From 5209be567a0a7df4d205d3dc2b971b8f03964593 Mon Sep 17 00:00:00 2001 From: Manuel Bottini Date: Wed, 13 Feb 2019 16:34:56 +0000 Subject: COMPMID-1999: Add support for GenerateProposals operator in CL Change-Id: Ie08a6874347085f96b00f25bdb605eee7d683c25 Signed-off-by: giuros01 Reviewed-on: https://review.mlplatform.org/719 Tested-by: Arm Jenkins Reviewed-by: Georgios Pinitas Reviewed-by: Michalis Spyrou --- src/runtime/CL/functions/CLComputeAllAnchors.cpp | 42 +++ .../CL/functions/CLGenerateProposalsLayer.cpp | 284 +++++++++++++++++++++ 2 files changed, 326 insertions(+) create mode 100644 src/runtime/CL/functions/CLComputeAllAnchors.cpp create mode 100644 src/runtime/CL/functions/CLGenerateProposalsLayer.cpp (limited to 'src/runtime/CL') diff --git a/src/runtime/CL/functions/CLComputeAllAnchors.cpp b/src/runtime/CL/functions/CLComputeAllAnchors.cpp new file mode 100644 index 0000000000..24c152f4d6 --- /dev/null +++ b/src/runtime/CL/functions/CLComputeAllAnchors.cpp @@ -0,0 +1,42 @@ +/* + * 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/CLComputeAllAnchors.h" + +#include "support/ToolchainSupport.h" + +namespace arm_compute +{ +void CLComputeAllAnchors::configure(const ICLTensor *anchors, ICLTensor *all_anchors, const ComputeAnchorsInfo &info) +{ + // Configure ComputeAllAnchors kernel + auto k = arm_compute::support::cpp14::make_unique(); + k->configure(anchors, all_anchors, info); + _kernel = std::move(k); +} + +Status CLComputeAllAnchors::validate(const ITensorInfo *anchors, const ITensorInfo *all_anchors, const ComputeAnchorsInfo &info) +{ + return CLComputeAllAnchorsKernel::validate(anchors, all_anchors, info); +} +} // namespace arm_compute diff --git a/src/runtime/CL/functions/CLGenerateProposalsLayer.cpp b/src/runtime/CL/functions/CLGenerateProposalsLayer.cpp new file mode 100644 index 0000000000..c50132ea04 --- /dev/null +++ b/src/runtime/CL/functions/CLGenerateProposalsLayer.cpp @@ -0,0 +1,284 @@ +/* + * 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 + _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 -- cgit v1.2.1