From 10b3826723e1e2f62a4e635801128ddf4438e50c Mon Sep 17 00:00:00 2001 From: Manuel Bottini Date: Fri, 19 Feb 2021 18:16:44 +0000 Subject: Port Arm(R) Neon(TM) Scale to new API Partially resolves: COMPMID-4190 Change-Id: I0c1e32ff6176775c9b7bf547899a791fd318ba0a Signed-off-by: Manuel Bottini Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5192 Comments-Addressed: Arm Jenkins Tested-by: Arm Jenkins Reviewed-by: TeresaARM Reviewed-by: Michele Di Giorgio Reviewed-by: Sheri Zhang --- src/runtime/NEON/functions/NEScale.cpp | 184 +++++++++------------------------ 1 file changed, 46 insertions(+), 138 deletions(-) (limited to 'src/runtime/NEON/functions/NEScale.cpp') diff --git a/src/runtime/NEON/functions/NEScale.cpp b/src/runtime/NEON/functions/NEScale.cpp index f91de32191..0fbad07d0f 100644 --- a/src/runtime/NEON/functions/NEScale.cpp +++ b/src/runtime/NEON/functions/NEScale.cpp @@ -23,191 +23,99 @@ */ #include "arm_compute/runtime/NEON/functions/NEScale.h" -#include "arm_compute/core/Coordinates.h" -#include "arm_compute/core/Error.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/ITensor.h" -#include "arm_compute/core/PixelValue.h" -#include "arm_compute/core/TensorInfo.h" -#include "arm_compute/core/Window.h" -#include "arm_compute/runtime/NEON/NEScheduler.h" -#include "arm_compute/runtime/TensorAllocator.h" -#include "src/core/NEON/kernels/NEScaleKernel.h" - +#include "arm_compute/core/Validate.h" +#include "arm_compute/runtime/Tensor.h" #include "src/core/utils/ScaleUtils.h" - +#include "src/runtime/cpu/operators/CpuScale.h" #include "support/Rounding.h" -#include -#include -#include - namespace arm_compute { -namespace -{ -void precompute_dx_dy_offsets(ITensor *dx, ITensor *dy, ITensor *offsets, float wr, float hr, SamplingPolicy sampling_policy, bool align_corners) +struct NEScale::Impl { - ARM_COMPUTE_ERROR_ON(nullptr == offsets); - ARM_COMPUTE_UNUSED(sampling_policy); - float sampling_offset = 0.0f; - if(sampling_policy == SamplingPolicy::CENTER) - { - sampling_offset = 0.5f; - } - - Window win; - win.set(Window::DimX, Window::Dimension(0, offsets->info()->dimension(0), 1)); - win.set(Window::DimY, Window::Dimension(0, offsets->info()->dimension(1), 1)); - - if(dx != nullptr && dy != nullptr) - { - // Pre-compute the offset and pixel's distance for BILINEAR interpolation - Iterator offsets_it(offsets, win); - Iterator dx_it(dx, win); - Iterator dy_it(dy, win); - - execute_window_loop(win, [&](const Coordinates & id) - { - const float in_x = (id.x() + sampling_offset) * wr - sampling_offset; - const float in_y = (id.y() + sampling_offset) * hr - sampling_offset; - const int in_xi = std::floor(in_x); - const int in_yi = std::floor(in_y); - - *reinterpret_cast(offsets_it.ptr()) = in_xi; - *reinterpret_cast(dx_it.ptr()) = in_x - in_xi; - *reinterpret_cast(dy_it.ptr()) = in_y - in_yi; - }, - offsets_it, dx_it, dy_it); - } - else - { - // Pre-compute the offset for NEAREST interpolation - Iterator offsets_it(offsets, win); - - execute_window_loop(win, [&](const Coordinates & id) - { - const float float_in_xi = (id.x() + sampling_offset) * wr; - const auto in_xi = static_cast(align_corners ? arm_compute::utils::rounding::round_half_away_from_zero(float_in_xi) : std::floor(float_in_xi)); - *reinterpret_cast(offsets_it.ptr()) = in_xi; - }, - offsets_it); - } -} -} // namespace + const ITensor *src{ nullptr }; + ITensor *dst{ nullptr }; + Tensor dx{ nullptr }; /**< Element's distance between the X real coordinate and the smallest X following integer */ + Tensor dy{ nullptr }; /**< Element's distance between the Y real coordinate and the smallest Y following integer */ + Tensor offsets{ nullptr }; /**< Offset to access the element with NEAREST interpolation or the top-left element with BILINEAR interpolation in the input tensor */ + std::unique_ptr op{ nullptr }; +}; NEScale::NEScale() - : _offsets(), _dx(), _dy() + : _impl(std::make_unique()) { } +NEScale::~NEScale() = default; void NEScale::configure(ITensor *input, ITensor *output, const ScaleKernelInfo &info) { - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_ERROR_THROW_ON(NEScale::validate(input->info(), output->info(), info)); - - const bool is_align_corners_used = info.align_corners && arm_compute::scale_utils::is_align_corners_allowed_sampling_policy(info.sampling_policy); + _impl->src = input; + _impl->dst = output; + _impl->op = std::make_unique(); + _impl->op->configure(input->info(), output->info(), info); + // Configure for size of allocation of internal tensors // Get data layout and width/height indices const DataLayout data_layout = info.data_layout == DataLayout::UNKNOWN ? input->info()->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); + // Compute the ratio between source width/height and destination width/height + const bool is_align_corners_used = info.align_corners && arm_compute::scale_utils::is_align_corners_allowed_sampling_policy(info.sampling_policy); + const auto wr = arm_compute::scale_utils::calculate_resize_ratio(input->info()->dimension(idx_width), output->info()->dimension(idx_width), is_align_corners_used); + const auto hr = arm_compute::scale_utils::calculate_resize_ratio(input->info()->dimension(idx_height), output->info()->dimension(idx_height), is_align_corners_used); + + // Area interpolation behaves as Nearest Neighbour in case of up-sampling + InterpolationPolicy policy_to_use = (info.interpolation_policy == InterpolationPolicy::AREA && wr <= 1.f && hr <= 1.f) ? InterpolationPolicy::NEAREST_NEIGHBOR : info.interpolation_policy; + // Get the tensor shape TensorShape shape(output->info()->dimension(idx_width)); shape.set(1, output->info()->dimension(idx_height), false); - // Compute the ratio between source width/height and destination width/height - const auto wr = arm_compute::scale_utils::calculate_resize_ratio(input->info()->dimension(idx_width), output->info()->dimension(idx_width), is_align_corners_used); - const auto hr = arm_compute::scale_utils::calculate_resize_ratio(input->info()->dimension(idx_height), output->info()->dimension(idx_height), is_align_corners_used); + const TensorInfo tensor_info_dxdy(shape, Format::F32); + const TensorInfo tensor_info_offsets(shape, Format::S32); - // Area interpolation behaves as Nearest Neighbour in case of up-sampling - const auto policy_to_use = (info.interpolation_policy == InterpolationPolicy::AREA && wr <= 1.f && hr <= 1.f) ? InterpolationPolicy::NEAREST_NEIGHBOR : info.interpolation_policy; - - auto scale_kernel = std::make_unique(); + _impl->dx.allocator()->init(tensor_info_dxdy); + _impl->dy.allocator()->init(tensor_info_dxdy); + _impl->offsets.allocator()->init(tensor_info_offsets); switch(policy_to_use) { case InterpolationPolicy::NEAREST_NEIGHBOR: { - TensorInfo tensor_info_offsets(shape, Format::S32); - _offsets.allocator()->init(tensor_info_offsets); - - scale_kernel->configure(input, nullptr, nullptr, &_offsets, output, info); - // Allocate once the configure methods have been called - _offsets.allocator()->allocate(); - - // Pre-compute offsets for nearest interpolation - precompute_dx_dy_offsets(nullptr, nullptr, &_offsets, wr, hr, info.sampling_policy, is_align_corners_used); + _impl->offsets.allocator()->allocate(); break; } case InterpolationPolicy::BILINEAR: { - TensorInfo tensor_info_offsets(shape, Format::S32); - TensorInfo tensor_info_dxdy(shape, Format::F32); - - _offsets.allocator()->init(tensor_info_offsets); - _dx.allocator()->init(tensor_info_dxdy); - _dy.allocator()->init(tensor_info_dxdy); - - scale_kernel->configure(input, &_dx, &_dy, &_offsets, output, info); - // Allocate once the configure methods have been called - _offsets.allocator()->allocate(); - _dx.allocator()->allocate(); - _dy.allocator()->allocate(); - - // Pre-compute dx, dy and offsets for bilinear interpolation - precompute_dx_dy_offsets(&_dx, &_dy, &_offsets, wr, hr, info.sampling_policy, is_align_corners_used); + _impl->dx.allocator()->allocate(); + _impl->dy.allocator()->allocate(); + _impl->offsets.allocator()->allocate(); break; } case InterpolationPolicy::AREA: { - scale_kernel->configure(input, nullptr, nullptr, nullptr, output, info); break; } default: ARM_COMPUTE_ERROR("Unsupported interpolation mode"); } - _kernel = std::move(scale_kernel); } Status NEScale::validate(const ITensorInfo *input, const ITensorInfo *output, const ScaleKernelInfo &info) { - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_RETURN_ERROR_ON(info.sampling_policy != SamplingPolicy::CENTER && info.sampling_policy != SamplingPolicy::TOP_LEFT); - - ITensorInfo *offsets = nullptr; - ITensorInfo *dx = nullptr; - ITensorInfo *dy = nullptr; - - // Get data layout and width/height indices - const DataLayout data_layout = info.data_layout == DataLayout::UNKNOWN ? input->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); - - // Get the tensor shape of auxilary buffers - const TensorShape shape(output->dimension(idx_width), output->dimension(idx_height)); - - TensorInfo tensor_info_offsets(shape, Format::S32); - TensorInfo tensor_info_dx(shape, Format::F32); - TensorInfo tensor_info_dy(shape, Format::F32); - - switch(info.interpolation_policy) - { - case InterpolationPolicy::NEAREST_NEIGHBOR: - offsets = &tensor_info_offsets; - break; - case InterpolationPolicy::BILINEAR: - offsets = &tensor_info_offsets; - dx = &tensor_info_dx; - dy = &tensor_info_dy; - break; - default: - break; - } + return cpu::CpuScale::validate(input, output, info); +} - ARM_COMPUTE_RETURN_ON_ERROR(NEScaleKernel::validate(input->clone().get(), dx, dy, offsets, output->clone().get(), info)); - return Status{}; +void NEScale::run() +{ + ITensorPack pack; + pack.add_tensor(TensorType::ACL_SRC, _impl->src); + pack.add_tensor(TensorType::ACL_DST, _impl->dst); + pack.add_tensor(TensorType::ACL_INT_0, &_impl->dx); + pack.add_tensor(TensorType::ACL_INT_1, &_impl->dy); + pack.add_tensor(TensorType::ACL_INT_2, &_impl->offsets); + _impl->op->run(pack); } } // namespace arm_compute -- cgit v1.2.1