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Enable more deep learning tests using Intel's Inference Engine backend

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This commit is contained in:
Dmitry Kurtaev
2018-08-27 15:45:44 +03:00
parent 6477262e63
commit 3e027df583
12 changed files with 167 additions and 83 deletions
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modules/dnn/test/test_torch_importer.cpp
+33 -11
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@@ -242,15 +242,23 @@ TEST_P(Test_Torch_layers, net_residual)
runTorchNet("net_residual", "", false, true);
}
typedef testing::TestWithParam<Target> Test_Torch_nets;
class Test_Torch_nets : public DNNTestLayer {};
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(DNN_BACKEND_OPENCV);
net.setPreferableTarget(GetParam());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat sample = imread(findDataFile("cv/shared/lena.png", false));
Mat sampleF32(sample.size(), CV_32FC3);
@@ -264,11 +272,16 @@ TEST_P(Test_Torch_nets, OpenFace_accuracy)
Mat out = net.forward();
Mat outRef = readTorchBlob(_tf("net_openface_output.dat"), true);
normAssert(out, outRef);
normAssert(out, outRef, "", default_l1, default_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);
@@ -276,8 +289,8 @@ TEST_P(Test_Torch_nets, ENet_accuracy)
ASSERT_TRUE(!net.empty());
}
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(GetParam());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat sample = imread(_tf("street.png", false));
Mat inputBlob = blobFromImage(sample, 1./255);
@@ -314,6 +327,7 @@ TEST_P(Test_Torch_nets, ENet_accuracy)
// -model models/instance_norm/feathers.t7
TEST_P(Test_Torch_nets, FastNeuralStyle_accuracy)
{
checkBackend();
std::string models[] = {"dnn/fast_neural_style_eccv16_starry_night.t7",
"dnn/fast_neural_style_instance_norm_feathers.t7"};
std::string targets[] = {"dnn/lena_starry_night.png", "dnn/lena_feathers.png"};
@@ -323,8 +337,8 @@ TEST_P(Test_Torch_nets, FastNeuralStyle_accuracy)
const string model = findDataFile(models[i], false);
Net net = readNetFromTorch(model);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(GetParam());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat img = imread(findDataFile("dnn/googlenet_1.png", false));
Mat inputBlob = blobFromImage(img, 1.0, Size(), Scalar(103.939, 116.779, 123.68), false);
@@ -341,12 +355,20 @@ TEST_P(Test_Torch_nets, FastNeuralStyle_accuracy)
Mat ref = imread(findDataFile(targets[i]));
Mat refBlob = blobFromImage(ref, 1.0, Size(), Scalar(), false);
normAssert(out, refBlob, "", 0.5, 1.1);
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
{
double normL1 = cvtest::norm(refBlob, out, cv::NORM_L1) / refBlob.total();
if (target == DNN_TARGET_MYRIAD)
EXPECT_LE(normL1, 4.0f);
else
EXPECT_LE(normL1, 0.6f);
}
else
normAssert(out, refBlob, "", 0.5, 1.1);
}
}
INSTANTIATE_TEST_CASE_P(/**/, Test_Torch_nets, availableDnnTargets());
INSTANTIATE_TEST_CASE_P(/**/, Test_Torch_nets, dnnBackendsAndTargets());
// Test a custom layer
// https://github.com/torch/nn/blob/master/doc/convolution.md#nn.SpatialUpSamplingNearest