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Merge remote-tracking branch 'upstream/3.4' into merge-3.4

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This commit is contained in:
Alexander Alekhin
2019-04-30 19:59:49 +00:00
parent 076ee65c85 163d5e4d39
commit e28e3c9491
37 changed files with 1159 additions and 439 deletions
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modules/dnn/test/test_backends.cpp
+20 -2
View File
@@ -45,7 +45,7 @@ public:
netDefault.setInput(inp);
Mat outDefault = netDefault.forward(outputLayer).clone();
Net net = readNet(weights, proto);
net = readNet(weights, proto);
net.setInput(inp);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
@@ -94,6 +94,8 @@ public:
else
normAssert(ref, out, msg, l1, lInf);
}
Net net;
};
TEST_P(DNNTestNetwork, AlexNet)
@@ -103,6 +105,7 @@ TEST_P(DNNTestNetwork, AlexNet)
Size(227, 227), "prob",
target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_alexnet.yml" :
"dnn/halide_scheduler_alexnet.yml");
expectNoFallbacksFromIE(net);
}
TEST_P(DNNTestNetwork, ResNet_50)
@@ -112,6 +115,7 @@ TEST_P(DNNTestNetwork, ResNet_50)
Size(224, 224), "prob",
target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_resnet_50.yml" :
"dnn/halide_scheduler_resnet_50.yml");
expectNoFallbacksFromIE(net);
}
TEST_P(DNNTestNetwork, SqueezeNet_v1_1)
@@ -120,6 +124,7 @@ TEST_P(DNNTestNetwork, SqueezeNet_v1_1)
Size(227, 227), "prob",
target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_squeezenet_v1_1.yml" :
"dnn/halide_scheduler_squeezenet_v1_1.yml");
expectNoFallbacksFromIE(net);
}
TEST_P(DNNTestNetwork, GoogLeNet)
@@ -127,6 +132,7 @@ TEST_P(DNNTestNetwork, GoogLeNet)
applyTestTag(target == DNN_TARGET_CPU ? "" : CV_TEST_TAG_MEMORY_512MB);
processNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt",
Size(224, 224), "prob");
expectNoFallbacksFromIE(net);
}
TEST_P(DNNTestNetwork, Inception_5h)
@@ -142,6 +148,7 @@ TEST_P(DNNTestNetwork, Inception_5h)
target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_inception_5h.yml" :
"dnn/halide_scheduler_inception_5h.yml",
l1, lInf);
expectNoFallbacksFromIE(net);
}
TEST_P(DNNTestNetwork, ENet)
@@ -168,6 +175,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe)
float detectionConfThresh = (target == DNN_TARGET_MYRIAD) ? 0.252 : 0.0;
processNet("dnn/MobileNetSSD_deploy.caffemodel", "dnn/MobileNetSSD_deploy.prototxt",
inp, "detection_out", "", diffScores, diffSquares, detectionConfThresh);
expectNoFallbacksFromIE(net);
}
TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe_Different_Width_Height)
@@ -185,7 +193,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe_Different_Width_Height)
float diffSquares = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.09 : 0.0;
processNet("dnn/MobileNetSSD_deploy.caffemodel", "dnn/MobileNetSSD_deploy.prototxt",
inp, "detection_out", "", diffScores, diffSquares);
expectNoFallbacksFromIE(net);
}
TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow)
@@ -200,6 +208,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow)
float detectionConfThresh = (target == DNN_TARGET_MYRIAD) ? 0.216 : 0.2;
processNet("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", "dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt",
inp, "detection_out", "", l1, lInf, detectionConfThresh);
expectNoFallbacksFromIE(net);
}
TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow_Different_Width_Height)
@@ -217,6 +226,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow_Different_Width_Height)
float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.06 : 0.0;
processNet("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", "dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt",
inp, "detection_out", "", l1, lInf);
expectNoFallbacksFromIE(net);
}
TEST_P(DNNTestNetwork, MobileNet_SSD_v2_TensorFlow)
@@ -230,6 +240,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_v2_TensorFlow)
float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.062 : 0.0;
processNet("dnn/ssd_mobilenet_v2_coco_2018_03_29.pb", "dnn/ssd_mobilenet_v2_coco_2018_03_29.pbtxt",
inp, "detection_out", "", l1, lInf, 0.25);
expectNoFallbacksFromIE(net);
}
TEST_P(DNNTestNetwork, SSD_VGG16)
@@ -244,6 +255,7 @@ TEST_P(DNNTestNetwork, SSD_VGG16)
Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
processNet("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel",
"dnn/ssd_vgg16.prototxt", inp, "detection_out", "", scoreThreshold, lInf);
expectNoFallbacksFromIE(net);
}
TEST_P(DNNTestNetwork, OpenPose_pose_coco)
@@ -262,6 +274,7 @@ TEST_P(DNNTestNetwork, OpenPose_pose_coco)
const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.072 : 0.0;
processNet("dnn/openpose_pose_coco.caffemodel", "dnn/openpose_pose_coco.prototxt",
Size(46, 46), "", "", l1, lInf);
expectNoFallbacksFromIE(net);
}
TEST_P(DNNTestNetwork, OpenPose_pose_mpi)
@@ -280,6 +293,7 @@ TEST_P(DNNTestNetwork, OpenPose_pose_mpi)
const float lInf = (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16) ? 0.16 : 0.0;
processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi.prototxt",
Size(46, 46), "", "", l1, lInf);
expectNoFallbacksFromIE(net);
}
TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
@@ -296,6 +310,7 @@ TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
// See https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/pose/poseParameters.cpp
processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi_faster_4_stages.prototxt",
Size(46, 46));
expectNoFallbacksFromIE(net);
}
TEST_P(DNNTestNetwork, OpenFace)
@@ -324,6 +339,7 @@ TEST_P(DNNTestNetwork, opencv_face_detector)
Mat inp = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);
processNet("dnn/opencv_face_detector.caffemodel", "dnn/opencv_face_detector.prototxt",
inp, "detection_out");
expectNoFallbacksFromIE(net);
}
TEST_P(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
@@ -342,6 +358,7 @@ TEST_P(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0731 : 0.0;
processNet("dnn/ssd_inception_v2_coco_2017_11_17.pb", "dnn/ssd_inception_v2_coco_2017_11_17.pbtxt",
inp, "detection_out", "", l1, lInf);
expectNoFallbacksFromIE(net);
}
TEST_P(DNNTestNetwork, DenseNet_121)
@@ -360,6 +377,7 @@ TEST_P(DNNTestNetwork, DenseNet_121)
l1 = 0.1; lInf = 0.6;
}
processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", Size(224, 224), "", "", l1, lInf);
expectNoFallbacksFromIE(net);
}
TEST_P(DNNTestNetwork, FastNeuralStyle_eccv16)
modules/dnn/test/test_caffe_importer.cpp
+35 -21
View File
@@ -207,60 +207,72 @@ TEST(Reproducibility_SSD, Accuracy)
normAssertDetections(ref, out);
}
typedef testing::TestWithParam<Target> Reproducibility_MobileNet_SSD;
typedef testing::TestWithParam<tuple<Backend, Target> > Reproducibility_MobileNet_SSD;
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;
int backendId = get<0>(GetParam());
int targetId = get<1>(GetParam());
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableBackend(backendId);
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();
Mat out = net.forward().clone();
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;
const float scores_diff = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 1.5e-2 : 1e-5;
const float boxes_iou_diff = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 6.3e-2 : 1e-4;
Mat ref = blobFromNPY(_tf("mobilenet_ssd_caffe_out.npy"));
normAssertDetections(ref, out, "", 0.0, scores_diff, boxes_iou_diff);
normAssertDetections(ref, out, "", FLT_MIN, 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);
Mat zerosOut = net.forward();
zerosOut = zerosOut.reshape(1, zerosOut.total() / 7);
const int numDetections = out.rows;
const int numDetections = zerosOut.rows;
ASSERT_NE(numDetections, 0);
for (int i = 0; i < numDetections; ++i)
{
float confidence = out.ptr<float>(i)[2];
float confidence = zerosOut.ptr<float>(i)[2];
ASSERT_EQ(confidence, 0);
}
// There is something wrong with Reshape layer in Myriad plugin and
// regression with DLIE/OCL_FP16 target.
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
{
if ((targetId == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2) ||
targetId == DNN_TARGET_OPENCL_FP16)
return;
}
// 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);
// For Inference Engine backend there is -1 delimiter which points the end of detections.
const int numRealDetections = ref.size[2];
EXPECT_EQ(outBatch.size[2], 2 * numDetections);
out = out.reshape(1, numDetections).rowRange(0, numRealDetections);
outBatch = outBatch.reshape(1, 2 * numDetections);
for (int i = 0; i < 2; ++i)
{
Mat pred = outBatch.rowRange(i * numRealDetections, (i + 1) * numRealDetections);
EXPECT_EQ(countNonZero(pred.col(0) != i), 0);
normAssert(pred.colRange(1, 7), out.colRange(1, 7));
}
}
INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_MobileNet_SSD,
Values(DNN_TARGET_CPU, DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16));
INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_MobileNet_SSD, dnnBackendsAndTargets());
typedef testing::TestWithParam<Target> Reproducibility_ResNet50;
TEST_P(Reproducibility_ResNet50, Accuracy)
@@ -405,6 +417,7 @@ TEST_P(Test_Caffe_nets, Colorization)
l1 = 0.6; lInf = 15;
}
normAssert(out, ref, "", l1, lInf);
expectNoFallbacksFromIE(net);
}
TEST_P(Test_Caffe_nets, DenseNet_121)
@@ -436,6 +449,7 @@ TEST_P(Test_Caffe_nets, DenseNet_121)
l1 = 0.11; lInf = 0.5;
}
normAssert(out, ref, "", l1, lInf);
expectNoFallbacksFromIE(net);
}
TEST(Test_Caffe, multiple_inputs)
modules/dnn/test/test_common.hpp
+25
View File
@@ -111,6 +111,31 @@ public:
}
}
void expectNoFallbacks(Net& net)
{
// Check if all the layers are supported with current backend and target.
// Some layers might be fused so their timings equal to zero.
std::vector<double> timings;
net.getPerfProfile(timings);
std::vector<String> names = net.getLayerNames();
CV_Assert(names.size() == timings.size());
for (int i = 0; i < names.size(); ++i)
{
Ptr<dnn::Layer> l = net.getLayer(net.getLayerId(names[i]));
bool fused = !timings[i];
if ((!l->supportBackend(backend) || l->preferableTarget != target) && !fused)
CV_Error(Error::StsNotImplemented, "Layer [" + l->name + "] of type [" +
l->type + "] is expected to has backend implementation");
}
}
void expectNoFallbacksFromIE(Net& net)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
expectNoFallbacks(net);
}
protected:
void checkBackend(Mat* inp = 0, Mat* ref = 0)
{
modules/dnn/test/test_onnx_importer.cpp
+33 -5
View File
@@ -28,7 +28,8 @@ public:
};
void testONNXModels(const String& basename, const Extension ext = npy,
const double l1 = 0, const float lInf = 0, const bool useSoftmax = false)
const double l1 = 0, const float lInf = 0, const bool useSoftmax = false,
bool checkNoFallbacks = true)
{
String onnxmodel = _tf("models/" + basename + ".onnx");
Mat inp, ref;
@@ -67,6 +68,8 @@ public:
ref = netSoftmax.forward();
}
normAssert(ref, out, "", l1 ? l1 : default_l1, lInf ? lInf : default_lInf);
if (checkNoFallbacks)
expectNoFallbacksFromIE(net);
}
};
@@ -81,6 +84,13 @@ TEST_P(Test_ONNX_layers, Convolution)
testONNXModels("convolution");
}
TEST_P(Test_ONNX_layers, Convolution3D)
{
if (backend != DNN_BACKEND_INFERENCE_ENGINE || target != DNN_TARGET_CPU)
throw SkipTestException("Only DLIE backend on CPU is supported");
testONNXModels("conv3d");
testONNXModels("conv3d_bias");
}
TEST_P(Test_ONNX_layers, Two_convolution)
{
@@ -96,10 +106,11 @@ TEST_P(Test_ONNX_layers, Two_convolution)
TEST_P(Test_ONNX_layers, Deconvolution)
{
testONNXModels("deconvolution");
testONNXModels("two_deconvolution");
testONNXModels("deconvolution_group");
testONNXModels("deconvolution_output_shape");
testONNXModels("deconvolution", npy, 0, 0, false, false);
testONNXModels("two_deconvolution", npy, 0, 0, false, false);
testONNXModels("deconvolution_group", npy, 0, 0, false, false);
testONNXModels("deconvolution_output_shape", npy, 0, 0, false, false);
testONNXModels("deconv_adjpad_2d", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, Dropout)
@@ -137,6 +148,20 @@ TEST_P(Test_ONNX_layers, AveragePooling)
testONNXModels("average_pooling");
}
TEST_P(Test_ONNX_layers, MaxPooling3D)
{
if (backend != DNN_BACKEND_INFERENCE_ENGINE || target != DNN_TARGET_CPU)
throw SkipTestException("Only DLIE backend on CPU is supported");
testONNXModels("max_pool3d");
}
TEST_P(Test_ONNX_layers, AvePooling3D)
{
if (backend != DNN_BACKEND_INFERENCE_ENGINE || target != DNN_TARGET_CPU)
throw SkipTestException("Only DLIE backend on CPU is supported");
testONNXModels("ave_pool3d");
}
TEST_P(Test_ONNX_layers, BatchNormalization)
{
testONNXModels("batch_norm");
@@ -198,6 +223,7 @@ TEST_P(Test_ONNX_layers, MultyInputs)
Mat out = net.forward();
normAssert(ref, out, "", default_l1, default_lInf);
expectNoFallbacksFromIE(net);
}
TEST_P(Test_ONNX_layers, DynamicReshape)
@@ -235,6 +261,7 @@ TEST_P(Test_ONNX_nets, Alexnet)
Mat out = net.forward();
normAssert(out, ref, "", default_l1, default_lInf);
expectNoFallbacksFromIE(net);
}
TEST_P(Test_ONNX_nets, Squeezenet)
@@ -267,6 +294,7 @@ TEST_P(Test_ONNX_nets, Googlenet)
Mat out = net.forward();
normAssert(ref, out, "", default_l1, default_lInf);
expectNoFallbacksFromIE(net);
}
TEST_P(Test_ONNX_nets, CaffeNet)
modules/dnn/test/test_tf_importer.cpp
+34
View File
@@ -131,6 +131,13 @@ TEST_P(Test_TensorFlow_layers, conv)
runTensorFlowNet("conv_pool_nchw");
}
TEST_P(Test_TensorFlow_layers, Convolution3D)
{
if (backend != DNN_BACKEND_INFERENCE_ENGINE || target != DNN_TARGET_CPU)
throw SkipTestException("Only DLIE backend on CPU is supported");
runTensorFlowNet("conv3d");
}
TEST_P(Test_TensorFlow_layers, padding)
{
runTensorFlowNet("padding_valid");
@@ -212,6 +219,20 @@ TEST_P(Test_TensorFlow_layers, ave_pool_same)
runTensorFlowNet("ave_pool_same");
}
TEST_P(Test_TensorFlow_layers, MaxPooling3D)
{
if (backend != DNN_BACKEND_INFERENCE_ENGINE || target != DNN_TARGET_CPU)
throw SkipTestException("Only DLIE backend on CPU is supported");
runTensorFlowNet("max_pool3d");
}
TEST_P(Test_TensorFlow_layers, AvePooling3D)
{
if (backend != DNN_BACKEND_INFERENCE_ENGINE || target != DNN_TARGET_CPU)
throw SkipTestException("Only DLIE backend on CPU is supported");
runTensorFlowNet("ave_pool3d");
}
TEST_P(Test_TensorFlow_layers, deconvolution)
{
runTensorFlowNet("deconvolution");
@@ -335,6 +356,7 @@ TEST_P(Test_TensorFlow_nets, MobileNet_SSD)
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0043 : default_l1;
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.037 : default_lInf;
normAssertDetections(ref, out, "", 0.2, scoreDiff, iouDiff);
expectNoFallbacksFromIE(net);
}
TEST_P(Test_TensorFlow_nets, Inception_v2_SSD)
@@ -372,6 +394,7 @@ TEST_P(Test_TensorFlow_nets, Inception_v2_SSD)
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);
expectNoFallbacksFromIE(net);
}
TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD)
@@ -402,6 +425,7 @@ TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD)
float scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 7e-3 : 1.5e-5;
float iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.012 : 1e-3;
normAssertDetections(ref, out, "", 0.3, scoreDiff, iouDiff);
expectNoFallbacksFromIE(net);
}
TEST_P(Test_TensorFlow_nets, Faster_RCNN)
@@ -460,6 +484,7 @@ TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD_PPN)
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.048 : 1.1e-5;
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.058 : default_lInf;
normAssertDetections(ref, out, "", 0.45, scoreDiff, iouDiff);
expectNoFallbacksFromIE(net);
}
TEST_P(Test_TensorFlow_nets, opencv_face_detector_uint8)
@@ -489,6 +514,7 @@ TEST_P(Test_TensorFlow_nets, opencv_face_detector_uint8)
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);
expectNoFallbacksFromIE(net);
}
// inp = cv.imread('opencv_extra/testdata/cv/ximgproc/sources/08.png')
@@ -553,6 +579,7 @@ TEST_P(Test_TensorFlow_nets, EAST_text_detection)
}
normAssert(scores, blobFromNPY(refScoresPath), "scores", l1_scores, lInf_scores);
normAssert(geometry, blobFromNPY(refGeometryPath), "geometry", l1_geometry, lInf_geometry);
expectNoFallbacksFromIE(net);
}
INSTANTIATE_TEST_CASE_P(/**/, Test_TensorFlow_nets, dnnBackendsAndTargets());
@@ -654,6 +681,13 @@ TEST_P(Test_TensorFlow_layers, relu6)
runTensorFlowNet("keras_relu6", /*hasText*/ true);
}
TEST_P(Test_TensorFlow_layers, subpixel)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("");
runTensorFlowNet("subpixel");
}
TEST_P(Test_TensorFlow_layers, keras_mobilenet_head)
{
runTensorFlowNet("keras_mobilenet_head");