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);
}
}
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, 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);
}
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);
}
