0.5.0 (14/12/2022) Inference on Android with ONNX Runtime

Features:

• Added Android inference support
  • Built Android artifacts for "impl", "onnx" and "visualization" modules #422
  • Added Android-specific models to the model zoo
    • Classification #438:
      • EfficientNet4Lite
      • MobilenetV1
    • Object Detection:
      • SSDMobileNetV1 #440
      • EfficientDetLite0 #443
    • Pose Detection #442:
      • MoveNetSinglePoseLighting
      • MoveNetSinglePoseThunder
    • Face Detection #461:
      • UltraFace320
      • UltraFace640
    • Face Alignment #441:
      • Fan2d106
  • Implemented preprocessing operations working on Android Bitmap #416
    #478:
    • Resize
    • Rotate
    • Crop
    • ConvertToFloatArray
  • Added utility functions to convert ImageProxy to Bitmap #458
  • Added NNAPI execution provider #420
  • Added api to create OnnxInferenceModel from the ByteArray representation #415
  • Introduced a gradle task to download model hub models before the build #444
  • Added utility functions to draw detection results on Android Canvas #450
• Implemented new preprocessing API #425
  • Introduced an Operation interface to represent a preprocessing operation for any input and output
  • Added PreprocessingPipeline class to combine operations together in a type-safe manner
  • Re-implemented old operations with the new API
  • Added convenience functions such as pipeline to start a new preprocessing pipeline,
    call to invoke operations defined elsewhere, onResult to access intermediate preprocessing results
  • Converted ModelType#preprocessInput function to Operation #429
  • Converted common preprocessing functions for models trained on ImageNet to Operation #429
• Added new ONNX features
  • Added execution providers support (CPU, CUDA, NNAPI) and convenient extensions for inference with them
    #386
  • Introduced OnnxInferenceModel#predictRaw function which allows custom OrtSession.Result processing and extension functions
    to extract common data types from the result #465
  • Added validation of input shape #385
• Added Imagenet enum to represent different Imagenet dataset labels and added support for zero indexed COCO labels
  #438 #446
• Implemented unified summary printing for Tensorflow and ONNX models #368
• Added FlatShape interface to allow manipulating the detected shapes in a unified way #480
• Introduced DataLoader interface for loading and preprocessing data for dataset implementations #424
• Improved swing visualization utilities #379
  #388
• Simplified Layer interface to leave only build function to be implemented and remove explicit output shape computation
  #408

Breaking changes:

• Refactored module structure and packages #412 #469
  • Extracted "tensorflow" module for learning and inference with Tensorflow backend
  • Extracted "impl" module for implementation classes and utilities
  • Moved preprocessing operation implementations to the "impl" module
  • Removed dependency of "api" module on the "dataset" module
  • Changed packages for "api", "impl", "dataset" and "onnx" so that they match the corresponding module name
• Preprocessing classes such as Preprocessing, ImagePreprocessing, ImagePreprocessor,
  ImageSaver, ImageShape, TensorPreprocessing, Preprocessor got removed in favor of the new preprocessing API #425
• Removed Sharpen preprocessor since the ModelType#preprocessor field was introduced, which can be used in the preprocessing
  pipeline using the call function #429

Bugfixes:

• Fix loading of jpeg files not supported by standard java ImageIO #384
• Updated ONNX Runtime version to enable inference on M1 chips #361
• Fixed channel ordering in for image recognition models #400
• Avoid warnings from loadWeightsForFrozenLayers function for layers without parameters #382

New documentation and examples:

• Inference with KotlinDL and ONNX Runtime on desktop and Android
• KotlinDL ONNX Model Zoo
• Sample Android App

Thanks to our contributors:

• Nikita Ermolenko (@ermolenkodev)
• Julia Beliaeva (@juliabeliaeva)
• Burak Akgün (@mbakgun)
• Pavel Gorgulov (@devcrocod)