From 5c02d32ccac16885ee537ff3a10095b149a6e810 Mon Sep 17 00:00:00 2001
From: Dmitry Kurtaev <dmitry.kurtaev@gmail.com>
Date: Thu, 13 Nov 2025 14:19:46 +0800
Subject: [PATCH] Merge pull request #28000 from
 dkurt:d.kurtaev:reset_winograd_impl

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

resolves https://github.com/opencv/opencv/issues/27580

- [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
---
 modules/dnn/src/layers/convolution_layer.cpp | 11 +++--
 modules/dnn/test/test_layers.cpp             | 43 ++++++++++++++++++++
 2 files changed, 51 insertions(+), 3 deletions(-)

diff --git a/modules/dnn/src/layers/convolution_layer.cpp b/modules/dnn/src/layers/convolution_layer.cpp
index 13069f53bc..c33eda428e 100644
--- a/modules/dnn/src/layers/convolution_layer.cpp
+++ b/modules/dnn/src/layers/convolution_layer.cpp
@@ -253,6 +253,7 @@ public:
     Ptr<ActivationLayer> activ;
 
     Ptr<FastConv> fastConvImpl;
+    bool canUseWinograd = false;
 
 #ifdef HAVE_OPENCL
     Ptr<OCL4DNNConvSpatial<float> > convolutionOp;
@@ -448,6 +449,13 @@ public:
 #ifdef HAVE_OPENCL
         convolutionOp.release();
 #endif
+
+        // Winograd only works when input h and w >= 12.
+        canUseWinograd = useWinograd && inputs[0].dims == 4 && inputs[0].size[2] >= 12 && inputs[0].size[3] >= 12;
+        if (fastConvImpl && (fastConvImpl->conv_type == CONV_TYPE_WINOGRAD3X3) ^ canUseWinograd)
+        {
+            fastConvImpl.reset();
+        }
     }
 
     bool setActivation(const Ptr<ActivationLayer>& layer) CV_OVERRIDE
@@ -1292,9 +1300,6 @@ public:
                 int K = outputs[0].size[1];
                 int C = inputs[0].size[1];
 
-                // Winograd only works when input h and w >= 12.
-                bool canUseWinograd = useWinograd && conv_dim == CONV_2D && inputs[0].size[2] >= 12 && inputs[0].size[3] >= 12;
-
                 CV_Assert(outputs[0].size[1] % ngroups == 0);
                 fastConvImpl = initFastConv(weightsMat, &biasvec[0], ngroups, K, C, kernel_size, strides,
                                             dilations, pads_begin, pads_end, conv_dim,
diff --git a/modules/dnn/test/test_layers.cpp b/modules/dnn/test/test_layers.cpp
index 48c968fb6a..9da907758c 100644
--- a/modules/dnn/test/test_layers.cpp
+++ b/modules/dnn/test/test_layers.cpp
@@ -2788,4 +2788,47 @@ INSTANTIATE_TEST_CASE_P(TestLayerFusion, ConvolutionActivationEltwiseFusion, Com
                         TestLayerFusion::dnnBackendsAndTargetsForFusionTests()
 ));
 
+TEST(ConvolutionWinograd, Accuracy)
+{
+    Mat weights({2, 1, 3, 3}, CV_32F);
+    randn(weights, 0, 1);
+
+    // Check convolution can switch between implementations on changed shape.
+    auto getNet = [&]() {
+        Net net;
+        LayerParams lp;
+        lp.name = "conv";
+        lp.type = "Convolution";
+        lp.set("kernel_size", 3);
+        lp.set("num_output", 2);
+        lp.set("pad", 0);
+        lp.set("stride", 1);
+        lp.set("bias_term", false);
+
+        lp.blobs.push_back(weights);
+        net.addLayerToPrev(lp.name, lp.type, lp);
+        return net;
+    };
+
+    Mat inpSmall({1, 1, 5, 5}, CV_32F);
+    Mat inpLarge({1, 1, 64, 64}, CV_32F);
+    randn(inpSmall, 0, 1);
+    randn(inpLarge, 0, 1);
+
+    Net net1 = getNet();
+    Net net2 = getNet();
+    net1.setInput(inpSmall);
+    net2.setInput(inpLarge);
+    Mat refSmall = net1.forward();
+    Mat refLarge = net2.forward();
+
+    net1.setInput(inpLarge);
+    net2.setInput(inpSmall);
+    Mat outLarge = net1.forward();
+    Mat outSmall = net2.forward();
+
+    normAssert(outSmall, refSmall, "Small input after large", 0.0, 0.0);
+    normAssert(outLarge, refLarge, "Large input after small", 0.0, 0.0);
+}
+
 }} // namespace