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[GPU] Fix Experimental Detectron ROI Feature Extractor pyramid levels indexing and Experimental Detectron DO in case of class_agnostic_box_regression option (#14700)

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Sergey Shlyapnikov 2022-12-21 15:11:33 +04:00 committed by GitHub
parent 425a3a1974
commit 901e7982d6
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4 changed files with 44 additions and 10 deletions
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5
src/plugins/intel_gpu/src/kernel_selector/cl_kernels/experimental_detectron_detection_output_ref.cl
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@ -286,8 +286,13 @@ KERNEL(eddo_ref_stage_1)
__global ScoreClassIndex* score_class_index_map,
__global uint* detection_count) {
size_t total_detections_num = 0;
// FIXME: figure out how to parallelize this!!!
#ifdef CLASS_AGNOSTIC_BOX_REGRESSION
for (int class_idx = 1; class_idx < NUM_CLASSES; ++class_idx) {
#else
for (int class_idx = 0; class_idx < NUM_CLASSES; ++class_idx) {
#endif
FUNC_CALL(nms_cf)
(&refined_scores[ROI_COUNT * class_idx],
&refined_boxes[ROI_COUNT * 4 * class_idx],

16
src/plugins/intel_gpu/src/kernel_selector/cl_kernels/experimental_detectron_roi_feature_extractor_ref.cl
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@ -5,6 +5,12 @@
#include "include/batch_headers/common.cl"
#include "include/batch_headers/data_types.cl"
inline int FUNC(get_pyramid_level_index)(uint level, uint c, uint y, uint x) {
uint idx = 0;
LEVELS_IDX_CALC_FUNCS;
return idx;
}
inline int FUNC(get_pyramid_level_for_roi)(const __global INPUT0_TYPE* current_roi) {
const INPUT0_TYPE canonical_scale = 224.0;
const int canonical_level = 2;
@ -63,8 +69,6 @@ KERNEL(experimental_detectron_roi_feature_extractor_ref)(const __global INPUT0_T
const uint level_offset = LEVEL_SIZES[3 * level + 2];
INPUT0_TYPE output_val = 0.0;
const __global INPUT1_TYPE* data = current_level_ptr + level_offset + c * level_h * level_w;
INPUT0_TYPE current_bin_start_h = roi_start_h + y * bin_height;
INPUT0_TYPE current_bin_start_w = roi_start_w + x * bin_width;
for (int iy = 0; iy < roi_bin_grid_h; iy++) {
@ -114,10 +118,10 @@ KERNEL(experimental_detectron_roi_feature_extractor_ref)(const __global INPUT0_T
INPUT0_TYPE w3 = ly * hx;
INPUT0_TYPE w4 = ly * lx;
output_val += w1 * data[y_low * level_w + x_low] +
w2 * data[y_low * level_w + x_high] +
w3 * data[y_high * level_w + x_low] +
w4 * data[y_high * level_w + x_high];
output_val += w1 * current_level_ptr[FUNC_CALL(get_pyramid_level_index)(level, c, y_low, x_low)] +
w2 * current_level_ptr[FUNC_CALL(get_pyramid_level_index)(level, c, y_low, x_high)] +
w3 * current_level_ptr[FUNC_CALL(get_pyramid_level_index)(level, c, y_high, x_low)] +
w4 * current_level_ptr[FUNC_CALL(get_pyramid_level_index)(level, c, y_high, x_high)];
}
}
output_val /= TO_INPUT0_TYPE(roi_bin_grid_h * roi_bin_grid_w);

13
src/plugins/intel_gpu/src/kernel_selector/kernels/ed_rfe/roi_feature_extractor_kernel_ref.cpp
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@ -65,6 +65,16 @@ namespace {
result += "}";
return result;
}
std::string GetIndexCalculationFuncs(size_t levels_num) {
std::string result = "if (level == 0) { idx = INPUT1_GET_INDEX(0, c, y, x); }";
std::string idx = "";
for (size_t i = 1; i < levels_num; i++) {
idx = std::to_string(i + 1);
result += " else if (level == " + std::to_string(i) + ") { idx = INPUT" + idx + "_GET_INDEX(0, c, y, x); }";
}
return result;
}
} // namespace
JitConstants ExperimentalDetectronROIFeatureExtractorRef::GetJitConstants(const experimental_detectron_roi_feature_extractor_params& params) const {
@ -79,7 +89,8 @@ JitConstants ExperimentalDetectronROIFeatureExtractorRef::GetJitConstants(const
MakeJitConstant("INPUT_LEVEL_PARAMS", GetInputLevelParams(levels_num)),
MakeJitConstant("LEVEL_PTRS", GetDefinedLevelPtrs(levels_num)),
MakeJitConstant("SPATIAL_SCALES", GetDefinedSpatialScales(params.pyramid_scales, levels_num)),
MakeJitConstant("LEVEL_SIZES", GetDefinedLevelSizes(levels_num))});
MakeJitConstant("LEVEL_SIZES", GetDefinedLevelSizes(levels_num)),
MakeJitConstant("LEVELS_IDX_CALC_FUNCS", GetIndexCalculationFuncs(levels_num))});
return jit;
}

20
src/plugins/intel_gpu/tests/test_cases/experimental_detectron_roi_feature_extractor_gpu_test.cpp
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@ -122,11 +122,18 @@ TEST(experimental_detectron_roi_feature_extractor_gpu_fp32, two_levels) {
const std::vector<int64_t> pyramid_scales = {4, 224};
const int sampling_ratio = 2;
const bool aligned = false;
auto level_1_layout = layout{data_types::f32, format::bfyx, {1, 2, 3, 2}};
auto level_2_layout = layout{data_types::f32, format::bfyx, {1, 2, 3, 2}};
auto roi_input = engine.allocate_memory({data_types::f32, format::bfyx, tensor(batch(rois_num), feature(rois_feature_dim))});
auto level_1 = engine.allocate_memory({data_types::f32, format::bfyx, {1, 2, 3, 2}});
auto level_2 = engine.allocate_memory({data_types::f32, format::bfyx, {1, 2, 3, 2}});
auto level_1 = engine.allocate_memory(level_1_layout);
auto level_2 = engine.allocate_memory(level_2_layout);
auto second_output = engine.allocate_memory({ data_types::f32, format::bfyx, tensor(batch(rois_num), feature(rois_feature_dim))});
auto level_1_padded_layout = level_1_layout;
level_1_padded_layout.data_padding = padding({0, 0, 1, 1}, {0, 0, 1, 1});
auto level_2_padded_layout = level_2_layout;
level_2_padded_layout.data_padding = padding({0, 1, 1, 1}, {0, 1, 1, 1});
std::vector<float> rois {0.0f, 56.0f, 112.0f, 168.0f, 4.0f, 5.0f, 6.0f, 7.0f};
set_values(roi_input, rois);
set_values(level_1, {0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f});
@ -135,6 +142,8 @@ TEST(experimental_detectron_roi_feature_extractor_gpu_fp32, two_levels) {
const std::string input_rois_id = "InputRois";
const std::string input_level_1_id = "InputLevel1";
const std::string input_level_2_id = "InputLevel2";
const std::string input_level_1_pad_id = "InputLevel1_padding";
const std::string input_level_2_pad_id = "InputLevel2_padding";
const std::string second_output_w_id = "second_output_w";
const std::string second_output_r_id = "second_output_r";
const std::string feature_extractor_id = "experimental_detectron_roi_feature_extractor";
@ -143,9 +152,14 @@ TEST(experimental_detectron_roi_feature_extractor_gpu_fp32, two_levels) {
topology.add(input_layout(input_rois_id, roi_input->get_layout()));
topology.add(input_layout(input_level_1_id, level_1->get_layout()));
topology.add(input_layout(input_level_2_id, level_2->get_layout()));
topology.add(reorder(input_level_1_pad_id, input_info(input_level_1_id), level_1_padded_layout));
topology.add(reorder(input_level_2_pad_id, input_info(input_level_2_id), level_2_padded_layout));
topology.add(mutable_data(second_output_w_id, second_output));
topology.add(experimental_detectron_roi_feature_extractor(feature_extractor_id,
{ input_info(input_rois_id), input_info(input_level_1_id), input_info(input_level_2_id), input_info(second_output_w_id) },
{ input_info(input_rois_id),
input_info(input_level_1_pad_id),
input_info(input_level_2_pad_id),
input_info(second_output_w_id) },
output_dim,
pyramid_scales,
sampling_ratio,