COMPMID-1329: Add support for GenerateProposals operator in CL
Change-Id: Ib0798cc17496b7817f5b5769b25d98913a33a69d
diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index ccc9aec..fde9608 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -275,6 +275,7 @@
{ "gemmlowp_output_stage_quantize_down", "gemmlowp.cl" },
{ "gemmlowp_output_stage_quantize_down_fixedpoint", "gemmlowp.cl" },
{ "gemmlowp_output_stage_quantize_down_float", "gemmlowp.cl" },
+ { "generate_proposals_compute_all_anchors", "generate_proposals.cl" },
{ "harris_score_3x3", "harris_corners.cl" },
{ "harris_score_5x5", "harris_corners.cl" },
{ "harris_score_7x7", "harris_corners.cl" },
@@ -655,6 +656,10 @@
#include "./cl_kernels/gemv.clembed"
},
{
+ "generate_proposals.cl",
+#include "./cl_kernels/generate_proposals.clembed"
+ },
+ {
"harris_corners.cl",
#include "./cl_kernels/harris_corners.clembed"
},
diff --git a/src/core/CL/cl_kernels/bounding_box_transform.cl b/src/core/CL/cl_kernels/bounding_box_transform.cl
index d330188..14a0fad 100644
--- a/src/core/CL/cl_kernels/bounding_box_transform.cl
+++ b/src/core/CL/cl_kernels/bounding_box_transform.cl
@@ -28,11 +28,11 @@
/** Perform a padded copy of input tensor to the output tensor. Padding values are defined at compile time
*
* @attention The following variables must be passed at compile time:
- * -# -DDATA_TYPE = Tensor data type. Supported data types: F16/F32
+ * -# -DDATA_TYPE= Tensor data type. Supported data types: F16/F32
* -# -DWEIGHT{X,Y,W,H}= Weights [wx, wy, ww, wh] for the deltas
* -# -DIMG_WIDTH= Original image width
* -# -DIMG_HEIGHT= Original image height
- * -# -DBOX_FIELDS=Number of fields that are used to represent a box in boxes
+ * -# -DBOX_FIELDS= Number of fields that are used to represent a box in boxes
*
* @param[in] boxes_ptr Pointer to the boxes tensor. Supported data types: F16/F32
* @param[in] boxes_stride_x Stride of the boxes tensor in X dimension (in bytes)
@@ -97,7 +97,7 @@
// Useful vector constant definitions
const VEC_DATA_TYPE(DATA_TYPE, 4)
- max_values = (VEC_DATA_TYPE(DATA_TYPE, 4))(IMG_WIDTH, IMG_HEIGHT, IMG_WIDTH, IMG_HEIGHT);
+ max_values = (VEC_DATA_TYPE(DATA_TYPE, 4))(IMG_WIDTH - 1, IMG_HEIGHT - 1, IMG_WIDTH - 1, IMG_HEIGHT - 1);
const VEC_DATA_TYPE(DATA_TYPE, 4)
sign = (VEC_DATA_TYPE(DATA_TYPE, 4))(-1, -1, 1, 1);
const VEC_DATA_TYPE(DATA_TYPE, 4)
diff --git a/src/core/CL/cl_kernels/generate_proposals.cl b/src/core/CL/cl_kernels/generate_proposals.cl
new file mode 100644
index 0000000..bc6f4b5
--- /dev/null
+++ b/src/core/CL/cl_kernels/generate_proposals.cl
@@ -0,0 +1,88 @@
+/*
+ * 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 "helpers.h"
+
+/** Generate all the region of interests based on the image size and the anchors passed in. For each element (x,y) of the
+ * grid, it will generate NUM_ANCHORS rois, given by shifting the grid position to match the anchor.
+ *
+ * @attention The following variables must be passed at compile time:
+ * -# -DDATA_TYPE= Tensor data type. Supported data types: F16/F32
+ * -# -DHEIGHT= Height of the feature map on which this kernel is applied
+ * -# -DWIDTH= Width of the feature map on which this kernel is applied
+ * -# -DNUM_ANCHORS= Number of anchors to be used to generate the rois per each pixel
+ * -# -DSTRIDE= Stride to be applied at each different pixel position (i.e., x_range = (1:WIDTH)*STRIDE and y_range = (1:HEIGHT)*STRIDE
+ * -# -DNUM_ROI_FIELDS= Number of fields used to represent a roi
+ *
+ * @param[in] anchors_ptr Pointer to the anchors tensor. Supported data types: F16/F32
+ * @param[in] anchors_stride_x Stride of the anchors tensor in X dimension (in bytes)
+ * @param[in] anchors_step_x anchors_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] anchors_stride_y Stride of the anchors tensor in Y dimension (in bytes)
+ * @param[in] anchors_step_y anchors_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] anchors_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] anchors_step_z anchors_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] anchors_offset_first_element_in_bytes The offset of the first element in the boxes tensor
+ * @param[out] rois_ptr Pointer to the rois. Supported data types: same as @p in_ptr
+ * @param[out] rois_stride_x Stride of the rois in X dimension (in bytes)
+ * @param[out] rois_step_x pred_boxes_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[out] rois_stride_y Stride of the rois in Y dimension (in bytes)
+ * @param[out] rois_step_y pred_boxes_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[out] rois_stride_z Stride of the rois in Z dimension (in bytes)
+ * @param[out] rois_step_z pred_boxes_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[out] rois_offset_first_element_in_bytes The offset of the first element in the rois
+ */
+#if defined(DATA_TYPE) && defined(WIDTH) && defined(HEIGHT) && defined(NUM_ANCHORS) && defined(STRIDE) && defined(NUM_ROI_FIELDS)
+__kernel void generate_proposals_compute_all_anchors(
+ VECTOR_DECLARATION(anchors),
+ VECTOR_DECLARATION(rois))
+{
+ Vector anchors = CONVERT_TO_VECTOR_STRUCT_NO_STEP(anchors);
+ Vector rois = CONVERT_TO_VECTOR_STRUCT(rois);
+
+ const size_t idx = get_global_id(0);
+ // Find the index of the anchor
+ const size_t anchor_idx = idx % NUM_ANCHORS;
+
+ // Find which shift is this thread using
+ const size_t shift_idx = idx / NUM_ANCHORS;
+
+ // Compute the shift on the X and Y direction (the shift depends exclusively by the index thread id)
+ const DATA_TYPE
+ shift_x = (DATA_TYPE)(shift_idx % WIDTH) * STRIDE;
+ const DATA_TYPE
+ shift_y = (DATA_TYPE)(shift_idx / WIDTH) * STRIDE;
+
+ const VEC_DATA_TYPE(DATA_TYPE, NUM_ROI_FIELDS)
+ shift = (VEC_DATA_TYPE(DATA_TYPE, NUM_ROI_FIELDS))(shift_x, shift_y, shift_x, shift_y);
+
+ // Read the given anchor
+ const VEC_DATA_TYPE(DATA_TYPE, NUM_ROI_FIELDS)
+ anchor = vload4(0, (__global DATA_TYPE *)vector_offset(&anchors, anchor_idx * NUM_ROI_FIELDS));
+
+ // Apply the shift to the anchor
+ const VEC_DATA_TYPE(DATA_TYPE, NUM_ROI_FIELDS)
+ shifted_anchor = anchor + shift;
+
+ vstore4(shifted_anchor, 0, (__global DATA_TYPE *)rois.ptr);
+}
+#endif //defined(DATA_TYPE) && defined(WIDTH) && defined(HEIGHT) && defined(NUM_ANCHORS) && defined(STRIDE) && defined(NUM_ROI_FIELDS)
diff --git a/src/core/CL/kernels/CLGenerateProposalsLayerKernel.cpp b/src/core/CL/kernels/CLGenerateProposalsLayerKernel.cpp
new file mode 100644
index 0000000..5d100a4
--- /dev/null
+++ b/src/core/CL/kernels/CLGenerateProposalsLayerKernel.cpp
@@ -0,0 +1,128 @@
+/*
+ * 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/core/CL/kernels/CLGenerateProposalsLayerKernel.h"
+
+#include "arm_compute/core/AccessWindowStatic.h"
+#include "arm_compute/core/CL/CLHelpers.h"
+#include "arm_compute/core/CL/CLKernelLibrary.h"
+#include "arm_compute/core/CL/CLValidate.h"
+#include "arm_compute/core/CL/ICLArray.h"
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/CL/OpenCL.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Window.h"
+
+namespace arm_compute
+{
+namespace
+{
+Status validate_arguments(const ITensorInfo *anchors, const ITensorInfo *all_anchors, const ComputeAnchorsInfo &info)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(anchors, all_anchors);
+ ARM_COMPUTE_RETURN_ERROR_ON(anchors->dimension(0) != info.values_per_roi());
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(anchors, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON(anchors->num_dimensions() > 2);
+ if(all_anchors->total_size() > 0)
+ {
+ size_t feature_height = info.feat_height();
+ size_t feature_width = info.feat_width();
+ size_t num_anchors = anchors->dimension(1);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(all_anchors, anchors);
+ ARM_COMPUTE_RETURN_ERROR_ON(all_anchors->num_dimensions() > 2);
+ ARM_COMPUTE_RETURN_ERROR_ON(all_anchors->dimension(0) != info.values_per_roi());
+ ARM_COMPUTE_RETURN_ERROR_ON(all_anchors->dimension(1) != feature_height * feature_width * num_anchors);
+ }
+ return Status{};
+}
+} // namespace
+
+CLComputeAllAnchorsKernel::CLComputeAllAnchorsKernel()
+ : _anchors(nullptr), _all_anchors(nullptr)
+{
+}
+
+void CLComputeAllAnchorsKernel::configure(const ICLTensor *anchors, ICLTensor *all_anchors, const ComputeAnchorsInfo &info)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(anchors, all_anchors);
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(anchors->info(), all_anchors->info(), info));
+
+ // Metadata
+ const size_t num_anchors = anchors->info()->dimension(1);
+ const DataType data_type = anchors->info()->data_type();
+ const float width = info.feat_width();
+ const float height = info.feat_height();
+
+ // Initialize the output if empty
+ const TensorShape output_shape(info.values_per_roi(), width * height * num_anchors);
+ auto_init_if_empty(*all_anchors->info(), output_shape, 1, data_type);
+
+ // Set instance variables
+ _anchors = anchors;
+ _all_anchors = all_anchors;
+
+ // Set build options
+ CLBuildOptions build_opts;
+ build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
+ build_opts.add_option("-DWIDTH=" + float_to_string_with_full_precision(width));
+ build_opts.add_option("-DHEIGHT=" + float_to_string_with_full_precision(height));
+ build_opts.add_option("-DSTRIDE=" + float_to_string_with_full_precision(1.f / info.spatial_scale()));
+ build_opts.add_option("-DNUM_ANCHORS=" + support::cpp11::to_string(num_anchors));
+ build_opts.add_option("-DNUM_ROI_FIELDS=" + support::cpp11::to_string(info.values_per_roi()));
+
+ // Create kernel
+ _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("generate_proposals_compute_all_anchors", build_opts.options()));
+
+ // The tensor all_anchors can be interpreted as an array of structs (each structs has values_per_roi fields).
+ // This means we don't need to pad on the X dimension, as we know in advance how many fields
+ // compose the struct.
+ Window win = calculate_max_window(*all_anchors->info(), Steps(info.values_per_roi()));
+ ICLKernel::configure_internal(win);
+}
+
+Status CLComputeAllAnchorsKernel::validate(const ITensorInfo *anchors, const ITensorInfo *all_anchors, const ComputeAnchorsInfo &info)
+{
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(anchors, all_anchors, info));
+ return Status{};
+}
+
+void CLComputeAllAnchorsKernel::run(const Window &window, cl::CommandQueue &queue)
+{
+ ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+ ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
+
+ // Collapse everything on the first dimension
+ Window collapsed = window.collapse(ICLKernel::window(), Window::DimX);
+
+ // Set arguments
+ unsigned int idx = 0;
+ add_1D_tensor_argument(idx, _anchors, collapsed);
+ add_1D_tensor_argument(idx, _all_anchors, collapsed);
+
+ // Note that we don't need to loop over the slices, as we are launching exactly
+ // as many threads as all the anchors generated
+ enqueue(queue, *this, collapsed);
+}
+} // namespace arm_compute
diff --git a/src/core/CPP/kernels/CPPBoxWithNonMaximaSuppressionLimitKernel.cpp b/src/core/CPP/kernels/CPPBoxWithNonMaximaSuppressionLimitKernel.cpp
index 89413fc..2b9934c 100644
--- a/src/core/CPP/kernels/CPPBoxWithNonMaximaSuppressionLimitKernel.cpp
+++ b/src/core/CPP/kernels/CPPBoxWithNonMaximaSuppressionLimitKernel.cpp
@@ -54,7 +54,7 @@
areas[i] = (x2[i] - x1[i] + 1.0) * (y2[i] - y1[i] + 1.0);
}
- // Note: Soft NMS scores have already been initialize with input scores
+ // Note: Soft NMS scores have already been initialized with input scores
while(!inds.empty())
{
@@ -150,17 +150,21 @@
for(unsigned int j = 0; j < sorted_indices_temp.size(); ++j)
{
- const auto xx1 = std::max(x1[sorted_indices_temp.at(j)], x1[i]);
- const auto yy1 = std::max(y1[sorted_indices_temp.at(j)], y1[i]);
- const auto xx2 = std::min(x2[sorted_indices_temp.at(j)], x2[i]);
- const auto yy2 = std::min(y2[sorted_indices_temp.at(j)], y2[i]);
+ const float xx1 = std::max(x1[sorted_indices_temp.at(j)], x1[i]);
+ const float yy1 = std::max(y1[sorted_indices_temp.at(j)], y1[i]);
+ const float xx2 = std::min(x2[sorted_indices_temp.at(j)], x2[i]);
+ const float yy2 = std::min(y2[sorted_indices_temp.at(j)], y2[i]);
- const auto w = std::max((xx2 - xx1 + 1.f), 0.f);
- const auto h = std::max((yy2 - yy1 + 1.f), 0.f);
- const auto inter = w * h;
- const auto ovr = inter / (areas[i] + areas[sorted_indices_temp.at(j)] - inter);
+ const float w = std::max((xx2 - xx1 + 1.f), 0.f);
+ const float h = std::max((yy2 - yy1 + 1.f), 0.f);
+ const float inter = w * h;
+ const float ovr = inter / (areas[i] + areas[sorted_indices_temp.at(j)] - inter);
+ const float ctr_x = xx1 + (w / 2);
+ const float ctr_y = yy1 + (h / 2);
- if(ovr <= info.nms())
+ // If suppress_size is specified, filter the boxes based on their size and position
+ const bool keep_size = !info.suppress_size() || (w >= info.min_size() && h >= info.min_size() && ctr_x < info.im_width() && ctr_y < info.im_height());
+ if(ovr <= info.nms() && keep_size)
{
new_indices.push_back(j);
}
@@ -214,8 +218,9 @@
for(int b = 0; b < batch_size; ++b)
{
const int num_boxes = _batch_splits_in == nullptr ? 1 : static_cast<int>(*reinterpret_cast<T *>(_batch_splits_in->ptr_to_element(Coordinates(b))));
- // Skip first class
- for(int j = 1; j < num_classes; ++j)
+ // Skip first class if there is more than 1 except if the number of classes is 1.
+ const int j_start = (num_classes == 1 ? 0 : 1);
+ for(int j = j_start; j < num_classes; ++j)
{
std::vector<T> cur_scores(scores_count);
std::vector<int> inds;
@@ -290,7 +295,7 @@
// Write results
int cur_out_idx = 0;
- for(int j = 1; j < num_classes; ++j)
+ for(int j = j_start; j < num_classes; ++j)
{
auto &cur_keep = keeps[j];
auto cur_out_scores = reinterpret_cast<T *>(_scores_out->ptr_to_element(Coordinates(cur_start_idx + cur_out_idx)));
diff --git a/src/runtime/CL/functions/CLComputeAllAnchors.cpp b/src/runtime/CL/functions/CLComputeAllAnchors.cpp
new file mode 100644
index 0000000..409d3c9
--- /dev/null
+++ b/src/runtime/CL/functions/CLComputeAllAnchors.cpp
@@ -0,0 +1,42 @@
+/*
+ * 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/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 0000000..80ed0e5
--- /dev/null
+++ b/src/runtime/CL/functions/CLGenerateProposalsLayer.cpp
@@ -0,0 +1,251 @@
+/*
+ * 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)
+{
+ 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(values_per_roi, scores_nms_size), 1, data_type);
+
+ // 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(proposals, scores_out, num_valid_proposals);
+
+ 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(proposals->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_NOT_IN(num_valid_proposals, 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