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Merge pull request #16036 from dkurt:dnn_backport_15203

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This commit is contained in:
Alexander Alekhin 2019-12-05 09:56:00 +00:00
commit 986c5084a4
4 changed files with 63 additions and 15 deletions
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30
modules/dnn/src/caffe/caffe_importer.cpp
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@ -424,6 +424,36 @@ public:
}
dstNet.setInputsNames(netInputs);
std::vector<MatShape> inp_shapes;
if (net.input_shape_size() > 0 || (layersSize > 0 && net.layer(0).has_input_param() &&
net.layer(0).input_param().shape_size() > 0)) {
int size = (net.input_shape_size() > 0) ? net.input_shape_size() :
net.layer(0).input_param().shape_size();
for (int inp_id = 0; inp_id < size; inp_id++)
{
const caffe::BlobShape &_input_shape = (net.input_shape_size() > 0) ?
net.input_shape(inp_id) :
net.layer(0).input_param().shape(inp_id);
MatShape shape;
for (int i = 0; i < _input_shape.dim_size(); i++) {
shape.push_back((int)_input_shape.dim(i));
}
inp_shapes.push_back(shape);
}
}
else if (net.input_dim_size() > 0) {
MatShape shape;
for (int dim = 0; dim < net.input_dim_size(); dim++) {
shape.push_back(net.input_dim(dim));
}
inp_shapes.push_back(shape);
}
for (int inp_id = 0; inp_id < inp_shapes.size(); inp_id++) {
dstNet.setInput(Mat(inp_shapes[inp_id], CV_32F), netInputs[inp_id]);
}
addedBlobs.clear();
}

21
modules/dnn/src/dnn.cpp
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@ -2772,6 +2772,18 @@ struct Net::Impl
{
std::vector<LayerPin>& inputLayerIds = layers[id].inputBlobsId;
if (inOutShapes[0].in[0].empty() && !layers[0].outputBlobs.empty())
{
ShapesVec shapes;
for (int i = 0; i < layers[0].outputBlobs.size(); i++)
{
Mat& inp = layers[0].outputBlobs[i];
CV_Assert(inp.total());
shapes.push_back(shape(inp));
}
inOutShapes[0].in = shapes;
}
if (inOutShapes[id].in.empty())
{
for(int i = 0; i < inputLayerIds.size(); i++)
@ -2934,14 +2946,23 @@ Net Net::readFromModelOptimizer(const String& xml, const String& bin)
#endif
std::vector<String> inputsNames;
std::vector<MatShape> inp_shapes;
for (auto& it : ieNet.getInputsInfo())
{
inputsNames.push_back(it.first);
std::vector<size_t> dims = it.second->getTensorDesc().getDims();
inp_shapes.push_back(std::vector<int>(dims.begin(), dims.end()));
}
Net cvNet;
cvNet.setInputsNames(inputsNames);
// set empty input to determine input shapes
for (int inp_id = 0; inp_id < inputsNames.size(); ++inp_id)
{
cvNet.setInput(Mat(inp_shapes[inp_id], CV_32F), inputsNames[inp_id]);
}
Ptr<BackendNode> backendNode;
#ifdef HAVE_DNN_NGRAPH
if (DNN_BACKEND_INFERENCE_ENGINE_NGRAPH == getInferenceEngineBackendTypeParam())

11
modules/dnn/test/test_caffe_importer.cpp
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@ -182,6 +182,17 @@ TEST_P(Reproducibility_AlexNet, Accuracy)
ASSERT_FALSE(net.empty());
}
// Test input layer size
std::vector<MatShape> inLayerShapes;
std::vector<MatShape> outLayerShapes;
net.getLayerShapes(MatShape(), 0, inLayerShapes, outLayerShapes);
ASSERT_FALSE(inLayerShapes.empty());
ASSERT_EQ(inLayerShapes[0].size(), 4);
ASSERT_EQ(inLayerShapes[0][0], 1);
ASSERT_EQ(inLayerShapes[0][1], 3);
ASSERT_EQ(inLayerShapes[0][2], 227);
ASSERT_EQ(inLayerShapes[0][3], 227);
const float l1 = 1e-5;
const float lInf = (targetId == DNN_TARGET_OPENCL_FP16) ? 3e-3 : 1e-4;

16
modules/dnn/test/test_ie_models.cpp
View File

@ -254,20 +254,6 @@ void runIE(Target target, const std::string& xmlPath, const std::string& binPath
infRequest.Infer();
}
std::vector<String> getOutputsNames(const Net& net)
{
std::vector<String> names;
if (names.empty())
{
std::vector<int> outLayers = net.getUnconnectedOutLayers();
std::vector<String> layersNames = net.getLayerNames();
names.resize(outLayers.size());
for (size_t i = 0; i < outLayers.size(); ++i)
names[i] = layersNames[outLayers[i] - 1];
}
return names;
}
void runCV(Backend backendId, Target targetId, const std::string& xmlPath, const std::string& binPath,
const std::map<std::string, cv::Mat>& inputsMap,
std::map<std::string, cv::Mat>& outputsMap)
@ -279,7 +265,7 @@ void runCV(Backend backendId, Target targetId, const std::string& xmlPath, const
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
std::vector<String> outNames = getOutputsNames(net);
std::vector<String> outNames = net.getUnconnectedOutLayersNames();
std::vector<Mat> outs;
net.forward(outs, outNames);