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Merge pull request #28945 from varun-jaiswal17:add-perf-test

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requires (YOLO26n , RetinaFace model download) : https://github.com/opencv/opencv_extra/pull/1357

added performance tests for 14 modern deep learning models to `modules/dnn/perf/perf_net.cpp.` 

**Tests added**
- RT_DETR_L
- RF_DETR
- Grounding_DINO
- BlazeFace
- RetinaFace
- YOLO26n
- YOLO26m_Seg
- SegFormer_B2_Clothes
- Depth_Anything_V2
- SigLIP
- OWLv2
- RAFT
- SAM2_Encoder
- SAM2_Decoder

Models are available at: https://drive.google.com/drive/folders/1lpYAWSLMtxcmfDtYjiw0qwzZX0bCH-i4

### 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
This commit is contained in:
Varun Jaiswal
2026-05-22 00:45:35 +05:30
committed by GitHub
parent c9147d6e1f
commit fe94255a5f
1 changed files with 214 additions and 0 deletions
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modules/dnn/perf/perf_net.cpp
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@@ -474,6 +474,220 @@ PERF_TEST_P_(DNNTestNetwork, FacePaint)
processNet("dnn/onnx/models/face_paint_512_v2_0.onnx", "", cv::Size(512, 512));
}
// Model: https://huggingface.co/vietanhdev/segment-anything-2-onnx-models/blob/main/sam2_hiera_large.encoder.onnx
PERF_TEST_P_(DNNTestNetwork, SAM2_Encoder)
{
applyTestTag(CV_TEST_TAG_MEMORY_2GB, CV_TEST_TAG_VERYLONG);
Mat sample = imread(findDataFile("dnn/dog416.png"));
Mat inp = blobFromImage(sample, 1.0 / 255.0, Size(1024, 1024), Scalar(), true);
processNet("dnn/onnx/models/sam2_hiera_large.encoder.onnx", "", inp);
}
// Model: https://huggingface.co/vietanhdev/segment-anything-2-onnx-models/blob/main/sam2_hiera_large.decoder.onnx
PERF_TEST_P_(DNNTestNetwork, SAM2_Decoder)
{
applyTestTag(CV_TEST_TAG_MEMORY_1GB, CV_TEST_TAG_VERYLONG);
// Synthetic encoder outputs used as decoder inputs
int shp_embed[4] = {1, 256, 64, 64};
int shp_feat0[4] = {1, 32, 256, 256};
int shp_feat1[4] = {1, 64, 128, 128};
Mat image_embed(4, shp_embed, CV_32F);
Mat high_res_feats_0(4, shp_feat0, CV_32F);
Mat high_res_feats_1(4, shp_feat1, CV_32F);
randu(image_embed, 0.0f, 1.0f);
randu(high_res_feats_0, 0.0f, 1.0f);
randu(high_res_feats_1, 0.0f, 1.0f);
// Single point prompt at center of image, label=1 (foreground)
int shp_pts[3] = {1, 1, 2};
int shp_lbl[2] = {1, 1};
int shp_mask[4] = {1, 1, 256, 256};
int shp_hasmask[1] = {1};
float point_coords_data[2] = {512.0f, 512.0f};
float point_labels_data[1] = {1.0f};
float has_mask_input_data[1]= {0.0f};
Mat point_coords(3, shp_pts, CV_32F, point_coords_data);
Mat point_labels(2, shp_lbl, CV_32F, point_labels_data);
Mat mask_input(4, shp_mask, CV_32F, Scalar(0));
Mat has_mask_input(1, shp_hasmask, CV_32F, has_mask_input_data);
processNet("dnn/onnx/models/sam2_hiera_large.decoder.onnx", "",
{std::make_tuple(image_embed, "image_embed"),
std::make_tuple(high_res_feats_0, "high_res_feats_0"),
std::make_tuple(high_res_feats_1, "high_res_feats_1"),
std::make_tuple(point_coords, "point_coords"),
std::make_tuple(point_labels, "point_labels"),
std::make_tuple(mask_input, "mask_input"),
std::make_tuple(has_mask_input, "has_mask_input")});
}
// Model: https://github.com/opencv/opencv_zoo/tree/main/models/optical_flow_estimation_raft
PERF_TEST_P_(DNNTestNetwork, RAFT)
{
applyTestTag(CV_TEST_TAG_MEMORY_2GB, CV_TEST_TAG_VERYLONG);
// RAFT takes two consecutive frames to estimate optical flow between them
Mat frame0 = imread(findDataFile("gpu/opticalflow/frame0.png"));
Mat frame1 = imread(findDataFile("gpu/opticalflow/frame1.png"));
Mat blob0 = blobFromImage(frame0, 1.0, Size(480, 360), Scalar(), true);
Mat blob1 = blobFromImage(frame1, 1.0, Size(480, 360), Scalar(), true);
processNet("dnn/onnx/models/optical_flow_estimation_raft_2023aug.onnx", "",
{std::make_tuple(blob0, "0"),
std::make_tuple(blob1, "1")});
}
// Model: https://huggingface.co/onnx-community/owlv2-base-patch16-finetuned-ONNX
PERF_TEST_P_(DNNTestNetwork, OWLv2)
{
applyTestTag(CV_TEST_TAG_MEMORY_1GB, CV_TEST_TAG_VERYLONG);
// Image input: [1, 3, 960, 960] (60x60 patches x 16 = 960)
Mat sample = imread(findDataFile("dnn/dog416.png"));
Mat pixel_values = blobFromImage(sample, 1.0 / 255.0, Size(960, 960), Scalar(), true);
// Text query tokens: "a dog" with CLIP tokenizer, seq_len=16
// [BOS=49406, "a"=320, "dog"=1929, EOS=49407, pad=0, ...]
const int seq_len = 16;
int shp[2] = {1, seq_len};
int64_t input_ids_data[seq_len] = {49406, 320, 1929, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
int64_t attention_mask_data[seq_len]= {1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
Mat input_ids(2, shp, CV_64S, input_ids_data);
Mat attention_mask(2, shp, CV_64S, attention_mask_data);
processNet("dnn/onnx/models/owlv2_base_patch_16.onnx", "",
{std::make_tuple(input_ids, "input_ids"),
std::make_tuple(pixel_values, "pixel_values"),
std::make_tuple(attention_mask, "attention_mask")});
}
// Model: https://drive.google.com/file/d/1IU7iktOUbvNPFnDJb_ivl3LxYIdpEp3f/view?usp=drive_link
PERF_TEST_P_(DNNTestNetwork, YOLO26m_Seg)
{
applyTestTag(CV_TEST_TAG_MEMORY_512MB, CV_TEST_TAG_VERYLONG);
Mat sample = imread(findDataFile("dnn/dog416.png"));
Mat inp = blobFromImage(sample, 1.0 / 255.0, Size(640, 640), Scalar(), true);
processNet("dnn/onnx/models/yolo26m-seg.onnx", "", inp);
}
// Model: https://drive.google.com/file/d/17OWMXSiefFMmj46CT42Fd2q5kl_jHRBC/view?usp=drive_link
PERF_TEST_P_(DNNTestNetwork, YOLO26n)
{
applyTestTag(CV_TEST_TAG_MEMORY_512MB);
Mat sample = imread(findDataFile("dnn/dog416.png"));
Mat inp = blobFromImage(sample, 1.0 / 255.0, Size(640, 640), Scalar(), true);
processNet("dnn/onnx/models/yolo26n.onnx", "", inp);
}
// Model: https://huggingface.co/Xenova/segformer_b2_clothes/blob/main/onnx/model.onnx
PERF_TEST_P_(DNNTestNetwork, SegFormer_B2_Clothes)
{
applyTestTag(CV_TEST_TAG_MEMORY_512MB, CV_TEST_TAG_VERYLONG);
Mat sample = imread(findDataFile("dnn/dog416.png"));
Mat inp = blobFromImage(sample, 1.0 / 255.0, Size(512, 512), Scalar(), true);
processNet("dnn/onnx/models/segformer_b2_clothes.onnx", "", inp);
}
// Model: https://huggingface.co/Xenova/siglip-base-patch16-224/blob/main/onnx/model.onnx
PERF_TEST_P_(DNNTestNetwork, SigLIP)
{
applyTestTag(CV_TEST_TAG_MEMORY_512MB, CV_TEST_TAG_VERYLONG);
// Image input: [1, 3, 224, 224] normalized to [-1, 1]
Mat sample = imread(findDataFile("dnn/dog416.png"));
Mat pixel_values = blobFromImage(sample, 1.0 / 255.0, Size(224, 224), Scalar(0.5, 0.5, 0.5), true);
pixel_values = (pixel_values - 0.5f) / 0.5f;
// Text input: dummy token IDs for "a photo of a dog", seq_len=64
const int seq_len = 64;
int shp[2] = {1, seq_len};
Mat input_ids(2, shp, CV_64S, Scalar(0));
// BOS=1, "a photo of a dog"=some tokens, EOS=2
int64_t* ids = input_ids.ptr<int64_t>();
ids[0] = 1; ids[1] = 263; ids[2] = 2514; ids[3] = 275; ids[4] = 262; ids[5] = 3914; ids[6] = 2;
processNet("dnn/onnx/models/siglip_base_patch16_224.onnx", "",
{std::make_tuple(input_ids, "input_ids"),
std::make_tuple(pixel_values, "pixel_values")});
}
// Model: https://huggingface.co/onnx-community/depth-anything-v2-small/blob/main/onnx/model.onnx
PERF_TEST_P_(DNNTestNetwork, Depth_Anything_V2)
{
applyTestTag(CV_TEST_TAG_MEMORY_512MB, CV_TEST_TAG_VERYLONG);
Mat sample = imread(findDataFile("dnn/street.png"));
Mat inp = blobFromImage(sample, 1.0 / 255.0, Size(518, 518), Scalar(), true);
processNet("dnn/onnx/models/depth_anything_v2_small.onnx", "", inp);
}
// Model: https://drive.google.com/file/d/1G2begS7rrEmWnI-xj2K5UL3PQ7H_0svc/view?usp=drive_link
PERF_TEST_P_(DNNTestNetwork, RetinaFace)
{
applyTestTag(CV_TEST_TAG_MEMORY_512MB);
processNet("dnn/onnx/models/retinaface_10g.onnx", "", cv::Size(640, 640));
}
// Model: https://huggingface.co/onnx-community/grounding-dino-tiny-ONNX
PERF_TEST_P_(DNNTestNetwork, Grounding_DINO)
{
applyTestTag(CV_TEST_TAG_MEMORY_2GB, CV_TEST_TAG_VERYLONG);
// Image input
Mat sample = imread(findDataFile("dnn/dog416.png"));
Mat img = blobFromImage(sample, 1.0 / 255.0, Size(800, 800), Scalar(), true);
// Text token inputs (dummy tokens for "dog ." as query text, seq_len=7)
const int seq_len = 7;
int64_t input_ids_data[seq_len] = {101, 3899, 1012, 102, 0, 0, 0};
int64_t attention_mask_data[seq_len] = {1, 1, 1, 1, 0, 0, 0};
int64_t token_type_ids_data[seq_len] = {0, 0, 0, 0, 0, 0, 0};
int64_t position_ids_data[seq_len] = {0, 1, 2, 3, 0, 0, 0};
uint8_t text_token_mask_data[seq_len]= {1, 1, 1, 1, 0, 0, 0};
int shp[2] = {1, seq_len};
Mat input_ids(2, shp, CV_64S, input_ids_data);
Mat attention_mask(2, shp, CV_64S, attention_mask_data);
Mat token_type_ids(2, shp, CV_64S, token_type_ids_data);
Mat position_ids(2, shp, CV_64S, position_ids_data);
Mat text_token_mask(2, shp, CV_8U, text_token_mask_data);
processNet("dnn/onnx/models/groundingdino_swint_ogc.onnx", "",
{std::make_tuple(img, "img"),
std::make_tuple(input_ids, "input_ids"),
std::make_tuple(attention_mask, "attention_mask"),
std::make_tuple(token_type_ids, "token_type_ids"),
std::make_tuple(position_ids, "position_ids"),
std::make_tuple(text_token_mask, "text_token_mask")});
}
// Model: https://drive.google.com/file/d/1P6a7oS_dV5y09FsCA4XDZK1-WcdZbWFh/view?usp=drive_link
PERF_TEST_P_(DNNTestNetwork, RF_DETR)
{
applyTestTag(CV_TEST_TAG_MEMORY_1GB, CV_TEST_TAG_VERYLONG);
Mat sample = imread(findDataFile("dnn/dog416.png"));
Mat inp = blobFromImage(sample, 1.0 / 255.0, Size(560, 560), Scalar(), true);
processNet("dnn/onnx/models/rfdetr.onnx", "", inp);
}
// Model: https://drive.google.com/file/d/1OrSmlXURayVQgW8nrrxjggzPMN7xPRGJ/view?usp=sharing
PERF_TEST_P_(DNNTestNetwork, RT_DETR_L)
{
applyTestTag(CV_TEST_TAG_MEMORY_1GB, CV_TEST_TAG_VERYLONG);
Mat sample = imread(findDataFile("dnn/dog416.png"));
Mat inp = blobFromImage(sample, 1.0 / 255.0, Size(640, 640), Scalar(), true);
processNet("dnn/onnx/models/rtdetr-l.onnx", "", inp);
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork, dnnBackendsAndTargets());
} // namespace