| /* |
| * Copyright (c) 2016, 2017 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/NEON/kernels/NEMinMaxLocationKernel.h" |
| |
| #include "arm_compute/core/Coordinates.h" |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/IAccessWindow.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/Window.h" |
| |
| #include <algorithm> |
| #include <arm_neon.h> |
| #include <climits> |
| #include <cstddef> |
| |
| namespace arm_compute |
| { |
| NEMinMaxKernel::NEMinMaxKernel() |
| : _func(), _input(nullptr), _min(), _max(), _mtx() |
| { |
| } |
| |
| void NEMinMaxKernel::configure(const IImage *input, void *min, void *max) |
| { |
| ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(input); |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S16, DataType::F32); |
| ARM_COMPUTE_ERROR_ON(nullptr == min); |
| ARM_COMPUTE_ERROR_ON(nullptr == max); |
| |
| _input = input; |
| _min = min; |
| _max = max; |
| |
| switch(_input->info()->data_type()) |
| { |
| case DataType::U8: |
| _func = &NEMinMaxKernel::minmax_U8; |
| break; |
| case DataType::S16: |
| _func = &NEMinMaxKernel::minmax_S16; |
| break; |
| case DataType::F32: |
| _func = &NEMinMaxKernel::minmax_F32; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Unsupported data type"); |
| break; |
| } |
| |
| // Configure kernel window |
| constexpr unsigned int num_elems_processed_per_iteration = 1; |
| |
| Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration)); |
| |
| INEKernel::configure(win); |
| } |
| |
| void NEMinMaxKernel::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); |
| ARM_COMPUTE_ERROR_ON(_func == nullptr); |
| |
| (this->*_func)(window); |
| } |
| |
| void NEMinMaxKernel::reset() |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| switch(_input->info()->data_type()) |
| { |
| case DataType::U8: |
| *static_cast<int32_t *>(_min) = UCHAR_MAX; |
| *static_cast<int32_t *>(_max) = 0; |
| break; |
| case DataType::S16: |
| *static_cast<int32_t *>(_min) = SHRT_MAX; |
| *static_cast<int32_t *>(_max) = SHRT_MIN; |
| break; |
| case DataType::F32: |
| *static_cast<float *>(_min) = std::numeric_limits<float>::max(); |
| *static_cast<float *>(_max) = std::numeric_limits<float>::lowest(); |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Unsupported data type"); |
| break; |
| } |
| } |
| |
| template <typename T> |
| void NEMinMaxKernel::update_min_max(const T min, const T max) |
| { |
| std::lock_guard<arm_compute::Mutex> lock(_mtx); |
| |
| using type = typename std::conditional<std::is_same<T, float>::value, float, int32_t>::type; |
| |
| auto min_ptr = static_cast<type *>(_min); |
| auto max_ptr = static_cast<type *>(_max); |
| |
| if(min < *min_ptr) |
| { |
| *min_ptr = min; |
| } |
| |
| if(max > *max_ptr) |
| { |
| *max_ptr = max; |
| } |
| } |
| |
| void NEMinMaxKernel::minmax_U8(Window win) |
| { |
| uint8x8_t carry_min = vdup_n_u8(UCHAR_MAX); |
| uint8x8_t carry_max = vdup_n_u8(0); |
| |
| uint8_t carry_max_scalar = 0; |
| uint8_t carry_min_scalar = UCHAR_MAX; |
| |
| const int x_start = win.x().start(); |
| const int x_end = win.x().end(); |
| |
| // Handle X dimension manually to split into two loops |
| // First one will use vector operations, second one processes the left over pixels |
| win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| Iterator input(_input, win); |
| |
| execute_window_loop(win, [&](const Coordinates & id) |
| { |
| int x = x_start; |
| |
| // Vector loop |
| for(; x <= x_end - 16; x += 16) |
| { |
| const uint8x16_t pixels = vld1q_u8(input.ptr() + x); |
| const uint8x8_t tmp_min = vmin_u8(vget_high_u8(pixels), vget_low_u8(pixels)); |
| const uint8x8_t tmp_max = vmax_u8(vget_high_u8(pixels), vget_low_u8(pixels)); |
| carry_min = vmin_u8(tmp_min, carry_min); |
| carry_max = vmax_u8(tmp_max, carry_max); |
| } |
| |
| // Process leftover pixels |
| for(; x < x_end; ++x) |
| { |
| const uint8_t pixel = input.ptr()[x]; |
| carry_min_scalar = std::min(pixel, carry_min_scalar); |
| carry_max_scalar = std::max(pixel, carry_max_scalar); |
| } |
| }, |
| input); |
| |
| // Reduce result |
| carry_min = vpmin_u8(carry_min, carry_min); |
| carry_max = vpmax_u8(carry_max, carry_max); |
| carry_min = vpmin_u8(carry_min, carry_min); |
| carry_max = vpmax_u8(carry_max, carry_max); |
| carry_min = vpmin_u8(carry_min, carry_min); |
| carry_max = vpmax_u8(carry_max, carry_max); |
| |
| // Extract max/min values |
| const uint8_t min_i = std::min(vget_lane_u8(carry_min, 0), carry_min_scalar); |
| const uint8_t max_i = std::max(vget_lane_u8(carry_max, 0), carry_max_scalar); |
| |
| // Perform reduction of local min/max values |
| update_min_max(min_i, max_i); |
| } |
| |
| void NEMinMaxKernel::minmax_S16(Window win) |
| { |
| int16x4_t carry_min = vdup_n_s16(SHRT_MAX); |
| int16x4_t carry_max = vdup_n_s16(SHRT_MIN); |
| |
| int16_t carry_max_scalar = SHRT_MIN; |
| int16_t carry_min_scalar = SHRT_MAX; |
| |
| const int x_start = win.x().start(); |
| const int x_end = win.x().end(); |
| |
| // Handle X dimension manually to split into two loops |
| // First one will use vector operations, second one processes the left over pixels |
| win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| Iterator input(_input, win); |
| |
| execute_window_loop(win, [&](const Coordinates & id) |
| { |
| int x = x_start; |
| const auto in_ptr = reinterpret_cast<const int16_t *const>(input.ptr()); |
| |
| // Vector loop |
| for(; x <= x_end - 16; x += 16) |
| { |
| const int16x8x2_t pixels = vld2q_s16(in_ptr + x); |
| const int16x8_t tmp_min1 = vminq_s16(pixels.val[0], pixels.val[1]); |
| const int16x8_t tmp_max1 = vmaxq_s16(pixels.val[0], pixels.val[1]); |
| const int16x4_t tmp_min2 = vmin_s16(vget_high_s16(tmp_min1), vget_low_s16(tmp_min1)); |
| const int16x4_t tmp_max2 = vmax_s16(vget_high_s16(tmp_max1), vget_low_s16(tmp_max1)); |
| carry_min = vmin_s16(tmp_min2, carry_min); |
| carry_max = vmax_s16(tmp_max2, carry_max); |
| } |
| |
| // Process leftover pixels |
| for(; x < x_end; ++x) |
| { |
| const int16_t pixel = in_ptr[x]; |
| carry_min_scalar = std::min(pixel, carry_min_scalar); |
| carry_max_scalar = std::max(pixel, carry_max_scalar); |
| } |
| |
| }, |
| input); |
| |
| // Reduce result |
| carry_min = vpmin_s16(carry_min, carry_min); |
| carry_max = vpmax_s16(carry_max, carry_max); |
| carry_min = vpmin_s16(carry_min, carry_min); |
| carry_max = vpmax_s16(carry_max, carry_max); |
| |
| // Extract max/min values |
| const int16_t min_i = std::min(vget_lane_s16(carry_min, 0), carry_min_scalar); |
| const int16_t max_i = std::max(vget_lane_s16(carry_max, 0), carry_max_scalar); |
| |
| // Perform reduction of local min/max values |
| update_min_max(min_i, max_i); |
| } |
| |
| void NEMinMaxKernel::minmax_F32(Window win) |
| { |
| float32x2_t carry_min = vdup_n_f32(std::numeric_limits<float>::max()); |
| float32x2_t carry_max = vdup_n_f32(std::numeric_limits<float>::lowest()); |
| |
| float carry_min_scalar = std::numeric_limits<float>::max(); |
| float carry_max_scalar = std::numeric_limits<float>::lowest(); |
| |
| const int x_start = win.x().start(); |
| const int x_end = win.x().end(); |
| |
| // Handle X dimension manually to split into two loops |
| // First one will use vector operations, second one processes the left over pixels |
| win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| Iterator input(_input, win); |
| |
| execute_window_loop(win, [&](const Coordinates & id) |
| { |
| int x = x_start; |
| const auto in_ptr = reinterpret_cast<const float *const>(input.ptr()); |
| |
| // Vector loop |
| for(; x <= x_end - 8; x += 8) |
| { |
| const float32x4x2_t pixels = vld2q_f32(in_ptr + x); |
| const float32x4_t tmp_min1 = vminq_f32(pixels.val[0], pixels.val[1]); |
| const float32x4_t tmp_max1 = vmaxq_f32(pixels.val[0], pixels.val[1]); |
| const float32x2_t tmp_min2 = vmin_f32(vget_high_f32(tmp_min1), vget_low_f32(tmp_min1)); |
| const float32x2_t tmp_max2 = vmax_f32(vget_high_f32(tmp_max1), vget_low_f32(tmp_max1)); |
| carry_min = vmin_f32(tmp_min2, carry_min); |
| carry_max = vmax_f32(tmp_max2, carry_max); |
| } |
| |
| // Process leftover pixels |
| for(; x < x_end; ++x) |
| { |
| const float pixel = in_ptr[x]; |
| carry_min_scalar = std::min(pixel, carry_min_scalar); |
| carry_max_scalar = std::max(pixel, carry_max_scalar); |
| } |
| |
| }, |
| input); |
| |
| // Reduce result |
| carry_min = vpmin_f32(carry_min, carry_min); |
| carry_max = vpmax_f32(carry_max, carry_max); |
| carry_min = vpmin_f32(carry_min, carry_min); |
| carry_max = vpmax_f32(carry_max, carry_max); |
| |
| // Extract max/min values |
| const float min_i = std::min(vget_lane_f32(carry_min, 0), carry_min_scalar); |
| const float max_i = std::max(vget_lane_f32(carry_max, 0), carry_max_scalar); |
| |
| // Perform reduction of local min/max values |
| update_min_max(min_i, max_i); |
| } |
| |
| NEMinMaxLocationKernel::NEMinMaxLocationKernel() |
| : _func(nullptr), _input(nullptr), _min(nullptr), _max(nullptr), _min_count(nullptr), _max_count(nullptr), _min_loc(nullptr), _max_loc(nullptr) |
| { |
| } |
| |
| bool NEMinMaxLocationKernel::is_parallelisable() const |
| { |
| return false; |
| } |
| |
| template <unsigned int...> |
| struct index_seq |
| { |
| index_seq() = default; |
| index_seq(const index_seq &) = default; |
| index_seq &operator=(const index_seq &) = default; |
| index_seq(index_seq &&) noexcept = default; |
| index_seq &operator=(index_seq &&) noexcept = default; |
| virtual ~index_seq() = default; |
| }; |
| template <unsigned int N, unsigned int... S> |
| struct gen_index_seq : gen_index_seq < N - 1, N - 1, S... > |
| { |
| }; |
| template <unsigned int... S> |
| struct gen_index_seq<0u, S...> : index_seq<S...> |
| { |
| using type = index_seq<S...>; |
| }; |
| |
| template <class T, unsigned int... N> |
| struct NEMinMaxLocationKernel::create_func_table<T, index_seq<N...>> |
| { |
| static const NEMinMaxLocationKernel::MinMaxLocFunction func_table[sizeof...(N)]; |
| }; |
| |
| template <class T, unsigned int... N> |
| const NEMinMaxLocationKernel::MinMaxLocFunction NEMinMaxLocationKernel::create_func_table<T, index_seq<N...>>::func_table[sizeof...(N)] = |
| { |
| &NEMinMaxLocationKernel::minmax_loc<T, bool(N & 8), bool(N & 4), bool(N & 2), bool(N & 1)>... |
| }; |
| |
| void NEMinMaxLocationKernel::configure(const IImage *input, void *min, void *max, |
| ICoordinates2DArray *min_loc, ICoordinates2DArray *max_loc, |
| uint32_t *min_count, uint32_t *max_count) |
| { |
| ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(input); |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S16, DataType::F32); |
| ARM_COMPUTE_ERROR_ON(nullptr == min); |
| ARM_COMPUTE_ERROR_ON(nullptr == max); |
| |
| _input = input; |
| _min = min; |
| _max = max; |
| _min_count = min_count; |
| _max_count = max_count; |
| _min_loc = min_loc; |
| _max_loc = max_loc; |
| |
| unsigned int count_min = (nullptr != min_count ? 1 : 0); |
| unsigned int count_max = (nullptr != max_count ? 1 : 0); |
| unsigned int loc_min = (nullptr != min_loc ? 1 : 0); |
| unsigned int loc_max = (nullptr != max_loc ? 1 : 0); |
| |
| unsigned int table_idx = (count_min << 3) | (count_max << 2) | (loc_min << 1) | loc_max; |
| |
| switch(input->info()->data_type()) |
| { |
| case DataType::U8: |
| _func = create_func_table<uint8_t, gen_index_seq<16>::type>::func_table[table_idx]; |
| break; |
| case DataType::S16: |
| _func = create_func_table<int16_t, gen_index_seq<16>::type>::func_table[table_idx]; |
| break; |
| case DataType::F32: |
| _func = create_func_table<float, gen_index_seq<16>::type>::func_table[table_idx]; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Unsupported data type"); |
| break; |
| } |
| |
| constexpr unsigned int num_elems_processed_per_iteration = 1; |
| |
| // Configure kernel window |
| Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration)); |
| |
| update_window_and_padding(win, AccessWindowHorizontal(input->info(), 0, num_elems_processed_per_iteration)); |
| |
| INEKernel::configure(win); |
| } |
| |
| void NEMinMaxLocationKernel::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); |
| ARM_COMPUTE_ERROR_ON(_func == nullptr); |
| |
| (this->*_func)(window); |
| } |
| |
| template <class T, bool count_min, bool count_max, bool loc_min, bool loc_max> |
| void NEMinMaxLocationKernel::minmax_loc(const Window &win) |
| { |
| if(count_min || count_max || loc_min || loc_max) |
| { |
| Iterator input(_input, win); |
| |
| size_t min_count = 0; |
| size_t max_count = 0; |
| |
| // Clear min location array |
| if(loc_min) |
| { |
| _min_loc->clear(); |
| } |
| |
| // Clear max location array |
| if(loc_max) |
| { |
| _max_loc->clear(); |
| } |
| |
| using type = typename std::conditional<std::is_same<T, float>::value, float, int32_t>::type; |
| |
| auto min_ptr = static_cast<type *>(_min); |
| auto max_ptr = static_cast<type *>(_max); |
| |
| execute_window_loop(win, [&](const Coordinates & id) |
| { |
| auto in_ptr = reinterpret_cast<const T *>(input.ptr()); |
| int32_t idx = id.x(); |
| int32_t idy = id.y(); |
| |
| const T pixel = *in_ptr; |
| Coordinates2D p{ idx, idy }; |
| |
| if(count_min || loc_min) |
| { |
| if(*min_ptr == pixel) |
| { |
| if(count_min) |
| { |
| ++min_count; |
| } |
| |
| if(loc_min) |
| { |
| _min_loc->push_back(p); |
| } |
| } |
| } |
| |
| if(count_max || loc_max) |
| { |
| if(*max_ptr == pixel) |
| { |
| if(count_max) |
| { |
| ++max_count; |
| } |
| |
| if(loc_max) |
| { |
| _max_loc->push_back(p); |
| } |
| } |
| } |
| }, |
| input); |
| |
| if(count_min) |
| { |
| *_min_count = min_count; |
| } |
| |
| if(count_max) |
| { |
| *_max_count = max_count; |
| } |
| } |
| } |
| } // namespace arm_compute |