text: /*
   * Copyright (c) 2018-2019 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/CPP/functions/CPPBoxWithNonMaximaSuppressionLimit.h"
  
  #include "arm_compute/core/CPP/kernels/CPPBoxWithNonMaximaSuppressionLimitKernel.h"
  #include "arm_compute/runtime/Scheduler.h"
  
  namespace arm_compute
  {
  namespace
  {
  void dequantize_tensor(const ITensor *input, ITensor *output)
  {
      const UniformQuantizationInfo qinfo     = input->info()->quantization_info().uniform();
      const DataType                data_type = input->info()->data_type();
  
      Window window;
      window.use_tensor_dimensions(input->info()->tensor_shape());
      Iterator input_it(input, window);
      Iterator output_it(output, window);
  
      switch(data_type)
      {
          case DataType::QASYMM8:
              execute_window_loop(window, [&](const Coordinates &)
              {
                  *reinterpret_cast<float *>(output_it.ptr()) = dequantize(*reinterpret_cast<const uint8_t *>(input_it.ptr()), qinfo.scale, qinfo.offset);
              },
              input_it, output_it);
              break;
          case DataType::QASYMM16:
              execute_window_loop(window, [&](const Coordinates &)
              {
                  *reinterpret_cast<float *>(output_it.ptr()) = dequantize(*reinterpret_cast<const uint16_t *>(input_it.ptr()), qinfo.scale, qinfo.offset);
              },
              input_it, output_it);
              break;
          default:
              ARM_COMPUTE_ERROR("Unsupported data type");
      }
  }
  
  void quantize_tensor(const ITensor *input, ITensor *output)
  {
      const UniformQuantizationInfo qinfo     = output->info()->quantization_info().uniform();
      const DataType                data_type = output->info()->data_type();
  
      Window window;
      window.use_tensor_dimensions(input->info()->tensor_shape());
      Iterator input_it(input, window);
      Iterator output_it(output, window);
  
      switch(data_type)
      {
          case DataType::QASYMM8:
              execute_window_loop(window, [&](const Coordinates &)
              {
                  *reinterpret_cast<uint8_t *>(output_it.ptr()) = quantize_qasymm8(*reinterpret_cast<const float *>(input_it.ptr()), qinfo);
              },
              input_it, output_it);
              break;
          case DataType::QASYMM16:
              execute_window_loop(window, [&](const Coordinates &)
              {
                  *reinterpret_cast<uint16_t *>(output_it.ptr()) = quantize_qasymm16(*reinterpret_cast<const float *>(input_it.ptr()), qinfo);
              },
              input_it, output_it);
              break;
          default:
              ARM_COMPUTE_ERROR("Unsupported data type");
      }
  }
  } // namespace
  
  CPPBoxWithNonMaximaSuppressionLimit::CPPBoxWithNonMaximaSuppressionLimit(std::shared_ptr<IMemoryManager> memory_manager)
      : _memory_group(std::move(memory_manager)),
        _box_with_nms_limit_kernel(),
        _scores_in(),
        _boxes_in(),
        _batch_splits_in(),
        _scores_out(),
        _boxes_out(),
        _classes(),
        _batch_splits_out(),
        _keeps(),
        _scores_in_f32(),
        _boxes_in_f32(),
        _batch_splits_in_f32(),
        _scores_out_f32(),
        _boxes_out_f32(),
        _classes_f32(),
        _batch_splits_out_f32(),
        _keeps_f32(),
        _is_qasymm8(false)
  {
  }
  
  void CPPBoxWithNonMaximaSuppressionLimit::configure(const ITensor *scores_in, const ITensor *boxes_in, const ITensor *batch_splits_in, ITensor *scores_out, ITensor *boxes_out, ITensor *classes,
                                                      ITensor *batch_splits_out, ITensor *keeps, ITensor *keeps_size, const BoxNMSLimitInfo info)
  {
      ARM_COMPUTE_ERROR_ON_NULLPTR(scores_in, boxes_in, scores_out, boxes_out, classes);
  
      _is_qasymm8 = scores_in->info()->data_type() == DataType::QASYMM8;
  
      _scores_in        = scores_in;
      _boxes_in         = boxes_in;
      _batch_splits_in  = batch_splits_in;
      _scores_out       = scores_out;
      _boxes_out        = boxes_out;
      _classes          = classes;
      _batch_splits_out = batch_splits_out;
      _keeps            = keeps;
  
      if(_is_qasymm8)
      {
          // Manage intermediate buffers
          _memory_group.manage(&_scores_in_f32);
          _memory_group.manage(&_boxes_in_f32);
          _memory_group.manage(&_scores_out_f32);
          _memory_group.manage(&_boxes_out_f32);
          _memory_group.manage(&_classes_f32);
          _scores_in_f32.allocator()->init(scores_in->info()->clone()->set_data_type(DataType::F32));
          _boxes_in_f32.allocator()->init(boxes_in->info()->clone()->set_data_type(DataType::F32));
          if(batch_splits_in != nullptr)
          {
              _memory_group.manage(&_batch_splits_in_f32);
              _batch_splits_in_f32.allocator()->init(batch_splits_in->info()->clone()->set_data_type(DataType::F32));
          }
          _scores_out_f32.allocator()->init(scores_out->info()->clone()->set_data_type(DataType::F32));
          _boxes_out_f32.allocator()->init(boxes_out->info()->clone()->set_data_type(DataType::F32));
          _classes_f32.allocator()->init(classes->info()->clone()->set_data_type(DataType::F32));
          if(batch_splits_out != nullptr)
          {
              _memory_group.manage(&_batch_splits_out_f32);
              _batch_splits_out_f32.allocator()->init(batch_splits_out->info()->clone()->set_data_type(DataType::F32));
          }
          if(keeps != nullptr)
          {
              _memory_group.manage(&_keeps_f32);
              _keeps_f32.allocator()->init(keeps->info()->clone()->set_data_type(DataType::F32));
          }
  
          _box_with_nms_limit_kernel.configure(&_scores_in_f32, &_boxes_in_f32, (batch_splits_in != nullptr) ? &_batch_splits_in_f32 : nullptr,
                                               &_scores_out_f32, &_boxes_out_f32, &_classes_f32,
                                               (batch_splits_out != nullptr) ? &_batch_splits_out_f32 : nullptr, (keeps != nullptr) ? &_keeps_f32 : nullptr,
                                               keeps_size, info);
      }
      else
      {
          _box_with_nms_limit_kernel.configure(scores_in, boxes_in, batch_splits_in, scores_out, boxes_out, classes, batch_splits_out, keeps, keeps_size, info);
      }
  
      if(_is_qasymm8)
      {
          _scores_in_f32.allocator()->allocate();
          _boxes_in_f32.allocator()->allocate();
          if(_batch_splits_in != nullptr)
          {
              _batch_splits_in_f32.allocator()->allocate();
          }
          _scores_out_f32.allocator()->allocate();
          _boxes_out_f32.allocator()->allocate();
          _classes_f32.allocator()->allocate();
          if(batch_splits_out != nullptr)
          {
              _batch_splits_out_f32.allocator()->allocate();
          }
          if(keeps != nullptr)
          {
              _keeps_f32.allocator()->allocate();
          }
      }
  }
  
  Status validate(const ITensorInfo *scores_in, const ITensorInfo *boxes_in, const ITensorInfo *batch_splits_in, const ITensorInfo *scores_out, const ITensorInfo *boxes_out, const ITensorInfo *classes,
                  const ITensorInfo *batch_splits_out, const ITensorInfo *keeps, const ITensorInfo *keeps_size, const BoxNMSLimitInfo info)
  {
      ARM_COMPUTE_UNUSED(batch_splits_in, batch_splits_out, keeps, keeps_size, info);
      ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(scores_in, boxes_in, scores_out, boxes_out, classes);
      ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(scores_in, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
  
      const bool is_qasymm8 = scores_in->data_type() == DataType::QASYMM8;
      if(is_qasymm8)
      {
          ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(boxes_in, 1, DataType::QASYMM16);
          ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(boxes_in, boxes_out);
          ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(boxes_in, boxes_out);
          const UniformQuantizationInfo boxes_qinfo = boxes_in->quantization_info().uniform();
          ARM_COMPUTE_RETURN_ERROR_ON(boxes_qinfo.scale != 0.125f);
          ARM_COMPUTE_RETURN_ERROR_ON(boxes_qinfo.offset != 0);
      }
  
      return Status{};
  }
  
  void CPPBoxWithNonMaximaSuppressionLimit::run()
  {
      // Acquire all the temporaries
      MemoryGroupResourceScope scope_mg(_memory_group);
  
      if(_is_qasymm8)
      {
          dequantize_tensor(_scores_in, &_scores_in_f32);
          dequantize_tensor(_boxes_in, &_boxes_in_f32);
          if(_batch_splits_in != nullptr)
          {
              dequantize_tensor(_batch_splits_in, &_batch_splits_in_f32);
          }
      }
  
      Scheduler::get().schedule(&_box_with_nms_limit_kernel, Window::DimY);
  
      if(_is_qasymm8)
      {
          quantize_tensor(&_scores_out_f32, _scores_out);
          quantize_tensor(&_boxes_out_f32, _boxes_out);
          quantize_tensor(&_classes_f32, _classes);
          if(_batch_splits_out != nullptr)
          {
              quantize_tensor(&_batch_splits_out_f32, _batch_splits_out);
          }
          if(_keeps != nullptr)
          {
              quantize_tensor(&_keeps_f32, _keeps);
          }
      }
  }
  } // namespace arm_compute
  