YoloV5 inference support of ONNX models

[thhart]

Description

adds support for YoloV5 object detection models via ONNX

Changes

added YoloV5Translator with following features: input squeezer, output parser and non max suppression boxes

Howto

YoloV5 supports at least two output formats, to use models here it is important to specify model.model[-1].export = False in export.py.
An ONNX export can be done like this: ultralytics/yolov5#251

Example

         final YoloV5Translator translator = new YoloV5Translator(YoloV5Translator.builder().optSynsetArtifactName("synset.txt"));
         Criteria<Image, DetectedObjects> criteria = Criteria.builder()
               .setTypes(Image.class, DetectedObjects.class) // defines input and output data type
               .optDevice(Device.cpu())
               .optTranslator(translator)
               .optModelUrls("file:///path-to-model/") // search models in specified path
               .optModelName("yolov5-exported-model.onnx")
               .optEngine("OnnxRuntime")
               .build();
         final ZooModel<Image, DetectedObjects> model = ModelZoo.loadModel(criteria);
         Predictor<Image, DetectedObjects> predictor = model.newPredictor(translator);
         final File input = new File("a-file-to-analyze.jpg");
         BufferedImageFactory factory = new BufferedImageFactory();
         final BufferedImage ix = ImageIO.read(input); //should be scaled or padded to model dims
         DetectedObjects detection = predictor.predict(factory.fromImage(ix));

[stu1130]

Thanks for your contribution!
Could you run
./gradlew fJ build

[frankfliu]

This looks good to me.

Would you mind import this mode into ONNX runtime model zoo in separate PR?

[thhart]

Would you mind import this mode into ONNX runtime model zoo in separate PR?

Please note I use DJL with own models however I can understand it can be good to have a sample model available and also for testing maybe, if I find some time I will PR a standard model.

[hongyaohongyao]

cost memory leak