arm_compute v17.12
diff --git a/tests/validation/reference/PoolingLayer.cpp b/tests/validation/reference/PoolingLayer.cpp
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+/*
+ * Copyright (c) 2017 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 "PoolingLayer.h"
+
+#include "arm_compute/core/Types.h"
+#include "tests/validation/FixedPoint.h"
+#include "tests/validation/Helpers.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace reference
+{
+namespace
+{
+TensorShape calculate_output_shape(TensorShape shape, PoolingLayerInfo info)
+{
+ TensorShape dst_shape = shape;
+ const int pool_size = info.is_global_pooling() ? shape.x() : info.pool_size();
+ const std::pair<unsigned int, unsigned int> scaled_dims = arm_compute::scaled_dimensions(shape.x(),
+ shape.y(),
+ pool_size,
+ pool_size,
+ info.pad_stride_info());
+ dst_shape.set(0, scaled_dims.first);
+ dst_shape.set(1, scaled_dims.second);
+
+ return dst_shape;
+}
+} // namespace
+
+template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type>
+SimpleTensor<T> pooling_layer(const SimpleTensor<T> &src, PoolingLayerInfo info)
+{
+ ARM_COMPUTE_ERROR_ON(info.is_global_pooling() && (src.shape().x() != src.shape().y()));
+
+ const int pool_size = info.is_global_pooling() ? src.shape().x() : info.pool_size();
+ PoolingType type = info.pool_type();
+ int pool_stride_x = info.pad_stride_info().stride().first;
+ int pool_stride_y = info.pad_stride_info().stride().second;
+ int pad_x = info.pad_stride_info().pad().first;
+ int pad_y = info.pad_stride_info().pad().second;
+ bool exclude_padding = info.exclude_padding();
+
+ const auto w_src = static_cast<int>(src.shape()[0]);
+ const auto h_src = static_cast<int>(src.shape()[1]);
+ const int upper_dims = src.shape().total_size() / (w_src * h_src);
+
+ // Create reference
+ SimpleTensor<T> dst{ calculate_output_shape(src.shape(), info), src.data_type(), 1, src.fixed_point_position() };
+
+ const auto w_dst = static_cast<int>(dst.shape()[0]);
+ const auto h_dst = static_cast<int>(dst.shape()[1]);
+
+ if(type == PoolingType::MAX)
+ {
+ for(int r = 0; r < upper_dims; ++r)
+ {
+ for(int h = 0; h < h_dst; ++h)
+ {
+ for(int w = 0; w < w_dst; ++w)
+ {
+ int wstart = w * pool_stride_x - pad_x;
+ int hstart = h * pool_stride_y - pad_y;
+ int wend = std::min(wstart + pool_size, w_src);
+ int hend = std::min(hstart + pool_size, h_src);
+ wstart = std::max(wstart, 0);
+ hstart = std::max(hstart, 0);
+
+ T max_val = std::numeric_limits<T>::lowest();
+ for(int y = hstart; y < hend; ++y)
+ {
+ for(int x = wstart; x < wend; ++x)
+ {
+ const T val = src[r * h_src * w_src + y * w_src + x];
+ if(val > max_val)
+ {
+ max_val = val;
+ }
+ }
+ }
+
+ dst[r * h_dst * w_dst + h * w_dst + w] = max_val;
+ }
+ }
+ }
+ }
+ else // Average or l2 pooling
+ {
+ for(int r = 0; r < upper_dims; ++r)
+ {
+ for(int h = 0; h < h_dst; ++h)
+ {
+ for(int w = 0; w < w_dst; ++w)
+ {
+ T avg_val(0);
+ int wstart = w * pool_stride_x - pad_x;
+ int hstart = h * pool_stride_y - pad_y;
+ int wend = std::min(wstart + pool_size, w_src + pad_x);
+ int hend = std::min(hstart + pool_size, h_src + pad_y);
+ int pool = (hend - hstart) * (wend - wstart);
+ wstart = std::max(wstart, 0);
+ hstart = std::max(hstart, 0);
+ wend = std::min(wend, w_src);
+ hend = std::min(hend, h_src);
+ // Exclude padding pixels from the average
+ if(exclude_padding)
+ {
+ pool = (hend - hstart) * (wend - wstart);
+ }
+
+ if(type == PoolingType::AVG)
+ {
+ for(int y = hstart; y < hend; ++y)
+ {
+ for(int x = wstart; x < wend; ++x)
+ {
+ avg_val += src[r * h_src * w_src + y * w_src + x];
+ }
+ }
+ dst[r * h_dst * w_dst + h * w_dst + w] = avg_val / pool;
+ }
+ else
+ {
+ for(int y = hstart; y < hend; ++y)
+ {
+ for(int x = wstart; x < wend; ++x)
+ {
+ const T val = src[r * h_src * w_src + y * w_src + x];
+ avg_val += val * val;
+ }
+ }
+ dst[r * h_dst * w_dst + h * w_dst + w] = std::sqrt(avg_val / pool);
+ }
+ }
+ }
+ }
+ }
+
+ return dst;
+}
+
+template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type>
+SimpleTensor<T> pooling_layer(const SimpleTensor<T> &src, PoolingLayerInfo info)
+{
+ ARM_COMPUTE_ERROR_ON(info.is_global_pooling() && (src.shape().x() != src.shape().y()));
+
+ const int pool_size = info.is_global_pooling() ? src.shape().x() : info.pool_size();
+ PoolingType type = info.pool_type();
+ int pool_stride_x = info.pad_stride_info().stride().first;
+ int pool_stride_y = info.pad_stride_info().stride().second;
+ int pad_x = info.pad_stride_info().pad().first;
+ int pad_y = info.pad_stride_info().pad().second;
+ bool exclude_padding = info.exclude_padding();
+
+ const auto w_src = static_cast<int>(src.shape()[0]);
+ const auto h_src = static_cast<int>(src.shape()[1]);
+ const int upper_dims = src.shape().total_size() / (w_src * h_src);
+
+ // Create reference
+ SimpleTensor<T> dst{ calculate_output_shape(src.shape(), info), src.data_type(), 1, src.fixed_point_position() };
+
+ const auto w_dst = static_cast<int>(dst.shape()[0]);
+ const auto h_dst = static_cast<int>(dst.shape()[1]);
+
+ if(type == PoolingType::MAX)
+ {
+ for(int r = 0; r < upper_dims; ++r)
+ {
+ for(int h = 0; h < h_dst; ++h)
+ {
+ for(int w = 0; w < w_dst; ++w)
+ {
+ int wstart = w * pool_stride_x - pad_x;
+ int hstart = h * pool_stride_y - pad_y;
+ int wend = std::min(wstart + pool_size, w_src);
+ int hend = std::min(hstart + pool_size, h_src);
+ wstart = std::max(wstart, 0);
+ hstart = std::max(hstart, 0);
+
+ T max_val = std::numeric_limits<T>::lowest();
+ for(int y = hstart; y < hend; ++y)
+ {
+ for(int x = wstart; x < wend; ++x)
+ {
+ const T val = src[r * h_src * w_src + y * w_src + x];
+ if(val > max_val)
+ {
+ max_val = val;
+ }
+ }
+ }
+
+ dst[r * h_dst * w_dst + h * w_dst + w] = max_val;
+ }
+ }
+ }
+ }
+ else // Average or l2 pooling
+ {
+ for(int r = 0; r < upper_dims; ++r)
+ {
+ for(int h = 0; h < h_dst; ++h)
+ {
+ for(int w = 0; w < w_dst; ++w)
+ {
+ int wstart = w * pool_stride_x - pad_x;
+ int hstart = h * pool_stride_y - pad_y;
+ int wend = std::min(wstart + pool_size, w_src + pad_x);
+ int hend = std::min(hstart + pool_size, h_src + pad_y);
+ int pool = (hend - hstart) * (wend - wstart);
+ wstart = std::max(wstart, 0);
+ hstart = std::max(hstart, 0);
+ wend = std::min(wend, w_src);
+ hend = std::min(hend, h_src);
+ // Exclude padding pixels from the average
+ if(exclude_padding)
+ {
+ pool = (hend - hstart) * (wend - wstart);
+ }
+
+ using namespace fixed_point_arithmetic;
+
+ const int fixed_point_position = src.fixed_point_position();
+ const fixed_point<T> const_1(1, fixed_point_position);
+ const fixed_point<T> invpool_fp(1.f / static_cast<float>(pool), fixed_point_position);
+ fixed_point<T> avg_val(0, fixed_point_position, true);
+
+ if(type == PoolingType::AVG)
+ {
+ for(int y = hstart; y < hend; ++y)
+ {
+ for(int x = wstart; x < wend; ++x)
+ {
+ const fixed_point<T> in_fp(src[r * h_src * w_src + y * w_src + x], fixed_point_position, true);
+ avg_val = add(avg_val, in_fp);
+ }
+ }
+ dst[r * h_dst * w_dst + h * w_dst + w] = mul(avg_val, invpool_fp).raw();
+ }
+ else
+ {
+ for(int y = hstart; y < hend; ++y)
+ {
+ for(int x = wstart; x < wend; ++x)
+ {
+ const fixed_point<T> in_fp(src[r * h_src * w_src + y * w_src + x], fixed_point_position, true);
+ avg_val = add(avg_val, mul(in_fp, in_fp));
+ }
+ }
+ auto res = div(const_1, (inv_sqrt(mul(avg_val, invpool_fp))));
+ dst[r * h_dst * w_dst + h * w_dst + w] = res.raw();
+ }
+ }
+ }
+ }
+ }
+
+ return dst;
+}
+
+template <>
+SimpleTensor<uint8_t> pooling_layer<uint8_t>(const SimpleTensor<uint8_t> &src, PoolingLayerInfo info)
+{
+ SimpleTensor<float> src_tmp = convert_from_asymmetric(src);
+ SimpleTensor<float> dst_tmp = pooling_layer<float>(src_tmp, info);
+ SimpleTensor<uint8_t> dst = convert_to_asymmetric(dst_tmp, src.quantization_info());
+ return dst;
+}
+
+template SimpleTensor<float> pooling_layer(const SimpleTensor<float> &src, PoolingLayerInfo info);
+template SimpleTensor<half> pooling_layer(const SimpleTensor<half> &src, PoolingLayerInfo info);
+template SimpleTensor<qint8_t> pooling_layer(const SimpleTensor<qint8_t> &src, PoolingLayerInfo info);
+template SimpleTensor<qint16_t> pooling_layer(const SimpleTensor<qint16_t> &src, PoolingLayerInfo info);
+} // namespace reference
+} // namespace validation
+} // namespace test
+} // namespace arm_compute