arm_compute v18.11
diff --git a/src/runtime/CL/functions/CLGenerateProposalsLayer.cpp b/src/runtime/CL/functions/CLGenerateProposalsLayer.cpp
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+++ b/src/runtime/CL/functions/CLGenerateProposalsLayer.cpp
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+/*
+ * Copyright (c) 2018 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<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(),
+ _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));
+
+ const DataType data_type = deltas->info()->data_type();
+ const int num_anchors = scores->info()->dimension(2);
+ const int feat_width = scores->info()->dimension(0);
+ const int feat_height = scores->info()->dimension(1);
+ 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
+ _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();
+
+ const TensorShape flatten_shape_scores(1, total_num_anchors);
+ _scores_flattened.allocator()->init(TensorInfo(flatten_shape_scores, 1, data_type));
+
+ // Permute and reshape scores
+ _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();
+
+ // 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(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);
+
+ const int num_anchors = scores->dimension(2);
+ const int feat_width = scores->dimension(0);
+ const int feat_height = scores->dimension(1);
+ 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);
+ ARM_COMPUTE_RETURN_ON_ERROR(CLPermuteKernel::validate(deltas, &deltas_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_permuted_info = scores->clone()->set_tensor_shape(TensorShape(num_anchors, feat_width, feat_height)).set_is_resizable(true);
+ ARM_COMPUTE_RETURN_ON_ERROR(CLPermuteKernel::validate(scores, &scores_permuted_info, PermutationVector{ 2, 0, 1 }));
+
+ 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
+ CLScheduler::get().enqueue(_permute_deltas_kernel, false);
+ CLScheduler::get().enqueue(_flatten_deltas_kernel, false);
+ CLScheduler::get().enqueue(_permute_scores_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