Decouple CpuPoolingKernel data type and data layout
1. Decouple data layout for CpuPoolingKernel: NCHW & NHWC
2. Decouple data type for CpuPoolingKernel NHWC
Partially solves: COMPMID-3999
Signed-off-by: Sheri Zhang <sheri.zhang@arm.com>
Change-Id: I3c6535eebdddeb467b7c68a7287a16959b5b9695
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5039
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/core/cpu/kernels/CpuPoolingKernel.cpp b/src/core/cpu/kernels/CpuPoolingKernel.cpp
index a29aef4..21afad2 100644
--- a/src/core/cpu/kernels/CpuPoolingKernel.cpp
+++ b/src/core/cpu/kernels/CpuPoolingKernel.cpp
@@ -33,6 +33,8 @@
#include "src/core/NEON/NEAsymm.h"
#include "src/core/NEON/NEFixedPoint.h"
#include "src/core/NEON/NEMath.h"
+#include "src/core/common/Registrars.h"
+#include "src/core/cpu/kernels/pooling/neon/list.h"
#include "src/core/helpers/AutoConfiguration.h"
#include "src/core/helpers/WindowHelpers.h"
#include "support/ToolchainSupport.h"
@@ -46,91 +48,138 @@
{
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)
+using namespace misc::shape_calculator;
+
+struct PoolingSelectorData
{
- return quantize_qasymm8_signed(val, info);
-}
+ DataType dt;
+ DataLayout dl;
+ int pool_stride_x;
+ Size2D pool_size;
+};
-template <typename T>
-inline typename std::enable_if<std::is_same<T, uint8_t>::value, uint8_t>::type
-quantize(float val, const UniformQuantizationInfo &info)
+using PoolingSelectorPtr = std::add_pointer<bool(const PoolingSelectorData &data)>::type;
+using PoolingKernelPtr = std::add_pointer<void(const ITensor *, ITensor *, ITensor *, PoolingLayerInfo &, const Window &, const Window &)>::type;
+struct PoolingKernel
{
- return quantize_qasymm8(val, info);
-}
+ const char *name;
+ const PoolingSelectorPtr is_selected;
+ PoolingKernelPtr ukernel;
+};
-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)
+static const PoolingKernel available_kernels[] =
{
- 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));
-}
+ "poolingMxN_qasymm8_neon_nhwc",
+ [](const PoolingSelectorData & data) { return ((data.dl == DataLayout::NHWC) && (data.dt == DataType::QASYMM8)); },
+ REGISTER_QASYMM8_NEON(arm_compute::cpu::poolingMxN_qasymm8_neon_nhwc)
+ },
+ {
+ "poolingMxN_qasymm8_signed_neon_nhwc",
+ [](const PoolingSelectorData & data) { return ((data.dl == DataLayout::NHWC) && (data.dt == DataType::QASYMM8_SIGNED)); },
+ REGISTER_QASYMM8_SIGNED_NEON(arm_compute::cpu::poolingMxN_qasymm8_signed_neon_nhwc)
+ },
+#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
+ {
+ "poolingMxN_fp16_neon_nhwc",
+ [](const PoolingSelectorData & data) { return ((data.dl == DataLayout::NHWC) && (data.dt == DataType::F16)); },
+ REGISTER_FP16_NEON(arm_compute::cpu::poolingMxN_fp16_neon_nhwc)
+ },
+#endif /* defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) */
+ {
+ "poolingMxN_fp32_neon_nhwc",
+ [](const PoolingSelectorData & data) { return ((data.dl == DataLayout::NHWC) && (data.dt == DataType::F32)); },
+ REGISTER_FP32_NEON(arm_compute::cpu::poolingMxN_fp32_neon_nhwc)
+ },
+#if defined(ENABLE_NCHW_KERNELS)
+ {
+ "pooling2_qasymm8_neon_nchw",
+ [](const PoolingSelectorData & data) { return ((data.dl == DataLayout::NCHW) && (data.dt == DataType::QASYMM8) && (data.pool_size.x() == data.pool_size.y()) && (data.pool_size.x() == 2) && (data.pool_stride_x < 3)); },
+ REGISTER_QASYMM8_NEON(arm_compute::cpu::pooling2_quantized_neon_nchw<uint8_t>)
+ },
+ {
+ "pooling3_qasymm8_neon_nchw",
+ [](const PoolingSelectorData & data) { return ((data.dl == DataLayout::NCHW) && (data.dt == DataType::QASYMM8) && (data.pool_size.x() == data.pool_size.y()) && (data.pool_size.x() == 3) && (data.pool_stride_x < 3)); },
+ REGISTER_QASYMM8_NEON(arm_compute::cpu::pooling3_quantized_neon_nchw<uint8_t>)
+ },
+ {
+ "poolingMxN_qasymm8_neon_nchw",
+ [](const PoolingSelectorData & data) { return ((data.dl == DataLayout::NCHW) && (data.dt == DataType::QASYMM8)); },
+ REGISTER_QASYMM8_NEON(arm_compute::cpu::poolingMxN_quantized_neon_nchw<uint8_t>)
+ },
+ {
+ "pooling2_qasymm8_signed_neon_nchw",
+ [](const PoolingSelectorData & data) { return ((data.dl == DataLayout::NCHW) && (data.dt == DataType::QASYMM8_SIGNED) && (data.pool_size.x() == data.pool_size.y()) && (data.pool_size.x() == 2) && (data.pool_stride_x < 3)); },
+ REGISTER_QASYMM8_SIGNED_NEON(arm_compute::cpu::pooling2_quantized_neon_nchw<int8_t>)
+ },
+ {
+ "pooling3_qasymm8_signed_neon_nchw",
+ [](const PoolingSelectorData & data) { return ((data.dl == DataLayout::NCHW) && (data.dt == DataType::QASYMM8_SIGNED) && (data.pool_size.x() == data.pool_size.y()) && (data.pool_size.x() == 3) && (data.pool_stride_x < 3)); },
+ REGISTER_QASYMM8_SIGNED_NEON(arm_compute::cpu::pooling3_quantized_neon_nchw<int8_t>)
+ },
+ {
+ "poolingMxN_qasymm8_signed_neon_nchw",
+ [](const PoolingSelectorData & data) { return ((data.dl == DataLayout::NCHW) && (data.dt == DataType::QASYMM8_SIGNED)); },
+ REGISTER_QASYMM8_SIGNED_NEON(arm_compute::cpu::poolingMxN_quantized_neon_nchw<int8_t>)
+ },
+#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
+ {
+ "pooling2_fp16_neon_nchw",
+ [](const PoolingSelectorData & data) { return ((data.dl == DataLayout::NCHW) && (data.dt == DataType::F16) && (data.pool_size.x() == data.pool_size.y()) && (data.pool_size.x() == 2)); },
+ REGISTER_FP16_NEON(arm_compute::cpu::pooling2_fp16_neon_nchw)
+ },
+ {
+ "pooling3_fp16_neon_nchw",
+ [](const PoolingSelectorData & data) { return ((data.dl == DataLayout::NCHW) && (data.dt == DataType::F16) && (data.pool_size.x() == data.pool_size.y()) && (data.pool_size.x() == 3)); },
+ REGISTER_FP16_NEON(arm_compute::cpu::pooling3_fp16_neon_nchw)
+ },
+ {
+ "poolingMxN_fp16_neon_nchw",
+ [](const PoolingSelectorData & data) { return ((data.dl == DataLayout::NCHW) && (data.dt == DataType::F16)); },
+ REGISTER_FP16_NEON(arm_compute::cpu::poolingMxN_fp16_neon_nchw)
+ },
+#endif /* defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) */
+ {
+ "pooling2_fp32_neon_nchw",
+ [](const PoolingSelectorData & data) { return ((data.dl == DataLayout::NCHW) && (data.dt == DataType::F32) && (data.pool_size.x() == data.pool_size.y()) && (data.pool_size.x() == 2)); },
+ REGISTER_FP32_NEON(arm_compute::cpu::pooling2_fp32_neon_nchw)
+ },
+ {
+ "pooling3_fp32_neon_nchw",
+ [](const PoolingSelectorData & data) { return ((data.dl == DataLayout::NCHW) && (data.dt == DataType::F32) && (data.pool_size.x() == data.pool_size.y()) && (data.pool_size.x() == 3)); },
+ REGISTER_FP32_NEON(arm_compute::cpu::pooling3_fp32_neon_nchw)
+ },
+ {
+ "pooling7_fp32_neon_nchw",
+ [](const PoolingSelectorData & data) { return ((data.dl == DataLayout::NCHW) && (data.dt == DataType::F32) && (data.pool_size.x() == data.pool_size.y()) && (data.pool_size.x() == 7)); },
+ REGISTER_FP32_NEON(arm_compute::cpu::pooling7_fp32_neon_nchw)
+ },
+ {
+ "poolingMxN_fp32_neon_nchw",
+ [](const PoolingSelectorData & data) { return ((data.dl == DataLayout::NCHW) && (data.dt == DataType::F32)); },
+ REGISTER_FP32_NEON(arm_compute::cpu::poolingMxN_fp32_neon_nchw)
+ },
+#endif /* defined(ENABLE_NCHW_KERNELS) */
+};
-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)
+/** Micro-kernel selector
+ *
+ * @param[in] data Selection data passed to help pick the appropriate micro-kernel
+ *
+ * @return A matching micro-kernel else nullptr
+ */
+const PoolingKernel *get_implementation(DataType dt, DataLayout dl, int pool_stride_x, Size2D pool_size)
{
- 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)
+ for(const auto &uk : available_kernels)
{
- start_y = std::max(0, start_y);
- }
-
- std::array<T, 8> elems =
- {
+ if(uk.is_selected({ dt, dl, pool_stride_x, pool_size }))
{
- 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),
+ return &uk;
}
- };
-
- 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);
+ return nullptr;
}
Status validate_arguments(const ITensorInfo *src, const ITensorInfo *dst, const PoolingLayerInfo &pool_info,
@@ -172,6 +221,9 @@
}
}
+ const auto *uk = get_implementation(src->data_type(), src->data_layout(), pool_stride_x, pool_size);
+ ARM_COMPUTE_RETURN_ERROR_ON(uk == nullptr || uk->ukernel == nullptr);
+
return Status{};
}
@@ -321,116 +373,6 @@
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
@@ -443,7 +385,6 @@
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;
@@ -471,135 +412,12 @@
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());
+ _pool_info = pool_info;
+ _data_layout = src->data_layout();
+ _pool_size = pool_size;
+ _pool_stride_x = pad_stride_info.stride().first;
- // 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)
+ if(_data_layout == DataLayout::NHWC)
{
// Configure kernel window
Window win = calculate_max_window(*dst, Steps());
@@ -618,1890 +436,6 @@
}
}
-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);
@@ -2547,23 +481,21 @@
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 ITensor *src = tensors.get_const_tensor(TensorType::ACL_SRC_0);
+ ITensor *dst = tensors.get_tensor(TensorType::ACL_DST_0);
+ ITensor *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;
+ 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;
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())
+ switch(src->info()->data_type())
{
case DataType::QASYMM8:
case DataType::QASYMM8_SIGNED:
@@ -2593,12 +525,19 @@
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));
+ 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);
+ const auto *uk = get_implementation(src->info()->data_type(), src->info()->data_layout(), _pool_stride_x, _pool_size);
+ ARM_COMPUTE_ERROR_ON(uk == nullptr || uk->ukernel == nullptr);
+
+ uk->ukernel(src, dst, indices, _pool_info, window_src, window);
+}
+
+const char *CpuPoolingKernel::name() const
+{
+ return "CpuPoolingKernel";
}
} // namespace kernels
} // namespace cpu
diff --git a/src/core/cpu/kernels/CpuPoolingKernel.h b/src/core/cpu/kernels/CpuPoolingKernel.h
index 036e436..87d8f67 100644
--- a/src/core/cpu/kernels/CpuPoolingKernel.h
+++ b/src/core/cpu/kernels/CpuPoolingKernel.h
@@ -38,10 +38,6 @@
class CpuPoolingKernel : public ICpuKernel
{
public:
- const char *name() const override
- {
- return "CpuPoolingKernel";
- }
/** Default constructor */
CpuPoolingKernel() = default;
ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(CpuPoolingKernel);
@@ -70,155 +66,16 @@
// Inherited methods overridden:
void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
- BorderSize border_size() const override;
+ BorderSize border_size() const override;
+ const char *name() 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 };
+ Size2D _pool_size{};
+ int _pool_stride_x{};
};
} // namespace kernels
} // namespace cpu
diff --git a/src/core/cpu/kernels/pooling/neon/fp16.cpp b/src/core/cpu/kernels/pooling/neon/fp16.cpp
new file mode 100644
index 0000000..314be37
--- /dev/null
+++ b/src/core/cpu/kernels/pooling/neon/fp16.cpp
@@ -0,0 +1,315 @@
+/*
+ * Copyright (c) 2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/utils/misc/Traits.h"
+#include "src/core/NEON/wrapper/intrinsics/intrinsics.h"
+#include "src/core/cpu/kernels/pooling/neon/list.h"
+#include "src/core/helpers/WindowHelpers.h"
+
+#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS)
+
+namespace arm_compute
+{
+namespace cpu
+{
+namespace
+{
+void pooling2_f16_maxpool_indices(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window)
+{
+ const int window_start_x = window.x().start();
+ const int window_end_x = window.x().end();
+ const int window_step_x = 8;
+
+ Window window_out = window;
+ window_out.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ Iterator in(src, window_src);
+ Iterator out(dst0, window_out);
+ Iterator indices(dst1, window_out);
+
+ const int pool_pad_top = pool_info.pad_stride_info.pad_top();
+ const int pool_pad_left = pool_info.pad_stride_info.pad_left();
+
+ int pool_stride_x = 0;
+ int pool_stride_y = 0;
+ std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride();
+
+ 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 *>(in.ptr() + in_x0_offset) + x_off;
+ const auto in_x1_ptr = reinterpret_cast<const float16_t *>(in.ptr() + in_x1_offset) + x_off;
+ const auto in_x2_ptr = reinterpret_cast<const float16_t *>(in.ptr() + in_x2_offset) + x_off;
+ const auto in_x3_ptr = reinterpret_cast<const float16_t *>(in.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 *>(out.ptr()) + x_off, vres);
+
+ const uint32_t offset_base = offset_no_padding<float16_t>(in.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 *>(in.ptr() + in_x0_offset) + x_off);
+ const auto x1 = *(reinterpret_cast<const float16_t *>(in.ptr() + in_x1_offset) + x_off);
+ const auto x2 = *(reinterpret_cast<const float16_t *>(in.ptr() + in_x2_offset) + x_off);
+ const auto x3 = *(reinterpret_cast<const float16_t *>(in.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 *>(out.ptr()) + x_off) = res;
+
+ const uint32_t offset_base = offset_no_padding<float16_t>(in.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;
+ }
+ },
+ in, out, indices);
+}
+}
+
+void poolingMxN_fp16_neon_nhwc(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window)
+{
+ if(pool_info.pool_size == Size2D(2, 2) && pool_info.pool_type == PoolingType::MAX && dst1)
+ {
+ pooling2_f16_maxpool_indices(src, dst0, dst1, pool_info, 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 in(src, window_src);
+ Iterator out(dst0, window_out);
+
+ const int pool_size_x = pool_info.is_global_pooling ? src->info()->tensor_shape().y() : pool_info.pool_size.width;
+ const int pool_size_y = pool_info.is_global_pooling ? src->info()->tensor_shape().z() : pool_info.pool_size.height;
+ const int pool_pad_right = pool_info.pad_stride_info.pad_right();
+ const int pool_pad_top = pool_info.pad_stride_info.pad_top();
+ const int pool_pad_left = pool_info.pad_stride_info.pad_left();
+ const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom();
+ int pool_stride_x = 0;
+ int pool_stride_y = 0;
+ std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride();
+ const int upper_bound_w = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = src->info()->dimension(2) + (pool_info.exclude_padding ? 0 : pool_pad_bottom);
+
+ 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(pool_info.pool_type != PoolingType::MAX)
+ {
+ // Calculate scale
+ const float scale = calculate_avg_scale(pool_info.exclude_padding, DataLayout::NHWC, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x,
+ pool_stride_y);
+ const 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 *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int>
+ (src->info()->strides_in_bytes().z())) + x_off);
+
+ // Get power of 2 in case of l2 pooling and accumulate
+ if(pool_info.pool_type == PoolingType::L2)
+ {
+ vres = 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 *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int>
+ (src->info()->strides_in_bytes().z())) + x_off);
+ vres = vmaxq_f16(vres, data);
+ }
+ }
+ }
+
+ // Calculate square-root in case of l2 pooling
+ if(pool_info.pool_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 *>(out.ptr()) + x_off, vres);
+ }
+
+ // Left-overs loop
+ for(; x_off < window_end_x; ++x_off)
+ {
+ float16_t res = 0.0f;
+
+ if(pool_info.pool_type != PoolingType::MAX)
+ {
+ // Calculate scale
+ const float16_t scale = calculate_avg_scale(pool_info.exclude_padding, DataLayout::NHWC, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x,
+ pool_stride_y);
+
+ for(int y = pool_start_y; y < pool_end_y; ++y)
+ {
+ for(int x = pool_start_x; x < pool_end_x; ++x)
+ {
+ const float data = *(reinterpret_cast<const float16_t *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int>
+ (src->info()->strides_in_bytes().z())) + x_off);
+
+ // Get power of 2 in case of l2 pooling and accumulate
+ if(pool_info.pool_type == PoolingType::L2)
+ {
+ res += data * data;
+ }
+ else
+ {
+ res += data;
+ }
+ }
+ }
+
+ // Divide by scale
+ res *= scale;
+ }
+ else
+ {
+ res = std::numeric_limits<float>::lowest();
+ for(int y = pool_start_y; y < pool_end_y; ++y)
+ {
+ for(int x = pool_start_x; x < pool_end_x; ++x)
+ {
+ const float16_t data = *(reinterpret_cast<const float16_t *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int>
+ (src->info()->strides_in_bytes().z())) + x_off);
+ res = std::max(res, data);
+ }
+ }
+ }
+
+ // Calculate square-root in case of l2 pooling
+ if(pool_info.pool_type == PoolingType::L2)
+ {
+ res = std::sqrt(res);
+ }
+
+ // Store result
+ *(reinterpret_cast<float16_t *>(out.ptr()) + x_off) = res;
+ }
+ },
+ in, out);
+}
+} // namespace cpu
+} // namespace arm_compute
+
+#endif /* defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS) */
\ No newline at end of file
diff --git a/src/core/cpu/kernels/pooling/neon/fp32.cpp b/src/core/cpu/kernels/pooling/neon/fp32.cpp
new file mode 100644
index 0000000..e319047
--- /dev/null
+++ b/src/core/cpu/kernels/pooling/neon/fp32.cpp
@@ -0,0 +1,312 @@
+/*
+ * Copyright (c) 2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/utils/misc/Traits.h"
+#include "src/core/NEON/wrapper/intrinsics/intrinsics.h"
+#include "src/core/cpu/kernels/pooling/neon/list.h"
+#include "src/core/helpers/WindowHelpers.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+namespace
+{
+void pooling2_f32_maxpool_indices(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window)
+{
+ const int window_start_x = window.x().start();
+ const int window_end_x = window.x().end();
+ const int window_step_x = 4;
+
+ Window window_out = window;
+ window_out.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ Iterator in(src, window_src);
+ Iterator out(dst0, window_out);
+ Iterator indices(dst1, window_out);
+
+ const int pool_pad_top = pool_info.pad_stride_info.pad_top();
+ const int pool_pad_left = pool_info.pad_stride_info.pad_left();
+
+ int pool_stride_x = 0;
+ int pool_stride_y = 0;
+ std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride();
+
+ float32x4_t vres;
+ float res;
+
+ const int pad_right = src->info()->padding().right;
+ const int in_stride_y = static_cast<int>(src->info()->strides_in_bytes().y());
+ const int in_stride_z = static_cast<int>(src->info()->strides_in_bytes().z());
+
+ execute_window_loop(window_out, [&](const Coordinates & id)
+ {
+ const int idx_width = id.y() * pool_stride_x;
+ const int idx_height = id.z() * pool_stride_y;
+ const int pool_limit_y = pool_pad_top - idx_height;
+ const int pool_limit_x = pool_pad_left - idx_width;
+
+ const int pool_start_y = std::max(0, window_src.z().start() + pool_limit_y);
+ const int pool_start_x = std::max(0, window_src.y().start() + pool_limit_x);
+
+ const int in_x0_offset = (pool_start_x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (pool_start_y - pool_pad_top) * static_cast<int>(src->info()->strides_in_bytes().z());
+ const int in_x1_offset = (pool_start_x + 1 - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (pool_start_y - pool_pad_top) * static_cast<int>
+ (src->info()->strides_in_bytes().z());
+ const int in_x2_offset = (pool_start_x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (pool_start_y + 1 - pool_pad_top) * static_cast<int>
+ (src->info()->strides_in_bytes().z());
+ const int in_x3_offset = (pool_start_x + 1 - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (pool_start_y + 1 - pool_pad_top) * static_cast<int>
+ (src->info()->strides_in_bytes().z());
+
+ int x_off = window_start_x;
+ for(; x_off <= (window_end_x - window_step_x); x_off += window_step_x)
+ {
+ const auto in_x0_ptr = reinterpret_cast<const float *>(in.ptr() + in_x0_offset);
+ const auto in_x1_ptr = reinterpret_cast<const float *>(in.ptr() + in_x1_offset);
+ const auto in_x2_ptr = reinterpret_cast<const float *>(in.ptr() + in_x2_offset);
+ const auto in_x3_ptr = reinterpret_cast<const float *>(in.ptr() + in_x3_offset);
+ const auto v_x0 = vld1q_f32(in_x0_ptr + x_off);
+ const auto v_x1 = vld1q_f32(in_x1_ptr + x_off);
+ const auto v_x2 = vld1q_f32(in_x2_ptr + x_off);
+ const auto v_x3 = vld1q_f32(in_x3_ptr + x_off);
+ vres = vmaxq_f32(vmaxq_f32(v_x2, v_x3), vmaxq_f32(v_x0, v_x1));
+ // Store result
+ vst1q_f32(reinterpret_cast<float *>(out.ptr()) + x_off, vres);
+
+ const uint32_t offset_base = offset_no_padding<float>(in.offset(), id, *src->info(), pool_stride_x, pool_stride_y);
+ const uint32_t offset_x0 = (uint32_t)offset_base / sizeof(float) + x_off;
+ const uint32_t offset_x1 = (uint32_t)offset_x0 + in_stride_y / sizeof(float) - pad_right;
+ const uint32_t offset_x2 = (uint32_t)offset_x0 + in_stride_z / sizeof(float) - pad_right * src->info()->tensor_shape()[1];
+ const uint32_t offset_x3 = (uint32_t)offset_x2 + in_stride_y / sizeof(float) - pad_right;
+ const uint32x4_t voffset_x0 = { offset_x0, offset_x0 + 1, offset_x0 + 2, offset_x0 + 3 };
+ const uint32x4_t voffset_x1 = { offset_x1, offset_x1 + 1, offset_x1 + 2, offset_x1 + 3 };
+ const uint32x4_t voffset_x2 = { offset_x2, offset_x2 + 1, offset_x2 + 2, offset_x2 + 3 };
+ const uint32x4_t voffset_x3 = { offset_x3, offset_x3 + 1, offset_x3 + 2, offset_x3 + 3 };
+ const uint32x4_t tmp_indices0 = vbslq_u32(vcgeq_f32(v_x0, v_x1), voffset_x0, voffset_x1);
+ const uint32x4_t tmp_indices1 = vbslq_u32(vcgeq_f32(v_x2, v_x3), voffset_x2, voffset_x3);
+ const uint32x4_t tmp_indices2 = vbslq_u32(vcgeq_f32(vmaxq_f32(v_x0, v_x1), vmaxq_f32(v_x2, v_x3)), tmp_indices0, tmp_indices1);
+
+ // Store indices
+ vst1q_u32(reinterpret_cast<uint32_t *>(indices.ptr()) + x_off, tmp_indices2);
+ }
+
+ // Left-overs loop
+ for(; x_off < window_end_x; ++x_off)
+ {
+ const auto x0 = *(reinterpret_cast<const float *>(in.ptr() + in_x0_offset) + x_off);
+ const auto x1 = *(reinterpret_cast<const float *>(in.ptr() + in_x1_offset) + x_off);
+ const auto x2 = *(reinterpret_cast<const float *>(in.ptr() + in_x2_offset) + x_off);
+ const auto x3 = *(reinterpret_cast<const float *>(in.ptr() + in_x3_offset) + x_off);
+ res = std::max(std::max(x2, x3), std::max(x0, x1));
+
+ // Store result
+ *(reinterpret_cast<float *>(out.ptr()) + x_off) = res;
+
+ const uint32_t offset_base = offset_no_padding<float>(in.offset(), id, *src->info(), pool_stride_x, pool_stride_y);
+ const uint32_t offset_x0 = (uint32_t)offset_base / sizeof(float) + x_off;
+ const uint32_t offset_x1 = (uint32_t)offset_x0 + in_stride_y / sizeof(float) - pad_right;
+ const uint32_t offset_x2 = (uint32_t)offset_x0 + in_stride_z / sizeof(float) - pad_right * src->info()->tensor_shape()[1];
+ const uint32_t offset_x3 = (uint32_t)offset_x2 + in_stride_y / sizeof(float) - pad_right;
+ const uint32_t tmp_idx0 = (x0 >= x1) ? offset_x0 : offset_x1;
+ const uint32_t tmp_idx1 = (x2 >= x3) ? offset_x2 : offset_x3;
+ const uint32_t tmp_idx2 = (std::max(x0, x1) >= std::max(x2, x3)) ? tmp_idx0 : tmp_idx1;
+
+ // Store indices
+ *(reinterpret_cast<uint32_t *>(indices.ptr()) + x_off) = tmp_idx2;
+ }
+ },
+ in, out, indices);
+}
+}
+
+void poolingMxN_fp32_neon_nhwc(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window)
+{
+ if(pool_info.pool_size == Size2D(2, 2) && pool_info.pool_type == PoolingType::MAX && dst1)
+ {
+ pooling2_f32_maxpool_indices(src, dst0, dst1, pool_info, window_src, window);
+ }
+ else
+ {
+ const int window_start_x = window.x().start();
+ const int window_end_x = window.x().end();
+ const int window_step_x = 4;
+
+ Window window_out = window;
+ window_out.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ Iterator in(src, window_src);
+ Iterator out(dst0, window_out);
+
+ const int pool_size_x = pool_info.is_global_pooling ? src->info()->tensor_shape().y() : pool_info.pool_size.width;
+ const int pool_size_y = pool_info.is_global_pooling ? src->info()->tensor_shape().z() : pool_info.pool_size.height;
+ const int pool_pad_right = pool_info.pad_stride_info.pad_right();
+ const int pool_pad_top = pool_info.pad_stride_info.pad_top();
+ const int pool_pad_left = pool_info.pad_stride_info.pad_left();
+ const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom();
+ int pool_stride_x = 0;
+ int pool_stride_y = 0;
+ std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride();
+ const int upper_bound_w = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = src->info()->dimension(2) + (pool_info.exclude_padding ? 0 : pool_pad_bottom);
+
+ float32x4_t vres;
+
+ execute_window_loop(window_out, [&](const Coordinates & id)
+ {
+ const int idx_width = id.y() * pool_stride_x;
+ const int idx_height = id.z() * pool_stride_y;
+ const int pool_limit_y = pool_pad_top - idx_height;
+ const int pool_limit_x = pool_pad_left - idx_width;
+
+ const int pool_start_y = std::max(0, window_src.z().start() + pool_limit_y);
+ const int pool_end_y = std::min(pool_size_y, window_src.z().end() + pool_limit_y);
+ const int pool_start_x = std::max(0, window_src.y().start() + pool_limit_x);
+ const int pool_end_x = std::min(pool_size_x, window_src.y().end() + pool_limit_x);
+
+ int x_off = window_start_x;
+ for(; x_off <= (window_end_x - window_step_x); x_off += window_step_x)
+ {
+ if(pool_info.pool_type != PoolingType::MAX)
+ {
+ // Calculate scale
+ const float scale = calculate_avg_scale(pool_info.exclude_padding, DataLayout::NHWC, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x,
+ pool_stride_y);
+ const float32x4_t scale_v = vdupq_n_f32(scale);
+
+ // Perform pooling
+ vres = vdupq_n_f32(0.0f);
+
+ for(int y = pool_start_y; y < pool_end_y; ++y)
+ {
+ for(int x = pool_start_x; x < pool_end_x; ++x)
+ {
+ const float32x4_t data = vld1q_f32(reinterpret_cast<const float *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int>
+ (src->info()->strides_in_bytes().z())) + x_off);
+
+ // Get power of 2 in case of l2 pooling and accumulate
+ if(pool_info.pool_type == PoolingType::L2)
+ {
+ vres = vmlaq_f32(vres, data, data);
+ }
+ else
+ {
+ vres = vaddq_f32(vres, data);
+ }
+ }
+ }
+ // Divide by scale
+ vres = vmulq_f32(vres, scale_v);
+ }
+ else
+ {
+ vres = vdupq_n_f32(std::numeric_limits<float>::lowest());
+ for(int y = pool_start_y; y < pool_end_y; ++y)
+ {
+ for(int x = pool_start_x; x < pool_end_x; ++x)
+ {
+ const float32x4_t data = vld1q_f32(reinterpret_cast<const float *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int>
+ (src->info()->strides_in_bytes().z())) + x_off);
+ vres = vmaxq_f32(vres, data);
+ }
+ }
+ }
+
+ // Calculate square-root in case of l2 pooling
+ if(pool_info.pool_type == PoolingType::L2)
+ {
+ float32x4_t l2_res = { static_cast<float>(sqrt(vgetq_lane_f32(vres, 0))),
+ static_cast<float>(sqrt(vgetq_lane_f32(vres, 1))),
+ static_cast<float>(sqrt(vgetq_lane_f32(vres, 2))),
+ static_cast<float>(sqrt(vgetq_lane_f32(vres, 3)))
+ };
+ vres = l2_res;
+ }
+
+ // Store result
+ vst1q_f32(reinterpret_cast<float *>(out.ptr()) + x_off, vres);
+ }
+
+ // Left-overs loop
+ for(; x_off < window_end_x; ++x_off)
+ {
+ float res = 0.0f;
+
+ if(pool_info.pool_type != PoolingType::MAX)
+ {
+ // Calculate scale
+ const float scale = calculate_avg_scale(pool_info.exclude_padding, DataLayout::NHWC, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x,
+ pool_stride_y);
+
+ for(int y = pool_start_y; y < pool_end_y; ++y)
+ {
+ for(int x = pool_start_x; x < pool_end_x; ++x)
+ {
+ const float data = *(reinterpret_cast<const float *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int>
+ (src->info()->strides_in_bytes().z())) + x_off);
+
+ // Get power of 2 in case of l2 pooling and accumulate
+ if(pool_info.pool_type == PoolingType::L2)
+ {
+ res += data * data;
+ }
+ else
+ {
+ res += data;
+ }
+ }
+ }
+
+ // Divide by scale
+ res *= scale;
+ }
+ else
+ {
+ res = std::numeric_limits<float>::lowest();
+ for(int y = pool_start_y; y < pool_end_y; ++y)
+ {
+ for(int x = pool_start_x; x < pool_end_x; ++x)
+ {
+ const float data = *(reinterpret_cast<const float *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int>
+ (src->info()->strides_in_bytes().z())) + x_off);
+ res = std::max(res, data);
+ }
+ }
+ }
+
+ // Calculate square-root in case of l2 pooling
+ if(pool_info.pool_type == PoolingType::L2)
+ {
+ res = std::sqrt(res);
+ }
+
+ // Store result
+ *(reinterpret_cast<float *>(out.ptr()) + x_off) = res;
+ }
+ },
+ in, out);
+ }
+}
+} // namespace cpu
+} // namespace arm_compute
\ No newline at end of file
diff --git a/src/core/cpu/kernels/pooling/neon/list.h b/src/core/cpu/kernels/pooling/neon/list.h
new file mode 100644
index 0000000..3435ee6
--- /dev/null
+++ b/src/core/cpu/kernels/pooling/neon/list.h
@@ -0,0 +1,97 @@
+/*
+ * 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 SRC_CORE_NEON_KERNELS_POOLING_LIST_H
+#define SRC_CORE_NEON_KERNELS_POOLING_LIST_H
+
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/utils/misc/Traits.h"
+#include "src/core/NEON/wrapper/wrapper.h"
+#include "src/core/cpu/kernels/pooling/neon/quantized.h"
+#include <arm_neon.h>
+
+namespace arm_compute
+{
+namespace cpu
+{
+#define DECLARE_POOLING_KERNEL(func_name) \
+ void func_name(const ITensor *src0, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &, const Window &window_src, const Window &window)
+
+DECLARE_POOLING_KERNEL(poolingMxN_qasymm8_neon_nhwc);
+DECLARE_POOLING_KERNEL(poolingMxN_qasymm8_signed_neon_nhwc);
+DECLARE_POOLING_KERNEL(poolingMxN_fp16_neon_nhwc);
+DECLARE_POOLING_KERNEL(poolingMxN_fp32_neon_nhwc);
+
+#if defined(ENABLE_NCHW_KERNELS)
+
+#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS)
+DECLARE_POOLING_KERNEL(pooling2_fp16_neon_nchw);
+DECLARE_POOLING_KERNEL(pooling3_fp16_neon_nchw);
+DECLARE_POOLING_KERNEL(poolingMxN_fp16_neon_nchw);
+#endif /* defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS) */
+
+DECLARE_POOLING_KERNEL(pooling2_fp32_neon_nchw);
+DECLARE_POOLING_KERNEL(pooling3_fp32_neon_nchw);
+DECLARE_POOLING_KERNEL(pooling7_fp32_neon_nchw);
+DECLARE_POOLING_KERNEL(poolingMxN_fp32_neon_nchw);
+#endif /* defined(ENABLE_NCHW_KERNELS) */
+
+#undef DECLARE_POOLING_KERNEL
+
+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;
+ }
+}
+} // namespace cpu
+} // namespace arm_compute
+
+#endif // SRC_CORE_NEON_KERNELS_POOLING_LIST_H
\ No newline at end of file
diff --git a/src/core/cpu/kernels/pooling/neon/nchw/all.cpp b/src/core/cpu/kernels/pooling/neon/nchw/all.cpp
new file mode 100644
index 0000000..47ac7b4
--- /dev/null
+++ b/src/core/cpu/kernels/pooling/neon/nchw/all.cpp
@@ -0,0 +1,700 @@
+/*
+ * Copyright (c) 2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/utils/misc/Traits.h"
+#include "src/core/NEON/wrapper/intrinsics/intrinsics.h"
+#include "src/core/cpu/kernels/pooling/neon/list.h"
+#include "src/core/helpers/WindowHelpers.h"
+
+#ifdef ENABLE_NCHW_KERNELS
+namespace arm_compute
+{
+namespace cpu
+{
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+void pooling3_fp16_neon_nchw(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window)
+{
+ ARM_COMPUTE_UNUSED(dst1);
+ ARM_COMPUTE_UNUSED(pool_info.pool_type);
+ ARM_COMPUTE_UNUSED(pool_info.exclude_padding);
+
+ Iterator in(src, window_src);
+ Iterator out(dst0, 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) + (pool_info.exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = src->info()->dimension(1) + (pool_info.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 + in.offset()));
+ float16x4_t middle_data = vld1_f16(reinterpret_cast<const float16_t *>(src_middle_ptr + in.offset()));
+ float16x4_t bottom_data = vld1_f16(reinterpret_cast<const float16_t *>(src_bottom_ptr + in.offset()));
+ float16x4_t res = {};
+
+ // Get power of 2 in case of l2 pooling
+ if(pool_info.pool_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(pool_info.pool_type != PoolingType::MAX)
+ {
+ // Calculate scale
+ const float scale = calculate_avg_scale(pool_info.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(pool_info.pool_type == PoolingType::L2)
+ {
+ res = vinv_f16(vinvsqrt_f16(res));
+ }
+
+ *(reinterpret_cast<float16_t *>(out.ptr())) = vget_lane_f16(res, 0);
+ },
+ in, out);
+}
+
+template <typename T>
+inline typename std::enable_if<std::is_same<T, float16_t>::value, float32x2_t>::type
+f16_to_f32(float16x4_t in)
+{
+ float32x2_t out = { static_cast<float>(vget_lane_f16(in, 0)), static_cast<float>(vget_lane_f16(in, 1)) };
+ return out;
+}
+#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 in)
+{
+ return in;
+}
+
+template <typename T>
+void pooling2_nchw_maxpool_indices(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window)
+{
+ Iterator in(src, window_src);
+ Iterator out(dst0, window);
+ Iterator indices(dst1, 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 + in.offset()));
+ auto bottom_data = wrapper::vload(reinterpret_cast<const T *>(src_bottom_ptr + in.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 *>(out.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>(in.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);
+ },
+ in, out, indices);
+}
+
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+void pooling2_fp16_neon_nchw(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window)
+{
+ if(pool_info.pool_type == PoolingType::MAX && dst1)
+ {
+ pooling2_nchw_maxpool_indices<float16_t>(src, dst0, dst1, pool_info, window_src, window);
+ }
+ else
+ {
+ Iterator in(src, window_src);
+ Iterator out(dst0, 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) + (pool_info.exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = src->info()->dimension(1) + (pool_info.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 + in.offset()));
+ float16x4_t bottom_data = vld1_f16(reinterpret_cast<const float16_t *>(src_bottom_ptr + in.offset()));
+ float16x4_t res = {};
+
+ // Get power of 2 in case of l2 pooling
+ if(pool_info.pool_type == PoolingType::L2)
+ {
+ top_data = vmul_f16(top_data, top_data);
+ bottom_data = vmul_f16(bottom_data, bottom_data);
+ }
+
+ if(pool_info.pool_type != PoolingType::MAX)
+ {
+ const float scale = calculate_avg_scale(pool_info.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(pool_info.pool_type == PoolingType::L2)
+ {
+ res = vinv_f16(vinvsqrt_f16(res));
+ }
+
+ // Store result
+ *(reinterpret_cast<float16_t *>(out.ptr())) = vget_lane_f16(res, 0);
+ },
+ in, out);
+ }
+}
+
+void poolingMxN_fp16_neon_nchw(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window)
+{
+ ARM_COMPUTE_UNUSED(dst1);
+ Iterator in(src, window_src);
+ Iterator out(dst0, 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) + (pool_info.exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = src->info()->dimension(1) + (pool_info.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(pool_info.pool_type != PoolingType::MAX)
+ {
+ // Calculate scale
+ const float scale = calculate_avg_scale(pool_info.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 *>(in.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(pool_info.pool_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 *>(in.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(pool_info.pool_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 *>(in.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 *>(in.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(pool_info.pool_type == PoolingType::L2)
+ {
+ res = std::sqrt(res);
+ }
+
+ // Store result
+ *(reinterpret_cast<float16_t *>(out.ptr())) = res;
+ },
+ in, out);
+}
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+
+void poolingMxN_fp32_neon_nchw(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window)
+{
+ ARM_COMPUTE_UNUSED(dst1);
+ Iterator in(src, window_src);
+ Iterator out(dst0, 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) + (pool_info.exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_bottom);
+
+ execute_window_loop(window, [&](const Coordinates & id)
+ {
+ float res = 0.0f;
+
+ if(pool_info.pool_type != PoolingType::MAX)
+ {
+ // Calculate scale
+ const float scale = calculate_avg_scale(pool_info.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 *>(in.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(pool_info.pool_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 *>(in.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(pool_info.pool_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 *>(in.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 *>(in.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(pool_info.pool_type == PoolingType::L2)
+ {
+ res = std::sqrt(res);
+ }
+
+ // Store result
+ *(reinterpret_cast<float *>(out.ptr())) = res;
+ },
+ in, out);
+}
+
+void pooling2_fp32_neon_nchw(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window)
+{
+ if(pool_info.pool_type == PoolingType::MAX && dst1)
+ {
+ pooling2_nchw_maxpool_indices<float>(src, dst0, dst1, pool_info, window_src, window);
+ }
+ else
+ {
+ Iterator in(src, window_src);
+ Iterator out(dst0, 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) + (pool_info.exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = src->info()->dimension(1) + (pool_info.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 + in.offset());
+ const auto in_bottom_ptr = reinterpret_cast<const float *>(src_bottom_ptr + in.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(pool_info.pool_type == PoolingType::L2)
+ {
+ top_data = vmul_f32(top_data, top_data);
+ bottom_data = vmul_f32(bottom_data, bottom_data);
+ }
+
+ if(pool_info.pool_type != PoolingType::MAX)
+ {
+ // Calculate scale
+ float scale = calculate_avg_scale(pool_info.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(pool_info.pool_type == PoolingType::L2)
+ {
+ final_res = sqrt(final_res);
+ }
+
+ // Store result
+ *(reinterpret_cast<float *>(out.ptr())) = final_res;
+ },
+ in, out);
+ }
+}
+
+void pooling3_fp32_neon_nchw(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window)
+{
+ ARM_COMPUTE_UNUSED(dst1);
+ Iterator in(src, window_src);
+ Iterator out(dst0, 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) + (pool_info.exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = src->info()->dimension(1) + (pool_info.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 + in.offset()));
+ float32x4_t middle_data = vld1q_f32(reinterpret_cast<const float *>(src_middle_ptr + in.offset()));
+ float32x4_t bottom_data = vld1q_f32(reinterpret_cast<const float *>(src_bottom_ptr + in.offset()));
+ float32x2_t res = {};
+ float final_res = 0;
+
+ // Get power of 2 in case of l2 pooling
+ if(pool_info.pool_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(pool_info.pool_type != PoolingType::MAX)
+ {
+ // Calculate scale
+ float scale = calculate_avg_scale(pool_info.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(pool_info.pool_type == PoolingType::L2)
+ {
+ final_res = sqrt(final_res);
+ }
+
+ // Store result
+ *(reinterpret_cast<float *>(out.ptr())) = final_res;
+ },
+ in, out);
+}
+
+void pooling7_fp32_neon_nchw(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window)
+{
+ ARM_COMPUTE_UNUSED(dst1);
+ Iterator in(src, window_src);
+ Iterator out(dst0, 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) + (pool_info.exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = src->info()->dimension(1) + (pool_info.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(pool_info.pool_type != PoolingType::MAX)
+ {
+ // Calculate scale
+ float scale = calculate_avg_scale(pool_info.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] + in.offset()));
+ // Get power of 2 in case of l2 pooling
+ if(pool_info.pool_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] + in.offset()));
+ // Get power of 2 in case of l2 pooling
+ if(pool_info.pool_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] + in.offset()));
+ for(int i = 1; i < pool_size; ++i)
+ {
+ const float32x4x2_t data = vld2q_f32(reinterpret_cast<const float *>(src_ptrs[i] + in.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(pool_info.pool_type == PoolingType::L2)
+ {
+ final_res = sqrt(final_res);
+ }
+
+ // Store result
+ *(reinterpret_cast<float *>(out.ptr())) = final_res;
+ },
+ in, out);
+}
+} // namespace cpu
+} // namespace arm_compute
+
+#endif // ENABLE_NCHW_KERNELS
\ No newline at end of file
diff --git a/src/core/cpu/kernels/pooling/neon/qasymm8.cpp b/src/core/cpu/kernels/pooling/neon/qasymm8.cpp
new file mode 100644
index 0000000..af62ede
--- /dev/null
+++ b/src/core/cpu/kernels/pooling/neon/qasymm8.cpp
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/utils/misc/Traits.h"
+#include "src/core/NEON/wrapper/intrinsics/intrinsics.h"
+#include "src/core/cpu/kernels/pooling/neon/list.h"
+#include "src/core/helpers/WindowHelpers.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+void poolingMxN_qasymm8_neon_nhwc(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window)
+{
+ poolingMxN_q8_neon_nhwc<uint8_t>(src, dst0, dst1, pool_info, window_src, window);
+}
+} // namespace cpu
+} // namespace arm_compute
\ No newline at end of file
diff --git a/src/core/cpu/kernels/pooling/neon/qasymm8_signed.cpp b/src/core/cpu/kernels/pooling/neon/qasymm8_signed.cpp
new file mode 100644
index 0000000..2c4b095
--- /dev/null
+++ b/src/core/cpu/kernels/pooling/neon/qasymm8_signed.cpp
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/utils/misc/Traits.h"
+#include "src/core/NEON/wrapper/intrinsics/intrinsics.h"
+#include "src/core/cpu/kernels/pooling/neon/list.h"
+#include "src/core/helpers/WindowHelpers.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+void poolingMxN_qasymm8_signed_neon_nhwc(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window)
+{
+ poolingMxN_q8_neon_nhwc<int8_t>(src, dst0, dst1, pool_info, window_src, window);
+}
+} // namespace cpu
+} // namespace arm_compute
\ No newline at end of file
diff --git a/src/core/cpu/kernels/pooling/neon/quantized.h b/src/core/cpu/kernels/pooling/neon/quantized.h
new file mode 100644
index 0000000..81fb777
--- /dev/null
+++ b/src/core/cpu/kernels/pooling/neon/quantized.h
@@ -0,0 +1,863 @@
+/*
+ * 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 SRC_CORE_NEON_KERNELS_QUANTIZED_H
+#define SRC_CORE_NEON_KERNELS_QUANTIZED_H
+
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/utils/misc/Traits.h"
+#include "src/core/NEON/NEAsymm.h"
+#include "src/core/NEON/NEFixedPoint.h"
+#include "src/core/NEON/NEMath.h"
+#include "src/core/NEON/wrapper/wrapper.h"
+#include <arm_neon.h>
+
+namespace arm_compute
+{
+namespace cpu
+{
+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);
+}
+
+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);
+}
+
+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>
+void poolingMxN_q8_neon_nhwc(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window)
+{
+ ARM_COMPUTE_UNUSED(dst1);
+
+ 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 in(src, window_src);
+ Iterator out(dst0, 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) + (pool_info.exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = src->info()->dimension(2) + (pool_info.exclude_padding ? 0 : pool_pad_bottom);
+
+ const float32x4_t half_scale_v = vdupq_n_f32(0.5f);
+ const UniformQuantizationInfo src_qinfo = src->info()->quantization_info().uniform();
+ const UniformQuantizationInfo dst_qinfo = dst0->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(pool_info.pool_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(pool_info.exclude_padding, DataLayout::NHWC, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x,
+ pool_stride_y);
+
+ // 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 *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int>
+ (src->info()->strides_in_bytes().z())) + x_off);
+
+ 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 *>(out.ptr()) + x_off, wrapper::vgetlow(requantized_dst));
+ wrapper::vstore(reinterpret_cast<T *>(out.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 *>(out.ptr()) + x_off, res1);
+ wrapper::vstore(reinterpret_cast<T *>(out.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 *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int>
+ (src->info()->strides_in_bytes().z())) + x_off);
+ vres = wrapper::vmax(vres, data);
+ }
+ }
+
+ // Store result
+ wrapper::vstore(reinterpret_cast<T *>(out.ptr()) + x_off, (src_qinfo != dst_qinfo) ? vrequantize_pooling<q8x8_t, q8x16_t>(wrapper::vgetlow(vres), wrapper::vgethigh(vres),
+ requant_qinfo) :
+ vres);
+ }
+ }
+
+ if(pool_info.pool_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 *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int>
+ (src->info()->strides_in_bytes().z())) + x_off);
+ vres = wrapper::vmax(vres, data);
+ }
+ }
+
+ // Store result
+ wrapper::vstore(reinterpret_cast<T *>(out.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(pool_info.pool_type != PoolingType::MAX)
+ {
+ q32_t res = static_cast<q32_t>(0.f);
+
+ // Calculate scale
+ const float scale = calculate_avg_scale(pool_info.exclude_padding, DataLayout::NHWC, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x,
+ pool_stride_y);
+
+ // 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 *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int>
+ (src->info()->strides_in_bytes().z())) + x_off);
+ res += 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 *>(out.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 *>(out.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 *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int>
+ (src->info()->strides_in_bytes().z())) + x_off);
+ res = std::max(res, data);
+ }
+ }
+
+ // Store result
+ if(src_qinfo != dst_qinfo)
+ {
+ const float res_f = static_cast<float>(res);
+ *(reinterpret_cast<T *>(out.ptr()) + x_off) = quantize<T>(res_f, requant_qinfo);
+ }
+ else
+ {
+ *(reinterpret_cast<T *>(out.ptr()) + x_off) = res;
+ }
+ }
+ }
+
+ },
+ in, out);
+}
+
+#if defined(ENABLE_NCHW_KERNELS)
+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);
+}
+
+template <typename T>
+void pooling2_quantized_neon_nchw(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window)
+{
+ ARM_COMPUTE_UNUSED(dst1);
+ Iterator in(src, window_src);
+ Iterator out(dst0, 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) + (pool_info.exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = src->info()->dimension(1) + (pool_info.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 = dst0->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 + in.offset());
+ const auto bottom_data = wrapper::vloadq(src_bottom_ptr + in.offset());
+ q8x8_t lower_res = {};
+ q8x8_t upper_res = {};
+
+ if(pool_info.pool_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>(pool_info.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>(pool_info.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 *>(out.ptr()), res);
+ }
+ else
+ {
+ wrapper::vstore(reinterpret_cast<T *>(out.ptr()), lower_res);
+ }
+ },
+ in, out);
+}
+
+template <typename T>
+void pooling3_quantized_neon_nchw(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window)
+{
+ ARM_COMPUTE_UNUSED(dst1);
+ Iterator in(src, window_src);
+ Iterator out(dst0, 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) + (pool_info.exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_bottom);
+
+ const UniformQuantizationInfo &src_qinfo = src->info()->quantization_info().uniform();
+ const UniformQuantizationInfo &dst_qinfo = dst0->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 + in.offset());
+ const auto middle_data = wrapper::vloadq(src_middle_ptr + in.offset());
+ const auto bottom_data = wrapper::vloadq(src_bottom_ptr + in.offset());
+ q8x8_t fres = {};
+ q8x16_t fqres = {};
+
+ if(pool_info.pool_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>(pool_info.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>(pool_info.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>(pool_info.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 *>(out.ptr()), fqres);
+ }
+ else
+ {
+ if(src_qinfo != dst_qinfo)
+ {
+ fres = vrequantize_pooling<q8x8_t>(fres, requant_qinfo);
+ }
+ wrapper::vstore(reinterpret_cast<T *>(out.ptr()), fres);
+ }
+ },
+ in, out);
+}
+
+template <typename T>
+void poolingMxN_quantized_neon_nchw(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window)
+{
+ ARM_COMPUTE_UNUSED(dst1);
+ Iterator in(src, window_src);
+ Iterator out(dst0, 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) + (pool_info.exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_bottom);
+
+ const UniformQuantizationInfo &src_qinfo = src->info()->quantization_info().uniform();
+ const UniformQuantizationInfo &dst_qinfo = dst0->info()->quantization_info().uniform();
+
+ execute_window_loop(window, [&](const Coordinates & id)
+ {
+ T res = std::numeric_limits<T>::min();
+
+ if(pool_info.pool_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(pool_info.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 *>(in.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 *>(in.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 *>(in.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 *>(in.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 *>(out.ptr())) = res;
+ },
+ in, out);
+}
+#endif /* defined(ENABLE_NCHW_KERNELS) */
+} // namespace cpu
+} // namespace arm_compute
+
+#endif // SRC_CORE_NEON_KERNELS_QUANTIZED_H
\ No newline at end of file