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Merge pull request #28678 from omrope79:caffe-importer-cleanup

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Caffe importer cleanup #28678

Merge with: https://github.com/opencv/opencv_extra/pull/1324

### 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
- [x] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
omrope79
2026-06-02 19:58:10 +05:30
committed by GitHub
parent 42fc6939f8
commit b67ad9a422
34 changed files with 327 additions and 5674 deletions
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modules/dnn/test/test_int8_layers.cpp
+1 -253
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@@ -46,16 +46,7 @@ public:
String inpPath, outPath;
Net net, qnet;
if (importer == "Caffe")
{
String prototxt = _tf("layers/" + basename + ".prototxt");
String caffemodel = _tf("layers/" + basename + ".caffemodel");
net = readNetFromCaffe(prototxt, useCaffeModel ? caffemodel : String());
inpPath = _tf("layers/" + (useCommonInputBlob ? "blob" : basename + ".input"));
outPath = _tf("layers/" + basename);
}
else if (importer == "TensorFlow")
if (importer == "TensorFlow")
{
String netPath = _tf("tensorflow/" + basename + "_net.pb");
String netConfig = hasText ? _tf("tensorflow/" + basename + "_net.pbtxt") : "";
@@ -768,121 +759,6 @@ public:
}
};
TEST_P(Test_Int8_nets, AlexNet)
{
#if defined(OPENCV_32BIT_CONFIGURATION) && defined(HAVE_OPENCL)
applyTestTag(CV_TEST_TAG_MEMORY_2GB);
#else
applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
#endif
if (target == DNN_TARGET_OPENCL_FP16 && !ocl::Device::getDefault().isIntel())
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel())
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
Net net = readNetFromCaffe(findDataFile("dnn/bvlc_alexnet.prototxt"),
findDataFile("dnn/bvlc_alexnet.caffemodel", false));
Mat inp = imread(_tf("grace_hopper_227.png"));
Mat blob = blobFromImage(inp, 1.0, Size(227, 227), Scalar(), false);
Mat ref = blobFromNPY(_tf("caffe_alexnet_prob.npy"));
float l1 = 1e-4, lInf = 0.003;
testClassificationNet(net, blob, ref, l1, lInf);
}
TEST_P(Test_Int8_nets, GoogLeNet)
{
if (target == DNN_TARGET_OPENCL_FP16 && !ocl::Device::getDefault().isIntel())
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel())
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
Net net = readNetFromCaffe(findDataFile("dnn/bvlc_googlenet.prototxt"),
findDataFile("dnn/bvlc_googlenet.caffemodel", false));
std::vector<Mat> inpMats;
inpMats.push_back( imread(_tf("googlenet_0.png")) );
inpMats.push_back( imread(_tf("googlenet_1.png")) );
Mat blob = blobFromImages(inpMats, 1.0, Size(224, 224), Scalar(), false);
Mat ref = blobFromNPY(_tf("googlenet_prob.npy"));
float l1 = 2e-4, lInf = 0.07;
testClassificationNet(net, blob, ref, l1, lInf);
}
TEST_P(Test_Int8_nets, ResNet50)
{
applyTestTag(
target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB,
CV_TEST_TAG_DEBUG_VERYLONG
);
if (target == DNN_TARGET_OPENCL_FP16 && !ocl::Device::getDefault().isIntel())
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel())
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
Net net = readNetFromCaffe(findDataFile("dnn/ResNet-50-deploy.prototxt"),
findDataFile("dnn/ResNet-50-model.caffemodel", false));
Mat inp = imread(_tf("googlenet_0.png"));
Mat blob = blobFromImage(inp, 1.0, Size(224, 224), Scalar(), false);
Mat ref = blobFromNPY(_tf("resnet50_prob.npy"));
float l1 = 3e-4, lInf = 0.05;
testClassificationNet(net, blob, ref, l1, lInf);
{
SCOPED_TRACE("Per-tensor quantize");
testClassificationNet(net, blob, ref, l1, lInf, false);
}
}
TEST_P(Test_Int8_nets, DenseNet121)
{
applyTestTag(CV_TEST_TAG_MEMORY_512MB);
if (target == DNN_TARGET_OPENCL_FP16 && !ocl::Device::getDefault().isIntel())
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel())
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
Net net = readNetFromCaffe(findDataFile("dnn/DenseNet_121.prototxt", false),
findDataFile("dnn/DenseNet_121.caffemodel", false));
Mat inp = imread(_tf("dog416.png"));
Mat blob = blobFromImage(inp, 1.0 / 255.0, Size(224, 224), Scalar(), true, true);
Mat ref = blobFromNPY(_tf("densenet_121_output.npy"));
float l1 = 0.76, lInf = 3.31; // seems wrong
testClassificationNet(net, blob, ref, l1, lInf);
}
TEST_P(Test_Int8_nets, SqueezeNet_v1_1)
{
if(target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
if (target == DNN_TARGET_OPENCL_FP16 && !ocl::Device::getDefault().isIntel())
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel())
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
Net net = readNetFromCaffe(findDataFile("dnn/squeezenet_v1.1.prototxt"),
findDataFile("dnn/squeezenet_v1.1.caffemodel", false));
Mat inp = imread(_tf("googlenet_0.png"));
Mat blob = blobFromImage(inp, 1.0, Size(227, 227), Scalar(), false, true);
Mat ref = blobFromNPY(_tf("squeezenet_v1.1_prob.npy"));
float l1 = 3e-4, lInf = 0.056;
testClassificationNet(net, blob, ref, l1, lInf);
}
TEST_P(Test_Int8_nets, CaffeNet)
{
#if defined(OPENCV_32BIT_CONFIGURATION) && (defined(HAVE_OPENCL) || defined(_WIN32))
@@ -947,24 +823,6 @@ TEST_P(Test_Int8_nets, Shufflenet)
testONNXNet("shufflenet", default_l1, default_lInf);
}
TEST_P(Test_Int8_nets, MobileNet_SSD)
{
if (target == DNN_TARGET_OPENCL_FP16 && !ocl::Device::getDefault().isIntel())
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel())
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
Net net = readNetFromCaffe(findDataFile("dnn/MobileNetSSD_deploy_19e3ec3.prototxt", false),
findDataFile("dnn/MobileNetSSD_deploy_19e3ec3.caffemodel", false));
Mat inp = imread(_tf("street.png"));
Mat blob = blobFromImage(inp, 1.0 / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
Mat ref = blobFromNPY(_tf("mobilenet_ssd_caffe_out.npy"));
float confThreshold = FLT_MIN, scoreDiff = 0.084, iouDiff = 0.43;
testDetectionNet(net, blob, ref, confThreshold, scoreDiff, iouDiff);
}
TEST_P(Test_Int8_nets, MobileNet_v1_SSD)
{
if (target == DNN_TARGET_OPENCL_FP16 && !ocl::Device::getDefault().isIntel())
@@ -1025,31 +883,6 @@ TEST_P(Test_Int8_nets, Inception_v2_SSD)
testDetectionNet(net, blob, ref, confThreshold, scoreDiff, iouDiff);
}
TEST_P(Test_Int8_nets, opencv_face_detector)
{
if (target == DNN_TARGET_OPENCL_FP16 && !ocl::Device::getDefault().isIntel())
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel())
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
Net net = readNetFromCaffe(findDataFile("dnn/opencv_face_detector.prototxt"),
findDataFile("dnn/opencv_face_detector.caffemodel", false));
Mat inp = imread(findDataFile("gpu/lbpcascade/er.png"));
Mat blob = blobFromImage(inp, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);
Mat ref = (Mat_<float>(6, 7) << 0, 1, 0.99520785, 0.80997437, 0.16379407, 0.87996572, 0.26685631,
0, 1, 0.9934696, 0.2831718, 0.50738752, 0.345781, 0.5985168,
0, 1, 0.99096733, 0.13629119, 0.24892329, 0.19756334, 0.3310290,
0, 1, 0.98977017, 0.23901358, 0.09084064, 0.29902688, 0.1769477,
0, 1, 0.97203469, 0.67965847, 0.06876482, 0.73999709, 0.1513494,
0, 1, 0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801);
float confThreshold = 0.5, scoreDiff = 0.002, iouDiff = 0.4;
testDetectionNet(net, blob, ref, confThreshold, scoreDiff, iouDiff);
}
TEST_P(Test_Int8_nets, EfficientDet)
{
if (cvtest::skipUnstableTests)
@@ -1143,91 +976,6 @@ TEST_P(Test_Int8_nets, FasterRCNN_inceptionv2)
testDetectionNet(net, blob, ref, confThreshold, scoreDiff, iouDiff);
}
TEST_P(Test_Int8_nets, FasterRCNN_vgg16)
{
applyTestTag(
#if defined(OPENCV_32BIT_CONFIGURATION) && defined(HAVE_OPENCL)
CV_TEST_TAG_MEMORY_2GB,
#else
CV_TEST_TAG_MEMORY_2GB,
#endif
CV_TEST_TAG_LONG,
CV_TEST_TAG_DEBUG_VERYLONG
);
if (target == DNN_TARGET_OPENCL_FP16 && !ocl::Device::getDefault().isIntel())
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel())
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
Net net = readNetFromCaffe(findDataFile("dnn/faster_rcnn_vgg16.prototxt"),
findDataFile("dnn/VGG16_faster_rcnn_final.caffemodel", false));
Mat ref = (Mat_<float>(3, 7) << 0, 2, 0.949398, 99.2454, 210.141, 601.205, 462.849,
0, 7, 0.997022, 481.841, 92.3218, 722.685, 175.953,
0, 12, 0.993028, 133.221, 189.377, 350.994, 563.166);
float confThreshold = 0.8, scoreDiff = 0.048, iouDiff = 0.35;
testFaster(net, ref, confThreshold, scoreDiff, iouDiff);
}
TEST_P(Test_Int8_nets, FasterRCNN_zf)
{
applyTestTag(
#if defined(OPENCV_32BIT_CONFIGURATION) && defined(HAVE_OPENCL)
CV_TEST_TAG_MEMORY_2GB,
#else
(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB),
#endif
CV_TEST_TAG_DEBUG_VERYLONG
);
if (target == DNN_TARGET_OPENCL_FP16 && !ocl::Device::getDefault().isIntel())
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel())
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
Net net = readNetFromCaffe(findDataFile("dnn/faster_rcnn_zf.prototxt"),
findDataFile("dnn/ZF_faster_rcnn_final.caffemodel", false));
Mat ref = (Mat_<float>(3, 7) << 0, 2, 0.90121, 120.407, 115.83, 570.586, 528.395,
0, 7, 0.988779, 469.849, 75.1756, 718.64, 186.762,
0, 12, 0.967198, 138.588, 206.843, 329.766, 553.176);
float confThreshold = 0.8, scoreDiff = 0.021, iouDiff = 0.1;
testFaster(net, ref, confThreshold, scoreDiff, iouDiff);
}
TEST_P(Test_Int8_nets, RFCN)
{
applyTestTag(
(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_2GB),
CV_TEST_TAG_LONG,
CV_TEST_TAG_DEBUG_VERYLONG
);
if (target == DNN_TARGET_OPENCL_FP16 && !ocl::Device::getDefault().isIntel())
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel())
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
Net net = readNetFromCaffe(findDataFile("dnn/rfcn_pascal_voc_resnet50.prototxt"),
findDataFile("dnn/resnet50_rfcn_final.caffemodel", false));
Mat ref = (Mat_<float>(2, 7) << 0, 7, 0.991359, 491.822, 81.1668, 702.573, 178.234,
0, 12, 0.94786, 132.093, 223.903, 338.077, 566.16);
float confThreshold = 0.8, scoreDiff = 0.15, iouDiff = 0.11;
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) {
iouDiff = 0.12;
}
testFaster(net, ref, confThreshold, scoreDiff, iouDiff);
}
TEST_P(Test_Int8_nets, YOLOv3)
{
applyTestTag(