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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
2019-09-05 14:28:07 +03:00
parent 00f36a261b fafada28eb
commit bea2c75452
165 changed files with 1601 additions and 369 deletions
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modules/dnn/test/test_darknet_importer.cpp
+67 -12
View File
@@ -53,17 +53,6 @@ static std::string _tf(TString filename)
return (getOpenCVExtraDir() + "/dnn/") + filename;
}
static std::vector<String> getOutputsNames(const Net& net)
{
std::vector<String> names;
std::vector<int> outLayers = net.getUnconnectedOutLayers();
std::vector<String> layersNames = net.getLayerNames();
names.resize(outLayers.size());
for (size_t i = 0; i < outLayers.size(); ++i)
names[i] = layersNames[outLayers[i] - 1];
return names;
}
TEST(Test_Darknet, read_tiny_yolo_voc)
{
Net net = readNetFromDarknet(_tf("tiny-yolo-voc.cfg"));
@@ -159,7 +148,7 @@ public:
net.setPreferableTarget(target);
net.setInput(inp);
std::vector<Mat> outs;
net.forward(outs, getOutputsNames(net));
net.forward(outs, net.getUnconnectedOutLayersNames());
for (int b = 0; b < batch_size; ++b)
{
@@ -339,6 +328,62 @@ TEST_P(Test_Darknet_nets, TinyYoloVoc)
}
}
#ifdef HAVE_INF_ENGINE
static const std::chrono::milliseconds async_timeout(500);
typedef testing::TestWithParam<tuple<std::string, Target> > Test_Darknet_nets_async;
TEST_P(Test_Darknet_nets_async, Accuracy)
{
applyTestTag(CV_TEST_TAG_MEMORY_512MB);
std::string prefix = get<0>(GetParam());
int target = get<1>(GetParam());
const int numInputs = 2;
std::vector<Mat> inputs(numInputs);
int blobSize[] = {1, 3, 416, 416};
for (int i = 0; i < numInputs; ++i)
{
inputs[i].create(4, &blobSize[0], CV_32F);
randu(inputs[i], 0, 1);
}
Net netSync = readNet(findDataFile("dnn/" + prefix + ".cfg"),
findDataFile("dnn/" + prefix + ".weights", false));
netSync.setPreferableTarget(target);
// Run synchronously.
std::vector<Mat> refs(numInputs);
for (int i = 0; i < numInputs; ++i)
{
netSync.setInput(inputs[i]);
refs[i] = netSync.forward().clone();
}
Net netAsync = readNet(findDataFile("dnn/" + prefix + ".cfg"),
findDataFile("dnn/" + prefix + ".weights", false));
netAsync.setPreferableTarget(target);
// Run asynchronously. To make test more robust, process inputs in the reversed order.
for (int i = numInputs - 1; i >= 0; --i)
{
netAsync.setInput(inputs[i]);
AsyncArray out = netAsync.forwardAsync();
ASSERT_TRUE(out.valid());
Mat result;
EXPECT_TRUE(out.get(result, async_timeout));
normAssert(refs[i], result, format("Index: %d", i).c_str(), 0, 0);
}
}
INSTANTIATE_TEST_CASE_P(/**/, Test_Darknet_nets_async, Combine(
Values("yolo-voc", "tiny-yolo-voc", "yolov3"),
ValuesIn(getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE))
));
#endif
TEST_P(Test_Darknet_nets, YOLOv3)
{
applyTestTag(CV_TEST_TAG_LONG, (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB));
@@ -376,6 +421,16 @@ TEST_P(Test_Darknet_nets, YOLOv3)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL) // Test with 'batch size 2' is disabled for DLIE/OpenCL target
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
{
if (target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
if (target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
}
#endif
{
SCOPED_TRACE("batch size 2");
testDarknetModel(config_file, weights_file, ref, scoreDiff, iouDiff);
modules/dnn/test/test_halide_layers.cpp
+5
View File
@@ -554,6 +554,11 @@ TEST_P(ReLU, Accuracy)
Backend backendId = get<0>(get<1>(GetParam()));
Target targetId = get<1>(get<1>(GetParam()));
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD && negativeSlope < 0)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
#endif
LayerParams lp;
lp.set("negative_slope", negativeSlope);
lp.type = "ReLU";
modules/dnn/test/test_layers.cpp
+1 -1
View File
@@ -1112,7 +1112,7 @@ INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_DLDT_two_inputs, Combine(
class UnsupportedLayer : public Layer
{
public:
UnsupportedLayer(const LayerParams &params) {}
UnsupportedLayer(const LayerParams &params) : Layer(params) {}
static Ptr<Layer> create(const LayerParams& params)
{
modules/dnn/test/test_tf_importer.cpp
+17 -3
View File
@@ -145,8 +145,17 @@ TEST_P(Test_TensorFlow_layers, padding)
{
runTensorFlowNet("padding_valid");
runTensorFlowNet("spatial_padding");
runTensorFlowNet("keras_pad_concat");
runTensorFlowNet("mirror_pad");
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
{
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
if (target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
}
#endif
runTensorFlowNet("keras_pad_concat");
}
TEST_P(Test_TensorFlow_layers, padding_same)
@@ -472,7 +481,7 @@ TEST_P(Test_TensorFlow_nets, Faster_RCNN)
"faster_rcnn_resnet50_coco_2018_01_28"};
checkBackend();
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
@@ -573,6 +582,10 @@ TEST_P(Test_TensorFlow_nets, EAST_text_detection)
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16 &&
INF_ENGINE_VER_MAJOR_EQ(2019020000))
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
#endif
checkBackend();
@@ -673,7 +686,8 @@ TEST_P(Test_TensorFlow_layers, lstm)
TEST_P(Test_TensorFlow_layers, split)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD &&
getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2);
runTensorFlowNet("split");
if (backend == DNN_BACKEND_INFERENCE_ENGINE)