# Convert INDArray to double\[\] take a lot of time

**URL:** <https://community.konduit.ai/t/convert-indarray-to-double-take-a-lot-of-time/668>\
**Category:** DL4J\
**Created:** [June 29, 2020, 10:08pm UTC](https://community.konduit.ai/t/convert-indarray-to-double-take-a-lot-of-time/668 "2020-06-29T22:08:35Z")\
**Posts on this page:** 4\
**Page:** 1

<div class="post-metadata">

**Author:** ![lquintero07](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/lquintero07/32/254_2.png) [@lquintero07](https://community.konduit.ai/u/lquintero07)\
**Post date:** [June 29, 2020, 10:08pm UTC](https://community.konduit.ai/t/convert-indarray-to-double-take-a-lot-of-time/668/1 "2020-06-29T22:08:35Z")

</div>

Hi all, i was looking on dl4j a few days ago and i was trying to run code based on this post: [Face Recognition in JAVA](https://code-ai.mk/face-recognition-with-java/).

I found 2 problems:

1. Load the pretrained model is taken around 30 minutes. Is it ok?  

2. Convert INDArray to double (featuresArray.data().asDouble()) is taken a lot of time.  

Here is my pom.xml information:

```
<properties>
    <dl4j.version>1.0.0-beta7</dl4j.version>
    <ffmpeg.version>3.2.1-1.3</ffmpeg.version>
    <javacv.version>1.4.1</javacv.version>
</properties>
<dependencies>
	<dependency>
        <groupId>org.nd4j</groupId>
        <artifactId>nd4j-native-platform</artifactId>
        <version>${dl4j.version}</version>
    </dependency>
    <dependency>
        <groupId>org.deeplearning4j</groupId>
        <artifactId>deeplearning4j-core</artifactId>
        <version>${dl4j.version}</version>
    </dependency>        
    <dependency>
        <groupId>org.deeplearning4j</groupId>
        <artifactId>deeplearning4j-zoo</artifactId>
        <version>${dl4j.version}</version>
    </dependency>
    <dependency>
        <groupId>org.deeplearning4j</groupId>
        <artifactId>deeplearning4j-modelimport</artifactId>
        <version>${dl4j.version}</version>
    </dependency>
    <dependency>
        <groupId>org.datavec</groupId>
        <artifactId>datavec-data-image</artifactId>
        <version>${dl4j.version}</version>
    </dependency>           
    <!-- https://mvnrepository.com/artifact/org.bytedeco/openblas-platform -->
	<dependency>
	    <groupId>org.bytedeco</groupId>
	    <artifactId>openblas-platform</artifactId>
	    <version>0.3.9-1.5.3</version>
	</dependency>
    <dependency>
	    <groupId>org.slf4j</groupId>
	    <artifactId>slf4j-jdk14</artifactId>
	    <version>1.7.25</version>
	</dependency>    
</dependencies>

```

Full class code:

```
package com.ajmakoni.fr.dl4j.math; 
      
import java.io.File;
import java.io.IOException;   
import org.datavec.image.loader.NativeImageLoader;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.transferlearning.TransferLearningHelper;
import org.deeplearning4j.zoo.PretrainedType;
import org.deeplearning4j.zoo.ZooModel;
import org.deeplearning4j.zoo.model.VGG16;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.api.preprocessor.DataNormalization;
import org.nd4j.linalg.dataset.api.preprocessor.VGG16ImagePreProcessor;
import org.nd4j.linalg.factory.Nd4j;

public class VGG16Model {

 private TransferLearningHelper transferLearningHelper;
 private NativeImageLoader nativeImageLoader;
 private DataNormalization scaler;	  
 private boolean isInitialized;
 private ComputationGraph objComputationGraph = null;
 
 public VGG16Model() {
	 try {
		 String path = "C:\\Java\\ajmackoni\\Face Recognition\\Processing\\VideoFace";
		 File f = new File(path, "model.zip");
		 
	      System.out.println("Loading DL4J");
	      		      
	      if(!f.exists()) {
	    	  ZooModel objZooModel = VGG16.builder().build();
	    	  objComputationGraph = (ComputationGraph)objZooModel.initPretrained(PretrainedType.VGGFACE);
		      objComputationGraph.save(f);
	      }
	      else {
	    	  objComputationGraph = ComputationGraph.load(f, false);
	      }	      
	      
	      System.out.println(objComputationGraph.summary());
	      System.out.println("Loaded DL4J");
	      String name = "pool4";
	      transferLearningHelper = new TransferLearningHelper(objComputationGraph, name); //new TransferLearningHelper(objComputationGraph,"pool4");
	      nativeImageLoader = new NativeImageLoader(224, 224, 3);
	      scaler = new VGG16ImagePreProcessor();
	      isInitialized=true;
	    } catch (IOException e) {
	      e.printStackTrace();
	    }		 
 }
 
 public double[] getFeatures(File faceImageFile) {
	    if (!isInitialized) {
	    	return null;
	    }

	    try {
	      INDArray imageMatrix = nativeImageLoader.asMatrix(faceImageFile);
	      scaler.transform(imageMatrix);
	      
	      //objComputationGraph.feedForward(imageMatrix, false);

	      DataSet objDataSet = new DataSet(imageMatrix, Nd4j.create(new float[]{0,0}));

	      DataSet objFeaturized = transferLearningHelper.featurize(objDataSet);
	      INDArray featuresArray = objFeaturized.getFeatures();

	      int reshapeDimension=1;
	      for (long dimension : featuresArray.shape()) {
	        reshapeDimension *= dimension;
	      }

	      featuresArray = featuresArray.reshape(1,reshapeDimension);
	      DataType d = featuresArray.dataType();

	      double[] result = featuresArray.data().asDouble();
	      return result;
	    } catch (IOException e) {
	      e.printStackTrace();
	      return null;
	    }
 }
 
 public INDArray getFeatures2(File faceImageFile) {
	    if (!isInitialized) {
	    	return null;
	    }

	    try {
	      INDArray imageMatrix = nativeImageLoader.asMatrix(faceImageFile);
	      scaler.transform(imageMatrix);
	      
	      //objComputationGraph.feedForward(imageMatrix, false);

	      DataSet objDataSet = new DataSet(imageMatrix, Nd4j.create(new float[]{0,0}));

	      DataSet objFeaturized = transferLearningHelper.featurize(objDataSet);
	      INDArray featuresArray = objFeaturized.getFeatures();

	      int reshapeDimension=1;
	      for (long dimension : featuresArray.shape()) {
	        reshapeDimension *= dimension;
	      }

	      featuresArray = featuresArray.reshape(1,reshapeDimension);
	      DataType d = featuresArray.dataType();

	      return featuresArray;
	    } catch (IOException e) {
	      e.printStackTrace();
	      return null;
	    }
 }
}

```

Computer hardware:

 ![PC](https://canada1.discourse-cdn.com/flex035/uploads/konduit/original/1X/3ef50ce7a42d14c01231e2b940a8fed1054f6042.png)

Sample Image:  
 ![1b2f53f7783a422684b62ccbd3e7dbeb](https://canada1.discourse-cdn.com/flex035/uploads/konduit/original/1X/541ccd85b0dd29bb404011f83296c35be05b2e57.jpeg)

Dl4j Output :

```
00:28:22.896 [main] INFO org.nd4j.linalg.factory.Nd4jBackend - Loaded [CpuBackend] backend
00:28:59.772 [main] INFO org.nd4j.nativeblas.NativeOpsHolder - Number of threads used for linear algebra: 4
00:28:59.785 [main] WARN org.nd4j.linalg.cpu.nativecpu.CpuNDArrayFactory - ***********************************CPU Feature Check Warning***********************************
00:28:59.785 [main] WARN org.nd4j.linalg.cpu.nativecpu.CpuNDArrayFactory - Warning: Initializing ND4J with Generic x86 binary on a CPU with AVX/AVX2 support
00:28:59.785 [main] WARN org.nd4j.linalg.cpu.nativecpu.CpuNDArrayFactory - Using ND4J with AVX/AVX2 will improve performance. See deeplearning4j.org/cpu for more details
00:28:59.785 [main] WARN org.nd4j.linalg.cpu.nativecpu.CpuNDArrayFactory - Or set environment variable ND4J_IGNORE_AVX=true to suppress this warning
00:28:59.786 [main] WARN org.nd4j.linalg.cpu.nativecpu.CpuNDArrayFactory - *************************************************************************************************
00:29:01.756 [main] INFO org.nd4j.nativeblas.Nd4jBlas - Number of threads used for OpenMP BLAS: 4
00:29:02.463 [main] INFO org.nd4j.linalg.api.ops.executioner.DefaultOpExecutioner - Backend used: [CPU]; OS: [Windows 10]
00:29:02.463 [main] INFO org.nd4j.linalg.api.ops.executioner.DefaultOpExecutioner - Cores: [8]; Memory: [4.0GB];
00:29:02.463 [main] INFO org.nd4j.linalg.api.ops.executioner.DefaultOpExecutioner - Blas vendor: [OPENBLAS]

```

Could you give some ideas how to fix the problems?  
Thanks,

---

<div class="post-metadata">

**Author:** ![lquintero07](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/lquintero07/32/254_2.png) [@lquintero07](https://community.konduit.ai/u/lquintero07)\
**Post date:** [June 30, 2020, 5:58am UTC](https://community.konduit.ai/t/convert-indarray-to-double-take-a-lot-of-time/668/2 "2020-06-30T05:58:44Z")

</div>

Ooops, i found the solution, for first problem, seems there was some missing/bad references on pom.xml.

New pom.xml:

```
<properties>
    <dl4j.version>1.0.0-beta7</dl4j.version>
    <ffmpeg.version>3.2.1-1.3</ffmpeg.version>
    <javacv.version>1.4.1</javacv.version>
    <logback.version>1.1.7</logback.version> 
</properties>
<dependencies>
	<dependency>
        <groupId>org.nd4j</groupId>
        <artifactId>nd4j-native-platform</artifactId>
        <version>${dl4j.version}</version>
    </dependency>
                                                   
    <dependency>                                                            
        <groupId>ch.qos.logback</groupId>                                   
        <artifactId>logback-classic</artifactId>                            
        <version>${logback.version}</version>                               
    </dependency>
    <dependency>
        <groupId>org.deeplearning4j</groupId>
        <artifactId>deeplearning4j-utility-iterators</artifactId>
        <version>${dl4j.version}</version>
    </dependency>
    
    
    <dependency>
        <groupId>org.deeplearning4j</groupId>
        <artifactId>deeplearning4j-core</artifactId>
        <version>${dl4j.version}</version>
    </dependency>        
    <dependency>
        <groupId>org.deeplearning4j</groupId>
        <artifactId>deeplearning4j-zoo</artifactId>
        <version>${dl4j.version}</version>
    </dependency>
    <dependency>
        <groupId>org.deeplearning4j</groupId>
        <artifactId>deeplearning4j-modelimport</artifactId>
        <version>${dl4j.version}</version>
    </dependency>

    <dependency>
        <groupId>org.datavec</groupId>
        <artifactId>datavec-data-image</artifactId>
        <version>${dl4j.version}</version>
    </dependency>   
    
    <!-- https://mvnrepository.com/artifact/org.bytedeco/openblas-platform -->
	<dependency>
	    <groupId>org.bytedeco</groupId>
	    <artifactId>openblas-platform</artifactId>
	    <version>0.3.9-1.5.3</version>
	</dependency>
    
    
    <!--  
    <dependency>
        <groupId>org.nd4j</groupId>
        <artifactId>nd4j-cuda-10.0</artifactId>
        <version>${dl4j.version}</version>
    </dependency>
    <dependency>
	    <groupId>org.deeplearning4j</groupId>
	    <artifactId>deeplearning4j-cuda-10.0</artifactId>
	    <version>${dl4j.version}</version>
	</dependency>        
    <dependency>
	    <groupId>org.slf4j</groupId>
	    <artifactId>slf4j-jdk14</artifactId>
	    <version>1.7.25</version>
	</dependency>
	-->
</dependencies>

```

---

<div class="post-metadata">

**Author:** ![lquintero07](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/lquintero07/32/254_2.png) [@lquintero07](https://community.konduit.ai/u/lquintero07)\
**Post date:** [July 3, 2020, 5:00pm UTC](https://community.konduit.ai/t/convert-indarray-to-double-take-a-lot-of-time/668/3 "2020-07-03T17:00:37Z")

</div>

After some test i can find that his only happen when i want to debug my test application.

---

<div class="post-metadata">

**Author:** ![agibsonccc](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/agibsonccc/32/697_2.png) [@agibsonccc](https://community.konduit.ai/u/agibsonccc)\
**Post date:** [July 3, 2020, 10:50pm UTC](https://community.konduit.ai/t/convert-indarray-to-double-take-a-lot-of-time/668/4 "2020-07-03T22:50:03Z")

</div>

Hi,

Sorry we didn’t reply to this. Generally things are fast. If you use a debugger, everything is going to be slow because the ide has to call toString() on almost every object currently in memory.