Micro Java Programs for JVM

1.

public class Main {
    public static void main(String[] args) {
        for(int i=0; i<100; i++)
            foo();
    }

    private static void foo() {
        for(int i=0; i<100; i++)
            bar();
    }

    private static void bar() {
    }
}


2.

import java.lang.*;

class compile {

        synchronized public int inc() {
//        public int inc() {
             return 1;
        }

        public static void main (String args[]) {
                int sum = 0;
                compile test =new compile();
long startTime = System.currentTimeMillis();
             for(int i = 0; i <= 100; i++) {
                for(int j = 0; j < 10; j++) {
                   sum += test.inc();
                   sum += test.inc();
                   sum += test.inc();
                   sum += test.inc();
                   sum += test.inc();
                }
             }
long endTime = System.currentTimeMillis();
                System.err.println("sum = "+sum+" time: "+ (endTime - startTime));
        }
}

3.

class compute{

    public final double execute(double omega, double G[][], int
            num_iterations) {
        int M = G.length;
        int N = G[0].length;

        double omega_over_four = omega * 0.25;
        double one_minus_omega = 1.0 - omega;
        double [] Gi = null;
        double Gi_Sum = 0.0;
        // update interior points
        //
        int Mm1 = M-1;
        int Nm1 = N-1;
        for (int p=0; p<num_iterations; p++) {
            for (int i=1; i<Mm1; i++) {
                Gi = G[i];
                double[] Gim1 = G[i-1];
                double[] Gip1 = G[i+1];
                for (int j=1; j<Nm1; j++)
                    Gi[j] = omega_over_four * (Gim1[j] + Gip1[j] + Gi[j-1]
                            + Gi[j+1]) + one_minus_omega * Gi[j];
            }
        }
        for(int k=0;k<Gi.length;k++)Gi_Sum += Gi[k];

        return Gi_Sum;
    }

   public static void main(String args[]) {
      compute test = new compute();
      double[][] G = new double[200][200];
      for(int i = 0; i < 200; i++) {
         for(int j = 0; j < 200; j++) {
            G[i][j] = 1.0 + i + j;
         }
      }
long startTime = System.currentTimeMillis();
      test.execute(2.0, G, 10000);
long endTime = System.currentTimeMillis();
System.err.println("time: "+ (endTime - startTime));
   }
}

4.

class hello {

    public static void matmult(int x[][], int y[][], int z[][], int repeat) {
        int m = x.length;
        int k = x[0].length;
        int n = y[0].length;
for(int w = 0; w < repeat; w++) {
        for (int r = 0; r < m; r++) {
            for (int s = 0; s < n; s++) {
                z[r][s] = 0;
                for (int t = 0; t < k; t++) {
                    z[r][s] += x[r][t] * y[t][s];
                }
            }
        }
}
    }

    public static void main (String args[]) {
        int[][] x = {{3,3,3,3,3},{3,3,3,3,3},{3,3,3,3,3},{3,3,3,3,3},{3,3,3,3,3}};
        int[][] y = {{3,3,3,3,3},{3,3,3,3,3},{3,3,3,3,3},{3,3,3,3,3},{3,3,3,3,3}};
        int[][] z = {{0,0,0,0,0},{0,0,0,0,0},{0,0,0,0,0},{0,0,0,0,0},{0,0,0,0,0}};
long startTime = System.currentTimeMillis();
        hello.matmult(x, y, z, 900000);
long endTime = System.currentTimeMillis();
        System.err.println("time: "+ (endTime - startTime));
        }
}

5.

class matrix {

    public static void matmult( int y[], int val[], int row[], int col[], int x[], int NUM_ITERATIONS) {
        int M = row.length - 1;
        for (int reps=0; reps<NUM_ITERATIONS; reps++) {

            for (int r=0; r<M; r++) {
                int sum = 0;
                int rowR = row[r];
                int rowRp1 = row[r+1];
                for (int i=rowR; i<rowRp1; i++) {
//                    sum += x[ col[i] ] * val[i];   //case 1
//                    sum += x[ col[i] ];            //case 2
                }
                y[r] = sum;
            }
        }
    }

    public static void main (String args[]) {
        int x[] = {12, 3, 6, 7, 2, 3, 3, 9, 10, 20, 10, 12, 11, 143, 18, 12, 12, 17, 14, 19, 12, 12, 12, 12, 12};
        int y[] = {12, 3, 6, 7, 2, 3, 3, 9, 10, 20, 10, 12, 11, 143, 18, 12, 12, 17, 14, 19, 12, 12, 12, 12, 12};
        int val[] = {12, 3, 6, 7, 2, 3, 3, 9, 10, 20, 10, 12, 11, 143, 18, 12, 12, 17, 14, 19, 12, 12, 12, 12, 12};
        int row[] = {8, 20, 4, 20, 4, 13, 20, 7, 12, 12, 14, 20, 20, 20, 8, 2, 3, 20, 5, 9, 12};
        int col[] = {8, 20, 4, 20, 4, 13, 20, 7, 12, 12, 14, 20, 20, 20, 8, 2, 3, 20, 5, 9, 12};
        double sum = 0.0;
long startTime = System.currentTimeMillis();
        matrix.matmult(y, val, row, col, x, 10000000);
long endTime = System.currentTimeMillis();
        System.err.println("sum = "+sum+" time: "+ (endTime - startTime));
        }
}

6.

import java.lang.*;

class min {
        public min() {
        }

        synchronized public int inc() {
//        public int inc() {
             return 1;
        }

        public static void main (String args[]) {
                int sum = 0;
                min test =new min();
long startTime = System.currentTimeMillis();
             for(int i = 0; i <= 20000; i++) {
                for(int j = 0; j < 20000; j++) {
                   sum += test.inc();
/*
                   sum += test.inc();
                   sum += test.inc();
                   sum += test.inc();
                   sum += test.inc();
*/
                }
             }
long endTime = System.currentTimeMillis();
                System.err.println("sum = "+sum+" time: "+ (endTime - startTime));
        }
}

7.

class sparse_small {

    public static void matmult( double y[], double val[], int row[], int col[], double x[], int NUM_ITERATIONS) {
        int M = row.length - 1;
        for (int reps=0; reps<NUM_ITERATIONS; reps++) {

            for (int r=0; r<M; r++) {
                double sum = 0.0;
                int rowR = row[r];
                int rowRp1 = row[r+1];
                for (int i=rowR; i<rowRp1; i++) {
                    sum += x[ col[i] ] * val[i];
                }
                y[r] = sum;
            }
        }
    }

    public static void main (String args[]) {
        double x[] = {12.0, 3.9, 6.7, 7.8, 2.9, 3.2, 3.7, 9.9, 10.1, 20.3, 10, 12.3, 11.1, 143, 18, 12.4, 12.2, 17, 14, 19, 12, 12, 12, 12, 12};
        double y[] = {12.0, 3.9, 6.7, 7.8, 2.9, 3.2, 3.7, 9.9, 10.1, 20.3, 10, 12.3, 11.1, 143, 18, 12.4, 12.2, 17, 14, 19, 12, 12, 12, 12, 12};
        double val[] = {12.0, 3.9, 6.7, 7.8, 2.9, 3.2, 3.7, 9.9, 10.1, 20.3, 10, 12.3, 11.1, 143, 18, 12.4, 12.2, 17, 14, 19, 12, 12, 12, 12, 12};
        int row[] = {8, 20, 4, 20, 4, 13, 20, 7, 12, 12, 14, 20, 20, 20, 8, 2, 3, 20, 5, 9, 12};
        int col[] = {8, 20, 4, 20, 4, 13, 20, 7, 12, 12, 14, 20, 20, 20, 8, 2, 3, 20, 5, 9, 12};
        double sum = 0.0;
long startTime = System.currentTimeMillis();
        sparse_small.matmult(y, val, row, col, x, 10000000);
long endTime = System.currentTimeMillis();
        System.err.println("sum = "+sum+" time: "+ (endTime - startTime));
        }
}

8.

class sum {

    public static int cal(int low, int high) {
        int sum = 0;
        for (int i = low; i < high; i++) {
//            for (int j = -10; j < low; j++)
            sum += i;
        }
        return sum;
    }

    public static void main (String args[]) {
        int s = 0;
long startTime = System.currentTimeMillis();
//     for(int i = 0; i < 9999; i++)
        s = sum.cal(1, 900000);
long endTime = System.currentTimeMillis();
        System.err.println("s = "+s+"time: "+ (endTime - startTime));
        }
}


 

一、基础信息 数据集名称:Bottle Fin实例分割数据集 图片数量: 训练集:4418张图片 验证集:1104张图片 总计:5522张图片 分类类别: - 类别0: 数字0 - 类别1: 数字1 - 类别2: 数字2 - 类别3: 数字3 - 类别4: 数字4 - 类别5: 数字5 - 类别6: Bottle Fin 标注格式:YOLO格式,包含多边形坐标,适用于实例分割任务。 数据格式:图片格式常见如JPEG或PNG,具体未指定。 二、适用场景 实例分割AI模型开发:数据集支持实例分割任务,帮助构建能够精确识别和分割图像中多个对象的AI模型,适用于对象检测和分割应用。 工业自动化与质量控制:可能应用于制造、物流或零售领域,用于自动化检测和分类物体,提升生产效率。 计算机视觉研究:支持实例分割算法的学术研究,促进目标检测和分割技术的创新。 教育与实践培训:可用于高校或培训机构的计算机视觉课程,作为实例分割任务的实践资源,帮助学生理解多类别分割。 三、数据集优势 多类别设计:包含7个不同类别,涵盖数字和Bottle Fin对象,增强模型对多样对象的识别和分割能力。 高质量标注:标注采用YOLO格式的多边形坐标,确保分割边界的精确性,提升模型训练效果。 数据规模适中:拥有超过5500张图片,提供充足的样本用于模型训练和验证,支持稳健的AI开发。 即插即用兼容性:标注格式直接兼容主流深度学习框架(如YOLO),便于快速集成到各种实例分割项目中。
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