NECropResize.cpp

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/*
 * Copyright (c) 2019-2021 Arm Limited.
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 * SPDX-License-Identifier: MIT
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#include "arm_compute/runtime/NEON/functions/NECropResize.h"
 
#include "arm_compute/runtime/NEON/NEScheduler.h"
#include "arm_compute/runtime/Tensor.h"
 
#include "src/common/utils/Log.h"
#include "src/core/NEON/kernels/NECropKernel.h"
 
#include <cstddef>
 
namespace arm_compute
{
NECropResize::~NECropResize() = default;
 
NECropResize::NECropResize()
    : _output(nullptr),
      _num_boxes(0),
      _method(),
      _extrapolation_value(0),
      _crop(),
      _scale(),
      _crop_results(),
      _scaled_results()
{
}
 
Status NECropResize::validate(const ITensorInfo  *input,
                              const ITensorInfo  *boxes,
                              const ITensorInfo  *box_ind,
                              const ITensorInfo  *output,
                              Coordinates2D       crop_size,
                              InterpolationPolicy method,
                              float               extrapolation_value)
{
    ARM_COMPUTE_RETURN_ERROR_ON(crop_size.x <= 0 || crop_size.y <= 0);
    ARM_COMPUTE_RETURN_ERROR_ON(method == InterpolationPolicy::AREA);
    TensorInfo temp_info;
    ARM_COMPUTE_RETURN_ON_ERROR(NECropKernel::validate(input->clone().get(), boxes->clone().get(),
                                                       box_ind->clone().get(), &temp_info, boxes->tensor_shape()[1] - 1,
                                                       extrapolation_value));
    if (output->total_size() > 0)
    {
        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(output, DataType::F32);
        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output);
        TensorShape out_shape(input->tensor_shape()[0], crop_size.x, crop_size.y, boxes->tensor_shape()[1]);
        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), out_shape);
    }
    return Status{};
}
 
void NECropResize::configure(const ITensor      *input,
                             const ITensor      *boxes,
                             const ITensor      *box_ind,
                             ITensor            *output,
                             Coordinates2D       crop_size,
                             InterpolationPolicy method,
                             float               extrapolation_value)
{
    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
    ARM_COMPUTE_ERROR_THROW_ON(NECropResize::validate(input->info(), boxes->info(), box_ind->info(), output->info(),
                                                      crop_size, method, extrapolation_value));
    ARM_COMPUTE_LOG_PARAMS(input, boxes, box_ind, output, crop_size, method, extrapolation_value);
 
    _num_boxes = boxes->info()->tensor_shape()[1];
    TensorShape out_shape(input->info()->tensor_shape()[0], crop_size.x, crop_size.y);
 
    _output              = output;
    _method              = method;
    _extrapolation_value = extrapolation_value;
 
    // For each crop box:
    // - A crop kernel is used to extract the initial cropped image as specified by boxes[i] from the 3D image input[box_ind[i]].
    // - A tensor is required to hold this initial cropped image.
    // - A scale function is used to resize the cropped image to the size specified by crop_size.
    // - A tensor is required to hold the final scaled image before it is copied into the 4D output
    //   that will hold all final cropped and scaled 3D images.
    _crop.reserve(_num_boxes);
    _crop_results.reserve(_num_boxes);
    _scaled_results.reserve(_num_boxes);
    _scale.reserve(_num_boxes);
 
    for (unsigned int i = 0; i < _num_boxes; ++i)
    {
        auto       crop_tensor = std::make_unique<Tensor>();
        TensorInfo crop_result_info(1, DataType::F32);
        crop_result_info.set_data_layout(DataLayout::NHWC);
        crop_tensor->allocator()->init(crop_result_info);
 
        auto       scale_tensor = std::make_unique<Tensor>();
        TensorInfo scaled_result_info(out_shape, 1, DataType::F32);
        scaled_result_info.set_data_layout(DataLayout::NHWC);
        scale_tensor->allocator()->init(scaled_result_info);
 
        auto crop_kernel  = std::make_unique<NECropKernel>();
        auto scale_kernel = std::make_unique<NEScale>();
        crop_kernel->configure(input, boxes, box_ind, crop_tensor.get(), i, _extrapolation_value);
 
        _crop.emplace_back(std::move(crop_kernel));
        _scaled_results.emplace_back(std::move(scale_tensor));
        _crop_results.emplace_back(std::move(crop_tensor));
        _scale.emplace_back(std::move(scale_kernel));
    }
}
 
void NECropResize::run()
{
    ARM_COMPUTE_ERROR_ON_MSG(_output == nullptr, "Unconfigured function");
 
    for (unsigned int i = 0; i < _num_boxes; ++i)
    {
        // Size of the crop box in _boxes and thus the shape of _crop_results[i]
        // may not be known until run-time and so the kernels cannot be configured until then.
        _crop[i]->configure_output_shape();
        _crop_results[i]->allocator()->allocate();
        NEScheduler::get().schedule(_crop[i].get(), Window::DimZ);
 
        // Scale the cropped image.
        _scale[i]->configure(_crop_results[i].get(), _scaled_results[i].get(),
                             ScaleKernelInfo{_method, BorderMode::CONSTANT, PixelValue(_extrapolation_value),
                                             SamplingPolicy::TOP_LEFT, false});
        _scaled_results[i]->allocator()->allocate();
        _scale[i]->run();
 
        // Copy scaled image into output.
        std::copy_n(_scaled_results[i]->buffer(), _scaled_results[i]->info()->total_size(),
                    _output->ptr_to_element(Coordinates(0, 0, 0, i)));
    }
}
} // namespace arm_compute