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/*
* Copyright (c) 2017-2021 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 "src/core/NEON/kernels/NEROIPoolingLayerKernel.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/Window.h"
#include "src/core/CPP/Validate.h"
#include "src/core/helpers/AutoConfiguration.h"
#include "src/core/helpers/WindowHelpers.h"
#include "support/ToolchainSupport.h"
#include <cfloat>
namespace arm_compute
{
namespace
{
Status validate_arguments(const ITensorInfo *input,
const ITensorInfo *rois,
const ITensorInfo *output,
const ROIPoolingLayerInfo &pool_info)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output, rois);
//Validate arguments
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(rois, DataType::U16);
ARM_COMPUTE_RETURN_ERROR_ON(rois->dimension(0) != 5);
ARM_COMPUTE_RETURN_ERROR_ON(rois->num_dimensions() > 2);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(input, DataType::F32, DataType::QASYMM8);
ARM_COMPUTE_RETURN_ERROR_ON((pool_info.pooled_width() == 0) || (pool_info.pooled_height() == 0));
if (output->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
ARM_COMPUTE_RETURN_ERROR_ON((output->dimension(0) != pool_info.pooled_width()) ||
(output->dimension(1) != pool_info.pooled_height()));
ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(2) != output->dimension(2));
ARM_COMPUTE_RETURN_ERROR_ON(rois->dimension(1) != output->dimension(3));
}
return Status{};
}
/** Evaluate number needing to be stored in output tensor as quantized format.
*
* @param[in] input Source tensor. Data types supported: QASYMM8
* @param[out] output Destination tensor. Where output value will be stored, same datatype as input
* @param[in] region_start_x Beginning region of x coordinate of pooling region
* @param[in] region_start_y Beginning region of y coordinate of pooling region
* @param[in] region_end_x End of pooling region, x coordinate
* @param[in] region_end_y End of pooling region, y coordinate
* @param[in] fm Channel index of coordinate in output Tensor to store value
* @param[in] px Width index of coodinate in output Tensor to store value
* @param[in] py Height index of coordinate in output Tensor to store value
* @param[in] roi_batch Index of image to perform Pooling on in input Tensor
* @param[in] roi_indx Index of image of coordinate in output Tensor to store value
*/
template <typename T>
void template_eval(const ITensor *input,
const ITensor *output,
int region_start_x,
int region_start_y,
int region_end_x,
int region_end_y,
int fm,
int px,
int py,
int roi_batch,
int roi_indx)
{
if ((region_end_x <= region_start_x) || (region_end_y <= region_start_y))
{
*reinterpret_cast<T *>(output->ptr_to_element(Coordinates(px, py, fm, roi_indx))) = 0;
}
else
{
T curr_max = std::numeric_limits<T>::lowest(); // Min value of typename T
for (int j = region_start_y; j < region_end_y; ++j)
{
for (int i = region_start_x; i < region_end_x; ++i)
{
const auto val = *reinterpret_cast<const T *>(input->ptr_to_element(Coordinates(i, j, fm, roi_batch)));
curr_max = std::max(val, curr_max);
}
}
// if quantized datatype, requantize then store in output tensor
if (is_data_type_quantized(input->info()->data_type()))
{
// covert qasymm to new output quantization scale and offset
UniformQuantizationInfo uqinfo = compute_requantization_scale_offset(
input->info()->quantization_info().uniform(), output->info()->quantization_info().uniform());
*reinterpret_cast<T *>(output->ptr_to_element(Coordinates(px, py, fm, roi_indx))) =
quantize_qasymm8(curr_max, uqinfo);
}
else
{
*reinterpret_cast<T *>(output->ptr_to_element(Coordinates(px, py, fm, roi_indx))) = curr_max;
}
}
}
} // namespace
NEROIPoolingLayerKernel::NEROIPoolingLayerKernel()
: _input(nullptr), _rois(nullptr), _output(nullptr), _pool_info(0, 0, 0.f)
{
}
Status NEROIPoolingLayerKernel::validate(const ITensorInfo *input,
const ITensorInfo *rois,
const ITensorInfo *output,
const ROIPoolingLayerInfo &pool_info)
{
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, rois, output, pool_info));
return Status{};
}
void NEROIPoolingLayerKernel::configure(const ITensor *input,
const ITensor *rois,
const ITensor *output,
const ROIPoolingLayerInfo &pool_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, rois);
//Validate arguments
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), rois->info(), output->info(), pool_info));
// Output auto initialization if not yet initialized
TensorShape output_shape(pool_info.pooled_width(), pool_info.pooled_height(), input->info()->dimension(2),
rois->info()->dimension(1));
auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(),
output->info()->quantization_info());
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
ARM_COMPUTE_ERROR_ON((output->info()->dimension(0) != pool_info.pooled_width()) ||
(output->info()->dimension(1) != pool_info.pooled_height()));
// Set instance variables
_input = input;
_rois = rois;
_output = output;
_pool_info = pool_info;
// Configure kernel window
Window window;
window.set(Window::DimX, Window::Dimension(0, rois->info()->dimension(1)));
window.set(Window::DimY, Window::Dimension(0, 1));
INEKernel::configure(window);
}
void NEROIPoolingLayerKernel::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);
const size_t values_per_roi = _rois->info()->dimension(0);
const int roi_list_start = window.x().start();
const int roi_list_end = window.x().end();
const int width = _input->info()->dimension(Window::DimX);
const int height = _input->info()->dimension(Window::DimY);
const int fms = _input->info()->dimension(Window::DimZ);
const int pooled_w = _pool_info.pooled_width();
const int pooled_h = _pool_info.pooled_height();
const float spatial_scale = _pool_info.spatial_scale();
const auto *rois_ptr = reinterpret_cast<const uint16_t *>(_rois->buffer());
const auto data_type = _input->info()->data_type();
for (int roi_indx = roi_list_start; roi_indx < roi_list_end; ++roi_indx)
{
const unsigned int roi_batch = rois_ptr[values_per_roi * roi_indx];
const auto x1 = rois_ptr[values_per_roi * roi_indx + 1];
const auto y1 = rois_ptr[values_per_roi * roi_indx + 2];
const auto x2 = rois_ptr[values_per_roi * roi_indx + 3];
const auto y2 = rois_ptr[values_per_roi * roi_indx + 4];
// Scale ROI
const int roi_anchor_x = support::cpp11::round(x1 * spatial_scale);
const int roi_anchor_y = support::cpp11::round(y1 * spatial_scale);
const int roi_width = std::max(support::cpp11::round((x2 - x1) * spatial_scale), 1.f);
const int roi_height = std::max(support::cpp11::round((y2 - y1) * spatial_scale), 1.f);
// Iterate through all feature maps
for (int fm = 0; fm < fms; ++fm)
{
// Iterate through all output pixels
for (int py = 0; py < pooled_h; ++py)
{
for (int px = 0; px < pooled_w; ++px)
{
auto region_start_x = static_cast<int>(std::floor((static_cast<float>(px) / pooled_w) * roi_width));
auto region_end_x =
static_cast<int>(std::floor((static_cast<float>(px + 1) / pooled_w) * roi_width));
auto region_start_y =
static_cast<int>(std::floor((static_cast<float>(py) / pooled_h) * roi_height));
auto region_end_y =
static_cast<int>(std::floor((static_cast<float>(py + 1) / pooled_h) * roi_height));
region_start_x = std::min(std::max(region_start_x + roi_anchor_x, 0), width);
region_end_x = std::min(std::max(region_end_x + roi_anchor_x, 0), width);
region_start_y = std::min(std::max(region_start_y + roi_anchor_y, 0), height);
region_end_y = std::min(std::max(region_end_y + roi_anchor_y, 0), height);
switch (data_type)
{
case DataType::F32:
template_eval<float>(_input, _output, region_start_x, region_start_y, region_end_x,
region_end_y, fm, px, py, roi_batch, roi_indx);
break;
case DataType::QASYMM8:
template_eval<qasymm8_t>(_input, _output, region_start_x, region_start_y, region_end_x,
region_end_y, fm, px, py, roi_batch, roi_indx);
break;
default:
ARM_COMPUTE_ERROR("DataType not Supported");
break;
}
}
}
}
}
}
} // namespace arm_compute
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