From 44b31dd82c5c95c75778d48bb13e961e0587fd87 Mon Sep 17 00:00:00 2001
From: ramukhsuya <163312977+ramukhsuya@users.noreply.github.com>
Date: Thu, 18 Dec 2025 20:25:04 +0530
Subject: [PATCH] Merge pull request #28171 from
 ramukhsuya:tflite-maximum-support

dnn(tflite): add support for MAXIMUM layer #28171

Fixes #26433
This PR adds support for the `MAXIMUM` layer in the TFLite importer.
It maps the TFLite `MAXIMUM` opcode to the existing OpenCV Element-wise `Max` operation.

### 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
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
---
 modules/dnn/src/tflite/tflite_importer.cpp |  8 +++++--
 modules/dnn/test/test_tflite_importer.cpp  | 28 ++++++++++++++++++++++
 2 files changed, 34 insertions(+), 2 deletions(-)

diff --git a/modules/dnn/src/tflite/tflite_importer.cpp b/modules/dnn/src/tflite/tflite_importer.cpp
index 7bc4f55988..edcc3804e9 100644
--- a/modules/dnn/src/tflite/tflite_importer.cpp
+++ b/modules/dnn/src/tflite/tflite_importer.cpp
@@ -287,7 +287,8 @@ TFLiteImporter::DispatchMap TFLiteImporter::buildDispatchMap()
     dispatch["DEPTHWISE_CONV_2D"] = &TFLiteImporter::parseDWConvolution;
     dispatch["ADD"] = dispatch["MUL"] = dispatch["SUB"] =
         dispatch["SQRT"] = dispatch["DIV"] = dispatch["NEG"] =
-        dispatch["RSQRT"] = dispatch["SQUARED_DIFFERENCE"] = &TFLiteImporter::parseEltwise;
+        dispatch["RSQRT"] = dispatch["SQUARED_DIFFERENCE"] =
+        dispatch["MAXIMUM"] = &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;
@@ -577,7 +578,10 @@ void TFLiteImporter::parseEltwise(const Operator& op, const std::string& opcode,
     }
     else if (opcode == "SQRT" && !isOpInt8) {
         layerParams.type = "Sqrt";
-    } else {
+    }
+    else if (opcode == "MAXIMUM" && !isOpInt8) {
+        layerParams.set("operation", "max");
+    }else {
         CV_Error(Error::StsNotImplemented, cv::format("DNN/TFLite: Unknown opcode for %s Eltwise layer '%s'", isOpInt8 ? "INT8" : "FP32", opcode.c_str()));
     }
 
diff --git a/modules/dnn/test/test_tflite_importer.cpp b/modules/dnn/test/test_tflite_importer.cpp
index 186b0ff154..3cee776611 100644
--- a/modules/dnn/test/test_tflite_importer.cpp
+++ b/modules/dnn/test/test_tflite_importer.cpp
@@ -283,6 +283,34 @@ TEST_P(Test_TFLite, face_blendshapes)
     testModel("face_blendshapes", inp);
 }
 
+TEST_P(Test_TFLite, maximum)
+{
+    Net net = readNetFromTFLite(findDataFile("dnn/tflite/maximum.tflite"));
+
+    net.setPreferableBackend(backend);
+    net.setPreferableTarget(target);
+
+    Mat input_x = blobFromNPY(findDataFile("dnn/tflite/maximum_input_x.npy"));
+    Mat input_y = blobFromNPY(findDataFile("dnn/tflite/maximum_input_y.npy"));
+
+    net.setInput(input_x, "x");
+    net.setInput(input_y, "y");
+
+    Mat out = net.forward();
+    Mat ref = blobFromNPY(findDataFile("dnn/tflite/maximum_output.npy"));
+
+    double l1 = 1e-5;
+    double lInf = 1e-4;
+
+    if (target == DNN_TARGET_CUDA_FP16 || target == DNN_TARGET_OPENCL_FP16)
+    {
+        l1 = 1e-3;
+        lInf = 1e-3;
+    }
+
+    normAssert(ref, out, "", l1, lInf);
+}
+
 INSTANTIATE_TEST_CASE_P(/**/, Test_TFLite, dnnBackendsAndTargets());
 
 }}  // namespace