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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
2020-08-06 14:15:52 +00:00
parent dbab8d8c38 1067cd0649
commit fa25faa2d2
52 changed files with 1498 additions and 319 deletions
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modules/dnn/test/test_onnx_importer.cpp
+56
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@@ -114,6 +114,62 @@ TEST_P(Test_ONNX_layers, Convolution)
testONNXModels("convolution");
}
TEST_P(Test_ONNX_layers, Convolution_variable_weight)
{
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ||
backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
String basename = "conv_variable_w";
Net net = readNetFromONNX(_tf("models/" + basename + ".onnx"));
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
for (int i = 0; i < 2; i++)
{
Mat input = blobFromNPY(_tf("data/input_" + basename + format("_%d", i) + "_0.npy"));
Mat weights = blobFromNPY(_tf("data/input_" + basename + format("_%d", i) + "_1.npy"));
Mat ref = blobFromNPY(_tf("data/output_" + basename + format("_%d", i) + ".npy"));
net.setInput(input, "0");
net.setInput(weights, "1");
Mat out = net.forward();
normAssert(ref, out, "", default_l1, default_lInf);
}
}
TEST_P(Test_ONNX_layers, Convolution_variable_weight_bias)
{
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ||
backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
String basename = "conv_variable_wb";
Net net = readNetFromONNX(_tf("models/" + basename + ".onnx"));
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
for (int i = 0; i < 2; i++)
{
Mat input = blobFromNPY(_tf("data/input_" + basename + format("_%d", i) + "_0.npy"));
Mat weights = blobFromNPY(_tf("data/input_" + basename + format("_%d", i) + "_1.npy"));
Mat bias = blobFromNPY(_tf("data/input_" + basename + format("_%d", i) + "_2.npy"));
Mat ref = blobFromNPY(_tf("data/output_" + basename + format("_%d", i) + ".npy"));
net.setInput(input, "0");
net.setInput(weights, "1");
net.setInput(bias, "bias");
Mat out = net.forward();
normAssert(ref, out, "", default_l1, default_lInf);
}
}
TEST_P(Test_ONNX_layers, Gather)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)