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/*
* Copyright (c) 2016-2023 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/runtime/NEON/functions/NEScale.h"
#include "arm_compute/runtime/Tensor.h"
#include "src/common/utils/Log.h"
#include "src/core/utils/ScaleUtils.h"
#include "src/cpu/operators/CpuScale.h"
namespace arm_compute
{
struct NEScale::Impl
{
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() : _impl(std::make_unique<Impl>())
{
}
NEScale::~NEScale() = default;
void NEScale::configure(ITensor *input, ITensor *output, const ScaleKernelInfo &info)
{
ARM_COMPUTE_LOG_PARAMS(input, output, info);
_impl->src = input;
_impl->dst = output;
_impl->op = std::make_unique<cpu::CpuScale>();
_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);
bool precompute_indices_weights = arm_compute::scale_utils::is_precomputation_required(
data_layout, input->info()->data_type(), policy_to_use, info.border_mode);
if (precompute_indices_weights)
{
const TensorInfo tensor_info_dxdy(shape, Format::F32);
const TensorInfo tensor_info_offsets(shape, Format::S32);
_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:
{
// Allocate once the configure methods have been called
_impl->offsets.allocator()->allocate();
break;
}
case InterpolationPolicy::BILINEAR:
{
// Allocate once the configure methods have been called
_impl->dx.allocator()->allocate();
_impl->dy.allocator()->allocate();
_impl->offsets.allocator()->allocate();
break;
}
case InterpolationPolicy::AREA:
{
break;
}
default:
ARM_COMPUTE_ERROR("Unsupported interpolation mode");
}
}
else
{
if (policy_to_use != InterpolationPolicy::NEAREST_NEIGHBOR && policy_to_use != InterpolationPolicy::BILINEAR &&
policy_to_use != InterpolationPolicy::AREA)
{
ARM_COMPUTE_ERROR("Unsupported interpolation mode");
}
}
}
Status NEScale::validate(const ITensorInfo *input, const ITensorInfo *output, const ScaleKernelInfo &info)
{
return cpu::CpuScale::validate(input, output, info);
}
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
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