| /* |
| * Copyright (c) 2022 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 "Pooling3dLayer.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.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_3d_layer_internal(const SimpleTensor<T> &src, const Pooling3dLayerInfo &pool3d_info, SimpleTensor<uint32_t> *indices) |
| { |
| TensorShape pooled_shape = compute_pool3d_shape(src.shape(), pool3d_info); |
| SimpleTensor<T> dst{ pooled_shape, src.data_type(), 1 }; |
| |
| if(indices != nullptr) |
| { |
| *indices = SimpleTensor<uint32_t> { pooled_shape, DataType::U32, 1 }; |
| } |
| |
| const int idx_channel = 0; |
| const int idx_width = 1; |
| const int idx_height = 2; |
| const int idx_depth = 3; |
| const int idx_batch = 4; |
| |
| const int pool_size_width = pool3d_info.is_global_pooling ? src.shape()[idx_width] : pool3d_info.pool_size.width; |
| const int pool_size_height = pool3d_info.is_global_pooling ? src.shape()[idx_height] : pool3d_info.pool_size.height; |
| const int pool_size_depth = pool3d_info.is_global_pooling ? src.shape()[idx_depth] : pool3d_info.pool_size.depth; |
| |
| const int pool_stride_width = static_cast<int>(pool3d_info.stride.width); |
| const int pool_stride_height = static_cast<int>(pool3d_info.stride.height); |
| const int pool_stride_depth = static_cast<int>(pool3d_info.stride.depth); |
| |
| const int pad_left = static_cast<int>(pool3d_info.padding.left); |
| const int pad_top = static_cast<int>(pool3d_info.padding.top); |
| const int pad_front = static_cast<int>(pool3d_info.padding.front); |
| |
| const int pad_right = static_cast<int>(pool3d_info.padding.right); |
| const int pad_bottom = static_cast<int>(pool3d_info.padding.bottom); |
| const int pad_back = static_cast<int>(pool3d_info.padding.back); |
| |
| const int num_channels = static_cast<int>(src.shape()[idx_channel]); |
| const int num_batches = static_cast<int>(src.shape()[idx_batch]); |
| |
| ARM_COMPUTE_ERROR_ON(num_channels != static_cast<int>(dst.shape()[idx_channel])); |
| ARM_COMPUTE_ERROR_ON(num_batches != static_cast<int>(dst.shape()[idx_batch])); |
| |
| const int w_src = static_cast<int>(src.shape()[idx_width]); |
| const int h_src = static_cast<int>(src.shape()[idx_height]); |
| const int d_src = static_cast<int>(src.shape()[idx_depth]); |
| const int w_dst = static_cast<int>(dst.shape()[idx_width]); |
| const int h_dst = static_cast<int>(dst.shape()[idx_height]); |
| const int d_dst = static_cast<int>(dst.shape()[idx_depth]); |
| |
| const bool exclude_padding = pool3d_info.exclude_padding; |
| |
| const int height_stride_src = num_channels * w_src; |
| const int depth_stride_src = height_stride_src * h_src; |
| const int batch_stride_src = depth_stride_src * d_src; |
| const int height_stride_dst = num_channels * w_dst; |
| const int depth_stride_dst = height_stride_dst * h_dst; |
| const int batch_stride_dst = depth_stride_dst * d_dst; |
| |
| for(int b = 0; b < num_batches; ++b) |
| { |
| const int batch_offset_dst = b * batch_stride_dst; |
| const int batch_offset_src = b * batch_stride_src; |
| for(int c = 0; c < num_channels; ++c) |
| { |
| for(int d = 0; d < d_dst; ++d) |
| { |
| const int depth_offset_dst = d * depth_stride_dst; |
| for(int h = 0; h < h_dst; ++h) |
| { |
| const int height_offset_dst = h * height_stride_dst; |
| for(int w = 0; w < w_dst; ++w) |
| { |
| int wstart = w * pool_stride_width - pad_left; |
| int hstart = h * pool_stride_height - pad_top; |
| int dstart = d * pool_stride_depth - pad_front; |
| int wend = std::min(wstart + pool_size_width, w_src + pad_right); |
| int hend = std::min(hstart + pool_size_height, h_src + pad_bottom); |
| int dend = std::min(dstart + pool_size_depth, d_src + pad_back); |
| |
| // this may not be equal to pool_w * pool_h * pool_d because of |
| // DimensionRoundingType choice (CEIL) |
| int pool_size = (dend - dstart) * (hend - hstart) * (wend - wstart); |
| |
| // limit [start, end) to [0, w_src) |
| wstart = std::max(wstart, 0); |
| hstart = std::max(hstart, 0); |
| dstart = std::max(dstart, 0); |
| wend = std::min(wend, w_src); |
| hend = std::min(hend, h_src); |
| dend = std::min(dend, d_src); |
| |
| auto max_val = -std::numeric_limits<T>::infinity(); |
| int max_index{ 0 }; |
| T avg_val = static_cast<T>(0.f); |
| T l2_val = static_cast<T>(0.f); |
| |
| if(exclude_padding) |
| { |
| pool_size = (dend - dstart) * (hend - hstart) * (wend - wstart); |
| } |
| |
| for(int z = dstart; z < dend; ++z) |
| { |
| const int depth_offset_src = z * depth_stride_src; |
| for(int y = hstart; y < hend; ++y) |
| { |
| const int height_offset_src = y * height_stride_src; |
| for(int x = wstart; x < wend; ++x) |
| { |
| const auto val = static_cast<T>( |
| src[batch_offset_src + depth_offset_src + height_offset_src + x * num_channels + c]); |
| if(val > max_val) |
| { |
| max_val = val; |
| max_index = coord2index(src.shape(), Coordinates(c, x, y, z, 0)); |
| } |
| |
| avg_val += val; |
| l2_val += val * val; |
| } |
| } |
| } |
| |
| avg_val /= pool_size; |
| l2_val = static_cast<T>(std::sqrt(l2_val / pool_size)); |
| |
| int dst_index = batch_offset_dst + depth_offset_dst + height_offset_dst + w * num_channels + c; |
| switch(pool3d_info.pool_type) |
| { |
| case PoolingType::MAX: |
| dst[dst_index] = static_cast<T>(max_val); |
| break; |
| case PoolingType::AVG: |
| dst[dst_index] = static_cast<T>(avg_val); |
| break; |
| case PoolingType::L2: |
| dst[dst_index] = static_cast<T>(l2_val); |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Pooling Type should be either MAX, AVG or L2"); |
| } |
| |
| if(indices != nullptr) |
| { |
| (*indices)[dst_index] = max_index; |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| return dst; |
| } |
| |
| template SimpleTensor<float> pooling_3d_layer(const SimpleTensor<float> &src, const Pooling3dLayerInfo &pool3d_info, const QuantizationInfo &output_qinfo, SimpleTensor<uint32_t> *indices); |
| template SimpleTensor<half> pooling_3d_layer(const SimpleTensor<half> &src, const Pooling3dLayerInfo &pool3d_info, const QuantizationInfo &output_qinfo, SimpleTensor<uint32_t> *indices); |
| |
| template <typename T> |
| SimpleTensor<T> pooling_3d_layer(const SimpleTensor<T> &src, const Pooling3dLayerInfo &pool3d_info, const QuantizationInfo &output_qinfo, SimpleTensor<uint32_t> *indices) |
| { |
| ARM_COMPUTE_UNUSED(output_qinfo); |
| return pooling_3d_layer_internal<T>(src, pool3d_info, indices); |
| } |
| |
| template <> |
| SimpleTensor<int8_t> pooling_3d_layer<int8_t>(const SimpleTensor<int8_t> &src, const Pooling3dLayerInfo &pool3d_info, const QuantizationInfo &output_qinfo, SimpleTensor<uint32_t> *indices) |
| { |
| SimpleTensor<float> src_tmp = convert_from_asymmetric(src); |
| SimpleTensor<float> dst_tmp = pooling_3d_layer_internal<float>(src_tmp, pool3d_info, indices); |
| return convert_to_asymmetric<int8_t>(dst_tmp, output_qinfo); |
| } |
| |
| template <> |
| SimpleTensor<uint8_t> pooling_3d_layer<uint8_t>(const SimpleTensor<uint8_t> &src, const Pooling3dLayerInfo &pool3d_info, const QuantizationInfo &output_qinfo, SimpleTensor<uint32_t> *indices) |
| { |
| SimpleTensor<float> src_tmp = convert_from_asymmetric(src); |
| SimpleTensor<float> dst_tmp = pooling_3d_layer_internal<float>(src_tmp, pool3d_info, indices); |
| return convert_to_asymmetric<uint8_t>(dst_tmp, output_qinfo); |
| } |
| |
| } // namespace reference |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |