Remove Compute Vision Neon support

Resolves COMPMID-4150

Change-Id: I316e8ab97de796666c71eadfde894715fcf4a1aa
Signed-off-by: Michalis Spyrou <michalis.spyrou@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5141
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 0 insertions, 478 deletions

diff --git a/src/core/NEON/kernels/NEMinMaxLocationKernel.cpp b/src/core/NEON/kernels/NEMinMaxLocationKernel.cpp
deleted file mode 100644
index 402e6f1811..0000000000
--- a/src/core/NEON/kernels/NEMinMaxLocationKernel.cpp
+++ /dev/null
@@ -1,478 +0,0 @@
-/*
- * Copyright (c) 2016-2020 Arm Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#include "src/core/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 "arm_compute/core/utils/misc/Utility.h"
-#include "src/core/helpers/AutoConfiguration.h"
-#include "src/core/helpers/WindowHelpers.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)
-{
-    arm_compute::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 &)
-    {
-        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 &)
-    {
-        int        x      = x_start;
-        const auto in_ptr = reinterpret_cast<const int16_t *>(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 &)
-    {
-        int        x      = x_start;
-        const auto in_ptr = reinterpret_cast<const float *>(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 <class T, std::size_t... N>
-struct NEMinMaxLocationKernel::create_func_table<T, utility::index_sequence<N...>>
-{
-    static const std::array<NEMinMaxLocationKernel::MinMaxLocFunction, sizeof...(N)> func_table;
-};
-
-template <class T, std::size_t... N>
-const std::array<NEMinMaxLocationKernel::MinMaxLocFunction, sizeof...(N)> NEMinMaxLocationKernel::create_func_table<T, utility::index_sequence<N...>>::func_table
-{
-    &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, utility::index_sequence_t<16>>::func_table[table_idx];
-            break;
-        case DataType::S16:
-            _func = create_func_table<int16_t, utility::index_sequence_t<16>>::func_table[table_idx];
-            break;
-        case DataType::F32:
-            _func = create_func_table<float, utility::index_sequence_t<16>>::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