KotlinDL Files

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)