Merge pull request #27307 from dkurt:tflite_face_blendshape_model

dkurt · web-flow · commit 1e3ab44cff25 · 2025-05-19T10:45:18.000+03:00
TFLite fixes for Face Blendshapes V2 #27307 ### Pull Request Readiness Checklist * Scalars support * Better handling of 1D tensors * New ops import: SUB, SQRT, DIV, NEG, SQUARED_DIFFERENCE, SUM * Number of NHWC<->NCHW layouts compatibility improvements resolves #27211 **Merge with extra**: opencv/opencv_extra#1257 See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
diff --git a/modules/dnn/src/layers/convolution_layer.cpp b/modules/dnn/src/layers/convolution_layer.cpp
@@ -2015,7 +2015,7 @@ class DeConvolutionLayerImpl CV_FINAL : public BaseConvolutionLayerImpl
         if( weightsMat.empty() )
         {
             transpose(blobs[0].reshape(1, inpCn), weightsMat);
-            biasesMat = hasBias() ? blobs[1].reshape(1, outCn) : Mat::zeros(outCn, 1, CV_32F);
+            biasesMat = hasBias() ? blobs[1] : Mat::zeros(outCn, 1, CV_32F);
         }
 
         for (size_t ii = 0; ii < outputs.size(); ii++)
@@ -2041,7 +2041,7 @@ class DeConvolutionLayerImpl CV_FINAL : public BaseConvolutionLayerImpl
 
                     Mat convMat = convBlob.rowRange(_Range((g + n * ngroups) * inpGroupCn, inpGroupCn));
                     Mat wghtMat = weightsMat.colRange(_Range(g * inpGroupCn, inpGroupCn));
-                    Mat curBiasMat = biasesMat.rowRange(_Range(g * outGroupCn, outGroupCn));
+                    Mat curBiasMat = biasesMat.reshape(1, {outCn, 1}).rowRange(_Range(g * outGroupCn, outGroupCn));
 
                     //gemm(wghtMat, convMat, 1, colMat, 0, colMat, 0);
                     MatMulInvoker mminvoker(wghtMat, convMat, colMat, nstripes);
diff --git a/modules/dnn/src/layers/elementwise_layers.cpp b/modules/dnn/src/layers/elementwise_layers.cpp
@@ -257,8 +257,8 @@ class ElementWiseLayer : public Func::Layer
         {
             const Mat &src = inputs[i];
             Mat &dst = outputs[i];
-            CV_Assert(src.size == dst.size && src.type() == dst.type() &&
-                      src.isContinuous() && dst.isContinuous() && src.type() == CV_32F);
+            CV_Assert_N(src.size == dst.size, src.type() == dst.type(),
+                      src.isContinuous(), dst.isContinuous(), src.type() == CV_32F);
 
             const int nstripes = getNumThreads();
             PBody body(func, src, dst, nstripes);
diff --git a/modules/dnn/src/layers/eltwise_layer.cpp b/modules/dnn/src/layers/eltwise_layer.cpp
@@ -106,7 +106,7 @@ class EltwiseLayerImpl CV_FINAL : public EltwiseLayer
         if (params.has("operation"))
         {
             String operation = toLowerCase(params.get<String>("operation"));
-            if (operation == "prod")
+            if (operation == "prod" || operation == "mul")
                 op = PROD;
             else if (operation == "sum")
                 op = SUM;
diff --git a/modules/dnn/src/legacy_backend.hpp b/modules/dnn/src/legacy_backend.hpp
@@ -213,6 +213,7 @@ struct BlobManager
             {
                 reuse(bestBlobPin, lp);
                 dst = bestBlob.reshape(1, 1).colRange(0, targetTotal).reshape(1, shape);
+                dst.dims = shape.size();
                 return;
             }
         }
diff --git a/modules/dnn/src/tflite/tflite_importer.cpp b/modules/dnn/src/tflite/tflite_importer.cpp
@@ -72,7 +72,7 @@ class TFLiteImporter {
     void parseSoftmax(const Operator& op, const std::string& opcode, LayerParams& layerParams);
     void parseCast(const Operator& op, const std::string& opcode, LayerParams& layerParams);
     void parseTranspose(const Operator& op, const std::string& opcode, LayerParams& layerParams);
-    void parseGlobalPooling(const Operator& op, const std::string& opcode, LayerParams& layerParams);
+    void parseReduce(const Operator& op, const std::string& opcode, LayerParams& layerParams);
 
     void parseFusedActivation(const Operator& op, ActivationFunctionType activ);
     void parseActivation(const Operator& op, const std::string& opcode, LayerParams& layerParams, bool isFused);
@@ -81,16 +81,20 @@ 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 addFlattenLayer(int axis, int end_axis, const std::string& name, const std::pair<int, int>& inpId, int dtype);
+    int addConstLayer(const Mat& data, const std::string& name);
 
     inline bool isInt8(const Operator& op);
     inline void getQuantParams(const Operator& op, float& inpScale, int& inpZero, float& outScale, int& outZero);
 };
 
 Mat TFLiteImporter::parseTensor(const Tensor& tensor)
 {
+    std::vector<int> shape;
     const auto tensor_shape = tensor.shape();
-    CV_Assert(tensor_shape);
-    std::vector<int> shape(tensor_shape->begin(), tensor_shape->end());
+    if (tensor_shape && tensor_shape->size())
+        shape.assign(tensor_shape->begin(), tensor_shape->end());
+    else
+        shape.resize(1, 1);
     int bufferIdx = tensor.buffer();
     CV_Assert(bufferIdx != 0);  // 0th buffer is a no-data buffer
     const Buffer* buffer = model->buffers()->Get(bufferIdx);
@@ -118,7 +122,11 @@ Mat TFLiteImporter::parseTensor(const Tensor& tensor)
     default:
         CV_Error(Error::StsNotImplemented, format("Parse tensor with type %s", EnumNameTensorType(tensor.type())));
     }
-    return shape.empty() ? Mat() : Mat(shape, dtype, const_cast<void*>(data));
+    Mat res = Mat(shape, dtype, const_cast<void*>(data));
+    // workaround for scalars support
+    if (!tensor_shape || shape.size() == 1)
+        res.dims = 1;
+    return res;
 }
 
 TFLiteImporter::TFLiteImporter(Net& dstNet, const char* modelBuffer, size_t bufSize)
@@ -237,6 +245,8 @@ void TFLiteImporter::populateNet()
                     // Dequantize a buffer
                     Mat dataFP32;
                     data.convertTo(dataFP32, CV_32F);
+                    // workaround for scalars support
+                    dataFP32.dims = data.dims;
                     allTensors[op_outputs->Get(0)] = dataFP32;
                     continue;
                 }
@@ -259,6 +269,11 @@ void TFLiteImporter::populateNet()
             }
             throw;
         }
+        // Uncomment to finish model build aftet specific node
+        // if (op_outputs->Get(0) == 90)
+        // {
+        //     break;
+        // }
     }
 }
 
@@ -270,7 +285,9 @@ TFLiteImporter::DispatchMap TFLiteImporter::buildDispatchMap()
 
     dispatch["CONV_2D"] = &TFLiteImporter::parseConvolution;
     dispatch["DEPTHWISE_CONV_2D"] = &TFLiteImporter::parseDWConvolution;
-    dispatch["ADD"] = dispatch["MUL"] = &TFLiteImporter::parseEltwise;
+    dispatch["ADD"] = dispatch["MUL"] = dispatch["SUB"] =
+        dispatch["SQRT"] = dispatch["DIV"] = dispatch["NEG"] =
+        dispatch["RSQRT"] = dispatch["SQUARED_DIFFERENCE"] = &TFLiteImporter::parseEltwise;
     dispatch["RELU"] = dispatch["PRELU"] = dispatch["HARD_SWISH"] =
         dispatch["LOGISTIC"] = dispatch["LEAKY_RELU"] = &TFLiteImporter::parseActivation;
     dispatch["MAX_POOL_2D"] = dispatch["AVERAGE_POOL_2D"] = &TFLiteImporter::parsePooling;
@@ -290,8 +307,8 @@ TFLiteImporter::DispatchMap TFLiteImporter::buildDispatchMap()
     dispatch["CAST"] = &TFLiteImporter::parseCast;
     dispatch["TFLite_Detection_PostProcess"] = &TFLiteImporter::parseDetectionPostProcess;
     dispatch["TRANSPOSE"] = &TFLiteImporter::parseTranspose;
-    dispatch["MEAN"] = dispatch["REDUCE_MAX"] = &TFLiteImporter::parseGlobalPooling;
     dispatch["STRIDED_SLICE"] = &TFLiteImporter::parseStridedSlice;
+    dispatch["REDUCE_MAX"] = dispatch["MEAN"] = dispatch["SUM"] = &TFLiteImporter::parseReduce;
     return dispatch;
 }
 
@@ -374,6 +391,14 @@ void TFLiteImporter::addLayer(LayerParams& layerParams, const Operator& op) {
 void TFLiteImporter::parseConvolution(const Operator& op, const std::string& opcode, LayerParams& layerParams) {
     layerParams.type = "Convolution";
 
+    int inpId = op.inputs()->Get(0);
+    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);  // NHWC -> NCHW
+        layerIds[inpId] = std::make_pair(permId, 0);
+        layouts[op.outputs()->Get(0)] = DNN_LAYOUT_NHWC;
+    }
+
     auto options = reinterpret_cast<const Conv2DOptions*>(op.builtin_options());
     layerParams.set("pad_mode", EnumNamePadding(options->padding()));
     layerParams.set("stride_w", options->stride_w());
@@ -517,8 +542,9 @@ void TFLiteImporter::parsePadding(const Operator& op, const std::string& opcode,
 }
 
 void TFLiteImporter::parseEltwise(const Operator& op, const std::string& opcode, LayerParams& layerParams) {
+    bool isOpInt8 = isInt8(op);
     ActivationFunctionType activ = ActivationFunctionType_NONE;
-    layerParams.type = "Eltwise";
+    layerParams.type = isOpInt8 ? "Eltwise" : "NaryEltwise";
     if (opcode == "ADD") {
         auto options = reinterpret_cast<const AddOptions*>(op.builtin_options());
         activ = options->fused_activation_function();
@@ -527,12 +553,35 @@ void TFLiteImporter::parseEltwise(const Operator& op, const std::string& opcode,
     else if (opcode == "MUL") {
         auto options = reinterpret_cast<const MulOptions*>(op.builtin_options());
         activ = options->fused_activation_function();
-        layerParams.set("operation", "prod");
+        layerParams.set("operation", "mul");
+    }
+    else if (opcode == "DIV") {
+        auto options = reinterpret_cast<const DivOptions*>(op.builtin_options());
+        activ = options->fused_activation_function();
+        layerParams.set("operation", "div");
+    }
+    else if (opcode == "SUB" && !isOpInt8) {
+        auto options = reinterpret_cast<const SubOptions*>(op.builtin_options());
+        activ = options->fused_activation_function();
+        layerParams.set("operation", "sub");
+    }
+    else if (opcode == "NEG") {
+        layerParams.type = "Scale";
+        layerParams.blobs.resize(1, Mat(1, 1, CV_32F, Scalar(-1)));
+    }
+    else if (opcode == "SQUARED_DIFFERENCE" && !isOpInt8) {
+        layerParams.set("operation", "sub");
+    }
+    else if (opcode == "RSQRT" && !isOpInt8) {
+        layerParams.type = "Sqrt";
+    }
+    else if (opcode == "SQRT" && !isOpInt8) {
+        layerParams.type = "Sqrt";
     } else {
-        CV_Error(Error::StsNotImplemented, "Unknown opcode for Eltwise layer: " + opcode);
+        CV_Error(Error::StsNotImplemented, cv::format("DNN/TFLite: Unknown opcode for %s Eltwise layer '%s'", isOpInt8 ? "INT8" : "FP32", opcode.c_str()));
     }
 
-    if (isInt8(op)) {
+    if (isOpInt8) {
         const Tensor* out = modelTensors->Get(op.outputs()->Get(0));
         float outScale = out->quantization()->scale()->Get(0);
         int outZero = out->quantization()->zero_point()->Get(0);
@@ -559,8 +608,35 @@ void TFLiteImporter::parseEltwise(const Operator& op, const std::string& opcode,
         layerParams.set("scales", outScale);
         layerParams.set("zeropoints", outZero);
     }
+
+    // Force all inputs to be in graph, not as blobs
+    for (int idx : *op.inputs()) {
+        if (layerIds.find(idx) != layerIds.end()) {
+            continue;  // Output from a different layer
+        }
+        Mat blob = allTensors[idx];
+        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);
+    }
+
     addLayer(layerParams, op);
     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") {
+        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);
+    }
 }
 
 void TFLiteImporter::parsePooling(const Operator& op, const std::string& opcode, LayerParams& layerParams) {
@@ -654,14 +730,35 @@ void TFLiteImporter::parseConcat(const Operator& op, const std::string& opcode,
     auto options = reinterpret_cast<const ConcatenationOptions*>(op.builtin_options());
     int axis = options->axis();
 
-    DataLayout inpLayout = layouts[op.inputs()->Get(0)];
-    if (inpLayout == DNN_LAYOUT_NHWC) {
-        // OpenCV works in NCHW data layout. So change the axis correspondingly.
-        axis = normalize_axis(axis, 4);
-        static const int remap[] = {0, 2, 3, 1};
-        axis = remap[axis];
+    bool hasNHWCInput = false;
+    for (int idx : *op.inputs()) {
+        DataLayout inpLayout = layouts[idx];
+        if (inpLayout == DNN_LAYOUT_NHWC) {
+            // OpenCV works in NCHW data layout. So change the axis correspondingly.
+            axis = normalize_axis(axis, 4);
+            static const int remap[] = {0, 2, 3, 1};
+            axis = remap[axis];
+            hasNHWCInput = true;
+            break;
+        }
     }
     layerParams.set("axis", axis);
+
+    // Force all inputs to be in graph, not as blobs
+    for (int idx : *op.inputs()) {
+        if (layerIds.find(idx) != layerIds.end()) {
+            continue;  // Output from a different layer
+        }
+        Mat blob = allTensors[idx];
+        if (hasNHWCInput && blob.dims == 4)
+        {
+            Mat nchwBlob;
+            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);
+    }
     addLayer(layerParams, op);
     parseFusedActivation(op, options->fused_activation_function());
 }
@@ -770,35 +867,38 @@ void TFLiteImporter::parseTranspose(const Operator& op, const std::string& opcod
     addLayer(layerParams, op);
 }
 
-void TFLiteImporter::parseGlobalPooling(const Operator& op, const std::string& opcode, LayerParams& layerParams)
+void TFLiteImporter::parseReduce(const Operator& op, const std::string& opcode, LayerParams& layerParams)
 {
-    layerParams.type = "Pooling";
-    if(opcode == "MEAN") {
-        layerParams.set("pool", "ave");
+    layerParams.type = "Reduce";
+    if (opcode == "REDUCE_MAX") {
+        layerParams.set("reduce", "max");
     }
-    else if (opcode == "REDUCE_MAX") {
-        layerParams.set("pool", "max");
+    else if (opcode == "SUM") {
+        layerParams.set("reduce", "sum");
+    }
+    else if (opcode == "MEAN") {
+        layerParams.set("reduce", "mean");
     }
     else {
-        CV_Error(Error::StsNotImplemented, "Unsupported pooling " + opcode);
+        CV_Error(Error::StsNotImplemented, "Unsupported reducing " + opcode);
     }
-    layerParams.set("global_pooling", true);
     auto options = op.builtin_options_as_ReducerOptions();
-    bool keep_dims = options->keep_dims();
+    layerParams.set("keepdims", options->keep_dims());
 
-    if (!keep_dims) {
-        const auto name = layerParams.name;
-        layerParams.name += "/global_pooling";
-        addLayer(layerParams, op);
+    Mat axes = allTensors[op.inputs()->Get(1)].clone();
+    CV_CheckTypeEQ(axes.type(), CV_32S, "");
 
-        int out = op.outputs()->Get(0);
-        auto outId = layerIds[out];
-        int flattenId = addFlattenLayer(1, -1, name, outId, isInt8(op) ? CV_8S : CV_32F);
-        layerIds[out] = std::make_pair(flattenId, 0);
-    }
-    else {
-        addLayer(layerParams, op);
+    DataLayout inpLayout = layouts[op.inputs()->Get(0)];
+    if (inpLayout == DNN_LAYOUT_NHWC) {
+        static const int remap[] = {0, 2, 3, 1};
+        // OpenCV works in NCHW data layout. So change the axis correspondingly.
+        for (int i = 0; i < axes.total(); ++i) {
+            axes.at<int>(i) = remap[normalize_axis(axes.at<int>(i), 4)];
+        }
     }
+
+    layerParams.set("axes", DictValue::arrayInt(axes.ptr<int>(), axes.total()));
+    addLayer(layerParams, op);
 }
 
 int TFLiteImporter::addPermuteLayer(const std::vector<int>& order, const std::string& permName,
@@ -833,6 +933,13 @@ int TFLiteImporter::addFlattenLayer(int axis, int end_axis, const std::string& n
     return id;
 }
 
+int TFLiteImporter::addConstLayer(const Mat& blob, const std::string& name)
+{
+    LayerParams lp;
+    lp.blobs.push_back(blob.u ? blob : blob.clone());  // some tensors are owned by OpenCV
+    return dstNet.addLayer(name, "Const", lp);
+}
+
 void TFLiteImporter::parseDeconvolution(const Operator& op, const std::string& opcode, LayerParams& layerParams) {
     layerParams.type = "Deconvolution";
 
@@ -928,8 +1035,7 @@ void TFLiteImporter::parseStridedSlice(const Operator& op, const std::string& op
     int endMask = options->end_mask();
     if (options->new_axis_mask())
         CV_Error(Error::StsNotImplemented, "New axis during StridedSlice");
-    if (options->shrink_axis_mask())
-        CV_Error(Error::StsNotImplemented, "Shrink axis during StridedSlice");
+    int shrinkMask = options->shrink_axis_mask();
 
     Mat begins = allTensors[op.inputs()->Get(1)];
     Mat ends = allTensors[op.inputs()->Get(2)];
@@ -958,7 +1064,30 @@ void TFLiteImporter::parseStridedSlice(const Operator& op, const std::string& op
     layerParams.set("begin", DictValue::arrayInt((int*)begins.data, begins.total()));
     layerParams.set("end", DictValue::arrayInt((int*)ends.data, ends.total()));
     layerParams.set("steps", DictValue::arrayInt((int*)strides.data, strides.total()));
+
+    int lastShrinkAxis = -1;
+    for (int axis = 0; axis < num; ++axis)
+    {
+        if (shrinkMask & (1 << axis))
+            lastShrinkAxis = axis;
+    }
+    std::string layerName = layerParams.name;
+    if (lastShrinkAxis != -1)
+    {
+        layerParams.name += "/slice";
+    }
+
     addLayer(layerParams, op);
+
+    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);
+        layerIds[op.inputs()->Get(0)] = std::make_pair(layerId, 0);
+    }
 }
 
 void TFLiteImporter::parseFullyConnected(const Operator& op, const std::string& opcode, LayerParams& layerParams) {
diff --git a/modules/dnn/test/test_tflite_importer.cpp b/modules/dnn/test/test_tflite_importer.cpp

Original file line number	Diff line number	Diff line change
`@@ -2015,7 +2015,7 @@ class DeConvolutionLayerImpl CV_FINAL : public BaseConvolutionLayerImpl`
`2015`	`2015`	`if( weightsMat.empty() )`
`2016`	`2016`	`{`
`2017`	`2017`	`transpose(blobs[0].reshape(1, inpCn), weightsMat);`
`2018`		`- biasesMat = hasBias() ? blobs[1].reshape(1, outCn) : Mat::zeros(outCn, 1, CV_32F);`
	`2018`	`+ biasesMat = hasBias() ? blobs[1] : Mat::zeros(outCn, 1, CV_32F);`
`2019`	`2019`	`}`
`2020`	`2020`
`2021`	`2021`	`for (size_t ii = 0; ii < outputs.size(); ii++)`
`@@ -2041,7 +2041,7 @@ class DeConvolutionLayerImpl CV_FINAL : public BaseConvolutionLayerImpl`
`2041`	`2041`
`2042`	`2042`	`Mat convMat = convBlob.rowRange(_Range((g + n * ngroups) * inpGroupCn, inpGroupCn));`
`2043`	`2043`	`Mat wghtMat = weightsMat.colRange(_Range(g * inpGroupCn, inpGroupCn));`
`2044`		`- Mat curBiasMat = biasesMat.rowRange(_Range(g * outGroupCn, outGroupCn));`
	`2044`	`+ Mat curBiasMat = biasesMat.reshape(1, {outCn, 1}).rowRange(_Range(g * outGroupCn, outGroupCn));`
`2045`	`2045`
`2046`	`2046`	`//gemm(wghtMat, convMat, 1, colMat, 0, colMat, 0);`
`2047`	`2047`	`MatMulInvoker mminvoker(wghtMat, convMat, colMat, nstripes);`
Original file line number	Diff line number	Diff line change
`@@ -106,7 +106,7 @@ class EltwiseLayerImpl CV_FINAL : public EltwiseLayer`
`106`	`106`	`if (params.has("operation"))`
`107`	`107`	`{`
`108`	`108`	`String operation = toLowerCase(params.get<String>("operation"));`
`109`		`- if (operation == "prod")`
	`109`	`+ if (operation == "prod" \|\| operation == "mul")`
`110`	`110`	`op = PROD;`
`111`	`111`	`else if (operation == "sum")`
`112`	`112`	`op = SUM;`
Original file line number	Diff line number	Diff line change
`@@ -213,6 +213,7 @@ struct BlobManager`
`213`	`213`	`{`
`214`	`214`	`reuse(bestBlobPin, lp);`
`215`	`215`	`dst = bestBlob.reshape(1, 1).colRange(0, targetTotal).reshape(1, shape);`
	`216`	`+ dst.dims = shape.size();`
`216`	`217`	`return;`
`217`	`218`	`}`
`218`	`219`	`}`