Make NEON Pooling kernels and functions state-less
Partially resolves COMPMID-3999
Change-Id: Ib39d40694df5c5f0a9401488e0c3af3ac26e8c55
Signed-off-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/4984
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/core/cpu/kernels/CpuPoolingAssemblyWrapperKernel.cpp b/src/core/cpu/kernels/CpuPoolingAssemblyWrapperKernel.cpp
new file mode 100644
index 0000000..19a0e90
--- /dev/null
+++ b/src/core/cpu/kernels/CpuPoolingAssemblyWrapperKernel.cpp
@@ -0,0 +1,276 @@
+/*
+ * 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 "src/core/cpu/kernels/CpuPoolingAssemblyWrapperKernel.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
+#include "src/core/CPP/Validate.h"
+#include "src/core/NEON/INEKernel.h"
+#include "src/core/helpers/AutoConfiguration.h"
+#include "src/core/helpers/WindowHelpers.h"
+
+#include <arm_neon.h>
+
+namespace arm_compute
+{
+namespace cpu
+{
+namespace kernels
+{
+using namespace arm_compute::misc::shape_calculator;
+
+void CpuPoolingAssemblyWrapperKernel::configure(const ITensorInfo *src, ITensorInfo *dst, const PoolingLayerInfo &info, const CPUInfo &cpu_info)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
+
+ // dst initialization if not yet initialized
+ auto_init_if_empty(*dst, src->clone()->set_tensor_shape(compute_pool_shape(*src, info)));
+
+ const bool requantize = src->quantization_info() != dst->quantization_info();
+
+ switch(src->data_type())
+ {
+ case DataType::QASYMM8:
+ if(requantize)
+ {
+ create_arm_pooling_requant<uint8_t, uint8_t>(src, dst, info, cpu_info);
+ }
+ else
+ {
+ create_arm_pooling<uint8_t, uint8_t>(src, dst, info, cpu_info);
+ }
+ break;
+ case DataType::QASYMM8_SIGNED:
+ if(requantize)
+ {
+ create_arm_pooling_requant<int8_t, int8_t>(src, dst, info, cpu_info);
+ }
+ else
+ {
+ create_arm_pooling<int8_t, int8_t>(src, dst, info, cpu_info);
+ }
+ break;
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F16:
+ create_arm_pooling<float16_t, float16_t>(src, dst, info, cpu_info);
+ break;
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+ case DataType::F32:
+ create_arm_pooling<float, float>(src, dst, info, cpu_info);
+ break;
+ default:
+ break;
+ }
+
+ Window win = calculate_max_window(*dst, Steps());
+ INEKernel::configure(win);
+}
+
+Status CpuPoolingAssemblyWrapperKernel::validate(const ITensorInfo *src, const ITensorInfo *dst, const PoolingLayerInfo &info)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst);
+
+#ifndef __aarch64__
+ ARM_COMPUTE_RETURN_ERROR_MSG("32-bit is not supported by assembly kernels");
+#endif /* __aarch64__ */
+ ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(src);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((src->data_layout() != DataLayout::NHWC) || (info.data_layout != DataLayout::NHWC), "Only NHWC is supported by assembly kernels");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((info.pool_type != PoolingType::AVG) && (info.pool_type != PoolingType::MAX),
+ "Only AVG and MAX pooling are supported by assembly kernels");
+
+ if(dst->total_size() > 0)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst);
+
+ const auto src_qinfo = src->quantization_info().uniform();
+ const auto dst_qinfo = dst->quantization_info().uniform();
+
+ if(src_qinfo != dst_qinfo)
+ {
+ const float multiplier = src_qinfo.scale / dst_qinfo.scale;
+ int32_t dst_multiplier{};
+ int32_t dst_shift{};
+ ARM_COMPUTE_RETURN_ERROR_ON(quantization::calculate_quantized_multiplier(multiplier, &dst_multiplier, &dst_shift));
+ }
+ else
+ {
+ if(src->data_type() == DataType::QASYMM8)
+ {
+ const bool has_padding = info.pad_stride_info.has_padding();
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(!info.exclude_padding && has_padding, "Assembly kernels do not support padding for QASYMM8 with same src/dst quantization info");
+ }
+ }
+ }
+ else
+ {
+ if(src->data_type() == DataType::QASYMM8)
+ {
+ // If dst is not configured, the quantization info are the same
+ const bool has_padding = info.pad_stride_info.has_padding();
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(!info.exclude_padding && has_padding, "Assembly kernels do not support padding for QASYMM8 with same src/dst quantization info");
+ }
+ }
+ return Status{};
+}
+
+void CpuPoolingAssemblyWrapperKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(_kernel_asm.get());
+ ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+ ARM_COMPUTE_UNUSED(window);
+ ARM_COMPUTE_UNUSED(info);
+
+ ARM_COMPUTE_ERROR_ON(tensors.empty());
+
+ const ITensor *src = tensors.get_const_tensor(TensorType::ACL_SRC);
+ ITensor *dst = tensors.get_tensor(TensorType::ACL_DST_0);
+ ITensor *workspace = tensors.get_tensor(TensorType::ACL_DST_1);
+
+ const auto in_ptr = src->buffer() + src->info()->offset_first_element_in_bytes();
+ auto out_ptr = dst->buffer() + dst->info()->offset_first_element_in_bytes();
+ auto working_space = workspace->buffer() + workspace->info()->offset_first_element_in_bytes();
+
+ const auto src_shape = src->info()->tensor_shape();
+ const auto dst_shape = dst->info()->tensor_shape();
+ const auto src_padding = src->info()->padding();
+ const auto dst_padding = dst->info()->padding();
+
+ const size_t ld_src_col = src_shape[0] + src_padding.left + src_padding.right;
+ const size_t ld_src_row = ld_src_col * (src_shape[1] + src_padding.top + src_padding.bottom);
+ const size_t ld_src_batch = ld_src_row * src_shape[2];
+ const size_t ld_dst_col = dst_shape[0] + dst_padding.left + dst_padding.right;
+ const size_t ld_dst_row = ld_dst_col * (dst_shape[1] + dst_padding.top + dst_padding.bottom);
+ const size_t ld_dst_batch = ld_dst_row * dst_shape[2];
+
+ _kernel_asm->execute(in_ptr, ld_src_col, ld_src_row, ld_src_batch,
+ out_ptr, ld_dst_col, ld_dst_row, ld_dst_batch,
+ working_space, info.thread_id, info.num_threads);
+}
+
+size_t CpuPoolingAssemblyWrapperKernel::get_working_size(unsigned int num_threads) const
+{
+ return _kernel_asm->get_working_size(num_threads);
+}
+
+bool CpuPoolingAssemblyWrapperKernel::is_configured() const
+{
+ return _kernel_asm != nullptr;
+}
+
+template <typename Typesrc, typename Typedst>
+void CpuPoolingAssemblyWrapperKernel::create_arm_pooling(const ITensorInfo *src, ITensorInfo *dst, const PoolingLayerInfo &info, const CPUInfo &cpu_info)
+{
+ const arm_conv::pooling::PoolingType pool_type = (info.pool_type == PoolingType::AVG) ? arm_conv::pooling::PoolingType::AVERAGE : arm_conv::pooling::PoolingType::MAX;
+
+ arm_conv::pooling::PoolingWindow window{};
+ window.cols = static_cast<unsigned int>(info.pool_size.x());
+ window.rows = static_cast<unsigned int>(info.pool_size.y());
+
+ arm_conv::pooling::PoolingStride stride{};
+ std::tie(stride.cols, stride.rows) = info.pad_stride_info.stride();
+
+ const arm_conv::pooling::PaddingValues padding{ info.pad_stride_info.pad_left(), info.pad_stride_info.pad_top(), info.pad_stride_info.pad_right(), info.pad_stride_info.pad_bottom() };
+
+ constexpr unsigned int idx_width = 1;
+ constexpr unsigned int idx_height = 2;
+ constexpr unsigned int idx_channels = 0;
+ constexpr unsigned int idx_batches = 3;
+
+ const unsigned int n_batches = src->dimension(idx_batches);
+ const unsigned int src_rows = src->dimension(idx_height);
+ const unsigned int src_cols = src->dimension(idx_width);
+ const unsigned int n_channels = src->dimension(idx_channels);
+ const unsigned int dst_rows = dst->dimension(idx_height);
+ const unsigned int dst_cols = dst->dimension(idx_width);
+
+ arm_conv::pooling::PoolingArgs args(&cpu_info, pool_type, window, stride, info.exclude_padding, n_batches, src_rows, src_cols, n_channels, dst_rows, dst_cols, padding, nullptr);
+
+ // Configure assembly pooling kernel
+ auto pooling_kernel_asm = arm_conv::pooling::pooling<Typesrc, Typedst>(args);
+ if(pooling_kernel_asm == nullptr)
+ {
+ // Configuration not supported: Leave function unconfigured:
+ return;
+ }
+
+ _kernel_asm = std::move(pooling_kernel_asm);
+}
+
+template <typename Typesrc, typename Typedst>
+void CpuPoolingAssemblyWrapperKernel::create_arm_pooling_requant(const ITensorInfo *src, ITensorInfo *dst, const PoolingLayerInfo &info, const CPUInfo &cpu_info)
+{
+ const arm_conv::pooling::PoolingType pool_type = (info.pool_type == PoolingType::AVG) ? arm_conv::pooling::PoolingType::AVERAGE : arm_conv::pooling::PoolingType::MAX;
+
+ arm_conv::pooling::PoolingWindow window{};
+ window.cols = static_cast<unsigned int>(info.pool_size.x());
+ window.rows = static_cast<unsigned int>(info.pool_size.y());
+
+ arm_conv::pooling::PoolingStride stride{};
+ std::tie(stride.cols, stride.rows) = info.pad_stride_info.stride();
+
+ const arm_conv::pooling::PaddingValues padding{ info.pad_stride_info.pad_left(), info.pad_stride_info.pad_top(), info.pad_stride_info.pad_right(), info.pad_stride_info.pad_bottom() };
+
+ constexpr unsigned int idx_width = 1;
+ constexpr unsigned int idx_height = 2;
+ constexpr unsigned int idx_channels = 0;
+ constexpr unsigned int idx_batches = 3;
+
+ const unsigned int n_batches = src->dimension(idx_batches);
+ const unsigned int src_rows = src->dimension(idx_height);
+ const unsigned int src_cols = src->dimension(idx_width);
+ const unsigned int n_channels = src->dimension(idx_channels);
+ const unsigned int dst_rows = dst->dimension(idx_height);
+ const unsigned int dst_cols = dst->dimension(idx_width);
+
+ arm_conv::pooling::PoolingArgs args(&cpu_info, pool_type, window, stride, info.exclude_padding, n_batches, src_rows, src_cols, n_channels, dst_rows, dst_cols, padding, nullptr);
+
+ const auto src_qinfo = src->quantization_info().uniform();
+ const auto dst_qinfo = dst->quantization_info().uniform();
+
+ const float multiplier = src_qinfo.scale / dst_qinfo.scale;
+ int32_t dst_multiplier{};
+ int32_t dst_shift{};
+ quantization::calculate_quantized_multiplier(multiplier, &dst_multiplier, &dst_shift);
+
+ const arm_conv::pooling::Requantize32 requant_args(src_qinfo.offset,
+ dst_qinfo.offset,
+ dst_shift, // left shift
+ 0, // right shift
+ dst_multiplier);
+
+ // Configure assembly pooling kernel with requantization
+ auto pooling_kernel_asm = arm_conv::pooling::pooling<Typesrc, Typedst, arm_conv::pooling::Requantize32>(args, requant_args);
+ if(pooling_kernel_asm == nullptr)
+ {
+ // Configuration not supported: Leave function unconfigured:
+ return;
+ }
+
+ _kernel_asm = std::move(pooling_kernel_asm);
+}
+} // namespace kernels
+} // namespace cpu
+} // namespace arm_compute
diff --git a/src/core/cpu/kernels/CpuPoolingAssemblyWrapperKernel.h b/src/core/cpu/kernels/CpuPoolingAssemblyWrapperKernel.h
new file mode 100644
index 0000000..34ec452
--- /dev/null
+++ b/src/core/cpu/kernels/CpuPoolingAssemblyWrapperKernel.h
@@ -0,0 +1,123 @@
+/*
+ * 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.
+ */
+#ifndef ARM_COMPUTE_CPU_POOLING_ASSEMBLY_WRAPPER_KERNEL_H
+#define ARM_COMPUTE_CPU_POOLING_ASSEMBLY_WRAPPER_KERNEL_H
+
+#include "arm_compute/core/Types.h"
+#include "src/core/NEON/kernels/assembly/pooling.hpp"
+#include "src/core/common/Macros.h"
+#include "src/core/cpu/ICpuKernel.h"
+
+#include "pool_common.hpp"
+
+namespace arm_compute
+{
+namespace cpu
+{
+namespace kernels
+{
+/** This class is a wrapper for the assembly kernels.
+ *
+ * Some kernels were written in assembly and highly optimised for specific
+ * CPUs like A53 or A55. The arm compute library creates an instance of
+ * CpuPoolingAssemblyWrapperKernel and other auxiliary data structures to
+ * execute a single assembly kernel in the context of an NEFunction.
+ *
+ */
+class CpuPoolingAssemblyWrapperKernel final : public ICpuKernel
+{
+public:
+ /** Constructor
+ */
+ CpuPoolingAssemblyWrapperKernel() = default;
+ CpuPoolingAssemblyWrapperKernel(CpuPoolingAssemblyWrapperKernel &) = delete;
+ CpuPoolingAssemblyWrapperKernel(CpuPoolingAssemblyWrapperKernel &&) = default;
+ CpuPoolingAssemblyWrapperKernel &operator=(CpuPoolingAssemblyWrapperKernel &) = delete;
+
+ const char *name() const override
+ {
+ return "CpuPoolingAssemblyWrapperKernel";
+ }
+
+ /** Initialise the kernel's src and dst.
+ *
+ * @param[in] src Source tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
+ * @param[out] dst Destination tensor info to store the result of pooling. Data types supported: same as @p src.
+ * @param[in] info Pooling meta-data.
+ * @param[in] cpu_info CPU information needed to select the most appropriate kernel.
+ */
+ void configure(const ITensorInfo *src, ITensorInfo *dst, const PoolingLayerInfo &info, const CPUInfo &cpu_info);
+
+ /** Indicates whether or not this function can be used to process the given parameters.
+ *
+ * @param[in] src Source tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
+ * @param[in] dst Destination tensor to store the result of pooling. Data types supported: same as @p src.
+ * @param[in] info Pooling meta-data
+ *
+ * @return a status.
+ */
+ static Status validate(const ITensorInfo *src, const ITensorInfo *dst, const PoolingLayerInfo &info);
+
+ // Inherited methods overridden:
+ void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
+
+ /** Get size of the workspace needed by the assembly kernel.
+ *
+ * @param[in] num_threads Maximum number of threads that are going to be spawned.
+ *
+ * @return size of workspace
+ */
+ size_t get_working_size(unsigned int num_threads) const;
+
+ /** Was the asm kernel successfully configured?
+ *
+ * @return True if the asm kernel is configured and ready to run
+ */
+ bool is_configured() const;
+
+private:
+ /** Helper function to create the assembly kernel.
+ *
+ * @param[in] src Source tensor info.
+ * @param[in] dst Destination tensor info.
+ * @param[in] info Pooling layer meta-data.
+ */
+ template <typename Typesrc, typename Typedst>
+ void create_arm_pooling(const ITensorInfo *src, ITensorInfo *dst, const PoolingLayerInfo &info, const CPUInfo &cpu_info);
+
+ /** Helper function to create the assembly kernel with requantization support
+ *
+ * @param[in] src Source tensor info.
+ * @param[in] dst Destination tensor info.
+ * @param[in] info Pooling layer meta-data.
+ */
+ template <typename Typesrc, typename Typedst>
+ void create_arm_pooling_requant(const ITensorInfo *src, ITensorInfo *dst, const PoolingLayerInfo &info, const CPUInfo &cpu_info);
+
+ std::unique_ptr<arm_conv::pooling::IPoolingCommon> _kernel_asm{ nullptr };
+};
+} // namespace kernels
+} // namespace cpu
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_CPU_POOLING_ASSEMBLY_WRAPPER_KERNEL_H */
diff --git a/src/core/cpu/kernels/CpuPoolingKernel.cpp b/src/core/cpu/kernels/CpuPoolingKernel.cpp
new file mode 100644
index 0000000..a29aef4
--- /dev/null
+++ b/src/core/cpu/kernels/CpuPoolingKernel.cpp
@@ -0,0 +1,2605 @@
+/*
+ * Copyright (c) 2017-2021 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 "src/core/cpu/kernels/CpuPoolingKernel.h"
+
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "src/core/AccessWindowStatic.h"
+#include "src/core/CPP/Validate.h"
+#include "src/core/NEON/NEAsymm.h"
+#include "src/core/NEON/NEFixedPoint.h"
+#include "src/core/NEON/NEMath.h"
+#include "src/core/helpers/AutoConfiguration.h"
+#include "src/core/helpers/WindowHelpers.h"
+#include "support/ToolchainSupport.h"
+
+#include "src/core/NEON/wrapper/wrapper.h"
+#include <arm_neon.h>
+
+namespace arm_compute
+{
+namespace cpu
+{
+namespace kernels
+{
+using namespace misc::shape_calculator;
+
+namespace
+{
+template <typename T>
+inline typename std::enable_if<std::is_same<T, int8_t>::value, int8_t>::type
+quantize(float val, const UniformQuantizationInfo &info)
+{
+ return quantize_qasymm8_signed(val, info);
+}
+
+template <typename T>
+inline typename std::enable_if<std::is_same<T, uint8_t>::value, uint8_t>::type
+quantize(float val, const UniformQuantizationInfo &info)
+{
+ return quantize_qasymm8(val, info);
+}
+
+inline float calculate_avg_scale(bool exclude_padding, DataLayout data_layout, const Coordinates &id, const int pool_size_x, const int pool_size_y, const int upper_bound_w, const int upper_bound_h,
+ const int pad_x, const int pad_y, const int stride_x, const int stride_y)
+{
+ const unsigned int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+ const unsigned int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+
+ int start_x = id[idx_width] * stride_x - pad_x;
+ int start_y = id[idx_height] * stride_y - pad_y;
+
+ const int end_x = std::min(start_x + pool_size_x, upper_bound_w);
+ const int end_y = std::min(start_y + pool_size_y, upper_bound_h);
+ if(exclude_padding)
+ {
+ start_x = std::max(0, start_x);
+ start_y = std::max(0, start_y);
+ }
+ return 1.f / ((end_y - start_y) * (end_x - start_x));
+}
+
+template <typename T, typename TVec>
+inline void scale_vector_q16x8(bool exclude_padding, TVec &v, const Coordinates &id, int id_offset, int step,
+ const int pool_size, const int upper_bound_w, const int upper_bound_h,
+ const int pad_x, const int pad_y, const int stride_x, const int stride_y)
+{
+ int start_x = (id.x() + id_offset) * stride_x - pad_x;
+ int start_y = id.y() * stride_y - pad_y;
+ const int end_y = std::min(start_y + pool_size, upper_bound_h);
+ if(exclude_padding)
+ {
+ start_y = std::max(0, start_y);
+ }
+
+ std::array<T, 8> elems =
+ {
+ {
+ wrapper::vgetlane(v, 0),
+ wrapper::vgetlane(v, 1),
+ wrapper::vgetlane(v, 2),
+ wrapper::vgetlane(v, 3),
+ wrapper::vgetlane(v, 4),
+ wrapper::vgetlane(v, 5),
+ wrapper::vgetlane(v, 6),
+ wrapper::vgetlane(v, 7),
+ }
+ };
+
+ for(auto &el : elems)
+ {
+ int c_start_x = start_x;
+ const int end_x = std::min(c_start_x + pool_size, upper_bound_w);
+ if(exclude_padding)
+ {
+ c_start_x = std::max(0, c_start_x);
+ }
+ float scale = 1.f / ((end_y - start_y) * (end_x - c_start_x));
+ el *= scale;
+ start_x += step * stride_x;
+ }
+
+ v = wrapper::vsetlane(elems[0], v, 0);
+ v = wrapper::vsetlane(elems[1], v, 1);
+ v = wrapper::vsetlane(elems[2], v, 2);
+ v = wrapper::vsetlane(elems[3], v, 3);
+ v = wrapper::vsetlane(elems[4], v, 4);
+ v = wrapper::vsetlane(elems[5], v, 5);
+ v = wrapper::vsetlane(elems[6], v, 6);
+ v = wrapper::vsetlane(elems[7], v, 7);
+}
+
+Status validate_arguments(const ITensorInfo *src, const ITensorInfo *dst, const PoolingLayerInfo &pool_info,
+ unsigned int &pooled_w, unsigned int pooled_h, const ITensorInfo *indices, Size2D pool_size)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst);
+
+ int pool_stride_x = 0;
+ int pool_stride_y = 0;
+ PoolingType pool_type = pool_info.pool_type;
+ const PadStrideInfo pad_stride_info = pool_info.pad_stride_info;
+ std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();
+
+ ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(src);
+ if(indices)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32, DataType::F16);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(indices, 1, DataType::U32);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(pool_type != PoolingType::MAX, "Pooling indices only supported for MAX pooling method");
+ }
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON(pool_type == PoolingType::L2 && is_data_type_quantized(src->data_type()));
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_data_type_quantized(src->data_type()) && !pool_info.exclude_padding && (pool_info.pool_type == PoolingType::AVG) && pool_info.pad_stride_info.has_padding()
+ && (src->data_layout() == DataLayout::NHWC),
+ "exclude_padding equal false is not supported for AVG Pooling with padding on quantized types");
+
+ if(dst->total_size() != 0)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, dst);
+ ARM_COMPUTE_RETURN_ERROR_ON((dst->dimension(get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::WIDTH)) != pooled_w)
+ || (dst->dimension(get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::HEIGHT)) != pooled_h));
+
+ if(indices)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((pool_size != Size2D(2, 2)), "Pooling indices only supported for pool size 2x2");
+ ARM_COMPUTE_RETURN_ERROR_ON((indices->dimension(get_data_layout_dimension_index(indices->data_layout(), DataLayoutDimension::WIDTH)) != pooled_w)
+ || (indices->dimension(get_data_layout_dimension_index(indices->data_layout(), DataLayoutDimension::HEIGHT)) != pooled_h));
+ }
+ }
+
+ return Status{};
+}
+
+Status validate_arguments_pool_info(const unsigned int pool_size_x, const unsigned int pool_size_y)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON(pool_size_x == 0);
+ ARM_COMPUTE_RETURN_ERROR_ON(pool_size_y == 0);
+
+ return Status{};
+}
+
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *src, ITensorInfo *dst, ITensorInfo *indices, const PoolingLayerInfo &pool_info,
+ unsigned int &num_elems_processed_per_iteration,
+ BorderSize &border_size,
+ unsigned int pooled_w, unsigned int pooled_h, int pool_size_x, int pool_size_y)
+{
+ // dst auto inizialitation if not yet initialized
+ auto_init_if_empty(*dst, src->clone()->set_tensor_shape(compute_pool_shape(*src, pool_info)));
+ if(indices)
+ {
+ // Indices auto inizialitation if not yet initialized
+ auto_init_if_empty(*indices, (src->clone()->set_tensor_shape(compute_pool_shape(*src,
+ pool_info)))
+ .set_data_type(DataType::U32) /* we store the offset to the element */);
+ }
+ const auto data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? src->data_layout() : pool_info.data_layout;
+ unsigned int num_elems_read_per_iteration = 0;
+ unsigned int num_elems_horizontal_window = 0;
+ int pool_stride_x = 0;
+ int pool_stride_y = 0;
+ const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+ const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+ const int src_width = src->dimension(idx_width);
+ const int src_height = src->dimension(idx_height);
+ const PadStrideInfo pad_stride_info = pool_info.pad_stride_info;
+ std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();
+ const int pool_pad_right = pad_stride_info.pad_right();
+ const int pool_pad_top = pad_stride_info.pad_top();
+ const int pool_pad_left = pad_stride_info.pad_left();
+ const int pool_pad_bottom = pad_stride_info.pad_bottom();
+ const bool is_square = pool_size_x == pool_size_y;
+
+ // Check dst dimensions
+ std::tie(pooled_w, pooled_h) = scaled_dimensions(src->dimension(idx_width),
+ src->dimension(idx_height),
+ pool_size_x,
+ pool_size_y,
+ pad_stride_info);
+
+ //If it's not squared and optimized will be executed the MxN
+ num_elems_read_per_iteration = 1;
+ num_elems_processed_per_iteration = 1;
+ num_elems_horizontal_window = 1;
+
+ if(is_square)
+ {
+ switch(src->data_type())
+ {
+ case DataType::QASYMM8:
+ case DataType::QASYMM8_SIGNED:
+ switch(pool_size_x)
+ {
+ case 2:
+ num_elems_read_per_iteration = 16;
+ num_elems_processed_per_iteration = (pool_stride_x == 2) ? 8 : 15;
+ num_elems_horizontal_window = (pool_stride_x == 2) ? 8 : 16;
+ break;
+ case 3:
+ num_elems_read_per_iteration = 16;
+ num_elems_processed_per_iteration = (pool_stride_x == 2) ? 7 : 14;
+ num_elems_horizontal_window = (pool_stride_x == 2) ? 8 : 16;
+ break;
+ default:
+ break;
+ }
+ break;
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F16:
+ switch(pool_size_x)
+ {
+ case 2:
+ case 3:
+ num_elems_read_per_iteration = 4;
+ num_elems_processed_per_iteration = 1;
+ num_elems_horizontal_window = 1;
+ break;
+ default:
+ break;
+ }
+ break;
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+ case DataType::F32:
+ switch(pool_size_x)
+ {
+ case 2:
+ num_elems_read_per_iteration = 2;
+ break;
+ case 3:
+ num_elems_read_per_iteration = 4; // We use vload4 for pooling3
+ break;
+ case 7:
+ num_elems_read_per_iteration = 8; // We use vload8 for pooling7
+ break;
+ default:
+ break;
+ }
+ num_elems_processed_per_iteration = 1;
+ num_elems_horizontal_window = 1;
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Element size not supported");
+ break;
+ }
+ }
+
+ bool window_changed = false;
+ Window win{};
+ if(data_layout == DataLayout::NCHW)
+ {
+ // Number of iterations in X dimension
+ const int num_iterations_x = (pooled_w + num_elems_processed_per_iteration - 1) / num_elems_processed_per_iteration;
+ // Upper limit for the number of right/bottom border elements that are accessed
+ const int upper_bound_w = ((num_iterations_x - 1) * num_elems_processed_per_iteration * pool_stride_x - pool_pad_left + num_elems_read_per_iteration) - src_width;
+ const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_top + pool_size_y) - src_height;
+ border_size = BorderSize(pool_pad_top, pool_pad_right, pool_pad_bottom, pool_pad_left);
+ border_size.right = std::max(upper_bound_w, pool_pad_right);
+ border_size.bottom = std::max(upper_bound_h, pool_pad_bottom);
+ TensorShape dst_shape{ src->tensor_shape() };
+ dst_shape.set(0, pooled_w);
+ dst_shape.set(1, pooled_h);
+ TensorInfo dst_info(src->clone()->set_tensor_shape(dst_shape));
+ win = calculate_max_window(dst_info, Steps(num_elems_processed_per_iteration));
+ AccessWindowStatic src_access(src, -pool_pad_left, -pool_pad_top, src_width + border_size.right, src_height + border_size.bottom);
+ AccessWindowHorizontal dst_access(dst, 0, num_elems_horizontal_window);
+ if(indices)
+ {
+ AccessWindowHorizontal indices_access(indices, 0, num_elems_horizontal_window);
+ window_changed = update_window_and_padding(win, src_access, dst_access, indices_access);
+ }
+ else
+ {
+ window_changed = update_window_and_padding(win, src_access, dst_access);
+ }
+ dst_access.set_valid_region(win, ValidRegion(Coordinates(), dst->tensor_shape()));
+ }
+
+ Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+ return std::make_pair(err, win);
+}
+
+template <typename T>
+inline T vcvtq_q32_f32(float32x4_t values);
+
+template <>
+inline uint32x4_t vcvtq_q32_f32(float32x4_t values)
+{
+ return vcvtq_u32_f32(values);
+}
+
+template <>
+inline int32x4_t vcvtq_q32_f32(float32x4_t values)
+{
+ return vcvtq_s32_f32(values);
+}
+
+template <typename T>
+inline float32x4_t vcvtq_f32_q32(T values);
+
+template <>
+inline float32x4_t vcvtq_f32_q32(uint32x4_t values)
+{
+ return vcvtq_f32_u32(values);
+}
+
+template <>
+inline float32x4_t vcvtq_f32_q32(int32x4_t values)
+{
+ return vcvtq_f32_s32(values);
+}
+
+template <typename Tout>
+inline Tout vrequantize_pooling_with_scale(const float32x4x4_t &acc, const float quant_rescale, const float scale_pooling, const int32_t new_offset);
+
+template <>
+inline uint8x16_t vrequantize_pooling_with_scale(const float32x4x4_t &acc, const float quant_rescale, const float scale_pooling, const int32_t new_offset)
+{
+ const float new_scale = quant_rescale / scale_pooling;
+ return vquantize(acc, UniformQuantizationInfo(new_scale, new_offset));
+}
+
+template <>
+inline int8x16_t vrequantize_pooling_with_scale(const float32x4x4_t &acc, const float quant_rescale, const float scale_pooling, const int32_t new_offset)
+{
+ const float new_scale = quant_rescale / scale_pooling;
+ return vquantize_signed(acc, UniformQuantizationInfo(new_scale, new_offset));
+}
+
+template <typename Tin, typename Tout>
+inline Tout vrequantize_pooling(Tin vec1, Tin vec2, const UniformQuantizationInfo &requant_qinfo);
+
+template <>
+inline uint8x16_t vrequantize_pooling(uint8x8_t vec1, uint8x8_t vec2, const UniformQuantizationInfo &requant_qinfo)
+{
+ const float32x4x4_t acc =
+ {
+ {
+ vcvtq_f32_u32(vmovl_u16(vget_low_u16(vmovl_u8((vec1))))),
+ vcvtq_f32_u32(vmovl_u16(vget_high_u16(vmovl_u8((vec1))))),
+ vcvtq_f32_u32(vmovl_u16(vget_low_u16(vmovl_u8((vec2))))),
+ vcvtq_f32_u32(vmovl_u16(vget_high_u16(vmovl_u8((vec2))))),
+ }
+ };
+ return vquantize(acc, requant_qinfo);
+}
+
+template <>
+inline int8x16_t vrequantize_pooling(int8x8_t vec1, int8x8_t vec2, const UniformQuantizationInfo &requant_qinfo)
+{
+ const float32x4x4_t acc =
+ {
+ {
+ vcvtq_f32_s32(vmovl_s16(vget_low_s16(vmovl_s8((vec1))))),
+ vcvtq_f32_s32(vmovl_s16(vget_high_s16(vmovl_s8((vec1))))),
+ vcvtq_f32_s32(vmovl_s16(vget_low_s16(vmovl_s8((vec2))))),
+ vcvtq_f32_s32(vmovl_s16(vget_high_s16(vmovl_s8((vec2))))),
+ }
+ };
+ return vquantize_signed(acc, requant_qinfo);
+}
+
+template <typename T>
+inline T vrequantize_pooling(T &vec, const UniformQuantizationInfo &requant_qinfo);
+
+template <>
+inline uint8x8_t vrequantize_pooling(uint8x8_t &vec, const UniformQuantizationInfo &requant_qinfo)
+{
+ const float32x4x2_t acc =
+ {
+ {
+ vcvtq_f32_u32(vmovl_u16(vget_low_u16(vmovl_u8((vec))))),
+ vcvtq_f32_u32(vmovl_u16(vget_high_u16(vmovl_u8((vec))))),
+ }
+ };
+ return vquantize(acc, requant_qinfo);
+}
+
+template <>
+inline int8x8_t vrequantize_pooling(int8x8_t &vec, const UniformQuantizationInfo &requant_qinfo)
+{
+ const float32x4x2_t acc =
+ {
+ {
+ vcvtq_f32_s32(vmovl_s16(vget_low_s16(vmovl_s8((vec))))),
+ vcvtq_f32_s32(vmovl_s16(vget_high_s16(vmovl_s8((vec))))),
+ }
+ };
+ return vquantize_signed(acc, requant_qinfo);
+}
+
+} // namespace
+
+BorderSize CpuPoolingKernel::border_size() const
+{
+ return _border_size;
+}
+
+void CpuPoolingKernel::configure(ITensorInfo *src, ITensorInfo *dst, const PoolingLayerInfo &pool_info, ITensorInfo *indices)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
+ const PadStrideInfo pad_stride_info = pool_info.pad_stride_info;
+ const bool is_global_pooling = pool_info.is_global_pooling;
+ const int pool_stride_x = pad_stride_info.stride().first;
+
+ // Get data layout
+ const auto data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? src->data_layout() : pool_info.data_layout;
+ const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+ const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+
+ // Update pool size in case of global pooling
+ const Size2D pool_size(
+ is_global_pooling ? src->dimension(idx_width) : pool_info.pool_size.width,
+ is_global_pooling ? src->dimension(idx_height) : pool_info.pool_size.height);
+
+ // Validate pool info before calling scaled_dimensions
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_pool_info(pool_size.x(), pool_size.y()));
+
+ // Check dst dimensions
+ unsigned int pooled_w;
+ unsigned int pooled_h;
+ std::tie(pooled_w, pooled_h) = scaled_dimensions(src->dimension(idx_width),
+ src->dimension(idx_height),
+ pool_size.x(),
+ pool_size.y(),
+ pad_stride_info);
+
+ // Perform validation step
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst, pool_info, pooled_w, pooled_h, indices, pool_size));
+
+ // Set instance variables
+ _pool_info = pool_info;
+ _data_layout = src->data_layout();
+ _is_square = (pool_size.x() == pool_size.y());
+
+ // Get data type
+ const DataType data_type = src->data_type();
+ const bool is_nchw = _data_layout == DataLayout::NCHW;
+
+ if(data_type == DataType::QASYMM8)
+ {
+ if(!is_nchw)
+ {
+ _func = &CpuPoolingKernel::poolingMxN_q8_nhwc<uint8_t>;
+ }
+ else
+ {
+ if(pool_size.x() == 2 && pool_stride_x < 3 && _is_square)
+ {
+ _func = &CpuPoolingKernel::pooling2_q8_nchw<uint8_t>;
+ }
+ else if(pool_size.x() == 3 && pool_stride_x < 3 && _is_square)
+ {
+ _func = &CpuPoolingKernel::pooling3_q8_nchw<uint8_t>;
+ }
+ else
+ {
+ _func = &CpuPoolingKernel::poolingMxN_q8_nchw<uint8_t>;
+ }
+ }
+ }
+ else if(data_type == DataType::QASYMM8_SIGNED)
+ {
+ if(!is_nchw)
+ {
+ _func = &CpuPoolingKernel::poolingMxN_q8_nhwc<int8_t>;
+ }
+ else
+ {
+ if(pool_size.x() == 2 && pool_stride_x < 3 && _is_square)
+ {
+ _func = &CpuPoolingKernel::pooling2_q8_nchw<int8_t>;
+ }
+ else if(pool_size.x() == 3 && pool_stride_x < 3 && _is_square)
+ {
+ _func = &CpuPoolingKernel::pooling3_q8_nchw<int8_t>;
+ }
+ else
+ {
+ _func = &CpuPoolingKernel::poolingMxN_q8_nchw<int8_t>;
+ }
+ }
+ }
+ else if(data_type == DataType::F16)
+ {
+ if(!is_nchw)
+ {
+ _func = &CpuPoolingKernel::poolingMxN_f16_nhwc;
+ }
+ else
+ {
+ if(_is_square)
+ {
+ switch(pool_size.x())
+ {
+ case 2:
+ {
+ _func = &CpuPoolingKernel::pooling2_f16_nchw;
+ }
+ break;
+ case 3:
+ {
+ _func = &CpuPoolingKernel::pooling3_f16_nchw;
+ }
+ break;
+ default:
+ {
+ _func = &CpuPoolingKernel::poolingMxN_f16_nchw;
+ break;
+ }
+ }
+ }
+ else
+ {
+ _func = &CpuPoolingKernel::poolingMxN_f16_nchw;
+ }
+ }
+ }
+ else if(data_type == DataType::F32)
+ {
+ if(!is_nchw)
+ {
+ _func = &CpuPoolingKernel::poolingMxN_f32_nhwc;
+ }
+ else
+ {
+ if(_is_square)
+ {
+ switch(pool_size.x())
+ {
+ case 2:
+ {
+ _func = &CpuPoolingKernel::pooling2_f32_nchw;
+ break;
+ }
+ case 3:
+ {
+ _func = &CpuPoolingKernel::pooling3_f32_nchw;
+ break;
+ }
+ case 7:
+ {
+ _func = &CpuPoolingKernel::pooling7_f32_nchw;
+ break;
+ }
+ default:
+ {
+ _func = &CpuPoolingKernel::poolingMxN_f32_nchw;
+ break;
+ }
+ }
+ }
+ else
+ {
+ _func = &CpuPoolingKernel::poolingMxN_f32_nchw;
+ }
+ }
+ }
+
+ if(!is_nchw)
+ {
+ // Configure kernel window
+ Window win = calculate_max_window(*dst, Steps());
+ Coordinates coord;
+ coord.set_num_dimensions(dst->num_dimensions());
+ dst->set_valid_region(ValidRegion(coord, dst->tensor_shape()));
+ ICpuKernel::configure(win);
+ }
+ else
+ {
+ // Configure kernel window
+ auto win_config = validate_and_configure_window(src, dst, indices, pool_info, _num_elems_processed_per_iteration,
+ _border_size, pooled_w, pooled_h, pool_size.x(), pool_size.y());
+ ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+ ICpuKernel::configure(win_config.second);
+ }
+}
+
+template <typename T>
+inline uint32_t offset_no_padding(uint32_t padded_offset, const Coordinates &id, const ITensorInfo &info, int pool_stride_x, int pool_stride_y)
+{
+ const int pad_left = info.padding().left;
+ const int pad_right = info.padding().right;
+ const int pad_top = info.padding().top;
+ const int pad_bottom = info.padding().bottom;
+ const int in_stride_y = static_cast<int>(info.strides_in_bytes().y());
+ const int in_stride_w = static_cast<int>(info.strides_in_bytes()[3]);
+ const int pad_horiz = pad_left + pad_right;
+ const int pad_vert = pad_top + pad_bottom;
+
+ if(info.data_layout() == DataLayout::NCHW)
+ {
+ const uint32_t offset_base = padded_offset
+ - sizeof(T) * pad_horiz * id.y() * pool_stride_y /* subtract padding elems per row */
+ - pad_top * sizeof(T) /* top padding */
+ - sizeof(T) * pad_horiz * info.tensor_shape()[1] * id.z() - pad_vert * in_stride_y * id.z() /* for each Z plane there are height*pad_right padding elems */
+ - in_stride_w * id[3];
+
+ return offset_base;
+ }
+ else
+ {
+ const uint32_t offset_base = padded_offset
+ - sizeof(T) * pad_horiz * id.y() * pool_stride_x // subtract padding elems per row
+ - pad_top * sizeof(T) // top padding
+ - sizeof(T) * pad_horiz * info.tensor_shape()[1] * id.z() * pool_stride_y // for each Z plane there are width*pad_right padding elems
+ - in_stride_w * id[3];
+
+ return offset_base;
+ }
+}
+
+template <typename T>
+void CpuPoolingKernel::pooling2_q8_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding)
+{
+ Iterator src(_src, window_src);
+ Iterator dst(_dst, window);
+
+ /** NEON vector types */
+ using q8x8_t = typename wrapper::traits::neon_vector<T, 8>::type;
+ using q8x16_t = typename wrapper::traits::neon_vector<T, 16>::type;
+ using q8x8x2_t = typename std::conditional<std::is_same<T, uint8_t>::value, uint8x8x2_t, int8x8x2_t>::type;
+ using q16_t = typename wrapper::traits::promote_t<T>;
+ using q16x4_t = typename wrapper::traits::neon_vector<q16_t, 4>::type;
+ using q16x8_t = typename wrapper::traits::neon_vector<q16_t, 8>::type;
+ using q16x8x2_t = typename wrapper::traits::neon_vector<q16_t, 16>::type;
+
+ constexpr int pool_size = 2;
+ int pool_stride_x = 0;
+ int pool_stride_y = 0;
+ 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();
+ std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info.stride();
+ const int upper_bound_w = _src->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = _src->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom);
+
+ const T *const src_top_ptr = reinterpret_cast<const T *>(_src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top))));
+ const T *const src_bottom_ptr = reinterpret_cast<const T *>(_src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1)));
+
+ const int scale_step_x = (pool_stride_x == 1) ? 2 : 1;
+
+ const UniformQuantizationInfo src_qinfo = _src->info()->quantization_info().uniform();
+ const UniformQuantizationInfo dst_qinfo = _dst->info()->quantization_info().uniform();
+ const bool have_different_qinfo = src_qinfo != dst_qinfo;
+
+ const float requant_scale = dst_qinfo.scale / src_qinfo.scale;
+ const int32_t requant_offset = dst_qinfo.offset - static_cast<int32_t>(static_cast<float>(src_qinfo.offset) / requant_scale);
+ const UniformQuantizationInfo requant_qinfo = UniformQuantizationInfo(requant_scale, requant_offset);
+
+ execute_window_loop(window, [&](const Coordinates & id)
+ {
+ const auto top_data = wrapper::vloadq(src_top_ptr + src.offset());
+ const auto bottom_data = wrapper::vloadq(src_bottom_ptr + src.offset());
+ q8x8_t lower_res = {};
+ q8x8_t upper_res = {};
+
+ if(pooling_type != PoolingType::MAX)
+ {
+ const q16x8x2_t top_data_q16 = { { wrapper::vmovl(wrapper::vgetlow(top_data)), wrapper::vmovl(wrapper::vgethigh(top_data)) } };
+ const q16x8x2_t bottom_data_q16 = { { wrapper::vmovl(wrapper::vgetlow(bottom_data)), wrapper::vmovl(wrapper::vgethigh(bottom_data)) } };
+
+ // Add rows
+ const q16x8x2_t vrsum =
+ {
+ {
+ wrapper::vadd(top_data_q16.val[0], bottom_data_q16.val[0]),
+ wrapper::vadd(top_data_q16.val[1], bottom_data_q16.val[1]),
+ }
+ };
+
+ // Pair-wise add row data
+ const q16x4_t vpsum_1 = wrapper::vpadd(wrapper::vgetlow(vrsum.val[0]), wrapper::vgethigh(vrsum.val[0]));
+ const q16x4_t vpsum_2 = wrapper::vpadd(wrapper::vgetlow(vrsum.val[1]), wrapper::vgethigh(vrsum.val[1]));
+
+ q16x8_t res_lower = wrapper::vcombine(vpsum_1, vpsum_2);
+
+ // Scale lower result
+ scale_vector_q16x8<q16_t, q16x8_t>(exclude_padding, res_lower, id, 0, scale_step_x,
+ pool_size, upper_bound_w, upper_bound_h,
+ pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y);
+ lower_res = wrapper::vmovn(res_lower);
+
+ // Compute upper result for stride_x == 1
+ if(pool_stride_x == 1)
+ {
+ // Shifted row sum
+ const q16x8x2_t vrsum_shifted =
+ {
+ {
+ wrapper::vext_1(vrsum.val[0], vrsum.val[1]),
+ wrapper::vext_1(vrsum.val[1], vrsum.val[1])
+ }
+ };
+
+ // Pair-wise add shifted row
+ q16x8_t res_upper = wrapper::vcombine(
+ wrapper::vpadd(wrapper::vgetlow(vrsum_shifted.val[0]), wrapper::vgethigh(vrsum_shifted.val[0])),
+ wrapper::vpadd(wrapper::vgetlow(vrsum_shifted.val[1]), wrapper::vgethigh(vrsum_shifted.val[1])));
+
+ // Scale upper result
+ scale_vector_q16x8<q16_t, q16x8_t>(exclude_padding, res_upper, id, 1, 2,
+ pool_size, upper_bound_w, upper_bound_h,
+ pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y);
+ upper_res = wrapper::vmovn(res_upper);
+ }
+ }
+ else
+ {
+ const q8x16_t max_data = wrapper::vmax(top_data, bottom_data);
+ lower_res = wrapper::vpmax(wrapper::vgetlow(max_data), wrapper::vgethigh(max_data));
+ if(pool_stride_x == 1)
+ {
+ const q8x16_t max_data_shifted = wrapper::vext_1(max_data, max_data);
+ upper_res = wrapper::vpmax(wrapper::vgetlow(max_data_shifted), wrapper::vgethigh(max_data_shifted));
+ }
+ }
+
+ if(have_different_qinfo)
+ {
+ const auto requantized_dst = vrequantize_pooling<q8x8_t, q8x16_t>(lower_res, upper_res, requant_qinfo);
+ lower_res = wrapper::vgetlow(requantized_dst);
+ upper_res = wrapper::vgethigh(requantized_dst);
+ }
+
+ // Store result
+ if(pool_stride_x == 1)
+ {
+ const q8x8x2_t res = { { lower_res, upper_res } };
+ wrapper::vstore(reinterpret_cast<T *>(dst.ptr()), res);
+ }
+ else
+ {
+ wrapper::vstore(reinterpret_cast<T *>(dst.ptr()), lower_res);
+ }
+ },
+ src, dst);
+}
+
+void CpuPoolingKernel::pooling3_f16_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding)
+{
+ ARM_COMPUTE_UNUSED(pooling_type);
+ ARM_COMPUTE_UNUSED(exclude_padding);
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ Iterator src(_src, window_src);
+ Iterator dst(_dst, window);
+
+ constexpr const int pool_size = 3;
+ 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(0) + (exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = _src->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom);
+
+ const unsigned char *const src_top_ptr = _src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top)));
+ const unsigned char *const src_middle_ptr = _src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1));
+ const unsigned char *const src_bottom_ptr = _src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 2));
+
+ execute_window_loop(window, [&](const Coordinates & id)
+ {
+ float16x4_t top_data = vld1_f16(reinterpret_cast<const float16_t *>(src_top_ptr + src.offset()));
+ float16x4_t middle_data = vld1_f16(reinterpret_cast<const float16_t *>(src_middle_ptr + src.offset()));
+ float16x4_t bottom_data = vld1_f16(reinterpret_cast<const float16_t *>(src_bottom_ptr + src.offset()));
+ float16x4_t res = {};
+
+ // Get power of 2 in case of l2 pooling
+ if(pooling_type == PoolingType::L2)
+ {
+ top_data = vmul_f16(top_data, top_data);
+ middle_data = vmul_f16(middle_data, middle_data);
+ bottom_data = vmul_f16(bottom_data, bottom_data);
+ }
+
+ if(pooling_type != PoolingType::MAX)
+ {
+ // Calculate scale
+ const float scale = calculate_avg_scale(exclude_padding, DataLayout::NCHW, id, pool_size, pool_size, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y);
+ const float16x4_t scale_v = vdup_n_f16(scale);
+ // Perform pooling
+ const float16x4_t sum_data = vadd_f16(vadd_f16(top_data, bottom_data), middle_data);
+ res = vpadd_f16(vset_lane_f16(0.f, sum_data, 3), sum_data);
+ res = vmul_f16(vpadd_f16(res, res), scale_v);
+ }
+ else
+ {
+ const float16x4_t max_data = vmax_f16(vmax_f16(top_data, bottom_data), middle_data);
+ res = vpmax_f16(vset_lane_f16(-std::numeric_limits<float>::max(), max_data, 3), max_data);
+ res = vpmax_f16(res, res);
+ }
+
+ // Calculate square-root in case of l2 pooling
+ if(pooling_type == PoolingType::L2)
+ {
+ res = vinv_f16(vinvsqrt_f16(res));
+ }
+
+ *(reinterpret_cast<float16_t *>(dst.ptr())) = vget_lane_f16(res, 0);
+ },
+ src, dst);
+#else /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+ ARM_COMPUTE_UNUSED(window_src);
+ ARM_COMPUTE_UNUSED(window);
+ ARM_COMPUTE_ERROR("FP16 Not supported! Recompile the library with arch=arm64-v8.2-a");
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+}
+
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+template <typename T>
+inline typename std::enable_if<std::is_same<T, float16_t>::value, float32x2_t>::type
+f16_to_f32(float16x4_t src)
+{
+ float32x2_t dst = { static_cast<float>(vget_lane_f16(src, 0)), static_cast<float>(vget_lane_f16(src, 1)) };
+ return dst;
+}
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+
+template <typename T>
+inline typename std::enable_if<std::is_same<T, float>::value, float32x2_t>::type
+f16_to_f32(float32x2_t src)
+{
+ return src;
+}
+
+template <typename T>
+void CpuPoolingKernel::pooling2_nchw_maxpool_indices(const Window &window_src, const Window &window)
+{
+ Iterator src(_src, window_src);
+ Iterator dst(_dst, window);
+ Iterator indices(_indices, window);
+ 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();
+ const uint8_t *const src_top_ptr = _src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top)));
+ const uint8_t *const src_bottom_ptr = _src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1));
+ const int pad_left = _src->info()->padding().left;
+ const int pad_right = _src->info()->padding().right;
+ const int in_stride_y = static_cast<int>(_src->info()->strides_in_bytes().y());
+
+ execute_window_loop(window, [&](const Coordinates & id)
+ {
+ auto top_data = wrapper::vload(reinterpret_cast<const T *>(src_top_ptr + src.offset()));
+ auto bottom_data = wrapper::vload(reinterpret_cast<const T *>(src_bottom_ptr + src.offset()));
+ float32x2_t top_data_f32 = f16_to_f32<T>(top_data);
+ float32x2_t bottom_data_f32 = f16_to_f32<T>(bottom_data);
+
+ // Calculate max data, compare top first, then bottom, to make sue the first max is recorded.
+ const float32x2_t max_data_top = vpmax_f32(top_data_f32, top_data_f32);
+ const float32x2_t max_data_bottom = vpmax_f32(bottom_data_f32, bottom_data_f32);
+ const float32x2_t max_data = vmax_f32(max_data_top, max_data_bottom);
+ *(reinterpret_cast<T *>(dst.ptr())) = static_cast<T>(vget_lane_f32(max_data, 0));
+
+ // Calculate max data indice, which will be used in max unpool.
+ const uint32_t offset_base = offset_no_padding<T>(src.offset(), id, *_src->info(), pool_stride_x, pool_stride_y);
+ const uint32_t offset_top = (uint32_t)(offset_base / sizeof(T));
+ const uint32_t offset_bottom = offset_top + in_stride_y / sizeof(T) - pad_right - pad_left;
+ const uint32x2_t voffset_top = { offset_top, offset_top + 1u };
+ const uint32x2_t voffset_bottom = { offset_bottom, offset_bottom + 1u };
+ const uint32x2_t tmp_indices_top = vbsl_u32(vcge_f32(top_data_f32, vrev64_f32(top_data_f32)), voffset_top, vrev64_u32(voffset_top));
+ const uint32x2_t tmp_indices_bottom = vbsl_u32(vcge_f32(bottom_data_f32, vrev64_f32(bottom_data_f32)), voffset_bottom, vrev64_u32(voffset_bottom));
+ *(reinterpret_cast<int *>(indices.ptr())) = vget_lane_u32(vbsl_u32(vcge_f32(max_data_top, max_data_bottom), tmp_indices_top, tmp_indices_bottom), 0);
+ },
+ src, dst, indices);
+}
+
+void CpuPoolingKernel::pooling2_f16_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding)
+{
+ ARM_COMPUTE_UNUSED(pooling_type);
+ ARM_COMPUTE_UNUSED(exclude_padding);
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ if(pooling_type == PoolingType::MAX && _indices)
+ {
+ pooling2_nchw_maxpool_indices<float16_t>(window_src, window);
+ }
+ else
+ {
+ Iterator src(_src, window_src);
+ Iterator dst(_dst, window);
+ constexpr int pool_size = 2;
+ 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, 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(0) + (exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = _src->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom);
+
+ const unsigned char *const src_top_ptr = _src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top)));
+ const unsigned char *const src_bottom_ptr = _src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1));
+
+ execute_window_loop(window, [&](const Coordinates & id)
+ {
+ float16x4_t top_data = vld1_f16(reinterpret_cast<const float16_t *>(src_top_ptr + src.offset()));
+ float16x4_t bottom_data = vld1_f16(reinterpret_cast<const float16_t *>(src_bottom_ptr + src.offset()));
+ float16x4_t res = {};
+
+ // Get power of 2 in case of l2 pooling
+ if(pooling_type == PoolingType::L2)
+ {
+ top_data = vmul_f16(top_data, top_data);
+ bottom_data = vmul_f16(bottom_data, bottom_data);
+ }
+
+ if(pooling_type != PoolingType::MAX)
+ {
+ const float scale = calculate_avg_scale(exclude_padding, DataLayout::NCHW, id, pool_size, pool_size, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y);
+ const float16x4_t scale_v = vdup_n_f16(scale);
+
+ const float16x4_t sum_data = vadd_f16(top_data, bottom_data);
+ res = vmul_f16(vpadd_f16(sum_data, sum_data), scale_v);
+ }
+ else
+ {
+ const float16x4_t max_data = vmax_f16(top_data, bottom_data);
+ res = vpmax_f16(max_data, max_data);
+ }
+
+ // Calculate square-root in case of l2 pooling
+ if(pooling_type == PoolingType::L2)
+ {
+ res = vinv_f16(vinvsqrt_f16(res));
+ }
+
+ // Store result
+ *(reinterpret_cast<float16_t *>(dst.ptr())) = vget_lane_f16(res, 0);
+ },
+ src, dst);
+ }
+#else /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+ ARM_COMPUTE_UNUSED(window_src);
+ ARM_COMPUTE_UNUSED(window);
+ ARM_COMPUTE_ERROR("FP16 Not supported! Recompile the library with arch=arm64-v8.2-a");
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+}
+
+template <typename T>
+void CpuPoolingKernel::pooling3_q8_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding)
+{
+ Iterator src(_src, window_src);
+ Iterator dst(_dst, window);
+
+ /** NEON vector types */
+ using q8x8_t = typename wrapper::traits::neon_vector<T, 8>::type;
+ using q8x16_t = typename wrapper::traits::neon_vector<T, 16>::type;
+ using q8x8x2_t = typename std::conditional<std::is_same<T, uint8_t>::value, uint8x8x2_t, int8x8x2_t>::type;
+ using q16_t = typename wrapper::traits::promote_t<T>;
+ using q16x8_t = typename wrapper::traits::neon_vector<q16_t, 8>::type;
+ using q16x8x2_t = typename wrapper::traits::neon_vector<q16_t, 16>::type;
+
+ constexpr int pool_size = 3;
+ 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(0) + (exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = _src->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom);
+
+ const UniformQuantizationInfo &src_qinfo = _src->info()->quantization_info().uniform();
+ const UniformQuantizationInfo &dst_qinfo = _dst->info()->quantization_info().uniform();
+
+ const float requant_scale = dst_qinfo.scale / src_qinfo.scale;
+ const int32_t requant_offset = dst_qinfo.offset - static_cast<int32_t>(static_cast<float>(src_qinfo.offset) / requant_scale);
+ const UniformQuantizationInfo requant_qinfo = UniformQuantizationInfo(requant_scale, requant_offset);
+
+ const T *const src_top_ptr = reinterpret_cast<const T *>(_src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top))));
+ const T *const src_middle_ptr = reinterpret_cast<const T *>(_src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1)));
+ const T *const src_bottom_ptr = reinterpret_cast<const T *>(_src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 2)));
+
+ execute_window_loop(window, [&](const Coordinates & id)
+ {
+ const auto top_data = wrapper::vloadq(src_top_ptr + src.offset());
+ const auto middle_data = wrapper::vloadq(src_middle_ptr + src.offset());
+ const auto bottom_data = wrapper::vloadq(src_bottom_ptr + src.offset());
+ q8x8_t fres = {};
+ q8x16_t fqres = {};
+
+ if(pooling_type == PoolingType::AVG)
+ {
+ // Convert data to u16
+ const q16x8x2_t top_data_q16 = { { wrapper::vmovl(wrapper::vgetlow(top_data)), wrapper::vmovl(wrapper::vgethigh(top_data)) } };
+ const q16x8x2_t middle_data_q16 = { { wrapper::vmovl(wrapper::vgetlow(middle_data)), wrapper::vmovl(wrapper::vgethigh(middle_data)) } };
+ const q16x8x2_t bottom_data_q16 = { { wrapper::vmovl(wrapper::vgetlow(bottom_data)), wrapper::vmovl(wrapper::vgethigh(bottom_data)) } };
+
+ // Calculate row sums
+ const q16x8x2_t vrsum =
+ {
+ {
+ wrapper::vadd(wrapper::vadd(top_data_q16.val[0], bottom_data_q16.val[0]), middle_data_q16.val[0]),
+ wrapper::vadd(wrapper::vadd(top_data_q16.val[1], bottom_data_q16.val[1]), middle_data_q16.val[1]),
+ }
+ };
+ const q16x8x2_t vrsum_shifted_1 =
+ {
+ {
+ wrapper::vext_1(vrsum.val[0], vrsum.val[1]),
+ wrapper::vext_1(vrsum.val[1], vrsum.val[1])
+ }
+ };
+ const q16x8x2_t vrsum_shifted_2 =
+ {
+ {
+ wrapper::vext_2(vrsum.val[0], vrsum.val[1]),
+ wrapper::vext_2(vrsum.val[1], vrsum.val[1])
+ }
+ };
+ // Calculate final sum
+ q16x8x2_t final_sum =
+ {
+ {
+ wrapper::vadd(wrapper::vadd(vrsum.val[0], vrsum_shifted_1.val[0]), vrsum_shifted_2.val[0]),
+ wrapper::vadd(wrapper::vadd(vrsum.val[1], vrsum_shifted_1.val[1]), vrsum_shifted_2.val[1]),
+ }
+ };
+ if(pool_stride_x == 2)
+ {
+ q16x8_t res =
+ {
+ wrapper::vgetlane(final_sum.val[0], 0),
+ wrapper::vgetlane(final_sum.val[0], 2),
+ wrapper::vgetlane(final_sum.val[0], 4),
+ wrapper::vgetlane(final_sum.val[0], 6),
+ wrapper::vgetlane(final_sum.val[1], 0),
+ wrapper::vgetlane(final_sum.val[1], 2),
+ wrapper::vgetlane(final_sum.val[1], 4),
+ wrapper::vgetlane(final_sum.val[1], 6),
+ };
+
+ scale_vector_q16x8<q16_t, q16x8_t>(exclude_padding, res, id, 0, 1,
+ pool_size, upper_bound_w, upper_bound_h,
+ pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y);
+ fres = wrapper::vmovn(res);
+ }
+ else
+ {
+ // Scale lower result
+ scale_vector_q16x8<q16_t, q16x8_t>(exclude_padding, final_sum.val[0], id, 0, 1,
+ pool_size, upper_bound_w, upper_bound_h,
+ pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y);
+ // Scale lower result
+ scale_vector_q16x8<q16_t, q16x8_t>(exclude_padding, final_sum.val[1], id, 8, 1,
+ pool_size, upper_bound_w, upper_bound_h,
+ pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y);
+ fqres = wrapper::vcombine(wrapper::vmovn(final_sum.val[0]), wrapper::vmovn(final_sum.val[1]));
+ }
+ }
+ else
+ {
+ const q8x16_t max_data = wrapper::vmax(wrapper::vmax(top_data, bottom_data), middle_data);
+ const q8x16_t max_data_shift1 = wrapper::vext_1(max_data, max_data);
+ const q8x16_t max_data_shift2 = wrapper::vext_2(max_data, max_data);
+ const q8x16_t final_max = wrapper::vmax(wrapper::vmax(max_data, max_data_shift1), max_data_shift2);
+
+ if(pool_stride_x == 2)
+ {
+ const q8x8x2_t table = { { wrapper::vgetlow(final_max), wrapper::vgethigh(final_max) } };
+ static const q8x8_t lookup_val = { 0, 2, 4, 6, 8, 10, 12, 14 };
+ fres = wrapper::vtbl(table, lookup_val);
+ }
+ else
+ {
+ fqres = final_max;
+ }
+ }
+
+ // Store result
+ if(pool_stride_x == 1)
+ {
+ if(src_qinfo != dst_qinfo)
+ {
+ fqres = vrequantize_pooling<q8x8_t, q8x16_t>(wrapper::vgetlow(fqres), wrapper::vgethigh(fqres), requant_qinfo);
+ }
+ wrapper::vstore(reinterpret_cast<T *>(dst.ptr()), fqres);
+ }
+ else
+ {
+ if(src_qinfo != dst_qinfo)
+ {
+ fres = vrequantize_pooling<q8x8_t>(fres, requant_qinfo);
+ }
+ wrapper::vstore(reinterpret_cast<T *>(dst.ptr()), fres);
+ }
+ },
+ src, dst);
+}
+
+void CpuPoolingKernel::poolingMxN_f16_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding)
+{
+ ARM_COMPUTE_UNUSED(pooling_type);
+ ARM_COMPUTE_UNUSED(exclude_padding);
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ Iterator src(_src, window_src);
+ Iterator dst(_dst, window);
+
+ const int pool_size_x = _pool_info.is_global_pooling ? _src->info()->tensor_shape().x() : _pool_info.pool_size.width;
+ const int pool_size_y = _pool_info.is_global_pooling ? _src->info()->tensor_shape().y() : _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(0) + (exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = _src->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom);
+
+ execute_window_loop(window, [&](const Coordinates & id)
+ {
+ float16_t res = 0.0f;
+ float16x8_t vres = vdupq_n_f16(0.0f);
+
+ if(pooling_type != PoolingType::MAX)
+ {
+ // Calculate scale
+ const float scale = calculate_avg_scale(exclude_padding, DataLayout::NCHW, 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);
+
+ // Perform pooling
+
+ for(int y = 0; y < pool_size_y; ++y)
+ {
+ int x = 0;
+ for(; x <= (pool_size_x - 8); x += 8)
+ {
+ const float16x8_t data = vld1q_f16(reinterpret_cast<const float16_t *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int>
+ (_src->info()->strides_in_bytes().y())));
+
+ // Get power of 2 in case of l2 pooling and accumulate
+ if(pooling_type == PoolingType::L2)
+ {
+ vres = vaddq_f16(vres, vmulq_f16(data, data));
+ }
+ else
+ {
+ vres = vaddq_f16(vres, data);
+ }
+ }
+
+ // Leftover for loop
+ for(; x < pool_size_x; ++x)
+ {
+ float16_t data = *(reinterpret_cast<const float16_t *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().x())
+ + (y - pool_pad_top) * static_cast<int>(_src->info()->strides_in_bytes().y())));
+
+ // Get power of 2 in case of l2 pooling
+ if(pooling_type == PoolingType::L2)
+ {
+ data *= data;
+ }
+
+ res += data;
+ }
+ }
+
+ // Reduction
+ float16x4_t tmp = vpadd_f16(vget_high_f16(vres), vget_low_f16(vres));
+ res += vget_lane_f16(tmp, 0);
+ res += vget_lane_f16(tmp, 1);
+ res += vget_lane_f16(tmp, 2);
+ res += vget_lane_f16(tmp, 3);
+
+ // Divide by scale
+ res *= scale;
+ }
+ else
+ {
+ float16x8_t vres = vdupq_n_f16(std::numeric_limits<float>::lowest());
+ res = std::numeric_limits<float>::lowest();
+
+ for(int y = 0; y < pool_size_y; ++y)
+ {
+ int x = 0;
+ for(; x <= (pool_size_x - 8); x += 8)
+ {
+ const float16x8_t data = vld1q_f16(reinterpret_cast<const float16_t *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int>
+ (_src->info()->strides_in_bytes().y())));
+ vres = vmaxq_f16(vres, data);
+ }
+
+ // Leftover for loop
+ for(; x < pool_size_x; ++x)
+ {
+ const float16_t data = *(reinterpret_cast<const float16_t *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().x())
+ + (y - pool_pad_top) * static_cast<int>(_src->info()->strides_in_bytes().y())));
+ res = std::max(res, data);
+ }
+ }
+
+ float16x4_t tmp = vpmax_f16(vget_high_f16(vres), vget_low_f16(vres));
+ res = std::max(res, vget_lane_f16(tmp, 0));
+ res = std::max(res, vget_lane_f16(tmp, 1));
+ res = std::max(res, vget_lane_f16(tmp, 2));
+ res = std::max(res, vget_lane_f16(tmp, 3));
+ }
+
+ // Calculate square-root in case of l2 pooling
+ if(pooling_type == PoolingType::L2)
+ {
+ res = std::sqrt(res);
+ }
+
+ // Store result
+ *(reinterpret_cast<float16_t *>(dst.ptr())) = res;
+ },
+ src, dst);
+
+#else /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+ ARM_COMPUTE_UNUSED(window_src);
+ ARM_COMPUTE_UNUSED(window);
+ ARM_COMPUTE_ERROR("FP16 Not supported! Recompile the library with arch=arm64-v8.2-a");
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+}
+
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+void CpuPoolingKernel::pooling2_f16_nhwc_maxpool_indices(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 = 8;
+
+ Window window_out = window;
+ window_out.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ Iterator src(_src, window_src);
+ Iterator dst(_dst, window_out);
+ Iterator indices(_indices, 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();
+
+ 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 float16_t *>(src.ptr() + in_x0_offset) + x_off;
+ const auto in_x1_ptr = reinterpret_cast<const float16_t *>(src.ptr() + in_x1_offset) + x_off;
+ const auto in_x2_ptr = reinterpret_cast<const float16_t *>(src.ptr() + in_x2_offset) + x_off;
+ const auto in_x3_ptr = reinterpret_cast<const float16_t *>(src.ptr() + in_x3_offset) + x_off;
+ const auto v_x0 = vld1q_f16(in_x0_ptr);
+ const auto v_x1 = vld1q_f16(in_x1_ptr);
+ const auto v_x2 = vld1q_f16(in_x2_ptr);
+ const auto v_x3 = vld1q_f16(in_x3_ptr);
+ float16x8_t vres = vmaxq_f16(vmaxq_f16(v_x2, v_x3), vmaxq_f16(v_x0, v_x1));
+ // Store result
+ vst1q_f16(reinterpret_cast<float16_t *>(dst.ptr()) + x_off, vres);
+
+ const uint32_t offset_base = offset_no_padding<float16_t>(src.offset(), id, *_src->info(), pool_stride_x, pool_stride_y);
+ const uint32_t offset_x0 = (uint32_t)offset_base / sizeof(float16_t) + x_off;
+ const uint32_t offset_x1 = (uint32_t)offset_x0 + in_stride_y / sizeof(float16_t) - pad_right;
+ const uint32_t offset_x2 = (uint32_t)offset_x0 + in_stride_z / sizeof(float16_t) - pad_right * _src->info()->tensor_shape()[1];
+ const uint32_t offset_x3 = (uint32_t)offset_x2 + in_stride_y / sizeof(float16_t) - pad_right;
+ const uint32x4_t voffset_x0_0 = { offset_x0, offset_x0 + 1, offset_x0 + 2, offset_x0 + 3 };
+ const uint32x4_t voffset_x0_1 = { offset_x0 + 4, offset_x0 + 5, offset_x0 + 6, offset_x0 + 7 };
+ const uint16x8_t voffset_x0 = vcombine_u16(vmovn_u32(voffset_x0_0), vmovn_u32(voffset_x0_1));
+ const uint32x4_t voffset_x1_0 = { offset_x1, offset_x1 + 1, offset_x1 + 2, offset_x1 + 3 };
+ const uint32x4_t voffset_x1_1 = { offset_x1 + 4, offset_x1 + 5, offset_x1 + 6, offset_x1 + 7 };
+ const uint16x8_t voffset_x1 = vcombine_u16(vmovn_u32(voffset_x1_0), vmovn_u32(voffset_x1_1));
+ const uint32x4_t voffset_x2_0 = { offset_x2, offset_x2 + 1, offset_x2 + 2, offset_x2 + 3 };
+ const uint32x4_t voffset_x2_1 = { offset_x2 + 4, offset_x2 + 5, offset_x2 + 6, offset_x2 + 7 };
+ const uint16x8_t voffset_x2 = vcombine_u16(vmovn_u32(voffset_x2_0), vmovn_u32(voffset_x2_1));
+ const uint32x4_t voffset_x3_0 = { offset_x3, offset_x3 + 1, offset_x3 + 2, offset_x3 + 3 };
+ const uint32x4_t voffset_x3_1 = { offset_x3 + 4, offset_x3 + 5, offset_x3 + 6, offset_x3 + 7 };
+ const uint16x8_t voffset_x3 = vcombine_u16(vmovn_u32(voffset_x3_0), vmovn_u32(voffset_x3_1));
+ const uint16x8_t tmp_indices0 = vbslq_u16(vcgeq_f16(v_x0, v_x1), voffset_x0, voffset_x1);
+ const uint16x8_t tmp_indices1 = vbslq_u16(vcgeq_f16(v_x2, v_x3), voffset_x2, voffset_x3);
+ const uint16x8_t tmp_indices2 = vbslq_u16(vcgeq_f16(vmaxq_f16(v_x0, v_x1), vmaxq_f16(v_x2, v_x3)), tmp_indices0, tmp_indices1);
+ const uint32x4_t tmp_indeces3_0 = vmovl_u16(vget_low_u16(tmp_indices2));
+ const uint32x4_t tmp_indeces3_1 = vmovl_u16(vget_high_u16(tmp_indices2));
+ // Store indicies
+ vst1q_u32(reinterpret_cast<uint32_t *>(indices.ptr()) + x_off, tmp_indeces3_0);
+ vst1q_u32(reinterpret_cast<uint32_t *>(indices.ptr() + 16) + x_off, tmp_indeces3_1);
+ }
+
+ // Left-overs loop
+ for(; x_off < window_end_x; ++x_off)
+ {
+ const auto x0 = *(reinterpret_cast<const float16_t *>(src.ptr() + in_x0_offset) + x_off);
+ const auto x1 = *(reinterpret_cast<const float16_t *>(src.ptr() + in_x1_offset) + x_off);
+ const auto x2 = *(reinterpret_cast<const float16_t *>(src.ptr() + in_x2_offset) + x_off);
+ const auto x3 = *(reinterpret_cast<const float16_t *>(src.ptr() + in_x3_offset) + x_off);
+ float16_t res = std::max(std::max(x2, x3), std::max(x0, x1));
+
+ // Store result
+ *(reinterpret_cast<float16_t *>(dst.ptr()) + x_off) = res;
+
+ const uint32_t offset_base = offset_no_padding<float16_t>(src.offset(), id, *_src->info(), pool_stride_x, pool_stride_y);
+ const uint32_t offset_x0 = (uint32_t)offset_base / sizeof(float16_t) + x_off;
+ const uint32_t offset_x1 = (uint32_t)offset_x0 + in_stride_y / sizeof(float16_t) - pad_right;
+ const uint32_t offset_x2 = (uint32_t)offset_x0 + in_stride_z / sizeof(float16_t) - pad_right * _src->info()->tensor_shape()[1];
+ const uint32_t offset_x3 = (uint32_t)offset_x2 + in_stride_y / sizeof(float16_t) - 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;
+ }
+ },
+ src, dst, indices);
+}
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+
+void CpuPoolingKernel::poolingMxN_f16_nhwc(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding)
+{
+ ARM_COMPUTE_UNUSED(pooling_type);
+ ARM_COMPUTE_UNUSED(exclude_padding);
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ if(_pool_info.pool_size == Size2D(2, 2) && pooling_type == PoolingType::MAX && _indices)
+ {
+ pooling2_f16_nhwc_maxpool_indices(window_src, window);
+ }
+ const int window_start_x = window.x().start();
+ const int window_end_x = window.x().end();
+ const int window_step_x = 8;
+
+ Window window_out = window;
+ window_out.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ Iterator src(_src, window_src);
+ Iterator dst(_dst, 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) + (exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = _src->info()->dimension(2) + (exclude_padding ? 0 : pool_pad_bottom);
+
+ float16x8_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(pooling_type != PoolingType::MAX)
+ {
+ // Calculate scale
+ const float scale = calculate_avg_scale(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 float16x8_t scale_v = vdupq_n_f16(scale);
+
+ // Perform pooling
+ vres = vdupq_n_f16(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 float16x8_t data = vld1q_f16(reinterpret_cast<const float16_t *>(src.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(pooling_type == PoolingType::L2)
+ {
+ vres = vaddq_f16(vres, vmulq_f16(data, data));
+ }
+ else
+ {
+ vres = vaddq_f16(vres, data);
+ }
+ }
+ }
+ // Divide by scale
+ vres = vmulq_f16(vres, scale_v);
+ }
+ else
+ {
+ vres = vdupq_n_f16(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 float16x8_t data = vld1q_f16(reinterpret_cast<const float16_t *>(src.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_f16(vres, data);
+ }
+ }
+ }
+
+ // Calculate square-root in case of l2 pooling
+ if(pooling_type == PoolingType::L2)
+ {
+ float16x8_t sqrt_reciprocal = vrsqrteq_f16(vres);
+ vres = vmulq_f16(vres, vmulq_f16(vrsqrtsq_f16(vmulq_f16(vres, sqrt_reciprocal), sqrt_reciprocal), sqrt_reciprocal));
+ }
+
+ // Store result
+ vst1q_f16(reinterpret_cast<float16_t *>(dst.ptr()) + x_off, vres);
+ }
+
+ // Left-overs loop
+ for(; x_off < window_end_x; ++x_off)
+ {
+ float16_t res = 0.0f;
+
+ if(pooling_type != PoolingType::MAX)
+ {
+ // Calculate scale
+ const float16_t scale = calculate_avg_scale(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 float16_t *>(src.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(pooling_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 float16_t data = *(reinterpret_cast<const float16_t *>(src.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(pooling_type == PoolingType::L2)
+ {
+ res = std::sqrt(res);
+ }
+
+ // Store result
+ *(reinterpret_cast<float16_t *>(dst.ptr()) + x_off) = res;
+ }
+ },
+ src, dst);
+
+#else /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+ ARM_COMPUTE_UNUSED(window_src);
+ ARM_COMPUTE_UNUSED(window);
+ ARM_COMPUTE_ERROR("FP16 Not supported! Recompile the library with arch=arm64-v8.2-a");
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+}
+
+void CpuPoolingKernel::poolingMxN_f32_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding)
+{
+ Iterator src(_src, window_src);
+ Iterator dst(_dst, window);
+
+ const int pool_size_x = _pool_info.is_global_pooling ? _src->info()->tensor_shape().x() : _pool_info.pool_size.width;
+ const int pool_size_y = _pool_info.is_global_pooling ? _src->info()->tensor_shape().y() : _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(0) + (exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = _src->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom);
+
+ execute_window_loop(window, [&](const Coordinates & id)
+ {
+ float res = 0.0f;
+
+ if(pooling_type != PoolingType::MAX)
+ {
+ // Calculate scale
+ const float scale = calculate_avg_scale(exclude_padding, DataLayout::NCHW, 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);
+
+ // Perform pooling
+ float32x4_t vres = vdupq_n_f32(0.0f);
+
+ for(int y = 0; y < pool_size_y; ++y)
+ {
+ int x = 0;
+ for(; x <= (pool_size_x - 4); x += 4)
+ {
+ const float32x4_t data = vld1q_f32(reinterpret_cast<const float *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int>
+ (_src->info()->strides_in_bytes().y())));
+
+ // Get power of 2 in case of l2 pooling and accumulate
+ if(pooling_type == PoolingType::L2)
+ {
+ vres = vmlaq_f32(vres, data, data);
+ }
+ else
+ {
+ vres = vaddq_f32(vres, data);
+ }
+ }
+
+ // Leftover for loop
+ for(; x < pool_size_x; ++x)
+ {
+ float data = *(reinterpret_cast<const float *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int>
+ (_src->info()->strides_in_bytes().y())));
+
+ // Get power of 2 in case of l2 pooling
+ if(pooling_type == PoolingType::L2)
+ {
+ data *= data;
+ }
+
+ res += data;
+ }
+ }
+
+#if defined(__aarch64__)
+ // Reduction operation available on 64 bit architectures only
+ res += vaddvq_f32(vres);
+#else // __aarch64__
+ // Reduction
+ float32x2_t tmp = vpadd_f32(vget_high_f32(vres), vget_low_f32(vres));
+ tmp = vpadd_f32(tmp, tmp);
+
+ res += vget_lane_f32(tmp, 0);
+#endif // __aarch64__
+ // Divide by scale
+ res *= scale;
+ }
+ else
+ {
+ float32x4_t vres = vdupq_n_f32(std::numeric_limits<float>::lowest());
+ res = std::numeric_limits<float>::lowest();
+
+ for(int y = 0; y < pool_size_y; ++y)
+ {
+ int x = 0;
+ for(; x <= (pool_size_x - 4); x += 4)
+ {
+ const float32x4_t data = vld1q_f32(reinterpret_cast<const float *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int>
+ (_src->info()->strides_in_bytes().y())));
+ vres = vmaxq_f32(vres, data);
+ }
+
+ // Leftover for loop
+ for(; x < pool_size_x; ++x)
+ {
+ const float data = *(reinterpret_cast<const float *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int>
+ (_src->info()->strides_in_bytes().y())));
+ res = std::max(res, data);
+ }
+ }
+#if defined(__aarch64__)
+ // Reduction operation available on 64 bit architectures only
+ res = std::max(vmaxvq_f32(vres), res);
+#else // __aarch64__
+ float32x2_t tmp = vpmax_f32(vget_high_f32(vres), vget_low_f32(vres));
+ tmp = vpmax_f32(tmp, tmp);
+
+ res = std::max(res, vget_lane_f32(tmp, 0));
+#endif // __aarch64__
+ }
+
+ // Calculate square-root in case of l2 pooling
+ if(pooling_type == PoolingType::L2)
+ {
+ res = std::sqrt(res);
+ }
+
+ // Store result
+ *(reinterpret_cast<float *>(dst.ptr())) = res;
+ },
+ src, dst);
+}
+
+void CpuPoolingKernel::pooling2_f32_nchw(const Window &window_src, const Window &window, PoolingType pooling_type,
+ bool exclude_padding)
+{
+ if(pooling_type == PoolingType::MAX && _indices)
+ {
+ pooling2_nchw_maxpool_indices<float>(window_src, window);
+ }
+ else
+ {
+ Iterator src(_src, window_src);
+ Iterator dst(_dst, window);
+ constexpr int pool_size = 2;
+ 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(0) + (exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = _src->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom);
+
+ const uint8_t *const src_top_ptr = _src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top)));
+ const uint8_t *const src_bottom_ptr = _src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1));
+
+ execute_window_loop(window, [&](const Coordinates & id)
+ {
+ const auto in_top_ptr = reinterpret_cast<const float *>(src_top_ptr + src.offset());
+ const auto in_bottom_ptr = reinterpret_cast<const float *>(src_bottom_ptr + src.offset());
+ float32x2_t top_data = vld1_f32(in_top_ptr);
+ float32x2_t bottom_data = vld1_f32(in_bottom_ptr);
+ float32x2_t res = {};
+ float final_res = 0;
+ // Get power of 2 in case of l2 pooling
+ if(pooling_type == PoolingType::L2)
+ {
+ top_data = vmul_f32(top_data, top_data);
+ bottom_data = vmul_f32(bottom_data, bottom_data);
+ }
+
+ if(pooling_type != PoolingType::MAX)
+ {
+ // Calculate scale
+ float scale = calculate_avg_scale(exclude_padding, DataLayout::NCHW, id, pool_size, pool_size, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y);
+ const float32x2_t scale_v = vdup_n_f32(scale);
+
+ // Perform pooling
+ const float32x2_t sum_data = vadd_f32(top_data, bottom_data);
+ res = vmul_f32(vpadd_f32(sum_data, sum_data), scale_v);
+ }
+ else
+ {
+ const float32x2_t max_data = vmax_f32(top_data, bottom_data);
+ res = vpmax_f32(max_data, max_data);
+ }
+ final_res = vget_lane_f32(res, 0);
+
+ // Calculate square-root in case of l2 pooling
+ if(pooling_type == PoolingType::L2)
+ {
+ final_res = sqrt(final_res);
+ }
+
+ // Store result
+ *(reinterpret_cast<float *>(dst.ptr())) = final_res;
+ },
+ src, dst);
+ }
+}
+
+void CpuPoolingKernel::pooling3_f32_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding)
+{
+ Iterator src(_src, window_src);
+ Iterator dst(_dst, window);
+
+ constexpr const int pool_size = 3;
+ 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(0) + (exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = _src->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom);
+
+ const uint8_t *const src_top_ptr = _src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top)));
+ const uint8_t *const src_middle_ptr = _src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1));
+ const uint8_t *const src_bottom_ptr = _src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 2));
+
+ execute_window_loop(window, [&](const Coordinates & id)
+ {
+ float32x4_t top_data = vld1q_f32(reinterpret_cast<const float *>(src_top_ptr + src.offset()));
+ float32x4_t middle_data = vld1q_f32(reinterpret_cast<const float *>(src_middle_ptr + src.offset()));
+ float32x4_t bottom_data = vld1q_f32(reinterpret_cast<const float *>(src_bottom_ptr + src.offset()));
+ float32x2_t res = {};
+ float final_res = 0;
+
+ // Get power of 2 in case of l2 pooling
+ if(pooling_type == PoolingType::L2)
+ {
+ top_data = vmulq_f32(top_data, top_data);
+ middle_data = vmulq_f32(middle_data, middle_data);
+ bottom_data = vmulq_f32(bottom_data, bottom_data);
+ }
+
+ if(pooling_type != PoolingType::MAX)
+ {
+ // Calculate scale
+ float scale = calculate_avg_scale(exclude_padding, DataLayout::NCHW, id, pool_size, pool_size, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y);
+ const float32x2_t scale_v = vdup_n_f32(scale);
+
+ // Perform pooling
+ const float32x4_t sum_data = vaddq_f32(vaddq_f32(top_data, bottom_data), middle_data);
+ res = vpadd_f32(vget_high_f32(vsetq_lane_f32(0.f, sum_data, 3)), vget_low_f32(sum_data));
+ res = vmul_f32(vpadd_f32(res, res), scale_v);
+ }
+ else
+ {
+ const float32x4_t max_data = vmaxq_f32(vmaxq_f32(top_data, bottom_data), middle_data);
+ res = vpmax_f32(vget_high_f32(vsetq_lane_f32(-std::numeric_limits<float>::max(), max_data, 3)), vget_low_f32(max_data));
+ res = vpmax_f32(res, res);
+ }
+ final_res = vget_lane_f32(res, 0);
+
+ // Calculate square-root in case of l2 pooling
+ if(pooling_type == PoolingType::L2)
+ {
+ final_res = sqrt(final_res);
+ }
+
+ // Store result
+ *(reinterpret_cast<float *>(dst.ptr())) = final_res;
+ },
+ src, dst);
+}
+
+void CpuPoolingKernel::pooling7_f32_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding)
+{
+ Iterator src(_src, window_src);
+ Iterator dst(_dst, window);
+
+ constexpr const int pool_size = 7;
+ 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(0) + (exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = _src->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom);
+
+ std::array<const uint8_t *, pool_size> src_ptrs{ {} };
+ for(int i = 0; i < pool_size; ++i)
+ {
+ src_ptrs[i] = _src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + i));
+ }
+
+ execute_window_loop(window, [&](const Coordinates & id)
+ {
+ float32x2_t res = {};
+ float final_res = 0.f;
+ if(pooling_type != PoolingType::MAX)
+ {
+ // Calculate scale
+ float scale = calculate_avg_scale(exclude_padding, DataLayout::NCHW, id, pool_size, pool_size, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y);
+ const float32x2_t scale_v = vdup_n_f32(scale);
+
+ // Perform pooling
+ float32x4x2_t data = vld2q_f32(reinterpret_cast<const float *>(src_ptrs[0] + src.offset()));
+ // Get power of 2 in case of l2 pooling
+ if(pooling_type == PoolingType::L2)
+ {
+ data.val[0] = vmulq_f32(data.val[0], data.val[0]);
+ data.val[1] = vmulq_f32(data.val[1], data.val[1]);
+ }
+ float32x4_t sum_data = vaddq_f32(data.val[0], vsetq_lane_f32(0.f, data.val[1], 3));
+ for(int i = 1; i < pool_size; ++i)
+ {
+ data = vld2q_f32(reinterpret_cast<const float *>(src_ptrs[i] + src.offset()));
+ // Get power of 2 in case of l2 pooling
+ if(pooling_type == PoolingType::L2)
+ {
+ data.val[0] = vmulq_f32(data.val[0], data.val[0]);
+ data.val[1] = vmulq_f32(data.val[1], data.val[1]);
+ }
+ sum_data = vaddq_f32(sum_data, data.val[0]);
+ sum_data = vaddq_f32(sum_data, vsetq_lane_f32(0.f, data.val[1], 3));
+ }
+ res = vpadd_f32(vget_high_f32(sum_data), vget_low_f32(sum_data));
+ res = vmul_f32(vpadd_f32(res, res), scale_v);
+ }
+ else
+ {
+ float32x4x2_t max_data = vld2q_f32(reinterpret_cast<const float *>(src_ptrs[0] + src.offset()));
+ for(int i = 1; i < pool_size; ++i)
+ {
+ const float32x4x2_t data = vld2q_f32(reinterpret_cast<const float *>(src_ptrs[i] + src.offset()));
+ max_data = vmax2q_f32(max_data, data);
+ }
+ res = vpmax_f32(vget_high_f32(vsetq_lane_f32(-std::numeric_limits<float>::max(), max_data.val[1], 3)), vget_low_f32(max_data.val[1]));
+ res = vpmax_f32(res, vpmax_f32(vget_high_f32(max_data.val[0]), vget_low_f32(max_data.val[0])));
+ res = vpmax_f32(res, res);
+ }
+ final_res = vget_lane_f32(res, 0);
+
+ // Calculate square-root in case of l2 pooling
+ if(pooling_type == PoolingType::L2)
+ {
+ final_res = sqrt(final_res);
+ }
+
+ // Store result
+ *(reinterpret_cast<float *>(dst.ptr())) = final_res;
+ },
+ src, dst);
+}
+
+void CpuPoolingKernel::poolingMxN_f32_nhwc(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding)
+{
+ if(_pool_info.pool_size == Size2D(2, 2) && pooling_type == PoolingType::MAX && _indices)
+ {
+ pooling2_f32_nhwc_maxpool_indices(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 src(_src, window_src);
+ Iterator dst(_dst, 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) + (exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = _src->info()->dimension(2) + (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(pooling_type != PoolingType::MAX)
+ {
+ // Calculate scale
+ const float scale = calculate_avg_scale(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 *>(src.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(pooling_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 *>(src.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(pooling_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 *>(dst.ptr()) + x_off, vres);
+ }
+
+ // Left-overs loop
+ for(; x_off < window_end_x; ++x_off)
+ {
+ float res = 0.0f;
+
+ if(pooling_type != PoolingType::MAX)
+ {
+ // Calculate scale
+ const float scale = calculate_avg_scale(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 *>(src.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(pooling_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 *>(src.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(pooling_type == PoolingType::L2)
+ {
+ res = std::sqrt(res);
+ }
+
+ // Store result
+ *(reinterpret_cast<float *>(dst.ptr()) + x_off) = res;
+ }
+ },
+ src, dst);
+ }
+}
+
+void CpuPoolingKernel::pooling2_f32_nhwc_maxpool_indices(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 src(_src, window_src);
+ Iterator dst(_dst, window_out);
+ Iterator indices(_indices, 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 *>(src.ptr() + in_x0_offset);
+ const auto in_x1_ptr = reinterpret_cast<const float *>(src.ptr() + in_x1_offset);
+ const auto in_x2_ptr = reinterpret_cast<const float *>(src.ptr() + in_x2_offset);
+ const auto in_x3_ptr = reinterpret_cast<const float *>(src.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 *>(dst.ptr()) + x_off, vres);
+
+ const uint32_t offset_base = offset_no_padding<float>(src.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 *>(src.ptr() + in_x0_offset) + x_off);
+ const auto x1 = *(reinterpret_cast<const float *>(src.ptr() + in_x1_offset) + x_off);
+ const auto x2 = *(reinterpret_cast<const float *>(src.ptr() + in_x2_offset) + x_off);
+ const auto x3 = *(reinterpret_cast<const float *>(src.ptr() + in_x3_offset) + x_off);
+ res = std::max(std::max(x2, x3), std::max(x0, x1));
+
+ // Store result
+ *(reinterpret_cast<float *>(dst.ptr()) + x_off) = res;
+
+ const uint32_t offset_base = offset_no_padding<float>(src.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;
+ }
+ },
+ src, dst, indices);
+}
+
+template <typename T>
+void CpuPoolingKernel::poolingMxN_q8_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding)
+{
+ Iterator src(_src, window_src);
+ Iterator dst(_dst, window);
+
+ /** NEON vector types */
+ using q8x8_t = typename wrapper::traits::neon_vector<T, 8>::type;
+ using q16_t = typename wrapper::traits::promote_t<T>;
+ using q16x8_t = typename wrapper::traits::neon_vector<q16_t, 8>::type;
+ using q32_t = typename wrapper::traits::promote_t<q16_t>;
+ using q32x4_t = typename wrapper::traits::neon_vector<q32_t, 4>::type;
+
+ const int pool_size_x = _pool_info.is_global_pooling ? _src->info()->tensor_shape().x() : _pool_info.pool_size.width;
+ const int pool_size_y = _pool_info.is_global_pooling ? _src->info()->tensor_shape().y() : _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(0) + (exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = _src->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom);
+
+ const UniformQuantizationInfo &src_qinfo = _src->info()->quantization_info().uniform();
+ const UniformQuantizationInfo &dst_qinfo = _dst->info()->quantization_info().uniform();
+
+ execute_window_loop(window, [&](const Coordinates & id)
+ {
+ T res = std::numeric_limits<T>::min();
+
+ if(pooling_type != PoolingType::MAX)
+ {
+ q32x4_t vres = wrapper::vdup_n(static_cast<q32_t>(0.f), wrapper::traits::vector_128_tag{});
+ q32_t sres = 0;
+
+ // Calculate scale
+ const float scale = calculate_avg_scale(exclude_padding, DataLayout::NCHW, 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);
+
+ // Perform pooling
+ for(int y = 0; y < pool_size_y; ++y)
+ {
+ int x = 0;
+ for(; x <= (pool_size_x - 8); x += 8)
+ {
+ const q8x8_t data = wrapper::vload(reinterpret_cast<const T *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int>
+ (_src->info()->strides_in_bytes().y())));
+
+ const q16x8_t data_q16 = wrapper::vmovl(data);
+ vres = wrapper::vadd(vres, wrapper::vaddl(wrapper::vgethigh(data_q16), wrapper::vgetlow(data_q16)));
+ }
+
+ // Leftover for loop
+ for(; x < pool_size_x; ++x)
+ {
+ T data = *(reinterpret_cast<const T *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int>
+ (_src->info()->strides_in_bytes().y())));
+ sres += data;
+ }
+ }
+
+ // Reduction
+ const auto tmp = wrapper::vpadd(wrapper::vgethigh(vres), wrapper::vgetlow(vres));
+ sres += wrapper::vgetlane(tmp, 0) + wrapper::vgetlane(tmp, 1);
+
+ // Divide by scale
+ res = static_cast<T>(support::cpp11::round(sres * scale));
+ }
+ else
+ {
+ q8x8_t vres = wrapper::vdup_n(std::numeric_limits<T>::min(), wrapper::traits::vector_64_tag{});
+
+ for(int y = 0; y < pool_size_y; ++y)
+ {
+ int x = 0;
+ for(; x <= (pool_size_x - 8); x += 8)
+ {
+ const q8x8_t data = wrapper::vload(reinterpret_cast<const T *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int>
+ (_src->info()->strides_in_bytes().y())));
+ vres = wrapper::vmax(vres, data);
+ }
+ // Leftover for loop
+ for(; x < pool_size_x; ++x)
+ {
+ const T data = *(reinterpret_cast<const T *>(src.ptr() + (x - pool_pad_left) * static_cast<int>(_src->info()->strides_in_bytes().x()) + (y - pool_pad_top) * static_cast<int>
+ (_src->info()->strides_in_bytes().y())));
+ res = std::max(res, data);
+ }
+ }
+
+ // Reduce max
+ vres = wrapper::vpmax(vres, vres);
+ vres = wrapper::vpmax(vres, vres);
+ vres = wrapper::vpmax(vres, vres);
+
+ // Get max value
+ res = std::max(res, wrapper::vgetlane(vres, 0));
+ }
+ // Store result
+ res = (src_qinfo != dst_qinfo) ? Qasymm8QuantizationHelper<T>::quantize(Qasymm8QuantizationHelper<T>::dequantize(res, src_qinfo), dst_qinfo) : res;
+ *(reinterpret_cast<T *>(dst.ptr())) = res;
+ },
+ src, dst);
+}
+
+template <typename T>
+void CpuPoolingKernel::poolingMxN_q8_nhwc(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding)
+{
+ const int window_start_x = window.x().start();
+ const int window_end_x = window.x().end();
+ const int window_step_x = 16;
+ const int window_half_step_x = window_step_x / 2;
+
+ Window window_out = window;
+ window_out.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ Iterator src(_src, window_src);
+ Iterator dst(_dst, window_out);
+
+ using q8x8_t = typename wrapper::traits::neon_vector<T, 8>::type;
+ using q8x16_t = typename wrapper::traits::neon_vector<T, 16>::type;
+ using q16_t = typename wrapper::traits::promote_t<T>;
+ using q16x8_t = typename wrapper::traits::neon_vector<q16_t, 8>::type;
+ using q32_t = typename wrapper::traits::promote_t<q16_t>;
+ using q32x4_t = typename wrapper::traits::neon_vector<q32_t, 4>::type;
+
+ 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) + (exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = _src->info()->dimension(2) + (exclude_padding ? 0 : pool_pad_bottom);
+
+ const float32x4_t half_scale_v = vdupq_n_f32(0.5f);
+ const UniformQuantizationInfo src_qinfo = _src->info()->quantization_info().uniform();
+ const UniformQuantizationInfo dst_qinfo = _dst->info()->quantization_info().uniform();
+
+ const float quant_rescale = dst_qinfo.scale / src_qinfo.scale;
+ // "new_offset" doesn't have to consider the "half_scale_v" in its computation
+ // With a requantization performed in a single step there won't be uncertainties introduced
+ const int32_t new_offset = dst_qinfo.offset - static_cast<int32_t>(static_cast<float>(src_qinfo.offset) / quant_rescale);
+
+ const float requant_scale = dst_qinfo.scale / src_qinfo.scale;
+ const int32_t requant_offset = dst_qinfo.offset - static_cast<int32_t>(static_cast<float>(src_qinfo.offset) / requant_scale);
+ const UniformQuantizationInfo requant_qinfo = UniformQuantizationInfo(requant_scale, requant_offset);
+
+ 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(pooling_type != PoolingType::MAX)
+ {
+ q32x4_t vres1 = wrapper::vdup_n(static_cast<q32_t>(0.f), wrapper::traits::vector_128_tag{});
+ q32x4_t vres2 = wrapper::vdup_n(static_cast<q32_t>(0.f), wrapper::traits::vector_128_tag{});
+ q32x4_t vres3 = wrapper::vdup_n(static_cast<q32_t>(0.f), wrapper::traits::vector_128_tag{});
+ q32x4_t vres4 = wrapper::vdup_n(static_cast<q32_t>(0.f), wrapper::traits::vector_128_tag{});
+
+ // Calculate scale
+ const float scale = calculate_avg_scale(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);
+
+ // Perform pooling
+ for(int y = pool_start_y; y < pool_end_y; ++y)
+ {
+ for(int x = pool_start_x; x < pool_end_x; ++x)
+ {
+ const q8x16_t data = wrapper::vloadq(reinterpret_cast<const T *>(src.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);
+
+ const q16x8_t data_q16 = wrapper::vmovl(wrapper::vgetlow(data));
+ const q16x8_t data2_q16 = wrapper::vmovl(wrapper::vgethigh(data));
+ vres1 = wrapper::vadd(vres1, wrapper::vmovl(wrapper::vgetlow(data_q16)));
+ vres2 = wrapper::vadd(vres2, wrapper::vmovl(wrapper::vgethigh(data_q16)));
+ vres3 = wrapper::vadd(vres3, wrapper::vmovl(wrapper::vgetlow(data2_q16)));
+ vres4 = wrapper::vadd(vres4, wrapper::vmovl(wrapper::vgethigh(data2_q16)));
+ }
+ }
+
+ if(src_qinfo != dst_qinfo)
+ {
+ const float32x4x4_t vres =
+ {
+ {
+ vcvtq_f32_q32(vres1),
+ vcvtq_f32_q32(vres2),
+ vcvtq_f32_q32(vres3),
+ vcvtq_f32_q32(vres4),
+ }
+ };
+ const auto requantized_dst = vrequantize_pooling_with_scale<q8x16_t>(vres, quant_rescale, scale, new_offset);
+ // Store result
+ wrapper::vstore(reinterpret_cast<T *>(dst.ptr()) + x_off, wrapper::vgetlow(requantized_dst));
+ wrapper::vstore(reinterpret_cast<T *>(dst.ptr()) + x_off + 8, wrapper::vgethigh(requantized_dst));
+ }
+ else
+ {
+ const float32x4_t scale_v = vdupq_n_f32(scale);
+ // Divide by scale and add 0.5f to round to nearest instead of rounding towards zero
+ vres1 = vcvtq_q32_f32<q32x4_t>(wrapper::vmla(half_scale_v, vcvtq_f32_q32(vres1), scale_v));
+ vres2 = vcvtq_q32_f32<q32x4_t>(wrapper::vmla(half_scale_v, vcvtq_f32_q32(vres2), scale_v));
+ vres3 = vcvtq_q32_f32<q32x4_t>(wrapper::vmla(half_scale_v, vcvtq_f32_q32(vres3), scale_v));
+ vres4 = vcvtq_q32_f32<q32x4_t>(wrapper::vmla(half_scale_v, vcvtq_f32_q32(vres4), scale_v));
+
+ const q8x8_t res1 = wrapper::vmovn(wrapper::vcombine(wrapper::vmovn(vres1), wrapper::vmovn(vres2)));
+ const q8x8_t res2 = wrapper::vmovn(wrapper::vcombine(wrapper::vmovn(vres3), wrapper::vmovn(vres4)));
+ // Store result
+ wrapper::vstore(reinterpret_cast<T *>(dst.ptr()) + x_off, res1);
+ wrapper::vstore(reinterpret_cast<T *>(dst.ptr()) + x_off + 8, res2);
+ }
+ }
+ else
+ {
+ q8x16_t vres = wrapper::vdup_n(std::numeric_limits<T>::min(), wrapper::traits::vector_128_tag{});
+
+ for(int y = pool_start_y; y < pool_end_y; ++y)
+ {
+ for(int x = pool_start_x; x < pool_end_x; ++x)
+ {
+ const q8x16_t data = wrapper::vloadq(reinterpret_cast<const T *>(src.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 = wrapper::vmax(vres, data);
+ }
+ }
+
+ // Store result
+ wrapper::vstore(reinterpret_cast<T *>(dst.ptr()) + x_off, (src_qinfo != dst_qinfo) ? vrequantize_pooling<q8x8_t, q8x16_t>(wrapper::vgetlow(vres), wrapper::vgethigh(vres),
+ requant_qinfo) :
+ vres);
+ }
+ }
+
+ if(pooling_type == PoolingType::MAX)
+ {
+ for(; x_off <= (window_end_x - window_half_step_x); x_off += window_half_step_x)
+ {
+ q8x8_t vres = wrapper::vdup_n(std::numeric_limits<T>::min(), wrapper::traits::vector_64_tag{});
+ for(int y = pool_start_y; y < pool_end_y; ++y)
+ {
+ for(int x = pool_start_x; x < pool_end_x; ++x)
+ {
+ const q8x8_t data = wrapper::vload(reinterpret_cast<const T *>(src.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 = wrapper::vmax(vres, data);
+ }
+ }
+
+ // Store result
+ wrapper::vstore(reinterpret_cast<T *>(dst.ptr()) + x_off,
+ (src_qinfo != dst_qinfo) ? vrequantize_pooling<q8x8_t>(vres, requant_qinfo) : vres);
+ }
+ }
+
+ // Left-overs loop
+ for(; x_off < window_end_x; ++x_off)
+ {
+ if(pooling_type != PoolingType::MAX)
+ {
+ q32_t res = static_cast<q32_t>(0.f);
+
+ // Calculate scale
+ const float scale = calculate_avg_scale(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);
+
+ // Perform pooling
+ for(int y = pool_start_y; y < pool_end_y; ++y)
+ {
+ for(int x = pool_start_x; x < pool_end_x; ++x)
+ {
+ const T data = *(reinterpret_cast<const T *>(src.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 += data;
+ }
+ }
+
+ if(src_qinfo != dst_qinfo)
+ {
+ const float res_f = static_cast<float>(res);
+ const float new_scale = quant_rescale / scale;
+ const auto requantized_dst = quantize<T>(res_f, UniformQuantizationInfo(new_scale, new_offset));
+
+ // Store result
+ *(reinterpret_cast<T *>(dst.ptr()) + x_off) = requantized_dst;
+ }
+ else
+ {
+ // Divide by scale and add 0.5f to round to nearest instead of rounding towards zero
+ res = static_cast<T>(0.5f + static_cast<float>(res) * scale);
+
+ // Store result
+ *(reinterpret_cast<T *>(dst.ptr()) + x_off) = res;
+ }
+ }
+ else
+ {
+ T res = std::numeric_limits<T>::min();
+
+ for(int y = pool_start_y; y < pool_end_y; ++y)
+ {
+ for(int x = pool_start_x; x < pool_end_x; ++x)
+ {
+ const T data = *(reinterpret_cast<const T *>(src.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);
+ }
+ }
+
+ // Store result
+ if(src_qinfo != dst_qinfo)
+ {
+ const float res_f = static_cast<float>(res);
+ *(reinterpret_cast<T *>(dst.ptr()) + x_off) = quantize<T>(res_f, requant_qinfo);
+ }
+ else
+ {
+ *(reinterpret_cast<T *>(dst.ptr()) + x_off) = res;
+ }
+ }
+ }
+
+ },
+ src, dst);
+}
+
+Status CpuPoolingKernel::validate(const ITensorInfo *src, const ITensorInfo *dst, const PoolingLayerInfo &pool_info, const ITensorInfo *indices)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src);
+
+ unsigned int pooled_w = 0;
+ unsigned int pooled_h = 0;
+ unsigned int num_elems_processed_per_iteration = 0;
+ BorderSize border_size(0);
+
+ const bool is_global_pooling = pool_info.is_global_pooling;
+ unsigned int pool_size_x = 0;
+ unsigned int pool_size_y = 0;
+
+ // Get data layout
+ const auto data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? src->data_layout() : pool_info.data_layout;
+ const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+ const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+
+ pool_size_x = is_global_pooling ? src->dimension(idx_width) : pool_info.pool_size.width;
+ pool_size_y = is_global_pooling ? src->dimension(idx_height) : pool_info.pool_size.height;
+
+ // Validate pool info before calling scaled_dimensions
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_pool_info(pool_size_x, pool_size_y));
+
+ // Check dst dimensions
+ std::tie(pooled_w, pooled_h) = scaled_dimensions(src->dimension(idx_width),
+ src->dimension(idx_height),
+ pool_size_x,
+ pool_size_y,
+ pool_info.pad_stride_info);
+
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst, pool_info, pooled_w, pooled_h, indices, Size2D(pool_size_x, pool_size_y)));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src->clone().get(), dst->clone().get(),
+ (indices) ? indices->clone().get() : nullptr, pool_info, num_elems_processed_per_iteration, border_size, pooled_w, pooled_h,
+ pool_size_x, pool_size_y)
+ .first);
+
+ return Status{};
+}
+
+void CpuPoolingKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
+{
+ ARM_COMPUTE_UNUSED(info);
+ ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+ ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window);
+ ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+ _src = tensors.get_const_tensor(TensorType::ACL_SRC_0);
+ _dst = tensors.get_tensor(TensorType::ACL_DST_0);
+ _indices = tensors.get_tensor(TensorType::ACL_DST_1);
+
+ const unsigned int pool_stride_x = _pool_info.pad_stride_info.stride().first;
+ const unsigned int pool_stride_y = _pool_info.pad_stride_info.stride().second;
+ const unsigned int pool_size = _pool_info.pool_size.width;
+ const bool exclude_padding = _pool_info.exclude_padding;
+
+ Window window_src(window);
+ if(_data_layout == DataLayout::NCHW)
+ {
+ // Set step for src in x and y direction for the src
+ unsigned int window_x_inc = 0;
+ switch(_src->info()->data_type())
+ {
+ case DataType::QASYMM8:
+ case DataType::QASYMM8_SIGNED:
+ {
+ window_x_inc = pool_stride_x;
+ if((pool_size == 2 || pool_size == 3) && pool_stride_x < 3)
+ {
+ window_x_inc = (pool_stride_x == 2) ? _num_elems_processed_per_iteration * 2 : _num_elems_processed_per_iteration;
+ }
+ break;
+ }
+
+ case DataType::F16:
+ case DataType::F32:
+ {
+ window_x_inc = pool_stride_x;
+ break;
+ }
+ default:
+ {
+ ARM_COMPUTE_ERROR("Not supported");
+ }
+ }
+ window_src.set(Window::DimX, Window::Dimension(window.x().start() * pool_stride_x, window.x().end() * pool_stride_x, window_x_inc));
+ window_src.set(Window::DimY, Window::Dimension(window.y().start() * pool_stride_y, window.y().end() * pool_stride_y, pool_stride_y));
+ }
+ else
+ {
+ window_src.set(Window::DimX, Window::Dimension(0, 1, 1));
+ window_src.set(Window::DimY, Window::Dimension(0, _src->info()->dimension(1), pool_stride_x));
+ window_src.set(Window::DimZ, Window::Dimension(0, _src->info()->dimension(2), pool_stride_y));
+ }
+
+ // Run function
+ (this->*_func)(window_src, window, _pool_info.pool_type, exclude_padding);
+}
+} // namespace kernels
+} // namespace cpu
+} // namespace arm_compute
diff --git a/src/core/cpu/kernels/CpuPoolingKernel.h b/src/core/cpu/kernels/CpuPoolingKernel.h
new file mode 100644
index 0000000..036e436
--- /dev/null
+++ b/src/core/cpu/kernels/CpuPoolingKernel.h
@@ -0,0 +1,226 @@
+/*
+ * Copyright (c) 2017-2021 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.
+ */
+#ifndef ARM_COMPUTE_CPU_POOLING_KERNEL_H
+#define ARM_COMPUTE_CPU_POOLING_KERNEL_H
+
+#include "arm_compute/core/Types.h"
+#include "src/core/common/Macros.h"
+#include "src/core/cpu/ICpuKernel.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+namespace kernels
+{
+/** Interface for the pooling layer kernel */
+class CpuPoolingKernel : public ICpuKernel
+{
+public:
+ const char *name() const override
+ {
+ return "CpuPoolingKernel";
+ }
+ /** Default constructor */
+ CpuPoolingKernel() = default;
+ ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(CpuPoolingKernel);
+ /** Configure kernel for a given list of arguments
+ *
+ * @note F16 are supported for pool sizes 2 and 3 only
+ *
+ * @param[in] src Source tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
+ * @param[out] dst Destination tensor info. Data types supported: Same as @p src.
+ * @param[in] pool_info Contains pooling operation information described in @ref PoolingLayerInfo.
+ * @param[out] indices (optional) The indices of the maximal values. Data type supported: U32.
+ */
+ void configure(ITensorInfo *src, ITensorInfo *dst, const PoolingLayerInfo &pool_info, ITensorInfo *indices = nullptr);
+ /** Static function to check if given info will lead to a valid configuration of @ref CpuPoolingKernel
+ *
+ * @note F16 are supported for pool sizes 2 and 3 only
+ *
+ * @param[in] src Source tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
+ * @param[in] dst Destination tensor info. Data types supported: Same as @p src.
+ * @param[in] pool_info Contains pooling operation information described in @ref PoolingLayerInfo.
+ * @param[in] indices (optional) The indices of the maximal values. Data type supported: U32.
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *src, const ITensorInfo *dst, const PoolingLayerInfo &pool_info, const ITensorInfo *indices = nullptr);
+
+ // Inherited methods overridden:
+ void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
+ BorderSize border_size() const override;
+
+private:
+ /** Function to perform 2x2 pooling.
+ *
+ * @param[in] window_src src region on which to execute the kernel.
+ * @param[in] window dst region on which to execute the kernel.
+ * @param[in] pooling_type Pooling operation to be computed.
+ * @param[in] exclude_padding Flag to specify exclusion of padding from the operation.
+ */
+ void pooling2_f32_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding = false);
+ /** Function to perform 2x2 pooling and compute the pooling indices. The indices can be used for max unpool.
+ *
+ * @param[in] window_src src region on which to execute the kernel.
+ * @param[in] window dst region on which to execute the kernel.
+ */
+ void pooling2_f32_nhwc_maxpool_indices(const Window &window_src, const Window &window);
+ /** Function to perform MxN pooling for 32-bit floating point values.
+ *
+ * @param[in] window_src src region on which to execute the kernel.
+ * @param[in] window dst region on which to execute the kernel.
+ * @param[in] pooling_type Pooling operation to be computed.
+ * @param[in] exclude_padding Flag to specify exclusion of padding from the operation.
+ */
+ void poolingMxN_f32_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding = false);
+ /** Function to perform MxN pooling for 32-bit floating point values (NHWC).
+ *
+ * @param[in] window_src src region on which to execute the kernel.
+ * @param[in] window dst region on which to execute the kernel.
+ * @param[in] pooling_type Pooling operation to be computed.
+ * @param[in] exclude_padding Flag to specify exclusion of padding from the operation.
+ */
+ void poolingMxN_f32_nhwc(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding = false);
+ /** Function to perform 7x7 pooling.
+ *
+ * @param[in] window_src src region on which to execute the kernel.
+ * @param[in] window dst region on which to execute the kernel.
+ * @param[in] pooling_type Pooling operation to be computed.
+ * @param[in] exclude_padding Flag to specify exclusion of padding from the operation.
+ */
+ void pooling7_f32_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding = false);
+ /** Function to perform 3x3 pooling.
+ *
+ * @param[in] window_src src region on which to execute the kernel.
+ * @param[in] window dst region on which to execute the kernel.
+ * @param[in] pooling_type Pooling operation to be computed.
+ * @param[in] exclude_padding Flag to specify exclusion of padding from the operation.
+ */
+ void pooling3_f32_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding = false);
+ /** Function to perform 2x2 pooling for float16_t.
+ *
+ * @param[in] window_src src region on which to execute the kernel.
+ * @param[in] window dst region on which to execute the kernel.
+ * @param[in] pooling_type Pooling operation to be computed.
+ * @param[in] exclude_padding Flag to specify exclusion of padding from the operation.
+ */
+ void pooling2_f16_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding = false);
+ /** Function to perform 2x2 pooling and compute the pooling indices for FP32/FP16. The indices can be used for max unpool.
+ *
+ * @param[in] window_src src region on which to execute the kernel.
+ * @param[in] window dst region on which to execute the kernel.
+ */
+ template <typename T>
+ void pooling2_nchw_maxpool_indices(const Window &window_src, const Window &window);
+ /** Function to perform 2x2 pooling and compute the pooling indices. The indices can be used for max unpool.
+ *
+ * @param[in] window_src src region on which to execute the kernel.
+ * @param[in] window dst region on which to execute the kernel.
+ */
+ void pooling2_f16_nhwc_maxpool_indices(const Window &window_src, const Window &window);
+ /** Function to perform 3x3 pooling.
+ *
+ * @param[in] window_src src region on which to execute the kernel.
+ * @param[in] window dst region on which to execute the kernel.
+ * @param[in] pooling_type Pooling operation to be computed.
+ * @param[in] exclude_padding Flag to specify exclusion of padding from the operation.
+ */
+ void pooling3_f16_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding = false);
+ /** Function to perform MxN pooling for 16-bit floating point values.
+ *
+ * @param[in] window_src src region on which to execute the kernel.
+ * @param[in] window dst region on which to execute the kernel.
+ * @param[in] pooling_type Pooling operation to be computed.
+ * @param[in] exclude_padding Flag to specify exclusion of padding from the operation.
+ */
+ void poolingMxN_f16_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding = false);
+ /** Function to perform MxN pooling for 16-bit floating point values. (NHWC)
+ *
+ * @param[in] window_src src region on which to execute the kernel.
+ * @param[in] window dst region on which to execute the kernel.
+ * @param[in] pooling_type Pooling operation to be computed.
+ * @param[in] exclude_padding Flag to specify exclusion of padding from the operation.
+ */
+ void poolingMxN_f16_nhwc(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding = false);
+ /** Template function to perform 2x2 pooling for 8bit quantized fixed point. (NCHW)
+ *
+ * @param[in] window_src src region on which to execute the kernel.
+ * @param[in] window dst region on which to execute the kernel.
+ * @param[in] pooling_type Pooling operation to be computed.
+ * @param[in] exclude_padding Flag to specify exclusion of padding from the operation.
+ */
+ template <typename T>
+ void pooling2_q8_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding = false);
+ /** Template function to perform 3x3 pooling for 8bit quantized fixed point. (NCHW)
+ *
+ * @param[in] window_src src region on which to execute the kernel.
+ * @param[in] window dst region on which to execute the kernel.
+ * @param[in] pooling_type Pooling operation to be computed.
+ * @param[in] exclude_padding Flag to specify exclusion of padding from the operation.
+ */
+ template <typename T>
+ void pooling3_q8_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding = false);
+ /** Template function to perform MxN pooling for 8-bit quantized. (NCHW)
+ *
+ * @param[in] window_src src region on which to execute the kernel.
+ * @param[in] window dst region on which to execute the kernel.
+ * @param[in] pooling_type Pooling operation to be computed.
+ * @param[in] exclude_padding Flag to specify exclusion of padding from the operation.
+ */
+ template <typename T>
+ void poolingMxN_q8_nchw(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding = false);
+ /** Template function to perform MxN pooling for 8-bit quantized. (NHWC)
+ *
+ * @param[in] window_src src region on which to execute the kernel.
+ * @param[in] window dst region on which to execute the kernel.
+ * @param[in] pooling_type Pooling operation to be computed.
+ * @param[in] exclude_padding Flag to specify exclusion of padding from the operation.
+ */
+ template <typename T>
+ void poolingMxN_q8_nhwc(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding = false);
+ /** Common signature for all the specialised Pooling functions
+ *
+ * @param[in] window_src src region on which to execute the kernel.
+ * @param[in] window dst region on which to execute the kernel.
+ * @param[in] pooling_type Pooling operation to be computed.
+ * @param[in] exclude_padding Flag to specify exclusion of padding from the operation.
+ */
+ using PoolingFunction = void (CpuPoolingKernel::*)(const Window &window_src, const Window &window, PoolingType pooling_type, bool exclude_padding);
+
+private:
+ PoolingFunction _func{ nullptr };
+ const ITensor *_src{ nullptr };
+ ITensor *_dst{ nullptr };
+ ITensor *_indices{ nullptr };
+ PoolingLayerInfo _pool_info{};
+ DataLayout _data_layout{ DataLayout::UNKNOWN };
+ unsigned int _num_elems_processed_per_iteration{ 0 };
+ BorderSize _border_size{ 0 };
+ bool _is_square{ false };
+};
+} // namespace kernels
+} // namespace cpu
+} // namespace arm_compute
+#endif /*ARM_COMPUTE_CPU_POOLING_KERNEL_H */