基于DataScience集群, 您可以使用Spark计算引擎进行大规模的图片Inference, 充分发挥各个Worker节点CPU或GPU的计算力,快速处理TB或PB级海量图片数据。

前提条件

背景信息

本文从以下场景为您介绍分布式Inference解决方案:
注意 DataScience已经可以运行CPU计算, 如果您需要运行GPU计算, 请联系产品运维人员升级Cuda至10.1版本,或者提交工单处理。

图片存储在HDFS

本场景优点是,您可以使用DataScience集群自带的HDFS,无需单独购买其他存储,缺点是对于大量的小文件(例如几KB的图片),HDFS吞吐效率偏低。

您可以在IDE中查看DistributedPredictHDFS.java,代码如下。
public class DistributedPredictHDFS {
    public static void main(String[] args) throws Exception{
        System.out.println("Start DistributedPredictHDFS Job.");

        String imagesPath = args[0];
        String modelPath  = args[1];
        String resultPath = args[2];

        SparkConf conf = new SparkConf().setAppName("DistributedPredictHDFSOnSpark");
        JavaSparkContext sc = new JavaSparkContext(conf);
        JavaPairRDD<String, PortableDataStream> imageStream = sc.binaryFiles(imagesPath, 128);
        System.out.println("Partitions: "+ imageStream.getNumPartitions());

        JavaRDD<String> result = imageStream.mapPartitions(new FlatMapFunction<Iterator<Tuple2<String, PortableDataStream>>, String>() {
            private static final long serialVersionUID = 1L;

            @Override
            public Iterator<String> call(Iterator<Tuple2<String, PortableDataStream>> iterator) throws Exception{

                ImageClassificationTranslator translator =
                        ImageClassificationTranslator.builder()
                                .addTransform(new Resize(224, 224))
                                .addTransform(new ToTensor())
                                .build();

                Criteria<Image, Classifications> criteria =
                            Criteria.builder()
                        .optApplication(Application.CV.IMAGE_CLASSIFICATION)
                        .setTypes(Image.class, Classifications.class) // defines input and output data type
                        .optTranslator(translator)
                        .optModelUrls(modelPath)
                        .build();

                System.out.println("Enginename: "+Engine.getInstance().getEngineName());
                System.out.println("ModelName: " + criteria.getModelName());
                System.out.println("criteriainfo: " + criteria.toString());
                List<String> list = new ArrayList<>();
                ZooModel<Image, Classifications> model ;
                Predictor<Image, Classifications> predictor;
                model = ModelZoo.loadModel(criteria);
                predictor = model.newPredictor() ;
                int idx = 0;
                List<Image> imagelist = new ArrayList<>();
                while(iterator.hasNext()){
                    Tuple2<String, PortableDataStream> item = iterator.next();

                    String name = item._1();
                    PortableDataStream content = item._2();

                    Image img = ImageFactory.getInstance().fromInputStream(content.open());
                    imagelist.add(img);
                    idx++;

                    if(imagelist.size()%32 == 0) {
                        List<Classifications> results = predictor.batchPredict(imagelist);
                        System.out.println("index: " + idx + "" + name + " " + img.getWidth() + " " + img.getHeight() + " " + results.toString());
                        System.out.println("index: " + idx);
                        list.add(results.toString());
                        imagelist.clear();
                    }
                }
                /*
                process_insufficient_images().
                */
                return list.iterator();
            }
        });
        result.saveAsTextFile(resultPath);

    }
}
  1. 通过SSH方式连接集群,详情请参见使用SSH连接主节点
  2. 运行如下代码。
    #!/bin/sh
    hadoop fs -put -f images hdfs://emr-header-1:9000/
    hadoop fs -put tensorflow_MobileNet.zip hdfs://emr-header-1:9000/
    hadoop fs -rm -r hdfs://emr-header-1:9000/predict_result
    spark-submit --master yarn-cluster \
    --conf spark.driver.extraClassPath=/usr/local/dstools/jars/protobuf-java-3.15.3.jar \
    --conf spark.executor.extraClassPath=/usr/local/dstools/jars/protobuf-java-3.15.3.jar \
    --num-executors 6 --executor-cores 18 --executor-memory 16G \
    --class com.alibaba.datascience.DistributedPredictHDFS distributedinference-0.1-SNAPSHOT.jar \
    hdfs://emr-header-1:9000/images/ \
    hdfs://emr-header-1:9000/tensorflow_MobileNet.zip \
    hdfs://emr-header-1:9000/predict_result

图片存储在HBase

本场景是小文件存储在HBase,优点是IO效率较高,您可以使用E-MapReduce的Hadoop集群,或者单独购买的HBase服务。HBase产品详情,请参见HBase产品首页

您可以在IDE中查看DistributedPredictHBase.java,代码如下。
public class DistributedPredictHBase {
    public static void main(String[] args) throws Exception{
        System.out.println("Start DistributedPredictHBase Job.");

        String hbasePath  = args[0];
        String modelPath  = args[1];
        String resultPath = args[2];

        SparkConf conf = new SparkConf().setAppName("DistributedPredictHBaseOnSpark");
        JavaSparkContext sc = new JavaSparkContext(conf);


        Scan scan = new Scan();
        ClientProtos.Scan proto = ProtobufUtil.toScan(scan);
        String scanToString = Base64.encodeBytes(proto.toByteArray());

        /* 0 */
        String tablename0 = "image0";
        Configuration hbconf0 = HBaseConfiguration.create();
        hbconf0.set(TableInputFormat.INPUT_TABLE, tablename0);
        hbconf0.set(TableInputFormat.SCAN_BATCHSIZE, "256");
        hbconf0.set(TableInputFormat.SCAN, scanToString);
        hbconf0.set("hbase.zookeeper.quorum", hbasePath);
        hbconf0.set("hbase.zookeeper.property.clientPort", "2181");
        JavaPairRDD<ImmutableBytesWritable, Result> HBaseRdd0 = sc.newAPIHadoopRDD(hbconf0, TableInputFormat.class,
                ImmutableBytesWritable.class, Result.class);
        for(int i=1;i<8;i++) {
            /* 1 */
            String tablename = "image" + i;
            Configuration hbconf = HBaseConfiguration.create();
            hbconf.set(TableInputFormat.INPUT_TABLE, tablename);
            hbconf.set(TableInputFormat.SCAN_BATCHSIZE, "256");
            hbconf.set(TableInputFormat.SCAN, scanToString);
            hbconf.set("hbase.zookeeper.quorum", hbasePath);
            hbconf.set("hbase.zookeeper.property.clientPort", "2181");
            JavaPairRDD<ImmutableBytesWritable, Result> HBaseRdd = sc.newAPIHadoopRDD(hbconf, TableInputFormat.class,
                    ImmutableBytesWritable.class, Result.class);
            HBaseRdd0 = HBaseRdd0.union(HBaseRdd);
        }

        System.out.println("Partitions: "+ HBaseRdd0.getNumPartitions());

        JavaRDD<String> resultx = HBaseRdd0.mapPartitions(new FlatMapFunction<Iterator<Tuple2<ImmutableBytesWritable, Result>>, String>() {

            private static final long serialVersionUID = 1L;

            @Override
            public Iterator<String> call(Iterator<Tuple2<ImmutableBytesWritable, Result>> iterator) throws Exception {
                // TODO Auto-generated method stub
                ImageClassificationTranslator translator =
                        ImageClassificationTranslator.builder()
                                .addTransform(new Resize(224, 224))
                                .addTransform(new ToTensor())
                                .build();

                Criteria<Image, Classifications> criteria =
                        Criteria.builder()
                                .optApplication(Application.CV.IMAGE_CLASSIFICATION)
                                .setTypes(Image.class, Classifications.class) // defines input and output data type
                                .optTranslator(translator)
                                .optModelUrls(modelPath)
                                .build();

                System.out.println("Enginename: "+Engine.getInstance().getEngineName());
                System.out.println("ModelName: " + criteria.getModelName());
                System.out.println("criteriainfo: " + criteria.toString());
                List<String> list = new ArrayList<>();
                ZooModel<Image, Classifications> model ;
                Predictor<Image, Classifications> predictor;
                model = ModelZoo.loadModel(criteria);
                predictor = model.newPredictor() ;
                int idx = 0;
                List<Image> imagelist = new ArrayList<>();
                List<String> rows = new ArrayList<String>();
                while (iterator.hasNext()) {

                    Result result = iterator.next()._2();
                    String rowKey = Bytes.toString(result.getRow());
                    byte[] body = result.getValue("f".getBytes(), "body".getBytes());
                    InputStream input = new ByteArrayInputStream(body);
                    Image img = ImageFactory.getInstance().fromInputStream(input);
                    imagelist.add(img);
                    idx++;

                    if(imagelist.size()%64 == 0) {
                        List<Classifications> results = predictor.batchPredict(imagelist);
                        //System.out.println("index: " + idx + " " + img.getWidth() + " " + img.getHeight() + " " + results.toString());
                        System.out.println("index: " + idx + " " + rowKey);
                        results.clear();
                        imagelist.clear();
                    }
                    rows.add(rowKey);
                }
                /*
                process_insufficient_images().
                */
                return rows.iterator();
           }
        });
        resultx.saveAsTextFile(resultPath);


    }
}
  1. 通过SSH方式连接集群,详情请参见使用SSH连接主节点
  2. 运行如下代码。
    #!/bin/sh
    hadoop fs -rm -r hdfs://emr-header-1:9000/predict_result
    hadoop fs -put tensorflow_MobileNet.zip hdfs://emr-header-1:9000/
    spark-submit --master yarn-cluster \
    --conf spark.driver.extraClassPath=/usr/local/dstools/jars/protobuf-java-3.15.3.jar \
    --conf spark.executor.extraClassPath=/usr/local/dstools/jars/protobuf-java-3.15.3.jar \
    --num-executors 16 --executor-cores 4 --executor-memory 24G \
    --class com.alibaba.datascience.DistributedPredictHBase distributedinference-0.1-SNAPSHOT.jar \
    192.168.0.7 \
    hdfs://emr-header-1:9000/tensorflow_MobileNet.zip \
    hdfs://emr-header-1:9000/predict_result

图片存储在OSS

您可以在IDE中查看DistributedPredictOSS.java,代码如下。
public class DistributedPredictOSS {
    public static void main(String[] args) throws Exception{
        System.out.println("Start DistributedPredictOSS Job.");

        String ossPath  = args[0];
        String modelPath  = args[1];
        String resultPath = args[2];

        SparkConf conf = new SparkConf().setAppName("DistributedPredictOSSOnSpark");
        JavaSparkContext sc = new JavaSparkContext(conf);

        JavaPairRDD<String, PortableDataStream> imageStream = sc.binaryFiles(ossPath, 128);
        System.out.println("Partitions: "+ imageStream.getNumPartitions());

        JavaRDD<String> result = imageStream.mapPartitions(new FlatMapFunction<Iterator<Tuple2<String, PortableDataStream>>, String>() {
            private static final long serialVersionUID = 1L;

            @Override
            public Iterator<String> call(Iterator<Tuple2<String, PortableDataStream>> iterator) throws Exception{

                ImageClassificationTranslator translator =
                        ImageClassificationTranslator.builder()
                                .addTransform(new Resize(224, 224))
                                .addTransform(new ToTensor())
                                .build();

                Criteria<Image, Classifications> criteria =
                        Criteria.builder()
                                .optApplication(Application.CV.IMAGE_CLASSIFICATION)
                                .setTypes(Image.class, Classifications.class) // defines input and output data type
                                .optTranslator(translator)
                                .optModelUrls(modelPath)
                                .build();

                System.out.println("Enginename: "+Engine.getInstance().getEngineName());
                System.out.println("ModelName: " + criteria.getModelName());
                System.out.println("criteriainfo: " + criteria.toString());
                List<String> list = new ArrayList<>();
                ZooModel<Image, Classifications> model ;
                Predictor<Image, Classifications> predictor;
                model = ModelZoo.loadModel(criteria);
                predictor = model.newPredictor() ;
                int idx = 0;
                List<Image> imagelist = new ArrayList<>();
                while(iterator.hasNext()){
                    Tuple2<String, PortableDataStream> item = iterator.next();

                    String name = item._1();
                    PortableDataStream content = item._2();

                    Image img = ImageFactory.getInstance().fromInputStream(content.open());
                    imagelist.add(img);
                    idx++;

                    if(imagelist.size()%32 == 0) {
                        List<Classifications> results = predictor.batchPredict(imagelist);
                        //System.out.println("index: " + idx + "" + name + " " + img.getWidth() + " " + img.getHeight() + " " + results.toString());
                        System.out.println("index: " + idx);
                        list.add(results.toString());
                        imagelist.clear();
                    }
                }
                /*
                process_insufficient_images().
                */
                return list.iterator();
            }
        });
        result.saveAsTextFile(resultPath);

    }
}
  1. 通过SSH方式连接集群,详情请参见使用SSH连接主节点
  2. 运行如下代码。
    #!/bin/sh
    hadoop fs -rm -r hdfs://emr-header-1:9000/predict_result
    hadoop fs -put tensorflow_MobileNet.zip hdfs://emr-header-1:9000/
    ossutil -i <yourAccessKeyId> -k <yourAccessKeySecret> -e oss-cn-huhehaote.aliyuncs.com cp -r images oss://bucket/images
    spark-submit --master yarn-cluster \
    --conf spark.driver.extraClassPath=/usr/local/dstools/jars/protobuf-java-3.15.3.jar \
    --conf spark.executor.extraClassPath=/usr/local/dstools/jars/protobuf-java-3.15.3.jar \
    --num-executors 16 --executor-cores 4 --executor-memory 16G \
    --class com.alibaba.datascience.DistributedPredictOSS distributedinference-0.1-SNAPSHOT.jar \
    oss://bucket/images/ \
    hdfs://emr-header-1:9000/tensorflow_MobileNet.zip \
    hdfs://emr-header-1:9000/predict_result
    • yourAccessKeyId:阿里云账号的AccessKey ID。
    • yourAccessKeySecret:阿里云账号的AccessKey Secret。

多图片合并成大文件存储在HDFS

本场景优点是吞吐率高,缺点是需要您先使用ConvertImageToBase64工具,将图片转成Base64编码, 合并成一个大文件存储于HDFS。

您可以在IDE中查看DistributedPredictHDFSBigFile.java,代码如下。
public class DistributedPredictHDFSBigFile {
    public static void main(String[] args) throws Exception{
        System.out.println("Start DistributedPredictHDFSBigFile Job.");

        String imagesPath = args[0];
        String modelPath  = args[1];
        String resultPath = args[2];

        SparkConf conf = new SparkConf().setAppName("DistributedPredictHDFSBigFileOnSpark");
        JavaSparkContext sc = new JavaSparkContext(conf);
        JavaRDD<String> imageStream_base64 = sc.textFile(imagesPath);
        System.out.println("Partitions: "+ imageStream_base64.getNumPartitions());

        JavaRDD<byte[]> imageStream_bytes =  imageStream_base64.map(new Function<String, byte[]>() {
            @Override
            public byte[] call(String in) throws Exception {
                byte[] out = Base64.getDecoder().decode(in);
                return out;
            }
        });
        System.out.println("Partitions: "+ imageStream_bytes.getNumPartitions());

        JavaRDD<String> result = imageStream_bytes.mapPartitions(new FlatMapFunction<Iterator<byte[]>, String>() {
            private static final long serialVersionUID = 1L;

            @Override
            public Iterator<String> call(Iterator<byte[]> iterator) throws Exception{
                ImageClassificationTranslator translator =
                        ImageClassificationTranslator.builder()
                                .addTransform(new Resize(224, 224))
                                .addTransform(new ToTensor())
                                .build();

                Criteria<Image, Classifications> criteria =
                        Criteria.builder()
                                .optApplication(Application.CV.IMAGE_CLASSIFICATION)
                                .setTypes(Image.class, Classifications.class) // defines input and output data type
                                .optTranslator(translator)
                                .optModelUrls(modelPath)
                                .build();

                System.out.println("Enginename: "+Engine.getInstance().getEngineName());
                System.out.println("ModelName: " + criteria.getModelName());
                System.out.println("criteriainfo: " + criteria.toString());
                List<String> list = new ArrayList<>();
                ZooModel<Image, Classifications> model ;
                Predictor<Image, Classifications> predictor;
                model = ModelZoo.loadModel(criteria);
                predictor = model.newPredictor() ;
                int idx = 0;
                List<Image> imagelist = new ArrayList<>();
                while(iterator.hasNext()){
                    byte[] body = iterator.next();

                    InputStream input = new ByteArrayInputStream(body);
                    Image img = ImageFactory.getInstance().fromInputStream(input);
                    imagelist.add(img);
                    idx++;

                    if(imagelist.size()%32 == 0) {
                        List<Classifications> results = predictor.batchPredict(imagelist);
                        System.out.println("index: " + idx + " " + img.getWidth() + " " + img.getHeight() + " " + results.toString());
                        System.out.println("index: " + idx);
                        list.add(results.toString());
                        imagelist.clear();
                    }
                }
                /*
                process_insufficient_images().
                */
                return list.iterator();
            }

        });
        result.saveAsTextFile(resultPath);

    }
}
  1. 通过SSH方式连接集群,详情请参见使用SSH连接主节点
  2. 运行如下代码。
    #!/bin/sh
    hadoop fs -rm -r hdfs://emr-header-1:9000/predict_result
    hadoop fs -put tensorflow_MobileNet.zip hdfs://emr-header-1:9000/
    hadoop fs -put -f images.base64 hdfs://emr-header-1:9000/
    spark-submit --master yarn-cluster \
    --conf spark.driver.extraClassPath=/usr/local/dstools/jars/protobuf-java-3.15.3.jar \
    --conf spark.executor.extraClassPath=/usr/local/dstools/jars/protobuf-java-3.15.3.jar \
    --num-executors 16 --executor-cores 4 --executor-memory 24G \
    --class com.alibaba.datascience.DistributedPredictHDFSBigFile distributedinference-0.1-SNAPSHOT.jar \
    hdfs://emr-header-1:9000/images.base64 \
    hdfs://emr-header-1:9000/tensorflow_MobileNet.zip \
    hdfs://emr-header-1:9000/predict_result

图片转BASE64

您可以在IDE中查看ConvertImageToBase64.java,代码如下。
public class ConvertImageToBase64 {
    public static void main(String[] args) throws Exception {
        String filename = "car.jpg";

        File file = new File(filename);
        FileInputStream fis = new FileInputStream(file);
        byte[] fileBytes = new byte[(int) file.length()];
        fis.read(fileBytes);
        String encoded = Base64.getEncoder().encodeToString(fileBytes);

        /* decode */
        byte[] decoded = Base64.getDecoder().decode(encoded);

        encoded += '\n';
        OutputStream out = new BufferedOutputStream(new FileOutputStream(filename+".base64", false));
        out.write(encoded.getBytes());

    }
}
  1. 通过SSH方式连接集群,详情请参见使用SSH连接主节点
  2. 运行如下代码。
    #!/bin/sh
    java -cp distributedinference-0.1-SNAPSHOT.jar com.alibaba.datascience.ConvertImageToBase64

导入图片至HBase

HBase提供高性能的Key-Value存储,可以把海量图片存储到HBase里,显著提升了HDFS的IO效率。

您可以在IDE中查看ImportImageToHBase.java,代码如下。
public class ImportImageToHBase {
    public static void main(String[] args) throws Exception {
        String h_table = args[0];
        String filename = "car.jpg";
        Configuration configuration = HBaseConfiguration.create();
        configuration.set("hbase.zookeeper.quorum", "192.168.0.7:2181");
        Connection connection = ConnectionFactory.createConnection(configuration);

        Table table = connection.getTable(TableName.valueOf(h_table));

        /* put image into hbase*/
        File file = new File(filename);
        FileInputStream fis = new FileInputStream(file);
        byte[] fileBytes = new byte[(int) file.length()];
        fis.read(fileBytes);
        int i;
        for(i=0;i<100000;i++) {
            String key = String.valueOf(i) + ".jpg";
            Put put = new Put(key.getBytes());
            put.addColumn("f".getBytes(), "body".getBytes(), fileBytes);
            try {
                table.put(put);
            } catch (IOException e) {
                e.printStackTrace();
            }
        }
        fis.close();
        System.out.println("put 100000 images done!");

        /* get image from hbase*/
        Get get = new Get(Bytes.toBytes(filename));
        Result result = table.get(get);
        byte[] body = result.getValue("f".getBytes(), "body".getBytes());
        OutputStream out = new BufferedOutputStream(new FileOutputStream(filename+".bk", false));
        out.write(body);
    }
}
  1. 通过SSH方式连接集群,详情请参见使用SSH连接主节点
  2. 运行如下代码。
    #!/bin/sh
    java -cp distributedinference-0.1-SNAPSHOT.jar com.alibaba.datascience.ImportImageToHBase