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/*
* Copyright (c) 2018-2019 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 "ROIAlignLayer.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "tests/validation/Helpers.h"
#include <algorithm>
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace reference
{
namespace
{
/** Average pooling over an aligned window */
template <typename T>
inline T roi_align_1x1(const T *input, TensorShape input_shape,
float region_start_x,
float bin_size_x,
int grid_size_x,
float region_end_x,
float region_start_y,
float bin_size_y,
int grid_size_y,
float region_end_y,
int pz)
{
if((region_end_x <= region_start_x) || (region_end_y <= region_start_y))
{
return T(0);
}
else
{
float avg = 0;
// Iterate through the aligned pooling region
for(int iy = 0; iy < grid_size_y; ++iy)
{
for(int ix = 0; ix < grid_size_x; ++ix)
{
// Align the window in the middle of every bin
float y = region_start_y + (iy + 0.5) * bin_size_y / float(grid_size_y);
float x = region_start_x + (ix + 0.5) * bin_size_x / float(grid_size_x);
// Interpolation in the [0,0] [0,1] [1,0] [1,1] square
const int y_low = y;
const int x_low = x;
const int y_high = y_low + 1;
const int x_high = x_low + 1;
const float ly = y - y_low;
const float lx = x - x_low;
const float hy = 1. - ly;
const float hx = 1. - lx;
const float w1 = hy * hx;
const float w2 = hy * lx;
const float w3 = ly * hx;
const float w4 = ly * lx;
const size_t idx1 = coord2index(input_shape, Coordinates(x_low, y_low, pz));
T data1 = input[idx1];
const size_t idx2 = coord2index(input_shape, Coordinates(x_high, y_low, pz));
T data2 = input[idx2];
const size_t idx3 = coord2index(input_shape, Coordinates(x_low, y_high, pz));
T data3 = input[idx3];
const size_t idx4 = coord2index(input_shape, Coordinates(x_high, y_high, pz));
T data4 = input[idx4];
avg += w1 * data1 + w2 * data2 + w3 * data3 + w4 * data4;
}
}
avg /= grid_size_x * grid_size_y;
return T(avg);
}
}
/** Clamp the value between lower and upper */
template <typename T>
T clamp(T value, T lower, T upper)
{
return std::max(lower, std::min(value, upper));
}
SimpleTensor<float> convert_rois_from_asymmetric(SimpleTensor<uint16_t> rois)
{
const UniformQuantizationInfo &quantization_info = rois.quantization_info().uniform();
SimpleTensor<float> dst{ rois.shape(), DataType::F32, 1, QuantizationInfo(), rois.data_layout() };
for(int i = 0; i < rois.num_elements(); i += 5)
{
dst[i] = static_cast<float>(rois[i]); // batch idx
dst[i + 1] = dequantize_qasymm16(rois[i + 1], quantization_info);
dst[i + 2] = dequantize_qasymm16(rois[i + 2], quantization_info);
dst[i + 3] = dequantize_qasymm16(rois[i + 3], quantization_info);
dst[i + 4] = dequantize_qasymm16(rois[i + 4], quantization_info);
}
return dst;
}
} // namespace
template <typename T, typename TRois>
SimpleTensor<T> roi_align_layer(const SimpleTensor<T> &src, const SimpleTensor<TRois> &rois, const ROIPoolingLayerInfo &pool_info, const QuantizationInfo &output_qinfo)
{
ARM_COMPUTE_UNUSED(output_qinfo);
const size_t values_per_roi = rois.shape()[0];
const size_t num_rois = rois.shape()[1];
DataType dst_data_type = src.data_type();
const auto *rois_ptr = static_cast<const TRois *>(rois.data());
TensorShape input_shape = src.shape();
TensorShape output_shape(pool_info.pooled_width(), pool_info.pooled_height(), src.shape()[2], num_rois);
SimpleTensor<T> dst(output_shape, dst_data_type);
// Iterate over every pixel of the input image
for(size_t px = 0; px < pool_info.pooled_width(); ++px)
{
for(size_t py = 0; py < pool_info.pooled_height(); ++py)
{
for(size_t pw = 0; pw < num_rois; ++pw)
{
const unsigned int roi_batch = rois_ptr[values_per_roi * pw];
const auto x1 = float(rois_ptr[values_per_roi * pw + 1]);
const auto y1 = float(rois_ptr[values_per_roi * pw + 2]);
const auto x2 = float(rois_ptr[values_per_roi * pw + 3]);
const auto y2 = float(rois_ptr[values_per_roi * pw + 4]);
const float roi_anchor_x = x1 * pool_info.spatial_scale();
const float roi_anchor_y = y1 * pool_info.spatial_scale();
const float roi_dims_x = std::max((x2 - x1) * pool_info.spatial_scale(), 1.0f);
const float roi_dims_y = std::max((y2 - y1) * pool_info.spatial_scale(), 1.0f);
float bin_size_x = roi_dims_x / pool_info.pooled_width();
float bin_size_y = roi_dims_y / pool_info.pooled_height();
float region_start_x = px * bin_size_x + roi_anchor_x;
float region_start_y = py * bin_size_y + roi_anchor_y;
float region_end_x = (px + 1) * bin_size_x + roi_anchor_x;
float region_end_y = (py + 1) * bin_size_y + roi_anchor_y;
region_start_x = clamp(region_start_x, 0.0f, float(input_shape[0]));
region_start_y = clamp(region_start_y, 0.0f, float(input_shape[1]));
region_end_x = clamp(region_end_x, 0.0f, float(input_shape[0]));
region_end_y = clamp(region_end_y, 0.0f, float(input_shape[1]));
const int roi_bin_grid_x = (pool_info.sampling_ratio() > 0) ? pool_info.sampling_ratio() : int(ceil(bin_size_x));
const int roi_bin_grid_y = (pool_info.sampling_ratio() > 0) ? pool_info.sampling_ratio() : int(ceil(bin_size_y));
// Move input and output pointer across the fourth dimension
const size_t input_stride_w = input_shape[0] * input_shape[1] * input_shape[2];
const size_t output_stride_w = output_shape[0] * output_shape[1] * output_shape[2];
const T *input_ptr = src.data() + roi_batch * input_stride_w;
T *output_ptr = dst.data() + px + py * output_shape[0] + pw * output_stride_w;
for(int pz = 0; pz < int(input_shape[2]); ++pz)
{
// For every pixel pool over an aligned region
*(output_ptr + pz * output_shape[0] * output_shape[1]) = roi_align_1x1(input_ptr, input_shape,
region_start_x,
bin_size_x,
roi_bin_grid_x,
region_end_x,
region_start_y,
bin_size_y,
roi_bin_grid_y,
region_end_y, pz);
}
}
}
}
return dst;
}
template SimpleTensor<float> roi_align_layer(const SimpleTensor<float> &src, const SimpleTensor<float> &rois, const ROIPoolingLayerInfo &pool_info, const QuantizationInfo &output_qinfo);
template SimpleTensor<half> roi_align_layer(const SimpleTensor<half> &src, const SimpleTensor<half> &rois, const ROIPoolingLayerInfo &pool_info, const QuantizationInfo &output_qinfo);
template <>
SimpleTensor<uint8_t> roi_align_layer(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint16_t> &rois, const ROIPoolingLayerInfo &pool_info, const QuantizationInfo &output_qinfo)
{
SimpleTensor<float> src_tmp = convert_from_asymmetric(src);
SimpleTensor<float> rois_tmp = convert_rois_from_asymmetric(rois);
SimpleTensor<float> dst_tmp = roi_align_layer<float, float>(src_tmp, rois_tmp, pool_info, output_qinfo);
SimpleTensor<uint8_t> dst = convert_to_asymmetric<uint8_t>(dst_tmp, output_qinfo);
return dst;
}
} // namespace reference
} // namespace validation
} // namespace test
} // namespace arm_compute
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