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author | Georgios Pinitas <georgios.pinitas@arm.com> | 2021-06-25 06:00:17 +0100 |
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committer | Georgios Pinitas <georgios.pinitas@arm.com> | 2021-06-25 13:39:14 +0000 |
commit | cd060c47c1bad06f2aad8f0f8f94a72c4f75b919 (patch) | |
tree | 2dfa26ae13ac8f24f2a9b0f3560f4cf1fe0ed714 /src/core/cpu/kernels/pool2d/neon/fp32.cpp | |
parent | 680705c24eec90273f70fc79c584d69a3e8f89e1 (diff) | |
download | ComputeLibrary-cd060c47c1bad06f2aad8f0f8f94a72c4f75b919.tar.gz |
Rename pooling implementation folder to pool2d
Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Change-Id: I82a298e059a92d82095186d120e4934a8eeb4e3e
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5854
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'src/core/cpu/kernels/pool2d/neon/fp32.cpp')
-rw-r--r-- | src/core/cpu/kernels/pool2d/neon/fp32.cpp | 314 |
1 files changed, 314 insertions, 0 deletions
diff --git a/src/core/cpu/kernels/pool2d/neon/fp32.cpp b/src/core/cpu/kernels/pool2d/neon/fp32.cpp new file mode 100644 index 0000000000..c82cad0ffd --- /dev/null +++ b/src/core/cpu/kernels/pool2d/neon/fp32.cpp @@ -0,0 +1,314 @@ +/* + * Copyright (c) 2021 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 "arm_compute/core/Helpers.h" +#include "arm_compute/core/ITensor.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/core/utils/misc/Traits.h" +#include "src/core/NEON/wrapper/intrinsics/intrinsics.h" +#include "src/core/cpu/kernels/pool2d/neon/list.h" +#include "src/core/helpers/WindowHelpers.h" + +namespace arm_compute +{ +namespace cpu +{ +namespace +{ +void pooling2_f32_maxpool_indices(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window) +{ + const int window_start_x = window.x().start(); + const int window_end_x = window.x().end(); + const int window_step_x = 4; + + Window window_out = window; + window_out.set(Window::DimX, Window::Dimension(0, 1, 1)); + + Iterator in(src, window_src); + Iterator out(dst0, window_out); + Iterator indices(dst1, window_out); + + const int pool_pad_top = pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = pool_info.pad_stride_info.pad_left(); + + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride(); + + float32x4_t vres; + float res; + + const int pad_right = src->info()->padding().right; + const int pad_left = src->info()->padding().left; + const int pad_horizontal = pad_right + pad_left; + const int in_stride_y = static_cast<int>(src->info()->strides_in_bytes().y()); + const int in_stride_z = static_cast<int>(src->info()->strides_in_bytes().z()); + + execute_window_loop(window_out, [&](const Coordinates & id) + { + const int idx_width = id.y() * pool_stride_x; + const int idx_height = id.z() * pool_stride_y; + const int pool_limit_y = pool_pad_top - idx_height; + const int pool_limit_x = pool_pad_left - idx_width; + + const int pool_start_y = std::max(0, window_src.z().start() + pool_limit_y); + const int pool_start_x = std::max(0, window_src.y().start() + pool_limit_x); + + const int in_x0_offset = (pool_start_x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (pool_start_y - pool_pad_top) * static_cast<int>(src->info()->strides_in_bytes().z()); + const int in_x1_offset = (pool_start_x + 1 - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (pool_start_y - pool_pad_top) * static_cast<int> + (src->info()->strides_in_bytes().z()); + const int in_x2_offset = (pool_start_x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (pool_start_y + 1 - pool_pad_top) * static_cast<int> + (src->info()->strides_in_bytes().z()); + const int in_x3_offset = (pool_start_x + 1 - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (pool_start_y + 1 - pool_pad_top) * static_cast<int> + (src->info()->strides_in_bytes().z()); + + int x_off = window_start_x; + for(; x_off <= (window_end_x - window_step_x); x_off += window_step_x) + { + const auto in_x0_ptr = reinterpret_cast<const float *>(in.ptr() + in_x0_offset); + const auto in_x1_ptr = reinterpret_cast<const float *>(in.ptr() + in_x1_offset); + const auto in_x2_ptr = reinterpret_cast<const float *>(in.ptr() + in_x2_offset); + const auto in_x3_ptr = reinterpret_cast<const float *>(in.ptr() + in_x3_offset); + const auto v_x0 = vld1q_f32(in_x0_ptr + x_off); + const auto v_x1 = vld1q_f32(in_x1_ptr + x_off); + const auto v_x2 = vld1q_f32(in_x2_ptr + x_off); + const auto v_x3 = vld1q_f32(in_x3_ptr + x_off); + vres = vmaxq_f32(vmaxq_f32(v_x2, v_x3), vmaxq_f32(v_x0, v_x1)); + // Store result + vst1q_f32(reinterpret_cast<float *>(out.ptr()) + x_off, vres); + + const uint32_t offset_base = offset_no_padding<float>(in.offset(), id, *src->info(), pool_stride_x, pool_stride_y, DataLayout::NHWC); + const uint32_t offset_x0 = (uint32_t)offset_base / sizeof(float) + x_off; + const uint32_t offset_x1 = (uint32_t)offset_x0 + in_stride_y / sizeof(float) - pad_horizontal; + const uint32_t offset_x2 = (uint32_t)offset_x0 + in_stride_z / sizeof(float) - pad_horizontal * src->info()->tensor_shape()[1]; + const uint32_t offset_x3 = (uint32_t)offset_x2 + in_stride_y / sizeof(float) - pad_horizontal; + const uint32x4_t voffset_x0 = { offset_x0, offset_x0 + 1, offset_x0 + 2, offset_x0 + 3 }; + const uint32x4_t voffset_x1 = { offset_x1, offset_x1 + 1, offset_x1 + 2, offset_x1 + 3 }; + const uint32x4_t voffset_x2 = { offset_x2, offset_x2 + 1, offset_x2 + 2, offset_x2 + 3 }; + const uint32x4_t voffset_x3 = { offset_x3, offset_x3 + 1, offset_x3 + 2, offset_x3 + 3 }; + const uint32x4_t tmp_indices0 = vbslq_u32(vcgeq_f32(v_x0, v_x1), voffset_x0, voffset_x1); + const uint32x4_t tmp_indices1 = vbslq_u32(vcgeq_f32(v_x2, v_x3), voffset_x2, voffset_x3); + const uint32x4_t tmp_indices2 = vbslq_u32(vcgeq_f32(vmaxq_f32(v_x0, v_x1), vmaxq_f32(v_x2, v_x3)), tmp_indices0, tmp_indices1); + + // Store indices + vst1q_u32(reinterpret_cast<uint32_t *>(indices.ptr()) + x_off, tmp_indices2); + } + + // Left-overs loop + for(; x_off < window_end_x; ++x_off) + { + const auto x0 = *(reinterpret_cast<const float *>(in.ptr() + in_x0_offset) + x_off); + const auto x1 = *(reinterpret_cast<const float *>(in.ptr() + in_x1_offset) + x_off); + const auto x2 = *(reinterpret_cast<const float *>(in.ptr() + in_x2_offset) + x_off); + const auto x3 = *(reinterpret_cast<const float *>(in.ptr() + in_x3_offset) + x_off); + res = std::max(std::max(x2, x3), std::max(x0, x1)); + + // Store result + *(reinterpret_cast<float *>(out.ptr()) + x_off) = res; + + const uint32_t offset_base = offset_no_padding<float>(in.offset(), id, *src->info(), pool_stride_x, pool_stride_y, DataLayout::NHWC); + const uint32_t offset_x0 = (uint32_t)offset_base / sizeof(float) + x_off; + const uint32_t offset_x1 = (uint32_t)offset_x0 + in_stride_y / sizeof(float) - pad_horizontal; + const uint32_t offset_x2 = (uint32_t)offset_x0 + in_stride_z / sizeof(float) - pad_horizontal * src->info()->tensor_shape()[1]; + const uint32_t offset_x3 = (uint32_t)offset_x2 + in_stride_y / sizeof(float) - pad_horizontal; + const uint32_t tmp_idx0 = (x0 >= x1) ? offset_x0 : offset_x1; + const uint32_t tmp_idx1 = (x2 >= x3) ? offset_x2 : offset_x3; + const uint32_t tmp_idx2 = (std::max(x0, x1) >= std::max(x2, x3)) ? tmp_idx0 : tmp_idx1; + + // Store indices + *(reinterpret_cast<uint32_t *>(indices.ptr()) + x_off) = tmp_idx2; + } + }, + in, out, indices); +} +} + +void poolingMxN_fp32_neon_nhwc(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window) +{ + if(pool_info.pool_size == Size2D(2, 2) && pool_info.pool_type == PoolingType::MAX && dst1) + { + pooling2_f32_maxpool_indices(src, dst0, dst1, pool_info, window_src, window); + } + else + { + const int window_start_x = window.x().start(); + const int window_end_x = window.x().end(); + const int window_step_x = 4; + + Window window_out = window; + window_out.set(Window::DimX, Window::Dimension(0, 1, 1)); + + Iterator in(src, window_src); + Iterator out(dst0, window_out); + + const int pool_size_x = pool_info.is_global_pooling ? src->info()->tensor_shape().y() : pool_info.pool_size.width; + const int pool_size_y = pool_info.is_global_pooling ? src->info()->tensor_shape().z() : pool_info.pool_size.height; + const int pool_pad_right = pool_info.pad_stride_info.pad_right(); + const int pool_pad_top = pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = pool_info.pad_stride_info.pad_left(); + const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom(); + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride(); + const int upper_bound_w = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_right); + const int upper_bound_h = src->info()->dimension(2) + (pool_info.exclude_padding ? 0 : pool_pad_bottom); + + float32x4_t vres; + + execute_window_loop(window_out, [&](const Coordinates & id) + { + const int idx_width = id.y() * pool_stride_x; + const int idx_height = id.z() * pool_stride_y; + const int pool_limit_y = pool_pad_top - idx_height; + const int pool_limit_x = pool_pad_left - idx_width; + + const int pool_start_y = std::max(0, window_src.z().start() + pool_limit_y); + const int pool_end_y = std::min(pool_size_y, window_src.z().end() + pool_limit_y); + const int pool_start_x = std::max(0, window_src.y().start() + pool_limit_x); + const int pool_end_x = std::min(pool_size_x, window_src.y().end() + pool_limit_x); + + int x_off = window_start_x; + for(; x_off <= (window_end_x - window_step_x); x_off += window_step_x) + { + if(pool_info.pool_type != PoolingType::MAX) + { + // Calculate scale + const float scale = calculate_avg_scale(pool_info.exclude_padding, DataLayout::NHWC, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, + pool_stride_y); + const float32x4_t scale_v = vdupq_n_f32(scale); + + // Perform pooling + vres = vdupq_n_f32(0.0f); + + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const float32x4_t data = vld1q_f32(reinterpret_cast<const float *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> + (src->info()->strides_in_bytes().z())) + x_off); + + // Get power of 2 in case of l2 pooling and accumulate + if(pool_info.pool_type == PoolingType::L2) + { + vres = vmlaq_f32(vres, data, data); + } + else + { + vres = vaddq_f32(vres, data); + } + } + } + // Divide by scale + vres = vmulq_f32(vres, scale_v); + } + else + { + vres = vdupq_n_f32(std::numeric_limits<float>::lowest()); + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const float32x4_t data = vld1q_f32(reinterpret_cast<const float *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> + (src->info()->strides_in_bytes().z())) + x_off); + vres = vmaxq_f32(vres, data); + } + } + } + + // Calculate square-root in case of l2 pooling + if(pool_info.pool_type == PoolingType::L2) + { + float32x4_t l2_res = { static_cast<float>(sqrt(vgetq_lane_f32(vres, 0))), + static_cast<float>(sqrt(vgetq_lane_f32(vres, 1))), + static_cast<float>(sqrt(vgetq_lane_f32(vres, 2))), + static_cast<float>(sqrt(vgetq_lane_f32(vres, 3))) + }; + vres = l2_res; + } + + // Store result + vst1q_f32(reinterpret_cast<float *>(out.ptr()) + x_off, vres); + } + + // Left-overs loop + for(; x_off < window_end_x; ++x_off) + { + float res = 0.0f; + + if(pool_info.pool_type != PoolingType::MAX) + { + // Calculate scale + const float scale = calculate_avg_scale(pool_info.exclude_padding, DataLayout::NHWC, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, + pool_stride_y); + + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const float data = *(reinterpret_cast<const float *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> + (src->info()->strides_in_bytes().z())) + x_off); + + // Get power of 2 in case of l2 pooling and accumulate + if(pool_info.pool_type == PoolingType::L2) + { + res += data * data; + } + else + { + res += data; + } + } + } + + // Divide by scale + res *= scale; + } + else + { + res = std::numeric_limits<float>::lowest(); + for(int y = pool_start_y; y < pool_end_y; ++y) + { + for(int x = pool_start_x; x < pool_end_x; ++x) + { + const float data = *(reinterpret_cast<const float *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int> + (src->info()->strides_in_bytes().z())) + x_off); + res = std::max(res, data); + } + } + } + + // Calculate square-root in case of l2 pooling + if(pool_info.pool_type == PoolingType::L2) + { + res = std::sqrt(res); + } + + // Store result + *(reinterpret_cast<float *>(out.ptr()) + x_off) = res; + } + }, + in, out); + } +} +} // namespace cpu +} // namespace arm_compute
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