From 6b04d456b39a145d1996ae0c031ef5e62d2f4e6a Mon Sep 17 00:00:00 2001
From: Teddy-Yangjiale <12411723@mail.sustech.edu.cn>
Date: Thu, 23 Jul 2026 10:17:18 +0800
Subject: [PATCH] add accuracy test for fastGemmThin

---
 modules/dnn/test/test_fast_gemm.cpp | 85 +++++++++++++++++++++++++++++
 1 file changed, 85 insertions(+)
 create mode 100644 modules/dnn/test/test_fast_gemm.cpp

diff --git a/modules/dnn/test/test_fast_gemm.cpp b/modules/dnn/test/test_fast_gemm.cpp
new file mode 100644
index 0000000000..23de8dd0bf
--- /dev/null
+++ b/modules/dnn/test/test_fast_gemm.cpp
@@ -0,0 +1,85 @@
+// 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.
+
+#include "test_precomp.hpp"
+
+namespace opencv_test { namespace {
+
+static Mat referenceMatMul(const Mat& A, const Mat& B, bool trans_a, float alpha)
+{
+    const int M = trans_a ? A.cols : A.rows;
+    const int K = trans_a ? A.rows : A.cols;
+    const int N = B.cols;
+    Mat expected(M, N, CV_32F);
+
+    for (int m = 0; m < M; m++)
+    {
+        float* dst = expected.ptr<float>(m);
+        for (int n = 0; n < N; n++)
+        {
+            float acc = 0.f;
+            for (int k = 0; k < K; k++)
+            {
+                const float a = trans_a ? A.ptr<float>(k)[m] : A.ptr<float>(m)[k];
+                acc += a * B.ptr<float>(k)[n];
+            }
+            dst[n] = alpha * acc;
+        }
+    }
+    return expected;
+}
+
+// Exercises the fastGemmThin path (constant-B MatMul): M covers every register
+// block width and the multi-block remainder, N covers full strips and partial
+// column tails for any VLEN <= 512.
+TEST(DNN_FastGemmThin, MatMulAccuracy)
+{
+    static const int m_values[] = { 1, 2, 3, 4, 5 };
+    static const int k_values[] = { 1, 3, 4, 7, 16, 64 };
+    static const int n_values[] = { 16, 19, 64, 67, 80, 83 };
+    static const float alpha_values[] = { 1.f, -0.5f };
+
+    RNG rng(0x5EED);
+    for (int M : m_values)
+    {
+        for (int N : n_values)
+        {
+            for (int K : k_values)
+            {
+                Mat B(K, N, CV_32F);
+                rng.fill(B, RNG::UNIFORM, -1.f, 1.f);
+
+                for (int trans_a = 0; trans_a <= 1; trans_a++)
+                {
+                    Mat A(trans_a ? K : M, trans_a ? M : K, CV_32F);
+                    rng.fill(A, RNG::UNIFORM, -1.f, 1.f);
+
+                    for (float alpha : alpha_values)
+                    {
+                        LayerParams lp;
+                        lp.type = "MatMul";
+                        lp.name = "thin_matmul";
+                        lp.set("transA", trans_a != 0);
+                        lp.set("transB", false);
+                        lp.set("alpha", alpha);
+                        lp.blobs.push_back(B);
+
+                        Net net;
+                        net.addLayerToPrev(lp.name, lp.type, lp);
+                        net.setInputsNames(std::vector<String>{ "A" });
+                        net.setInput(A, "A");
+                        Mat actual = net.forward();
+                        Mat expected = referenceMatMul(A, B, trans_a != 0, alpha);
+
+                        EXPECT_LE(cv::norm(expected, actual, NORM_INF), 2e-5f * std::max(K, 1))
+                            << "M=" << M << ", N=" << N << ", K=" << K
+                            << ", trans_a=" << trans_a << ", alpha=" << alpha;
+                    }
+                }
+            }
+        }
+    }
+}
+
+}} // namespace