Merge pull request #19428 from alalek:dnn_drop_misbehaved_clamp

modules/dnn/include/opencv2/dnn/shape_utils.hpp

@@ -205,21 +205,33 @@ static inline std::ostream& operator<<(std::ostream &out, const MatShape& shape)
     return out;
 }
 
-inline int clamp(int ax, int dims)
+/// @brief Converts axis from `[-dims; dims)` (similar to Python's slice notation) to `[0; dims)` range.
+static inline
+int normalize_axis(int axis, int dims)
 {
-    return ax < 0 ? ax + dims : ax;
+    CV_Check(axis, axis >= -dims && axis < dims, "");
+    axis = (axis < 0) ? (dims + axis) : axis;
+    CV_DbgCheck(axis, axis >= 0 && axis < dims, "");
+    return axis;
 }
 
-inline int clamp(int ax, const MatShape& shape)
+static inline
+int normalize_axis(int axis, const MatShape& shape)
 {
-    return clamp(ax, (int)shape.size());
+    return normalize_axis(axis, (int)shape.size());
 }
 
-inline Range clamp(const Range& r, int axisSize)
+static inline
+Range normalize_axis_range(const Range& r, int axisSize)
 {
-    Range clamped(std::max(r.start, 0),
+    if (r == Range::all())
+        return Range(0, axisSize);
+    CV_CheckGE(r.start, 0, "");
+    Range clamped(r.start,
                   r.end > 0 ? std::min(r.end, axisSize) : axisSize + r.end + 1);
-    CV_Assert_N(clamped.start < clamped.end, clamped.end <= axisSize);
+    CV_DbgCheckGE(clamped.start, 0, "");
+    CV_CheckLT(clamped.start, clamped.end, "");
+    CV_CheckLE(clamped.end, axisSize, "");
     return clamped;
 }
 

modules/dnn/src/dnn.cpp

@@ -2598,7 +2598,7 @@ struct Net::Impl : public detail::NetImplBase
                 // the concatenation optimization is applied with batch_size > 1.
                 // so, for now, we only apply this optimization in the most popular
                 // case batch_size == 1.
-                int axis = clamp(concatLayer->axis, output.dims);
+                int axis = normalize_axis(concatLayer->axis, output.dims);
                 if( output.total(0, axis) == 1 )
                 {
                     size_t i, ninputs = ld.inputBlobsId.size();

modules/dnn/src/layers/concat_layer.cpp

@@ -72,7 +72,7 @@ public:
     {
         CV_Assert(inputs.size() > 0);
         outputs.resize(1, inputs[0]);
-        int cAxis = clamp(axis, inputs[0]);
+        int cAxis = normalize_axis(axis, inputs[0]);
 
         int axisSum = 0;
         for (size_t i = 0; i < inputs.size(); i++)
@@ -192,7 +192,7 @@ public:
         inps.getUMatVector(inputs);
         outs.getUMatVector(outputs);
 
-        int cAxis = clamp(axis, inputs[0].dims);
+        int cAxis = normalize_axis(axis, inputs[0].dims);
         if (padding)
             return false;
 
@@ -246,7 +246,7 @@ public:
         inputs_arr.getMatVector(inputs);
         outputs_arr.getMatVector(outputs);
 
-        int cAxis = clamp(axis, inputs[0].dims);
+        int cAxis = normalize_axis(axis, inputs[0].dims);
         Mat& outMat = outputs[0];
 
         if (padding)
@@ -306,7 +306,7 @@ public:
         InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
 
         InferenceEngine::Builder::ConcatLayer ieLayer(name);
-        ieLayer.setAxis(clamp(axis, input->getDims().size()));
+        ieLayer.setAxis(normalize_axis(axis, input->getDims().size()));
         ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(inputs.size()));
         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
     }
@@ -319,7 +319,7 @@ public:
     {
         InferenceEngine::DataPtr data = ngraphDataNode(inputs[0]);
         const int numDims = data->getDims().size();
-        const int cAxis = clamp(axis, numDims);
+        const int cAxis = normalize_axis(axis, numDims);
         std::vector<size_t> maxDims(numDims, 0);
 
         CV_Assert(inputs.size() == nodes.size());

modules/dnn/src/layers/flatten_layer.cpp

@@ -82,8 +82,8 @@ public:
         }
 
         int numAxes = inputs[0].size();
-        int startAxis = clamp(_startAxis, numAxes);
-        int endAxis = clamp(_endAxis, numAxes);
+        int startAxis = normalize_axis(_startAxis, numAxes);
+        int endAxis = normalize_axis(_endAxis, numAxes);
 
         CV_Assert(startAxis >= 0);
         CV_Assert(endAxis >= startAxis && endAxis < (int)numAxes);
@@ -113,8 +113,8 @@ public:
         inputs_arr.getMatVector(inputs);
 
         int numAxes = inputs[0].dims;
-        _startAxis = clamp(_startAxis, numAxes);
-        _endAxis = clamp(_endAxis, numAxes);
+        _startAxis = normalize_axis(_startAxis, numAxes);
+        _endAxis = normalize_axis(_endAxis, numAxes);
     }
 
 #ifdef HAVE_OPENCL
@@ -186,8 +186,8 @@ virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inp
         std::vector<size_t> dims = ieInpNode->get_shape();
 
         int numAxes = dims.size();
-        int startAxis = clamp(_startAxis, numAxes);
-        int endAxis = clamp(_endAxis, numAxes);
+        int startAxis = normalize_axis(_startAxis, numAxes);
+        int endAxis = normalize_axis(_endAxis, numAxes);
 
         CV_Assert(startAxis >= 0);
         CV_Assert(endAxis >= startAxis && endAxis < numAxes);

modules/dnn/src/layers/fully_connected_layer.cpp

@@ -129,7 +129,7 @@ public:
             CV_CheckEQ(blobs[0].dims, 2, "");
             numOutput = blobs[0].size[0];
             CV_Assert(!bias || (size_t)numOutput == blobs[1].total());
-            cAxis = clamp(axis, inputs[0]);
+            cAxis = normalize_axis(axis, inputs[0]);
         }
 
         MatShape outShape(cAxis + 1);
@@ -352,7 +352,7 @@ public:
             return true;
         }
 
-        int axisCan = clamp(axis, inputs[0].dims);
+        int axisCan = normalize_axis(axis, inputs[0].dims);
         int numOutput = blobs[0].size[0];
         int innerSize = blobs[0].size[1];
         int outerSize = total(shape(inputs[0]), 0, axisCan);
@@ -473,7 +473,7 @@ public:
 
         if (!blobs.empty())
         {
-            int axisCan = clamp(axis, input[0].dims);
+            int axisCan = normalize_axis(axis, input[0].dims);
             int outerSize = input[0].total(0, axisCan);
 
             for (size_t i = 0; i < input.size(); i++)

modules/dnn/src/layers/normalize_bbox_layer.cpp

@@ -118,8 +118,8 @@ public:
 
         const UMat& inp0 = inputs[0];
         UMat& buffer = internals[0];
-        startAxis = clamp(startAxis, inp0.dims);
-        endAxis = clamp(endAxis, inp0.dims);
+        startAxis = normalize_axis(startAxis, inp0.dims);
+        endAxis = normalize_axis(endAxis, inp0.dims);
 
         size_t num = total(shape(inp0.size), 0, startAxis);
         size_t numPlanes = total(shape(inp0.size), startAxis, endAxis + 1);
@@ -203,8 +203,8 @@ public:
 
         const Mat& inp0 = inputs[0];
         Mat& buffer = internals[0];
-        startAxis = clamp(startAxis, inp0.dims);
-        endAxis = clamp(endAxis, inp0.dims);
+        startAxis = normalize_axis(startAxis, inp0.dims);
+        endAxis = normalize_axis(endAxis, inp0.dims);
 
         const float* inpData = inp0.ptr<float>();
         float* outData = outputs[0].ptr<float>();

modules/dnn/src/layers/reshape_layer.cpp

@@ -60,14 +60,7 @@ static void computeShapeByReshapeMask(const MatShape &srcShape,
     int srcShapeSize = (int)srcShape.size();
     int maskShapeSize = (int)maskShape.size();
 
-    if (srcRange == Range::all())
-        srcRange = Range(0, srcShapeSize);
-    else
-    {
-        int sz = srcRange.size();
-        srcRange.start = clamp(srcRange.start, srcShapeSize);
-        srcRange.end = srcRange.end == INT_MAX ? srcShapeSize : srcRange.start + sz;
-    }
+    srcRange = normalize_axis_range(srcRange, srcShapeSize);
 
     bool explicitMask = !maskShape.empty();  // All mask values are positive.
     for (int i = 0, n = maskShape.size(); i < n && explicitMask; ++i)

modules/dnn/src/layers/scale_layer.cpp

@@ -240,7 +240,7 @@ public:
             numChannels = blobs[0].total();
 
         std::vector<size_t> shape(ieInpNode0->get_shape().size(), 1);
-        int cAxis = clamp(axis, shape.size());
+        int cAxis = normalize_axis(axis, shape.size());
         shape[cAxis] = numChannels;
 
         auto node = ieInpNode0;

modules/dnn/src/layers/slice_layer.cpp

@@ -146,7 +146,7 @@ public:
                 for (int j = 0; j < sliceRanges[i].size(); ++j)
                 {
                     if (shapesInitialized || inpShape[j] > 0)
-                        outputs[i][j] = clamp(sliceRanges[i][j], inpShape[j]).size();
+                        outputs[i][j] = normalize_axis_range(sliceRanges[i][j], inpShape[j]).size();
                 }
             }
         }
@@ -209,7 +209,7 @@ public:
             // Clamp.
             for (int j = 0; j < finalSliceRanges[i].size(); ++j)
             {
-                finalSliceRanges[i][j] = clamp(finalSliceRanges[i][j], inpShape[j]);
+                finalSliceRanges[i][j] = normalize_axis_range(finalSliceRanges[i][j], inpShape[j]);
             }
         }
 
@@ -601,7 +601,7 @@ public:
         CV_Assert(inputs.size() == 2);
 
         MatShape dstShape = inputs[0];
-        int start = clamp(axis, dstShape);
+        int start = normalize_axis(axis, dstShape);
         for (int i = start; i < dstShape.size(); i++)
         {
             dstShape[i] = inputs[1][i];
@@ -620,7 +620,7 @@ public:
         const Mat &inpSzBlob = inputs[1];
 
         int dims = inpBlob.dims;
-        int start_axis = clamp(axis, dims);
+        int start_axis = normalize_axis(axis, dims);
 
         std::vector<int> offset_final(dims, 0);
         if (offset.size() == 1)

modules/dnn/src/layers/softmax_layer.cpp

@@ -82,7 +82,7 @@ public:
     {
         bool inplace = Layer::getMemoryShapes(inputs, requiredOutputs, outputs, internals);
         MatShape shape = inputs[0];
-        int cAxis = clamp(axisRaw, shape.size());
+        int cAxis = normalize_axis(axisRaw, shape.size());
         shape[cAxis] = 1;
         internals.assign(1, shape);
         return inplace;
@@ -115,7 +115,7 @@ public:
 
         UMat& src = inputs[0];
         UMat& dstMat = outputs[0];
-        int axis = clamp(axisRaw, src.dims);
+        int axis = normalize_axis(axisRaw, src.dims);
 
         if (softmaxOp.empty())
         {
@@ -207,7 +207,7 @@ public:
         const Mat &src = inputs[0];
         Mat &dst = outputs[0];
 
-        int axis = clamp(axisRaw, src.dims);
+        int axis = normalize_axis(axisRaw, src.dims);
         size_t outerSize = src.total(0, axis), channels = src.size[axis],
                 innerSize = src.total(axis + 1);
 
@@ -318,7 +318,7 @@ public:
         InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
 
         InferenceEngine::Builder::SoftMaxLayer ieLayer(name);
-        ieLayer.setAxis(clamp(axisRaw, input->getDims().size()));
+        ieLayer.setAxis(normalize_axis(axisRaw, input->getDims().size()));
 
         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
     }
@@ -329,7 +329,7 @@ public:
                                         const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
     {
         auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
-        int axis = clamp(axisRaw, ieInpNode->get_shape().size());
+        int axis = normalize_axis(axisRaw, ieInpNode->get_shape().size());
         auto softmax = std::make_shared<ngraph::op::v1::Softmax>(ieInpNode, axis);
         if (logSoftMax)
             return Ptr<BackendNode>(new InfEngineNgraphNode(std::make_shared<ngraph::op::v0::Log>(softmax)));

modules/dnn/src/onnx/onnx_importer.cpp

@@ -503,7 +503,7 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto_)
                 MatShape targetShape;
                 std::vector<bool> shouldDelete(inpShape.size(), false);
                 for (int i = 0; i < axes.size(); i++) {
-                    int axis = clamp(axes.get<int>(i), inpShape.size());
+                    int axis = normalize_axis(axes.get<int>(i), inpShape.size());
                     shouldDelete[axis] = true;
                 }
                 for (int axis = 0; axis < inpShape.size(); ++axis){
@@ -515,7 +515,7 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto_)
 
                 if (inpShape.size() == 3 && axes.size() <= 2)
                 {
-                    int axis = clamp(axes.get<int>(0), inpShape.size());
+                    int axis = normalize_axis(axes.get<int>(0), inpShape.size());
                     CV_CheckNE(axis, 0, "");
 
                     LayerParams reshapeLp;
@@ -539,8 +539,8 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto_)
                     avgLp.set("pool", pool);
                     if (axes.size() == 2)
                     {
-                        CV_CheckEQ(clamp(axes.get<int>(0), inpShape.size()), 1, "Unsupported mode");
-                        CV_CheckEQ(clamp(axes.get<int>(1), inpShape.size()), 2, "Unsupported mode");
+                        CV_CheckEQ(normalize_axis(axes.get<int>(0), inpShape.size()), 1, "Unsupported mode");
+                        CV_CheckEQ(normalize_axis(axes.get<int>(1), inpShape.size()), 2, "Unsupported mode");
                         avgLp.set("global_pooling", true);
                     }
                     else
@@ -560,9 +560,9 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto_)
 
                     CV_Assert(axes.size() <= inpShape.size() - 2);
                     std::vector<int> kernel_size(inpShape.size() - 2, 1);
-                    if (axes.size() == 1 && (clamp(axes.get<int>(0), inpShape.size()) <= 1))
+                    if (axes.size() == 1 && (normalize_axis(axes.get<int>(0), inpShape.size()) <= 1))
                     {
-                        int axis = clamp(axes.get<int>(0), inpShape.size());
+                        int axis = normalize_axis(axes.get<int>(0), inpShape.size());
                         MatShape newShape = inpShape;
                         newShape[axis + 1] = total(newShape, axis + 1);
                         newShape.resize(axis + 2);
@@ -584,7 +584,7 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto_)
                     else
                     {
                         for (int i = 0; i < axes.size(); i++) {
-                            int axis = clamp(axes.get<int>(i), inpShape.size());
+                            int axis = normalize_axis(axes.get<int>(i), inpShape.size());
                             CV_Assert_N(axis >= 2 + i, axis < inpShape.size());
                             kernel_size[axis - 2] = inpShape[axis];
                         }
@@ -1376,7 +1376,7 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto_)
             if (constBlobs.find(node_proto.input(0)) != constBlobs.end())
             {
                 Mat input = getBlob(node_proto, 0);
-                int axis = clamp(layerParams.get<int>("axis", 1), input.dims);
+                int axis = normalize_axis(layerParams.get<int>("axis", 1), input.dims);
 
                 std::vector<int> out_size(&input.size[0], &input.size[0] + axis);
                 out_size.push_back(input.total(axis));