| /* | |
| * Copyright (c) 2016-2022 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/cpu/kernels/CpuScaleKernel.h" | |
| #include "arm_compute/core/Helpers.h" | |
| #include "arm_compute/core/Window.h" | |
| #include "src/core/common/Registrars.h" | |
| #include "src/core/helpers/ScaleHelpers.h" | |
| #include "src/core/helpers/WindowHelpers.h" | |
| #include "src/cpu/kernels/scale/neon/list.h" | |
| #include "src/cpu/kernels/scale/sve/list.h" | |
| #include "support/Rounding.h" | |
| #include <arm_neon.h> | |
| #include <map> | |
| namespace arm_compute | |
| { | |
| namespace cpu | |
| { | |
| namespace kernels | |
| { | |
| namespace | |
| { | |
| static const std::vector<CpuScaleKernel::ScaleKernel> available_kernels = | |
| { | |
| { | |
| "sve_fp16_scale", | |
| [](const ScaleKernelDataTypeISASelectorData & data) | |
| { | |
| return data.dt == DataType::F16 && data.isa.sve && data.isa.fp16 && data.interpolation_policy != InterpolationPolicy::BILINEAR; | |
| }, | |
| REGISTER_FP16_SVE(arm_compute::cpu::fp16_sve_scale) | |
| }, | |
| { | |
| "sve_fp32_scale", | |
| [](const ScaleKernelDataTypeISASelectorData & data) | |
| { | |
| return data.dt == DataType::F32 && data.isa.sve && data.interpolation_policy != InterpolationPolicy::BILINEAR; | |
| }, | |
| REGISTER_FP32_SVE(arm_compute::cpu::fp32_sve_scale) | |
| }, | |
| { | |
| "sve_qu8_scale", | |
| [](const ScaleKernelDataTypeISASelectorData & data) | |
| { | |
| return data.dt == DataType::QASYMM8 && data.isa.sve && data.interpolation_policy != InterpolationPolicy::BILINEAR; | |
| }, | |
| REGISTER_QASYMM8_SVE(arm_compute::cpu::qasymm8_sve_scale) | |
| }, | |
| { | |
| "sve_qs8_scale", | |
| [](const ScaleKernelDataTypeISASelectorData & data) | |
| { | |
| return data.dt == DataType::QASYMM8_SIGNED && data.isa.sve && data.interpolation_policy != InterpolationPolicy::BILINEAR; | |
| }, | |
| REGISTER_QASYMM8_SIGNED_SVE(arm_compute::cpu::qasymm8_signed_sve_scale) | |
| }, | |
| { | |
| "sve_u8_scale", | |
| [](const ScaleKernelDataTypeISASelectorData & data) | |
| { | |
| return data.dt == DataType::U8 && data.isa.sve && data.interpolation_policy != InterpolationPolicy::BILINEAR; | |
| }, | |
| REGISTER_INTEGER_SVE(arm_compute::cpu::u8_sve_scale) | |
| }, | |
| { | |
| "sve_s16_scale", | |
| [](const ScaleKernelDataTypeISASelectorData & data) | |
| { | |
| return data.dt == DataType::S16 && data.isa.sve && data.interpolation_policy != InterpolationPolicy::BILINEAR; | |
| }, | |
| REGISTER_INTEGER_SVE(arm_compute::cpu::s16_sve_scale) | |
| }, | |
| { | |
| "neon_fp16_scale", | |
| [](const ScaleKernelDataTypeISASelectorData & data) { return data.dt == DataType::F16 && data.isa.fp16; }, | |
| REGISTER_FP16_NEON(arm_compute::cpu::common_neon_scale<float16_t>) | |
| }, | |
| { | |
| "neon_fp32_scale", | |
| [](const ScaleKernelDataTypeISASelectorData & data) { return data.dt == DataType::F32; }, | |
| REGISTER_FP32_NEON(arm_compute::cpu::common_neon_scale<float>) | |
| }, | |
| { | |
| "neon_qu8_scale", | |
| [](const ScaleKernelDataTypeISASelectorData & data) { return data.dt == DataType::QASYMM8; }, | |
| REGISTER_QASYMM8_NEON(arm_compute::cpu::qasymm8_neon_scale) | |
| }, | |
| { | |
| "neon_qs8_scale", | |
| [](const ScaleKernelDataTypeISASelectorData & data) { return data.dt == DataType::QASYMM8_SIGNED; }, | |
| REGISTER_QASYMM8_SIGNED_NEON(arm_compute::cpu::qasymm8_signed_neon_scale) | |
| }, | |
| { | |
| "neon_u8_scale", | |
| [](const ScaleKernelDataTypeISASelectorData & data) { return data.dt == DataType::U8; }, | |
| REGISTER_INTEGER_NEON(arm_compute::cpu::u8_neon_scale) | |
| }, | |
| { | |
| "neon_s8_scale", | |
| [](const ScaleKernelDataTypeISASelectorData & data) { return data.dt == DataType::S8; }, | |
| REGISTER_INTEGER_NEON(arm_compute::cpu::s8_neon_scale) | |
| }, | |
| { | |
| "neon_s16_scale", | |
| [](const ScaleKernelDataTypeISASelectorData & data) { return data.dt == DataType::S16; }, | |
| REGISTER_INTEGER_NEON(arm_compute::cpu::s16_neon_scale) | |
| }, | |
| }; | |
| Status validate_arguments(const ITensorInfo *src, const ITensorInfo *dx, const ITensorInfo *dy, | |
| const ITensorInfo *offsets, ITensorInfo *dst, const ScaleKernelInfo &info) | |
| { | |
| const auto *uk = CpuScaleKernel::get_implementation(ScaleKernelDataTypeISASelectorData{ src->data_type(), CPUInfo::get().get_isa(), info.interpolation_policy }); | |
| ARM_COMPUTE_RETURN_ERROR_ON(uk == nullptr || uk->ukernel == nullptr); | |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(dst); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst); | |
| ARM_COMPUTE_RETURN_ERROR_ON(dst == src); | |
| ARM_COMPUTE_RETURN_ERROR_ON(info.sampling_policy != SamplingPolicy::CENTER && info.sampling_policy != SamplingPolicy::TOP_LEFT); | |
| ARM_COMPUTE_UNUSED(info.constant_border_value); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.use_padding, "Padding is not supported"); | |
| const DataLayout data_layout = info.data_layout == DataLayout::UNKNOWN ? src->data_layout() : info.data_layout; | |
| const auto width_index = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); | |
| const auto height_index = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); | |
| const auto output_width = dst->dimension(width_index); | |
| const auto output_height = dst->dimension(height_index); | |
| ARM_COMPUTE_RETURN_ERROR_ON(output_width == 0); | |
| ARM_COMPUTE_RETURN_ERROR_ON(output_height == 0); | |
| ARM_COMPUTE_RETURN_ERROR_ON((src->data_type() == DataType::S8) && (data_layout != DataLayout::NHWC || info.interpolation_policy != InterpolationPolicy::BILINEAR | |
| || info.border_mode != BorderMode::REPLICATE)); | |
| if(info.interpolation_policy == InterpolationPolicy::NEAREST_NEIGHBOR && offsets != nullptr) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(offsets, 1, DataType::S32); | |
| } | |
| if(info.interpolation_policy == InterpolationPolicy::BILINEAR && offsets != nullptr) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(offsets, 1, DataType::S32); | |
| if(dx != nullptr && dy != nullptr) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dx, 1, DataType::F32); | |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dy, 1, DataType::F32); | |
| } | |
| } | |
| ARM_COMPUTE_RETURN_ERROR_ON(info.align_corners && !scale_utils::is_align_corners_allowed_sampling_policy(info.sampling_policy)); | |
| if(info.interpolation_policy == InterpolationPolicy::AREA) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON(data_layout != DataLayout::NCHW); | |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::U8); | |
| } | |
| return Status{}; | |
| } | |
| } // namespace | |
| void CpuScaleKernel::configure(const ITensorInfo *src, const ITensorInfo *dx, const ITensorInfo *dy, const ITensorInfo *offsets, | |
| ITensorInfo *dst, const ScaleKernelInfo &info) | |
| { | |
| ARM_COMPUTE_UNUSED(dx, dy, offsets); | |
| ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); | |
| // Perform validation step | |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, | |
| dx, | |
| dy, | |
| offsets, | |
| dst, | |
| info)); | |
| const auto *uk = CpuScaleKernel::get_implementation(ScaleKernelDataTypeISASelectorData{ src->data_type(), CPUInfo::get().get_isa(), info.interpolation_policy }); | |
| ARM_COMPUTE_ERROR_ON_NULLPTR(uk); | |
| _run_method = uk->ukernel; | |
| _name = std::string("CpuScaleKernel").append("/").append(uk->name).append("_").append(string_from_interpolation_policy(info.interpolation_policy)); | |
| // Get data layout and width/height indices | |
| _data_layout = info.data_layout == DataLayout::UNKNOWN ? src->data_layout() : info.data_layout; | |
| 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); | |
| _policy = info.interpolation_policy; | |
| _border_mode = info.border_mode; | |
| _constant_border_value = info.constant_border_value; | |
| _align_corners = info.align_corners; | |
| if(info.sampling_policy == SamplingPolicy::CENTER) | |
| { | |
| _sampling_offset = 0.5f; | |
| } | |
| // Compute the ratio between source width/height and destination width/height | |
| const auto wr = scale_utils::calculate_resize_ratio(src->dimension(idx_width), dst->dimension(idx_width), _align_corners); | |
| const auto hr = scale_utils::calculate_resize_ratio(src->dimension(idx_height), dst->dimension(idx_height), _align_corners); | |
| // Area interpolation behaves as Nearest Neighbour in case of up-sampling | |
| _policy = (_policy == InterpolationPolicy::AREA && wr <= 1.f && hr <= 1.f) ? InterpolationPolicy::NEAREST_NEIGHBOR : _policy; | |
| if(_border_mode == BorderMode::UNDEFINED) | |
| { | |
| _border_mode = BorderMode::CONSTANT; | |
| _constant_border_value = PixelValue(); | |
| } | |
| #ifdef ENABLE_NCHW_KERNELS | |
| // Configure scale function to run | |
| if(_data_layout == DataLayout::NCHW) | |
| { | |
| std::string function_to_call("scale_"); | |
| function_to_call += string_from_data_type(src->data_type()) + "_"; | |
| function_to_call += string_from_data_layout(_data_layout) + "_"; | |
| function_to_call += string_from_interpolation_policy(_policy); | |
| static std::map<std::string, ScaleFunctionPtr> map_function = | |
| { | |
| { "scale_U8_NCHW_AREA_CONSTANT", &CpuScaleKernel::scale_area_nchw_u8 }, | |
| { "scale_U8_NCHW_BILINEAR", &CpuScaleKernel::scale_bilinear_nchw<uint8_t> }, | |
| { "scale_U8_NCHW_NEAREST_NEIGHBOUR", &CpuScaleKernel::scale_nearest_nchw<uint8_t> }, | |
| { "scale_QASYMM8_NCHW_BILINEAR", &CpuScaleKernel::scale_bilinear_qasymm<uint8_t> }, | |
| { "scale_QASYMM8_NCHW_NEAREST_NEIGHBOUR", &CpuScaleKernel::scale_nearest_nchw<uint8_t> }, | |
| { "scale_QASYMM8_SIGNED_NCHW_BILINEAR", &CpuScaleKernel::scale_bilinear_qasymm<int8_t> }, | |
| { "scale_QASYMM8_SIGNED_NCHW_NEAREST_NEIGHBOUR", &CpuScaleKernel::scale_nearest_nchw<int8_t> }, | |
| { "scale_S16_NCHW_BILINEAR", &CpuScaleKernel::scale_bilinear_nchw<int16_t> }, | |
| { "scale_S16_NCHW_NEAREST_NEIGHBOUR", &CpuScaleKernel::scale_nearest_nchw<int16_t> }, | |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC | |
| { "scale_F16_NCHW_BILINEAR", &CpuScaleKernel::scale_bilinear_nchw<float16_t> }, | |
| { "scale_F16_NCHW_NEAREST_NEIGHBOUR", &CpuScaleKernel::scale_nearest_nchw<float16_t> }, | |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ | |
| { "scale_F32_NCHW_BILINEAR", &CpuScaleKernel::scale_bilinear_nchw<float> }, | |
| { "scale_F32_NCHW_NEAREST_NEIGHBOUR", &CpuScaleKernel::scale_nearest_nchw<float> }, | |
| }; | |
| auto it = map_function.find(function_to_call); | |
| if(it != map_function.end()) | |
| { | |
| _func = it->second; | |
| } | |
| } | |
| #endif // ENABLE_NCHW_KERNELS | |
| // Configure window | |
| Window win = calculate_max_window(*dst, Steps()); | |
| ICpuKernel::configure(win); | |
| } | |
| #ifdef ENABLE_NCHW_KERNELS | |
| template <typename T> | |
| void CpuScaleKernel::scale_nearest_nchw(const ITensor *src, ITensor *dst, const ITensor *dx, const ITensor *dy, const ITensor *offsets, const Window &window) | |
| { | |
| ARM_COMPUTE_UNUSED(dx, dy); | |
| const size_t in_stride_x = src->info()->dimension(0) + src->info()->padding().left + src->info()->padding().right; | |
| // Compute the ratio between source height and destination height | |
| const auto hr = scale_utils::calculate_resize_ratio(src->info()->dimension(1), dst->info()->dimension(1), _align_corners); | |
| // Don't increment in X and Y direction for the input tensor | |
| // A pointer to the start of this plane is needed as base for the precomputed offsets | |
| Window win_in(window); | |
| win_in.set(Window::DimX, Window::Dimension(0, 0, 0)); | |
| win_in.set(Window::DimY, Window::Dimension(0, 0, 0)); | |
| // Set offsets window | |
| Window win_off; | |
| win_off.set(Window::DimX, window[Window::DimX]); | |
| win_off.set(Window::DimY, window[Window::DimY]); | |
| for(size_t d = Window::DimZ; d < offsets->info()->num_dimensions(); ++d) | |
| { | |
| win_off.set(d, Window::Dimension(0, 0, 0)); | |
| } | |
| // Create iterators | |
| Iterator src_i(src, win_in); | |
| Iterator dst_i(dst, window); | |
| Iterator offsets_i(offsets, win_off); | |
| execute_window_loop(window, [&](const Coordinates & id) | |
| { | |
| const auto offsets_ptr = reinterpret_cast<const int32_t *>(offsets_i.ptr()); | |
| const auto in_yi = static_cast<int32_t>(_align_corners ? utils::rounding::round_half_away_from_zero((id.y() + _sampling_offset) * hr) : std::floor(( | |
| id.y() + _sampling_offset) | |
| * hr)); | |
| const int32_t offset_row = in_yi * in_stride_x; | |
| *reinterpret_cast<T *>(dst_i.ptr()) = *(reinterpret_cast<const T *>(src_i.ptr()) + offsets_ptr[0] + offset_row); | |
| }, | |
| src_i, offsets_i, dst_i); | |
| } | |
| template <typename T> | |
| void CpuScaleKernel::scale_bilinear_nchw(const ITensor *src, ITensor *dst, const ITensor *dx, const ITensor *dy, const ITensor *offsets, const Window &window) | |
| { | |
| // Compute the ratio between source height and destination height | |
| const auto hr = scale_utils::calculate_resize_ratio(src->info()->dimension(1), dst->info()->dimension(1), _align_corners); | |
| Window win_off; | |
| win_off.set(Window::DimX, window.x()); | |
| win_off.set(Window::DimY, window.y()); | |
| // Don't increment in X and Y direction for the input tensor | |
| // A pointer to the start of this plane is needed as base for the precomputed offsets | |
| Window win_in(window); | |
| win_in.set(Window::DimX, Window::Dimension(0, 0, 0)); | |
| win_in.set(Window::DimY, Window::Dimension(0, 0, 0)); | |
| for(size_t d = Window::DimZ; d < offsets->info()->num_dimensions(); ++d) | |
| { | |
| win_off.set(d, Window::Dimension(0, 0, 0)); | |
| } | |
| Iterator src_i(src, win_in); | |
| Iterator dst_i(dst, window); | |
| Iterator offsets_i(offsets, win_off); | |
| Iterator dx_i(dx, win_off); | |
| Iterator dy_i(dy, win_off); | |
| const int32_t in_dim_w = src->info()->dimension(0); | |
| const int32_t in_dim_h = src->info()->dimension(1); | |
| const int32_t in_stride_w = in_dim_w + src->info()->padding().left + src->info()->padding().right; | |
| if(_border_mode == BorderMode::CONSTANT) | |
| { | |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC | |
| using ConstType = typename std::conditional<std::is_same<T, float16_t>::value, half, T>::type; | |
| #else /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ | |
| using ConstType = T; | |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ | |
| const T const_border_value = static_cast<T>(_constant_border_value.get<ConstType>()); | |
| execute_window_loop(window, [&](const Coordinates & id) | |
| { | |
| const int32_t index_h = std::floor((id.y() + _sampling_offset) * hr - _sampling_offset); | |
| const auto index_w = *(reinterpret_cast<const int32_t *>(offsets_i.ptr())); | |
| const auto dx_val = *(reinterpret_cast<const float *>(dx_i.ptr())); | |
| const auto dy_val = *(reinterpret_cast<const float *>(dy_i.ptr())); | |
| const auto pixel_row_ptr = reinterpret_cast<const T *>(src_i.ptr()); | |
| const auto a00 = (0 <= index_w && index_w < in_dim_w && 0 <= index_h && index_h < in_dim_h) ? (*(pixel_row_ptr + index_w + index_h * in_stride_w)) : const_border_value; | |
| const auto a01 = (-1 <= index_w && index_w < in_dim_w - 1 && 0 <= index_h && index_h < in_dim_h) ? (*(pixel_row_ptr + index_w + 1 + index_h * in_stride_w)) : const_border_value; | |
| const auto a10 = (0 <= index_w && index_w < in_dim_w && -1 <= index_h | |
| && index_h < in_dim_h - 1) ? | |
| (*(pixel_row_ptr + index_w + index_h * in_stride_w + in_stride_w)) : | |
| const_border_value; | |
| const auto a11 = (-1 <= index_w && index_w < in_dim_w - 1 && -1 <= index_h | |
| && index_h < in_dim_h - 1) ? | |
| (*(pixel_row_ptr + index_w + 1 + index_h * in_stride_w + in_stride_w)) : | |
| const_border_value; | |
| *reinterpret_cast<T *>(dst_i.ptr()) = static_cast<T>(scale_helpers::delta_bilinear(a00, a01, a10, a11, dx_val, dy_val)); | |
| }, | |
| src_i, offsets_i, dx_i, dy_i, dst_i); | |
| } | |
| else if(_border_mode == BorderMode::REPLICATE) | |
| { | |
| execute_window_loop(window, [&](const Coordinates & id) | |
| { | |
| const int index_h = std::floor((id.y() + _sampling_offset) * hr - _sampling_offset); | |
| const auto index_w = *(reinterpret_cast<const int32_t *>(offsets_i.ptr())); | |
| const auto dx_val = *(reinterpret_cast<const float *>(dx_i.ptr())); | |
| const auto dy_val = *(reinterpret_cast<const float *>(dy_i.ptr())); | |
| const auto pixel_row_ptr = reinterpret_cast<const T *>(src_i.ptr()); | |
| auto clamped_x = utility::clamp<int>(index_w, 0, in_dim_w - 1); | |
| auto clamped_x1 = utility::clamp<int>(index_w + 1, 0, in_dim_w - 1); | |
| auto clamped_y = utility::clamp<int>(index_h, 0, in_dim_h - 1); | |
| auto clamped_y1 = utility::clamp<int>(index_h + 1, 0, in_dim_h - 1); | |
| const auto a00 = *(pixel_row_ptr + clamped_x + clamped_y * in_stride_w); | |
| const auto a01 = *(pixel_row_ptr + clamped_x1 + clamped_y * in_stride_w); | |
| const auto a10 = *(pixel_row_ptr + clamped_x + clamped_y1 * in_stride_w); | |
| const auto a11 = *(pixel_row_ptr + clamped_x1 + clamped_y1 * in_stride_w); | |
| *reinterpret_cast<T *>(dst_i.ptr()) = static_cast<T>(scale_helpers::delta_bilinear(a00, a01, a10, a11, dx_val, dy_val)); | |
| }, | |
| src_i, offsets_i, dx_i, dy_i, dst_i); | |
| } | |
| else | |
| { | |
| ARM_COMPUTE_ERROR("Not implemented"); | |
| } | |
| } | |
| void CpuScaleKernel::scale_area_nchw_u8(const ITensor *src, ITensor *dst, const ITensor *dx, const ITensor *dy, const ITensor *offsets, const Window &window) | |
| { | |
| ARM_COMPUTE_UNUSED(dx, dy, offsets); | |
| using namespace scale_helpers; | |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::U8); | |
| // Don't increment in width/height/channels for the input tensor | |
| // A pointer to the start of this plane is needed as base for the precomputed offsets | |
| Window win_in(window); | |
| win_in.set(Window::DimX, Window::Dimension(0, 0, 0)); | |
| win_in.set(Window::DimY, Window::Dimension(0, 0, 0)); | |
| win_in.set(Window::DimZ, Window::Dimension(0, 0, 0)); | |
| Iterator src_i(src, win_in); | |
| Iterator dst_i(dst, window); | |
| const auto wr = scale_utils::calculate_resize_ratio(src->info()->dimension(0), dst->info()->dimension(0), _align_corners); | |
| const auto hr = scale_utils::calculate_resize_ratio(src->info()->dimension(1), dst->info()->dimension(1), _align_corners); | |
| const auto w = src->info()->dimension(0); | |
| const auto h = src->info()->dimension(1); | |
| const size_t in_stride = src->info()->strides_in_bytes()[1]; | |
| execute_window_loop(window, [&](const Coordinates & id) | |
| { | |
| const auto in_ptr = reinterpret_cast<const uint8_t *>(src_i.ptr()); | |
| uint8x8_t tmp0 = vdup_n_u8(0); | |
| tmp0 = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x(), id.y()), tmp0, 0); | |
| tmp0 = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 1, id.y()), tmp0, 1); | |
| tmp0 = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 2, id.y()), tmp0, 2); | |
| tmp0 = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 3, id.y()), tmp0, 3); | |
| tmp0 = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 4, id.y()), tmp0, 4); | |
| tmp0 = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 5, id.y()), tmp0, 5); | |
| tmp0 = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 6, id.y()), tmp0, 6); | |
| tmp0 = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 7, id.y()), tmp0, 7); | |
| uint8x8_t tmp1 = vdup_n_u8(0); | |
| tmp1 = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 8, id.y()), tmp1, 0); | |
| tmp1 = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 9, id.y()), tmp1, 1); | |
| tmp1 = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 10, id.y()), tmp1, 2); | |
| tmp1 = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 11, id.y()), tmp1, 3); | |
| tmp1 = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 12, id.y()), tmp1, 4); | |
| tmp1 = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 13, id.y()), tmp1, 5); | |
| tmp1 = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 14, id.y()), tmp1, 6); | |
| tmp1 = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 15, id.y()), tmp1, 7); | |
| vst1q_u8(dst_i.ptr(), vcombine_u8(tmp0, tmp1)); | |
| }, | |
| src_i, dst_i); | |
| } | |
| template <typename T> | |
| void CpuScaleKernel::scale_bilinear_qasymm(const ITensor *src, ITensor *dst, const ITensor *dx, const ITensor *dy, const ITensor *offsets, const Window &window) | |
| { | |
| // Get data layout and width/height indices | |
| 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); | |
| // Compute the ratio between source height and destination height | |
| const auto hr = scale_utils::calculate_resize_ratio(src->info()->dimension(idx_height), dst->info()->dimension(idx_height), _align_corners); | |
| Window win_off; | |
| win_off.set(Window::DimX, Window::Dimension(0, 0, 0)); | |
| win_off.set(Window::DimY, Window::Dimension(0, 0, 0)); | |
| // Don't increment in X and Y direction for the input tensor | |
| // A pointer to the start of this plane is needed as base for the precomputed offsets | |
| Window win_in(window); | |
| win_in.set(idx_width, Window::Dimension(0, 0, 0)); | |
| win_in.set(idx_height, Window::Dimension(0, 0, 0)); | |
| for(size_t d = Window::DimZ; d < offsets->info()->num_dimensions(); ++d) | |
| { | |
| win_off.set(d, Window::Dimension(0, 0, 0)); | |
| } | |
| Iterator src_i(src, win_in); | |
| Iterator dst_i(dst, window); | |
| const int32_t in_dim_w = src->info()->dimension(idx_width); | |
| const int32_t in_dim_h = src->info()->dimension(idx_height); | |
| const int32_t stride_w = src->info()->strides_in_bytes()[idx_width]; | |
| const int32_t stride_h = src->info()->strides_in_bytes()[idx_height]; | |
| const UniformQuantizationInfo iq_info = src->info()->quantization_info().uniform(); | |
| const UniformQuantizationInfo oq_info = dst->info()->quantization_info().uniform(); | |
| if(_border_mode == BorderMode::CONSTANT) | |
| { | |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC | |
| using ConstType = typename std::conditional<std::is_same<T, float16_t>::value, half, T>::type; | |
| #else /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ | |
| using ConstType = T; | |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ | |
| const T const_border_value = static_cast<T>(_constant_border_value.get<ConstType>()); | |
| execute_window_loop(window, [&](const Coordinates & id) | |
| { | |
| const int32_t index_h = std::floor((id[idx_height] + _sampling_offset) * hr - _sampling_offset); | |
| const int32_t index_w = *(reinterpret_cast<const int32_t *>(offsets->ptr_to_element(Coordinates(id[idx_width], id[idx_height])))); | |
| const auto dx_val = *(reinterpret_cast<const float *>(dx->ptr_to_element(Coordinates(id[idx_width], id[idx_height])))); | |
| const auto dy_val = *(reinterpret_cast<const float *>(dy->ptr_to_element(Coordinates(id[idx_width], id[idx_height])))); | |
| const auto pixel_row_ptr = reinterpret_cast<const T *>(src_i.ptr()); | |
| const auto a00 = (0 <= index_w && index_w < in_dim_w && 0 <= index_h && index_h < in_dim_h) ? | |
| (*(pixel_row_ptr + index_w * stride_w + index_h * stride_h)) : | |
| const_border_value; | |
| const auto a01 = (-1 <= index_w && index_w < in_dim_w - 1 && 0 <= index_h && index_h < in_dim_h) ? | |
| (*(pixel_row_ptr + (index_w + 1) * stride_w + index_h * stride_h)) : | |
| const_border_value; | |
| const auto a10 = (0 <= index_w && index_w < in_dim_w && -1 <= index_h && index_h < in_dim_h - 1) ? | |
| (*(pixel_row_ptr + index_w * stride_w + (index_h + 1) * stride_h)) : | |
| const_border_value; | |
| const auto a11 = (-1 <= index_w && index_w < in_dim_w - 1 && -1 <= index_h && index_h < in_dim_h - 1) ? | |
| (*(pixel_row_ptr + (index_w + 1) * stride_w + (index_h + 1) * stride_h)) : | |
| const_border_value; | |
| const float inp00 = Qasymm8QuantizationHelper<T>::dequantize(a00, iq_info); | |
| const float inp01 = Qasymm8QuantizationHelper<T>::dequantize(a01, iq_info); | |
| const float inp10 = Qasymm8QuantizationHelper<T>::dequantize(a10, iq_info); | |
| const float inp11 = Qasymm8QuantizationHelper<T>::dequantize(a11, iq_info); | |
| *reinterpret_cast<T *>(dst_i.ptr()) = Qasymm8QuantizationHelper<T>::quantize(scale_helpers::delta_bilinear(inp00, inp01, inp10, inp11, dx_val, dy_val), oq_info); | |
| }, | |
| src_i, dst_i); | |
| } | |
| else if(_border_mode == BorderMode::REPLICATE) | |
| { | |
| execute_window_loop(window, [&](const Coordinates & id) | |
| { | |
| const int index_h = std::floor((id[idx_height] + _sampling_offset) * hr - _sampling_offset); | |
| const int32_t index_w = *(reinterpret_cast<const int32_t *>(offsets->ptr_to_element(Coordinates(id[idx_width], id[idx_height])))); | |
| const auto dx_val = *(reinterpret_cast<const float *>(dx->ptr_to_element(Coordinates(id[idx_width], id[idx_height])))); | |
| const auto dy_val = *(reinterpret_cast<const float *>(dy->ptr_to_element(Coordinates(id[idx_width], id[idx_height])))); | |
| const auto pixel_row_ptr = reinterpret_cast<const T *>(src_i.ptr()); | |
| auto clamped_w = utility::clamp<int>(index_w, 0, in_dim_w - 1); | |
| auto clamped_w1 = utility::clamp<int>(index_w + 1, 0, in_dim_w - 1); | |
| auto clamped_h = utility::clamp<int>(index_h, 0, in_dim_h - 1); | |
| auto clamped_h1 = utility::clamp<int>(index_h + 1, 0, in_dim_h - 1); | |
| const auto a00 = *(pixel_row_ptr + clamped_w * stride_w + clamped_h * stride_h); | |
| const auto a01 = *(pixel_row_ptr + clamped_w1 * stride_w + clamped_h * stride_h); | |
| const auto a10 = *(pixel_row_ptr + clamped_w * stride_w + clamped_h1 * stride_h); | |
| const auto a11 = *(pixel_row_ptr + clamped_w1 * stride_w + clamped_h1 * stride_h); | |
| const float inp00 = Qasymm8QuantizationHelper<T>::dequantize(a00, iq_info); | |
| const float inp01 = Qasymm8QuantizationHelper<T>::dequantize(a01, iq_info); | |
| const float inp10 = Qasymm8QuantizationHelper<T>::dequantize(a10, iq_info); | |
| const float inp11 = Qasymm8QuantizationHelper<T>::dequantize(a11, iq_info); | |
| *reinterpret_cast<T *>(dst_i.ptr()) = Qasymm8QuantizationHelper<T>::quantize(scale_helpers::delta_bilinear(inp00, inp01, inp10, inp11, dx_val, dy_val), oq_info); | |
| }, | |
| src_i, dst_i); | |
| } | |
| else | |
| { | |
| ARM_COMPUTE_ERROR("Not implemented"); | |
| } | |
| } | |
| #endif // ENABLE_NCHW_KERNELS | |
| Status CpuScaleKernel::validate(const ITensorInfo *input, const ITensorInfo *dx, const ITensorInfo *dy, | |
| const ITensorInfo *offsets, ITensorInfo *output, const ScaleKernelInfo &info) | |
| { | |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, dx, dy, offsets, output, info)); | |
| return Status{}; | |
| } | |
| void CpuScaleKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) | |
| { | |
| ARM_COMPUTE_UNUSED(info); | |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); | |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window); | |
| ARM_COMPUTE_ERROR_ON(_func == nullptr && _data_layout == DataLayout::NCHW); | |
| ARM_COMPUTE_ERROR_ON(_run_method == nullptr && _data_layout == DataLayout::NHWC); | |
| const auto src = tensors.get_const_tensor(TensorType::ACL_SRC); | |
| auto dst = tensors.get_tensor(TensorType::ACL_DST); | |
| const auto dx = tensors.get_const_tensor(TensorType::ACL_INT_0); | |
| const auto dy = tensors.get_const_tensor(TensorType::ACL_INT_1); | |
| const auto offsets = tensors.get_const_tensor(TensorType::ACL_INT_2); | |
| if(_data_layout == DataLayout::NCHW) | |
| { | |
| (this->*_func)(src, dst, dx, dy, offsets, window); | |
| } | |
| else | |
| { | |
| _run_method(src, dst, offsets, dx, dy, _policy, _border_mode, _constant_border_value, _sampling_offset, _align_corners, window); | |
| } | |
| } | |
| const char *CpuScaleKernel::name() const | |
| { | |
| return _name.c_str(); | |
| } | |
| const std::vector<CpuScaleKernel::ScaleKernel> &CpuScaleKernel::get_available_kernels() | |
| { | |
| return available_kernels; | |
| } | |
| } // namespace kernels | |
| } // namespace cpu | |
| } // namespace arm_compute |