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/core/CL/CLKernelLibrary.cpp | 5 + src/core/CL/cl_kernels/bounding_box_transform.cl | 4 +- src/core/CL/cl_kernels/generate_proposals.cl | 88 ++++++++++++++ .../CL/kernels/CLGenerateProposalsLayerKernel.cpp | 128 +++++++++++++++++++++ .../CPPBoxWithNonMaximaSuppressionLimitKernel.cpp | 35 +++--- 5 files changed, 243 insertions(+), 17 deletions(-) create mode 100644 src/core/CL/cl_kernels/generate_proposals.cl create mode 100644 src/core/CL/kernels/CLGenerateProposalsLayerKernel.cpp (limited to 'src/core') diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp index a7d371dabc..4ecb885440 100644 --- a/src/core/CL/CLKernelLibrary.cpp +++ b/src/core/CL/CLKernelLibrary.cpp @@ -307,6 +307,7 @@ const std::map CLKernelLibrary::_kernel_program_map = { "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" }, @@ -704,6 +705,10 @@ const std::map CLKernelLibrary::_program_source_map = { "gemv.cl", #include "./cl_kernels/gemv.clembed" + }, + { + "generate_proposals.cl", +#include "./cl_kernels/generate_proposals.clembed" }, { "harris_corners.cl", diff --git a/src/core/CL/cl_kernels/bounding_box_transform.cl b/src/core/CL/cl_kernels/bounding_box_transform.cl index 77db5d9311..e6f470a962 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) 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 0000000000..a947dad523 --- /dev/null +++ b/src/core/CL/cl_kernels/generate_proposals.cl @@ -0,0 +1,88 @@ +/* + * 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 "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 0000000000..f16422f815 --- /dev/null +++ b/src/core/CL/kernels/CLGenerateProposalsLayerKernel.cpp @@ -0,0 +1,128 @@ +/* + * 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/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(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 5e4b80aa5a..02150ff275 100644 --- a/src/core/CPP/kernels/CPPBoxWithNonMaximaSuppressionLimitKernel.cpp +++ b/src/core/CPP/kernels/CPPBoxWithNonMaximaSuppressionLimitKernel.cpp @@ -54,7 +54,7 @@ std::vector SoftNMS(const ITensor *proposals, std::vector> & 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 @@ std::vector NonMaximaSuppression(const ITensor *proposals, std::vector 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 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); - - if(ovr <= info.nms()) + 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 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 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 @@ void CPPBoxWithNonMaximaSuppressionLimitKernel::run_nmslimit() for(int b = 0; b < batch_size; ++b) { const int num_boxes = _batch_splits_in == nullptr ? 1 : static_cast(*reinterpret_cast(_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 cur_scores(scores_count); std::vector inds; @@ -290,7 +295,7 @@ void CPPBoxWithNonMaximaSuppressionLimitKernel::run_nmslimit() // 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(_scores_out->ptr_to_element(Coordinates(cur_start_idx + cur_out_idx))); -- cgit v1.2.1