COMPMID-1999: Add support for GenerateProposals operator in CL

Change-Id: Ie08a6874347085f96b00f25bdb605eee7d683c25
Signed-off-by: giuros01 <giuseppe.rossini@arm.com>
Reviewed-on: https://review.mlplatform.org/719
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-by: Michalis Spyrou <michalis.spyrou@arm.com>

2 files changed, 326 insertions, 0 deletions

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<CLComputeAllAnchorsKernel>();
+    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<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 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<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, 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