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Revert "Fix experimental detectron do ref impl (#10621)" (#12683) (#13009)

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* Revert "Fix experimental detectron do ref impl (#10621)"

This reverts commit d87233863d.

* Disabled Experimental Detectron per agreement with GPU team. Ticket to fix it: 90209
This commit is contained in:
Evgenya Stepyreva
2022-09-12 18:16:13 +04:00
committed by GitHub
parent dcc8f926e1
commit af16ea1d79
8 changed files with 247 additions and 266 deletions
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10
docs/template_plugin/backend/evaluates_map.cpp
View File

@@ -2112,11 +2112,11 @@ bool evaluate(const shared_ptr<op::v6::ExperimentalDetectronDetectionOutput>& op
const HostTensorVector& outputs,
const HostTensorVector& inputs) {
const auto attrs = op->get_attrs();
const size_t output_roi_count = attrs.max_detections_per_image;
size_t rois_num = attrs.max_detections_per_image;
const Shape output_boxes_shape = Shape{output_roi_count, 4};
const Shape output_classes_shape = Shape{output_roi_count};
const Shape output_scores_shape = Shape{output_roi_count};
const Shape output_boxes_shape = Shape{rois_num, 4};
const Shape output_classes_shape = Shape{rois_num};
const Shape output_scores_shape = Shape{rois_num};
const auto output_type = op->get_input_element_type(0);
@@ -2124,7 +2124,6 @@ bool evaluate(const shared_ptr<op::v6::ExperimentalDetectronDetectionOutput>& op
const auto input_deltas_data = get_floats(inputs[1], inputs[1]->get_shape());
const auto input_scores_data = get_floats(inputs[2], inputs[2]->get_shape());
const auto input_im_info_data = get_floats(inputs[3], inputs[3]->get_shape());
const auto input_roi_count = inputs[0]->get_shape()[0];
std::vector<float> output_boxes(shape_size(output_boxes_shape));
std::vector<int32_t> output_classes(shape_size(output_classes_shape));
@@ -2141,7 +2140,6 @@ bool evaluate(const shared_ptr<op::v6::ExperimentalDetectronDetectionOutput>& op
input_deltas_data.data(),
input_scores_data.data(),
input_im_info_data.data(),
input_roi_count,
attrs,
output_boxes.data(),
output_scores.data(),

135
docs/template_plugin/tests/functional/op_reference/experimental_detectron_detection_output.cpp
View File

@@ -2,10 +2,9 @@
// SPDX-License-Identifier: Apache-2.0
//
#include "openvino/op/experimental_detectron_detection_output.hpp"
#include <gtest/gtest.h>
#include "openvino/op/experimental_detectron_detection_output.hpp"
#include "base_reference_test.hpp"
using namespace reference_tests;
@@ -19,13 +18,9 @@ struct ExperimentalDOParams {
ExperimentalDOParams(const Attrs& attrs,
const size_t num_rois,
const element::Type& iType,
const std::vector<IT>& roisValues,
const std::vector<IT>& deltasValues,
const std::vector<IT>& scoresValues,
const std::vector<IT>& imageSizeInfoValues,
const std::vector<IT>& refBoxesValues,
const std::vector<int32_t>& refClassesValues,
const std::vector<IT>& refScoresValues,
const std::vector<IT>& roisValues, const std::vector<IT>& deltasValues,
const std::vector<IT>& scoresValues, const std::vector<IT>& imageSizeInfoValues,
const std::vector<IT>& refBoxesValues, const std::vector<int32_t>& refClassesValues, const std::vector<IT>& refScoresValues,
const std::string& testcaseName = "")
: attrs(attrs),
inType(iType),
@@ -38,11 +33,11 @@ struct ExperimentalDOParams {
refClassesData(CreateTensor(ov::element::i32, refClassesValues)),
refScoresData(CreateTensor(iType, refScoresValues)),
testcaseName(testcaseName) {
roisShape = Shape{num_rois, 4};
deltasShape = Shape{num_rois, static_cast<size_t>(attrs.num_classes * 4)};
scoresShape = Shape{num_rois, static_cast<size_t>(attrs.num_classes)};
imageSizeInfoShape = Shape{1, 3};
}
roisShape = Shape{num_rois, 4};
deltasShape = Shape{num_rois, static_cast<size_t>(attrs.num_classes * 4)};
scoresShape = Shape{num_rois, static_cast<size_t>(attrs.num_classes)};
imageSizeInfoShape = Shape{1, 3};
}
Attrs attrs;
PartialShape roisShape;
@@ -61,8 +56,7 @@ struct ExperimentalDOParams {
std::string testcaseName;
};
class ReferenceExperimentalDOLayerTest : public testing::TestWithParam<ExperimentalDOParams>,
public CommonReferenceTest {
class ReferenceExperimentalDOLayerTest : public testing::TestWithParam<ExperimentalDOParams>, public CommonReferenceTest {
public:
void SetUp() override {
auto params = GetParam();
@@ -90,9 +84,12 @@ private:
const auto deltas = std::make_shared<op::v0::Parameter>(params.inType, params.deltasShape);
const auto scores = std::make_shared<op::v0::Parameter>(params.inType, params.scoresShape);
const auto im_info = std::make_shared<op::v0::Parameter>(params.inType, params.imageSizeInfoShape);
const auto ExperimentalDO =
std::make_shared<op::v6::ExperimentalDetectronDetectionOutput>(rois, deltas, scores, im_info, params.attrs);
return std::make_shared<ov::Model>(ExperimentalDO->outputs(), ParameterVector{rois, deltas, scores, im_info});
const auto ExperimentalDO = std::make_shared<op::v6::ExperimentalDetectronDetectionOutput>(rois,
deltas,
scores,
im_info,
params.attrs);
return std::make_shared<ov::Model>(ExperimentalDO->outputs(), ParameterVector {rois, deltas, scores, im_info});
}
};
@@ -104,54 +101,68 @@ template <element::Type_t IN_ET>
std::vector<ExperimentalDOParams> generateExperimentalDOFloatParams() {
using T = typename element_type_traits<IN_ET>::value_type;
std::vector<ExperimentalDOParams> experimentalDOParams{
ExperimentalDOParams(
Attrs{
0.01000000074505806f, // score_threshold
0.2f, // nms_threshold
2.0f, // max_delta_log_wh
2, // num_classes
500, // post_nms_count
5, // max_detections_per_image
true, // class_agnostic_box_regression
{10.0f, 10.0f, 5.0f, 5.0f} // deltas_weights
},
16,
IN_ET,
std::vector<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, 8.0f, 5.0f, 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, 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, 1.0f, 1.0f, 1.0f},
std::vector<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,
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,
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,
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, 1.0f, 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, 1.0f, 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},
std::vector<ExperimentalDOParams> experimentalDOParams {
ExperimentalDOParams(Attrs{0.01000000074505806f, // score_threshold
0.2f, // nms_threshold
2.0f, // max_delta_log_wh
2, // num_classes
500, // post_nms_count
5, // max_detections_per_image
true, // class_agnostic_box_regression
{10.0f, 10.0f, 5.0f, 5.0f} // deltas_weights
},
16,
IN_ET,
std::vector<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,
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,
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, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f},
std::vector<T>{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,
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,
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,
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,
std::vector<T>{0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.8f, 0.9f, 0.5f,
0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f},
std::vector<T>{16.0f, 12.0f, 1.0f},
std::vector<T>{4.8929863f, 0.892986298f, 12.0f, 12.1070137f, 0.0f, 0.892986298f, 10.1070137f,
12.1070137f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f},
std::vector<int32_t>{0, 1, 0, 0, 0},
std::vector<T>{0.8f, 0.9f, 0.0f, 0.0f, 0.0f}),
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, 1.0f,
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, 1.0f},
std::vector<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},
std::vector<T>{1.0f, 1.0f, 1.0f},
std::vector<T>{0.8929862f,
0.892986297607421875,
12.10701370239257812,
12.10701370239257812,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0},
std::vector<int32_t>{1, 0, 0, 0, 0},
std::vector<T>{1.0f, 0.0f, 0.0f, 0.0f, 0.0f}),
};
return experimentalDOParams;
}
std::vector<ExperimentalDOParams> generateExperimentalDOCombinedParams() {
const std::vector<std::vector<ExperimentalDOParams>> ExperimentalDOTypeParams{
const std::vector<std::vector<ExperimentalDOParams>> ExperimentalDOTypeParams {
generateExperimentalDOFloatParams<element::Type_t::f32>(),
generateExperimentalDOFloatParams<element::Type_t::f16>(),
generateExperimentalDOFloatParams<element::Type_t::bf16>(),
};
};
std::vector<ExperimentalDOParams> combinedParams;
for (const auto& params : ExperimentalDOTypeParams) {
@@ -160,8 +171,6 @@ std::vector<ExperimentalDOParams> generateExperimentalDOCombinedParams() {
return combinedParams;
}
INSTANTIATE_TEST_SUITE_P(smoke_ExperimentalDetectronDetectionOutput_With_Hardcoded_Refs,
ReferenceExperimentalDOLayerTest,
testing::ValuesIn(generateExperimentalDOCombinedParams()),
ReferenceExperimentalDOLayerTest::getTestCaseName);
} // namespace
INSTANTIATE_TEST_SUITE_P(smoke_ExperimentalDetectronDetectionOutput_With_Hardcoded_Refs, ReferenceExperimentalDOLayerTest,
testing::ValuesIn(generateExperimentalDOCombinedParams()), ReferenceExperimentalDOLayerTest::getTestCaseName);
} // namespace

1
src/core/reference/include/ngraph/runtime/reference/experimental_detectron_detection_output.hpp
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@@ -34,7 +34,6 @@ void experimental_detectron_detection_output(const float* input_rois,
const float* input_deltas,
const float* input_scores,
const float* input_im_info,
size_t roi_count,
const op::v6::ExperimentalDetectronDetectionOutput::Attributes& attrs,
float* output_boxes,
float* output_scores,

15
src/core/reference/src/runtime/reference/experimental_detectron_detection_output.cpp
View File

@@ -54,7 +54,7 @@ void refine_boxes(const float* boxes,
const float ctr_x = x0 + 0.5f * ww;
const float ctr_y = y0 + 0.5f * hh;
for (int class_idx = 0; class_idx < classes_num; ++class_idx) {
for (int class_idx = 1; class_idx < classes_num; ++class_idx) {
const int64_t deltas_base_offset = classes_num * 4 * roi_idx + 4 * class_idx;
const float dx = deltas[deltas_base_offset + 0] / weights[0];
const float dy = deltas[deltas_base_offset + 1] / weights[1];
@@ -76,10 +76,10 @@ void refine_boxes(const float* boxes,
float y1_new = pred_ctr_y + 0.5f * pred_h - coordinates_offset;
// adjust new corner locations to be within the image region,
x0_new = std::min(std::max(0.0f, x0_new), img_W);
y0_new = std::min(std::max(0.0f, y0_new), img_H);
x1_new = std::min(std::max(0.0f, x1_new), img_W);
y1_new = std::min(std::max(0.0f, y1_new), img_H);
x0_new = std::max<float>(0.0f, x0_new);
y0_new = std::max<float>(0.0f, y0_new);
x1_new = std::max<float>(0.0f, x1_new);
y1_new = std::max<float>(0.0f, y1_new);
// recompute new width & height
const float box_w = x1_new - x0_new + coordinates_offset;
@@ -214,7 +214,6 @@ void experimental_detectron_detection_output(const float* boxes,
const float* input_deltas,
const float* input_scores,
const float* input_im_info,
size_t roi_count,
const op::v6::ExperimentalDetectronDetectionOutput::Attributes& attrs,
float* output_boxes,
float* output_scores,
@@ -222,7 +221,7 @@ void experimental_detectron_detection_output(const float* boxes,
const float img_H = input_im_info[0];
const float img_W = input_im_info[1];
const int64_t classes_num = attrs.num_classes;
const int64_t rois_num = static_cast<int64_t>(roi_count);
const int64_t rois_num = static_cast<int64_t>(attrs.max_detections_per_image);
const int64_t max_detections_per_image = static_cast<int64_t>(attrs.max_detections_per_image);
const int64_t max_detections_per_class = attrs.post_nms_count;
const float score_threshold = attrs.score_threshold;
@@ -256,7 +255,7 @@ void experimental_detectron_detection_output(const float* boxes,
std::vector<int64_t> detections_per_class(classes_num, 0);
int64_t total_detections_num = 0;
for (int64_t class_idx = 0; class_idx < classes_num; ++class_idx) {
for (int64_t class_idx = 1; class_idx < classes_num; ++class_idx) {
nms_cf(&refined_scores[rois_num * class_idx],
&refined_boxes[rois_num * 4 * class_idx],
&refined_boxes_areas[rois_num * class_idx],

70
src/frontends/tests/onnx/onnx_import_org_openvino.in.cpp
View File

@@ -272,34 +272,54 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_model_experimental_detectron_detection_output)
auto test_case = test::TestCase(function, s_device);
// rois
test_case.add_input<float>({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, 8.0f, 5.0f, 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, 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, 1.0f, 1.0f, 1.0f});
test_case.add_input<float>({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,
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,
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, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f});
// deltas
test_case.add_input<float>(
{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, 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, 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, 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, 1.0f, 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, 1.0f, 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});
// scores
test_case.add_input<float>({0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.8f, 0.9f, 0.5f,
0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f});
// im_info
test_case.add_input<float>({16.0f, 12.0f, 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, 1.0f, 1.0f, 1.0f,
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,
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,
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,
test_case.add_expected_output<float>(
Shape{5, 4},
{4.8929863f, 0.892986298f, 12.0f, 12.1070137f, 0.0f, 0.892986298f, 10.1070137f, 12.1070137f, 0.0f, 0.0f,
0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f});
test_case.add_expected_output<int>(Shape{5}, {0, 1, 0, 0, 0});
test_case.add_expected_output<float>(Shape{5}, {0.8f, 0.9f, 0.0f, 0.0f, 0.0f});
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, 1.0f,
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, 1.0f});
// scores
test_case.add_input<float>({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});
// im_info
test_case.add_input<float>({1.0f, 1.0f, 1.0f});
test_case.add_expected_output<float>(Shape{5, 4},
{
0.8929862f,
0.892986297607421875,
12.10701370239257812,
12.10701370239257812,
0,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
});
test_case.add_expected_output<int>(Shape{5}, {1, 0, 0, 0, 0});
test_case.add_expected_output<float>(Shape{5}, {1.0f, 0.0f, 0.0f, 0.0f, 0.0f});
test_case.run();
}

116
src/plugins/intel_cpu/src/nodes/experimental_detectron_detection_output.cpp
View File

@@ -2,13 +2,12 @@
// SPDX-License-Identifier: Apache-2.0
//
#include "experimental_detectron_detection_output.h"
#include <ngraph/op/experimental_detectron_detection_output.hpp>
#include <string>
#include <vector>
#include <ngraph/op/experimental_detectron_detection_output.hpp>
#include "ie_parallel.hpp"
#include "experimental_detectron_detection_output.h"
using namespace InferenceEngine;
@@ -39,19 +38,13 @@ struct Indexer {
}
};
static void refine_boxes(const float* boxes,
const float* deltas,
const float* weights,
const float* scores,
float* refined_boxes,
float* refined_boxes_areas,
float* refined_scores,
const int rois_num,
const int classes_num,
const float img_H,
const float img_W,
const float max_delta_log_wh,
float coordinates_offset) {
static
void refine_boxes(const float* boxes, const float* deltas, const float* weights, const float* scores,
float* refined_boxes, float* refined_boxes_areas, float* refined_scores,
const int rois_num, const int classes_num,
const float img_H, const float img_W,
const float max_delta_log_wh,
float coordinates_offset) {
Indexer box_idx({rois_num, 4});
Indexer delta_idx({rois_num, classes_num, 4});
Indexer score_idx({rois_num, classes_num});
@@ -76,7 +69,7 @@ static void refine_boxes(const float* boxes,
const float ctr_x = x0 + 0.5f * ww;
const float ctr_y = y0 + 0.5f * hh;
for (int class_idx = 0; class_idx < classes_num; ++class_idx) {
for (int class_idx = 1; class_idx < classes_num; ++class_idx) {
const float dx = deltas[delta_idx({roi_idx, class_idx, 0})] / weights[0];
const float dy = deltas[delta_idx({roi_idx, class_idx, 1})] / weights[1];
const float d_log_w = deltas[delta_idx({roi_idx, class_idx, 2})] / weights[2];
@@ -97,10 +90,10 @@ static void refine_boxes(const float* boxes,
float y1_new = pred_ctr_y + 0.5f * pred_h - coordinates_offset;
// adjust new corner locations to be within the image region,
x0_new = std::min(std::max(0.0f, x0_new), img_W);
y0_new = std::min(std::max(0.0f, y0_new), img_H);
x1_new = std::min(std::max(0.0f, x1_new), img_W);
y1_new = std::min(std::max(0.0f, y1_new), img_H);
x0_new = std::max<float>(0.0f, x0_new);
y0_new = std::max<float>(0.0f, y0_new);
x1_new = std::max<float>(0.0f, x1_new);
y1_new = std::max<float>(0.0f, y1_new);
// recompute new width & height
const float box_w = x1_new - x0_new + coordinates_offset;
@@ -123,22 +116,21 @@ static bool SortScorePairDescend(const std::pair<float, std::pair<int, int>>& pa
return (pair1.first > pair2.first) || ((pair1.first == pair2.first) && (pair1.second.second < pair2.second.second));
}
struct ConfidenceComparator {
explicit ConfidenceComparator(const float* conf_data) : _conf_data(conf_data) {}
bool operator()(int idx1, int idx2) {
if (_conf_data[idx1] > _conf_data[idx2])
return true;
if (_conf_data[idx1] < _conf_data[idx2])
return false;
if (_conf_data[idx1] > _conf_data[idx2]) return true;
if (_conf_data[idx1] < _conf_data[idx2]) return false;
return idx1 < idx2;
}
const float* _conf_data;
};
static inline float JaccardOverlap(const float* decoded_bbox,
const float* bbox_sizes,
static inline float JaccardOverlap(const float *decoded_bbox,
const float *bbox_sizes,
const int idx1,
const int idx2,
const float coordinates_offset = 1) {
@@ -161,7 +153,7 @@ static inline float JaccardOverlap(const float* decoded_bbox,
float intersect_xmax = (std::min)(xmax1, xmax2);
float intersect_ymax = (std::min)(ymax1, ymax2);
float intersect_width = intersect_xmax - intersect_xmin + coordinates_offset;
float intersect_width = intersect_xmax - intersect_xmin + coordinates_offset;
float intersect_height = intersect_ymax - intersect_ymin + coordinates_offset;
if (intersect_width <= 0 || intersect_height <= 0) {
@@ -175,6 +167,7 @@ static inline float JaccardOverlap(const float* decoded_bbox,
return intersect_size / (bbox1_size + bbox2_size - intersect_size);
}
static void nms_cf(const float* conf_data,
const float* bboxes,
const float* sizes,
@@ -196,10 +189,8 @@ static void nms_cf(const float* conf_data,
int num_output_scores = (pre_nms_topn == -1 ? count : (std::min)(pre_nms_topn, count));
std::partial_sort_copy(indices,
indices + count,
buffer,
buffer + num_output_scores,
std::partial_sort_copy(indices, indices + count,
buffer, buffer + num_output_scores,
ConfidenceComparator(conf_data));
detections = 0;
@@ -232,13 +223,11 @@ bool ExperimentalDetectronDetectionOutput::needPrepareParams() const {
return false;
}
bool ExperimentalDetectronDetectionOutput::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op,
std::string& errorMessage) noexcept {
bool ExperimentalDetectronDetectionOutput::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
try {
const auto doOp = ngraph::as_type_ptr<const ngraph::op::v6::ExperimentalDetectronDetectionOutput>(op);
if (!doOp) {
errorMessage =
"Node is not an instance of the ExperimentalDetectronDetectionOutput from the operations set v6.";
errorMessage = "Node is not an instance of the ExperimentalDetectronDetectionOutput from the operations set v6.";
return false;
}
} catch (...) {
@@ -247,10 +236,9 @@ bool ExperimentalDetectronDetectionOutput::isSupportedOperation(const std::share
return true;
}
ExperimentalDetectronDetectionOutput::ExperimentalDetectronDetectionOutput(const std::shared_ptr<ngraph::Node>& op,
const dnnl::engine& eng,
WeightsSharing::Ptr& cache)
: Node(op, eng, cache) {
ExperimentalDetectronDetectionOutput::ExperimentalDetectronDetectionOutput
(const std::shared_ptr<ngraph::Node>& op, const dnnl::engine& eng,
WeightsSharing::Ptr &cache) : Node(op, eng, cache) {
std::string errorMessage;
if (!isSupportedOperation(op, errorMessage)) {
IE_THROW(NotImplemented) << errorMessage;
@@ -277,10 +265,11 @@ void ExperimentalDetectronDetectionOutput::initSupportedPrimitiveDescriptors() {
for (int i = 0; i < inputShapes.size(); ++i)
inDataConf.emplace_back(LayoutType::ncsp, Precision::FP32);
addSupportedPrimDesc(
inDataConf,
{{LayoutType::ncsp, Precision::FP32}, {LayoutType::ncsp, Precision::I32}, {LayoutType::ncsp, Precision::FP32}},
impl_desc_type::ref_any);
addSupportedPrimDesc(inDataConf,
{{LayoutType::ncsp, Precision::FP32},
{LayoutType::ncsp, Precision::I32},
{LayoutType::ncsp, Precision::FP32}},
impl_desc_type::ref_any);
}
void ExperimentalDetectronDetectionOutput::execute(dnnl::stream strm) {
@@ -288,14 +277,14 @@ void ExperimentalDetectronDetectionOutput::execute(dnnl::stream strm) {
assert(classes_num_ == static_cast<int>(getParentEdgeAt(INPUT_SCORES)->getMemory().getStaticDims()[1]));
assert(4 * classes_num_ == static_cast<int>(getParentEdgeAt(INPUT_DELTAS)->getMemory().getStaticDims()[1]));
const auto* boxes = reinterpret_cast<const float*>(getParentEdgeAt(INPUT_ROIS)->getMemoryPtr()->GetPtr());
const auto* deltas = reinterpret_cast<const float*>(getParentEdgeAt(INPUT_DELTAS)->getMemoryPtr()->GetPtr());
const auto* scores = reinterpret_cast<const float*>(getParentEdgeAt(INPUT_SCORES)->getMemoryPtr()->GetPtr());
const auto* im_info = reinterpret_cast<const float*>(getParentEdgeAt(INPUT_IM_INFO)->getMemoryPtr()->GetPtr());
const auto* boxes = reinterpret_cast<const float *>(getParentEdgeAt(INPUT_ROIS)->getMemoryPtr()->GetPtr());
const auto* deltas = reinterpret_cast<const float *>(getParentEdgeAt(INPUT_DELTAS)->getMemoryPtr()->GetPtr());
const auto* scores = reinterpret_cast<const float *>(getParentEdgeAt(INPUT_SCORES)->getMemoryPtr()->GetPtr());
const auto* im_info = reinterpret_cast<const float *>(getParentEdgeAt(INPUT_IM_INFO)->getMemoryPtr()->GetPtr());
auto* output_boxes = reinterpret_cast<float*>(getChildEdgesAtPort(OUTPUT_BOXES)[0]->getMemoryPtr()->GetPtr());
auto* output_scores = reinterpret_cast<float*>(getChildEdgesAtPort(OUTPUT_SCORES)[0]->getMemoryPtr()->GetPtr());
auto* output_classes = reinterpret_cast<int32_t*>(getChildEdgesAtPort(OUTPUT_CLASSES)[0]->getMemoryPtr()->GetPtr());
auto* output_boxes = reinterpret_cast<float *>(getChildEdgesAtPort(OUTPUT_BOXES)[0]->getMemoryPtr()->GetPtr());
auto* output_scores = reinterpret_cast<float *>(getChildEdgesAtPort(OUTPUT_SCORES)[0]->getMemoryPtr()->GetPtr());
auto* output_classes = reinterpret_cast<int32_t *>(getChildEdgesAtPort(OUTPUT_CLASSES)[0]->getMemoryPtr()->GetPtr());
const float img_H = im_info[0];
const float img_W = im_info[1];
@@ -307,17 +296,10 @@ void ExperimentalDetectronDetectionOutput::execute(dnnl::stream strm) {
Indexer refined_box_idx({classes_num_, rois_num, 4});
Indexer refined_score_idx({classes_num_, rois_num});
refine_boxes(boxes,
deltas,
&deltas_weights_[0],
scores,
&refined_boxes[0],
&refined_boxes_areas[0],
&refined_scores[0],
rois_num,
classes_num_,
img_H,
img_W,
refine_boxes(boxes, deltas, &deltas_weights_[0], scores,
&refined_boxes[0], &refined_boxes_areas[0], &refined_scores[0],
rois_num, classes_num_,
img_H, img_W,
max_delta_log_wh_,
1.0f);
@@ -327,7 +309,7 @@ void ExperimentalDetectronDetectionOutput::execute(dnnl::stream strm) {
std::vector<int> detections_per_class(classes_num_, 0);
int total_detections_num = 0;
for (int class_idx = 0; class_idx < classes_num_; ++class_idx) {
for (int class_idx = 1; class_idx < classes_num_; ++class_idx) {
nms_cf(&refined_scores[refined_score_idx({class_idx, 0})],
&refined_boxes[refined_box_idx({class_idx, 0, 0})],
&refined_boxes_areas[refined_score_idx({class_idx, 0})],
@@ -373,7 +355,7 @@ void ExperimentalDetectronDetectionOutput::execute(dnnl::stream strm) {
memset(output_classes, 0, max_detections_per_image_ * sizeof(output_classes[0]));
int i = 0;
for (const auto& detection : conf_index_class_map) {
for (const auto & detection : conf_index_class_map) {
float score = detection.first;
int cls = detection.second.first;
int idx = detection.second.second;
@@ -391,6 +373,6 @@ bool ExperimentalDetectronDetectionOutput::created() const {
return getType() == Type::ExperimentalDetectronDetectionOutput;
}
} // namespace node
} // namespace intel_cpu
} // namespace ov
} // namespace node
} // namespace intel_cpu
} // namespace ov

2
src/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/experimental_detectron_detection_output.cpp
View File

@@ -47,7 +47,7 @@ const std::vector<std::vector<InputShape>> inputShapes = {
static_shapes_to_test_representation({{16, 4}, {16, 8}, {16, 2}, {1, 3}}),
};
INSTANTIATE_TEST_SUITE_P(smoke_ExperimentalDetectronDetectionOutput,
INSTANTIATE_TEST_SUITE_P(DISABLED_smoke_ExperimentalDetectronDetectionOutput,
ExperimentalDetectronDetectionOutputLayerTest,
::testing::Combine(::testing::ValuesIn(inputShapes),
::testing::ValuesIn(score_threshold),

164
src/tests/functional/shared_test_classes/src/single_layer/experimental_detectron_detection_output.cpp
View File

@@ -3,19 +3,16 @@
//
#include "shared_test_classes/single_layer/experimental_detectron_detection_output.hpp"
#include <common_test_utils/ov_tensor_utils.hpp>
#include "common_test_utils/data_utils.hpp"
#include "ngraph_functions/builders.hpp"
#include "common_test_utils/data_utils.hpp"
#include <common_test_utils/ov_tensor_utils.hpp>
namespace ov {
namespace test {
namespace subgraph {
namespace {
std::ostream& operator<<(std::ostream& ss,
const ngraph::opset6::ExperimentalDetectronDetectionOutput::Attributes& attributes) {
std::ostream& operator <<(std::ostream& ss, const ngraph::opset6::ExperimentalDetectronDetectionOutput::Attributes& attributes) {
ss << "score_threshold=" << attributes.score_threshold << "_";
ss << "nms_threshold=" << attributes.nms_threshold << "_";
ss << "max_delta_log_wh=" << attributes.max_delta_log_wh << "_";
@@ -26,25 +23,26 @@ std::ostream& operator<<(std::ostream& ss,
ss << "deltas_weights=" << CommonTestUtils::vec2str(attributes.deltas_weights);
return ss;
}
} // namespace
} // namespace
std::string ExperimentalDetectronDetectionOutputLayerTest::getTestCaseName(
const testing::TestParamInfo<ExperimentalDetectronDetectionOutputTestParams>& obj) {
const testing::TestParamInfo<ExperimentalDetectronDetectionOutputTestParams>& obj) {
std::vector<ov::test::InputShape> inputShapes;
ngraph::opset6::ExperimentalDetectronDetectionOutput::Attributes attributes;
ElementType netPrecision;
std::string targetName;
std::tie(inputShapes,
attributes.score_threshold,
attributes.nms_threshold,
attributes.max_delta_log_wh,
attributes.num_classes,
attributes.post_nms_count,
attributes.max_detections_per_image,
attributes.class_agnostic_box_regression,
attributes.deltas_weights,
netPrecision,
targetName) = obj.param;
std::tie(
inputShapes,
attributes.score_threshold,
attributes.nms_threshold,
attributes.max_delta_log_wh,
attributes.num_classes,
attributes.post_nms_count,
attributes.max_detections_per_image,
attributes.class_agnostic_box_regression,
attributes.deltas_weights,
netPrecision,
targetName) = obj.param;
std::ostringstream result;
@@ -67,17 +65,18 @@ void ExperimentalDetectronDetectionOutputLayerTest::SetUp() {
ElementType netPrecision;
std::string targetName;
std::tie(inputShapes,
attributes.score_threshold,
attributes.nms_threshold,
attributes.max_delta_log_wh,
attributes.num_classes,
attributes.post_nms_count,
attributes.max_detections_per_image,
attributes.class_agnostic_box_regression,
attributes.deltas_weights,
netPrecision,
targetName) = this->GetParam();
std::tie(
inputShapes,
attributes.score_threshold,
attributes.nms_threshold,
attributes.max_delta_log_wh,
attributes.num_classes,
attributes.post_nms_count,
attributes.max_detections_per_image,
attributes.class_agnostic_box_regression,
attributes.deltas_weights,
netPrecision,
targetName) = this->GetParam();
if (netPrecision == element::f16)
abs_threshold = 0.01;
@@ -88,74 +87,49 @@ void ExperimentalDetectronDetectionOutputLayerTest::SetUp() {
init_input_shapes(inputShapes);
auto params = ngraph::builder::makeDynamicParams(netPrecision, {inputDynamicShapes});
auto paramsOuts =
ngraph::helpers::convert2OutputVector(ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(params));
auto experimentalDetectron =
std::make_shared<ngraph::opset6::ExperimentalDetectronDetectionOutput>(params[0], // input_rois
params[1], // input_deltas
params[2], // input_scores
params[3], // input_im_info
attributes);
function = std::make_shared<ov::Model>(ov::OutputVector{experimentalDetectron->output(0),
experimentalDetectron->output(1),
experimentalDetectron->output(2)},
"ExperimentalDetectronDetectionOutput");
auto paramsOuts = ngraph::helpers::convert2OutputVector(ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(params));
auto experimentalDetectron = std::make_shared<ngraph::opset6::ExperimentalDetectronDetectionOutput>(
params[0], // input_rois
params[1], // input_deltas
params[2], // input_scores
params[3], // input_im_info
attributes);
function = std::make_shared<ov::Model>(
ov::OutputVector{experimentalDetectron->output(0), experimentalDetectron->output(1)},
"ExperimentalDetectronDetectionOutput");
}
namespace {
template <typename T>
std::vector<T> getValues(const std::vector<float>& values) {
std::vector<T> result(values.begin(), values.end());
return result;
}
template <typename T>
std::vector<ov::Tensor> generateInputTensors() {
const auto netPrecision = ov::element::from<T>();
std::vector<ov::Tensor> inputTensors = {
void ExperimentalDetectronDetectionOutputLayerTest::generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) {
static const std::vector<ov::Tensor> inputTensors = {
// 16 x 4 = 64
ov::test::utils::create_tensor<T>(
netPrecision,
Shape{16, 4},
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, 8.0f, 5.0f, 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, 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, 1.0f, 1.0f, 1.0f})),
ov::test::utils::create_tensor<float>(ov::element::f32, Shape{16, 4}, {
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,
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,
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, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f
}),
// 16 x 8
ov::test::utils::create_tensor<T>(
netPrecision,
Shape{16, 8},
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,
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,
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,
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, 1.0f, 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, 1.0f, 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})),
ov::test::utils::create_tensor<float>(ov::element::f32, Shape{16, 8}, {
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,
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,
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,
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,
1.0f, 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,
1.0f, 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<T>(
netPrecision,
Shape{16, 2},
getValues<T>({0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.8f, 0.9f, 0.5f,
0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f})),
ov::test::utils::create_tensor<float>(ov::element::f32, Shape{16, 2}, {
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<T>(netPrecision, Shape{1, 3}, getValues<T>({16.0f, 12.0f, 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>();
ov::test::utils::create_tensor<float>(ov::element::f32, Shape{1, 3}, {1.0f, 1.0f, 1.0f})
};
inputs.clear();
const auto& funcInputs = function->inputs();
@@ -168,6 +142,6 @@ void ExperimentalDetectronDetectionOutputLayerTest::generate_inputs(
}
}
} // namespace subgraph
} // namespace test
} // namespace ov
} // namespace subgraph
} // namespace test
} // namespace ov