static void runTorchNet(String prefix, int targetId = DNN_TARGET_CPU, String outLayerName = "",
bool check2ndBlob = false, bool isBinary = false)
{
String suffix = (isBinary) ? ".dat" : ".txt";
Net net = readNetFromTorch(_tf(prefix + "_net" + suffix), isBinary);
ASSERT_FALSE(net.empty());
net.setPreferableBackend(DNN_BACKEND_DEFAULT);
net.setPreferableTarget(targetId);
Mat inp, outRef;
ASSERT_NO_THROW( inp = readTorchBlob(_tf(prefix + "_input" + suffix), isBinary) );
ASSERT_NO_THROW( outRef = readTorchBlob(_tf(prefix + "_output" + suffix), isBinary) );
if (outLayerName.empty())
outLayerName = net.getLayerNames().back();
net.setInput(inp, "0");
std::vector<Mat> outBlobs;
net.forward(outBlobs, outLayerName);
normAssert(outRef, outBlobs[0]);
if (check2ndBlob)
{
Mat out2 = outBlobs[1];
Mat ref2 = readTorchBlob(_tf(prefix + "_output_2" + suffix), isBinary);
normAssert(out2, ref2);
}
}
TEST_P(Test_Torch_nets, ENet_accuracy)
{
Net net;
{
const string model = findDataFile("dnn/Enet-model-best.net", false);
net = readNetFromTorch(model, true);
ASSERT_TRUE(!net.empty());
}
net.setPreferableTarget(GetParam());
Mat sample = imread(_tf("street.png", false));
Mat inputBlob = blobFromImage(sample, 1./255);
net.setInput(inputBlob, "");
Mat out = net.forward();
Mat ref = blobFromNPY(_tf("torch_enet_prob.npy", false));
// Due to numerical instability in Pooling-Unpooling layers (indexes jittering)
// thresholds for ENet must be changed. Accuracy of results was checked on
// Cityscapes dataset and difference in mIOU with Torch is 10E-4%
normAssert(ref, out, "", 0.00044, 0.44);
const int N = 3;
for (int i = 0; i < N; i++)
{
net.setInput(inputBlob, "");
Mat out = net.forward();
normAssert(ref, out, "", 0.00044, 0.44);
}
}
TEST_P(Reproducibility_SqueezeNet_v1_1, Accuracy)
{
Net net = readNetFromCaffe(findDataFile("dnn/squeezenet_v1.1.prototxt", false),
findDataFile("dnn/squeezenet_v1.1.caffemodel", false));
int targetId = GetParam();
net.setPreferableTarget(targetId);
Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(227,227), Scalar(), false);
ASSERT_TRUE(!input.empty());
Mat out;
if (targetId == DNN_TARGET_OPENCL)
{
// Firstly set a wrong input blob and run the model to receive a wrong output.
// Then set a correct input blob to check CPU->GPU synchronization is working well.
net.setInput(input * 2.0f);
out = net.forward();
}
net.setInput(input);
out = net.forward();
Mat ref = blobFromNPY(_tf("squeezenet_v1.1_prob.npy"));
normAssert(ref, out);
}
TEST_P(Reproducibility_ResNet50, Accuracy)
{
Net net = readNetFromCaffe(findDataFile("dnn/ResNet-50-deploy.prototxt", false),
findDataFile("dnn/ResNet-50-model.caffemodel", false));
int targetId = GetParam();
net.setPreferableTarget(targetId);
float l1 = (targetId == DNN_TARGET_OPENCL_FP16) ? 3e-5 : 1e-5;
float lInf = (targetId == DNN_TARGET_OPENCL_FP16) ? 6e-3 : 1e-4;
Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(224,224), Scalar(), false);
ASSERT_TRUE(!input.empty());
net.setInput(input);
Mat out = net.forward();
Mat ref = blobFromNPY(_tf("resnet50_prob.npy"));
normAssert(ref, out, "", l1, lInf);
if (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16)
{
UMat out_umat;
net.forward(out_umat);
normAssert(ref, out_umat, "out_umat", l1, lInf);
std::vector<UMat> out_umats;
net.forward(out_umats);
normAssert(ref, out_umats[0], "out_umat_vector", l1, lInf);
}
}
void runTorchNet(const String& prefix, String outLayerName = "",
bool check2ndBlob = false, bool isBinary = false,
double l1 = 0.0, double lInf = 0.0)
{
String suffix = (isBinary) ? ".dat" : ".txt";
Mat inp, outRef;
ASSERT_NO_THROW( inp = readTorchBlob(_tf(prefix + "_input" + suffix), isBinary) );
ASSERT_NO_THROW( outRef = readTorchBlob(_tf(prefix + "_output" + suffix), isBinary) );
checkBackend(backend, target, &inp, &outRef);
Net net = readNetFromTorch(_tf(prefix + "_net" + suffix), isBinary);
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
if (outLayerName.empty())
outLayerName = net.getLayerNames().back();
net.setInput(inp);
std::vector<Mat> outBlobs;
net.forward(outBlobs, outLayerName);
l1 = l1 ? l1 : default_l1;
lInf = lInf ? lInf : default_lInf;
normAssert(outRef, outBlobs[0], "", l1, lInf);
if (check2ndBlob && backend != DNN_BACKEND_INFERENCE_ENGINE)
{
Mat out2 = outBlobs[1];
Mat ref2 = readTorchBlob(_tf(prefix + "_output_2" + suffix), isBinary);
normAssert(out2, ref2, "", l1, lInf);
}
}
TEST(Reproducibility_FCN, Accuracy)
{
Net net;
{
const string proto = findDataFile("dnn/fcn8s-heavy-pascal.prototxt", false);
const string model = findDataFile("dnn/fcn8s-heavy-pascal.caffemodel", false);
net = readNetFromCaffe(proto, model);
ASSERT_FALSE(net.empty());
}
Mat sample = imread(_tf("street.png"));
ASSERT_TRUE(!sample.empty());
std::vector<int> layerIds;
std::vector<size_t> weights, blobs;
net.getMemoryConsumption(shape(1,3,227,227), layerIds, weights, blobs);
net.setInput(blobFromImage(sample, 1.0f, Size(500, 500), Scalar(), false), "data");
Mat out = net.forward("score");
Mat refData = imread(_tf("caffe_fcn8s_prob.png"), IMREAD_ANYDEPTH);
int shape[] = {1, 21, 500, 500};
Mat ref(4, shape, CV_32FC1, refData.data);
normAssert(ref, out);
}
TEST(Reproducibility_DenseNet_121, Accuracy)
{
const string proto = findDataFile("dnn/DenseNet_121.prototxt", false);
const string model = findDataFile("dnn/DenseNet_121.caffemodel", false);
Mat inp = imread(_tf("dog416.png"));
inp = blobFromImage(inp, 1.0 / 255, Size(224, 224));
Mat ref = blobFromNPY(_tf("densenet_121_output.npy"));
Net net = readNetFromCaffe(proto, model);
net.setInput(inp);
Mat out = net.forward();
normAssert(out, ref);
}
TEST(Test_Darknet, read_yolo_voc_stream)
{
Mat ref;
Mat sample = imread(_tf("dog416.png"));
Mat inp = blobFromImage(sample, 1.0/255, Size(416, 416), Scalar(), true, false);
const std::string cfgFile = findDataFile("dnn/yolo-voc.cfg", false);
const std::string weightsFile = findDataFile("dnn/yolo-voc.weights", false);
// Import by paths.
{
Net net = readNetFromDarknet(cfgFile, weightsFile);
net.setInput(inp);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
ref = net.forward();
}
// Import from bytes array.
{
std::string cfg, weights;
readFileInMemory(cfgFile, cfg);
readFileInMemory(weightsFile, weights);
Net net = readNetFromDarknet(&cfg[0], cfg.size(), &weights[0], weights.size());
net.setInput(inp);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat out = net.forward();
normAssert(ref, out);
}
}
TEST_P(Test_Torch_nets, OpenFace_accuracy)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("Test is enabled starts from OpenVINO 2018R3");
#endif
checkBackend();
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("");
const string model = findDataFile("dnn/openface_nn4.small2.v1.t7", false);
Net net = readNetFromTorch(model);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat sample = imread(findDataFile("cv/shared/lena.png", false));
Mat sampleF32(sample.size(), CV_32FC3);
sample.convertTo(sampleF32, sampleF32.type());
sampleF32 /= 255;
resize(sampleF32, sampleF32, Size(96, 96), 0, 0, INTER_NEAREST);
Mat inputBlob = blobFromImage(sampleF32, 1.0, Size(), Scalar(), /*swapRB*/true);
net.setInput(inputBlob);
Mat out = net.forward();
Mat outRef = readTorchBlob(_tf("net_openface_output.dat"), true);
normAssert(out, outRef, "", default_l1, default_lInf);
}
void testONNXModels(const String& basename, const Extension ext = npy,
const double l1 = 0, const float lInf = 0, const bool useSoftmax = false,
bool checkNoFallbacks = true)
{
String onnxmodel = _tf("models/" + basename + ".onnx");
Mat inp, ref;
if (ext == npy) {
inp = blobFromNPY(_tf("data/input_" + basename + ".npy"));
ref = blobFromNPY(_tf("data/output_" + basename + ".npy"));
}
else if (ext == pb) {
inp = readTensorFromONNX(_tf("data/input_" + basename + ".pb"));
ref = readTensorFromONNX(_tf("data/output_" + basename + ".pb"));
}
else
CV_Error(Error::StsUnsupportedFormat, "Unsupported extension");
checkBackend(&inp, &ref);
Net net = readNetFromONNX(onnxmodel);
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(inp);
Mat out = net.forward("");
if (useSoftmax)
{
LayerParams lp;
Net netSoftmax;
netSoftmax.addLayerToPrev("softmaxLayer", "SoftMax", lp);
netSoftmax.setPreferableBackend(DNN_BACKEND_OPENCV);
netSoftmax.setInput(out);
out = netSoftmax.forward();
netSoftmax.setInput(ref);
ref = netSoftmax.forward();
}
normAssert(ref, out, "", l1 ? l1 : default_l1, lInf ? lInf : default_lInf);
if (checkNoFallbacks)
expectNoFallbacksFromIE(net);
}
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);
}
// https://github.com/richzhang/colorization
TEST(Reproducibility_Colorization, Accuracy)
{
const float l1 = 3e-5;
const float lInf = 3e-3;
Mat inp = blobFromNPY(_tf("colorization_inp.npy"));
Mat ref = blobFromNPY(_tf("colorization_out.npy"));
Mat kernel = blobFromNPY(_tf("colorization_pts_in_hull.npy"));
const string proto = findDataFile("dnn/colorization_deploy_v2.prototxt", false);
const string model = findDataFile("dnn/colorization_release_v2.caffemodel", false);
Net net = readNetFromCaffe(proto, model);
net.getLayer(net.getLayerId("class8_ab"))->blobs.push_back(kernel);
net.getLayer(net.getLayerId("conv8_313_rh"))->blobs.push_back(Mat(1, 313, CV_32F, 2.606));
net.setInput(inp);
Mat out = net.forward();
normAssert(out, ref, "", 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());
}
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_ONNX_nets, Alexnet)
{
applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
const String model = _tf("models/alexnet.onnx");
Net net = readNetFromONNX(model);
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat inp = imread(_tf("../grace_hopper_227.png"));
Mat ref = blobFromNPY(_tf("../caffe_alexnet_prob.npy"));
checkBackend(&inp, &ref);
net.setInput(blobFromImage(inp, 1.0f, Size(227, 227), Scalar(), false));
ASSERT_FALSE(net.empty());
Mat out = net.forward();
normAssert(out, ref, "", default_l1, default_lInf);
expectNoFallbacksFromIE(net);
}
TEST_P(Test_ONNX_layers, MultyInputs)
{
const String model = _tf("models/multy_inputs.onnx");
Net net = readNetFromONNX(model);
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat inp1 = blobFromNPY(_tf("data/input_multy_inputs_0.npy"));
Mat inp2 = blobFromNPY(_tf("data/input_multy_inputs_1.npy"));
Mat ref = blobFromNPY(_tf("data/output_multy_inputs.npy"));
checkBackend(&inp1, &ref);
net.setInput(inp1, "0");
net.setInput(inp2, "1");
Mat out = net.forward();
normAssert(ref, out, "", default_l1, default_lInf);
expectNoFallbacksFromIE(net);
}
TEST(Reproducibility_GoogLeNet_fp16, Accuracy)
{
const float l1 = 1e-5;
const float lInf = 3e-3;
const string proto = findDataFile("dnn/bvlc_googlenet.prototxt", false);
const string model = findDataFile("dnn/bvlc_googlenet.caffemodel", false);
shrinkCaffeModel(model, "bvlc_googlenet.caffemodel_fp16");
Net net = readNetFromCaffe(proto, "bvlc_googlenet.caffemodel_fp16");
std::vector<Mat> inpMats;
inpMats.push_back( imread(_tf("googlenet_0.png")) );
inpMats.push_back( imread(_tf("googlenet_1.png")) );
ASSERT_TRUE(!inpMats[0].empty() && !inpMats[1].empty());
net.setInput(blobFromImages(inpMats, 1.0f, Size(), Scalar(), false), "data");
Mat out = net.forward("prob");
Mat ref = blobFromNPY(_tf("googlenet_prob.npy"));
normAssert(out, ref, "", l1, lInf);
}
TEST_P(Test_Torch_nets, ENet_accuracy)
{
checkBackend();
if (backend == DNN_BACKEND_INFERENCE_ENGINE ||
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("");
Net net;
{
const string model = findDataFile("dnn/Enet-model-best.net", false);
net = readNetFromTorch(model, true);
ASSERT_TRUE(!net.empty());
}
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat sample = imread(_tf("street.png", false));
Mat inputBlob = blobFromImage(sample, 1./255, Size(), Scalar(), /*swapRB*/true);
net.setInput(inputBlob, "");
Mat out = net.forward();
Mat ref = blobFromNPY(_tf("torch_enet_prob.npy", false));
// Due to numerical instability in Pooling-Unpooling layers (indexes jittering)
// thresholds for ENet must be changed. Accuracy of results was checked on
// Cityscapes dataset and difference in mIOU with Torch is 10E-4%
normAssert(ref, out, "", 0.00044, /*target == DNN_TARGET_CPU ? 0.453 : */0.552);
normAssertSegmentation(ref, out);
const int N = 3;
for (int i = 0; i < N; i++)
{
net.setInput(inputBlob, "");
Mat out = net.forward();
normAssert(ref, out, "", 0.00044, /*target == DNN_TARGET_CPU ? 0.453 : */0.552);
normAssertSegmentation(ref, out);
}
}
TEST(Reproducibility_SSD, Accuracy)
{
Net net;
{
const string proto = findDataFile("dnn/ssd_vgg16.prototxt", false);
const string model = findDataFile("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel", false);
net = readNetFromCaffe(proto, model);
ASSERT_FALSE(net.empty());
}
Mat sample = imread(_tf("street.png"));
ASSERT_TRUE(!sample.empty());
if (sample.channels() == 4)
cvtColor(sample, sample, COLOR_BGRA2BGR);
Mat in_blob = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
net.setInput(in_blob, "data");
Mat out = net.forward("detection_out");
Mat ref = blobFromNPY(_tf("ssd_out.npy"));
normAssertDetections(ref, out);
}
TEST_P(Reproducibility_AlexNet, Accuracy)
{
bool readFromMemory = get<0>(GetParam());
Net net;
{
const string proto = findDataFile("dnn/bvlc_alexnet.prototxt", false);
const string model = findDataFile("dnn/bvlc_alexnet.caffemodel", false);
if (readFromMemory)
{
string dataProto;
ASSERT_TRUE(readFileInMemory(proto, dataProto));
string dataModel;
ASSERT_TRUE(readFileInMemory(model, dataModel));
net = readNetFromCaffe(dataProto.c_str(), dataProto.size(),
dataModel.c_str(), dataModel.size());
}
else
net = readNetFromCaffe(proto, model);
ASSERT_FALSE(net.empty());
}
int targetId = get<1>(GetParam());
const float l1 = 1e-5;
const float lInf = (targetId == DNN_TARGET_OPENCL_FP16) ? 3e-3 : 1e-4;
net.setPreferableTarget(targetId);
Mat sample = imread(_tf("grace_hopper_227.png"));
ASSERT_TRUE(!sample.empty());
net.setInput(blobFromImage(sample, 1.0f, Size(227, 227), Scalar(), false), "data");
Mat out = net.forward("prob");
Mat ref = blobFromNPY(_tf("caffe_alexnet_prob.npy"));
normAssert(ref, out, "", l1, lInf);
}
TEST_P(Test_Torch_nets, OpenFace_accuracy)
{
const string model = findDataFile("dnn/openface_nn4.small2.v1.t7", false);
Net net = readNetFromTorch(model);
net.setPreferableTarget(GetParam());
Mat sample = imread(findDataFile("cv/shared/lena.png", false));
Mat sampleF32(sample.size(), CV_32FC3);
sample.convertTo(sampleF32, sampleF32.type());
sampleF32 /= 255;
resize(sampleF32, sampleF32, Size(96, 96), 0, 0, INTER_NEAREST);
Mat inputBlob = blobFromImage(sampleF32);
net.setInput(inputBlob);
Mat out = net.forward();
Mat outRef = readTorchBlob(_tf("net_openface_output.dat"), true);
normAssert(out, outRef);
}
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;
}
TEST(Test_Caffe, read_googlenet)
{
Net net = readNetFromCaffe(_tf("bvlc_googlenet.prototxt"));
ASSERT_FALSE(net.empty());
}
TEST(Torch_Importer, simple_read)
{
Net net;
ASSERT_NO_THROW(net = readNetFromTorch(_tf("net_simple_net.txt"), false));
ASSERT_FALSE(net.empty());
}
TEST(Test_Caffe, read_gtsrb)
{
Net net = readNetFromCaffe(_tf("gtsrb.prototxt"));
ASSERT_FALSE(net.empty());
}
// Test object detection network from Darknet framework.
void testDarknetModel(const std::string& cfg, const std::string& weights,
const std::vector<std::vector<int> >& refClassIds,
const std::vector<std::vector<float> >& refConfidences,
const std::vector<std::vector<Rect2d> >& refBoxes,
double scoreDiff, double iouDiff, float confThreshold = 0.24, float nmsThreshold = 0.4)
{
checkBackend();
Mat img1 = imread(_tf("dog416.png"));
Mat img2 = imread(_tf("street.png"));
std::vector<Mat> samples(2);
samples[0] = img1; samples[1] = img2;
// determine test type, whether batch or single img
int batch_size = refClassIds.size();
CV_Assert(batch_size == 1 || batch_size == 2);
samples.resize(batch_size);
Mat inp = blobFromImages(samples, 1.0/255, Size(416, 416), Scalar(), true, false);
Net net = readNet(findDataFile("dnn/" + cfg, false),
findDataFile("dnn/" + weights, false));
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(inp);
std::vector<Mat> outs;
net.forward(outs, getOutputsNames(net));
for (int b = 0; b < batch_size; ++b)
{
std::vector<int> classIds;
std::vector<float> confidences;
std::vector<Rect2d> boxes;
for (int i = 0; i < outs.size(); ++i)
{
Mat out;
if (batch_size > 1){
// get the sample slice from 3D matrix (batch, box, classes+5)
Range ranges[3] = {Range(b, b+1), Range::all(), Range::all()};
out = outs[i](ranges).reshape(1, outs[i].size[1]);
}else{
out = outs[i];
}
for (int j = 0; j < out.rows; ++j)
{
Mat scores = out.row(j).colRange(5, out.cols);
double confidence;
Point maxLoc;
minMaxLoc(scores, 0, &confidence, 0, &maxLoc);
if (confidence > confThreshold) {
float* detection = out.ptr<float>(j);
double centerX = detection[0];
double centerY = detection[1];
double width = detection[2];
double height = detection[3];
boxes.push_back(Rect2d(centerX - 0.5 * width, centerY - 0.5 * height,
width, height));
confidences.push_back(confidence);
classIds.push_back(maxLoc.x);
}
}
}
// here we need NMS of boxes
std::vector<int> indices;
NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, indices);
std::vector<int> nms_classIds;
std::vector<float> nms_confidences;
std::vector<Rect2d> nms_boxes;
for (size_t i = 0; i < indices.size(); ++i)
{
int idx = indices[i];
Rect2d box = boxes[idx];
float conf = confidences[idx];
int class_id = classIds[idx];
nms_boxes.push_back(box);
nms_confidences.push_back(conf);
nms_classIds.push_back(class_id);
}
normAssertDetections(refClassIds[b], refConfidences[b], refBoxes[b], nms_classIds,
nms_confidences, nms_boxes, format("batch size %d, sample %d\n", batch_size, b).c_str(), confThreshold, scoreDiff, iouDiff);
}
}
TEST(Test_Darknet, read_yolo_voc)
{
Net net = readNetFromDarknet(_tf("yolo-voc.cfg"));
ASSERT_FALSE(net.empty());
}
TEST_P(Test_ONNX_nets, Googlenet)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("");
const String model = _tf("models/googlenet.onnx");
Net net = readNetFromONNX(model);
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
std::vector<Mat> images;
images.push_back( imread(_tf("../googlenet_0.png")) );
images.push_back( imread(_tf("../googlenet_1.png")) );
Mat inp = blobFromImages(images, 1.0f, Size(), Scalar(), false);
Mat ref = blobFromNPY(_tf("../googlenet_prob.npy"));
checkBackend(&inp, &ref);
net.setInput(inp);
ASSERT_FALSE(net.empty());
Mat out = net.forward();
normAssert(ref, out, "", default_l1, default_lInf);
expectNoFallbacksFromIE(net);
}
TEST_P(Reproducibility_MobileNet_SSD, Accuracy)
{
const string proto = findDataFile("dnn/MobileNetSSD_deploy.prototxt", false);
const string model = findDataFile("dnn/MobileNetSSD_deploy.caffemodel", false);
Net net = readNetFromCaffe(proto, model);
int targetId = GetParam();
const float l1 = (targetId == DNN_TARGET_OPENCL_FP16) ? 1.5e-4 : 1e-5;
const float lInf = (targetId == DNN_TARGET_OPENCL_FP16) ? 4e-4 : 1e-4;
net.setPreferableTarget(targetId);
Mat sample = imread(_tf("street.png"));
Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
net.setInput(inp);
Mat out = net.forward();
const float scores_diff = (targetId == DNN_TARGET_OPENCL_FP16) ? 4e-4 : 1e-5;
const float boxes_iou_diff = (targetId == DNN_TARGET_OPENCL_FP16) ? 5e-3 : 1e-4;
Mat ref = blobFromNPY(_tf("mobilenet_ssd_caffe_out.npy"));
normAssertDetections(ref, out, "", 0.0, scores_diff, boxes_iou_diff);
// Check that detections aren't preserved.
inp.setTo(0.0f);
net.setInput(inp);
out = net.forward();
out = out.reshape(1, out.total() / 7);
const int numDetections = out.rows;
ASSERT_NE(numDetections, 0);
for (int i = 0; i < numDetections; ++i)
{
float confidence = out.ptr<float>(i)[2];
ASSERT_EQ(confidence, 0);
}
// Check batching mode.
ref = ref.reshape(1, numDetections);
inp = blobFromImages(std::vector<Mat>(2, sample), 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
net.setInput(inp);
Mat outBatch = net.forward();
// Output blob has a shape 1x1x2Nx7 where N is a number of detection for
// a single sample in batch. The first numbers of detection vectors are batch id.
outBatch = outBatch.reshape(1, outBatch.total() / 7);
EXPECT_EQ(outBatch.rows, 2 * numDetections);
normAssert(outBatch.rowRange(0, numDetections), ref, "", l1, lInf);
normAssert(outBatch.rowRange(numDetections, 2 * numDetections).colRange(1, 7), ref.colRange(1, 7),
"", l1, lInf);
}
