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

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

 #include "arm_compute/core/CL/ICLTensor.h"
 #include "arm_compute/core/Types.h"
 #include "support/ToolchainSupport.h"

 namespace arm_compute
 {
 CLGenerateProposalsLayer::CLGenerateProposalsLayer(std::shared_ptr<IMemoryManager> memory_manager)
     : _memory_group(std::move(memory_manager)),
       _permute_deltas_kernel(),
       _flatten_deltas_kernel(),
       _permute_scores_kernel(),
       _flatten_scores_kernel(),
       _compute_anchors_kernel(),
       _bounding_box_kernel(),
       _memset_kernel(),
       _padded_copy_kernel(),
       _cpp_nms_kernel(),
       _is_nhwc(false),
       _deltas_permuted(),
       _deltas_flattened(),
       _scores_permuted(),
       _scores_flattened(),
       _all_anchors(),
       _all_proposals(),
       _keeps_nms_unused(),
       _classes_nms_unused(),
       _proposals_4_roi_values(),
       _num_valid_proposals(nullptr),
       _scores_out(nullptr)
 {
 }

 void CLGenerateProposalsLayer::configure(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));

     _is_nhwc                         = scores->info()->data_layout() == DataLayout::NHWC;
     const DataType data_type         = deltas->info()->data_type();
     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();

     // Compute all the anchors
     _memory_group.manage(&_all_anchors);
     _compute_anchors_kernel.configure(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, data_type));

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

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

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

     // Bounding box transform
     _memory_group.manage(&_all_proposals);
     BoundingBoxTransformInfo bbox_info(info.im_width(), info.im_height(), 1.f);
     _bounding_box_kernel.configure(&_all_anchors, &_all_proposals, &_deltas_flattened, bbox_info);
     _deltas_flattened.allocator()->allocate();
     _all_anchors.allocator()->allocate();

     // 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, data_type);
     auto_init_if_empty(*_proposals_4_roi_values.info(), TensorShape(values_per_roi, scores_nms_size), 1, data_type);
     auto_init_if_empty(*num_valid_proposals->info(), TensorShape(1), 1, DataType::U32);

     // Initialize temporaries (unused) outputs
     _classes_nms_unused.allocator()->init(TensorInfo(TensorShape(1, 1), 1, data_type));
     _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_kernel.configure(&_scores_flattened, &_all_proposals, 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.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
     _padded_copy_kernel.configure(&_proposals_4_roi_values, proposals, PaddingList{ { 1, 0 } });
     _proposals_4_roi_values.allocator()->allocate();

     _memset_kernel.configure(proposals, PixelValue());
 }

 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_LAYOUT_NOT_IN(scores, DataLayout::NCHW, DataLayout::NHWC);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(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();

     ARM_COMPUTE_RETURN_ERROR_ON(num_images > 1);

     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(CLPermuteKernel::validate(deltas, &deltas_permuted_info, PermutationVector{ 2, 0, 1 }));
         ARM_COMPUTE_RETURN_ON_ERROR(CLPermuteKernel::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(CLReshapeLayerKernel::validate(&deltas_permuted_info, &deltas_flattened_info));

     TensorInfo scores_flattened_info(deltas->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(CLReshapeLayerKernel::validate(&scores_permuted_info, &scores_flattened_info));
     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(CLCopyKernel::validate(&proposals_4_roi_values, proposals, PaddingList{ { 0, 1 } }));
     ARM_COMPUTE_RETURN_ON_ERROR(CLMemsetKernel::validate(proposals, PixelValue()));

     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));
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(proposals, deltas);
     }

     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.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
     CPPScheduler::get().schedule(&_cpp_nms_kernel, Window::DimX);

     // 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.unmap();
 }

 void CLGenerateProposalsLayer::run()
 {
     // Acquire all the temporaries
     _memory_group.acquire();

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

     // Transpose and reshape the inputs
     if(!_is_nhwc)
     {
         CLScheduler::get().enqueue(_permute_deltas_kernel, false);
         CLScheduler::get().enqueue(_permute_scores_kernel, false);
     }
     CLScheduler::get().enqueue(_flatten_deltas_kernel, false);
     CLScheduler::get().enqueue(_flatten_scores_kernel, false);

     // Build the boxes
     CLScheduler::get().enqueue(_bounding_box_kernel, false);
     // Non maxima suppression
     run_cpp_nms_kernel();
     // Add dummy batch indexes
     CLScheduler::get().enqueue(_memset_kernel, true);
     CLScheduler::get().enqueue(_padded_copy_kernel, true);

     // Release all the temporaries
     _memory_group.release();
 }
 } // namespace arm_compute
	/*
	* Copyright (c) 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/CL/functions/CLGenerateProposalsLayer.h"

	#include "arm_compute/core/CL/ICLTensor.h"
	#include "arm_compute/core/Types.h"
	#include "support/ToolchainSupport.h"

	namespace arm_compute
	{
	CLGenerateProposalsLayer::CLGenerateProposalsLayer(std::shared_ptr<IMemoryManager> memory_manager)
	: _memory_group(std::move(memory_manager)),
	_permute_deltas_kernel(),
	_flatten_deltas_kernel(),
	_permute_scores_kernel(),
	_flatten_scores_kernel(),
	_compute_anchors_kernel(),
	_bounding_box_kernel(),
	_memset_kernel(),
	_padded_copy_kernel(),
	_cpp_nms_kernel(),
	_is_nhwc(false),
	_deltas_permuted(),
	_deltas_flattened(),
	_scores_permuted(),
	_scores_flattened(),
	_all_anchors(),
	_all_proposals(),
	_keeps_nms_unused(),
	_classes_nms_unused(),
	_proposals_4_roi_values(),
	_num_valid_proposals(nullptr),
	_scores_out(nullptr)
	{
	}

	void CLGenerateProposalsLayer::configure(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));

	_is_nhwc = scores->info()->data_layout() == DataLayout::NHWC;
	const DataType data_type = deltas->info()->data_type();
	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();

	// Compute all the anchors
	_memory_group.manage(&_all_anchors);
	_compute_anchors_kernel.configure(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, data_type));

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

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

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

	// Bounding box transform
	_memory_group.manage(&_all_proposals);
	BoundingBoxTransformInfo bbox_info(info.im_width(), info.im_height(), 1.f);
	_bounding_box_kernel.configure(&_all_anchors, &_all_proposals, &_deltas_flattened, bbox_info);
	_deltas_flattened.allocator()->allocate();
	_all_anchors.allocator()->allocate();

	// 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, data_type);
	auto_init_if_empty(*_proposals_4_roi_values.info(), TensorShape(values_per_roi, scores_nms_size), 1, data_type);
	auto_init_if_empty(*num_valid_proposals->info(), TensorShape(1), 1, DataType::U32);

	// Initialize temporaries (unused) outputs
	_classes_nms_unused.allocator()->init(TensorInfo(TensorShape(1, 1), 1, data_type));
	_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_kernel.configure(&_scores_flattened, &_all_proposals, 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.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
	_padded_copy_kernel.configure(&_proposals_4_roi_values, proposals, PaddingList{ { 1, 0 } });
	_proposals_4_roi_values.allocator()->allocate();

	_memset_kernel.configure(proposals, PixelValue());
	}

	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_LAYOUT_NOT_IN(scores, DataLayout::NCHW, DataLayout::NHWC);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(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();

	ARM_COMPUTE_RETURN_ERROR_ON(num_images > 1);

	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(CLPermuteKernel::validate(deltas, &deltas_permuted_info, PermutationVector{ 2, 0, 1 }));
	ARM_COMPUTE_RETURN_ON_ERROR(CLPermuteKernel::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(CLReshapeLayerKernel::validate(&deltas_permuted_info, &deltas_flattened_info));

	TensorInfo scores_flattened_info(deltas->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(CLReshapeLayerKernel::validate(&scores_permuted_info, &scores_flattened_info));
	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(CLCopyKernel::validate(&proposals_4_roi_values, proposals, PaddingList{ { 0, 1 } }));
	ARM_COMPUTE_RETURN_ON_ERROR(CLMemsetKernel::validate(proposals, PixelValue()));

	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));
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(proposals, deltas);
	}

	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.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
	CPPScheduler::get().schedule(&_cpp_nms_kernel, Window::DimX);

	// 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.unmap();
	}

	void CLGenerateProposalsLayer::run()
	{
	// Acquire all the temporaries
	_memory_group.acquire();

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

	// Transpose and reshape the inputs
	if(!_is_nhwc)
	{
	CLScheduler::get().enqueue(_permute_deltas_kernel, false);
	CLScheduler::get().enqueue(_permute_scores_kernel, false);
	}
	CLScheduler::get().enqueue(_flatten_deltas_kernel, false);
	CLScheduler::get().enqueue(_flatten_scores_kernel, false);

	// Build the boxes
	CLScheduler::get().enqueue(_bounding_box_kernel, false);
	// Non maxima suppression
	run_cpp_nms_kernel();
	// Add dummy batch indexes
	CLScheduler::get().enqueue(_memset_kernel, true);
	CLScheduler::get().enqueue(_padded_copy_kernel, true);

	// Release all the temporaries
	_memory_group.release();
	}
	} // namespace arm_compute