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Diffstat (limited to 'tests/validation/reference/Pooling3dLayer.cpp')
| -rw-r--r-- | tests/validation/reference/Pooling3dLayer.cpp | 220 |
1 files changed, 220 insertions, 0 deletions
diff --git a/tests/validation/reference/Pooling3dLayer.cpp b/tests/validation/reference/Pooling3dLayer.cpp new file mode 100644 index 0000000000..2e8f3a0b92 --- /dev/null +++ b/tests/validation/reference/Pooling3dLayer.cpp @@ -0,0 +1,220 @@ +/* + * 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 |