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Diffstat (limited to 'tests/validation/reference/ROIPoolingLayer.cpp')
| -rw-r--r-- | tests/validation/reference/ROIPoolingLayer.cpp | 147 |
1 files changed, 147 insertions, 0 deletions
diff --git a/tests/validation/reference/ROIPoolingLayer.cpp b/tests/validation/reference/ROIPoolingLayer.cpp new file mode 100644 index 0000000000..8dc3014763 --- /dev/null +++ b/tests/validation/reference/ROIPoolingLayer.cpp @@ -0,0 +1,147 @@ +/* + * Copyright (c) 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 "ROIPoolingLayer.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 +{ +template <> +SimpleTensor<float> roi_pool_layer(const SimpleTensor<float> &src, const SimpleTensor<uint16_t> &rois, const ROIPoolingLayerInfo &pool_info, const QuantizationInfo &output_qinfo) +{ + ARM_COMPUTE_UNUSED(output_qinfo); + + const size_t num_rois = rois.shape()[1]; + const size_t values_per_roi = rois.shape()[0]; + DataType output_data_type = src.data_type(); + + TensorShape input_shape = src.shape(); + TensorShape output_shape(pool_info.pooled_width(), pool_info.pooled_height(), src.shape()[2], num_rois); + SimpleTensor<float> output(output_shape, output_data_type); + + const int pooled_w = pool_info.pooled_width(); + const int pooled_h = pool_info.pooled_height(); + const float spatial_scale = pool_info.spatial_scale(); + + // get sizes of x and y dimensions in src tensor + const int width = src.shape()[0]; + const int height = src.shape()[1]; + + // Move 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 auto *rois_ptr = reinterpret_cast<const uint16_t *>(rois.data()); + + // Iterate through pixel width (X-Axis) + for(size_t pw = 0; pw < num_rois; ++pw) + { + const unsigned int roi_batch = rois_ptr[values_per_roi * pw]; + const auto x1 = rois_ptr[values_per_roi * pw + 1]; + const auto y1 = rois_ptr[values_per_roi * pw + 2]; + const auto x2 = rois_ptr[values_per_roi * pw + 3]; + const auto y2 = rois_ptr[values_per_roi * pw + 4]; + + //Iterate through pixel height (Y-Axis) + for(size_t fm = 0; fm < input_shape[2]; ++fm) + { + // Iterate through regions of interest index + for(size_t py = 0; py < pool_info.pooled_height(); ++py) + { + // 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 over feature map (Z axis) + for(size_t px = 0; px < pool_info.pooled_width(); ++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); + + // Iterate through the pooling region + if((region_end_x <= region_start_x) || (region_end_y <= region_start_y)) + { + /* Assign element in tensor 'output' at coordinates px, py, fm, roi_indx, to 0 */ + auto out_ptr = output.data() + px + py * output_shape[0] + fm * output_shape[0] * output_shape[1] + pw * output_stride_w; + *out_ptr = 0; + } + else + { + float curr_max = -std::numeric_limits<float>::max(); + for(int j = region_start_y; j < region_end_y; ++j) + { + for(int i = region_start_x; i < region_end_x; ++i) + { + /* Retrieve element from input tensor at coordinates(i, j, fm, roi_batch) */ + float in_element = *(src.data() + i + j * input_shape[0] + fm * input_shape[0] * input_shape[1] + roi_batch * input_stride_w); + curr_max = std::max(in_element, curr_max); + } + } + + /* Assign element in tensor 'output' at coordinates px, py, fm, roi_indx, to curr_max */ + auto out_ptr = output.data() + px + py * output_shape[0] + fm * output_shape[0] * output_shape[1] + pw * output_stride_w; + *out_ptr = curr_max; + } + } + } + } + } + + return output; +} + +/* + Template genericised method to allow calling of roi_pooling_layer with quantized 8 bit datatype +*/ +template <> +SimpleTensor<uint8_t> roi_pool_layer(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint16_t> &rois, const ROIPoolingLayerInfo &pool_info, const QuantizationInfo &output_qinfo) +{ + const SimpleTensor<float> src_tmp = convert_from_asymmetric(src); + SimpleTensor<float> dst_tmp = roi_pool_layer<float>(src_tmp, rois, 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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