first_element_in_bytes() + depth_offset * out->info()->strides_in_bytes()[2];
uint8_t *output_ptr = out->buffer() + output_offset_to_first_elements_in_bytes;
Iterator input(in, window);
Iterator output(out, window);
execute_window_loop(window, [&](const Coordinates & id)
{
const auto in_ptr = reinterpret_cast<const T *>(input_ptr + input.offset());
const auto out_ptr = reinterpret_cast<T *>(output_ptr + output.offset());
storeq(out_ptr, loadq(in_ptr));
},
input, output);
}
} // namespace
NEDepthConcatenateLayerKernel::NEDepthConcatenateLayerKernel()
: _func(nullptr), _input(nullptr), _output(nullptr), _top_bottom(0), _left_right(0), _depth_offset(0)
{
}
BorderSize NEDepthConcatenateLayerKernel::border_size() const
{
return BorderSize(_top_bottom, _left_right);
}
void NEDepthConcatenateLayerKernel::configure(const ITensor *input, unsigned int depth_offset, ITensor *output)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
ARM_COMPUTE_ERROR_ON(input->info()->dimension(2) + depth_offset > output->info()->dimension(2));
ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) > output->info()->dimension(0));
ARM_COMPUTE_ERROR_ON(input->info()->dimension(1) > output->info()->dimension(1));
ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(3, input, output);
// The gaps between the two lowest dimensions of input and output need to be divisible by 2
// Otherwise it is not clear how the padding should be added onto the input tensor
ARM_COMPUTE_ERROR_ON((output->info()->dimension(0) - input->info()->dimension(0)) % 2);
ARM_COMPUTE_ERROR_ON((output->info()->dimension(1) - input->info()->dimension(1)) % 2);
_func = nullptr;
_input = input;
_output = output;
_depth_offset = depth_offset;
_left_right = (output->info()->dimension(0) - input->info()->dimension(0)) / 2;
_top_bottom = (output->info()->dimension(1) - input->info()->dimension(1)) / 2;
switch(input->info()->data_type())
{
case DataType::F16:
_func = &depth_concat<uint16_t>;
break;
case DataType::F32:
_func = &depth_concat<uint32_t>;
break;
default:
ARM_COMPUTE_ERROR("Unsupported data type.");
}
const unsigned int num_elems_processed_per_iteration = 16 / input->info()->element_size();
const unsigned int num_elems_read_per_iteration = 16 / input->info()->element_size();
const unsigned int num_rows_read_per_iteration = 1;
// The window needs to be based on input as we copy all the depths of input
Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration));
win.set(Window::DimZ, Window::Dimension(0, input->info()->tensor_shape().z(), 1));
AccessWindowRectangle input_access(input->info(), -_left_right, -_top_bottom, num_elems_read_per_iteration, num_rows_read_per_iteration);
AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
update_window_and_padding(win, input_access, output_access);
output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
INEKernel::configure(win);
}
void NEDepthConcatenateLayerKernel::run(const Window &window, const ThreadInfo &info)
{
ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
ARM_COMPUTE_ERROR_ON(_func == nullptr);
(*_func)(_input, _output, std::make_pair(_left_right, _top_bottom), _depth_offset, window);
}