/* * Copyright (c) 2019-2020 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/NEGenerateProposalsLayer.h" #include "arm_compute/core/Types.h" #include "arm_compute/runtime/NEON/NEScheduler.h" namespace arm_compute { NEGenerateProposalsLayer::NEGenerateProposalsLayer(std::shared_ptr memory_manager) : _memory_group(memory_manager), _permute_deltas_kernel(), _flatten_deltas(), _permute_scores_kernel(), _flatten_scores(), _compute_anchors_kernel(), _bounding_box_kernel(), _pad_kernel(), _dequantize_anchors(), _dequantize_deltas(), _quantize_all_proposals(), _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) { } void NEGenerateProposalsLayer::configure(const ITensor *scores, const ITensor *deltas, const ITensor *anchors, ITensor *proposals, ITensor *scores_out, ITensor *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(NEGenerateProposalsLayer::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 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(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_kernel.configure(deltas, &_deltas_permuted, PermutationVector{ 2, 0, 1 }); _flatten_deltas.configure(&_deltas_permuted, &_deltas_flattened); _deltas_permuted.allocator()->allocate(); } else { _flatten_deltas.configure(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_kernel.configure(scores, &_scores_permuted, PermutationVector{ 2, 0, 1 }); _flatten_scores.configure(&_scores_permuted, &_scores_flattened); _scores_permuted.allocator()->allocate(); } else { _flatten_scores.configure(scores, &_scores_flattened); } Tensor *anchors_to_use = &_all_anchors; Tensor *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(&_all_anchors, &_all_anchors_f32); _all_anchors.allocator()->allocate(); anchors_to_use = &_all_anchors_f32; // Dequantize deltas to float _dequantize_deltas.configure(&_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(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(&_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(std::min(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); const BoxNMSLimitInfo box_nms_info(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()); _cpp_nms.configure(&_scores_flattened /*scores_in*/, _all_proposals_to_use /*boxes_in,*/, nullptr /* batch_splits_in*/, scores_out /* scores_out*/, &_proposals_4_roi_values /*boxes_out*/, &_classes_nms_unused /*classes*/, nullptr /*batch_splits_out*/, &_keeps_nms_unused /*keeps*/, num_valid_proposals /* keeps_size*/, box_nms_info); _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(&_proposals_4_roi_values, proposals, PaddingList{ { 1, 0 } }); _proposals_4_roi_values.allocator()->allocate(); } Status NEGenerateProposalsLayer::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(NEComputeAllAnchorsKernel::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(NEPermuteKernel::validate(deltas, &deltas_permuted_info, PermutationVector{ 2, 0, 1 })); ARM_COMPUTE_RETURN_ON_ERROR(NEPermuteKernel::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(NEReshapeLayer::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(NEReshapeLayer::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(NEDequantizationLayerKernel::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(NEDequantizationLayerKernel::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(NEBoundingBoxTransformKernel::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(NEQuantizationLayerKernel::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(NEBoundingBoxTransformKernel::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(NEPadLayerKernel::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 NEGenerateProposalsLayer::run() { // Acquire all the temporaries MemoryGroupResourceScope scope_mg(_memory_group); // Compute all the anchors NEScheduler::get().schedule(&_compute_anchors_kernel, Window::DimY); // Transpose and reshape the inputs if(!_is_nhwc) { NEScheduler::get().schedule(&_permute_deltas_kernel, Window::DimY); NEScheduler::get().schedule(&_permute_scores_kernel, Window::DimY); } _flatten_deltas.run(); _flatten_scores.run(); if(_is_qasymm8) { NEScheduler::get().schedule(&_dequantize_anchors, Window::DimY); NEScheduler::get().schedule(&_dequantize_deltas, Window::DimY); } // Build the boxes NEScheduler::get().schedule(&_bounding_box_kernel, Window::DimY); if(_is_qasymm8) { NEScheduler::get().schedule(&_quantize_all_proposals, Window::DimY); } // Non maxima suppression _cpp_nms.run(); // Add dummy batch indexes NEScheduler::get().schedule(&_pad_kernel, Window::DimY); } } // namespace arm_compute