Remove padding in cpuPool2d NCHW
Remove padding from all cpuPool2d NCHW kernels (FP16,FP32 & Quantized)
Resolves: COMPMID-4728, COMPMID-4823
Signed-off-by: Freddie Liardet <frederick.liardet@arm.com>
Change-Id: Ida619f67cd6606b33828f2d9dee925aeb794cc50
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/6358
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Pablo Marquez Tello <pablo.tello@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/arm_compute/core/Types.h b/arm_compute/core/Types.h
index 37ba9f9..b2b0982 100644
--- a/arm_compute/core/Types.h
+++ b/arm_compute/core/Types.h
@@ -675,8 +675,8 @@
* @param[in] stride_x Stride, in elements, across x.
* @param[in] stride_y Stride, in elements, across y.
* @param[in] pad_left Padding across x on the left, in elements.
- * @param[in] pad_top Padding across y on the top, in elements.
* @param[in] pad_right Padding across x on the right, in elements.
+ * @param[in] pad_top Padding across y on the top, in elements.
* @param[in] pad_bottom Padding across y on the bottom, in elements.
* @param[in] round Dimensions rounding.
*/
diff --git a/src/cpu/kernels/CpuPool2dKernel.cpp b/src/cpu/kernels/CpuPool2dKernel.cpp
index d7fb75e..90ddefa 100644
--- a/src/cpu/kernels/CpuPool2dKernel.cpp
+++ b/src/cpu/kernels/CpuPool2dKernel.cpp
@@ -239,7 +239,6 @@
std::pair<Status, Window> validate_and_configure_window(ITensorInfo *src, ITensorInfo *dst, ITensorInfo *indices, const PoolingLayerInfo &pool_info,
unsigned int &num_elems_processed_per_iteration,
- BorderSize &border_size,
int pool_size_x, int pool_size_y)
{
// dst auto inizialitation if not yet initialized
@@ -251,29 +250,22 @@
pool_info)))
.set_data_type(DataType::U32) /* we store the offset to the element */);
}
- const auto data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? src->data_layout() : pool_info.data_layout;
- unsigned int num_elems_read_per_iteration = 0;
- unsigned int num_elems_horizontal_window = 0;
- int pool_stride_x = 0;
- int pool_stride_y = 0;
- const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
- const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
- const int src_width = src->dimension(idx_width);
- const int src_height = src->dimension(idx_height);
- const PadStrideInfo pad_stride_info = pool_info.pad_stride_info;
+ const auto data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? src->data_layout() : pool_info.data_layout;
+ ARM_COMPUTE_ERROR_ON(src->data_layout() != DataLayout::NCHW);
+
+ int pool_stride_x = 0;
+ int pool_stride_y = 0;
+ const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+ const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+ const PadStrideInfo pad_stride_info = pool_info.pad_stride_info;
+
std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();
- const int pool_pad_right = pad_stride_info.pad_right();
- const int pool_pad_top = pad_stride_info.pad_top();
- const int pool_pad_left = pad_stride_info.pad_left();
- const int pool_pad_bottom = pad_stride_info.pad_bottom();
- const bool is_square = pool_size_x == pool_size_y;
- const unsigned int pooled_w = dst->dimension(idx_width);
- const unsigned int pooled_h = dst->dimension(idx_height);
+ const bool is_square = pool_size_x == pool_size_y;
+ const unsigned int pooled_w = dst->dimension(idx_width);
+ const unsigned int pooled_h = dst->dimension(idx_height);
//If it's not squared and optimized will be executed the MxN
- num_elems_read_per_iteration = 1;
num_elems_processed_per_iteration = 1;
- num_elems_horizontal_window = 1;
if(is_square)
{
@@ -284,14 +276,10 @@
switch(pool_size_x)
{
case 2:
- num_elems_read_per_iteration = 16;
num_elems_processed_per_iteration = (pool_stride_x == 2) ? 8 : 15;
- num_elems_horizontal_window = (pool_stride_x == 2) ? 8 : 16;
break;
case 3:
- num_elems_read_per_iteration = 16;
num_elems_processed_per_iteration = (pool_stride_x == 2) ? 7 : 14;
- num_elems_horizontal_window = (pool_stride_x == 2) ? 8 : 16;
break;
default:
break;
@@ -299,36 +287,11 @@
break;
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case DataType::F16:
- switch(pool_size_x)
- {
- case 2:
- case 3:
- num_elems_read_per_iteration = 4;
- num_elems_processed_per_iteration = 1;
- num_elems_horizontal_window = 1;
- break;
- default:
- break;
- }
+ num_elems_processed_per_iteration = 1;
break;
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
case DataType::F32:
- switch(pool_size_x)
- {
- case 2:
- num_elems_read_per_iteration = 2;
- break;
- case 3:
- num_elems_read_per_iteration = 4; // We use vload4 for pooling3
- break;
- case 7:
- num_elems_read_per_iteration = 8; // We use vload8 for pooling7
- break;
- default:
- break;
- }
num_elems_processed_per_iteration = 1;
- num_elems_horizontal_window = 1;
break;
default:
ARM_COMPUTE_ERROR("Element size not supported");
@@ -338,47 +301,18 @@
bool window_changed = false;
Window win{};
- if(data_layout == DataLayout::NCHW)
- {
- // Number of iterations in X dimension
- const int num_iterations_x = (pooled_w + num_elems_processed_per_iteration - 1) / num_elems_processed_per_iteration;
- // Upper limit for the number of right/bottom border elements that are accessed
- const int upper_bound_w = ((num_iterations_x - 1) * num_elems_processed_per_iteration * pool_stride_x - pool_pad_left + num_elems_read_per_iteration) - src_width;
- const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_top + pool_size_y) - src_height;
- border_size = BorderSize(pool_pad_top, pool_pad_right, pool_pad_bottom, pool_pad_left);
- border_size.right = std::max(upper_bound_w, pool_pad_right);
- border_size.bottom = std::max(upper_bound_h, pool_pad_bottom);
- TensorShape dst_shape{ src->tensor_shape() };
- dst_shape.set(0, pooled_w);
- dst_shape.set(1, pooled_h);
- TensorInfo dst_info(src->clone()->set_tensor_shape(dst_shape));
- win = calculate_max_window(dst_info, Steps(num_elems_processed_per_iteration));
- AccessWindowStatic src_access(src, -pool_pad_left, -pool_pad_top, ceil_to_multiple(src_width + border_size.right, pool_size_x), src_height + border_size.bottom);
- AccessWindowHorizontal dst_access(dst, 0, num_elems_horizontal_window);
- if(indices)
- {
- AccessWindowHorizontal indices_access(indices, 0, num_elems_horizontal_window);
- window_changed = update_window_and_padding(win, src_access, dst_access, indices_access);
- }
- else
- {
- window_changed = update_window_and_padding(win, src_access, dst_access);
- }
- dst_access.set_valid_region(win, ValidRegion(Coordinates(), dst->tensor_shape()));
-
- border_size = src->padding();
- }
+ // Upper limit for the number of right/bottom border elements that are accessed
+ TensorShape dst_shape{ src->tensor_shape() };
+ dst_shape.set(0, pooled_w);
+ dst_shape.set(1, pooled_h);
+ TensorInfo dst_info(src->clone()->set_tensor_shape(dst_shape));
+ win = calculate_max_window(dst_info, Steps(num_elems_processed_per_iteration));
Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
return std::make_pair(err, win);
}
} // namespace
-BorderSize CpuPool2dKernel::border_size() const
-{
- return _border_size;
-}
-
void CpuPool2dKernel::configure(ITensorInfo *src, ITensorInfo *dst, const PoolingLayerInfo &pool_info, ITensorInfo *indices)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
@@ -419,7 +353,7 @@
{
// Configure kernel window
auto win_config = validate_and_configure_window(src, dst, indices, pool_info, _num_elems_processed_per_iteration,
- _border_size, pool_size.x(), pool_size.y());
+ pool_size.x(), pool_size.y());
ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
ICpuKernel::configure(win_config.second);
}
@@ -430,7 +364,6 @@
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src);
unsigned int num_elems_processed_per_iteration = 0;
- BorderSize border_size(0);
const bool is_global_pooling = pool_info.is_global_pooling;
@@ -444,7 +377,7 @@
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst, pool_info, indices, Size2D(pool_size_x, pool_size_y)));
ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src->clone().get(), dst->clone().get(),
- (indices) ? indices->clone().get() : nullptr, pool_info, num_elems_processed_per_iteration, border_size,
+ (indices) ? indices->clone().get() : nullptr, pool_info, num_elems_processed_per_iteration,
pool_size_x, pool_size_y)
.first);
diff --git a/src/cpu/kernels/CpuPool2dKernel.h b/src/cpu/kernels/CpuPool2dKernel.h
index 70fe52d..aedeb7f 100644
--- a/src/cpu/kernels/CpuPool2dKernel.h
+++ b/src/cpu/kernels/CpuPool2dKernel.h
@@ -60,7 +60,6 @@
// Inherited methods overridden:
void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
- BorderSize border_size() const override;
const char *name() const override;
private:
@@ -70,7 +69,6 @@
PoolingLayerInfo _pool_info{};
DataLayout _data_layout{ DataLayout::UNKNOWN };
unsigned int _num_elems_processed_per_iteration{ 0 };
- BorderSize _border_size{ 0 };
Size2D _pool_size{};
int _pool_stride_x{};
PoolingKernelPtr _run_method{ nullptr };
diff --git a/src/cpu/kernels/pool2d/neon/nchw/all.cpp b/src/cpu/kernels/pool2d/neon/nchw/all.cpp
index 3ca7701..109fc1b 100644
--- a/src/cpu/kernels/pool2d/neon/nchw/all.cpp
+++ b/src/cpu/kernels/pool2d/neon/nchw/all.cpp
@@ -28,18 +28,55 @@
#include "src/core/NEON/wrapper/intrinsics/intrinsics.h"
#include "src/core/helpers/WindowHelpers.h"
#include "src/cpu/kernels/pool2d/neon/list.h"
+#include <limits>
#ifdef ENABLE_NCHW_KERNELS
namespace arm_compute
{
namespace cpu
{
+#define READ_2_RIGHT_BOUNDARY_AWARE(height, width, pad_left, pad_top, x, y, ptr, fval) \
+ (x == width + pad_left - 1) ? vset_lane_f32(*(ptr), vdup_n_f32(fval), 0) : vld1_f32(ptr)
+#define READ_2_LEFT_BOUNDARY_AWARE(height, width, pad_left, pad_top, x, y, ptr, fval) \
+ (x == pad_left - 1) ? vset_lane_f32(*(1 + ptr), vdup_n_f32(fval), 1) : READ_2_RIGHT_BOUNDARY_AWARE(height, width, pad_left, pad_top, x, y, ptr, fval)
+#define READ_2_BOUNDARY_AWARE(height, width, pad_left, pad_top, x, y, ptr, fval) \
+ ((y < pad_top) || (x < pad_left - 1) || (y >= height + pad_top) || (x > width + pad_left - 1)) ? vdup_n_f32(fval) : READ_2_LEFT_BOUNDARY_AWARE(height, width, pad_left, pad_top, x, y, ptr, fval)
+
+#define READ_4_BOUNDARY_AWARE(height, width, pad_left, pad_top, x, y, ptr, fval) \
+ vcombine_f32(READ_2_BOUNDARY_AWARE(height, width, pad_left, pad_top, x, y, ptr, fval), \
+ READ_2_BOUNDARY_AWARE(height, width, pad_left, pad_top, (x + 2), y, (ptr + 2), fval))
+
+float32x4x2_t read_8_boundary_aware(int height, int width, int pad_left, int pad_top, int x, int y, const float *ptr, float fval)
+{
+ float32x4x2_t vec;
+ vec.val[0] = READ_4_BOUNDARY_AWARE(height, width, pad_left, pad_top, x, y, ptr, fval);
+ vec.val[1] = READ_4_BOUNDARY_AWARE(height, width, pad_left, pad_top, (x + 4), y, (ptr + 4), fval);
+ return vec;
+}
+
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+
+float16x4_t read_4_boundary_aware_fp16(int srcw, int srch, int pad_l, int pad_t, int x, int y, const float16_t *ptr, float16_t fval)
+{
+ float16_t vec[4];
+ const bool row_in_bounds((y >= pad_t) && (y < (srch + pad_t)));
+ for(int i = 0; i < 4; i++)
+ {
+ if(row_in_bounds && (x + i >= pad_l) && (x + i < (srcw + pad_l)))
+ {
+ vec[i] = *(ptr + i);
+ }
+ else
+ {
+ vec[i] = fval;
+ }
+ }
+ return wrapper::vload(vec);
+}
+
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);
@@ -52,19 +89,29 @@
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 int src_w = src->info()->dimension(0);
+ const int src_h = src->info()->dimension(1);
+ const int upper_bound_w = src_w + (pool_info.exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = src_h + (pool_info.exclude_padding ? 0 : pool_pad_bottom);
+ constexpr float16_t fp16_min = -100.0f;
+ const float16_t fill_value = (pool_info.pool_type == PoolingType::MAX) ? fp16_min : 0.f;
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 = {};
+ const auto x_val = id.x() * pool_stride_x;
+ const auto y_val_0 = id.y() * pool_stride_y;
+ const auto y_val_1 = (id.y() * pool_stride_y) + 1;
+ const auto y_val_2 = (id.y() * pool_stride_y) + 2;
+ float16x4_t top_data = read_4_boundary_aware_fp16(src_w, src_h, pool_pad_left, pool_pad_top,
+ x_val, y_val_0, reinterpret_cast<const float16_t *>(src_top_ptr + in.offset()), fill_value);
+ float16x4_t middle_data = read_4_boundary_aware_fp16(src_w, src_h, pool_pad_left, pool_pad_top,
+ x_val, y_val_1, reinterpret_cast<const float16_t *>(src_middle_ptr + in.offset()), fill_value);
+ float16x4_t bottom_data = read_4_boundary_aware_fp16(src_w, src_h, pool_pad_left, pool_pad_top,
+ x_val, y_val_2, reinterpret_cast<const float16_t *>(src_bottom_ptr + in.offset()), fill_value);
+ float16x4_t res = {};
// Get power of 2 in case of l2 pooling
if(pool_info.pool_type == PoolingType::L2)
@@ -88,7 +135,7 @@
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(vset_lane_f16(fp16_min, max_data, 3), max_data);
res = vpmax_f16(res, res);
}
@@ -120,6 +167,25 @@
}
template <typename T>
+auto read_2_boundary_aware(int srcw, int srch, int pad_l, int pad_t, int x, int y, const T *ptr, T fval)
+{
+ T vec[2];
+ const bool row_in_bounds((y >= pad_t) && (y < (srch + pad_t)));
+ for(int i = 0; i < 2; i++)
+ {
+ if(row_in_bounds && (x + i >= pad_l) && (x + i < (srcw + pad_l)))
+ {
+ vec[i] = *(ptr + i);
+ }
+ else
+ {
+ vec[i] = fval;
+ }
+ }
+ return wrapper::vload(vec);
+}
+
+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);
@@ -130,16 +196,25 @@
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 src_w = src->info()->dimension(0);
+ const int src_h = src->info()->dimension(1);
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());
+ constexpr T float_min = -100.0f;
+ const T fill_value = (pool_info.pool_type == PoolingType::MAX) ? float_min : 0.f;
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()));
+ const auto x_val = id.x() * pool_stride_x;
+ const auto y_val_0 = id.y() * pool_stride_y;
+ const auto y_val_1 = (id.y() * pool_stride_y) + 1;
+ auto top_data = read_2_boundary_aware(src_w, src_h, pool_pad_left, pool_pad_top,
+ x_val, y_val_0, reinterpret_cast<const T *>(src_top_ptr + in.offset()), fill_value);
+ auto bottom_data = read_2_boundary_aware(src_w, src_h, pool_pad_left, pool_pad_top,
+ x_val, y_val_1, reinterpret_cast<const T *>(src_bottom_ptr + in.offset()), fill_value);
float32x2_t top_data_f32 = f16_to_f32<T>(top_data);
float32x2_t bottom_data_f32 = f16_to_f32<T>(bottom_data);
@@ -180,17 +255,29 @@
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 int src_w = src->info()->dimension(0);
+ const int src_h = src->info()->dimension(1);
+ const int upper_bound_w = src_w + (pool_info.exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = src_h + (pool_info.exclude_padding ? 0 : pool_pad_bottom);
+ constexpr float16_t fp16_min = -100.0f;
+ const float16_t fill_value = (pool_info.pool_type == PoolingType::MAX) ? fp16_min : 0.0f;
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 = {};
+ const auto in_top_ptr = reinterpret_cast<const float16_t *>(src_top_ptr + in.offset());
+ const auto in_bottom_ptr = reinterpret_cast<const float16_t *>(src_bottom_ptr + in.offset());
+
+ const auto x_val = id.x() * pool_stride_x;
+ const auto y_val_0 = id.y() * pool_stride_y;
+ const auto y_val_1 = (id.y() * pool_stride_y) + 1;
+ float16x4_t top_data = read_4_boundary_aware_fp16(src_w, src_h, pool_pad_left, pool_pad_top,
+ x_val, y_val_0, in_top_ptr, fill_value);
+ float16x4_t bottom_data = read_4_boundary_aware_fp16(src_w, src_h, pool_pad_left, pool_pad_top,
+ x_val, y_val_1, in_bottom_ptr, fill_value);
+ float16x4_t res = {};
// Get power of 2 in case of l2 pooling
if(pool_info.pool_type == PoolingType::L2)
@@ -242,48 +329,35 @@
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 int src_w = src->info()->dimension(0);
+ const int src_h = src->info()->dimension(1);
+ const int upper_bound_w = src_w + (pool_info.exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = src_h + (pool_info.exclude_padding ? 0 : pool_pad_bottom);
+ constexpr float16_t fp16_min = -100.0f;
+ const float16_t fill_value = (pool_info.pool_type == PoolingType::MAX) ? fp16_min : 0.0f;
execute_window_loop(window, [&](const Coordinates & id)
{
- float16_t res = 0.0f;
- float16x8_t vres = vdupq_n_f16(0.0f);
+ float16_t 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);
+ const float16_t 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)
+ for(int x = 0; x < pool_size_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().x()) + (y - pool_pad_top) * static_cast<int>
- (src->info()->strides_in_bytes().y())));
+ const auto ptr = 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);
- }
- }
+ const int idx = x + id.x() * pool_stride_x - pool_pad_left;
+ const int idy = y + id.y() * pool_stride_y - pool_pad_top;
+ float16_t data = (idx < 0 || idy < 0 || idx >= src_w || idy >= src_h) ? fill_value : *ptr;
- // 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;
@@ -293,45 +367,26 @@
}
}
- // 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
+ else // if max pooling
{
- float16x8_t vres = vdupq_n_f16(std::numeric_limits<float>::lowest());
- res = std::numeric_limits<float>::lowest();
+ res = fp16_min;
for(int y = 0; y < pool_size_y; ++y)
{
- int x = 0;
- for(; x <= (pool_size_x - 8); x += 8)
+ for(int x = 0; x < pool_size_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().x()) + (y - pool_pad_top) * static_cast<int>
- (src->info()->strides_in_bytes().y())));
- vres = vmaxq_f16(vres, data);
- }
+ const auto ptr = 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()));
- // 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);
+ const int idx = x + id.x() * pool_stride_x - pool_pad_left;
+ const int idy = y + id.y() * pool_stride_y - pool_pad_top;
+ float16_t data = (idx < 0 || idy < 0 || idx >= src_w || idy >= src_h) ? fill_value : *ptr;
+ 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
@@ -362,8 +417,11 @@
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 int src_w = src->info()->dimension(0);
+ const int src_h = src->info()->dimension(1);
+ const int upper_bound_w = src_w + (pool_info.exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = src_h + (pool_info.exclude_padding ? 0 : pool_pad_bottom);
+ const float fill_value = (pool_info.pool_type == PoolingType::MAX) ? -std::numeric_limits<float>::max() : 0.0f;
execute_window_loop(window, [&](const Coordinates & id)
{
@@ -372,38 +430,21 @@
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);
+ 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)
+ for(int x = 0; x < pool_size_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().x()) + (y - pool_pad_top) * static_cast<int>
- (src->info()->strides_in_bytes().y())));
+ const auto ptr = 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);
- }
- }
+ const int idx = x + id.x() * pool_stride_x - pool_pad_left;
+ const int idy = y + id.y() * pool_stride_y - pool_pad_top;
+ float data = (idx < 0 || idy < 0 || idx >= src_w || idy >= src_h) ? fill_value : *ptr;
- // 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;
@@ -413,51 +454,26 @@
}
}
-#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
+ else // if max pooling
{
- float32x4_t vres = vdupq_n_f32(std::numeric_limits<float>::lowest());
- res = 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)
+ for(int x = 0; x < pool_size_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().x()) + (y - pool_pad_top) * static_cast<int>
- (src->info()->strides_in_bytes().y())));
- vres = vmaxq_f32(vres, data);
- }
+ const auto ptr = 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()));
- // 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);
+ const int idx = x + id.x() * pool_stride_x - pool_pad_left;
+ const int idy = y + id.y() * pool_stride_y - pool_pad_top;
+ float data = (idx < 0 || idy < 0 || idx >= src_w || idy >= src_h) ? fill_value : *ptr;
+ 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
@@ -490,20 +506,28 @@
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 int src_w = src->info()->dimension(0);
+ const int src_h = src->info()->dimension(1);
+ const int upper_bound_w = src_w + (pool_info.exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = src_h + (pool_info.exclude_padding ? 0 : pool_pad_bottom);
+ const float fill_value = (pool_info.pool_type == PoolingType::MAX) ? -std::numeric_limits<float>::max() : 0.0f;
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;
+ 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());
+
+ const auto x_val = id.x() * pool_stride_x;
+ const auto y_val_0 = id.y() * pool_stride_y;
+ const auto y_val_1 = (id.y() * pool_stride_y) + 1;
+ auto top_data = READ_2_BOUNDARY_AWARE(src_h, src_w, pool_pad_left, pool_pad_top, x_val, y_val_0, in_top_ptr, fill_value);
+ auto bottom_data = READ_2_BOUNDARY_AWARE(src_h, src_w, pool_pad_left, pool_pad_top, x_val, y_val_1, in_bottom_ptr, fill_value);
+ float32x2_t res = {};
+ float final_res = 0;
+
// Get power of 2 in case of l2 pooling
if(pool_info.pool_type == PoolingType::L2)
{
@@ -556,8 +580,11 @@
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 int src_w = src->info()->dimension(0);
+ const int src_h = src->info()->dimension(1);
+ const int upper_bound_w = src_w + (pool_info.exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = src_h + (pool_info.exclude_padding ? 0 : pool_pad_bottom);
+ const float fill_value = (pool_info.pool_type == PoolingType::MAX) ? -std::numeric_limits<float>::max() : 0.0f;
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));
@@ -565,11 +592,20 @@
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;
+ const auto in_top_ptr = reinterpret_cast<const float *>(src_top_ptr + in.offset());
+ const auto in_middle_ptr = reinterpret_cast<const float *>(src_middle_ptr + in.offset());
+ const auto in_bottom_ptr = reinterpret_cast<const float *>(src_bottom_ptr + in.offset());
+
+ const auto x_val = id.x() * pool_stride_x;
+ const auto y_val_0 = id.y() * pool_stride_y;
+ const auto y_val_1 = (id.y() * pool_stride_y) + 1;
+ const auto y_val_2 = (id.y() * pool_stride_y) + 2;
+ auto top_data = READ_4_BOUNDARY_AWARE(src_h, src_w, pool_pad_left, pool_pad_top, x_val, y_val_0, in_top_ptr, fill_value);
+ auto middle_data = READ_4_BOUNDARY_AWARE(src_h, src_w, pool_pad_left, pool_pad_top, x_val, y_val_1, in_middle_ptr, fill_value);
+ auto bottom_data = READ_4_BOUNDARY_AWARE(src_h, src_w, pool_pad_left, pool_pad_top, x_val, y_val_2, in_bottom_ptr, fill_value);
+
+ float32x2_t res = {};
+ float final_res = 0;
// Get power of 2 in case of l2 pooling
if(pool_info.pool_type == PoolingType::L2)
@@ -625,8 +661,11 @@
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 int src_w = src->info()->dimension(0);
+ const int src_h = src->info()->dimension(1);
+ const int upper_bound_w = src_w + (pool_info.exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = src_h + (pool_info.exclude_padding ? 0 : pool_pad_bottom);
+ const float fill_value = (pool_info.pool_type == PoolingType::MAX) ? -std::numeric_limits<float>::max() : 0.0f;
std::array<const uint8_t *, pool_size> src_ptrs{ {} };
for(int i = 0; i < pool_size; ++i)
@@ -636,8 +675,15 @@
execute_window_loop(window, [&](const Coordinates & id)
{
+ auto in_ptr = reinterpret_cast<const float *>(src_ptrs[0] + in.offset());
+
+ auto x_val = id.x() * pool_stride_x;
+ auto y_val = id.y() * pool_stride_y;
+ float32x4x2_t data = read_8_boundary_aware(src_h, src_w, pool_pad_left, pool_pad_top, x_val, y_val, in_ptr, fill_value);
+
float32x2_t res = {};
float final_res = 0.f;
+
if(pool_info.pool_type != PoolingType::MAX)
{
// Calculate scale
@@ -645,8 +691,6 @@
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)
{
@@ -656,7 +700,11 @@
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()));
+ in_ptr = reinterpret_cast<const float *>(src_ptrs[i] + in.offset());
+
+ x_val = id.x() * pool_stride_x;
+ y_val = (id.y() * pool_stride_y) + i;
+ data = read_8_boundary_aware(src_h, src_w, pool_pad_left, pool_pad_top, x_val, y_val, in_ptr, fill_value);
// Get power of 2 in case of l2 pooling
if(pool_info.pool_type == PoolingType::L2)
{
@@ -671,14 +719,17 @@
}
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);
+ in_ptr = reinterpret_cast<const float *>(src_ptrs[i] + in.offset());
+
+ x_val = id.x() * pool_stride_x;
+ y_val = (id.y() * pool_stride_y) + i;
+ float32x4x2_t temp = read_8_boundary_aware(src_h, src_w, pool_pad_left, pool_pad_top, x_val, y_val, in_ptr, fill_value);
+ data = vmax2q_f32(data, temp);
}
- 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(vget_high_f32(vsetq_lane_f32(-std::numeric_limits<float>::max(), data.val[1], 3)), vget_low_f32(data.val[1]));
+ res = vpmax_f32(res, vpmax_f32(vget_high_f32(data.val[0]), vget_low_f32(data.val[0])));
res = vpmax_f32(res, res);
}
final_res = vget_lane_f32(res, 0);
diff --git a/src/cpu/kernels/pool2d/neon/quantized.h b/src/cpu/kernels/pool2d/neon/quantized.h
index a16960a..386e043 100644
--- a/src/cpu/kernels/pool2d/neon/quantized.h
+++ b/src/cpu/kernels/pool2d/neon/quantized.h
@@ -467,6 +467,63 @@
}
template <typename T>
+auto load16_boundary_aware(int srcw, int srch, int pad_l, int pad_r, int pad_t, int pad_b, int x, int y, const T *ptr, T fval)
+{
+ ARM_COMPUTE_UNUSED(pad_b, pad_r);
+ T vec[16];
+ //handle reading a row out of the tensor
+ const bool row_in_bounds((y >= pad_t) && (y < (srch + pad_t)));
+ for(int i = 0; i < 16; i++)
+ {
+ if(row_in_bounds && (x + i >= pad_l) && (x + i < (srcw + pad_l)))
+ {
+ vec[i] = *(ptr + i);
+ }
+ else
+ {
+ vec[i] = fval;
+ }
+ }
+ return wrapper::vloadq(vec);
+}
+
+template <typename T, typename V, bool deinterleave>
+inline void write16_boundary_aware(int x, int dst_w, const V &lower, const V &upper, T *ptr)
+{
+ if(deinterleave)
+ {
+ for(int i = 0; i < 8 && (i * 2 + x) < dst_w; ++i)
+ {
+ *(ptr + i * 2) = lower[i];
+ }
+ for(int i = 0; i < 8 && (i * 2 + x + 1) < dst_w; ++i)
+ {
+ *(ptr + 1 + i * 2) = upper[i];
+ }
+ }
+ else
+ {
+ for(int i = 0; i < 8 && (i + x) < dst_w; ++i)
+ {
+ *(ptr + i) = lower[i];
+ }
+ for(int i = 0; i < 8 && (i + x + 8) < dst_w; ++i)
+ {
+ *(ptr + i + 8) = upper[i];
+ }
+ }
+}
+
+template <typename T, typename V>
+inline void write8_boundary_aware(int x, int dst_w, const V &v, T *ptr)
+{
+ for(int i = 0; i < 8 && (i + x) < dst_w; ++i)
+ {
+ *(ptr + i) = v[i];
+ }
+}
+
+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);
@@ -474,9 +531,8 @@
Iterator out(dst0, window);
/** SIMD 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 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 q16x4_t = typename wrapper::traits::neon_vector<q16_t, 4>::type;
using q16x8_t = typename wrapper::traits::neon_vector<q16_t, 8>::type;
@@ -490,14 +546,11 @@
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 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;
@@ -505,13 +558,25 @@
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 int src_w = src->info()->dimension(0);
+ const int src_h = src->info()->dimension(1);
+ const int dst_w = dst0->info()->dimension(0);
+
+ const T fill_value = (pool_info.pool_type == PoolingType::MAX) ? std::numeric_limits<T>::min() : T(0);
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 = {};
+ const auto x_val = id.x() * pool_stride_x;
+ const auto y_val_0 = id.y() * pool_stride_y;
+ const auto y_val_1 = (id.y() * pool_stride_y) + 1;
+
+ auto top_data = load16_boundary_aware(src_w, src_h, pool_pad_left, pool_pad_right, pool_pad_top, pool_pad_bottom,
+ x_val, y_val_0, reinterpret_cast<const T *>(src_top_ptr + in.offset()), fill_value);
+ auto bottom_data = load16_boundary_aware(src_w, src_h, pool_pad_left, pool_pad_right, pool_pad_top, pool_pad_bottom,
+ x_val, y_val_1, reinterpret_cast<const T *>(src_bottom_ptr + in.offset()), fill_value);
+
+ q8x8_t lower_res = {};
+ q8x8_t upper_res = {};
if(pool_info.pool_type != PoolingType::MAX)
{
@@ -580,16 +645,15 @@
lower_res = wrapper::vgetlow(requantized_dst);
upper_res = wrapper::vgethigh(requantized_dst);
}
-
+ auto out_ptr = reinterpret_cast<T *>(out.ptr());
// Store result
if(pool_stride_x == 1)
{
- const q8x8x2_t res = { { lower_res, upper_res } };
- wrapper::vstore(reinterpret_cast<T *>(out.ptr()), res);
+ write16_boundary_aware<T, q8x8_t, true>(id.x(), dst_w, lower_res, upper_res, out_ptr);
}
else
{
- wrapper::vstore(reinterpret_cast<T *>(out.ptr()), lower_res);
+ write8_boundary_aware<T, q8x8_t>(id.x(), dst_w, lower_res, out_ptr);
}
},
in, out);
@@ -632,13 +696,27 @@
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)));
+ const int src_w = src->info()->dimension(0);
+ const int src_h = src->info()->dimension(1);
+ const T fill_value = (pool_info.pool_type == PoolingType::AVG) ? T(0) : std::numeric_limits<T>::min();
+ const int dst_w = dst0->info()->dimension(0);
+
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 = {};
+ const auto x_val = id.x() * pool_stride_x;
+ const auto y_val_0 = id.y() * pool_stride_y;
+ const auto y_val_1 = (id.y() * pool_stride_y) + 1;
+ const auto y_val_2 = (id.y() * pool_stride_y) + 2;
+
+ auto top_data = load16_boundary_aware(src_w, src_h, pool_pad_left, pool_pad_right, pool_pad_top, pool_pad_bottom,
+ x_val, y_val_0, reinterpret_cast<const T *>(src_top_ptr + in.offset()), fill_value);
+ auto middle_data = load16_boundary_aware(src_w, src_h, pool_pad_left, pool_pad_right, pool_pad_top, pool_pad_bottom,
+ x_val, y_val_1, reinterpret_cast<const T *>(src_middle_ptr + in.offset()), fill_value);
+ auto bottom_data = load16_boundary_aware(src_w, src_h, pool_pad_left, pool_pad_right, pool_pad_top, pool_pad_bottom,
+ x_val, y_val_2, reinterpret_cast<const T *>(src_bottom_ptr + in.offset()), fill_value);
+
+ q8x8_t fres = {};
+ q8x16_t fqres = {};
if(pool_info.pool_type == PoolingType::AVG)
{
@@ -735,7 +813,7 @@
{
fqres = vrequantize_pooling<q8x8_t, q8x16_t>(wrapper::vgetlow(fqres), wrapper::vgethigh(fqres), requant_qinfo);
}
- wrapper::vstore(reinterpret_cast<T *>(out.ptr()), fqres);
+ write16_boundary_aware<T, q8x8_t, false>(id.x(), dst_w, wrapper::vgetlow(fqres), wrapper::vgethigh(fqres), reinterpret_cast<T *>(out.ptr()));
}
else
{
@@ -743,7 +821,7 @@
{
fres = vrequantize_pooling<q8x8_t>(fres, requant_qinfo);
}
- wrapper::vstore(reinterpret_cast<T *>(out.ptr()), fres);
+ write8_boundary_aware<T, q8x8_t>(id.x(), dst_w, fres, reinterpret_cast<T *>(out.ptr()));
}
},
in, out);
@@ -757,11 +835,8 @@
Iterator out(dst0, window);
/** SIMD 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;
+ using q16_t = typename wrapper::traits::promote_t<T>;
+ using q32_t = typename wrapper::traits::promote_t<q16_t>;
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;
@@ -775,8 +850,13 @@
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 UniformQuantizationInfo &src_qinfo = src->info()->quantization_info().uniform();
+ const UniformQuantizationInfo &dst_qinfo = dst0->info()->quantization_info().uniform();
+ const int src_w = src->info()->dimension(0);
+ const int src_h = src->info()->dimension(1);
+ const T fill_value = (pool_info.pool_type == PoolingType::AVG) ? T(0) : std::numeric_limits<T>::min();
+ const int stridex_in_bytes = static_cast<int>(src->info()->strides_in_bytes().x());
+ const int stridey_in_bytes = static_cast<int>(src->info()->strides_in_bytes().y());
execute_window_loop(window, [&](const Coordinates & id)
{
@@ -784,8 +864,7 @@
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;
+ 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,
@@ -794,61 +873,33 @@
// Perform pooling
for(int y = 0; y < pool_size_y; ++y)
{
- int x = 0;
- for(; x <= (pool_size_x - 8); x += 8)
+ for(int x = 0; x < pool_size_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().x()) + (y - pool_pad_top) * static_cast<int>
- (src->info()->strides_in_bytes().y())));
+ const auto in_ptr = reinterpret_cast<const T *>(in.ptr() + (x - pool_pad_left) * stridex_in_bytes + (y - pool_pad_top) * stridey_in_bytes);
- 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())));
+ const int idx = x + id.x() * pool_stride_x - pool_pad_left;
+ const int idy = y + id.y() * pool_stride_y - pool_pad_top;
+ const T data = (idx < 0 || idy < 0 || idx >= src_w || idy >= src_h) ? fill_value : *in_ptr;
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)
+ for(int x = 0; x < pool_size_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().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);
+ const auto in_ptr = reinterpret_cast<const T *>(in.ptr() + (x - pool_pad_left) * stridex_in_bytes + (y - pool_pad_top) * stridey_in_bytes);
+
+ const int idx = x + id.x() * pool_stride_x - pool_pad_left;
+ const int idy = y + id.y() * pool_stride_y - pool_pad_top;
+ const T data = (idx < 0 || idy < 0 || idx >= src_w || idy >= src_h) ? fill_value : *in_ptr;
+ 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;
diff --git a/src/cpu/operators/CpuPool2d.cpp b/src/cpu/operators/CpuPool2d.cpp
index a4ac871..eabbd5e 100644
--- a/src/cpu/operators/CpuPool2d.cpp
+++ b/src/cpu/operators/CpuPool2d.cpp
@@ -39,7 +39,6 @@
{
CpuPool2d::CpuPool2d()
: _pooling_layer_kernel(),
- _border_handler(),
_asm_glue(),
_is_global_pooling_layer(false),
_data_layout(DataLayout::NCHW),
@@ -86,28 +85,6 @@
auto k = std::make_unique<kernels::CpuPool2dKernel>();
k->configure(src, dst, pool_info, indices);
_pooling_layer_kernel = std::move(k);
-
- switch(_data_layout)
- {
- case DataLayout::NCHW:
- {
- // Configure border depending on operation required (quantize border in case of asymmetric data_type)
- BorderMode border_mode = (!indices && pool_info.pool_type == PoolingType::MAX) ? BorderMode::REPLICATE : BorderMode::CONSTANT;
- PixelValue zero_value((indices) ? std::numeric_limits<int>::min() : 0.f);
- if(is_data_type_quantized_asymmetric(src->data_type()) && !pool_info.exclude_padding)
- {
- zero_value = PixelValue(0, src->data_type(), src->quantization_info());
- }
- auto b = std::make_unique<NEFillBorderKernel>();
- b->configure(src, _pooling_layer_kernel->border_size(), border_mode, zero_value);
- _border_handler = std::move(b);
- break;
- }
- case DataLayout::NHWC:
- break;
- default:
- ARM_COMPUTE_ERROR("Data layout not supported");
- }
}
}
@@ -137,14 +114,9 @@
switch(_data_layout)
{
case DataLayout::NCHW:
- // Fill border
- NEScheduler::get().schedule_op(_border_handler.get(), Window::DimY, _border_handler->window(), tensors);
-
- // Run pooling layer
NEScheduler::get().schedule_op(_pooling_layer_kernel.get(), _is_global_pooling_layer ? Window::DimZ : Window::DimY, _pooling_layer_kernel->window(), tensors);
break;
case DataLayout::NHWC:
- // Run pooling layer
NEScheduler::get().schedule_op(_pooling_layer_kernel.get(), Window::DimX, _pooling_layer_kernel->window(), tensors);
break;
default:
diff --git a/src/cpu/operators/CpuPool2d.h b/src/cpu/operators/CpuPool2d.h
index 4716371..02c2609 100644
--- a/src/cpu/operators/CpuPool2d.h
+++ b/src/cpu/operators/CpuPool2d.h
@@ -73,7 +73,6 @@
private:
std::unique_ptr<INEKernel> _pooling_layer_kernel;
- std::unique_ptr<INEKernel> _border_handler;
std::unique_ptr<INEKernel> _asm_glue;
bool _is_global_pooling_layer;
diff --git a/tests/validation/NEON/PoolingLayer.cpp b/tests/validation/NEON/PoolingLayer.cpp
index a6ba5de..77a5015 100644
--- a/tests/validation/NEON/PoolingLayer.cpp
+++ b/tests/validation/NEON/PoolingLayer.cpp
@@ -193,9 +193,7 @@
FIXTURE_DATA_TEST_CASE(RunIndices, NEPoolingLayerIndicesFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallNoneUnitShapes(), combine(PoolingLayerIndicesDatasetFPSmall,
framework::dataset::make("DataType",
DataType::F16))),
- framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })
-
- ))
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_f16);