Gunes Bayir | 918a9fb | 2022-02-15 11:40:13 +0000 | [diff] [blame] | 1 | /* |
| 2 | * Copyright (c) 2022 Arm Limited. |
| 3 | * |
| 4 | * SPDX-License-Identifier: MIT |
| 5 | * |
| 6 | * Permission is hereby granted, free of charge, to any person obtaining a copy |
| 7 | * of this software and associated documentation files (the "Software"), to |
| 8 | * deal in the Software without restriction, including without limitation the |
| 9 | * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| 10 | * sell copies of the Software, and to permit persons to whom the Software is |
| 11 | * furnished to do so, subject to the following conditions: |
| 12 | * |
| 13 | * The above copyright notice and this permission notice shall be included in all |
| 14 | * copies or substantial portions of the Software. |
| 15 | * |
| 16 | * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| 17 | * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| 18 | * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| 19 | * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| 20 | * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| 21 | * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| 22 | * SOFTWARE. |
| 23 | */ |
| 24 | |
ramelg01 | 3751569 | 2022-02-26 22:06:20 +0000 | [diff] [blame] | 25 | #include "Pooling3dLayer.h" |
Gunes Bayir | 918a9fb | 2022-02-15 11:40:13 +0000 | [diff] [blame] | 26 | #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| 27 | #include "tests/validation/Helpers.h" |
| 28 | |
| 29 | namespace arm_compute |
| 30 | { |
| 31 | namespace test |
| 32 | { |
| 33 | namespace validation |
| 34 | { |
| 35 | namespace reference |
| 36 | { |
| 37 | using namespace arm_compute::misc::shape_calculator; |
| 38 | |
| 39 | template <typename T> |
ramelg01 | 3751569 | 2022-02-26 22:06:20 +0000 | [diff] [blame] | 40 | SimpleTensor<T> pooling_3d_layer_internal(const SimpleTensor<T> &src, const Pooling3dLayerInfo &pool3d_info, SimpleTensor<uint32_t> *indices) |
Gunes Bayir | 918a9fb | 2022-02-15 11:40:13 +0000 | [diff] [blame] | 41 | { |
| 42 | TensorShape pooled_shape = compute_pool3d_shape(src.shape(), pool3d_info); |
| 43 | SimpleTensor<T> dst{ pooled_shape, src.data_type(), 1 }; |
| 44 | |
| 45 | if(indices != nullptr) |
| 46 | { |
| 47 | *indices = SimpleTensor<uint32_t> { pooled_shape, DataType::U32, 1 }; |
| 48 | } |
| 49 | |
| 50 | const int idx_channel = 0; |
| 51 | const int idx_width = 1; |
| 52 | const int idx_height = 2; |
| 53 | const int idx_depth = 3; |
| 54 | const int idx_batch = 4; |
| 55 | |
| 56 | const int pool_size_width = pool3d_info.is_global_pooling ? src.shape()[idx_width] : pool3d_info.pool_size.width; |
| 57 | const int pool_size_height = pool3d_info.is_global_pooling ? src.shape()[idx_height] : pool3d_info.pool_size.height; |
| 58 | const int pool_size_depth = pool3d_info.is_global_pooling ? src.shape()[idx_depth] : pool3d_info.pool_size.depth; |
| 59 | |
ramelg01 | 3751569 | 2022-02-26 22:06:20 +0000 | [diff] [blame] | 60 | const int pool_stride_width = static_cast<int>(pool3d_info.stride.width); |
| 61 | const int pool_stride_height = static_cast<int>(pool3d_info.stride.height); |
| 62 | const int pool_stride_depth = static_cast<int>(pool3d_info.stride.depth); |
Gunes Bayir | 918a9fb | 2022-02-15 11:40:13 +0000 | [diff] [blame] | 63 | |
| 64 | const int pad_left = static_cast<int>(pool3d_info.padding.left); |
| 65 | const int pad_top = static_cast<int>(pool3d_info.padding.top); |
| 66 | const int pad_front = static_cast<int>(pool3d_info.padding.front); |
| 67 | |
| 68 | const int pad_right = static_cast<int>(pool3d_info.padding.right); |
| 69 | const int pad_bottom = static_cast<int>(pool3d_info.padding.bottom); |
| 70 | const int pad_back = static_cast<int>(pool3d_info.padding.back); |
| 71 | |
| 72 | const int num_channels = static_cast<int>(src.shape()[idx_channel]); |
| 73 | const int num_batches = static_cast<int>(src.shape()[idx_batch]); |
| 74 | |
| 75 | ARM_COMPUTE_ERROR_ON(num_channels != static_cast<int>(dst.shape()[idx_channel])); |
| 76 | ARM_COMPUTE_ERROR_ON(num_batches != static_cast<int>(dst.shape()[idx_batch])); |
| 77 | |
| 78 | const int w_src = static_cast<int>(src.shape()[idx_width]); |
| 79 | const int h_src = static_cast<int>(src.shape()[idx_height]); |
| 80 | const int d_src = static_cast<int>(src.shape()[idx_depth]); |
| 81 | const int w_dst = static_cast<int>(dst.shape()[idx_width]); |
| 82 | const int h_dst = static_cast<int>(dst.shape()[idx_height]); |
| 83 | const int d_dst = static_cast<int>(dst.shape()[idx_depth]); |
| 84 | |
| 85 | const bool exclude_padding = pool3d_info.exclude_padding; |
| 86 | |
| 87 | const int height_stride_src = num_channels * w_src; |
| 88 | const int depth_stride_src = height_stride_src * h_src; |
| 89 | const int batch_stride_src = depth_stride_src * d_src; |
| 90 | const int height_stride_dst = num_channels * w_dst; |
| 91 | const int depth_stride_dst = height_stride_dst * h_dst; |
| 92 | const int batch_stride_dst = depth_stride_dst * d_dst; |
| 93 | |
| 94 | for(int b = 0; b < num_batches; ++b) |
| 95 | { |
| 96 | const int batch_offset_dst = b * batch_stride_dst; |
| 97 | const int batch_offset_src = b * batch_stride_src; |
| 98 | for(int c = 0; c < num_channels; ++c) |
| 99 | { |
| 100 | for(int d = 0; d < d_dst; ++d) |
| 101 | { |
| 102 | const int depth_offset_dst = d * depth_stride_dst; |
| 103 | for(int h = 0; h < h_dst; ++h) |
| 104 | { |
| 105 | const int height_offset_dst = h * height_stride_dst; |
| 106 | for(int w = 0; w < w_dst; ++w) |
| 107 | { |
| 108 | int wstart = w * pool_stride_width - pad_left; |
| 109 | int hstart = h * pool_stride_height - pad_top; |
| 110 | int dstart = d * pool_stride_depth - pad_front; |
| 111 | int wend = std::min(wstart + pool_size_width, w_src + pad_right); |
| 112 | int hend = std::min(hstart + pool_size_height, h_src + pad_bottom); |
| 113 | int dend = std::min(dstart + pool_size_depth, d_src + pad_back); |
| 114 | |
| 115 | // this may not be equal to pool_w * pool_h * pool_d because of |
| 116 | // DimensionRoundingType choice (CEIL) |
| 117 | int pool_size = (dend - dstart) * (hend - hstart) * (wend - wstart); |
| 118 | |
| 119 | // limit [start, end) to [0, w_src) |
| 120 | wstart = std::max(wstart, 0); |
| 121 | hstart = std::max(hstart, 0); |
| 122 | dstart = std::max(dstart, 0); |
| 123 | wend = std::min(wend, w_src); |
| 124 | hend = std::min(hend, h_src); |
| 125 | dend = std::min(dend, d_src); |
| 126 | |
| 127 | auto max_val = -std::numeric_limits<T>::infinity(); |
| 128 | int max_index{ 0 }; |
| 129 | T avg_val = static_cast<T>(0.f); |
| 130 | T l2_val = static_cast<T>(0.f); |
| 131 | |
| 132 | if(exclude_padding) |
| 133 | { |
| 134 | pool_size = (dend - dstart) * (hend - hstart) * (wend - wstart); |
| 135 | } |
| 136 | |
| 137 | for(int z = dstart; z < dend; ++z) |
| 138 | { |
| 139 | const int depth_offset_src = z * depth_stride_src; |
| 140 | for(int y = hstart; y < hend; ++y) |
| 141 | { |
| 142 | const int height_offset_src = y * height_stride_src; |
| 143 | for(int x = wstart; x < wend; ++x) |
| 144 | { |
| 145 | const auto val = static_cast<T>( |
| 146 | src[batch_offset_src + depth_offset_src + height_offset_src + x * num_channels + c]); |
Gunes Bayir | 918a9fb | 2022-02-15 11:40:13 +0000 | [diff] [blame] | 147 | if(val > max_val) |
| 148 | { |
| 149 | max_val = val; |
| 150 | max_index = coord2index(src.shape(), Coordinates(c, x, y, z, 0)); |
| 151 | } |
| 152 | |
| 153 | avg_val += val; |
| 154 | l2_val += val * val; |
| 155 | } |
| 156 | } |
| 157 | } |
| 158 | |
| 159 | avg_val /= pool_size; |
| 160 | l2_val = static_cast<T>(std::sqrt(l2_val / pool_size)); |
| 161 | |
| 162 | int dst_index = batch_offset_dst + depth_offset_dst + height_offset_dst + w * num_channels + c; |
| 163 | switch(pool3d_info.pool_type) |
| 164 | { |
| 165 | case PoolingType::MAX: |
| 166 | dst[dst_index] = static_cast<T>(max_val); |
| 167 | break; |
| 168 | case PoolingType::AVG: |
| 169 | dst[dst_index] = static_cast<T>(avg_val); |
| 170 | break; |
| 171 | case PoolingType::L2: |
| 172 | dst[dst_index] = static_cast<T>(l2_val); |
| 173 | break; |
| 174 | default: |
| 175 | ARM_COMPUTE_ERROR("Pooling Type should be either MAX, AVG or L2"); |
| 176 | } |
| 177 | |
| 178 | if(indices != nullptr) |
| 179 | { |
| 180 | (*indices)[dst_index] = max_index; |
| 181 | } |
| 182 | } |
| 183 | } |
| 184 | } |
| 185 | } |
| 186 | } |
| 187 | |
| 188 | return dst; |
| 189 | } |
| 190 | |
ramelg01 | 3751569 | 2022-02-26 22:06:20 +0000 | [diff] [blame] | 191 | template SimpleTensor<float> pooling_3d_layer(const SimpleTensor<float> &src, const Pooling3dLayerInfo &pool3d_info, const QuantizationInfo &output_qinfo, SimpleTensor<uint32_t> *indices); |
| 192 | template SimpleTensor<half> pooling_3d_layer(const SimpleTensor<half> &src, const Pooling3dLayerInfo &pool3d_info, const QuantizationInfo &output_qinfo, SimpleTensor<uint32_t> *indices); |
Gunes Bayir | 918a9fb | 2022-02-15 11:40:13 +0000 | [diff] [blame] | 193 | |
| 194 | template <typename T> |
ramelg01 | 3751569 | 2022-02-26 22:06:20 +0000 | [diff] [blame] | 195 | SimpleTensor<T> pooling_3d_layer(const SimpleTensor<T> &src, const Pooling3dLayerInfo &pool3d_info, const QuantizationInfo &output_qinfo, SimpleTensor<uint32_t> *indices) |
Gunes Bayir | 918a9fb | 2022-02-15 11:40:13 +0000 | [diff] [blame] | 196 | { |
| 197 | ARM_COMPUTE_UNUSED(output_qinfo); |
ramelg01 | 3751569 | 2022-02-26 22:06:20 +0000 | [diff] [blame] | 198 | return pooling_3d_layer_internal<T>(src, pool3d_info, indices); |
Gunes Bayir | 918a9fb | 2022-02-15 11:40:13 +0000 | [diff] [blame] | 199 | } |
| 200 | |
| 201 | template <> |
ramelg01 | 3751569 | 2022-02-26 22:06:20 +0000 | [diff] [blame] | 202 | 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) |
Gunes Bayir | 918a9fb | 2022-02-15 11:40:13 +0000 | [diff] [blame] | 203 | { |
| 204 | SimpleTensor<float> src_tmp = convert_from_asymmetric(src); |
ramelg01 | 3751569 | 2022-02-26 22:06:20 +0000 | [diff] [blame] | 205 | SimpleTensor<float> dst_tmp = pooling_3d_layer_internal<float>(src_tmp, pool3d_info, indices); |
Gunes Bayir | 918a9fb | 2022-02-15 11:40:13 +0000 | [diff] [blame] | 206 | return convert_to_asymmetric<int8_t>(dst_tmp, output_qinfo); |
| 207 | } |
| 208 | |
| 209 | template <> |
ramelg01 | 3751569 | 2022-02-26 22:06:20 +0000 | [diff] [blame] | 210 | 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) |
Gunes Bayir | 918a9fb | 2022-02-15 11:40:13 +0000 | [diff] [blame] | 211 | { |
| 212 | SimpleTensor<float> src_tmp = convert_from_asymmetric(src); |
ramelg01 | 3751569 | 2022-02-26 22:06:20 +0000 | [diff] [blame] | 213 | SimpleTensor<float> dst_tmp = pooling_3d_layer_internal<float>(src_tmp, pool3d_info, indices); |
Gunes Bayir | 918a9fb | 2022-02-15 11:40:13 +0000 | [diff] [blame] | 214 | return convert_to_asymmetric<uint8_t>(dst_tmp, output_qinfo); |
| 215 | } |
| 216 | |
| 217 | } // namespace reference |
| 218 | } // namespace validation |
| 219 | } // namespace test |
ramelg01 | 3751569 | 2022-02-26 22:06:20 +0000 | [diff] [blame] | 220 | } // namespace arm_compute |