Merge pull request #28963 from abhishek-gola:custom_layers

Added custom layer support in new DNN engine #28963 Closes: https://github.com/opencv/opencv/issues/26200 Merge with: https://github.com/opencv/opencv_extra/pull/1358 ### Pull Request Readiness Checklist 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
2026-07-31 08:13:04 +04:00 · 2026-05-13 13:01:25 +05:30
parent 72e0bc2bf3
commit 914c0b2bc8
3 changed files with 168 additions and 3 deletions
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+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+// Copyright (C) 2026, BigVision LLC, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Companion sample for tutorial:
+//   doc/tutorials/dnn/dnn_custom_layers/dnn_custom_layers.md (ONNX section)
+//
+// Models used by this sample are generated by:
+//   opencv_extra/testdata/dnn/onnx/generate_custom_layer_models.py
+// and live at:
+//   opencv_extra/testdata/dnn/onnx/models/custom_layer_default_domain.onnx
+//   opencv_extra/testdata/dnn/onnx/models/custom_layer_custom_domain.onnx
+
+#include <opencv2/dnn.hpp>
+#include <opencv2/core.hpp>
+#include <iostream>
+#include <vector>
+#include <string>
+
+using namespace cv;
+using namespace cv::dnn;
+using namespace std;
+
+//! [CustomScaleBiasLayer]
+// y = scale * x + bias, with scale/bias read from ONNX node attributes.
+class CustomScaleBiasLayer CV_FINAL : public Layer
+{
+public:
+    CustomScaleBiasLayer(const LayerParams& params) : Layer(params)
+    {
+        scale = params.get<float>("scale", 1.f);
+        bias  = params.get<float>("bias",  0.f);
+    }
+
+    static Ptr<Layer> create(LayerParams& params)
+    {
+        return makePtr<CustomScaleBiasLayer>(params);
+    }
+
+    bool getMemoryShapes(const vector<MatShape>& inpts,
+                         const int /*requiredOutputs*/,
+                         vector<MatShape>& outShapes,
+                         vector<MatShape>& /*internals*/) const CV_OVERRIDE
+    {
+        outShapes.assign(1, inpts[0]);
+        return false;
+    }
+
+    void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr,
+                 OutputArrayOfArrays) CV_OVERRIDE
+    {
+        vector<Mat> inps, outs;
+        inputs_arr.getMatVector(inps);
+        outputs_arr.getMatVector(outs);
+        inps[0].convertTo(outs[0], outs[0].type(), scale, bias);
+    }
+
+private:
+    float scale, bias;
+};
+//! [CustomScaleBiasLayer]
+
+static void printShape(const string& tag, const Mat& m)
+{
+    cout << tag;
+    for (int d = 0; d < m.dims; ++d)
+        cout << (d ? "x" : "") << m.size[d];
+    cout << "\n";
+}
+
+int main(int argc, char** argv)
+{
+    const string keys =
+        "{ help  h |                                  | Print help message }"
+        "{ model m |                                  | Path to ONNX model containing the custom op }"
+        "{ op    o | MyCustomOp                       | Op key for registration. Default-domain: just the op_type "
+                                                       "(e.g. MyCustomOp). Custom-domain: <domain>.<op_type> "
+                                                       "(e.g. my.namespace.MyDomainOp) }";
+    CommandLineParser parser(argc, argv, keys);
+    parser.about("Demonstrates importing an ONNX model that contains a custom (non-standard) op "
+                 "by registering a user-defined layer with cv::dnn::LayerFactory.");
+    if (parser.has("help") || !parser.has("model"))
+    {
+        parser.printMessage();
+        return 0;
+    }
+
+    const string modelPath = parser.get<string>("model");
+    const string opKey     = parser.get<string>("op");
+
+    //! [Register CustomScaleBiasLayer]
+    // ONNX op-type lookup: layers in the default `ai.onnx` domain are registered
+    // under their op_type; layers in a non-default domain are registered under
+    // "<domain>.<op_type>" (e.g. "my.namespace.MyDomainOp").
+    LayerFactory::registerLayer(opKey, CustomScaleBiasLayer::create);
+    //! [Register CustomScaleBiasLayer]
+
+    Net net = readNetFromONNX(modelPath);
+    if (net.empty())
+    {
+        cerr << "Failed to load model: " << modelPath << "\n";
+        LayerFactory::unregisterLayer(opKey);
+        return 1;
+    }
+
+    // The companion ONNX models accept a 1x3x4x4 float input.
+    Mat input(vector<int>{1, 3, 4, 4}, CV_32F, Scalar(1.0f));
+    net.setInput(input);
+    Mat out = net.forward();
+
+    cout << "Loaded " << modelPath << " using custom op key '" << opKey << "'.\n";
+    printShape("Input shape:  ", input);
+    printShape("Output shape: ", out);
+    cout << "Output[0,0,0,0] = " << out.ptr<float>()[0]
+         << "  (= scale * 1.0 + bias for the registered op)\n";
+
+    LayerFactory::unregisterLayer(opKey);
+    return 0;
+}