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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+//
+//  CloneNetTest.cpp
+//  MNNTests
+//
+//  Created by MNN on 2019/09/10.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+
+#include <MNN/expr/ExprCreator.hpp>
+#include "MNNTestSuite.h"
+#include "MNN_generated.h"
+#include <MNN/expr/Module.hpp>
+#include "TestUtils.h"
+
+using namespace MNN;
+using namespace MNN::Express;
+
+const int width = 1280;
+const int height = 720;
+const int channel = 3;
+static std::shared_ptr<Module> _createModel() {
+    float fac = 0.23;
+    int res = 10;
+    float tail = 0.05;
+    std::vector<float> constdata(channel * height * width);
+    for (int j = 0; j < channel; ++j) {
+        for (int k = 0; k < height * width; k++) {
+            constdata[j * height * width + k] = (j * height * width + k) % (height * width) * fac + tail;
+        }
+    }
+    
+    auto x = _Input({1, channel, height, width}, NCHW, halide_type_of<float>());
+    x->setName("Input");
+    auto c = _Const(constdata.data(), {1, channel, height, width}, NCHW);
+    auto y = x + c;
+    y->setName("Output");
+    std::unique_ptr<NetT> net(new NetT);
+    Variable::save({y}, net.get());
+    flatbuffers::FlatBufferBuilder builder;
+    auto len = MNN::Net::Pack(builder, net.get());
+    builder.Finish(len);
+    return std::shared_ptr<Module>(Module::load({"Input"}, {"Output"}, builder.GetBufferPointer(), builder.GetSize()));
+}
+
+class CloneNetTest : public MNNTestCase {
+public:
+    virtual bool run(int precision) {
+        std::vector<float> inputData(channel * width * height);
+        for (int i = 0; i < channel * height * width; ++i){
+            inputData[i] = (rand() % 10) * 0.1;
+        }
+
+        MNN::BackendConfig config;
+        config.precision = (MNN::BackendConfig::PrecisionMode)MNN::BackendConfig::Precision_Normal;
+        config.memory = (MNN::BackendConfig::MemoryMode)MNN::BackendConfig::Memory_Normal;
+        std::shared_ptr<Executor> executor(Executor::newExecutor(getCurrentType(), config, 4));
+        ExecutorScope scope(executor);
+        
+        auto net = _createModel();
+        auto x = _Input({1, channel, height, width}, NCHW, halide_type_of<float>());
+        {
+            auto xPtr = x->writeMap<float>();
+            ::memcpy(xPtr, inputData.data(), channel * height * width * sizeof(float));
+            x->unMap();
+        }
+        
+        auto outputs = net->onForward({x});
+        outputs[0] = _Convert(outputs[0], NC4HW4);
+        auto refPtr = outputs[0]->readMap<float>();
+        auto size = outputs[0]->getInfo()->size;
+        
+        
+        // clone model
+
+        std::unique_ptr<Module> tempModule(Module::clone(net.get()));
+        
+        auto xClone = _Input({1, channel, height, width}, NCHW, halide_type_of<float>());
+        {
+            auto xPtr = xClone->writeMap<float>();
+            ::memcpy(xPtr, inputData.data(), channel * height * width * sizeof(float));
+            xClone->unMap();
+        }
+        auto outputsClone = tempModule->onForward({xClone});
+        outputsClone[0] = _Convert(outputsClone[0], NC4HW4);
+        auto outPtr = outputsClone[0]->readMap<float>();
+        
+        for (int i = 0; i < size; ++i) {
+            float targetValue = refPtr[i], computeResult = outPtr[i];
+            float diff = targetValue - computeResult;
+            if (fabsf(diff) > 0.001) {
+                MNN_PRINT("%d result Error: right=%f, error=%f\n", targetValue, computeResult);
+                return false;
+            }
+        }
+        
+        return true;
+    }
+};
+
+MNNTestSuiteRegister(CloneNetTest, "Clone/CloneNet");