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-rw-r--r--tests/validation/CPP/PoolingLayer.cpp302
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diff --git a/tests/validation/CPP/PoolingLayer.cpp b/tests/validation/CPP/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
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