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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
2021-12-03 12:32:49 +00:00
parent c3910807c5 35ff9af6ce
commit 8b4fa2605e
89 changed files with 1116 additions and 542 deletions
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modules/dnn/test/test_misc.cpp
+62 -1
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@@ -582,7 +582,8 @@ TEST_P(Async, create_layer_pipeline_set_and_forward_all)
if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
throw SkipTestException("No support for async forward");
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
// Exception: Default implementation fallbacks in asynchronous mode
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && dtype == CV_8U)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
@@ -832,4 +833,64 @@ INSTANTIATE_TEST_CASE_P(/**/, Test_Model_Optimizer,
#endif // HAVE_INF_ENGINE
typedef testing::TestWithParam<tuple<MatDepth, MatDepth, tuple<Backend, Target> > > Test_two_inputs;
TEST_P(Test_two_inputs, basic)
{
static const float kScale = 0.5f;
static const float kScaleInv = 1.0f / kScale;
Backend backendId = get<0>(get<2>(GetParam()));
Target targetId = get<1>(get<2>(GetParam()));
Net net;
LayerParams lp;
lp.type = "Eltwise";
lp.name = "testLayer";
lp.set("operation", "sum");
int eltwiseId = net.addLayerToPrev(lp.name, lp.type, lp); // connect to a first input
net.connect(0, 1, eltwiseId, 1); // connect to a second input
int inpSize[] = {1, 2, 3, 4};
Mat firstInp(4, &inpSize[0], get<0>(GetParam()));
Mat secondInp(4, &inpSize[0], get<1>(GetParam()));
randu(firstInp, 0, 100);
randu(secondInp, 0, 100);
#ifndef CV_CXX11
std::vector<String> input_names;
input_names.push_back("data");
input_names.push_back("second_input");
net.setInputsNames(input_names);
#else
net.setInputsNames({"data", "second_input"});
#endif
net.setInput(firstInp, "data", kScale);
net.setInput(secondInp, "second_input", kScaleInv);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
Mat out = net.forward();
Mat ref;
addWeighted(firstInp, kScale, secondInp, kScaleInv, 0, ref, CV_32F);
double l1 = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 0.06 : 1e-6;
double lInf = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 0.3 : 1e-5;
normAssert(out, ref, "", l1, lInf);
if (cvtest::debugLevel > 0 || HasFailure())
{
std::cout << "input1 scale=" << kScale << " input2 scale=" << kScaleInv << std::endl;
std::cout << "input1: " << firstInp.size << " " << firstInp.reshape(1, 1) << std::endl;
std::cout << "input2: " << secondInp.size << " " << secondInp.reshape(1, 1) << std::endl;
std::cout << "ref: " << ref.reshape(1, 1) << std::endl;
std::cout << "out: " << out.reshape(1, 1) << std::endl;
}
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_two_inputs, Combine(
Values(CV_32F, CV_8U),
Values(CV_32F, CV_8U),
dnnBackendsAndTargets()
));
}} // namespace