244 lines
11 KiB
C++
244 lines
11 KiB
C++
/*
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* Copyright (c) 2018-2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#include "ROIAlignLayer.h"
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#include "arm_compute/core/Types.h"
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#include "arm_compute/core/utils/misc/ShapeCalculator.h"
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#include "tests/validation/Helpers.h"
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#include <algorithm>
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namespace arm_compute
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{
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namespace test
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{
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namespace validation
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{
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namespace reference
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{
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namespace
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{
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/** Average pooling over an aligned window */
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inline float roi_align_1x1(const float *input, TensorShape input_shape,
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float region_start_x,
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float bin_size_x,
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int grid_size_x,
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float region_end_x,
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float region_start_y,
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float bin_size_y,
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int grid_size_y,
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float region_end_y,
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int pz)
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{
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if((region_end_x <= region_start_x) || (region_end_y <= region_start_y))
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{
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return 0;
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}
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else
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{
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float avg = 0;
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// Iterate through the aligned pooling region
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for(int iy = 0; iy < grid_size_y; ++iy)
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{
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for(int ix = 0; ix < grid_size_x; ++ix)
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{
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// Align the window in the middle of every bin
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float y = region_start_y + (iy + 0.5) * bin_size_y / float(grid_size_y);
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float x = region_start_x + (ix + 0.5) * bin_size_x / float(grid_size_x);
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// Interpolation in the [0,0] [0,1] [1,0] [1,1] square
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const int y_low = y;
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const int x_low = x;
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const int y_high = y_low + 1;
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const int x_high = x_low + 1;
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const float ly = y - y_low;
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const float lx = x - x_low;
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const float hy = 1. - ly;
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const float hx = 1. - lx;
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const float w1 = hy * hx;
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const float w2 = hy * lx;
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const float w3 = ly * hx;
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const float w4 = ly * lx;
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const size_t idx1 = coord2index(input_shape, Coordinates(x_low, y_low, pz));
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float data1 = input[idx1];
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const size_t idx2 = coord2index(input_shape, Coordinates(x_high, y_low, pz));
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float data2 = input[idx2];
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const size_t idx3 = coord2index(input_shape, Coordinates(x_low, y_high, pz));
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float data3 = input[idx3];
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const size_t idx4 = coord2index(input_shape, Coordinates(x_high, y_high, pz));
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float data4 = input[idx4];
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avg += w1 * data1 + w2 * data2 + w3 * data3 + w4 * data4;
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}
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}
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avg /= grid_size_x * grid_size_y;
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return avg;
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}
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}
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template <typename TI, typename TO>
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SimpleTensor<TO> float_converter(const SimpleTensor<TI> &tensor, DataType dst_dt)
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{
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SimpleTensor<TO> dst{ tensor.shape(), dst_dt, 1, QuantizationInfo(), tensor.data_layout() };
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#if defined(_OPENMP)
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#pragma omp parallel for
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#endif /* _OPENMP */
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for(int i = 0; i < tensor.num_elements(); ++i)
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{
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dst[i] = tensor[i];
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}
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return dst;
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}
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SimpleTensor<float> convert_rois_from_asymmetric(SimpleTensor<uint16_t> rois)
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{
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const UniformQuantizationInfo &quantization_info = rois.quantization_info().uniform();
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SimpleTensor<float> dst{ rois.shape(), DataType::F32, 1, QuantizationInfo(), rois.data_layout() };
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for(int i = 0; i < rois.num_elements(); i += 5)
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{
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dst[i] = static_cast<float>(rois[i]); // batch idx
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dst[i + 1] = dequantize_qasymm16(rois[i + 1], quantization_info);
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dst[i + 2] = dequantize_qasymm16(rois[i + 2], quantization_info);
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dst[i + 3] = dequantize_qasymm16(rois[i + 3], quantization_info);
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dst[i + 4] = dequantize_qasymm16(rois[i + 4], quantization_info);
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}
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return dst;
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}
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} // namespace
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template <>
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SimpleTensor<float> roi_align_layer(const SimpleTensor<float> &src, const SimpleTensor<float> &rois, const ROIPoolingLayerInfo &pool_info, const QuantizationInfo &output_qinfo)
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{
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ARM_COMPUTE_UNUSED(output_qinfo);
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const size_t values_per_roi = rois.shape()[0];
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const size_t num_rois = rois.shape()[1];
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DataType dst_data_type = src.data_type();
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const auto *rois_ptr = static_cast<const float *>(rois.data());
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TensorShape input_shape = src.shape();
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TensorShape output_shape(pool_info.pooled_width(), pool_info.pooled_height(), src.shape()[2], num_rois);
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SimpleTensor<float> dst(output_shape, dst_data_type);
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// Iterate over every pixel of the input image
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for(size_t px = 0; px < pool_info.pooled_width(); ++px)
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{
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for(size_t py = 0; py < pool_info.pooled_height(); ++py)
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{
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for(size_t pw = 0; pw < num_rois; ++pw)
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{
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const unsigned int roi_batch = rois_ptr[values_per_roi * pw];
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const auto x1 = float(rois_ptr[values_per_roi * pw + 1]);
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const auto y1 = float(rois_ptr[values_per_roi * pw + 2]);
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const auto x2 = float(rois_ptr[values_per_roi * pw + 3]);
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const auto y2 = float(rois_ptr[values_per_roi * pw + 4]);
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const float roi_anchor_x = x1 * pool_info.spatial_scale();
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const float roi_anchor_y = y1 * pool_info.spatial_scale();
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const float roi_dims_x = std::max((x2 - x1) * pool_info.spatial_scale(), 1.0f);
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const float roi_dims_y = std::max((y2 - y1) * pool_info.spatial_scale(), 1.0f);
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float bin_size_x = roi_dims_x / pool_info.pooled_width();
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float bin_size_y = roi_dims_y / pool_info.pooled_height();
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float region_start_x = px * bin_size_x + roi_anchor_x;
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float region_start_y = py * bin_size_y + roi_anchor_y;
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float region_end_x = (px + 1) * bin_size_x + roi_anchor_x;
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float region_end_y = (py + 1) * bin_size_y + roi_anchor_y;
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region_start_x = utility::clamp(region_start_x, 0.0f, float(input_shape[0]));
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region_start_y = utility::clamp(region_start_y, 0.0f, float(input_shape[1]));
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region_end_x = utility::clamp(region_end_x, 0.0f, float(input_shape[0]));
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region_end_y = utility::clamp(region_end_y, 0.0f, float(input_shape[1]));
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const int roi_bin_grid_x = (pool_info.sampling_ratio() > 0) ? pool_info.sampling_ratio() : int(ceil(bin_size_x));
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const int roi_bin_grid_y = (pool_info.sampling_ratio() > 0) ? pool_info.sampling_ratio() : int(ceil(bin_size_y));
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// Move input and output pointer across the fourth dimension
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const size_t input_stride_w = input_shape[0] * input_shape[1] * input_shape[2];
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const size_t output_stride_w = output_shape[0] * output_shape[1] * output_shape[2];
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const float *input_ptr = src.data() + roi_batch * input_stride_w;
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float *output_ptr = dst.data() + px + py * output_shape[0] + pw * output_stride_w;
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for(int pz = 0; pz < int(input_shape[2]); ++pz)
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{
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// For every pixel pool over an aligned region
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*(output_ptr + pz * output_shape[0] * output_shape[1]) = roi_align_1x1(input_ptr, input_shape,
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region_start_x,
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bin_size_x,
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roi_bin_grid_x,
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region_end_x,
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region_start_y,
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bin_size_y,
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roi_bin_grid_y,
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region_end_y, pz);
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}
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}
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}
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}
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return dst;
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}
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template <>
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SimpleTensor<half> roi_align_layer(const SimpleTensor<half> &src, const SimpleTensor<half> &rois, const ROIPoolingLayerInfo &pool_info, const QuantizationInfo &output_qinfo)
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{
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SimpleTensor<float> src_tmp = float_converter<half, float>(src, DataType::F32);
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SimpleTensor<float> rois_tmp = float_converter<half, float>(rois, DataType::F32);
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SimpleTensor<float> dst_tmp = roi_align_layer<float, float>(src_tmp, rois_tmp, pool_info, output_qinfo);
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SimpleTensor<half> dst = float_converter<float, half>(dst_tmp, DataType::F16);
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return dst;
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}
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template <>
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SimpleTensor<uint8_t> roi_align_layer(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint16_t> &rois, const ROIPoolingLayerInfo &pool_info, const QuantizationInfo &output_qinfo)
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{
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SimpleTensor<float> src_tmp = convert_from_asymmetric(src);
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SimpleTensor<float> rois_tmp = convert_rois_from_asymmetric(rois);
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SimpleTensor<float> dst_tmp = roi_align_layer<float, float>(src_tmp, rois_tmp, pool_info, output_qinfo);
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SimpleTensor<uint8_t> dst = convert_to_asymmetric<uint8_t>(dst_tmp, output_qinfo);
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return dst;
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}
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template <>
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SimpleTensor<int8_t> roi_align_layer(const SimpleTensor<int8_t> &src, const SimpleTensor<uint16_t> &rois, const ROIPoolingLayerInfo &pool_info, const QuantizationInfo &output_qinfo)
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{
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SimpleTensor<float> src_tmp = convert_from_asymmetric(src);
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SimpleTensor<float> rois_tmp = convert_rois_from_asymmetric(rois);
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SimpleTensor<float> dst_tmp = roi_align_layer<float, float>(src_tmp, rois_tmp, pool_info, output_qinfo);
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SimpleTensor<int8_t> dst = convert_to_asymmetric<int8_t>(dst_tmp, output_qinfo);
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return dst;
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}
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} // namespace reference
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} // namespace validation
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} // namespace test
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} // namespace arm_compute
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