src/core/NEON/kernels/NEPriorBoxLayerKernel.cpp

/*
 * Copyright (c) 2018-2020 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 "arm_compute/core/NEON/kernels/NEPriorBoxLayerKernel.h"

#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Validate.h"

#include <arm_neon.h>
#include <cstdint>

namespace arm_compute
{
namespace
{
Status validate_arguments(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, const PriorBoxLayerInfo &info)
{
    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output);
    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::F32);
    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input1, input2);
    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input1, input2);

    // Check variances
    const int var_size = info.variances().size();
    if(var_size > 1)
    {
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(var_size != 4, "Must provide 4 variance values");
        for(int i = 0; i < var_size; ++i)
        {
            ARM_COMPUTE_RETURN_ERROR_ON_MSG(var_size <= 0, "Must be greater than 0");
        }
    }
    ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.steps()[0] < 0.f, "Step x should be greater or equal to 0");
    ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.steps()[1] < 0.f, "Step y should be greater or equal to 0");

    if(!info.max_sizes().empty())
    {
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.max_sizes().size() != info.min_sizes().size(), "Max and min sizes dimensions should match");
    }

    for(unsigned int i = 0; i < info.max_sizes().size(); ++i)
    {
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.max_sizes()[i] < info.min_sizes()[i], "Max size should be greater than min size");
    }

    if(output != nullptr && output->total_size() != 0)
    {
        ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(1) != 2);
    }

    return Status{};
}
} // namespace

NEPriorBoxLayerKernel::NEPriorBoxLayerKernel()
    : _input1(nullptr), _input2(nullptr), _output(nullptr), _info()
{
}

void NEPriorBoxLayerKernel::store_coordinates(float *out, const int offset, const float center_x, const float center_y, const float box_width, const float box_height, const int width,
                                              const int height)
{
    float xmin = (center_x - box_width / 2.f) / width;
    float ymin = (center_y - box_height / 2.f) / height;
    float xmax = (center_x + box_width / 2.f) / width;
    float ymax = (center_y + box_height / 2.f) / height;

    float32x4_t vec_elements = { xmin, ymin, xmax, ymax };
    if(_info.clip())
    {
        static const float32x4_t CONST_0 = vdupq_n_f32(0.f);
        static const float32x4_t CONST_1 = vdupq_n_f32(1.f);
        vec_elements                     = vmaxq_f32(vminq_f32(vec_elements, CONST_1), CONST_0);
    }
    vst1q_f32(out + offset, vec_elements);
}

void NEPriorBoxLayerKernel::calculate_prior_boxes(const Window &window)
{
    const int num_priors = _info.aspect_ratios().size() * _info.min_sizes().size() + _info.max_sizes().size();

    const DataLayout data_layout = _input1->info()->data_layout();
    const int        width_idx   = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
    const int        height_idx  = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);

    const int layer_width  = _input1->info()->dimension(width_idx);
    const int layer_height = _input1->info()->dimension(height_idx);

    int img_width  = _info.img_size().x;
    int img_height = _info.img_size().y;
    if(img_width == 0 || img_height == 0)
    {
        img_width  = _input2->info()->dimension(width_idx);
        img_height = _input2->info()->dimension(height_idx);
    }

    float step_x = _info.steps()[0];
    float step_y = _info.steps()[1];
    if(step_x == 0.f || step_y == 0.f)
    {
        step_x = static_cast<float>(img_width) / layer_width;
        step_y = static_cast<float>(img_height) / layer_height;
    }

    Window slice = window.first_slice_window_2D();
    slice.set(Window::DimY, Window::Dimension(0, _output->info()->dimension(1), 2));

    Iterator output(_output, slice);
    execute_window_loop(slice, [&](const Coordinates & id)
    {
        float center_x = 0;
        float center_y = 0;
        int   idx      = id.x() / (4 * num_priors);
        center_x       = (static_cast<float>(idx % layer_width) + _info.offset()) * step_x;
        center_y       = (static_cast<float>(idx / layer_width) + _info.offset()) * step_y;

        float box_width;
        float box_height;
        int   offset = 0;

        auto out = reinterpret_cast<float *>(output.ptr());
        for(unsigned int i = 0; i < _info.min_sizes().size(); ++i)
        {
            const float min_size = _info.min_sizes().at(i);
            box_width            = min_size;
            box_height           = min_size;
            store_coordinates(out, offset, center_x, center_y, box_width, box_height, img_width, img_height);
            offset += 4;

            if(!_info.max_sizes().empty())
            {
                const float max_size = _info.max_sizes().at(i);
                box_width            = std::sqrt(min_size * max_size);
                box_height           = box_width;

                store_coordinates(out, offset, center_x, center_y, box_width, box_height, img_width, img_height);
                offset += 4;
            }

            // rest of priors
            for(auto ar : _info.aspect_ratios())
            {
                if(fabs(ar - 1.) < 1e-6)
                {
                    continue;
                }

                box_width  = min_size * sqrt(ar);
                box_height = min_size / sqrt(ar);

                store_coordinates(out, offset, center_x, center_y, box_width, box_height, img_width, img_height);
                offset += 4;
            }
        }

        // set the variance
        out = reinterpret_cast<float *>(_output->ptr_to_element(Coordinates(id.x(), 1)));
        float32x4_t var;
        if(_info.variances().size() == 1)
        {
            var = vdupq_n_f32(_info.variances().at(0));
        }
        else
        {
            const float32x4_t vars = { _info.variances().at(0), _info.variances().at(1), _info.variances().at(2), _info.variances().at(3) };
            var                    = vars;
        }
        for(int i = 0; i < num_priors; ++i)
        {
            vst1q_f32(out + 4 * i, var);
        }
    },
    output);
}

void NEPriorBoxLayerKernel::configure(const ITensor *input1, const ITensor *input2, ITensor *output, const PriorBoxLayerInfo &info)
{
    ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);

    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input1->info(), input2->info(), output->info(), info));

    _input1 = input1;
    _input2 = input2;
    _info   = info;
    _output = output;

    // Configure kernel window
    const int   num_priors = info.aspect_ratios().size() * info.min_sizes().size() + info.max_sizes().size();
    Window      win        = calculate_max_window(*output->info(), Steps(num_priors * 4));
    Coordinates coord;
    coord.set_num_dimensions(output->info()->num_dimensions());
    output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape()));

    INEKernel::configure(win);
}

Status NEPriorBoxLayerKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, const PriorBoxLayerInfo &info)
{
    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output);
    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input1, input2, output, info));

    return Status{};
}
void NEPriorBoxLayerKernel::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);

    // Run function
    calculate_prior_boxes(window);
}
} // namespace arm_compute