chore: import upstream snapshot with attribution

tools/train/source/demo/dataLoaderDemo.cpp

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+//
+//  dataLoaderDemo.cpp
+//  MNN
+//
+//  Created by MNN on 2019/11/20.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+
+#include <iostream>
+#include "DataLoader.hpp"
+#include "DemoUnit.hpp"
+#include "MNN_generated.h"
+#include "MnistDataset.hpp"
+#include "LambdaTransform.hpp"
+#include "RandomSampler.hpp"
+#include "Sampler.hpp"
+#include "StackTransform.hpp"
+#include "Transform.hpp"
+#include "TransformDataset.hpp"
+
+#ifdef MNN_USE_OPENCV
+#include <opencv2/opencv.hpp> // use opencv to show pictures
+using namespace cv;
+#endif
+
+using namespace std;
+using namespace MNN;
+using namespace MNN::Train;
+/*
+ * this is an demo for how to use the DataLoader
+ */
+
+class DataLoaderDemo : public DemoUnit {
+public:
+    // this function is an example to use the lambda transform
+    // here we use lambda transform to normalize data from 0~255 to 0~1
+    static Example func(Example example) {
+        // // an easier way to do this
+        auto cast       = _Cast(example.first[0], halide_type_of<float>());
+        example.first[0] = _Multiply(cast, _Const(1.0f / 255.0f));
+        return example;
+    }
+    virtual int run(int argc, const char* argv[]) override {
+        if (argc != 2) {
+            cout << "usage: ./runTrainDemo.out DataLoaderDemo /path/to/unzipped/mnist/data/" << endl;
+            return 0;
+        }
+
+        std::string root = argv[1];
+
+        // train data loader
+        const size_t trainDatasetSize = 60000;
+        auto trainDatasetOrigin = MnistDataset::create(root, MnistDataset::Mode::TRAIN);
+        auto trainDataset             = trainDatasetOrigin.mDataset;
+
+        // the lambda transform for one example, we also can do it in batch
+        auto trainTransform = std::make_shared<LambdaTransform>(func);
+
+        // // the stack transform, stack [1, 28, 28] to [n, 1, 28, 28]
+        // auto trainTransform = std::make_shared<StackTransform>();
+
+        const int trainBatchSize  = 7;
+        const int trainNumWorkers = 4;
+
+        auto trainDataLoader =
+            std::shared_ptr<DataLoader>(DataLoader::makeDataLoader(trainDataset, {trainTransform}, trainBatchSize, true, trainNumWorkers));
+
+        // test data loader
+        const size_t testDatasetSize = 10000;
+        auto testDatasetOrigin = MnistDataset::create(root, MnistDataset::Mode::TEST);
+        auto testDataset             = testDatasetOrigin.mDataset;
+
+        // the lambda transform for one example, we also can do it in batch
+        auto testTransform = std::make_shared<LambdaTransform>(func);
+
+        // // the stack transform, stack [1, 28, 28] to [n, 1, 28, 28]
+        // auto testTransform = std::make_shared<StackTransform>();
+
+        const int testBatchSize  = 3;
+        const int testNumWorkers = 4;
+
+        auto testDataLoader =
+            std::shared_ptr<DataLoader>(DataLoader::makeDataLoader(testDataset, {testTransform}, testBatchSize, false, testNumWorkers));
+
+        const size_t iterations = testDatasetSize / testBatchSize;
+
+        for (int i = 0; i < iterations; i++) {
+            auto trainData = trainDataLoader->next();
+            auto testData  = testDataLoader->next();
+
+            auto data  = trainData[0].first[0]->readMap<float>();
+            auto label = trainData[0].second[0]->readMap<uint8_t>();
+
+            cout << "index: " << i << " train label: " << int(label[0]) << endl;
+#ifdef MNN_USE_OPENCV
+            // only show the first picture in the batch
+            imshow("train", Mat(28, 28, CV_32FC1, (void*)data));
+#endif
+            data  = testData[0].first[0]->readMap<float>();
+            label = testData[0].second[0]->readMap<uint8_t>();
+
+            cout << "index: " << i << " test label: " << int(label[0]) << endl;
+#ifdef MNN_USE_OPENCV
+            // only show the first picture in the batch
+            imshow("test", Mat(28, 28, CV_32FC1, (void*)data));
+            waitKey(-1);
+#endif
+        }
+        // this will reset the sampler's internal state, not necessary here
+        trainDataLoader->reset();
+
+        // this will reset the sampler's internal state, necessary here, because the test dataset is exhausted
+        testDataLoader->reset();
+        return 0;
+    }
+};
+DemoUnitSetRegister(DataLoaderDemo, "DataLoaderDemo");