arm_compute v18.11
diff --git a/tests/validation/reference/PriorBoxLayer.cpp b/tests/validation/reference/PriorBoxLayer.cpp
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+/*
+ * Copyright (c) 2018 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 "PriorBoxLayer.h"
+
+#include "ActivationLayer.h"
+
+#include "tests/validation/Helpers.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace reference
+{
+template <typename T>
+SimpleTensor<T> prior_box_layer(const SimpleTensor<T> &src1, const SimpleTensor<T> &src2, const PriorBoxLayerInfo &info, const TensorShape &output_shape)
+{
+ const auto layer_width = static_cast<int>(src1.shape()[0]);
+ const auto layer_height = static_cast<int>(src1.shape()[1]);
+
+ int img_width = info.img_size().x;
+ int img_height = info.img_size().y;
+ if(img_width == 0 || img_height == 0)
+ {
+ img_width = static_cast<int>(src2.shape()[0]);
+ img_height = static_cast<int>(src2.shape()[1]);
+ }
+
+ 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_x = static_cast<float>(img_height) / layer_height;
+ }
+
+ // Calculate number of aspect ratios
+ const int num_priors = info.aspect_ratios().size() * info.min_sizes().size() + info.max_sizes().size();
+ const int total_elements = layer_width * layer_height * num_priors * 4;
+
+ SimpleTensor<T> result(output_shape, src1.data_type());
+
+ int idx = 0;
+ for(int y = 0; y < layer_height; ++y)
+ {
+ for(int x = 0; x < layer_width; ++x)
+ {
+ const float center_x = (x + info.offset()) * step_x;
+ const float center_y = (y + info.offset()) * step_y;
+ float box_width;
+ float box_height;
+ 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;
+ // (xmin, ymin, xmax, ymax)
+ result[idx++] = (center_x - box_width / 2.f) / img_width;
+ result[idx++] = (center_y - box_height / 2.f) / img_height;
+ result[idx++] = (center_x + box_width / 2.f) / img_width;
+ result[idx++] = (center_y + box_height / 2.f) / img_height;
+
+ if(!info.max_sizes().empty())
+ {
+ const float max_size = info.max_sizes().at(i);
+ box_width = sqrt(min_size * max_size);
+ box_height = box_width;
+
+ // (xmin, ymin, xmax, ymax)
+ result[idx++] = (center_x - box_width / 2.f) / img_width;
+ result[idx++] = (center_y - box_height / 2.f) / img_height;
+ result[idx++] = (center_x + box_width / 2.f) / img_width;
+ result[idx++] = (center_y + box_height / 2.f) / img_height;
+ }
+
+ // 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);
+
+ // (xmin, ymin, xmax, ymax)
+ result[idx++] = (center_x - box_width / 2.f) / img_width;
+ result[idx++] = (center_y - box_height / 2.f) / img_height;
+ result[idx++] = (center_x + box_width / 2.f) / img_width;
+ result[idx++] = (center_y + box_height / 2.f) / img_height;
+ }
+ }
+ }
+ }
+
+ // clip the coordinates
+ if(info.clip())
+ {
+ for(int i = 0; i < total_elements; ++i)
+ {
+ result[i] = std::min<T>(std::max<T>(result[i], 0.f), 1.f);
+ }
+ }
+
+ // set the variance.
+ if(info.variances().size() == 1)
+ {
+ std::fill_n(result.data() + idx, total_elements, info.variances().at(0));
+ }
+ else
+ {
+ for(int h = 0; h < layer_height; ++h)
+ {
+ for(int w = 0; w < layer_width; ++w)
+ {
+ for(int i = 0; i < num_priors; ++i)
+ {
+ for(int j = 0; j < 4; ++j)
+ {
+ result[idx++] = info.variances().at(j);
+ }
+ }
+ }
+ }
+ }
+
+ return result;
+}
+template SimpleTensor<float> prior_box_layer(const SimpleTensor<float> &src1, const SimpleTensor<float> &src2, const PriorBoxLayerInfo &info, const TensorShape &output_shape);
+
+} // namespace reference
+} // namespace validation
+} // namespace test
+} // namespace arm_compute