| /* |
| * Copyright (c) 2021-2023 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/helpers/WindowHelpers.h" |
| #include "src/core/NEON/wrapper/intrinsics/intrinsics.h" |
| #include "src/cpu/kernels/pool2d/neon/impl.h" |
| #include "src/cpu/kernels/pool2d/neon/list.h" |
| |
| #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS) |
| |
| namespace arm_compute |
| { |
| namespace cpu |
| { |
| #ifdef ENABLE_NCHW_KERNELS |
| |
| namespace |
| { |
| 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); |
| } |
| } // namespace |
| |
| 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); |
| |
| 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 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 float16_t fp16_min = get_initial_min<half_float::half>(pool_info.use_inf_as_limit); |
| 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) |
| { |
| 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) |
| { |
| 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_pool2d( |
| 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(fp16_min, 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 = vsqrt_f16(res); |
| } |
| |
| *(reinterpret_cast<float16_t *>(out.ptr())) = vget_lane_f16(res, 0); |
| }, |
| in, out); |
| } |
| #endif // ENABLE_NCHW_KERNELS |
| |
| 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 pad_left = src->info()->padding().left; |
| const int pad_horizontal = pad_right + pad_left; |
| const int in_stride_y = static_cast<int>(src->info()->strides_in_bytes().y()); |
| const int in_stride_z = static_cast<int>(src->info()->strides_in_bytes().z()); |
| |
| execute_window_loop( |
| window_out, |
| [&](const Coordinates &id) |
| { |
| const int idx_width = id.y() * pool_stride_x; |
| const int idx_height = id.z() * pool_stride_y; |
| const int pool_limit_y = pool_pad_top - idx_height; |
| const int pool_limit_x = pool_pad_left - idx_width; |
| |
| const int pool_start_y = std::max(0, window_src.z().start() + pool_limit_y); |
| const int pool_start_x = std::max(0, window_src.y().start() + pool_limit_x); |
| const int in_x0_offset = |
| (pool_start_x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + |
| (pool_start_y - pool_pad_top) * static_cast<int>(src->info()->strides_in_bytes().z()); |
| const int in_x1_offset = |
| (pool_start_x + 1 - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + |
| (pool_start_y - pool_pad_top) * static_cast<int>(src->info()->strides_in_bytes().z()); |
| const int in_x2_offset = |
| (pool_start_x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + |
| (pool_start_y + 1 - pool_pad_top) * static_cast<int>(src->info()->strides_in_bytes().z()); |
| const int in_x3_offset = |
| (pool_start_x + 1 - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + |
| (pool_start_y + 1 - pool_pad_top) * static_cast<int>(src->info()->strides_in_bytes().z()); |
| |
| int x_off = window_start_x; |
| for (; x_off <= (window_end_x - window_step_x); x_off += window_step_x) |
| { |
| const auto in_x0_ptr = reinterpret_cast<const 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, DataLayout::NHWC); |
| 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_horizontal; |
| const uint32_t offset_x2 = (uint32_t)offset_x0 + in_stride_z / sizeof(float16_t) - |
| pad_horizontal * src->info()->tensor_shape()[1]; |
| const uint32_t offset_x3 = (uint32_t)offset_x2 + in_stride_y / sizeof(float16_t) - pad_horizontal; |
| 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, DataLayout::NHWC); |
| 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_horizontal; |
| const uint32_t offset_x2 = (uint32_t)offset_x0 + in_stride_z / sizeof(float16_t) - |
| pad_horizontal * src->info()->tensor_shape()[1]; |
| const uint32_t offset_x3 = (uint32_t)offset_x2 + in_stride_y / sizeof(float16_t) - pad_horizontal; |
| const uint32_t tmp_idx0 = (x0 >= x1) ? offset_x0 : offset_x1; |
| const uint32_t tmp_idx1 = (x2 >= x3) ? offset_x2 : offset_x3; |
| const uint32_t tmp_idx2 = (std::max(x0, x1) >= std::max(x2, x3)) ? tmp_idx0 : tmp_idx1; |
| |
| // Store indices |
| *(reinterpret_cast<uint32_t *>(indices.ptr()) + x_off) = tmp_idx2; |
| } |
| }, |
| in, out, indices); |
| } |
| #ifdef ENABLE_NCHW_KERNELS |
| |
| 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 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 float16_t fp16_min = get_initial_min<half_float::half>(pool_info.use_inf_as_limit); |
| 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) |
| { |
| 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) |
| { |
| 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_pool2d( |
| 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 = vsqrt_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 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 float16_t fp16_min = get_initial_min<half_float::half>(pool_info.use_inf_as_limit); |
| 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; |
| |
| if (pool_info.pool_type != PoolingType::MAX) |
| { |
| // Calculate scale |
| const float16_t scale = calculate_avg_scale_pool2d( |
| 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) |
| { |
| for (int x = 0; x < pool_size_x; ++x) |
| { |
| 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())); |
| |
| 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; |
| |
| if (pool_info.pool_type == PoolingType::L2) |
| { |
| data *= data; |
| } |
| |
| res += data; |
| } |
| } |
| |
| // Divide by scale |
| res *= scale; |
| } |
| else // if max pooling |
| { |
| res = fp16_min; |
| |
| for (int y = 0; y < pool_size_y; ++y) |
| { |
| for (int x = 0; x < pool_size_x; ++x) |
| { |
| 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())); |
| |
| 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); |
| } |
| } |
| } |
| |
| // 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 // ENABLE_NCHW_KERNELS |
| |
| 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); |
| const float16_t min_value = get_initial_min<half_float::half>(pool_info.use_inf_as_limit); |
| 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_pool2d( |
| 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(min_value); |
| |
| 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_pool2d( |
| 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 = min_value; |
| 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) */ |