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Merged
merged 6 commits into from
Jun 13, 2025
Merged

Adoptions of TFLife fixes to new engine #27439

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96745d6
Adopt TFLite fixes for new engine
dkurt Jun 11, 2025
30a084d
model infered
dkurt Jun 12, 2025
e12d7db
Enable test
dkurt Jun 12, 2025
aeea1d0
Accuracy test pass
dkurt Jun 12, 2025
016b01e
Replace exceptions to warnings
dkurt Jun 12, 2025
38160f6
fix classic engine
dkurt Jun 12, 2025
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96 changes: 55 additions & 41 deletions modules/dnn/src/tflite/tflite_importer.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -86,7 +86,7 @@ class TFLiteImporter {
int addReshapeLayer(const std::vector<int>& shape, int axis, int num_axes,
const std::string& name, const std::pair<int, int>& inpId, int dtype, int inpTensorId);
int addFlattenLayer(int axis, int end_axis, const std::string& name, const std::pair<int, int>& inpId, int dtype, int outTensorId);
int addConstLayer(const Mat& data, const std::string& name);
void addConstLayer(const Mat& data, int tensorIdx);

inline bool isInt8(const Operator& op);
inline void getQuantParams(const Operator& op, float& inpScale, int& inpZero, float& outScale, int& outZero);
Expand Down Expand Up @@ -488,11 +488,13 @@ void TFLiteImporter::parseConvolution(const Operator& op, const std::string& opc
layerParams.type = "Convolution";

int inpId = op.inputs()->Get(0);
bool additionalPreLayer = false;
if (layouts[inpId] == DNN_LAYOUT_UNKNOWN && modelTensors->Get(inpId)->shape()->size() == 4)
{
int permId = addPermuteLayer({0, 3, 1, 2}, layerParams.name + "/permute_input", layerIds[inpId], isInt8(op) ? CV_8S : CV_32F, op.inputs()->Get(0)); // NHWC -> NCHW
layerIds[inpId] = std::make_pair(permId, 0);
layouts[op.outputs()->Get(0)] = DNN_LAYOUT_NHWC;
additionalPreLayer = true;
}

auto options = reinterpret_cast<const Conv2DOptions*>(op.builtin_options());
Expand Down Expand Up @@ -554,7 +556,7 @@ void TFLiteImporter::parseConvolution(const Operator& op, const std::string& opc

std::string fusedActivationType = EnumNameActivationFunctionType(options->fused_activation_function());
bool haveFusedActivation = fusedActivationType != "NONE";
addLayer(layerParams, op, false, haveFusedActivation);
addLayer(layerParams, op, additionalPreLayer, haveFusedActivation);
parseFusedActivation(op, options->fused_activation_function());
}

Expand Down Expand Up @@ -720,27 +722,33 @@ void TFLiteImporter::parseEltwise(const Operator& op, const std::string& opcode,
if (layouts[op.inputs()->Get(0)] == DNN_LAYOUT_NHWC && blob.dims == 1) {
blob = blob.reshape(1, {1, (int)blob.total(), 1, 1});
}
int constId = addConstLayer(blob, modelTensors->Get(idx)->name()->str());
layerIds[idx] = std::make_pair(constId, 0);
addConstLayer(blob, idx);
}


std::string fusedActivationType = EnumNameActivationFunctionType(activ);
bool haveFusedActivation = fusedActivationType != "NONE";
addLayer(layerParams, op, false, haveFusedActivation);
addLayer(layerParams, op, false, haveFusedActivation || opcode == "SQUARED_DIFFERENCE" || opcode == "RSQRT");
parseFusedActivation(op, activ);

// Layers that split on multiple operations
if (opcode == "SQUARED_DIFFERENCE") {
LayerParams lp;
lp.set("power", 2);
int id = dstNet.addLayerToPrev(layerParams.name + "/square", "Power", isOpInt8 ? CV_8S : CV_32F, lp);
layerIds[op.outputs()->Get(0)] = std::make_pair(id, 0);
}
else if (opcode == "RSQRT") {
if (opcode == "SQUARED_DIFFERENCE" || opcode == "RSQRT") {
if (haveFusedActivation)
CV_LOG_WARNING(NULL, format("%s with fused activation on new engine is not tested", opcode.c_str()));
LayerParams lp;
int id = dstNet.addLayerToPrev(layerParams.name + "/inv", "Reciprocal", isOpInt8 ? CV_8S : CV_32F, lp);
layerIds[op.outputs()->Get(0)] = std::make_pair(id, 0);
if (opcode == "RSQRT")
lp.type = "Reciprocal";
else {
lp.type = "Power";
lp.set("power", 2);
}
if (newEngine) {
std::string tensorName = modelTensors->Get(op.outputs()->Get(0))->name()->str();
addLayer(lp, {tensorName + "_additional_post_layer"}, {tensorName});
layerIds[op.outputs()->Get(0)] = std::make_pair(-1, -1);
} else {
int id = dstNet.addLayerToPrev(layerParams.name + "/post", lp.type, isOpInt8 ? CV_8S : CV_32F, lp);
layerIds[op.outputs()->Get(0)] = std::make_pair(id, 0);
}
}
}

Expand Down Expand Up @@ -866,8 +874,7 @@ void TFLiteImporter::parseConcat(const Operator& op, const std::string& opcode,
transposeND(blob, {0, 3, 1, 2}, nchwBlob);
blob = nchwBlob;
}
int constId = addConstLayer(blob, modelTensors->Get(idx)->name()->str());
layerIds[idx] = std::make_pair(constId, 0);
addConstLayer(blob, idx);
}

std::string fusedActivationType = EnumNameActivationFunctionType(options->fused_activation_function());
Expand Down Expand Up @@ -1079,11 +1086,23 @@ int TFLiteImporter::addFlattenLayer(int axis, int end_axis, const std::string& n
}
}

int TFLiteImporter::addConstLayer(const Mat& blob, const std::string& name)
void TFLiteImporter::addConstLayer(const Mat& blob, int tensorIdx)
{
const std::string& name = modelTensors->Get(tensorIdx)->name()->str();
LayerParams lp;
lp.blobs.push_back(blob.u ? blob : blob.clone()); // some tensors are owned by OpenCV
return dstNet.addLayer(name, "Const", lp);
if (newEngine)
{
lp.type = "Const";
lp.name = name;
addLayer(lp, {}, {name});
layerIds[tensorIdx] = std::make_pair(-1, -1);
}
else
{
int constId = dstNet.addLayer(name, "Const", lp);
layerIds[tensorIdx] = std::make_pair(constId, 0);
}
}

void TFLiteImporter::parseDeconvolution(const Operator& op, const std::string& opcode, LayerParams& layerParams) {
Expand Down Expand Up @@ -1218,21 +1237,31 @@ void TFLiteImporter::parseStridedSlice(const Operator& op, const std::string& op
lastShrinkAxis = axis;
}
std::string layerName = layerParams.name;
if (lastShrinkAxis != -1)
if (!newEngine && lastShrinkAxis != -1)
{
layerParams.name += "/slice";
}

addLayer(layerParams, op);
addLayer(layerParams, op, false, lastShrinkAxis != -1);

for (int axis = 0; axis < num; ++axis)
{
if (!(shrinkMask & (1 << axis)))
continue;
std::string name = (axis == lastShrinkAxis) ? layerName : format("%s/shrink_axis_%d", layerName.c_str(), axis);
int layerId = addFlattenLayer(axis, axis + 1, name,
layerIds[op.outputs()->Get(0)], isInt8(op) ? CV_8S : CV_32F, op.inputs()->Get(0));
layerIds[op.inputs()->Get(0)] = std::make_pair(layerId, 0);
if (newEngine)
{
if (axis != lastShrinkAxis)
CV_LOG_WARNING(NULL, "StridedSlice with multiple axes shrink in new engine is not tested");
addFlattenLayer(axis, axis + 1, layerName,
layerIds[op.outputs()->Get(0)], isInt8(op) ? CV_8S : CV_32F, op.outputs()->Get(0));
}
else
{
std::string name = (axis == lastShrinkAxis) ? layerName : format("%s/shrink_axis_%d", layerName.c_str(), axis);
int layerId = addFlattenLayer(axis, axis + 1, name,
layerIds[op.outputs()->Get(0)], isInt8(op) ? CV_8S : CV_32F, op.inputs()->Get(0));
layerIds[op.inputs()->Get(0)] = std::make_pair(layerId, 0);
}
}
}

Expand Down Expand Up @@ -1328,22 +1357,7 @@ void TFLiteImporter::parseDetectionPostProcess(const Operator& op, const std::st
layerParams.set("variance_encoded_in_target", true);
}

LayerParams priorsLP;
priorsLP.name = layerParams.name + "/priors";
priorsLP.type = "Const";
priorsLP.blobs.resize(1, priors);

if (newEngine)
{
std::string outTensorName = modelTensors->Get(op.inputs()->Get(2))->name()->str();
addLayer(priorsLP, {}, {outTensorName});
layerIds[op.inputs()->Get(2)] = std::make_pair(-1, -1);
}
else
{
int priorsId = dstNet.addLayer(priorsLP.name, priorsLP.type, priorsLP);
layerIds[op.inputs()->Get(2)] = std::make_pair(priorsId, 0);
}
addConstLayer(priors, op.inputs()->Get(2));
addLayer(layerParams, op);
}

Expand Down
2 changes: 1 addition & 1 deletion modules/dnn/test/test_tflite_importer.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -283,7 +283,7 @@ TEST_P(Test_TFLite, StridedSlice) {
testLayer("strided_slice");
}

TEST_P(Test_TFLite, DISABLED_face_blendshapes)
TEST_P(Test_TFLite, face_blendshapes)
{
Mat inp = blobFromNPY(findDataFile("dnn/tflite/face_blendshapes_inp.npy"));
testModel("face_blendshapes", inp);
Expand Down
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