JDL自动下载pytroch的动态库错误

Question

我想使用djl 加载我的模型,但是在自动下载动态库时出现了一些问题请问我该怎么解决?
这是我导入的一些依赖

<dependency>
      <groupId>ai.djl.pytorch</groupId>
      <artifactId>pytorch-model-zoo</artifactId>
      <version>0.25.0</version>
    </dependency>

    <dependency>
      <groupId>ai.djl.pytorch</groupId>
      <artifactId>pytorch-engine</artifactId>
      <version>0.25.0</version>
      <scope>runtime</scope>
    </dependency>
    <dependency>
      <groupId>ai.djl.pytorch</groupId>
      <artifactId>pytorch-native-auto</artifactId>
      <version>1.8.1</version>
    </dependency>

下面的是我加载的代码

public class Yolov5 {
    static Translator<Image, DetectedObjects> translator = YoloV5Translator.builder().optSynsetArtifactName("coco.names").build();
    static Criteria<Image, DetectedObjects> criteria =
            Criteria.builder()
                    .setTypes(Image.class, DetectedObjects.class)
                    .optDevice(Device.cpu())
                    .optModelUrls(Main.class.getResource("/yolov5s").getPath())
                    .optModelName("best2.pt")
                    .optTranslator(translator)
                    .optEngine("PyTorch")
                    .build();
    public static void detect(Mat mat) throws ModelNotFoundException, MalformedModelException, IOException {
        ZooModel<Image, DetectedObjects> model = ModelZoo.loadModel(criteria);
        Image img = MyUtils.mat2Image(mat);
        long startTime = System.currentTimeMillis();
        Rect rect = new Rect();
        Scalar color = new Scalar(0,255, 0);
        try (Predictor<Image, DetectedObjects> predictor = model.newPredictor()) {
            DetectedObjects results = predictor.predict(img);
//            System.out.println(results);
            for (DetectedObjects.DetectedObject obj : results.<DetectedObjects.DetectedObject>items()) {
                BoundingBox bbox = obj.getBoundingBox();
                Rectangle rectangle = bbox.getBounds();
                String showText = String.format("%s: %.2f", obj.getClassName(), obj.getProbability());
                rect.x = (int) rectangle.getX();
                rect.y = (int) rectangle.getY();
                rect.width = (int) rectangle.getWidth();
                rect.height = (int) rectangle.getHeight();
                // 画框

                Imgproc.rectangle(mat, rect, color, 2);
                //画名字
                Imgproc.putText(mat, showText,
                        new Point(rect.x, rect.y),
                        Imgproc.FONT_HERSHEY_COMPLEX,
                        rectangle.getWidth() / 200,
                        color);
            }
        } catch (TranslateException e) {
            e.printStackTrace();
        }

    }

}
package com.lgw.until;

import ai.djl.modality.cv.Image;
import ai.djl.modality.cv.ImageFactory;
import org.opencv.core.Mat;
import org.opencv.highgui.HighGui;

public class MyUtils {

    public static Image mat2Image(Mat mat) {
        return ImageFactory.getInstance().fromImage(HighGui.toBufferedImage(mat));
    }


}

运行时自动下载pytroch的动态库出现了一些问题
java.io.FileNotFoundException: https://publish.djl.ai/pytorch/1.8.1/jnilib/0.25.0/win-x86_64/cu111/djl_torch.dll
我打开这个连接出现了

```xml

<Error>
<Code>NoSuchKey</Code>
<Message>The specified key does not exist.</Message>
<Key>publish/pytorch/1.8.1/jnilib/0.21.0/win-x86_64/cu111/djl_torch.dll</Key>
<RequestId>YM27SCMB5C8AYR4M</RequestId>
<HostId>CESZYMpghat/pM8VZXOvXuUs1i0dY0DiyZ0H83szxjaW+9NtduR50lx2UE1fdUigrPQZoGND3RE=</HostId>
</Error>

我应该怎么解决?

Answer 1

【相关推荐】

• 你可以看下这个问题的回答https://ask.csdn.net/questions/7429099
• 你也可以参考下这篇文章：DJL Java环境下部署pytorch模型推理
• 除此之外, 这篇博客: DJL快速入门（纯Java跑深度学习模型）中的 3. 实战：DJL使用Pytorch模型完成图片分类 部分也许能够解决你的问题, 你可以仔细阅读以下内容或跳转源博客中阅读:
  

  这里使用Pytorch提供的resnet18模型完成一个图片分类任务。

  1. 首先引入依赖：

  <dependency>
      <groupId>ai.djl.pytorch</groupId>
      <artifactId>pytorch-engine</artifactId>
      <version>0.17.0</version>
      <scope>runtime</scope>
  </dependency>
  
  <dependency>
      <groupId>ai.djl.pytorch</groupId>
      <artifactId>pytorch-native-cpu</artifactId>
      <classifier>win-x86_64</classifier>
      <scope>runtime</scope>
      <version>1.11.0</version>
  </dependency>
  <dependency>
      <groupId>ai.djl.pytorch</groupId>
      <artifactId>pytorch-jni</artifactId>
      <version>1.11.0-0.17.0</version>
      <scope>runtime</scope>
  </dependency>
  
  <dependency>
      <groupId>ai.djl</groupId>
      <artifactId>api</artifactId>
      <version>0.17.0</version>
  </dependency>
  
  <dependency>
      <groupId>ai.djl</groupId>
      <artifactId>basicdataset</artifactId>
      <version>0.17.0</version>
  </dependency>
  
  <dependency>
      <groupId>ai.djl.opencv</groupId>
      <artifactId>opencv</artifactId>
      <version>0.17.0</version>
  </dependency>
  

  1. 导出pytorch的resnet18模型：

  import torch
  import torchvision
  
  # An instance of your model.
  model = torchvision.models.resnet18(pretrained=True)
  
  # Switch the model to eval model
  model.eval()
  
  # An example input you would normally provide to your model's forward() method.
  example = torch.rand(1, 3, 224, 224)
  
  # Use torch.jit.trace to generate a torch.jit.ScriptModule via tracing.
  traced_script_module = torch.jit.trace(model, example)
  
  # Save the TorchScript model
  traced_script_module.save("traced_resnet_model.pt")
  

  1. 将导出的模型拷贝到项目的model目录下：
     

  2. 创建Translator，这里我们定义输入为String类型，表示图片的输入路径；输出也为String，表示类别。将图片送入Resnet18网络，需要做一些预处理：

  ...
  preprocess = transforms.Compose([
      transforms.Resize(256),
      transforms.CenterCrop(224),
      transforms.ToTensor(),
      transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
  ])
  ...
  

  这里利用Java的NDArray的

  Translator<String/*filename*/, String/*class*/> translator = new Translator<String, String>() {
  
      @Override
      public NDList processInput(TranslatorContext ctx, String input) throws Exception {
          // 根据路径读取图片
          Image image = ImageFactory.getInstance().fromFile(Paths.get(input));
          NDArray ndArray = image.toNDArray(ctx.getNDManager());
          // 在图片送入resnet前要做一些预处理，官方的例子中使用transforms，但为了本文的前后呼应，我这里就用上面将的NDArray的操作来完成
          Resize resize = new Resize(256, 256);
          ndArray = resize.transform(ndArray); // 将图片的大小resize到256x256
  
          // py: transforms.CenterCrop(224)
          // NDArray没有CenterCrop方法，但是我们可以通过切片的方式实现
          ndArray = ndArray.get(new NDIndex("16:240, 16:240, :"));
  
          // ToTensor会将Shape的(224,224,3)转变为(3,224,224)，并且将值从0-255缩放到0-1
          ndArray = new ToTensor().transform(ndArray);
  
          // py: transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
          Normalize normalize = new Normalize(new float[]{0.485f, 0.456f, 0.406f}, new float[]{0.229f, 0.224f, 0.225f});
          ndArray = normalize.transform(ndArray);
  
          return new NDList(ndArray);  // resnet只接受一个Tensor
      }
  
      @Override
      public String processOutput(TranslatorContext ctx, NDList list) throws Exception {
          // resnet只返回一个tensor，所以要get(0)
          int index = list.get(0).argMax().toType(DataType.INT32, false).getInt();
          // 由于resnet可以识别1000种物体，这里我就只给index了。
          return index + "";
      }
  };
  

  1. 定义Criteria，然后实例化模型，并newPredictor

  Criteria<String, String> criteria = Criteria.builder()
          .setTypes(String.class, String.class)
          .optModelPath(Paths.get("model/traced_resnet_model.pt"))
          .optOption("mapLocation", "true")
          .optTranslator(translator)
          .build();
  
  ZooModel model = criteria.loadModel();
  Predictor predictor = model.newPredictor();
  

  1. 准备一张图片，我这里放在项目的test目录下：

  

  1. 进行预测

  System.out.println(predictor.predict("test/test.jpg"));
  

  由于resnet可以识别1000个物体，太多了，所以我只输出了index，全部的类别可以到

  该链接查找。最终输出为：

  258
  

  258对应的类别为Samoyed（萨摩耶），可以看得到预测对了。

  DJL更多的例子可以参考官方Demo。

  
  

  
  
  
  

  ---

如果你已经解决了该问题, 非常希望你能够分享一下解决方案, 写成博客, 将相关链接放在评论区, 以帮助更多的人 ^-^