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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_backends.cpp
+17 -7
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@@ -222,9 +222,12 @@ TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
TEST_P(DNNTestNetwork, OpenFace)
{
#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
if (backend == DNN_BACKEND_HALIDE ||
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16) ||
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("");
processNet("dnn/openface_nn4.small2.v1.t7", "", Size(96, 96), "");
}
@@ -253,12 +256,19 @@ TEST_P(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
TEST_P(DNNTestNetwork, DenseNet_121)
{
if ((backend == DNN_BACKEND_HALIDE) ||
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) ||
(backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL_FP16 ||
target == DNN_TARGET_MYRIAD)))
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", Size(224, 224), "", "caffe");
float l1 = 0.0, lInf = 0.0;
if (target == DNN_TARGET_OPENCL_FP16)
{
l1 = 9e-3; lInf = 5e-2;
}
else if (target == DNN_TARGET_MYRIAD)
{
l1 = 6e-2; lInf = 0.27;
}
processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", Size(224, 224), "", "", l1, lInf);
}
TEST_P(DNNTestNetwork, FastNeuralStyle_eccv16)
modules/dnn/test/test_caffe_importer.cpp
+18 -11
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@@ -374,14 +374,6 @@ TEST(Reproducibility_GoogLeNet_fp16, Accuracy)
TEST_P(Test_Caffe_nets, Colorization)
{
checkBackend();
if ((backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16) ||
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD) ||
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("");
const float l1 = 4e-4;
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"));
@@ -398,11 +390,15 @@ TEST_P(Test_Caffe_nets, Colorization)
net.setInput(inp);
Mat out = net.forward();
// Reference output values are in range [-29.1, 69.5]
const double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.21 : 4e-4;
const double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 5.3 : 3e-3;
normAssert(out, ref, "", l1, lInf);
}
TEST(Reproducibility_DenseNet_121, Accuracy)
TEST_P(Test_Caffe_nets, DenseNet_121)
{
checkBackend();
const string proto = findDataFile("dnn/DenseNet_121.prototxt", false);
const string model = findDataFile("dnn/DenseNet_121.caffemodel", false);
@@ -411,12 +407,23 @@ TEST(Reproducibility_DenseNet_121, Accuracy)
Mat ref = blobFromNPY(_tf("densenet_121_output.npy"));
Net net = readNetFromCaffe(proto, model);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(inp);
Mat out = net.forward();
normAssert(out, ref);
// Reference is an array of 1000 values from a range [-6.16, 7.9]
float l1 = default_l1, lInf = default_lInf;
if (target == DNN_TARGET_OPENCL_FP16)
{
l1 = 0.017; lInf = 0.067;
}
else if (target == DNN_TARGET_MYRIAD)
{
l1 = 0.097; lInf = 0.52;
}
normAssert(out, ref, "", l1, lInf);
}
TEST(Test_Caffe, multiple_inputs)
modules/dnn/test/test_ie_models.cpp
+2 -1
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@@ -177,7 +177,8 @@ TEST_P(DNNTestOpenVINO, models)
Target target = (dnn::Target)(int)get<0>(GetParam());
std::string modelName = get<1>(GetParam());
if (modelName == "semantic-segmentation-adas-0001" && target == DNN_TARGET_OPENCL_FP16)
if ((modelName == "semantic-segmentation-adas-0001" && target == DNN_TARGET_OPENCL_FP16) ||
(modelName == "vehicle-license-plate-detection-barrier-0106"))
throw SkipTestException("");
std::string precision = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? "FP16" : "FP32";
modules/dnn/test/test_layers.cpp
+1 -7
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@@ -127,15 +127,9 @@ TEST_P(Test_Caffe_layers, Softmax)
testLayerUsingCaffeModels("layer_softmax");
}
TEST_P(Test_Caffe_layers, LRN_spatial)
TEST_P(Test_Caffe_layers, LRN)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
testLayerUsingCaffeModels("layer_lrn_spatial");
}
TEST_P(Test_Caffe_layers, LRN_channels)
{
testLayerUsingCaffeModels("layer_lrn_channels");
}
modules/dnn/test/test_tf_importer.cpp
+23 -4
View File
@@ -399,8 +399,10 @@ TEST_P(Test_TensorFlow_nets, opencv_face_detector_uint8)
TEST_P(Test_TensorFlow_nets, EAST_text_detection)
{
checkBackend();
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
#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
std::string netPath = findDataFile("dnn/frozen_east_text_detection.pb", false);
std::string imgPath = findDataFile("cv/ximgproc/sources/08.png", false);
@@ -425,8 +427,25 @@ TEST_P(Test_TensorFlow_nets, EAST_text_detection)
Mat scores = outs[0];
Mat geometry = outs[1];
normAssert(scores, blobFromNPY(refScoresPath), "scores");
normAssert(geometry, blobFromNPY(refGeometryPath), "geometry", 1e-4, 3e-3);
// Scores are in range [0, 1]. Geometry values are in range [-0.23, 290]
double l1_scores = default_l1, lInf_scores = default_lInf;
double l1_geometry = default_l1, lInf_geometry = default_lInf;
if (target == DNN_TARGET_OPENCL_FP16)
{
lInf_scores = 0.11;
l1_geometry = 0.28; lInf_geometry = 5.94;
}
else if (target == DNN_TARGET_MYRIAD)
{
lInf_scores = 0.214;
l1_geometry = 0.47; lInf_geometry = 15.34;
}
else
{
l1_geometry = 1e-4, lInf_geometry = 3e-3;
}
normAssert(scores, blobFromNPY(refScoresPath), "scores", l1_scores, lInf_scores);
normAssert(geometry, blobFromNPY(refGeometryPath), "geometry", l1_geometry, lInf_geometry);
}
INSTANTIATE_TEST_CASE_P(/**/, Test_TensorFlow_nets, dnnBackendsAndTargets());
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