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Merge pull request #16088 from alalek:dnn_eltwise_layer_different_src_channels

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dnn(eltwise): fix handling of different number of channels

* dnn(test): reproducer for Eltwise layer issue from PR16063

* dnn(eltwise): rework support for inputs with different channels

* dnn(eltwise): get rid of finalize(), variableChannels

* dnn(eltwise): update input sorting by number of channels

- do not swap inputs if number of channels are same after truncation

* dnn(test): skip "shortcut" with batch size 2 on MYRIAD targets
This commit is contained in:
Alexander Alekhin
2019-12-11 20:16:58 +03:00
committed by GitHub
parent f2cce5fd8c
commit 5ee7abbe3c
7 changed files with 407 additions and 123 deletions
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modules/dnn/test/test_darknet_importer.cpp
+42
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@@ -99,6 +99,7 @@ class Test_Darknet_layers : public DNNTestLayer
public:
void testDarknetLayer(const std::string& name, bool hasWeights = false)
{
SCOPED_TRACE(name);
Mat inp = blobFromNPY(findDataFile("dnn/darknet/" + name + "_in.npy"));
Mat ref = blobFromNPY(findDataFile("dnn/darknet/" + name + "_out.npy"));
@@ -115,6 +116,47 @@ public:
net.setInput(inp);
Mat out = net.forward();
normAssert(out, ref, "", default_l1, default_lInf);
if (inp.size[0] == 1) // test handling of batch size
{
SCOPED_TRACE("batch size 2");
#if defined(INF_ENGINE_RELEASE)
if (target == DNN_TARGET_MYRIAD && name == "shortcut")
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
#endif
std::vector<int> sz2 = shape(inp);
sz2[0] = 2;
Net net2 = readNet(cfg, model);
net2.setPreferableBackend(backend);
net2.setPreferableTarget(target);
Range ranges0[4] = { Range(0, 1), Range::all(), Range::all(), Range::all() };
Range ranges1[4] = { Range(1, 2), Range::all(), Range::all(), Range::all() };
Mat inp2(sz2, inp.type(), Scalar::all(0));
inp.copyTo(inp2(ranges0));
inp.copyTo(inp2(ranges1));
net2.setInput(inp2);
Mat out2 = net2.forward();
EXPECT_EQ(0, cv::norm(out2(ranges0), out2(ranges1), NORM_INF)) << "Batch result is not equal: " << name;
Mat ref2 = ref;
if (ref.dims == 2 && out2.dims == 3)
{
int ref_3d_sizes[3] = {1, ref.rows, ref.cols};
ref2 = Mat(3, ref_3d_sizes, ref.type(), (void*)ref.data);
}
/*else if (ref.dims == 3 && out2.dims == 4)
{
int ref_4d_sizes[4] = {1, ref.size[0], ref.size[1], ref.size[2]};
ref2 = Mat(4, ref_4d_sizes, ref.type(), (void*)ref.data);
}*/
ASSERT_EQ(out2.dims, ref2.dims) << ref.dims;
normAssert(out2(ranges0), ref2, "", default_l1, default_lInf);
normAssert(out2(ranges1), ref2, "", default_l1, default_lInf);
}
}
};