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Merge pull request #24553 from skycat8:yolov5

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Add yolov5n to tests #24553

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [ X] I agree to contribute to the project under Apache 2 License.
- [ X] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ X] The PR is proposed to the proper branch
- [ X] There is a reference to the original bug report and related work
- [ X] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ X] The feature is well documented and sample code can be built with the project CMake
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skycat8 1 year ago committed by GitHub
parent 2c1ec4245d
commit 848dd12a1f
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1 changed files with 65 additions and 44 deletions
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  1. 109
      modules/dnn/test/test_onnx_importer.cpp

109
modules/dnn/test/test_onnx_importer.cpp
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@ -2605,28 +2605,15 @@ TEST_P(Test_ONNX_layers, CumSum)
testONNXModels("cumsum_3d_dim_2");
}
// This test is mainly to test:
// 1. identity node with constant input
// 2. limited support to range operator (all inputs are constant)
// 3. parseExpand with multiple broadcast axes
// 4. 1D mat dimension issue with the output of range operator
TEST_P(Test_ONNX_layers, YOLOv7)
static void testYOLO(const std::string& weightPath, const std::vector<int>& refClassIds,
const std::vector<float>& refScores, const std::vector<Rect2d>& refBoxes)
{
std::string weightPath = _tf("models/yolov7_not_simplified.onnx", false);
std::string imgPath = _tf("../dog_orig_size.png");
Size targetSize{640, 640};
float conf_threshold = 0.3;
float iou_threshold = 0.5;
// Reference, which is collected with input size of 640x640
std::vector<int> refClassIds{1, 16, 7};
std::vector<float> refScores{0.9614331f, 0.9589417f, 0.8679074f};
// [x1, y1, x2, y2] x 3
std::vector<Rect2d> refBoxes{Rect2d(105.973236f, 150.16716f, 472.59012f, 466.48834f),
Rect2d(109.97953f, 246.17862f, 259.83676f, 600.76624f),
Rect2d(385.96185f, 83.02809f, 576.07355f, 189.82793f)};
Mat img = imread(imgPath);
Mat inp = blobFromImage(img, 1/255.0, targetSize, Scalar(0, 0, 0), true, false);
@ -2636,40 +2623,42 @@ TEST_P(Test_ONNX_layers, YOLOv7)
std::vector<Mat> outs;
net.forward(outs, net.getUnconnectedOutLayersNames());
Mat preds = outs[3].reshape(1, outs[3].size[1]); // [1, 25200, 85]
// Retrieve
std::vector<int> classIds;
std::vector<float> confidences;
std::vector<Rect2d> boxes;
// each row is [cx, cy, w, h, conf_obj, conf_class1, ..., conf_class80]
for (int i = 0; i < preds.rows; ++i)
{
// filter out non objects
float obj_conf = preds.row(i).at<float>(4);
if (obj_conf < conf_threshold)
continue;
// get class id and conf
Mat scores = preds.row(i).colRange(5, preds.cols);
double conf;
Point maxLoc;
minMaxLoc(scores, 0, &conf, 0, &maxLoc);
conf *= obj_conf;
if (conf < conf_threshold)
continue;
// get bbox coords
float* det = preds.ptr<float>(i);
double cx = det[0];
double cy = det[1];
double w = det[2];
double h = det[3];
// [x1, y1, x2, y2]
boxes.push_back(Rect2d(cx - 0.5 * w, cy - 0.5 * h,
cx + 0.5 * w, cy + 0.5 * h));
classIds.push_back(maxLoc.x);
confidences.push_back(conf);
for (auto preds : outs)
{
preds = preds.reshape(1, preds.size[1]);
for (int i = 0; i < preds.rows; ++i)
{
// filter out non objects
float obj_conf = preds.row(i).at<float>(4);
if (obj_conf < conf_threshold)
continue;
// get class id and conf
Mat scores = preds.row(i).colRange(5, preds.cols);
double conf;
Point maxLoc;
minMaxLoc(scores, 0, &conf, 0, &maxLoc);
conf *= obj_conf;
if (conf < conf_threshold)
continue;
// get bbox coords
float* det = preds.ptr<float>(i);
double cx = det[0];
double cy = det[1];
double w = det[2];
double h = det[3];
// [x1, y1, x2, y2]
boxes.push_back(Rect2d(cx - 0.5 * w, cy - 0.5 * h,
cx + 0.5 * w, cy + 0.5 * h));
classIds.push_back(maxLoc.x);
confidences.push_back(conf);
}
}
// NMS
@ -2689,6 +2678,38 @@ TEST_P(Test_ONNX_layers, YOLOv7)
normAssertDetections(refClassIds, refScores, refBoxes, keep_classIds, keep_confidences, keep_boxes);
}
// This test is mainly to test:
// 1. identity node with constant input
// 2. limited support to range operator (all inputs are constant)
// 3. parseExpand with multiple broadcast axes
// 4. 1D mat dimension issue with the output of range operator
TEST_P(Test_ONNX_nets, YOLOv7)
{
std::string weightPath = _tf("models/yolov7_not_simplified.onnx", false);
// Reference, which is collected with input size of 640x640
std::vector<int> refClassIds{1, 16, 7};
std::vector<float> refScores{0.9614331f, 0.9589417f, 0.8679074f};
// [x1, y1, x2, y2] x 3
std::vector<Rect2d> refBoxes{Rect2d(105.973236f, 150.16716f, 472.59012f, 466.48834f),
Rect2d(109.97953f, 246.17862f, 259.83676f, 600.76624f),
Rect2d(385.96185f, 83.02809f, 576.07355f, 189.82793f)};
testYOLO(weightPath, refClassIds, refScores, refBoxes);
}
TEST_P(Test_ONNX_nets, YOLOv5n)
{
std::string weightPath = findDataFile("dnn/yolov5n.onnx", false);
// Reference, which is collected with input size of 640x640
std::vector<int> refClassIds{16, 2, 1};
std::vector<float> refScores{0.749053f, 0.616853f, 0.32506f};
// [x1, y1, x2, y2] x 4
std::vector<Rect2d> refBoxes{Rect2d(108.088f, 239.293f, 266.196f, 607.658f),
Rect2d(392.028f, 89.9233f, 579.152f, 190.447f),
Rect2d(120.278f, 159.76, 214.481f, 241.473f)};
testYOLO(weightPath, refClassIds, refScores, refBoxes);
}
TEST_P(Test_ONNX_layers, Tile)
{
testONNXModels("tile", pb);

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