/* * 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 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(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, 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 MemoryGroupResourceScope scope_mg(_memory_group); // 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); } } // namespace arm_compute