From 2c675820203f21b047252fc3c2f8b2ad5dfdc072 Mon Sep 17 00:00:00 2001
From: Abhishek Gola <abhishek.gola@bigvisionllc.com>
Date: Fri, 3 Jul 2026 17:49:10 +0530
Subject: [PATCH] slope fix

---
 modules/dnn/include/opencv2/dnn/all_layers.hpp |  2 ++
 modules/dnn/src/graph_const_args.cpp           |  5 +++++
 modules/dnn/src/layers/elementwise_layers.cpp  | 15 +++++++++++++++
 modules/dnn/src/onnx/onnx_importer2.cpp        | 14 +++++++++++---
 4 files changed, 33 insertions(+), 3 deletions(-)

diff --git a/modules/dnn/include/opencv2/dnn/all_layers.hpp b/modules/dnn/include/opencv2/dnn/all_layers.hpp
index bce0b862de..686bccda2d 100644
--- a/modules/dnn/include/opencv2/dnn/all_layers.hpp
+++ b/modules/dnn/include/opencv2/dnn/all_layers.hpp
@@ -1123,6 +1123,8 @@ CV__DNN_INLINE_NS_BEGIN
     {
     public:
         static Ptr<Layer> create(const LayerParams& params);
+        // Set the per-channel slope when it arrives as a second input, not a blob.
+        virtual void setSlope(const Mat& /*slope*/) {}
     };
 
     class CV_EXPORTS ELULayer : public ActivationLayer
diff --git a/modules/dnn/src/graph_const_args.cpp b/modules/dnn/src/graph_const_args.cpp
index 7cb449d440..706bd67538 100644
--- a/modules/dnn/src/graph_const_args.cpp
+++ b/modules/dnn/src/graph_const_args.cpp
@@ -68,6 +68,7 @@ struct ConstArgs
             Conv2Layer* conv = dynamic_cast<Conv2Layer*>(layer_ptr);
             ConvTranspose2Layer* deconv = dynamic_cast<ConvTranspose2Layer*>(layer_ptr);
             BatchNorm2Layer* bn = dynamic_cast<BatchNorm2Layer*>(layer_ptr);
+            ChannelsPReLULayer* prelu = dynamic_cast<ChannelsPReLULayer*>(layer_ptr);
             //ActivationLayer* activ = dynamic_cast<ActivationLayer*>(layer_ptr);
 
             if (tail_const) {
@@ -88,6 +89,10 @@ struct ConstArgs
                 } else if (bn && bn->freezeScaleBias()) {
                     // batch norm with constant parameters
                     unuse_tail = true;
+                } else if (prelu && ninputs == 2) {
+                    prelu->setSlope(netimpl->__tensors__[inputs[1].idx]);
+                    prelu->inputs.resize(1);
+                    unuse_tail = true;
                 }/* else if (activ && dynamic_cast<ReLU6Layer>(activ)) {
                     // [TODO] ...
                     unuse_tail = true;
diff --git a/modules/dnn/src/layers/elementwise_layers.cpp b/modules/dnn/src/layers/elementwise_layers.cpp
index 5405d938d2..269b3fe96a 100644
--- a/modules/dnn/src/layers/elementwise_layers.cpp
+++ b/modules/dnn/src/layers/elementwise_layers.cpp
@@ -3742,6 +3742,14 @@ class ChannelsPReLUImpl CV_FINAL : public ElementWiseLayer<ChannelsPReLUFunctor>
 public:
     using ElementWiseLayer<ChannelsPReLUFunctor>::ElementWiseLayer;
 
+    void setSlope(const Mat& slope) CV_OVERRIDE
+    {
+        slope.reshape(1, (int)slope.total()).convertTo(func.scale, CV_32F);
+#ifdef HAVE_OPENCL
+        func.scale_umat.release();
+#endif
+    }
+
     void forward(InputArrayOfArrays inputs_arr,
                  OutputArrayOfArrays outputs_arr,
                  OutputArrayOfArrays internals_arr) CV_OVERRIDE
@@ -3849,6 +3857,13 @@ private:
 
 Ptr<Layer> ChannelsPReLULayer::create(const LayerParams& params)
 {
+    if (params.blobs.empty())
+    {
+        // Slope comes as a second input; constArgs() fills the scale in later.
+        Ptr<ChannelsPReLUImpl> l(new ChannelsPReLUImpl(ChannelsPReLUFunctor()));
+        l->setParamsFrom(params);
+        return l;
+    }
     CV_Assert(params.blobs.size() == 1);
     Mat scale = params.blobs[0];
     float slope = *scale.ptr<float>();
diff --git a/modules/dnn/src/onnx/onnx_importer2.cpp b/modules/dnn/src/onnx/onnx_importer2.cpp
index 1eb9c7b154..961d207538 100644
--- a/modules/dnn/src/onnx/onnx_importer2.cpp
+++ b/modules/dnn/src/onnx/onnx_importer2.cpp
@@ -1458,9 +1458,17 @@ void ONNXImporter2::parsePRelu(LayerParams& layerParams, const opencv_onnx::Node
 {
     layerParams.type = "PReLU";
     CV_Assert(node_inputs.size() == 2);
-    CV_Assert(net.isConstArg(node_inputs[1]));
-    layerParams.blobs.push_back(net.argTensor(node_inputs[1]));
-    addLayer(layerParams, node_proto, 1);
+    if (net.isConstArg(node_inputs[1]))
+    {
+        layerParams.blobs.push_back(net.argTensor(node_inputs[1]));
+        addLayer(layerParams, node_proto, 1);
+    }
+    else
+    {
+        // Slope produced by a foldable subgraph (e.g. Reshape of an initializer):
+        // keep it as a second input for constFold()/constArgs() to resolve.
+        addLayer(layerParams, node_proto);
+    }
 }
 
 void ONNXImporter2::parseLpNormalization(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)