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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
2019-10-24 18:17:40 +00:00
parent 87a692b89e eabe679f78
commit 055ffc0425
61 changed files with 1438 additions and 394 deletions
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modules/dnn/test/test_darknet_importer.cpp
+25 -16
View File
@@ -390,12 +390,6 @@ 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));
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
#endif
// batchId, classId, confidence, left, top, right, bottom
Mat ref = (Mat_<float>(9, 7) << 0, 7, 0.952983f, 0.614622f, 0.150257f, 0.901369f, 0.289251f, // a truck
0, 1, 0.987908f, 0.150913f, 0.221933f, 0.742255f, 0.74626f, // a bicycle
@@ -413,23 +407,35 @@ TEST_P(Test_Darknet_nets, YOLOv3)
std::string config_file = "yolov3.cfg";
std::string weights_file = "yolov3.weights";
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD &&
getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
{
scoreDiff = 0.04;
iouDiff = 0.2;
}
#endif
{
SCOPED_TRACE("batch size 1");
testDarknetModel(config_file, weights_file, ref.rowRange(0, 3), scoreDiff, iouDiff);
}
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL)
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 defined(INF_ENGINE_RELEASE)
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);
if (INF_ENGINE_VER_MAJOR_LE(2018050000) && target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_2018R5);
else if (INF_ENGINE_VER_MAJOR_EQ(2019020000))
{
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);
}
else if (target == DNN_TARGET_MYRIAD &&
getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
}
#endif
@@ -444,6 +450,9 @@ INSTANTIATE_TEST_CASE_P(/**/, Test_Darknet_nets, dnnBackendsAndTargets());
TEST_P(Test_Darknet_layers, shortcut)
{
testDarknetLayer("shortcut");
testDarknetLayer("shortcut_leaky");
testDarknetLayer("shortcut_unequal");
testDarknetLayer("shortcut_unequal_2");
}
TEST_P(Test_Darknet_layers, upsample)
modules/dnn/test/test_layers.cpp
+58
View File
@@ -1493,4 +1493,62 @@ TEST(Layer_Test_Convolution, relu_fusion)
normAssert(input, output);
}
typedef testing::TestWithParam<tuple<bool, tuple<Backend, Target> > > Layer_Test_Eltwise_unequal;
TEST_P(Layer_Test_Eltwise_unequal, Accuracy)
{
bool weighted = get<0>(GetParam());
int backendId = get<0>(get<1>(GetParam()));
int targetId = get<1>(get<1>(GetParam()));
if (backendId == DNN_BACKEND_OPENCV && targetId == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
Net net;
LayerParams lp;
lp.type = "Eltwise";
lp.name = "testLayer";
const int inpShapes[][4] = {{1, 4, 2, 2}, {1, 5, 2, 2}, {1, 3, 2, 2}};
std::vector<String> inpNames(3);
std::vector<Mat> inputs(3);
size_t numOutValues = 1*4*2*2; // By the first input
std::vector<float> weights(3, 1);
if (weighted)
{
for (int i = 0; i < inputs.size(); ++i)
randu(Mat(1, 1, CV_32F, &weights[i]), -1, 1);
lp.set("coeff", DictValue::arrayReal<float*>(&weights[0], weights.size()));
}
int eltwiseId = net.addLayer(lp.name, lp.type, lp);
for (int i = 0; i < inputs.size(); ++i)
{
inputs[i].create(4, inpShapes[i], CV_32F);
randu(inputs[i], 0, 255);
inpNames[i] = format("input_%d", i);
net.connect(0, i, eltwiseId, i);
}
Mat ref(1, numOutValues, CV_32F, Scalar(0));
net.setInputsNames(inpNames);
for (int i = 0; i < inputs.size(); ++i)
{
net.setInput(inputs[i], inpNames[i]);
if (numOutValues >= inputs[i].total())
ref.colRange(0, inputs[i].total()) += weights[i] * inputs[i].reshape(1, 1);
else
ref += weights[i] * inputs[i].reshape(1, 1).colRange(0, numOutValues);
}
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
Mat out = net.forward();
normAssert(out.reshape(1, 1), ref);
}
INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_Eltwise_unequal, Combine(
testing::Bool(),
dnnBackendsAndTargets()
));
}} // namespace