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Merge pull request #11893 from dkurt:fix_11884

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This commit is contained in:
Alexander Alekhin 2018-07-05 09:56:50 +00:00
commit c0d0cf5e74
2 changed files with 36 additions and 3 deletions
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7
modules/dnn/src/dnn.cpp
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@ -2075,7 +2075,8 @@ Mat Net::forward(const String& outputName)
if (layerName.empty()) if (layerName.empty())
layerName = getLayerNames().back(); layerName = getLayerNames().back();
impl->setUpNet(); std::vector<LayerPin> pins(1, impl->getPinByAlias(layerName));
impl->setUpNet(pins);
impl->forwardToLayer(impl->getLayerData(layerName)); impl->forwardToLayer(impl->getLayerData(layerName));
return impl->getBlob(layerName); return impl->getBlob(layerName);
@ -2085,13 +2086,13 @@ void Net::forward(OutputArrayOfArrays outputBlobs, const String& outputName)
{ {
CV_TRACE_FUNCTION(); CV_TRACE_FUNCTION();
impl->setUpNet();
String layerName = outputName; String layerName = outputName;
if (layerName.empty()) if (layerName.empty())
layerName = getLayerNames().back(); layerName = getLayerNames().back();
std::vector<LayerPin> pins(1, impl->getPinByAlias(layerName));
impl->setUpNet(pins);
impl->forwardToLayer(impl->getLayerData(layerName)); impl->forwardToLayer(impl->getLayerData(layerName));
LayerPin pin = impl->getPinByAlias(layerName); LayerPin pin = impl->getPinByAlias(layerName);

32
modules/dnn/test/test_layers.cpp
View File

@ -1240,4 +1240,36 @@ INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_ShuffleChannel, Combine(
/*group*/ Values(1, 2, 3, 6) /*group*/ Values(1, 2, 3, 6)
)); ));
// Check if relu is not fused to convolution if we requested it's output
TEST(Layer_Test_Convolution, relu_fusion)
{
Net net;
{
LayerParams lp;
lp.set("kernel_size", 1);
lp.set("num_output", 1);
lp.set("bias_term", false);
lp.type = "Convolution";
lp.name = "testConv";
int weightsShape[] = {1, 1, 1, 1};
Mat weights(4, &weightsShape[0], CV_32F, Scalar(1));
lp.blobs.push_back(weights);
net.addLayerToPrev(lp.name, lp.type, lp);
}
{
LayerParams lp;
lp.type = "ReLU";
lp.name = "testReLU";
net.addLayerToPrev(lp.name, lp.type, lp);
}
int sz[] = {1, 1, 2, 3};
Mat input(4, &sz[0], CV_32F);
randu(input, -1.0, -0.1);
net.setInput(input);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat output = net.forward("testConv");
normAssert(input, output);
}
}} // namespace }} // namespace