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Import SSDs from TensorFlow by training config (#12188)

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* Remove TensorFlow and protobuf dependencies from object detection scripts

* Create text graphs for TensorFlow object detection networks from sample
This commit is contained in:
Dmitry Kurtaev
2018-09-03 17:08:40 +03:00
committed by Vadim Pisarevsky
parent e3af72bb68
commit c7cf8fb35c
10 changed files with 858 additions and 450 deletions
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modules/dnn/test/test_tf_importer.cpp
+25 -1
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@@ -315,6 +315,29 @@ TEST_P(Test_TensorFlow_nets, Inception_v2_SSD)
normAssertDetections(ref, out, "", 0.5, scoreDiff, iouDiff);
}
TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD)
{
checkBackend();
std::string model = findDataFile("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", false);
std::string proto = findDataFile("dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt", false);
Net net = readNetFromTensorflow(model, proto);
Mat img = imread(findDataFile("dnn/dog416.png", false));
Mat blob = blobFromImage(img, 1.0f, Size(300, 300), Scalar(), true, false);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(blob);
Mat out = net.forward();
Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco_2017_11_17.detection_out.npy"));
float scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 7e-3 : 1e-5;
float iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0098 : 1e-3;
normAssertDetections(ref, out, "", 0.3, scoreDiff, iouDiff);
}
TEST_P(Test_TensorFlow_nets, Faster_RCNN)
{
static std::string names[] = {"faster_rcnn_inception_v2_coco_2018_01_28",
@@ -360,7 +383,8 @@ TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD_PPN)
net.setInput(blob);
Mat out = net.forward();
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.008 : default_l1;
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.011 : default_l1;
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.021 : default_lInf;
normAssertDetections(ref, out, "", 0.4, scoreDiff, iouDiff);
}