Port Arm(R) Neon(TM) Scale to new API
Partially resolves: COMPMID-4190
Change-Id: I0c1e32ff6176775c9b7bf547899a791fd318ba0a
Signed-off-by: Manuel Bottini <manuel.bottini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5192
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: TeresaARM <teresa.charlinreyes@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-by: Sheri Zhang <sheri.zhang@arm.com>
diff --git a/src/runtime/NEON/functions/NEScale.cpp b/src/runtime/NEON/functions/NEScale.cpp
index f91de32..0fbad07 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 <cmath>
-#include <cstddef>
-#include <utility>
-
namespace arm_compute
{
-namespace
+struct NEScale::Impl
{
-void precompute_dx_dy_offsets(ITensor *dx, ITensor *dy, ITensor *offsets, float wr, float hr, SamplingPolicy sampling_policy, bool align_corners)
-{
- 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<int32_t *>(offsets_it.ptr()) = in_xi;
- *reinterpret_cast<float *>(dx_it.ptr()) = in_x - in_xi;
- *reinterpret_cast<float *>(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<size_t>(align_corners ? arm_compute::utils::rounding::round_half_away_from_zero(float_in_xi) : std::floor(float_in_xi));
- *reinterpret_cast<int32_t *>(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<cpu::CpuScale> op{ nullptr };
+};
NEScale::NEScale()
- : _offsets(), _dx(), _dy()
+ : _impl(std::make_unique<Impl>())
{
}
+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));
+ _impl->src = input;
+ _impl->dst = output;
+ _impl->op = std::make_unique<cpu::CpuScale>();
+ _impl->op->configure(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);
-
+ // 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<NEScaleKernel>();
+ _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);
+ return cpu::CpuScale::validate(input, output, info);
+}
- 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;
- }
-
- 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