CHANGELOG.md

[1.2.5] - 2024-05-04

Added

• Added exception in mltu.dataProvider.DataProvider to raise ValueError when dataset is not iterable
• Added custom training code for YoloV8 object detector: Tutorials\11_Yolov8\train_yolov8.py
• Added custom trained inference code for YoloV8 object detector:Tutorials\11_Yolov8\test_yolov8.py

Changed

• Fixed RandomElasticTransform in mltu.augmentors to handle elastic transformation not to exceed image boundaries
• Modified YoloPreprocessor in mltu.torch.yolo.preprocessors to output dictionary with np.arrays istead of lists

[1.2.4] - 2024-03-21

Added

• Added RandomElasticTransform to mltu.augmentors to work with Image objects
• Added xyxy_abs to mltu.annotations.detections.Detection object to return absolute bounding boxes

Changes

• Changed ImageShowCV2 transformer in mltu.transformers to display images when running with multiple threads

[1.2.3] - 2024-03-17

Added

• Added Tutorial how to run YOLOv8 pretrained Object Detection model Tutorials.11_Yolov8.README.md

[1.2.2] - 2024-03-15

Changed

• Bug fixed with loss_info local variable in mltu.torch.model.Model object

Added

• Added RandomColorMode and RandomZoom into mltu.augmentors

[1.2.1] - 2024-03-12

Changed

• Fixed many minor bugs

Added

• Added mltu.transformers.ImageNormalizer to normalize and transpose images
• Added mltu.torch.yolo.annotation.VOCAnnotationReader to read VOC annotation files
• Added mltu.torch.yolo.preprocessors.YoloPreprocessor to preprocess images and annotations for YoloV8 detection model

[1.2.0] - 2024-03-05

Changed

• Creating code to work with Ultralytics YoloV8 Detection model (training and inference)
• Updated previous tutorials to work with the latest mltu changes
• Updated mltu.augmentors.RandomRotate to work with Detections objects
• Changed to use importlib to import librosa in mltu.preprocessors to avoid import errors
• Changed mltu.torch.model.Model object to provide more flexibility in training and validation
• Improved mltu.torch.callbacks to provide more flexibility in training and validation

Added

• Added mltu.torch.detection module, that contains Detections and Detection objects, to handle detection annotations
• Added RandomDropBlock and RandomDropBlock augmentors into mltu.augmentors to work with Detections objects
• Added ModelEMA into mltu.torch.model to work with EMA (Exponential Moving Average) model
• Added FpsWrapper into mltu.inferenceModel to automatically calculate FPS (Frames Per Second) when using inference model
• Added mltu.torch.yolo.detector.BaseDetector as a base class for preprocessing and postprocessing detection models
• Added mltu.torch.yolo.detector.onnx_detector.Detector as a class to handle YoloV8 onnx model detection inference
• Added mltu.torch.yolo.detector.torch_detector.Detector as a class to handle YoloV8 torch model detection inference
• Added mltu.torch.yolo.loss.v8DetectionLoss as a class to handle YoloV8 detection loss in training
• Added mltu.torch.yolo.metrics.YoloMetrics as a class to handle YoloV8 detection metrics in training and validation
• Added mltu.torch.yolo.optimizer module, that contains AccumulativeOptimizer object and build_optimizer function, to handle YoloV8 detection optimizer in training
• Added YoloV8 Detection tutorial in Tutorials.11_yolov8 that shows how to do basic inference with torch and exported onnx models

[1.1.8] - 2024-01-02

Changed

• Fixed setup.py to include mltu.torch and mltu.tensorflow packages and other packages that are required for mltu to work properly

[1.1.7] - 2023-10-30

Changed

• Fixed bug in mltu.torch.dataProvider.SequenceHandler, to handle len sequence < queue_size case

[1.1.6] - 2023-10-30

Changed

• Fixed dependencies with librosa library

[1.1.5] - 2023-10-17

Changed

• Fixed dependencies with librosa library

[1.1.4] - 2023-09-29

Changed

• Improoved mltu.torch.dataProvider.DataProvider to hangle multiprocessing when it doesn't work to switch to multithreading

[1.1.3] - 2023-09-29

Changed

• Removed Librosa library dependency in requirements, now it is optional and required only with modules that use librosa

Added

• Created Tutorials.05_sound_to_text.train_no_limit.py that demonstrates how to train audio recognition model with mltu without audio length limit

[1.1.1] - 2023-09-26

Changed

• Included self._executor as generator in mltu.dataProvider.DataProvider object, to enable functionality to modify batch preprocessing without changing original code
• Introduced changes in mltu.torch.dataProvider.py to handle data in multiprocessing and multithreading modes, for faster preprocessing while torch models
• Modified mltu.transformers.AudioPadding object, to work with batches of raw audio data

Added

• Created tutorial 10_wav2vec2_torch (Audio to Text model) that shows how to train wav2vec2 model with mltu

[1.1.0] - 2023-08-28

Changed

• Changed mltu.transformers.SpectrogramPadding object, to pad spectrogram end with zeros instead of start

Added

• Created Tutorials/09_translation_transformer tutorial, that shows how to train translation transformer model
• Created mltu.tensorflow.tokenizers module, that contains CustomTokenizer for text data
• Created mltu.tensorflow.transformer.attention module, that contains BaseAttention, CrossAttention, GlobalSelfAttention and CausalSelfAttention layers
• Created mltu.tensorflow.transformer.layers module, that contains positional_encoding function, PositionalEmbedding, FeedForward, EncoderLayer, DecoderLayer, Encoder, Decoder layers and Transformer model
• Created mltu.tensorflow.transformer.callbacks module, that contains EncDecSplitCallback callback, to split Transformer model into separate encoder and decoder models
• Created mltu.tensorflow.transformer.utils module, that contains MaskedLoss loss and MaskedAccuracy metric, used for training Transformer models

[1.0.15] - 2023-07-15

Changed

• Fixed bug in mltu.dataProvider.DataProvider to work with batch_postprocessors.

[1.0.14] - 2023-07-13

Changed

• Included augment_annotation bool option to all mltu.augmentors to be able to choose whether to augment annotation or not
• Changed mltu.augmentors.RandomRotate to have @staticmethod of rotate_image to be able to use it without creating object

Added

• Added batch_postprocessor option to mltu.dataProvider.DataProvider to be able to postprocess batch after augmentation

[1.0.13] - 2023-07-13

• Removed because of bad build

[1.0.12] - 2023-06-08

Changed

• Moved onnx and tf2onnx import inside mltu.tensorflow.callbacks.Model2onnx to avoid import errors when not using this callback
• Removed onnx and tf2onnx install requirements from global requirements

Added

• Added RandomMirror and RandomFlip augmentors into mltu.augmentors
• Added u2net segmentation model into mltu.tensorflow.models

[1.0.11] - 2023-06-07

Changed

• Downgrade tf2onnx and onnx versions, they don't work with newest TensorFlow version

[1.0.10] - 2023-06-06

Changed

• Updated BaseModelConfigs in mltu.configs.py to include instance_attributes
• Some minor changes

[1.0.9] - 2023-05-24

Changed

• Fixed styling, changed all string from single quotes to double quotes, corrected comments, etc.

Added

• Added CVImage and PillowImage objects in mltu.annotations.image to handle image annotations

[1.0.8] - 2023-04-24

Added

• Added RandomSaltAndPepper augmentor into mltu.augmentors

[1.0.7] - 2023-04-14

Added

• Added SelfAttention layer into mltu.tensorflow.layers to use with Conv2D layers (need more testings).

[1.0.6] - 2023-04-13

Changed

• Fixed bug in mltu.dataProvider.DataProvider object to work without data_preprocessors when feeding loaded data in memory

Added

• Added RandomGaussianBlur augmentor into mltu.augmentors

[1.0.4] - 2023-03-22

Changed

• Fix ImageReader to work either with image path or np.ndarray
• Added metadata support to callbacks/tf2onnx when converting to onnx format

[1.0.3] - 2023-03-20

Changed

• Changed mltu.augmentors to work only with Image objects

Added

• Created Image object in mltu.annotations.image to handle image annotations

[1.0.2] - 2023-03-20

Changed

• changes OnnxInferenceModel in mltu.torch.inferenceModels to load custom metadata from saved ONNX model
• improved mltu.dataProvider to remove bad samples from dataset on epoch end

Added:

• added mltu.torch.losses, used to create PyTorch losses, that may be used in training and validation
• added CTC loss to mltu.torch.losses that can be used for training CTC based models
• added Model2onnx and Tensorboard callbacks to mltu.torch.callbacks, used to create PyTorch callbacks, that may be used in training and validation
• added CERMetric and WERMetric to mltu.torch.metrics, used to create PyTorch metrics, that may be used in training and validation
• created 08 pytorch tutorial, that shows how to use mltu.torch to train CTC based models

[1.0.1] - 2023-03-06

Changed

• In all tutorials removed stow dependency and replaced with os package, to make it easier to use on Windows 11

Added:

• added mltu.torch, that contains PyTorch utilities for training machine learning models
• added mltu.torch.dataProvider, used to create PyTorch data loaders for training and validation
• added mltu.torch.models, used to create PyTorch models, that wrapps whole model pipeline (training, validation, metrics, callbacks, etc.)
• added mltu.torch.callbacks, used to create PyTorch callbacks, that may be used in training and validation
• added mltu.torch.metrics, used to create PyTorch metrics, that may be used in training and validation
• added 07_pytorch_tutorial tutorial

[1.0.0] - 2023-03-06

Changed

• detaching TensorFlow from mltu, now mltu is only a collection of utilities for training machine learning models

Added:

• added 06_pytorch_introduction tutorial
• added mltu.tensorflow and mltu.torch into built package

[0.1.6] - 2023-02-26

Changed

Added:

• added 05_sound_to_text tutorial
• added WavReader to mltu/preprocessors, used to read wav files and convert them to numpy arrays

[0.1.7] - 2023-02-03

Changed

• added mltu.utils into built package

[0.1.5] - 2023-01-10

Changed

• seperated CWERMetric to CER and WER Metrics in mltu.metrics, Character/word rate was calculatted in a wrong way
• created @setter for augmentors and transformers in DataProvider, to properlly add augmentors and transformers to the pipeline
• augmentors and transformers must inherit from mltu.augmentors.base.Augmentor and mltu.transformers.base.Transformer respectively
• updated ImageShowCV2 transformer documentation
• fixed OnnxInferenceModel in mltu.inferenceModels to use CPU even if GPU is available with force_cpu=True flag

Added:

• added RandomSharpen to mltu.augmentors, used for simple image augmentation;
• added ImageShowCV2 to mltu.transformers, used to show image with cv2 for debugging purposes;
• added better explained documentation
• created unittests for CER and WER in mltu.utils.text_utils and TensorFlow verion of CER and WER mltu.metrics

[0.1.4] - 2023-12-21

Added:

• added mltu.augmentors (RandomBrightness, RandomRotate, RandomErodeDilate) - used for simple image augmentation;

[0.1.3] - 2023-12-20

Initial release of mltu (Machine Learning Training Utilities)

• Project to help with training machine learning models