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Merge pull request #16156 from TH3CHARLie:3.4

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* Eltwise::DIV support in Halide backend

* fix typo

* remove div from generated test suite to pass CI, switching to manual test...

* ensure divisor not near to zero

* use randu

* dnn(test): update test data for Eltwise.Accuracy/DIV layer test
This commit is contained in:
Xuanda Yang 2019-12-13 23:29:39 +08:00 committed by Alexander Alekhin
parent 60f81032bb
commit 3d60a9b96c
2 changed files with 30 additions and 6 deletions
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6
modules/dnn/src/layers/eltwise_layer.cpp
View File

@ -638,6 +638,12 @@ public:
for (int i = 2; i < inputBuffers.size(); ++i)
topExpr *= inputBuffers[i](x, y, c, n);
break;
case DIV:
topExpr = inputBuffers[0](x, y, c, n) /
inputBuffers[1](x, y, c, n);
for (int i = 2; i < inputBuffers.size(); ++i)
topExpr /= inputBuffers[i](x, y, c, n);
break;
case MAX:
topExpr = max(inputBuffers[0](x, y, c, n),
inputBuffers[1](x, y, c, n));

30
modules/dnn/test/test_halide_layers.cpp
View File

@ -16,10 +16,11 @@ using namespace cv;
using namespace cv::dnn;
using namespace testing;
static void test(Mat& input, Net& net, Backend backendId, Target targetId, bool skipCheck = false)
static void test(Mat& input, Net& net, Backend backendId, Target targetId, bool skipCheck = false, bool randInput = true)
{
DNNTestLayer::checkBackend(backendId, targetId);
randu(input, -1.0f, 1.0f);
if (randInput)
randu(input, -1.0f, 1.0f);
net.setInput(input);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
@ -776,6 +777,14 @@ TEST_P(Eltwise, Accuracy)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
bool convInputShift = 1;
int numEltwiseInputs = numConv;
if (op == "div")
{
numConv = 1;
convInputShift = 0; // first input is convolution
}
Net net;
std::vector<int> convLayerIds(numConv);
@ -815,20 +824,29 @@ TEST_P(Eltwise, Accuracy)
eltwiseParam.type = "Eltwise";
eltwiseParam.name = "testLayer";
int eltwiseId = net.addLayer(eltwiseParam.name, eltwiseParam.type, eltwiseParam);
net.connect(0, 0, eltwiseId, 0);
if (convInputShift == 1)
net.connect(0, 0, eltwiseId, 0);
for (int i = 0; i < numConv; ++i)
{
net.connect(convLayerIds[i], 0, eltwiseId, i + 1);
net.connect(convLayerIds[i], 0, eltwiseId, i + convInputShift);
}
if (convInputShift == 0)
net.connect(0, 0, eltwiseId, numConv);
for (int i = numConv; i < numEltwiseInputs; ++i)
{
net.connect(0, 0, eltwiseId, i + 1);
}
int sz[] = {1, inSize[0], inSize[1], inSize[2]};
Mat input(4, &sz[0], CV_32F);
test(input, net, backendId, targetId);
if (op == "div")
randu(input, 1.0f, 1.0f); // ensure no divisor value has absouluate value of less than 0.5
test(input, net, backendId, targetId, /*skipCheck*/false, (op == "div") ? false : true);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Eltwise, Combine(
/*input size*/ Values(Vec3i(1, 4, 5), Vec3i(2, 8, 6)),
/*operation*/ Values("prod", "sum", "max"),
/*operation*/ Values("prod", "sum", "div", "max"),
/*num convs*/ Values(1, 2, 3),
/*weighted(for sum only)*/ Bool(),
dnnBackendsAndTargetsWithHalide()