| /* |
| * 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 "src/core/CL/kernels/CLBoundingBoxTransformKernel.h" |
| #include "src/core/CL/kernels/CLDequantizationLayerKernel.h" |
| #include "src/core/CL/kernels/CLGenerateProposalsLayerKernel.h" |
| #include "src/core/CL/kernels/CLPadLayerKernel.h" |
| #include "src/core/CL/kernels/CLQuantizationLayerKernel.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<CLDequantizationLayerKernel>()), |
| _dequantize_deltas(std::make_unique<CLDequantizationLayerKernel>()), |
| _quantize_all_proposals(std::make_unique<CLQuantizationLayerKernel>()), |
| _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)); |
| |
| _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(CLDequantizationLayerKernel::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(CLDequantizationLayerKernel::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(CLQuantizationLayerKernel::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) |
| { |
| CLScheduler::get().enqueue(*_dequantize_anchors, false); |
| CLScheduler::get().enqueue(*_dequantize_deltas, false); |
| } |
| |
| // Build the boxes |
| CLScheduler::get().enqueue(*_bounding_box_kernel, false); |
| |
| if(_is_qasymm8) |
| { |
| CLScheduler::get().enqueue(*_quantize_all_proposals, false); |
| } |
| |
| // Non maxima suppression |
| run_cpp_nms_kernel(); |
| // Add dummy batch indexes |
| CLScheduler::get().enqueue(*_pad_kernel, true); |
| } |
| } // namespace arm_compute |