chore: import upstream snapshot with attribution

2026-07-13 13:33:03 +08:00
commit 5b57521aa1
8226 changed files with 3425766 additions and 0 deletions
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+//
+//  NormalizeTest.cpp
+//  MNNTests
+//
+//  Created by MNN on 2021/10/22.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+#include <cmath>
+#include <MNN/expr/Expr.hpp>
+#include <MNN/expr/ExprCreator.hpp>
+#include "MNNTestSuite.h"
+#include "TestUtils.h"
+
+using namespace MNN::Express;
+class NormalizeTest : public MNNTestCase {
+public:
+    static void _refNormalize(float* dst, const float* src, int batch, int channel, int area, float* scale, float eps) {
+        // Normalize
+        for (int b=0; b<batch; ++b) {
+            for (int x=0; x<area; ++x) {
+                auto dstX = dst + b * area * channel + x;
+                auto srcX = src + b * area * channel + x;
+                float sumSquare = 0.0f;
+                for (int c=0; c<channel; ++c) {
+                    sumSquare += (srcX[area * c] * srcX[area * c]);
+                }
+                float normalValue = 1.0f / sqrtf(sumSquare + eps);
+                for (int c=0; c<channel; ++c) {
+                    dstX[area*c] = srcX[area * c] * normalValue * scale[c];
+                }
+            }
+        }
+    }
+    virtual ~NormalizeTest() = default;
+    virtual bool run(int precision) {
+        auto input = _Input({1, 2, 2, 1}, NCHW);
+        // set input data
+        const float inpudata[] = {-1.0, -2.0, 3.0, 4.0};
+        auto inputPtr          = input->writeMap<float>();
+        memcpy(inputPtr, inpudata, 4 * sizeof(float));
+        input = _Convert(input, NC4HW4);
+        std::vector<float> scaleData = {0.5f, 0.5f};
+        float eps = 0.00f;
+        auto output = _Normalize(input, 0, 0, eps, scaleData);
+        output = _Convert(output, NCHW);
+        std::vector<float> expectedOutput(4);
+        _refNormalize(expectedOutput.data(), inpudata, 1, 2, 2, scaleData.data(), eps);
+        auto gotOutput                        = output->readMap<float>();
+        float errorScale = precision <= MNN::BackendConfig::Precision_High ? 1 : 1000;
+        if (!checkVectorByRelativeError<float>(gotOutput, expectedOutput.data(), 4, 1e-5 * errorScale)) {
+            MNN_ERROR("NormalizeTest test failed!\n");
+            return false;
+        }
+        return true;
+    }
+};
+MNNTestSuiteRegister(NormalizeTest, "op/normalize");