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Merge remote-tracking branch 'upstream/3.4' into merge-3.4

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This commit is contained in:
Alexander Alekhin
2021-03-06 17:29:12 +00:00
parent b450dd7a87 04a9ff88d8
commit d643a90efa
15 changed files with 650 additions and 369 deletions
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modules/dnn/src/darknet/darknet_io.cpp
+31
View File
@@ -558,6 +558,29 @@ namespace cv {
fused_layer_names.push_back(last_layer);
}
void setSAM(int from)
{
cv::dnn::LayerParams eltwise_param;
eltwise_param.name = "SAM-name";
eltwise_param.type = "Eltwise";
eltwise_param.set<std::string>("operation", "prod");
eltwise_param.set<std::string>("output_channels_mode", "same");
darknet::LayerParameter lp;
std::string layer_name = cv::format("sam_%d", layer_id);
lp.layer_name = layer_name;
lp.layer_type = eltwise_param.type;
lp.layerParams = eltwise_param;
lp.bottom_indexes.push_back(last_layer);
lp.bottom_indexes.push_back(fused_layer_names.at(from));
last_layer = layer_name;
net->layers.push_back(lp);
layer_id++;
fused_layer_names.push_back(last_layer);
}
void setUpsample(int scaleFactor)
{
cv::dnn::LayerParams param;
@@ -837,6 +860,14 @@ namespace cv {
from = from < 0 ? from + layers_counter : from;
setParams.setScaleChannels(from);
}
else if (layer_type == "sam")
{
std::string bottom_layer = getParam<std::string>(layer_params, "from", "");
CV_Assert(!bottom_layer.empty());
int from = std::atoi(bottom_layer.c_str());
from = from < 0 ? from + layers_counter : from;
setParams.setSAM(from);
}
else if (layer_type == "upsample")
{
int scaleFactor = getParam<int>(layer_params, "stride", 1);
modules/dnn/src/ie_ngraph.cpp
+6
View File
@@ -772,8 +772,14 @@ static InferenceEngine::Layout estimateLayout(const Mat& m)
{
if (m.dims == 4)
return InferenceEngine::Layout::NCHW;
else if (m.dims == 3)
return InferenceEngine::Layout::CHW;
else if (m.dims == 2)
return InferenceEngine::Layout::NC;
else if (m.dims == 1)
return InferenceEngine::Layout::C;
else if (m.dims == 5)
return InferenceEngine::Layout::NCDHW;
else
return InferenceEngine::Layout::ANY;
}
modules/dnn/src/tensorflow/tf_importer.cpp
+56 -14
View File
@@ -295,6 +295,22 @@ DataLayout getDataLayout(
return it != data_layouts.end() ? it->second : DATA_LAYOUT_UNKNOWN;
}
static
bool hasAllOnes(const Mat &inputs, int startPos, int endPos)
{
CV_CheckLE(inputs.dims, 2, "");
CV_CheckGE(startPos, 0, "");
CV_CheckLE(startPos, endPos, "");
CV_CheckLT((size_t)endPos, inputs.total(), "");
for (int i = startPos; i < endPos; i++)
{
if (inputs.at<int>(i) != 1 || inputs.at<int>(i)!= -1)
return false;
}
return true;
}
void setStrides(LayerParams &layerParams, const tensorflow::NodeDef &layer)
{
if (hasLayerAttr(layer, "strides"))
@@ -490,6 +506,9 @@ protected:
std::map<String, Mat> sharedWeights;
std::map<String, int> layer_id;
private:
void addPermuteLayer(const int* order, const std::string& permName, Pin& inpId);
};
TFImporter::TFImporter(Net& net, const char *model, const char *config)
@@ -895,6 +914,17 @@ void TFImporter::populateNet()
CV_LOG_DEBUG(NULL, "DNN/TF: ===================== Import completed =====================");
}
void TFImporter::addPermuteLayer(const int* order, const std::string& permName, Pin& inpId)
{
LayerParams permLP;
permLP.set("order", DictValue::arrayInt<const int*>(order, 4));
CV_Assert(layer_id.find(permName) == layer_id.end());
int permId = dstNet.addLayer(permName, "Permute", permLP);
layer_id[permName] = permId;
connect(layer_id, dstNet, inpId, permId, 0);
inpId = Pin(permName);
}
void TFImporter::parseNode(const tensorflow::NodeDef& layer_)
{
tensorflow::NodeDef layer = layer_;
@@ -1276,37 +1306,49 @@ void TFImporter::parseNode(const tensorflow::NodeDef& layer_)
if (value_id.find(layer.input(1)) != value_id.end())
{
Mat newShape = getTensorContent(getConstBlob(layer, value_id, 1));
if (newShape.total() == 4)
int newShapeSize = newShape.total();
bool hasSwap = false;
if (newShapeSize == 4 && hasAllOnes(newShape, 0, 2))
{
// NHWC->NCHW
std::swap(*newShape.ptr<int32_t>(0, 2), *newShape.ptr<int32_t>(0, 3));
std::swap(*newShape.ptr<int32_t>(0, 1), *newShape.ptr<int32_t>(0, 2));
hasSwap = true;
}
if (inpLayout == DATA_LAYOUT_NHWC)
{
if (newShape.total() != 4 || newShape.at<int>(1) == 1)
if (newShapeSize >= 2 || newShape.at<int>(1) == 1)
{
LayerParams permLP;
int order[] = {0, 2, 3, 1}; // From OpenCV's NCHW to NHWC.
permLP.set("order", DictValue::arrayInt<int*>(order, 4));
std::string permName = name + "/nchw";
CV_Assert(layer_id.find(permName) == layer_id.end());
int permId = dstNet.addLayer(permName, "Permute", permLP);
layer_id[permName] = permId;
connect(layer_id, dstNet, inpId, permId, 0);
inpId = Pin(permName);
inpLayout = DATA_LAYOUT_NCHW;
addPermuteLayer(order, name + "/nhwc", inpId);
if (newShapeSize < 4)
{
inpLayout = DATA_LAYOUT_NCHW;
}
else
{
inpLayout = DATA_LAYOUT_NHWC;
}
}
}
layerParams.set("dim", DictValue::arrayInt<int*>(newShape.ptr<int>(), newShape.total()));
layerParams.set("dim", DictValue::arrayInt<int*>(newShape.ptr<int>(), newShapeSize));
int id = dstNet.addLayer(name, "Reshape", layerParams);
layer_id[name] = id;
// one input only
connect(layer_id, dstNet, inpId, id, 0);
data_layouts[name] = newShape.total() == 2 ? DATA_LAYOUT_PLANAR : inpLayout;
inpId = Pin(name);
if ((inpLayout == DATA_LAYOUT_NHWC || inpLayout == DATA_LAYOUT_UNKNOWN || inpLayout == DATA_LAYOUT_PLANAR) &&
newShapeSize == 4 && !hasSwap)
{
int order[] = {0, 3, 1, 2}; // Transform back to OpenCV's NCHW.
addPermuteLayer(order, name + "/nchw", inpId);
inpLayout = DATA_LAYOUT_NCHW;
}
data_layouts[name] = newShapeSize == 2 ? DATA_LAYOUT_PLANAR : inpLayout;
}
else
{
modules/dnn/test/test_darknet_importer.cpp
+5
View File
@@ -803,6 +803,11 @@ TEST_P(Test_Darknet_layers, relu)
testDarknetLayer("relu");
}
TEST_P(Test_Darknet_layers, sam)
{
testDarknetLayer("sam", true);
}
INSTANTIATE_TEST_CASE_P(/**/, Test_Darknet_layers, dnnBackendsAndTargets());
}} // namespace
modules/dnn/test/test_tf_importer.cpp
+10
View File
@@ -478,6 +478,16 @@ TEST_P(Test_TensorFlow_layers, unfused_flatten)
runTensorFlowNet("unfused_flatten_unknown_batch");
}
TEST_P(Test_TensorFlow_layers, reshape_layer)
{
runTensorFlowNet("reshape_layer");
}
TEST_P(Test_TensorFlow_layers, reshape_nchw)
{
runTensorFlowNet("reshape_nchw");
}
TEST_P(Test_TensorFlow_layers, leaky_relu)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000)