src/runtime/CL/functions/CLGenerateProposalsLayer.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2019-2021 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/CL/functions/CLGenerateProposalsLayer.h"

 #include "arm_compute/core/CL/ICLTensor.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/runtime/CL/functions/CLDequantizationLayer.h"
 #include "arm_compute/runtime/CL/functions/CLQuantizationLayer.h"

 #include "src/common/utils/Log.h"
 #include "src/core/CL/kernels/CLBoundingBoxTransformKernel.h"
 #include "src/core/CL/kernels/CLGenerateProposalsLayerKernel.h"
 #include "src/core/CL/kernels/CLPadLayerKernel.h"
 #include "src/core/helpers/AutoConfiguration.h"

 namespace arm_compute
 {
 CLGenerateProposalsLayer::CLGenerateProposalsLayer(std::shared_ptr<IMemoryManager> memory_manager)
     : _memory_group(memory_manager),
       _permute_deltas(),
       _flatten_deltas(),
       _permute_scores(),
       _flatten_scores(),
       _compute_anchors_kernel(std::make_unique<CLComputeAllAnchorsKernel>()),
       _bounding_box_kernel(std::make_unique<CLBoundingBoxTransformKernel>()),
       _pad_kernel(std::make_unique<CLPadLayerKernel>()),
       _dequantize_anchors(std::make_unique<CLDequantizationLayer>()),
       _dequantize_deltas(std::make_unique<CLDequantizationLayer>()),
       _quantize_all_proposals(std::make_unique<CLQuantizationLayer>()),
       _cpp_nms(memory_manager),
       _is_nhwc(false),
       _is_qasymm8(false),
       _deltas_permuted(),
       _deltas_flattened(),
       _deltas_flattened_f32(),
       _scores_permuted(),
       _scores_flattened(),
       _all_anchors(),
       _all_anchors_f32(),
       _all_proposals(),
       _all_proposals_quantized(),
       _keeps_nms_unused(),
       _classes_nms_unused(),
       _proposals_4_roi_values(),
       _all_proposals_to_use(nullptr),
       _num_valid_proposals(nullptr),
       _scores_out(nullptr)
 {
 }

 CLGenerateProposalsLayer::~CLGenerateProposalsLayer() = default;

 void CLGenerateProposalsLayer::configure(const ICLTensor             *scores,
                                          const ICLTensor             *deltas,
                                          const ICLTensor             *anchors,
                                          ICLTensor                   *proposals,
                                          ICLTensor                   *scores_out,
                                          ICLTensor                   *num_valid_proposals,
                                          const GenerateProposalsInfo &info)
 {
     configure(CLKernelLibrary::get().get_compile_context(), scores, deltas, anchors, proposals, scores_out,
               num_valid_proposals, info);
 }

 void CLGenerateProposalsLayer::configure(const CLCompileContext      &compile_context,
                                          const ICLTensor             *scores,
                                          const ICLTensor             *deltas,
                                          const ICLTensor             *anchors,
                                          ICLTensor                   *proposals,
                                          ICLTensor                   *scores_out,
                                          ICLTensor                   *num_valid_proposals,
                                          const GenerateProposalsInfo &info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(scores, deltas, anchors, proposals, scores_out, num_valid_proposals);
     ARM_COMPUTE_ERROR_THROW_ON(CLGenerateProposalsLayer::validate(scores->info(), deltas->info(), anchors->info(),
                                                                   proposals->info(), scores_out->info(),
                                                                   num_valid_proposals->info(), info));
     ARM_COMPUTE_LOG_PARAMS(scores, deltas, anchors, proposals, scores_out, num_valid_proposals, info);

     _is_nhwc                        = scores->info()->data_layout() == DataLayout::NHWC;
     const DataType scores_data_type = scores->info()->data_type();
     _is_qasymm8                     = scores_data_type == DataType::QASYMM8;
     const int num_anchors           = scores->info()->dimension(
                   get_data_layout_dimension_index(scores->info()->data_layout(), DataLayoutDimension::CHANNEL));
     const int feat_width = scores->info()->dimension(
         get_data_layout_dimension_index(scores->info()->data_layout(), DataLayoutDimension::WIDTH));
     const int feat_height = scores->info()->dimension(
         get_data_layout_dimension_index(scores->info()->data_layout(), DataLayoutDimension::HEIGHT));
     const int    total_num_anchors = num_anchors * feat_width * feat_height;
     const int    pre_nms_topN      = info.pre_nms_topN();
     const int    post_nms_topN     = info.post_nms_topN();
     const size_t values_per_roi    = info.values_per_roi();

     const QuantizationInfo scores_qinfo   = scores->info()->quantization_info();
     const DataType         rois_data_type = (_is_qasymm8) ? DataType::QASYMM16 : scores_data_type;
     const QuantizationInfo rois_qinfo =
         (_is_qasymm8) ? QuantizationInfo(0.125f, 0) : scores->info()->quantization_info();

     // Compute all the anchors
     _memory_group.manage(&_all_anchors);
     _compute_anchors_kernel->configure(compile_context, anchors, &_all_anchors,
                                        ComputeAnchorsInfo(feat_width, feat_height, info.spatial_scale()));

     const TensorShape flatten_shape_deltas(values_per_roi, total_num_anchors);
     _deltas_flattened.allocator()->init(
         TensorInfo(flatten_shape_deltas, 1, scores_data_type, deltas->info()->quantization_info()));

     // Permute and reshape deltas
     _memory_group.manage(&_deltas_flattened);
     if (!_is_nhwc)
     {
         _memory_group.manage(&_deltas_permuted);
         _permute_deltas.configure(compile_context, deltas, &_deltas_permuted, PermutationVector{2, 0, 1});
         _flatten_deltas.configure(compile_context, &_deltas_permuted, &_deltas_flattened);
         _deltas_permuted.allocator()->allocate();
     }
     else
     {
         _flatten_deltas.configure(compile_context, deltas, &_deltas_flattened);
     }

     const TensorShape flatten_shape_scores(1, total_num_anchors);
     _scores_flattened.allocator()->init(TensorInfo(flatten_shape_scores, 1, scores_data_type, scores_qinfo));

     // Permute and reshape scores
     _memory_group.manage(&_scores_flattened);
     if (!_is_nhwc)
     {
         _memory_group.manage(&_scores_permuted);
         _permute_scores.configure(compile_context, scores, &_scores_permuted, PermutationVector{2, 0, 1});
         _flatten_scores.configure(compile_context, &_scores_permuted, &_scores_flattened);
         _scores_permuted.allocator()->allocate();
     }
     else
     {
         _flatten_scores.configure(compile_context, scores, &_scores_flattened);
     }

     CLTensor *anchors_to_use = &_all_anchors;
     CLTensor *deltas_to_use  = &_deltas_flattened;
     if (_is_qasymm8)
     {
         _all_anchors_f32.allocator()->init(TensorInfo(_all_anchors.info()->tensor_shape(), 1, DataType::F32));
         _deltas_flattened_f32.allocator()->init(TensorInfo(_deltas_flattened.info()->tensor_shape(), 1, DataType::F32));
         _memory_group.manage(&_all_anchors_f32);
         _memory_group.manage(&_deltas_flattened_f32);
         // Dequantize anchors to float
         _dequantize_anchors->configure(compile_context, &_all_anchors, &_all_anchors_f32);
         _all_anchors.allocator()->allocate();
         anchors_to_use = &_all_anchors_f32;
         // Dequantize deltas to float
         _dequantize_deltas->configure(compile_context, &_deltas_flattened, &_deltas_flattened_f32);
         _deltas_flattened.allocator()->allocate();
         deltas_to_use = &_deltas_flattened_f32;
     }
     // Bounding box transform
     _memory_group.manage(&_all_proposals);
     BoundingBoxTransformInfo bbox_info(info.im_width(), info.im_height(), 1.f);
     _bounding_box_kernel->configure(compile_context, anchors_to_use, &_all_proposals, deltas_to_use, bbox_info);
     deltas_to_use->allocator()->allocate();
     anchors_to_use->allocator()->allocate();

     _all_proposals_to_use = &_all_proposals;
     if (_is_qasymm8)
     {
         _memory_group.manage(&_all_proposals_quantized);
         // Requantize all_proposals to QASYMM16 with 0.125 scale and 0 offset
         _all_proposals_quantized.allocator()->init(
             TensorInfo(_all_proposals.info()->tensor_shape(), 1, DataType::QASYMM16, QuantizationInfo(0.125f, 0)));
         _quantize_all_proposals->configure(compile_context, &_all_proposals, &_all_proposals_quantized);
         _all_proposals.allocator()->allocate();
         _all_proposals_to_use = &_all_proposals_quantized;
     }
     // The original layer implementation first selects the best pre_nms_topN anchors (thus having a lightweight sort)
     // that are then transformed by bbox_transform. The boxes generated are then fed into a non-sorting NMS operation.
     // Since we are reusing the NMS layer and we don't implement any CL/sort, we let NMS do the sorting (of all the input)
     // and the filtering
     const int   scores_nms_size = std::min<int>(std::min<int>(post_nms_topN, pre_nms_topN), total_num_anchors);
     const float min_size_scaled = info.min_size() * info.im_scale();
     _memory_group.manage(&_classes_nms_unused);
     _memory_group.manage(&_keeps_nms_unused);

     // Note that NMS needs outputs preinitialized.
     auto_init_if_empty(*scores_out->info(), TensorShape(scores_nms_size), 1, scores_data_type, scores_qinfo);
     auto_init_if_empty(*_proposals_4_roi_values.info(), TensorShape(values_per_roi, scores_nms_size), 1, rois_data_type,
                        rois_qinfo);
     auto_init_if_empty(*num_valid_proposals->info(), TensorShape(1), 1, DataType::U32);

     // Initialize temporaries (unused) outputs
     _classes_nms_unused.allocator()->init(TensorInfo(TensorShape(scores_nms_size), 1, scores_data_type, scores_qinfo));
     _keeps_nms_unused.allocator()->init(*scores_out->info());

     // Save the output (to map and unmap them at run)
     _scores_out          = scores_out;
     _num_valid_proposals = num_valid_proposals;

     _memory_group.manage(&_proposals_4_roi_values);
     _cpp_nms.configure(&_scores_flattened, _all_proposals_to_use, nullptr, scores_out, &_proposals_4_roi_values,
                        &_classes_nms_unused, nullptr, &_keeps_nms_unused, num_valid_proposals,
                        BoxNMSLimitInfo(0.0f, info.nms_thres(), scores_nms_size, false, NMSType::LINEAR, 0.5f, 0.001f,
                                        true, min_size_scaled, info.im_width(), info.im_height()));
     _keeps_nms_unused.allocator()->allocate();
     _classes_nms_unused.allocator()->allocate();
     _all_proposals_to_use->allocator()->allocate();
     _scores_flattened.allocator()->allocate();

     // Add the first column that represents the batch id. This will be all zeros, as we don't support multiple images
     _pad_kernel->configure(compile_context, &_proposals_4_roi_values, proposals, PaddingList{{1, 0}});
     _proposals_4_roi_values.allocator()->allocate();
 }

 Status CLGenerateProposalsLayer::validate(const ITensorInfo           *scores,
                                           const ITensorInfo           *deltas,
                                           const ITensorInfo           *anchors,
                                           const ITensorInfo           *proposals,
                                           const ITensorInfo           *scores_out,
                                           const ITensorInfo           *num_valid_proposals,
                                           const GenerateProposalsInfo &info)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(scores, deltas, anchors, proposals, scores_out, num_valid_proposals);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(scores, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(scores, DataLayout::NCHW, DataLayout::NHWC);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(scores, deltas);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(scores, deltas);

     const int num_anchors =
         scores->dimension(get_data_layout_dimension_index(scores->data_layout(), DataLayoutDimension::CHANNEL));
     const int feat_width =
         scores->dimension(get_data_layout_dimension_index(scores->data_layout(), DataLayoutDimension::WIDTH));
     const int feat_height =
         scores->dimension(get_data_layout_dimension_index(scores->data_layout(), DataLayoutDimension::HEIGHT));
     const int num_images        = scores->dimension(3);
     const int total_num_anchors = num_anchors * feat_width * feat_height;
     const int values_per_roi    = info.values_per_roi();

     const bool is_qasymm8 = scores->data_type() == DataType::QASYMM8;

     ARM_COMPUTE_RETURN_ERROR_ON(num_images > 1);

     if (is_qasymm8)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(anchors, 1, DataType::QSYMM16);
         const UniformQuantizationInfo anchors_qinfo = anchors->quantization_info().uniform();
         ARM_COMPUTE_RETURN_ERROR_ON(anchors_qinfo.scale != 0.125f);
     }

     TensorInfo all_anchors_info(
         anchors->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true));
     ARM_COMPUTE_RETURN_ON_ERROR(CLComputeAllAnchorsKernel::validate(
         anchors, &all_anchors_info, ComputeAnchorsInfo(feat_width, feat_height, info.spatial_scale())));

     TensorInfo deltas_permuted_info =
         deltas->clone()
             ->set_tensor_shape(TensorShape(values_per_roi * num_anchors, feat_width, feat_height))
             .set_is_resizable(true);
     TensorInfo scores_permuted_info =
         scores->clone()->set_tensor_shape(TensorShape(num_anchors, feat_width, feat_height)).set_is_resizable(true);
     if (scores->data_layout() == DataLayout::NHWC)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(deltas, &deltas_permuted_info);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(scores, &scores_permuted_info);
     }
     else
     {
         ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(deltas, &deltas_permuted_info, PermutationVector{2, 0, 1}));
         ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(scores, &scores_permuted_info, PermutationVector{2, 0, 1}));
     }

     TensorInfo deltas_flattened_info(
         deltas->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true));
     ARM_COMPUTE_RETURN_ON_ERROR(CLReshapeLayer::validate(&deltas_permuted_info, &deltas_flattened_info));

     TensorInfo scores_flattened_info(
         scores->clone()->set_tensor_shape(TensorShape(1, total_num_anchors)).set_is_resizable(true));
     TensorInfo proposals_4_roi_values(
         deltas->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true));

     ARM_COMPUTE_RETURN_ON_ERROR(CLReshapeLayer::validate(&scores_permuted_info, &scores_flattened_info));

     TensorInfo *proposals_4_roi_values_to_use = &proposals_4_roi_values;
     TensorInfo  proposals_4_roi_values_quantized(
          deltas->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true));
     proposals_4_roi_values_quantized.set_data_type(DataType::QASYMM16)
         .set_quantization_info(QuantizationInfo(0.125f, 0));
     if (is_qasymm8)
     {
         TensorInfo all_anchors_f32_info(anchors->clone()
                                             ->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors))
                                             .set_is_resizable(true)
                                             .set_data_type(DataType::F32));
         ARM_COMPUTE_RETURN_ON_ERROR(CLDequantizationLayer::validate(&all_anchors_info, &all_anchors_f32_info));

         TensorInfo deltas_flattened_f32_info(deltas->clone()
                                                  ->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors))
                                                  .set_is_resizable(true)
                                                  .set_data_type(DataType::F32));
         ARM_COMPUTE_RETURN_ON_ERROR(
             CLDequantizationLayer::validate(&deltas_flattened_info, &deltas_flattened_f32_info));

         TensorInfo proposals_4_roi_values_f32(deltas->clone()
                                                   ->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors))
                                                   .set_is_resizable(true)
                                                   .set_data_type(DataType::F32));
         ARM_COMPUTE_RETURN_ON_ERROR(CLBoundingBoxTransformKernel::validate(
             &all_anchors_f32_info, &proposals_4_roi_values_f32, &deltas_flattened_f32_info,
             BoundingBoxTransformInfo(info.im_width(), info.im_height(), 1.f)));

         ARM_COMPUTE_RETURN_ON_ERROR(
             CLQuantizationLayer::validate(&proposals_4_roi_values_f32, &proposals_4_roi_values_quantized));
         proposals_4_roi_values_to_use = &proposals_4_roi_values_quantized;
     }
     else
     {
         ARM_COMPUTE_RETURN_ON_ERROR(
             CLBoundingBoxTransformKernel::validate(&all_anchors_info, &proposals_4_roi_values, &deltas_flattened_info,
                                                    BoundingBoxTransformInfo(info.im_width(), info.im_height(), 1.f)));
     }

     ARM_COMPUTE_RETURN_ON_ERROR(
         CLPadLayerKernel::validate(proposals_4_roi_values_to_use, proposals, PaddingList{{1, 0}}));

     if (num_valid_proposals->total_size() > 0)
     {
         ARM_COMPUTE_RETURN_ERROR_ON(num_valid_proposals->num_dimensions() > 1);
         ARM_COMPUTE_RETURN_ERROR_ON(num_valid_proposals->dimension(0) > 1);
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(num_valid_proposals, 1, DataType::U32);
     }

     if (proposals->total_size() > 0)
     {
         ARM_COMPUTE_RETURN_ERROR_ON(proposals->num_dimensions() > 2);
         ARM_COMPUTE_RETURN_ERROR_ON(proposals->dimension(0) != size_t(values_per_roi) + 1);
         ARM_COMPUTE_RETURN_ERROR_ON(proposals->dimension(1) != size_t(total_num_anchors));
         if (is_qasymm8)
         {
             ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(proposals, 1, DataType::QASYMM16);
             const UniformQuantizationInfo proposals_qinfo = proposals->quantization_info().uniform();
             ARM_COMPUTE_RETURN_ERROR_ON(proposals_qinfo.scale != 0.125f);
             ARM_COMPUTE_RETURN_ERROR_ON(proposals_qinfo.offset != 0);
         }
         else
         {
             ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(proposals, scores);
         }
     }

     if (scores_out->total_size() > 0)
     {
         ARM_COMPUTE_RETURN_ERROR_ON(scores_out->num_dimensions() > 1);
         ARM_COMPUTE_RETURN_ERROR_ON(scores_out->dimension(0) != size_t(total_num_anchors));
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(scores_out, scores);
     }

     return Status{};
 }

 void CLGenerateProposalsLayer::run_cpp_nms_kernel()
 {
     // Map inputs
     _scores_flattened.map(true);
     _all_proposals_to_use->map(true);

     // Map outputs
     _scores_out->map(CLScheduler::get().queue(), true);
     _proposals_4_roi_values.map(CLScheduler::get().queue(), true);
     _num_valid_proposals->map(CLScheduler::get().queue(), true);
     _keeps_nms_unused.map(true);
     _classes_nms_unused.map(true);

     // Run nms
     _cpp_nms.run();

     // Unmap outputs
     _keeps_nms_unused.unmap();
     _classes_nms_unused.unmap();
     _scores_out->unmap(CLScheduler::get().queue());
     _proposals_4_roi_values.unmap(CLScheduler::get().queue());
     _num_valid_proposals->unmap(CLScheduler::get().queue());

     // Unmap inputs
     _scores_flattened.unmap();
     _all_proposals_to_use->unmap();
 }

 void CLGenerateProposalsLayer::run()
 {
     // Acquire all the temporaries
     MemoryGroupResourceScope scope_mg(_memory_group);

     // Compute all the anchors
     CLScheduler::get().enqueue(*_compute_anchors_kernel, false);

     // Transpose and reshape the inputs
     if (!_is_nhwc)
     {
         _permute_deltas.run();
         _permute_scores.run();
     }
     _flatten_deltas.run();
     _flatten_scores.run();

     if (_is_qasymm8)
     {
         _dequantize_anchors->run();
         _dequantize_deltas->run();
     }

     // Build the boxes
     CLScheduler::get().enqueue(*_bounding_box_kernel, false);

     if (_is_qasymm8)
     {
         _quantize_all_proposals->run();
     }

     // Non maxima suppression
     run_cpp_nms_kernel();
     // Add dummy batch indexes
     CLScheduler::get().enqueue(*_pad_kernel, true);
 }
 } // namespace arm_compute
	/*
	* Copyright (c) 2019-2021 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/CL/functions/CLGenerateProposalsLayer.h"

	#include "arm_compute/core/CL/ICLTensor.h"
	#include "arm_compute/core/Types.h"
	#include "arm_compute/runtime/CL/functions/CLDequantizationLayer.h"
	#include "arm_compute/runtime/CL/functions/CLQuantizationLayer.h"

	#include "src/common/utils/Log.h"
	#include "src/core/CL/kernels/CLBoundingBoxTransformKernel.h"
	#include "src/core/CL/kernels/CLGenerateProposalsLayerKernel.h"
	#include "src/core/CL/kernels/CLPadLayerKernel.h"
	#include "src/core/helpers/AutoConfiguration.h"

	namespace arm_compute
	{
	CLGenerateProposalsLayer::CLGenerateProposalsLayer(std::shared_ptr<IMemoryManager> memory_manager)
	: _memory_group(memory_manager),
	_permute_deltas(),
	_flatten_deltas(),
	_permute_scores(),
	_flatten_scores(),
	_compute_anchors_kernel(std::make_unique<CLComputeAllAnchorsKernel>()),
	_bounding_box_kernel(std::make_unique<CLBoundingBoxTransformKernel>()),
	_pad_kernel(std::make_unique<CLPadLayerKernel>()),
	_dequantize_anchors(std::make_unique<CLDequantizationLayer>()),
	_dequantize_deltas(std::make_unique<CLDequantizationLayer>()),
	_quantize_all_proposals(std::make_unique<CLQuantizationLayer>()),
	_cpp_nms(memory_manager),
	_is_nhwc(false),
	_is_qasymm8(false),
	_deltas_permuted(),
	_deltas_flattened(),
	_deltas_flattened_f32(),
	_scores_permuted(),
	_scores_flattened(),
	_all_anchors(),
	_all_anchors_f32(),
	_all_proposals(),
	_all_proposals_quantized(),
	_keeps_nms_unused(),
	_classes_nms_unused(),
	_proposals_4_roi_values(),
	_all_proposals_to_use(nullptr),
	_num_valid_proposals(nullptr),
	_scores_out(nullptr)
	{
	}

	CLGenerateProposalsLayer::~CLGenerateProposalsLayer() = default;

	void CLGenerateProposalsLayer::configure(const ICLTensor *scores,
	const ICLTensor *deltas,
	const ICLTensor *anchors,
	ICLTensor *proposals,
	ICLTensor *scores_out,
	ICLTensor *num_valid_proposals,
	const GenerateProposalsInfo &info)
	{
	configure(CLKernelLibrary::get().get_compile_context(), scores, deltas, anchors, proposals, scores_out,
	num_valid_proposals, info);
	}

	void CLGenerateProposalsLayer::configure(const CLCompileContext &compile_context,
	const ICLTensor *scores,
	const ICLTensor *deltas,
	const ICLTensor *anchors,
	ICLTensor *proposals,
	ICLTensor *scores_out,
	ICLTensor *num_valid_proposals,
	const GenerateProposalsInfo &info)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(scores, deltas, anchors, proposals, scores_out, num_valid_proposals);
	ARM_COMPUTE_ERROR_THROW_ON(CLGenerateProposalsLayer::validate(scores->info(), deltas->info(), anchors->info(),
	proposals->info(), scores_out->info(),
	num_valid_proposals->info(), info));
	ARM_COMPUTE_LOG_PARAMS(scores, deltas, anchors, proposals, scores_out, num_valid_proposals, info);

	_is_nhwc = scores->info()->data_layout() == DataLayout::NHWC;
	const DataType scores_data_type = scores->info()->data_type();
	_is_qasymm8 = scores_data_type == DataType::QASYMM8;
	const int num_anchors = scores->info()->dimension(
	get_data_layout_dimension_index(scores->info()->data_layout(), DataLayoutDimension::CHANNEL));
	const int feat_width = scores->info()->dimension(
	get_data_layout_dimension_index(scores->info()->data_layout(), DataLayoutDimension::WIDTH));
	const int feat_height = scores->info()->dimension(
	get_data_layout_dimension_index(scores->info()->data_layout(), DataLayoutDimension::HEIGHT));
	const int total_num_anchors = num_anchors * feat_width * feat_height;
	const int pre_nms_topN = info.pre_nms_topN();
	const int post_nms_topN = info.post_nms_topN();
	const size_t values_per_roi = info.values_per_roi();

	const QuantizationInfo scores_qinfo = scores->info()->quantization_info();
	const DataType rois_data_type = (_is_qasymm8) ? DataType::QASYMM16 : scores_data_type;
	const QuantizationInfo rois_qinfo =
	(_is_qasymm8) ? QuantizationInfo(0.125f, 0) : scores->info()->quantization_info();

	// Compute all the anchors
	_memory_group.manage(&_all_anchors);
	_compute_anchors_kernel->configure(compile_context, anchors, &_all_anchors,
	ComputeAnchorsInfo(feat_width, feat_height, info.spatial_scale()));

	const TensorShape flatten_shape_deltas(values_per_roi, total_num_anchors);
	_deltas_flattened.allocator()->init(
	TensorInfo(flatten_shape_deltas, 1, scores_data_type, deltas->info()->quantization_info()));

	// Permute and reshape deltas
	_memory_group.manage(&_deltas_flattened);
	if (!_is_nhwc)
	{
	_memory_group.manage(&_deltas_permuted);
	_permute_deltas.configure(compile_context, deltas, &_deltas_permuted, PermutationVector{2, 0, 1});
	_flatten_deltas.configure(compile_context, &_deltas_permuted, &_deltas_flattened);
	_deltas_permuted.allocator()->allocate();
	}
	else
	{
	_flatten_deltas.configure(compile_context, deltas, &_deltas_flattened);
	}

	const TensorShape flatten_shape_scores(1, total_num_anchors);
	_scores_flattened.allocator()->init(TensorInfo(flatten_shape_scores, 1, scores_data_type, scores_qinfo));

	// Permute and reshape scores
	_memory_group.manage(&_scores_flattened);
	if (!_is_nhwc)
	{
	_memory_group.manage(&_scores_permuted);
	_permute_scores.configure(compile_context, scores, &_scores_permuted, PermutationVector{2, 0, 1});
	_flatten_scores.configure(compile_context, &_scores_permuted, &_scores_flattened);
	_scores_permuted.allocator()->allocate();
	}
	else
	{
	_flatten_scores.configure(compile_context, scores, &_scores_flattened);
	}

	CLTensor *anchors_to_use = &_all_anchors;
	CLTensor *deltas_to_use = &_deltas_flattened;
	if (_is_qasymm8)
	{
	_all_anchors_f32.allocator()->init(TensorInfo(_all_anchors.info()->tensor_shape(), 1, DataType::F32));
	_deltas_flattened_f32.allocator()->init(TensorInfo(_deltas_flattened.info()->tensor_shape(), 1, DataType::F32));
	_memory_group.manage(&_all_anchors_f32);
	_memory_group.manage(&_deltas_flattened_f32);
	// Dequantize anchors to float
	_dequantize_anchors->configure(compile_context, &_all_anchors, &_all_anchors_f32);
	_all_anchors.allocator()->allocate();
	anchors_to_use = &_all_anchors_f32;
	// Dequantize deltas to float
	_dequantize_deltas->configure(compile_context, &_deltas_flattened, &_deltas_flattened_f32);
	_deltas_flattened.allocator()->allocate();
	deltas_to_use = &_deltas_flattened_f32;
	}
	// Bounding box transform
	_memory_group.manage(&_all_proposals);
	BoundingBoxTransformInfo bbox_info(info.im_width(), info.im_height(), 1.f);
	_bounding_box_kernel->configure(compile_context, anchors_to_use, &_all_proposals, deltas_to_use, bbox_info);
	deltas_to_use->allocator()->allocate();
	anchors_to_use->allocator()->allocate();

	_all_proposals_to_use = &_all_proposals;
	if (_is_qasymm8)
	{
	_memory_group.manage(&_all_proposals_quantized);
	// Requantize all_proposals to QASYMM16 with 0.125 scale and 0 offset
	_all_proposals_quantized.allocator()->init(
	TensorInfo(_all_proposals.info()->tensor_shape(), 1, DataType::QASYMM16, QuantizationInfo(0.125f, 0)));
	_quantize_all_proposals->configure(compile_context, &_all_proposals, &_all_proposals_quantized);
	_all_proposals.allocator()->allocate();
	_all_proposals_to_use = &_all_proposals_quantized;
	}
	// The original layer implementation first selects the best pre_nms_topN anchors (thus having a lightweight sort)
	// that are then transformed by bbox_transform. The boxes generated are then fed into a non-sorting NMS operation.
	// Since we are reusing the NMS layer and we don't implement any CL/sort, we let NMS do the sorting (of all the input)
	// and the filtering
	const int scores_nms_size = std::min<int>(std::min<int>(post_nms_topN, pre_nms_topN), total_num_anchors);
	const float min_size_scaled = info.min_size() * info.im_scale();
	_memory_group.manage(&_classes_nms_unused);
	_memory_group.manage(&_keeps_nms_unused);

	// Note that NMS needs outputs preinitialized.
	auto_init_if_empty(*scores_out->info(), TensorShape(scores_nms_size), 1, scores_data_type, scores_qinfo);
	auto_init_if_empty(*_proposals_4_roi_values.info(), TensorShape(values_per_roi, scores_nms_size), 1, rois_data_type,
	rois_qinfo);
	auto_init_if_empty(*num_valid_proposals->info(), TensorShape(1), 1, DataType::U32);

	// Initialize temporaries (unused) outputs
	_classes_nms_unused.allocator()->init(TensorInfo(TensorShape(scores_nms_size), 1, scores_data_type, scores_qinfo));
	_keeps_nms_unused.allocator()->init(*scores_out->info());

	// Save the output (to map and unmap them at run)
	_scores_out = scores_out;
	_num_valid_proposals = num_valid_proposals;

	_memory_group.manage(&_proposals_4_roi_values);
	_cpp_nms.configure(&_scores_flattened, _all_proposals_to_use, nullptr, scores_out, &_proposals_4_roi_values,
	&_classes_nms_unused, nullptr, &_keeps_nms_unused, num_valid_proposals,
	BoxNMSLimitInfo(0.0f, info.nms_thres(), scores_nms_size, false, NMSType::LINEAR, 0.5f, 0.001f,
	true, min_size_scaled, info.im_width(), info.im_height()));
	_keeps_nms_unused.allocator()->allocate();
	_classes_nms_unused.allocator()->allocate();
	_all_proposals_to_use->allocator()->allocate();
	_scores_flattened.allocator()->allocate();

	// Add the first column that represents the batch id. This will be all zeros, as we don't support multiple images
	_pad_kernel->configure(compile_context, &_proposals_4_roi_values, proposals, PaddingList{{1, 0}});
	_proposals_4_roi_values.allocator()->allocate();
	}

	Status CLGenerateProposalsLayer::validate(const ITensorInfo *scores,
	const ITensorInfo *deltas,
	const ITensorInfo *anchors,
	const ITensorInfo *proposals,
	const ITensorInfo *scores_out,
	const ITensorInfo *num_valid_proposals,
	const GenerateProposalsInfo &info)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(scores, deltas, anchors, proposals, scores_out, num_valid_proposals);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(scores, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(scores, DataLayout::NCHW, DataLayout::NHWC);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(scores, deltas);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(scores, deltas);

	const int num_anchors =
	scores->dimension(get_data_layout_dimension_index(scores->data_layout(), DataLayoutDimension::CHANNEL));
	const int feat_width =
	scores->dimension(get_data_layout_dimension_index(scores->data_layout(), DataLayoutDimension::WIDTH));
	const int feat_height =
	scores->dimension(get_data_layout_dimension_index(scores->data_layout(), DataLayoutDimension::HEIGHT));
	const int num_images = scores->dimension(3);
	const int total_num_anchors = num_anchors * feat_width * feat_height;
	const int values_per_roi = info.values_per_roi();

	const bool is_qasymm8 = scores->data_type() == DataType::QASYMM8;

	ARM_COMPUTE_RETURN_ERROR_ON(num_images > 1);

	if (is_qasymm8)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(anchors, 1, DataType::QSYMM16);
	const UniformQuantizationInfo anchors_qinfo = anchors->quantization_info().uniform();
	ARM_COMPUTE_RETURN_ERROR_ON(anchors_qinfo.scale != 0.125f);
	}

	TensorInfo all_anchors_info(
	anchors->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true));
	ARM_COMPUTE_RETURN_ON_ERROR(CLComputeAllAnchorsKernel::validate(
	anchors, &all_anchors_info, ComputeAnchorsInfo(feat_width, feat_height, info.spatial_scale())));

	TensorInfo deltas_permuted_info =
	deltas->clone()
	->set_tensor_shape(TensorShape(values_per_roi * num_anchors, feat_width, feat_height))
	.set_is_resizable(true);
	TensorInfo scores_permuted_info =
	scores->clone()->set_tensor_shape(TensorShape(num_anchors, feat_width, feat_height)).set_is_resizable(true);
	if (scores->data_layout() == DataLayout::NHWC)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(deltas, &deltas_permuted_info);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(scores, &scores_permuted_info);
	}
	else
	{
	ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(deltas, &deltas_permuted_info, PermutationVector{2, 0, 1}));
	ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(scores, &scores_permuted_info, PermutationVector{2, 0, 1}));
	}

	TensorInfo deltas_flattened_info(
	deltas->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true));
	ARM_COMPUTE_RETURN_ON_ERROR(CLReshapeLayer::validate(&deltas_permuted_info, &deltas_flattened_info));

	TensorInfo scores_flattened_info(
	scores->clone()->set_tensor_shape(TensorShape(1, total_num_anchors)).set_is_resizable(true));
	TensorInfo proposals_4_roi_values(
	deltas->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true));

	ARM_COMPUTE_RETURN_ON_ERROR(CLReshapeLayer::validate(&scores_permuted_info, &scores_flattened_info));

	TensorInfo *proposals_4_roi_values_to_use = &proposals_4_roi_values;
	TensorInfo proposals_4_roi_values_quantized(
	deltas->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true));
	proposals_4_roi_values_quantized.set_data_type(DataType::QASYMM16)
	.set_quantization_info(QuantizationInfo(0.125f, 0));
	if (is_qasymm8)
	{
	TensorInfo all_anchors_f32_info(anchors->clone()
	->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors))
	.set_is_resizable(true)
	.set_data_type(DataType::F32));
	ARM_COMPUTE_RETURN_ON_ERROR(CLDequantizationLayer::validate(&all_anchors_info, &all_anchors_f32_info));

	TensorInfo deltas_flattened_f32_info(deltas->clone()
	->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors))
	.set_is_resizable(true)
	.set_data_type(DataType::F32));
	ARM_COMPUTE_RETURN_ON_ERROR(
	CLDequantizationLayer::validate(&deltas_flattened_info, &deltas_flattened_f32_info));

	TensorInfo proposals_4_roi_values_f32(deltas->clone()
	->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors))
	.set_is_resizable(true)
	.set_data_type(DataType::F32));
	ARM_COMPUTE_RETURN_ON_ERROR(CLBoundingBoxTransformKernel::validate(
	&all_anchors_f32_info, &proposals_4_roi_values_f32, &deltas_flattened_f32_info,
	BoundingBoxTransformInfo(info.im_width(), info.im_height(), 1.f)));

	ARM_COMPUTE_RETURN_ON_ERROR(
	CLQuantizationLayer::validate(&proposals_4_roi_values_f32, &proposals_4_roi_values_quantized));
	proposals_4_roi_values_to_use = &proposals_4_roi_values_quantized;
	}
	else
	{
	ARM_COMPUTE_RETURN_ON_ERROR(
	CLBoundingBoxTransformKernel::validate(&all_anchors_info, &proposals_4_roi_values, &deltas_flattened_info,
	BoundingBoxTransformInfo(info.im_width(), info.im_height(), 1.f)));
	}

	ARM_COMPUTE_RETURN_ON_ERROR(
	CLPadLayerKernel::validate(proposals_4_roi_values_to_use, proposals, PaddingList{{1, 0}}));

	if (num_valid_proposals->total_size() > 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON(num_valid_proposals->num_dimensions() > 1);
	ARM_COMPUTE_RETURN_ERROR_ON(num_valid_proposals->dimension(0) > 1);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(num_valid_proposals, 1, DataType::U32);
	}

	if (proposals->total_size() > 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON(proposals->num_dimensions() > 2);
	ARM_COMPUTE_RETURN_ERROR_ON(proposals->dimension(0) != size_t(values_per_roi) + 1);
	ARM_COMPUTE_RETURN_ERROR_ON(proposals->dimension(1) != size_t(total_num_anchors));
	if (is_qasymm8)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(proposals, 1, DataType::QASYMM16);
	const UniformQuantizationInfo proposals_qinfo = proposals->quantization_info().uniform();
	ARM_COMPUTE_RETURN_ERROR_ON(proposals_qinfo.scale != 0.125f);
	ARM_COMPUTE_RETURN_ERROR_ON(proposals_qinfo.offset != 0);
	}
	else
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(proposals, scores);
	}
	}

	if (scores_out->total_size() > 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON(scores_out->num_dimensions() > 1);
	ARM_COMPUTE_RETURN_ERROR_ON(scores_out->dimension(0) != size_t(total_num_anchors));
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(scores_out, scores);
	}

	return Status{};
	}

	void CLGenerateProposalsLayer::run_cpp_nms_kernel()
	{
	// Map inputs
	_scores_flattened.map(true);
	_all_proposals_to_use->map(true);

	// Map outputs
	_scores_out->map(CLScheduler::get().queue(), true);
	_proposals_4_roi_values.map(CLScheduler::get().queue(), true);
	_num_valid_proposals->map(CLScheduler::get().queue(), true);
	_keeps_nms_unused.map(true);
	_classes_nms_unused.map(true);

	// Run nms
	_cpp_nms.run();

	// Unmap outputs
	_keeps_nms_unused.unmap();
	_classes_nms_unused.unmap();
	_scores_out->unmap(CLScheduler::get().queue());
	_proposals_4_roi_values.unmap(CLScheduler::get().queue());
	_num_valid_proposals->unmap(CLScheduler::get().queue());

	// Unmap inputs
	_scores_flattened.unmap();
	_all_proposals_to_use->unmap();
	}

	void CLGenerateProposalsLayer::run()
	{
	// Acquire all the temporaries
	MemoryGroupResourceScope scope_mg(_memory_group);

	// Compute all the anchors
	CLScheduler::get().enqueue(*_compute_anchors_kernel, false);

	// Transpose and reshape the inputs
	if (!_is_nhwc)
	{
	_permute_deltas.run();
	_permute_scores.run();
	}
	_flatten_deltas.run();
	_flatten_scores.run();

	if (_is_qasymm8)
	{
	_dequantize_anchors->run();
	_dequantize_deltas->run();
	}

	// Build the boxes
	CLScheduler::get().enqueue(*_bounding_box_kernel, false);

	if (_is_qasymm8)
	{
	_quantize_all_proposals->run();
	}

	// Non maxima suppression
	run_cpp_nms_kernel();
	// Add dummy batch indexes
	CLScheduler::get().enqueue(*_pad_kernel, true);
	}
	} // namespace arm_compute