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diff --git a/src/core/cpu/kernels/pooling/neon/fp32.cpp b/src/core/cpu/kernels/pooling/neon/fp32.cpp
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+++ b/src/core/cpu/kernels/pooling/neon/fp32.cpp
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+/*
+ * 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/pooling/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 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);
+ 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_right;
+ const uint32_t offset_x2 = (uint32_t)offset_x0 + in_stride_z / sizeof(float) - pad_right * src->info()->tensor_shape()[1];
+ const uint32_t offset_x3 = (uint32_t)offset_x2 + in_stride_y / sizeof(float) - pad_right;
+ 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);
+ 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_right;
+ const uint32_t offset_x2 = (uint32_t)offset_x0 + in_stride_z / sizeof(float) - pad_right * src->info()->tensor_shape()[1];
+ const uint32_t offset_x3 = (uint32_t)offset_x2 + in_stride_y / sizeof(float) - pad_right;
+ 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 \ No newline at end of file
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