Method run

tools/train/source/demo/nnGradTest.cpp:352–422

class LoopGradTest : public DemoUnit {
public:
    virtual int run(int argc, const char* argv[]) override {
        MNN_PRINT("Test grad for Loop Binary\n");
        {
            int w = 4;
            int h = 5;
            auto input = _Input({w, h}, NHWC);
            auto target = _Input({w, h}, NHWC);
            auto targetAdd = _Input({w, h}, NHWC);
            auto inputPtr = input->writeMap<float>();
            auto targetPtr = target->writeMap<float>();
            auto targetPtrAdd = targetAdd->writeMap<float>();
            for (int y=0; y<h; ++y) {
                for (int x=0; x<w; ++x) {
                    auto value = ((float) (y * w) + (float)x) * 0.1f;
                    inputPtr[x + y * w] = value;
                    targetPtr[x + y * w] = value * (float) (y + 1) * 0.1f;
                    targetPtrAdd[x + y * w] = value + (float) (y + 1) * 0.1f;
                }
            }
            
            auto weight = _TrainableParam(0.0f, {1, h}, NHWC);
            auto weightAdd = _TrainableParam(0.0f, {1, h}, NHWC);
            std::shared_ptr<Module> _m(Module::createEmpty({weight, weightAdd}));
            std::shared_ptr<SGD> sgd(new SGD(_m));
            sgd->setLearningRate(0.01f);
            MNN_PRINT("Test grad for Binary Mul Loop\n");
            for (int i = 0; i < 1000; ++i) {
                auto compute = input * weight;
                auto loss = _ReduceMean(_Square(_Subtract(compute, target)), {});
                if (i % 100 == 0) {
                    MNN_PRINT("Loss = %f\n", loss->readMap<float>()[0]);
                }
                sgd->step(loss);
            }
            MNN_PRINT("Test grad for Binary Add Loop\n");
            for (int i = 0; i < 1000; ++i) {
                auto compute = input + weightAdd;
                auto loss = _ReduceMean(_Square(_Subtract(compute, target)), {});
                if (i % 100 == 0) {
                    MNN_PRINT("Loss = %f\n", loss->readMap<float>()[0]);
                }
                sgd->step(loss);
            }
        }
        MNN_PRINT("Test grad for Gather\n");
        {
            // set input data
            const float inpudata[] = {1.0,  2.0,  3.0,  4.0,  5.0,  6.0,  7.0,  8.0, 9.0, 10.0, 11.0, 12.0, 13.0,
                                      14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21,  0,   22.0, 23.0, 24.0};
            std::vector<float> inputDataRaw(0.0f, sizeof(inpudata) / sizeof(float));
            auto params = _TrainableParam(inputDataRaw.data(), {4, 3, 2}, NCHW, halide_type_of<float>());
            const int indices_data[]                = {1, 0, 1, 0};
            const std::vector<float> expectedOutput = {7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0,
                                                       7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0};
            std::shared_ptr<Module> _m(Module::createEmpty({params}));
            std::shared_ptr<SGD> sgd(new SGD(_m));
            sgd->setLearningRate(0.01f);
            for (int i = 0; i < 1000; ++i) {