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Merge pull request #20702 from zihaomu:tf_expand_dim_layer

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Add ExpandDims layer of tf_importer.cpp

* Add ExpandDims to tf_importer.

* add -1 expand test case.

* Support different dimensions of input.

* Compatible with 5-dimensional NDHWC data

* Code align

* support 3-dim input.

* 3-dim bug fixed.

* fixing error of code format.
This commit is contained in:
Zihao Mu 2021-10-05 00:37:38 +08:00 committed by GitHub
parent 59502594f8
commit 9085b933d8
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2 changed files with 144 additions and 3 deletions
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134
modules/dnn/src/tensorflow/tf_importer.cpp
View File

@ -555,7 +555,7 @@ protected:
std::map<String, int> layer_id;
private:
void addPermuteLayer(const int* order, const std::string& permName, Pin& inpId);
void addPermuteLayer(const int* order, const std::string& permName, Pin& inpId, int orderSize = 4);
void setPadding(LayerParams &layerParams, const tensorflow::NodeDef &layer, std::string& inputName, float value = 0.);
friend class TFLayerHandler;
@ -595,6 +595,7 @@ private:
void parseClipByValue (tensorflow::GraphDef& net, const tensorflow::NodeDef& layer, LayerParams& layerParams);
void parseLeakyRelu (tensorflow::GraphDef& net, const tensorflow::NodeDef& layer, LayerParams& layerParams);
void parseActivation (tensorflow::GraphDef& net, const tensorflow::NodeDef& layer, LayerParams& layerParams);
void parseExpandDims (tensorflow::GraphDef& net, const tensorflow::NodeDef& layer, LayerParams& layerParams);
void parseCustomLayer (tensorflow::GraphDef& net, const tensorflow::NodeDef& layer, LayerParams& layerParams);
};
@ -672,6 +673,7 @@ const TFImporter::DispatchMap TFImporter::buildDispatchMap()
dispatch["LeakyRelu"] = &TFImporter::parseLeakyRelu;
dispatch["Abs"] = dispatch["Tanh"] = dispatch["Sigmoid"] = dispatch["Relu"] =
dispatch["Elu"] = dispatch["Exp"] = dispatch["Identity"] = dispatch["Relu6"] = &TFImporter::parseActivation;
dispatch["ExpandDims"] = &TFImporter::parseExpandDims;
return dispatch;
}
@ -1113,6 +1115,123 @@ void TFImporter::parseReshape(tensorflow::GraphDef& net, const tensorflow::NodeD
}
}
void TFImporter::parseExpandDims(tensorflow::GraphDef& net, const tensorflow::NodeDef& layer, LayerParams& layerParams)
{
const std::string& name = layer.name();
const int num_inputs = layer.input_size();
CV_Assert(!netInputShapes.empty());
CV_CheckGT(num_inputs, 0, "");
Pin inpId = parsePin(layer.input(0));
DataLayout inpLayout = getDataLayout(layer.input(0), data_layouts);
// Get input shape
std::vector<MatShape> inShape_, outShape_;
int inpIdindex = layer_id.find(inpId.name)->second;
dstNet.getLayerShapes(netInputShapes, inpIdindex, inShape_, outShape_);
MatShape inpShape = outShape_[0];
std::vector<int> outShape = inpShape;
int outShapeSize = outShape.size();
CV_Assert(inpShape.size() >= 1);
// 2nd blob is dims tensor
int axis = getConstBlob(layer, value_id, 1).int_val().Get(0);
// Convert negative numbers to positive numbers, axis can be in range [-(D+1), D].
if(axis < 0)
{
axis = inpShape.size() + axis + 1;
}
CV_Assert(0 <= axis && axis <= inpShape.size());
// After ExpendDims, 3-dim data will become 4-dim data, and OpenCV retains 4-dim data as NCHW data layout.
// Convert OpenCV's NHC to NCH first.
if(outShapeSize == 3)
{
// If axis equal to outShapeSize, that mean we expand in Channel dimmension, and do not add permuteLayer.
if(axis != outShapeSize)
{
int order[] = {0, 2, 1}; // From OpenCV's NHC to NCH.
addPermuteLayer(order, name + "/nch", inpId, 3);
std::swap(outShape[1], outShape[2]);
}
axis = (axis != 0)?(axis % outShapeSize + 1):2;
}
if(inpShape.size() == 4)
{
if(axis == inpShape.size())
{
int order[] = {0, 2, 3, 1}; // From OpenCV's NCHW to NHWC.
addPermuteLayer(order, name + "/nhwc", inpId);
// Convert shape From OpenCV's NCHW to NHWC.
if(inpLayout == DATA_LAYOUT_NHWC)
{
std::swap(outShape[1], outShape[2]);
std::swap(outShape[2], outShape[3]);
}
}
if(inpLayout == DATA_LAYOUT_NHWC || inpLayout == DATA_LAYOUT_NCHW)
{
// toNCHW
axis = (axis != 0)?(axis % outShapeSize + 1):0;
}
}
// After ExpendDims, 5-dim data will become 6-dim data, and OpenCV retains 6-dim data as original data layout.
// Convert OpenCV's NCDHW to NDHWC first.
if (inpShape.size() == 5 && (inpLayout == DATA_LAYOUT_NDHWC || inpLayout == DATA_LAYOUT_UNKNOWN))
{
int order[] = {0, 2, 3, 4, 1}; // From OpenCV's NCDHW to NDHWC.
addPermuteLayer(order, name + "/ndhwc", inpId, 5);
// Convert shape From OpenCV's NCDHW to NDHWC.
if(inpLayout == DATA_LAYOUT_NDHWC)
{
std::swap(outShape[1], outShape[2]);
std::swap(outShape[2], outShape[3]);
std::swap(outShape[3], outShape[4]);
}
}
outShape.insert(outShape.begin() + axis, 1);
outShapeSize += 1;
// From OpenCV's NCDHW to NDHWC.
if((inpLayout != DATA_LAYOUT_NHWC && inpLayout != DATA_LAYOUT_NCHW) && outShapeSize == 5)
{
for(int i = 1; i < outShapeSize - 1; i++)
{
std::swap(outShape[outShapeSize - i - 1], outShape[outShapeSize - i]);
}
}
layerParams.set("dim", DictValue::arrayInt(&outShape[0], outShape.size()));
int id = dstNet.addLayer(name, "Reshape", layerParams);
layer_id[name] = id;
connect(layer_id, dstNet, inpId, id, 0);
if(outShapeSize == 5)
{
data_layouts[name] = DATA_LAYOUT_NDHWC;
}
else if(outShapeSize == 4)
{
data_layouts[name] = DATA_LAYOUT_NCHW;
}
else
{
data_layouts[name] = inpLayout;
}
}
// "Flatten" "Squeeze"
void TFImporter::parseFlatten(tensorflow::GraphDef& net, const tensorflow::NodeDef& layer, LayerParams& layerParams)
{
@ -1419,6 +1538,15 @@ void TFImporter::parsePlaceholder(tensorflow::GraphDef& net, const tensorflow::N
if (dims[0] == -1) // It's OK to have undetermined batch size
dims[0] = 1;
}
if (dims.size() == 5 && predictedLayout == DATA_LAYOUT_NDHWC)
{
std::swap(dims[3], dims[4]); // NDHWC->NDHCW
std::swap(dims[2], dims[3]); // NDHCW->NDCHW
std::swap(dims[1], dims[2]); // NDCHW->NCDHW
if (dims[0] == -1) // It's OK to have undetermined batch size
dims[0] = 1;
}
bool hasNeg = false;
for (int i = 0; i < dims.size() && !hasNeg; ++i)
{
@ -2882,10 +3010,10 @@ void TFImporter::populateNet()
CV_LOG_DEBUG(NULL, (DNN_DIAGNOSTICS_RUN? "DNN/TF: diagnostic run completed!" : "DNN/TF: import completed!"));
}
void TFImporter::addPermuteLayer(const int* order, const std::string& permName, Pin& inpId)
void TFImporter::addPermuteLayer(const int* order, const std::string& permName, Pin& inpId, int orderSize)
{
LayerParams permLP;
permLP.set("order", DictValue::arrayInt<const int*>(order, 4));
permLP.set("order", DictValue::arrayInt<const int*>(order, orderSize));
CV_Assert(layer_id.find(permName) == layer_id.end());
int permId = dstNet.addLayer(permName, "Permute", permLP);
layer_id[permName] = permId;

13
modules/dnn/test/test_tf_importer.cpp
View File

@ -593,6 +593,19 @@ TEST_P(Test_TensorFlow_layers, BiasAdd)
runTensorFlowNet("bias_add_1");
}
TEST_P(Test_TensorFlow_layers, ExpandDims)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
runTensorFlowNet("expand_dims_1");
runTensorFlowNet("expand_dims_2");
}
// TODO: fix it and add to l2_normalize
TEST_P(Test_TensorFlow_layers, l2_normalize_3d)
{