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openvino/ngraph/test/backend/detection_output.in.cpp
Ilya Churaev faeaf045a9 Graph comparator to ngraph util (#7729) 
* Moved FrameworkNode to nGraph

* Moved graph comparator to ngraph test util

* Fixed build

* Try to fix centos

* Fix export target

* Moved engine utils to separate library

* Removed ONNX util from common library

* Fixed build

* Fixed code style
2021-10-01 07:24:28 +03:00

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// Copyright (C) 2018-2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
// clang-format off
#ifdef ${BACKEND_NAME}_FLOAT_TOLERANCE_BITS
#define DEFAULT_FLOAT_TOLERANCE_BITS ${BACKEND_NAME}_FLOAT_TOLERANCE_BITS
#endif
#ifdef ${BACKEND_NAME}_DOUBLE_TOLERANCE_BITS
#define DEFAULT_DOUBLE_TOLERANCE_BITS ${BACKEND_NAME}_DOUBLE_TOLERANCE_BITS
#endif
// clang-format on
#include "gtest/gtest.h"
#include "ngraph/ngraph.hpp"
#include "engines_util/test_engines.hpp"
#include "engines_util/test_case.hpp"
#include "util/test_control.hpp"
using namespace std;
using namespace ngraph;
static string s_manifest = "${MANIFEST}";
using TestEngine = test::ENGINE_CLASS_NAME(${BACKEND_NAME});
NGRAPH_TEST(${BACKEND_NAME}, detection_output_3_inputs) {
op::DetectionOutputAttrs attrs;
attrs.num_classes = 3;
attrs.background_label_id = -1;
attrs.top_k = -1;
attrs.variance_encoded_in_target = true;
attrs.keep_top_k = {2};
attrs.code_type = "caffe.PriorBoxParameter.CORNER";
attrs.share_location = false;
attrs.nms_threshold = 0.5;
attrs.confidence_threshold = 0.3;
attrs.clip_after_nms = false;
attrs.clip_before_nms = true;
attrs.decrease_label_id = false;
attrs.normalized = true;
attrs.input_height = 0;
attrs.input_width = 0;
attrs.objectness_score = 0;
size_t num_prior_boxes = 2;
size_t num_loc_classes = attrs.share_location ? 1 : attrs.num_classes;
size_t prior_box_size = attrs.normalized ? 4 : 5;
size_t num_images = 2;
Shape loc_shape{num_images, num_prior_boxes * num_loc_classes * prior_box_size};
Shape conf_shape{num_images, num_prior_boxes * attrs.num_classes};
Shape prior_boxes_shape{1, attrs.variance_encoded_in_target ? 1UL : 2UL, num_prior_boxes * prior_box_size};
auto loc = make_shared<op::Parameter>(element::f32, loc_shape);
auto conf = make_shared<op::Parameter>(element::f32, conf_shape);
auto prior_boxes = make_shared<op::Parameter>(element::f32, prior_boxes_shape);
auto f = make_shared<Function>(make_shared<op::DetectionOutput>(loc, conf, prior_boxes, attrs),
ParameterVector{loc, conf, prior_boxes});
auto test_case = test::TestCase<TestEngine>(f);
// locations
test_case.add_input<float>({
// batch 0, class 0
0.1,
0.1,
0.2,
0.2,
0.0,
0.1,
0.2,
0.15,
// batch 0, class 1
0.3,
0.2,
0.5,
0.3,
0.2,
0.1,
0.42,
0.66,
// batch 0, class 2
0.05,
0.1,
0.2,
0.3,
0.2,
0.1,
0.33,
0.44,
// batch 1, class 0
0.2,
0.1,
0.4,
0.2,
0.1,
0.05,
0.2,
0.25,
// batch 1, class 1
0.1,
0.2,
0.5,
0.3,
0.1,
0.1,
0.12,
0.34,
// batch 1, class 2
0.25,
0.11,
0.4,
0.32,
0.2,
0.12,
0.38,
0.24,
});
test_case.add_input<float>({
// batch 0
0.1,
0.9,
0.4,
0.7,
0,
0.2,
// batch 1
0.7,
0.8,
0.42,
0.33,
0.81,
0.2,
});
test_case.add_input<float>({
// prior box 0
0.0,
0.5,
0.1,
0.2,
// prior box 1
0.0,
0.3,
0.1,
0.35,
});
Shape output_shape{1, 1, num_images * static_cast<size_t>(attrs.keep_top_k[0]), 7};
test_case.add_expected_output<float>(output_shape,
{0, 0, 0.7, 0.2, 0.4, 0.52, 1, 0, 1, 0.9, 0, 0.6, 0.3, 0.35,
1, 1, 0.81, 0.25, 0.41, 0.5, 0.67, 1, 1, 0.8, 0.1, 0.55, 0.3, 0.45});
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, detection_output_3_inputs_share_location) {
op::DetectionOutputAttrs attrs;
attrs.num_classes = 3;
attrs.background_label_id = -1;
attrs.top_k = -1;
attrs.variance_encoded_in_target = true;
attrs.keep_top_k = {2};
attrs.code_type = "caffe.PriorBoxParameter.CORNER";
attrs.share_location = true;
attrs.nms_threshold = 0.5;
attrs.confidence_threshold = 0.3;
attrs.clip_after_nms = false;
attrs.clip_before_nms = true;
attrs.decrease_label_id = false;
attrs.normalized = true;
attrs.input_height = 0;
attrs.input_width = 0;
attrs.objectness_score = 0;
size_t num_prior_boxes = 2;
size_t num_loc_classes = attrs.share_location ? 1 : attrs.num_classes;
size_t prior_box_size = attrs.normalized ? 4 : 5;
size_t num_images = 2;
Shape loc_shape{num_images, num_prior_boxes * num_loc_classes * prior_box_size};
Shape conf_shape{num_images, num_prior_boxes * attrs.num_classes};
Shape prior_boxes_shape{num_images, attrs.variance_encoded_in_target ? 1UL : 2UL, num_prior_boxes * prior_box_size};
auto loc = make_shared<op::Parameter>(element::f32, loc_shape);
auto conf = make_shared<op::Parameter>(element::f32, conf_shape);
auto prior_boxes = make_shared<op::Parameter>(element::f32, prior_boxes_shape);
auto f = make_shared<Function>(make_shared<op::DetectionOutput>(loc, conf, prior_boxes, attrs),
ParameterVector{loc, conf, prior_boxes});
auto test_case = test::TestCase<TestEngine>(f);
// locations
test_case.add_input<float>({
// batch 0
0.1,
0.1,
0.2,
0.2,
0.0,
0.1,
0.2,
0.15,
// batch 1
0.2,
0.1,
0.4,
0.2,
0.1,
0.05,
0.2,
0.25,
});
test_case.add_input<float>({
// batch 0
0.1,
0.9,
0.4,
0.7,
0,
0.2,
// batch 1
0.7,
0.8,
0.42,
0.33,
0.81,
0.2,
});
test_case.add_input<float>({
// batch 0
0.0,
0.5,
0.1,
0.2,
0.0,
0.3,
0.1,
0.35,
// batch 1
0.33,
0.2,
0.52,
0.37,
0.22,
0.1,
0.32,
0.36,
});
Shape output_shape{1, 1, num_images * static_cast<size_t>(attrs.keep_top_k[0]), 7};
test_case.add_expected_output<float>(output_shape,
{
0, 0, 0.7, 0, 0.4, 0.3, 0.5, 0, 1, 0.9, 0.1, 0.6, 0.3, 0.4,
1, 1, 0.81, 0.32, 0.15, 0.52, 0.61, 1, 1, 0.8, 0.53, 0.3, 0.92, 0.57,
});
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, detection_output_3_inputs_normalized) {
op::DetectionOutputAttrs attrs;
attrs.num_classes = 3;
attrs.background_label_id = -1;
attrs.top_k = -1;
attrs.variance_encoded_in_target = true;
attrs.keep_top_k = {2};
attrs.code_type = "caffe.PriorBoxParameter.CORNER";
attrs.share_location = true;
attrs.nms_threshold = 0.5;
attrs.confidence_threshold = 0.3;
attrs.clip_after_nms = false;
attrs.clip_before_nms = true;
attrs.decrease_label_id = false;
attrs.normalized = true;
attrs.input_height = 0;
attrs.input_width = 0;
attrs.objectness_score = 0;
size_t num_prior_boxes = 2;
size_t num_loc_classes = attrs.share_location ? 1 : attrs.num_classes;
size_t prior_box_size = attrs.normalized ? 4 : 5;
size_t num_images = 2;
Shape loc_shape{num_images, num_prior_boxes * num_loc_classes * prior_box_size};
Shape conf_shape{num_images, num_prior_boxes * attrs.num_classes};
Shape prior_boxes_shape{num_images, attrs.variance_encoded_in_target ? 1UL : 2UL, num_prior_boxes * prior_box_size};
auto loc = make_shared<op::Parameter>(element::f32, loc_shape);
auto conf = make_shared<op::Parameter>(element::f32, conf_shape);
auto prior_boxes = make_shared<op::Parameter>(element::f32, prior_boxes_shape);
auto f = make_shared<Function>(make_shared<op::DetectionOutput>(loc, conf, prior_boxes, attrs),
ParameterVector{loc, conf, prior_boxes});
auto test_case = test::TestCase<TestEngine>(f);
// locations
test_case.add_input<float>({
// batch 0
0.1,
0.1,
0.2,
0.2,
0.0,
0.1,
0.2,
0.15,
// batch 1
0.2,
0.1,
0.4,
0.2,
0.1,
0.05,
0.2,
0.25,
});
test_case.add_input<float>({
// batch 0
0.1,
0.9,
0.4,
0.7,
0,
0.2,
// batch 1
0.7,
0.8,
0.42,
0.33,
0.81,
0.2,
});
test_case.add_input<float>({
// batch 0
0.0,
0.5,
0.1,
0.2,
0.0,
0.3,
0.1,
0.35,
// batch 1
0.33,
0.2,
0.52,
0.37,
0.22,
0.1,
0.32,
0.36,
});
Shape output_shape{1, 1, num_images * static_cast<size_t>(attrs.keep_top_k[0]), 7};
test_case.add_expected_output<float>(output_shape,
{
0, 0, 0.7, 0, 0.4, 0.3, 0.5, 0, 1, 0.9, 0.1, 0.6, 0.3, 0.4,
1, 1, 0.81, 0.32, 0.15, 0.52, 0.61, 1, 1, 0.8, 0.53, 0.3, 0.92, 0.57,
});
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, detection_output_3_inputs_keep_all_bboxes) {
op::DetectionOutputAttrs attrs;
attrs.num_classes = 2;
attrs.background_label_id = -1;
attrs.top_k = -1;
attrs.variance_encoded_in_target = false;
attrs.keep_top_k = {-1};
attrs.code_type = "caffe.PriorBoxParameter.CORNER";
attrs.share_location = false;
attrs.nms_threshold = 0.5;
attrs.confidence_threshold = 0.3;
attrs.clip_after_nms = false;
attrs.clip_before_nms = true;
attrs.decrease_label_id = false;
attrs.normalized = true;
attrs.input_height = 0;
attrs.input_width = 0;
attrs.objectness_score = 0;
size_t num_prior_boxes = 2;
size_t num_loc_classes = attrs.share_location ? 1 : attrs.num_classes;
size_t prior_box_size = attrs.normalized ? 4 : 5;
size_t num_images = 3;
Shape loc_shape{num_images, num_prior_boxes * num_loc_classes * prior_box_size};
Shape conf_shape{num_images, num_prior_boxes * attrs.num_classes};
Shape prior_boxes_shape{num_images, attrs.variance_encoded_in_target ? 1UL : 2UL, num_prior_boxes * prior_box_size};
auto loc = make_shared<op::Parameter>(element::f32, loc_shape);
auto conf = make_shared<op::Parameter>(element::f32, conf_shape);
auto prior_boxes = make_shared<op::Parameter>(element::f32, prior_boxes_shape);
auto f = make_shared<Function>(make_shared<op::DetectionOutput>(loc, conf, prior_boxes, attrs),
ParameterVector{loc, conf, prior_boxes});
auto test_case = test::TestCase<TestEngine>(f);
// locations
test_case.add_input<float>({
// batch 0, class 0
0.1,
0.1,
0.2,
0.2,
0.0,
0.1,
0.2,
0.15,
// batch 0, class 1
0.3,
0.2,
0.5,
0.3,
0.2,
0.1,
0.42,
0.66,
// batch 1, class 0
0.05,
0.1,
0.2,
0.3,
0.2,
0.1,
0.33,
0.44,
// batch 1, class 1
0.2,
0.1,
0.4,
0.2,
0.1,
0.05,
0.2,
0.25,
// batch 2, class 0
0.1,
0.2,
0.5,
0.3,
0.1,
0.1,
0.12,
0.34,
// batch 2, class 1
0.25,
0.11,
0.4,
0.32,
0.2,
0.12,
0.38,
0.24,
});
test_case.add_input<float>({
// batch 0
0.1,
0.9,
0.4,
0.7,
// batch 1
0.7,
0.8,
0.42,
0.33,
// batch 1
0.1,
0.2,
0.32,
0.43,
});
test_case.add_input<float>({
// batch 0 priors
0.0,
0.5,
0.1,
0.2,
0.0,
0.3,
0.1,
0.35,
// batch 0 variances
0.12,
0.11,
0.32,
0.02,
0.02,
0.20,
0.09,
0.71,
// batch 1 priors
0.33,
0.2,
0.52,
0.37,
0.22,
0.1,
0.32,
0.36,
// batch 1 variances
0.01,
0.07,
0.12,
0.13,
0.41,
0.33,
0.2,
0.1,
// batch 2 priors
0.0,
0.3,
0.1,
0.35,
0.22,
0.1,
0.32,
0.36,
// batch 2 variances
0.32,
0.02,
0.13,
0.41,
0.33,
0.2,
0.02,
0.20,
});
Shape output_shape{1, 1, num_images * attrs.num_classes * num_prior_boxes, 7};
test_case.add_expected_output<float>(
output_shape,
{
0, 0, 0.4, 0.006, 0.34, 0.145, 0.563, 0, 1, 0.9, 0, 0.511, 0.164, 0.203,
0, 1, 0.7, 0.004, 0.32, 0.1378, 0.8186, 1, 0, 0.7, 0.3305, 0.207, 0.544, 0.409,
1, 0, 0.42, 0.302, 0.133, 0.4, 0.38, 1, 1, 0.8, 0.332, 0.207, 0.5596, 0.4272,
1, 1, 0.33, 0.261, 0.1165, 0.36, 0.385, 2, 0, 0.32, 0.3025, 0.122, 0.328, 0.424,
2, 1, 0.43, 0.286, 0.124, 0.3276, 0.408, -1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
});
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, detection_output_3_inputs_center_size) {
op::DetectionOutputAttrs attrs;
attrs.num_classes = 3;
attrs.background_label_id = -1;
attrs.top_k = -1;
attrs.variance_encoded_in_target = true;
attrs.keep_top_k = {2};
attrs.code_type = "caffe.PriorBoxParameter.CENTER_SIZE";
attrs.share_location = false;
attrs.nms_threshold = 0.5;
attrs.confidence_threshold = 0.3;
attrs.clip_after_nms = false;
attrs.clip_before_nms = true;
attrs.decrease_label_id = false;
attrs.normalized = true;
attrs.input_height = 0;
attrs.input_width = 0;
attrs.objectness_score = 0;
size_t num_prior_boxes = 2;
size_t num_loc_classes = attrs.share_location ? 1 : attrs.num_classes;
size_t prior_box_size = attrs.normalized ? 4 : 5;
size_t num_images = 2;
Shape loc_shape{num_images, num_prior_boxes * num_loc_classes * prior_box_size};
Shape conf_shape{num_images, num_prior_boxes * attrs.num_classes};
Shape prior_boxes_shape{num_images, attrs.variance_encoded_in_target ? 1UL : 2UL, num_prior_boxes * prior_box_size};
auto loc = make_shared<op::Parameter>(element::f32, loc_shape);
auto conf = make_shared<op::Parameter>(element::f32, conf_shape);
auto prior_boxes = make_shared<op::Parameter>(element::f32, prior_boxes_shape);
auto f = make_shared<Function>(make_shared<op::DetectionOutput>(loc, conf, prior_boxes, attrs),
ParameterVector{loc, conf, prior_boxes});
auto test_case = test::TestCase<TestEngine>(f);
// locations
test_case.add_input<float>({
// batch 0, class 0
0.1,
0.1,
0.2,
0.2,
0.0,
0.1,
0.2,
0.15,
// batch 0, class 1
0.3,
0.2,
0.5,
0.3,
0.2,
0.1,
0.42,
0.66,
// batch 0, class 2
0.05,
0.1,
0.2,
0.3,
0.2,
0.1,
0.33,
0.44,
// batch 1, class 0
0.2,
0.1,
0.4,
0.2,
0.1,
0.05,
0.2,
0.25,
// batch 1, class 1
0.1,
0.2,
0.5,
0.3,
0.1,
0.1,
0.12,
0.34,
// batch 1, class 2
0.25,
0.11,
0.4,
0.32,
0.2,
0.12,
0.38,
0.24,
});
test_case.add_input<float>({
// batch 0
0.1,
0.9,
0.4,
0.7,
0,
0.2,
// batch 1
0.7,
0.8,
0.42,
0.33,
0.81,
0.2,
});
test_case.add_input<float>({
// batch 0
0.0,
0.5,
0.1,
0.2,
0.0,
0.3,
0.1,
0.35,
// batch 1
0.33,
0.2,
0.52,
0.37,
0.22,
0.1,
0.32,
0.36,
});
Shape output_shape{1, 1, num_images * static_cast<size_t>(attrs.keep_top_k[0]), 7};
test_case.add_expected_output<float>(output_shape,
{
0, 0, 0.7, 0, 0.28163019, 0.14609808, 0.37836978,
0, 1, 0.9, 0, 0.49427515, 0.11107014, 0.14572485,
1, 1, 0.81, 0.22040875, 0.079573378, 0.36959124, 0.4376266,
1, 1, 0.8, 0.32796675, 0.18435785, 0.56003326, 0.40264216,
});
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, detection_output_5_inputs) {
op::DetectionOutputAttrs attrs;
attrs.num_classes = 2;
attrs.background_label_id = -1;
attrs.top_k = -1;
attrs.variance_encoded_in_target = true;
attrs.keep_top_k = {2};
attrs.code_type = "caffe.PriorBoxParameter.CORNER";
attrs.share_location = false;
attrs.nms_threshold = 0.5;
attrs.confidence_threshold = 0.3;
attrs.clip_after_nms = false;
attrs.clip_before_nms = true;
attrs.decrease_label_id = false;
attrs.normalized = true;
attrs.input_height = 0;
attrs.input_width = 0;
attrs.objectness_score = 0.6;
size_t num_prior_boxes = 2;
size_t num_loc_classes = attrs.share_location ? 1 : attrs.num_classes;
size_t prior_box_size = attrs.normalized ? 4 : 5;
size_t num_images = 2;
Shape loc_shape{num_images, num_prior_boxes * num_loc_classes * prior_box_size};
Shape conf_shape{num_images, num_prior_boxes * attrs.num_classes};
Shape prior_boxes_shape{num_images, attrs.variance_encoded_in_target ? 1UL : 2UL, num_prior_boxes * prior_box_size};
auto loc = make_shared<op::Parameter>(element::f32, loc_shape);
auto conf = make_shared<op::Parameter>(element::f32, conf_shape);
auto prior_boxes = make_shared<op::Parameter>(element::f32, prior_boxes_shape);
auto aux_loc = make_shared<op::Parameter>(element::f32, loc_shape);
auto aux_conf = make_shared<op::Parameter>(element::f32, conf_shape);
auto f = make_shared<Function>(make_shared<op::DetectionOutput>(loc, conf, prior_boxes, aux_conf, aux_loc, attrs),
ParameterVector{loc, conf, prior_boxes, aux_conf, aux_loc});
auto test_case = test::TestCase<TestEngine>(f);
// locations
test_case.add_input<float>({
// batch 0, class 0
0.1,
0.1,
0.2,
0.2,
0.0,
0.1,
0.2,
0.15,
// batch 0, class 1
0.3,
0.2,
0.5,
0.3,
0.2,
0.1,
0.42,
0.66,
// batch 1, class 0
0.2,
0.1,
0.4,
0.2,
0.1,
0.05,
0.2,
0.25,
// batch 1, class 1
0.1,
0.2,
0.5,
0.3,
0.1,
0.1,
0.12,
0.34,
});
// confidence
test_case.add_input<float>({
// batch 0
0.1,
0.9,
0.4,
0.7,
// batch 1
0.42,
0.33,
0.81,
0.2,
});
// prior boxes
test_case.add_input<float>({
// batch 0
0.0,
0.5,
0.1,
0.2,
0.0,
0.3,
0.1,
0.35,
// batch 1
0.33,
0.2,
0.52,
0.37,
0.22,
0.1,
0.32,
0.36,
});
// aux conf
test_case.add_input<float>({
// batch 0
0.1,
0.3,
0.5,
0.8,
// batch 1
0.5,
0.8,
0.01,
0.1,
});
// aux locations
test_case.add_input<float>({
// batch 0, class 0
0.1,
0.2,
0.5,
0.3,
0.1,
0.1,
0.12,
0.34,
// batch 0, class 1
0.25,
0.11,
0.4,
0.32,
0.2,
0.12,
0.38,
0.24,
// batch 1, class 0
0.3,
0.2,
0.5,
0.3,
0.2,
0.1,
0.42,
0.66,
// batch 1, class 1
0.05,
0.1,
0.2,
0.3,
0.2,
0.1,
0.33,
0.44,
});
Shape output_shape{1, 1, num_images * static_cast<size_t>(attrs.keep_top_k[0]), 7};
test_case.add_expected_output<float>(output_shape,
{
0, 0, 0.4, 0.55, 0.61, 1, 0.97, 0, 1, 0.7, 0.4, 0.52, 0.9, 1,
1, 0, 0.42, 0.83, 0.5, 1, 0.87, 1, 1, 0.33, 0.63, 0.35, 1, 1,
});
test_case.run();
}
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