[GPU] Add support class agnostic box regression for experimental detectron … (#13003)
* add support class agnostic box regression for experimental detectron detection output * add test
This commit is contained in:
Oleksandr Zhydkov
GitHub
parent
66b5f9acf2
commit
2d4d80a444
@ -79,6 +79,10 @@ JitConstants ExperimentalDetectronDetectionOutputKernelRef::GetJitConstants(
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MakeJitConstant("OUTPUT_INDICES_TYPE", "INPUT4_TYPE"),
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});
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if (params.class_agnostic_box_regression) {
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jit.AddConstant(MakeJitConstant("CLASS_AGNOSTIC_BOX_REGRESSION", true));
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}
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return jit;
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}
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@ -111,7 +115,7 @@ void ExperimentalDetectronDetectionOutputKernelRef::PrepareRefineBoxesKernel(
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const optional_params& options,
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clKernelData& kernel) const {
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const size_t roi_count = params.inputs[kScoresInputIdx].Batch().v;
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const size_t class_count = params.num_classes;
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const size_t class_count = params.class_agnostic_box_regression ? params.num_classes - 1 : params.num_classes;
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PrepareKernelCommon(params, options, {roi_count, class_count, 1}, "EDDO_STAGE_0_REFINE_BOXES", 0, kernel);
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@ -131,7 +131,12 @@ KERNEL(eddo_ref_stage_0)
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__global INPUT_TYPE* refined_scores) {
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const size_t roi_count = get_global_size(0);
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size_t roi_idx = get_global_id(0);
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#ifdef CLASS_AGNOSTIC_BOX_REGRESSION
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size_t class_idx = get_global_id(1) + 1;
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#else
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size_t class_idx = get_global_id(1);
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#endif
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INPUT_TYPE4 box = vload4(roi_idx, boxes);
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@ -160,7 +165,7 @@ KERNEL(eddo_ref_stage_0)
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// adjust new corner locations to be within the image region
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const INPUT_TYPE2 img_size = vload2(0, im_info).s10;
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new_box = clamp(new_box, ZERO4, img_size.xyxy);
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new_box = fmax(new_box, ZERO4);
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// recompute new width & height
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const INPUT_TYPE2 new_box_size = new_box.hi - new_box.lo + COORDINATE_OFFSET;
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@ -175,6 +175,72 @@ TEST_P(experimental_detectron_detection_output_test_f16, basic) {
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template <typename T>
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std::vector<ExperimentalDetectronDetectionOutputParams<T>> getExperimentalDetectronDetectionOutputParams() {
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std::vector<ExperimentalDetectronDetectionOutputParams<T>> params = {
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{
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0.01000000074505806f, // score_threshold
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0.2f, // nms_threshold
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2.0f, // max_delta_log_wh
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2, // num_classes
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500, // post_nms_count
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5, // max_detections_per_image
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true, // class_agnostic_box_regression
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{10.0f, 10.0f, 5.0f, 5.0f}, // deltas_weights
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16, // roi count
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// boxes
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getValues<T>({1.0f, 1.0f, 10.0f, 10.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f, 4.0f, 1.0f, 8.0f, 5.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}),
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// deltas
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getValues<T>(
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{5.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f, 1.0f, 4.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 8.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}),
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// scores
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getValues<T>({1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}),
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// im_info
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getValues<T>({1.0f, 1.0f, 1.0f}),
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// out_boxes
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getValues<T>({0.8929862f,
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0.892986297607421875,
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12.10701370239257812,
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12.10701370239257812,
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0.0f,
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0.0f,
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0.0f,
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0.0f,
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0.0f,
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0.0f,
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0.0f,
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0.0f,
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0.0f,
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0.0f,
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0.0f,
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0.0f,
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0.0f,
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0.0f,
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0.0f,
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0.0}),
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// out_classes
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std::vector<int32_t>{1, 0, 0, 0, 0},
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// out_scores
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getValues<T>({1.0f, 0.0f, 0.0f, 0.0f, 0.0f})
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},
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{
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0.01000000074505806f, // score_threshold
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0.2f, // nms_threshold
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@ -193,6 +259,7 @@ std::vector<ExperimentalDetectronDetectionOutputParams<T>> getExperimentalDetect
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1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}),
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// deltas
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getValues<T>({1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 4.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 8.0f, 1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 5.0f, 1.0f, 1.0f, 1.0f, -1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
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@ -203,16 +270,24 @@ std::vector<ExperimentalDetectronDetectionOutputParams<T>> getExperimentalDetect
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1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}),
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// scores
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getValues<T>({0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.8f, 0.9f, 0.5f,
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0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
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0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f}),
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// im_info
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getValues<T>({16.0f, 12.0f, 1.0f}),
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getValues<T>({4.8929863f, 0.892986298f, 12.0f, 12.1070137f, 0.0f, 0.892986298f, 10.1070137f,
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12.1070137f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
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0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}),
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std::vector<int32_t>{0, 1, 0, 0, 0},
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getValues<T>({0.8f, 0.9f, 0.0f, 0.0f, 0.0f}),
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// out_boxes
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getValues<T>({ 0.0f, 0.892987f, 10.107f, 12.107f, 0.0f, 0.0f, 0.0f, 0.0f,
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0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
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0.0f, 0.0f, 0.0f, 0.0f}),
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// out_classes
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std::vector<int32_t>{1, 0, 0, 0, 0},
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// out_scores
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getValues<T>({0.9f, 0.0f, 0.0f, 0.0f, 0.0f}),
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},
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{
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0.0500000007, // score_threshold
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@ -274,12 +349,12 @@ std::vector<ExperimentalDetectronDetectionOutputParams<T>> getExperimentalDetect
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}),
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// out_boxes
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getValues<T>({
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0, 2.97829, 6.57812, 4.90234, 0, 4.90234, 6.57812, 4.90234, 4.37184, 4.90234,
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6.03075, 4.90234, 5.95093, 3.66966, 6.57812, 4.90234, 0, 4.90234, 6.57812, 4.90234,
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1.31075, 4.90234, 6.57812, 4.90234, 3.24829, 4.90234, 6.57812, 4.90234, 0, 0,
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6.57812, 4.90234, 4.20346, 0, 6.57812, 4.90234, 0, 0, 6.57812, 4.90234,
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}),
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getValues<T>({ 0.0f, 2.97829f, 14.8295f, 11.1221f, 0.0f, 6.29737f, 16.2088f, 16.3451f,
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4.37184f, 6.41816f, 6.03075f, 15.934f, 5.95092f, 3.66966f, 6.81878f, 16.9983f,
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0.0f, 5.64766f, 17.3085f, 12.3716f, 1.31074f, 9.12453f, 13.1104f, 10.6441f,
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3.24828f, 7.11447f, 9.16656f, 10.1058f, 0.0f, 0.0f, 10.0008f, 14.6173f,
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4.20346f, 0.0f, 8.5746f, 18.8736f, 0.0f, 0.0f, 15.661f, 22.4114f}
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),
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// out_classes
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std::vector<int32_t>({
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@ -32,12 +32,16 @@ const std::vector<int64_t> num_classes = {2};
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const std::vector<int64_t> post_nms_count = {5, 25};
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// specifies maximual number of detections per image
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// there is assigning size_t rois_num = attrs.max_detections_per_image at docs/template_plugin/backend/evaluates_map.cpp:2117,
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// as a result we have to set max_detections_per_image equal to rois_num
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const std::vector<size_t> max_detections_per_image16 = {16};
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const std::vector<size_t> max_detections_per_image = {5, 25};
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// a flag specifies whether to delete background classes or not
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// `true` means background classes should be deleted,
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// `false` means background classes shouldn't be deleted.
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const std::vector<bool> class_agnostic_box_regression = {true, false};
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const bool class_agnostic_box_regression_true = true;
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const bool class_agnostic_box_regression_false = false;
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// specifies deltas of weights
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const std::vector<std::vector<float>> deltas_weights = {{10.0f, 10.0f, 5.0f, 5.0f}};
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@ -47,6 +51,37 @@ const std::vector<std::vector<InputShape>> inputShapes = {
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static_shapes_to_test_representation({{16, 4}, {16, 8}, {16, 2}, {1, 3}}),
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};
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INSTANTIATE_TEST_SUITE_P(smoke_ExperimentalDetectronDetectionOutput,
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ExperimentalDetectronDetectionOutputLayerTest,
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::testing::Combine(::testing::ValuesIn(inputShapes),
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::testing::ValuesIn(score_threshold),
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::testing::ValuesIn(nms_threshold),
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::testing::ValuesIn(max_delta_log_wh),
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::testing::ValuesIn(num_classes),
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::testing::ValuesIn(post_nms_count),
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::testing::ValuesIn(max_detections_per_image16),
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::testing::Values(class_agnostic_box_regression_true),
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::testing::ValuesIn(deltas_weights),
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::testing::ValuesIn(netPrecisions),
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::testing::Values(CommonTestUtils::DEVICE_GPU)),
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ExperimentalDetectronDetectionOutputLayerTest::getTestCaseName);
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INSTANTIATE_TEST_SUITE_P(DISABLED_smoke_ExperimentalDetectronDetectionOutputMaxDetectionsPerImage,
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ExperimentalDetectronDetectionOutputLayerTest,
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::testing::Combine(::testing::ValuesIn(inputShapes),
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::testing::ValuesIn(score_threshold),
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::testing::ValuesIn(nms_threshold),
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::testing::ValuesIn(max_delta_log_wh),
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::testing::ValuesIn(num_classes),
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::testing::ValuesIn(post_nms_count),
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::testing::ValuesIn(max_detections_per_image),
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::testing::Values(class_agnostic_box_regression_true),
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::testing::ValuesIn(deltas_weights),
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::testing::ValuesIn(netPrecisions),
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::testing::Values(CommonTestUtils::DEVICE_GPU)),
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ExperimentalDetectronDetectionOutputLayerTest::getTestCaseName);
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INSTANTIATE_TEST_SUITE_P(DISABLED_smoke_ExperimentalDetectronDetectionOutput,
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ExperimentalDetectronDetectionOutputLayerTest,
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::testing::Combine(::testing::ValuesIn(inputShapes),
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@ -56,7 +91,7 @@ INSTANTIATE_TEST_SUITE_P(DISABLED_smoke_ExperimentalDetectronDetectionOutput,
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::testing::ValuesIn(num_classes),
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::testing::ValuesIn(post_nms_count),
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::testing::ValuesIn(max_detections_per_image),
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::testing::ValuesIn(class_agnostic_box_regression),
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::testing::Values(class_agnostic_box_regression_false),
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::testing::ValuesIn(deltas_weights),
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::testing::ValuesIn(netPrecisions),
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::testing::Values(CommonTestUtils::DEVICE_GPU)),
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@ -99,18 +99,34 @@ void ExperimentalDetectronDetectionOutputLayerTest::SetUp() {
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"ExperimentalDetectronDetectionOutput");
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}
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void ExperimentalDetectronDetectionOutputLayerTest::generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) {
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static const std::vector<ov::Tensor> inputTensors = {
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namespace {
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template <typename T>
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std::vector<T> getValues(const std::vector<float>& values) {
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std::vector<T> result(values.begin(), values.end());
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return result;
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}
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template <typename T>
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std::vector<ov::Tensor> generateInputTensors() {
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const auto netPrecision = ov::element::from<T>();
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std::vector<ov::Tensor> inputTensors = {
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// 16 x 4 = 64
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ov::test::utils::create_tensor<float>(ov::element::f32, Shape{16, 4}, {
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ov::test::utils::create_tensor<T>(
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netPrecision,
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Shape{16, 4},
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getValues<T>({
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1.0f, 1.0f, 10.0f, 10.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f, 4.0f, 1.0f, 8.0f, 5.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f
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}),
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})),
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// 16 x 8
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ov::test::utils::create_tensor<float>(ov::element::f32, Shape{16, 8}, {
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ov::test::utils::create_tensor<T>(
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netPrecision,
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Shape{16, 8},
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getValues<T>({
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5.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f, 1.0f, 4.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 8.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
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@ -120,16 +136,29 @@ void ExperimentalDetectronDetectionOutputLayerTest::generate_inputs(const std::v
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1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f
|
||||
}),
|
||||
})),
|
||||
// 16 x 2 = 32
|
||||
ov::test::utils::create_tensor<float>(ov::element::f32, Shape{16, 2}, {
|
||||
ov::test::utils::create_tensor<T>(
|
||||
netPrecision,
|
||||
Shape{16, 2},
|
||||
getValues<T>({
|
||||
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
|
||||
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
|
||||
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f
|
||||
}),
|
||||
})),
|
||||
// 1 x 3 = 3
|
||||
ov::test::utils::create_tensor<float>(ov::element::f32, Shape{1, 3}, {1.0f, 1.0f, 1.0f})
|
||||
};
|
||||
ov::test::utils::create_tensor<T>(netPrecision, Shape{1, 3}, getValues<T>({1.0f, 1.0f, 1.0f}))};
|
||||
|
||||
return inputTensors;
|
||||
}
|
||||
} // namespace
|
||||
|
||||
void ExperimentalDetectronDetectionOutputLayerTest::generate_inputs(
|
||||
const std::vector<ngraph::Shape>& targetInputStaticShapes) {
|
||||
const auto netPrecision = std::get<9>(GetParam());
|
||||
|
||||
const std::vector<ov::Tensor> inputTensors =
|
||||
(netPrecision == element::f16) ? generateInputTensors<ov::float16>() : generateInputTensors<float>();
|
||||
|
||||
inputs.clear();
|
||||
const auto& funcInputs = function->inputs();
|
||||
|
||||
Loading…
Reference in New Issue
Block a user