mirror/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2024-11-25 03:30:34 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

Disable fusion to output layers

Browse Source
This commit is contained in:
Dmitry Kurtaev 2018-07-04 15:50:39 +03:00
parent a0baae8a55
commit f25a01bb5a
2 changed files with 36 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

7
modules/dnn/src/dnn.cpp
View File

@ -2075,7 +2075,8 @@ Mat Net::forward(const String& outputName)
if (layerName.empty())
layerName = getLayerNames().back();
impl->setUpNet();
std::vector<LayerPin> pins(1, impl->getPinByAlias(layerName));
impl->setUpNet(pins);
impl->forwardToLayer(impl->getLayerData(layerName));
return impl->getBlob(layerName);
@ -2085,13 +2086,13 @@ void Net::forward(OutputArrayOfArrays outputBlobs, const String& outputName)
{
CV_TRACE_FUNCTION();
impl->setUpNet();
String layerName = outputName;
if (layerName.empty())
layerName = getLayerNames().back();
std::vector<LayerPin> pins(1, impl->getPinByAlias(layerName));
impl->setUpNet(pins);
impl->forwardToLayer(impl->getLayerData(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)
));
// 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