| /* |
| * Copyright (c) 2017-2018 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 "PoolingLayer.h" |
| |
| #include "Permute.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "tests/validation/FixedPoint.h" |
| #include "tests/validation/Helpers.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| namespace reference |
| { |
| using namespace arm_compute::misc::shape_calculator; |
| |
| template <typename T> |
| SimpleTensor<T> pooling_layer_nchw(const SimpleTensor<T> &src, SimpleTensor<T> &dst, const PoolingLayerInfo &info) |
| { |
| ARM_COMPUTE_ERROR_ON(info.is_global_pooling() && (src.shape().x() != src.shape().y())); |
| |
| const int pool_size_x = info.is_global_pooling() ? src.shape().x() : info.pool_size().width; |
| const int pool_size_y = info.is_global_pooling() ? src.shape().y() : info.pool_size().height; |
| 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_left = info.pad_stride_info().pad_left(); |
| int pad_top = info.pad_stride_info().pad_top(); |
| int pad_right = info.pad_stride_info().pad_right(); |
| int pad_bottom = info.pad_stride_info().pad_bottom(); |
| 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); |
| |
| 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_left; |
| int hstart = h * pool_stride_y - pad_top; |
| int wend = std::min(wstart + pool_size_x, w_src); |
| int hend = std::min(hstart + pool_size_y, 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_left; |
| int hstart = h * pool_stride_y - pad_top; |
| int wend = std::min(wstart + pool_size_x, w_src + pad_right); |
| int hend = std::min(hstart + pool_size_y, h_src + pad_bottom); |
| 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, const PoolingLayerInfo &info) |
| { |
| ARM_COMPUTE_ERROR_ON(info.is_global_pooling() && (src.shape().x() != src.shape().y())); |
| |
| 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); |
| |
| const int pool_size_x = info.is_global_pooling() ? src.shape().x() : info.pool_size().width; |
| const int pool_size_y = info.is_global_pooling() ? src.shape().y() : info.pool_size().height; |
| 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_left = info.pad_stride_info().pad_left(); |
| int pad_top = info.pad_stride_info().pad_top(); |
| int pad_right = info.pad_stride_info().pad_right(); |
| int pad_bottom = info.pad_stride_info().pad_bottom(); |
| bool exclude_padding = info.exclude_padding(); |
| |
| // Create reference |
| TensorInfo src_info(src.shape(), 1, src.data_type(), src.fixed_point_position()); |
| src_info.set_data_layout(src.data_layout()); |
| |
| SimpleTensor<T> dst{ compute_pool_shape(src_info, 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_left; |
| int hstart = h * pool_stride_y - pad_top; |
| int wend = std::min(wstart + pool_size_x, w_src); |
| int hend = std::min(hstart + pool_size_y, 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_left; |
| int hstart = h * pool_stride_y - pad_top; |
| int wend = std::min(wstart + pool_size_x, w_src + pad_right); |
| int hend = std::min(hstart + pool_size_y, h_src + pad_bottom); |
| 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, const 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 <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type> |
| SimpleTensor<T> pooling_layer(const SimpleTensor<T> &src, const PoolingLayerInfo &info) |
| { |
| TensorInfo src_info(src.shape(), 1, src.data_type(), src.fixed_point_position()); |
| src_info.set_data_layout(src.data_layout()); |
| |
| SimpleTensor<T> dst{ compute_pool_shape(src_info, info), src.data_type(), 1, src.fixed_point_position() }; |
| |
| if(src.data_layout() == DataLayout::NHWC) |
| { |
| SimpleTensor<T> src_nchw = reference::permute<T>(src, PermutationVector(1U, 2U, 0U)); |
| SimpleTensor<T> dst_nchw = reference::permute<T>(dst, PermutationVector(1U, 2U, 0U)); |
| |
| pooling_layer_nchw<T>(src_nchw, dst_nchw, info); |
| |
| return reference::permute<T>(dst_nchw, PermutationVector(2U, 0U, 1U)); |
| } |
| else |
| { |
| return pooling_layer_nchw<T>(src, dst, info); |
| } |
| } |
| |
| template SimpleTensor<float> pooling_layer(const SimpleTensor<float> &src, const PoolingLayerInfo &info); |
| template SimpleTensor<half> pooling_layer(const SimpleTensor<half> &src, const PoolingLayerInfo &info); |
| template SimpleTensor<qint8_t> pooling_layer(const SimpleTensor<qint8_t> &src, const PoolingLayerInfo &info); |
| template SimpleTensor<qint16_t> pooling_layer(const SimpleTensor<qint16_t> &src, const PoolingLayerInfo &info); |
| } // namespace reference |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |