TEST_P(Test_TensorFlow_nets, opencv_face_detector_uint8)
{
std::string proto = findDataFile("dnn/opencv_face_detector.pbtxt", false);
std::string model = findDataFile("dnn/opencv_face_detector_uint8.pb", false);
Net net = readNetFromTensorflow(model, proto);
Mat img = imread(findDataFile("gpu/lbpcascade/er.png", false));
Mat blob = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);
net.setPreferableTarget(GetParam());
net.setInput(blob);
// Output has shape 1x1xNx7 where N - number of detections.
// An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
Mat out = net.forward();
// References are from test for Caffe model.
Mat ref = (Mat_<float>(6, 5) << 0.99520785, 0.80997437, 0.16379407, 0.87996572, 0.26685631,
0.9934696, 0.2831718, 0.50738752, 0.345781, 0.5985168,
0.99096733, 0.13629119, 0.24892329, 0.19756334, 0.3310290,
0.98977017, 0.23901358, 0.09084064, 0.29902688, 0.1769477,
0.97203469, 0.67965847, 0.06876482, 0.73999709, 0.1513494,
0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801);
normAssert(out.reshape(1, out.total() / 7).rowRange(0, 6).colRange(2, 7), ref, "", 2.8e-4, 3.4e-3);
}
TEST_P(Test_TensorFlow_nets, Inception_v2_SSD)
{
std::string proto = findDataFile("dnn/ssd_inception_v2_coco_2017_11_17.pbtxt", false);
std::string model = findDataFile("dnn/ssd_inception_v2_coco_2017_11_17.pb", false);
Net net = readNetFromTensorflow(model, proto);
Mat img = imread(findDataFile("dnn/street.png", false));
Mat blob = blobFromImage(img, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), true, false);
net.setPreferableTarget(GetParam());
net.setInput(blob);
// Output has shape 1x1xNx7 where N - number of detections.
// An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
Mat out = net.forward();
out = out.reshape(1, out.total() / 7);
Mat detections;
for (int i = 0; i < out.rows; ++i)
{
if (out.at<float>(i, 2) > 0.5)
detections.push_back(out.row(i).colRange(1, 7));
}
Mat ref = (Mat_<float>(5, 6) << 1, 0.90176028, 0.19872092, 0.36311883, 0.26461923, 0.63498729,
3, 0.93569964, 0.64865261, 0.45906419, 0.80675775, 0.65708131,
3, 0.75838411, 0.44668293, 0.45907149, 0.49459291, 0.52197015,
10, 0.95932811, 0.38349164, 0.32528657, 0.40387636, 0.39165527,
10, 0.93973452, 0.66561931, 0.37841269, 0.68074018, 0.42907384);
normAssert(detections, ref);
}
TEST_P(Test_TensorFlow_nets, opencv_face_detector_uint8)
{
checkBackend();
std::string proto = findDataFile("dnn/opencv_face_detector.pbtxt", false);
std::string model = findDataFile("dnn/opencv_face_detector_uint8.pb", false);
Net net = readNetFromTensorflow(model, proto);
Mat img = imread(findDataFile("gpu/lbpcascade/er.png", false));
Mat blob = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(blob);
// Output has shape 1x1xNx7 where N - number of detections.
// An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
Mat out = net.forward();
// References are from test for Caffe model.
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);
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 4e-3 : 3.4e-3;
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.024 : 1e-2;
normAssertDetections(ref, out, "", 0.9, scoreDiff, iouDiff);
}
TEST_P(Test_TensorFlow_nets, MobileNet_SSD)
{
std::string netPath = findDataFile("dnn/ssd_mobilenet_v1_coco.pb", false);
std::string netConfig = findDataFile("dnn/ssd_mobilenet_v1_coco.pbtxt", false);
std::string imgPath = findDataFile("dnn/street.png", false);
Mat inp;
resize(imread(imgPath), inp, Size(300, 300));
inp = blobFromImage(inp, 1.0f / 127.5, Size(), Scalar(127.5, 127.5, 127.5), true);
std::vector<String> outNames(3);
outNames[0] = "concat";
outNames[1] = "concat_1";
outNames[2] = "detection_out";
std::vector<Mat> target(outNames.size());
for (int i = 0; i < outNames.size(); ++i)
{
std::string path = findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco." + outNames[i] + ".npy", false);
target[i] = blobFromNPY(path);
}
Net net = readNetFromTensorflow(netPath, netConfig);
net.setPreferableTarget(GetParam());
net.setInput(inp);
std::vector<Mat> output;
net.forward(output, outNames);
normAssert(target[0].reshape(1, 1), output[0].reshape(1, 1), "", 1e-5, 1.5e-4);
normAssert(target[1].reshape(1, 1), output[1].reshape(1, 1), "", 1e-5, 3e-4);
normAssert(target[2].reshape(1, 1), output[2].reshape(1, 1), "", 4e-5, 1e-2);
}
TEST_P(Test_TensorFlow_nets, Faster_RCNN)
{
static std::string names[] = {"faster_rcnn_inception_v2_coco_2018_01_28",
"faster_rcnn_resnet50_coco_2018_01_28"};
checkBackend();
if ((backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU) ||
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("");
for (int i = 1; i < 2; ++i)
{
std::string proto = findDataFile("dnn/" + names[i] + ".pbtxt", false);
std::string model = findDataFile("dnn/" + names[i] + ".pb", false);
Net net = readNetFromTensorflow(model, proto);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat img = imread(findDataFile("dnn/dog416.png", false));
Mat blob = blobFromImage(img, 1.0f, Size(800, 600), Scalar(), true, false);
net.setInput(blob);
Mat out = net.forward();
Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/" + names[i] + ".detection_out.npy"));
normAssertDetections(ref, out, names[i].c_str(), 0.3);
}
}
TEST_P(Test_TensorFlow_nets, Inception_v2_SSD)
{
checkBackend();
std::string proto = findDataFile("dnn/ssd_inception_v2_coco_2017_11_17.pbtxt", false);
std::string model = findDataFile("dnn/ssd_inception_v2_coco_2017_11_17.pb", false);
Net net = readNetFromTensorflow(model, proto);
Mat img = imread(findDataFile("dnn/street.png", false));
Mat blob = blobFromImage(img, 1.0f, Size(300, 300), Scalar(), true, false);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(blob);
// Output has shape 1x1xNx7 where N - number of detections.
// An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
Mat out = net.forward();
Mat ref = (Mat_<float>(5, 7) << 0, 1, 0.90176028, 0.19872092, 0.36311883, 0.26461923, 0.63498729,
0, 3, 0.93569964, 0.64865261, 0.45906419, 0.80675775, 0.65708131,
0, 3, 0.75838411, 0.44668293, 0.45907149, 0.49459291, 0.52197015,
0, 10, 0.95932811, 0.38349164, 0.32528657, 0.40387636, 0.39165527,
0, 10, 0.93973452, 0.66561931, 0.37841269, 0.68074018, 0.42907384);
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0097 : default_l1;
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.09 : default_lInf;
normAssertDetections(ref, out, "", 0.5, scoreDiff, iouDiff);
}
void runTensorFlowNet(const std::string& prefix, bool hasText = false,
double l1 = 0.0, double lInf = 0.0, bool memoryLoad = false)
{
std::string netPath = path(prefix + "_net.pb");
std::string netConfig = (hasText ? path(prefix + "_net.pbtxt") : "");
std::string inpPath = path(prefix + "_in.npy");
std::string outPath = path(prefix + "_out.npy");
cv::Mat input = blobFromNPY(inpPath);
cv::Mat ref = blobFromNPY(outPath);
checkBackend(&input, &ref);
Net net;
if (memoryLoad)
{
// Load files into a memory buffers
string dataModel;
ASSERT_TRUE(readFileInMemory(netPath, dataModel));
string dataConfig;
if (hasText)
{
ASSERT_TRUE(readFileInMemory(netConfig, dataConfig));
}
net = readNetFromTensorflow(dataModel.c_str(), dataModel.size(),
dataConfig.c_str(), dataConfig.size());
}
else
net = readNetFromTensorflow(netPath, netConfig);
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(input);
cv::Mat output = net.forward();
normAssert(ref, output, "", l1 ? l1 : default_l1, lInf ? lInf : default_lInf);
}
TEST(Test_TensorFlow, Mask_RCNN)
{
std::string proto = findDataFile("dnn/mask_rcnn_inception_v2_coco_2018_01_28.pbtxt", false);
std::string model = findDataFile("dnn/mask_rcnn_inception_v2_coco_2018_01_28.pb", false);
Net net = readNetFromTensorflow(model, proto);
Mat img = imread(findDataFile("dnn/street.png", false));
Mat refDetections = blobFromNPY(path("mask_rcnn_inception_v2_coco_2018_01_28.detection_out.npy"));
Mat refMasks = blobFromNPY(path("mask_rcnn_inception_v2_coco_2018_01_28.detection_masks.npy"));
Mat blob = blobFromImage(img, 1.0f, Size(800, 800), Scalar(), true, false);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setInput(blob);
// Mask-RCNN predicts bounding boxes and segmentation masks.
std::vector<String> outNames(2);
outNames[0] = "detection_out_final";
outNames[1] = "detection_masks";
std::vector<Mat> outs;
net.forward(outs, outNames);
Mat outDetections = outs[0];
Mat outMasks = outs[1];
normAssertDetections(refDetections, outDetections, "", /*threshold for zero confidence*/1e-5);
// Output size of masks is NxCxHxW where
// N - number of detected boxes
// C - number of classes (excluding background)
// HxW - segmentation shape
const int numDetections = outDetections.size[2];
int masksSize[] = {1, numDetections, outMasks.size[2], outMasks.size[3]};
Mat masks(4, &masksSize[0], CV_32F);
std::vector<cv::Range> srcRanges(4, cv::Range::all());
std::vector<cv::Range> dstRanges(4, cv::Range::all());
outDetections = outDetections.reshape(1, outDetections.total() / 7);
for (int i = 0; i < numDetections; ++i)
{
// Get a class id for this bounding box and copy mask only for that class.
int classId = static_cast<int>(outDetections.at<float>(i, 1));
srcRanges[0] = dstRanges[1] = cv::Range(i, i + 1);
srcRanges[1] = cv::Range(classId, classId + 1);
outMasks(srcRanges).copyTo(masks(dstRanges));
}
cv::Range topRefMasks[] = {Range::all(), Range(0, numDetections), Range::all(), Range::all()};
normAssert(masks, refMasks(&topRefMasks[0]));
}
static void runTensorFlowNet(const std::string& prefix, int targetId = DNN_TARGET_CPU, bool hasText = false,
double l1 = 1e-5, double lInf = 1e-4,
bool memoryLoad = false)
{
std::string netPath = path(prefix + "_net.pb");
std::string netConfig = (hasText ? path(prefix + "_net.pbtxt") : "");
std::string inpPath = path(prefix + "_in.npy");
std::string outPath = path(prefix + "_out.npy");
Net net;
if (memoryLoad)
{
// Load files into a memory buffers
string dataModel;
ASSERT_TRUE(readFileInMemory(netPath, dataModel));
string dataConfig;
if (hasText)
ASSERT_TRUE(readFileInMemory(netConfig, dataConfig));
net = readNetFromTensorflow(dataModel.c_str(), dataModel.size(),
dataConfig.c_str(), dataConfig.size());
}
else
net = readNetFromTensorflow(netPath, netConfig);
ASSERT_FALSE(net.empty());
net.setPreferableBackend(DNN_BACKEND_DEFAULT);
net.setPreferableTarget(targetId);
cv::Mat input = blobFromNPY(inpPath);
cv::Mat target = blobFromNPY(outPath);
net.setInput(input);
cv::Mat output = net.forward();
normAssert(target, output, "", l1, lInf);
}
TEST(Test_TensorFlow, inception_accuracy)
{
Net net;
{
const string model = findDataFile("dnn/tensorflow_inception_graph.pb", false);
net = readNetFromTensorflow(model);
ASSERT_FALSE(net.empty());
}
Mat sample = imread(_tf("grace_hopper_227.png"));
ASSERT_TRUE(!sample.empty());
resize(sample, sample, Size(224, 224));
Mat inputBlob = blobFromImage(sample);
net.setInput(inputBlob, "input");
Mat out = net.forward("softmax2");
Mat ref = blobFromNPY(_tf("tf_inception_prob.npy"));
normAssert(ref, out);
}
TEST(Test_TensorFlow, inception_accuracy)
{
Net net;
{
const string model = findDataFile("dnn/tensorflow_inception_graph.pb", false);
net = readNetFromTensorflow(model);
ASSERT_FALSE(net.empty());
}
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat sample = imread(_tf("grace_hopper_227.png"));
ASSERT_TRUE(!sample.empty());
Mat inputBlob = blobFromImage(sample, 1.0, Size(224, 224), Scalar(), /*swapRB*/true);
net.setInput(inputBlob, "input");
Mat out = net.forward("softmax2");
Mat ref = blobFromNPY(_tf("tf_inception_prob.npy"));
normAssert(ref, out);
}
TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD_PPN)
{
checkBackend();
std::string proto = findDataFile("dnn/ssd_mobilenet_v1_ppn_coco.pbtxt", false);
std::string model = findDataFile("dnn/ssd_mobilenet_v1_ppn_coco.pb", false);
Net net = readNetFromTensorflow(model, proto);
Mat img = imread(findDataFile("dnn/dog416.png", false));
Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/ssd_mobilenet_v1_ppn_coco.detection_out.npy", 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();
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);
}
TEST_P(Test_TensorFlow_nets, MobileNet_SSD)
{
checkBackend();
if ((backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU) ||
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("");
std::string netPath = findDataFile("dnn/ssd_mobilenet_v1_coco.pb", false);
std::string netConfig = findDataFile("dnn/ssd_mobilenet_v1_coco.pbtxt", false);
std::string imgPath = findDataFile("dnn/street.png", false);
Mat inp;
resize(imread(imgPath), inp, Size(300, 300));
inp = blobFromImage(inp, 1.0f / 127.5, Size(), Scalar(127.5, 127.5, 127.5), true);
std::vector<String> outNames(3);
outNames[0] = "concat";
outNames[1] = "concat_1";
outNames[2] = "detection_out";
std::vector<Mat> refs(outNames.size());
for (int i = 0; i < outNames.size(); ++i)
{
std::string path = findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco." + outNames[i] + ".npy", false);
refs[i] = blobFromNPY(path);
}
Net net = readNetFromTensorflow(netPath, netConfig);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(inp);
std::vector<Mat> output;
net.forward(output, outNames);
normAssert(refs[0].reshape(1, 1), output[0].reshape(1, 1), "", 1e-5, 1.5e-4);
normAssert(refs[1].reshape(1, 1), output[1].reshape(1, 1), "", 1e-5, 3e-4);
normAssertDetections(refs[2], output[2], "", 0.2);
}
TEST(Test_TensorFlow, read_inception)
{
Net net;
{
const string model = findDataFile("dnn/tensorflow_inception_graph.pb", false);
net = readNetFromTensorflow(model);
ASSERT_FALSE(net.empty());
}
Mat sample = imread(_tf("grace_hopper_227.png"));
ASSERT_TRUE(!sample.empty());
Mat input;
resize(sample, input, Size(224, 224));
input -= 128; // mean sub
Mat inputBlob = blobFromImage(input);
net.setInput(inputBlob, "input");
Mat out = net.forward("softmax2");
std::cout << out.dims << std::endl;
}
