diff options
author | Pablo Tello <pablo.tello@arm.com> | 2019-09-13 10:20:25 +0100 |
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committer | Pablo Marquez <pablo.tello@arm.com> | 2019-09-20 09:27:12 +0000 |
commit | c9564cb3850b6675cef663d7cc0722567b55cc25 (patch) | |
tree | 1ab16f4e24240fed839967c6e1b7a34597adce18 /src/runtime/NEON/functions/NEGenerateProposalsLayer.cpp | |
parent | 7b9d7ca0d207d9f4d4b96222940eb96c2e10a0f1 (diff) | |
download | ComputeLibrary-c9564cb3850b6675cef663d7cc0722567b55cc25.tar.gz |
COMPMID-2257: Implement NEGenerateProposals.
Change-Id: I8d751f8b09f842a214c305a4530a71d82f16db8f
Signed-off-by: Pablo Tello <pablo.tello@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1943
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Diffstat (limited to 'src/runtime/NEON/functions/NEGenerateProposalsLayer.cpp')
-rw-r--r-- | src/runtime/NEON/functions/NEGenerateProposalsLayer.cpp | 264 |
1 files changed, 264 insertions, 0 deletions
diff --git a/src/runtime/NEON/functions/NEGenerateProposalsLayer.cpp b/src/runtime/NEON/functions/NEGenerateProposalsLayer.cpp new file mode 100644 index 0000000000..6e5da43a94 --- /dev/null +++ b/src/runtime/NEON/functions/NEGenerateProposalsLayer.cpp @@ -0,0 +1,264 @@ +/* + * 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/NEON/functions/NEGenerateProposalsLayer.h" + +#include "arm_compute/core/Types.h" +#include "arm_compute/runtime/NEON/NEScheduler.h" +#include "support/ToolchainSupport.h" + +namespace arm_compute +{ +NEGenerateProposalsLayer::NEGenerateProposalsLayer(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 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 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)); + _memory_group.manage(&_deltas_flattened); + + // Permute and reshape deltas + if(!_is_nhwc) + { + _memory_group.manage(&_deltas_permuted); + _permute_deltas_kernel.configure(deltas, &_deltas_permuted, PermutationVector{ 2, 0, 1 }); + _flatten_deltas_kernel.configure(&_deltas_permuted, &_deltas_flattened); + _deltas_permuted.allocator()->allocate(); + } + else + { + _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)); + _memory_group.manage(&_scores_flattened); + // Permute and reshape scores + if(!_is_nhwc) + { + _memory_group.manage(&_scores_permuted); + _permute_scores_kernel.configure(scores, &_scores_permuted, PermutationVector{ 2, 0, 1 }); + _flatten_scores_kernel.configure(&_scores_permuted, &_scores_flattened); + _scores_permuted.allocator()->allocate(); + } + else + { + _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(scores_nms_size), 1, DataType::U32); + + // Initialize temporaries (unused) outputs + _classes_nms_unused.allocator()->init(TensorInfo(TensorShape(8, 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); + + 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_kernel.configure(&_scores_flattened /*scores_in*/, + &_all_proposals /*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.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 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_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(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(NEReshapeLayerKernel::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(NEReshapeLayerKernel::validate(&scores_permuted_info, &scores_flattened_info)); + 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(NECopyKernel::validate(&proposals_4_roi_values, proposals, PaddingList{ { 0, 1 } })); + + 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 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); + } + + NEScheduler::get().schedule(&_flatten_deltas_kernel, Window::DimY); + NEScheduler::get().schedule(&_flatten_scores_kernel, Window::DimY); + + // Build the boxes + NEScheduler::get().schedule(&_bounding_box_kernel, Window::DimY); + + // Non maxima suppression + CPPScheduler::get().schedule(&_cpp_nms_kernel, Window::DimX); + + // Add dummy batch indexes + + NEScheduler::get().schedule(&_memset_kernel, Window::DimY); + NEScheduler::get().schedule(&_padded_copy_kernel, Window::DimY); +} +} // namespace arm_compute |