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Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[unReleased] - 2022-MM-DD

Added

  • Added requires wrapper (#1056)

Changed

  • Revision of the MoCo SSL model (#928)

Deprecated

Removed

[0.7.0] - 2022-06-30

Added

  • Improved YOLO model includes YOLOv4, YOLOv5, and YOLOX networks and training algorithms (#817)

Changed

  • Move SSL transforms to pl_bolts/transforms (#905)
  • Reviewed models.detection.yolo (#851)
  • Reviewed LogisticRegression (#950)
  • Bumped support of min python version to py3.8+ (#1021)
  • Update numpy compatibility to <1.25.0 (#959)
  • Update torchmetrics compatibility to <0.12.0 (#1016)
  • Update pytorch-lightning compatibility to >1.7.0,<2.0.0 ( #965, #973, #1006 )

Fixed

  • Dropped reference to torch._six (#993)

[0.6.0] - 2022-11-02

Added

  • Updated SparseML callback for latest PyTorch Lightning (#822)

  • Updated torch version to v1.10.X (#815)

  • Dataset specific args method to CIFAR10, ImageNet, MNIST, and STL10 (#890)

  • Migrate to use lightning-utilities (#907)

  • Support PyTorch Lightning v1.8 (#910)

  • Major revision of Bolts

    • under_review flag (#835, #837)
    • Reviewing GAN basics, VisionDataModule, MNISTDataModule, CIFAR10DataModule (#843)
    • Added tests, updated doc-strings for Dummy Datasets (#865)
    • Binary MNIST/EMNIST Datasets and Datamodules (#866)
    • FashionMNIST/EMNIST Datamodules (#871)
    • Revision ArrayDataset (#872)
    • BYOL weight update callback (#867)
    • Revision models.vision.unet, models.vision.segmentation (#880)
    • Revision of SimCLR transforms (#857)
    • Revision Metrics (#878, #887)
    • Revision of BYOL module and tests (#874)
    • Revision of MNIST module (#873)
    • Revision of dataset normalizations (#898)
    • Revision of SimSiam module and tests (#891)
    • Revision datasets.kitti_dataset.KittiDataset (#896)
    • SWAV improvements (#903)
    • minor dcgan-import fix (#921)

Fixed

  • Removing extra flatten (#809)
  • support number of channels!=3 in YOLOConfiguration (#806)
  • CVE-2007-4559 Patch (#894)

[0.5.0] - 2021-12-20

Added

  • Added YOLO model (#552)
  • Added SRGAN, SRImageLoggerCallback, TVTDataModule, SRCelebA, SRMNIST, SRSTL10 (#466)
  • Added nn.Module support for FasterRCNN backbone (#661)
  • Added RetinaNet with torchvision backbones (#529)
  • Added Python 3.9 support (#786)

Changed

  • VAE now uses deterministic KL divergence during training, previously estimated KL divergence by random sampling (#760)

Removed

  • Removed PyTorch 1.6 support (#786)
  • Removed Python 3.6 support (#785)

Fixed

  • Fixed doctest fails with ImportError: cannot import name 'Env' from 'gym' (#751)
  • Fixed MoCo v2 missing Cosine Annealing learning scheduler (#757)

[0.4.0] - 2021-09-09

Added

  • Added Soft Actor Critic (SAC) Model (#627)
  • Added EMNISTDataModule, BinaryEMNISTDataModule, and BinaryEMNIST dataset (#676)
  • Added Advantage Actor-Critic (A2C) Model (#598)
  • Added Torch ORT Callback (#720)
  • Added SparseML Callback (#724)

Changed

  • Changed the default values pin_memory=False, shuffle=False and num_workers=16 to pin_memory=True, shuffle=True and num_workers=0 of datamodules (#701)
  • Supporting deprecated attribute usage (#699)

Fixed

  • Fixed ImageNet val loader to use val transform instead of train transform (#713)
  • Fixed the MNIST download giving HTTP 404 with torchvision>=0.9.1 (#674)
  • Removed momentum updating from val step and add separate val queue (#631)
  • Fixed moving the queue to GPU when resuming checkpoint for SwAV model (#684)
  • Fixed FP16 support with vision GPT model (#694)
  • Removing bias from linear model regularisation (#669)
  • Fixed CPC module issue (#680)

[0.3.4] - 2021-06-17

Changed

  • Replaced load_boston with load_diabetes in the docs and tests (#629)
  • Added base encoder and MLP dimension arguments to BYOL constructor (#637)

Fixed

  • Fixed the MNIST download giving HTTP 503 (#633)
  • Fixed type annotation of ExperienceSource.__iter__ (#645)
  • Fixed pretrained_urls on Windows (#652)
  • Fixed logistic regression (#655, #664)
  • Fixed double softmax in SSLEvaluator (#663)

[0.3.3] - 2021-04-17

Changed

  • Suppressed missing package warnings, conditioned by WARN_MISSING_PACKAGE="1" (#617)
  • Updated all scripts to LARS (#613)

Fixed

  • Add missing dataclass requirements (#618)

[0.3.2] - 2021-03-20

Changed

  • Renamed SSL modules: CPCV2 >> CPC_v2 and MocoV2 >> Moco_v2 (#585)
  • Refactored setup.py to be typing friendly (#601)

[0.3.1] - 2021-03-09

Added

  • Added Pix2Pix model (#533)

Changed

  • Moved vision models (GPT2, ImageGPT, SemSegment, UNet) to pl_bolts.models.vision (#561)

Fixed

  • Fixed BYOL moving average update (#574)
  • Fixed custom gamma in rl (#550)
  • Fixed PyTorch 1.8 compatibility issue (#580, #579)
  • Fixed handling batchnorms in BatchGradientVerification (#569)
  • Corrected num_rows calculation in LatentDimInterpolator callback (#573)

[0.3.0] - 2021-01-20

Added

  • Added input_channels argument to UNet (#297)
  • Added SwAV (#239, #348, #323)
  • Added data monitor callbacks ModuleDataMonitor and TrainingDataMonitor (#285)
  • Added DCGAN module (#403)
  • Added VisionDataModule as parent class for BinaryMNISTDataModule, CIFAR10DataModule, FashionMNISTDataModule, and MNISTDataModule (#400)
  • Added GIoU loss (#347)
  • Added IoU loss (#469)
  • Added semantic segmentation model SemSegment with UNet backend (#259)
  • Added pption to normalize latent interpolation images (#438)
  • Added flags to datamodules (#388)
  • Added metric GIoU (#347)
  • Added Intersection over Union Metric/Loss (#469)
  • Added SimSiam model (#407)
  • Added gradient verification callback (#465)
  • Added Backbones to FRCNN (#475)

Changed

  • Decoupled datamodules from models (#332, #270)
  • Set PyTorch Lightning 1.0 as the minimum requirement (#274)
  • Moved pl_bolts.callbacks.self_supervised.BYOLMAWeightUpdate to pl_bolts.callbacks.byol_updates.BYOLMAWeightUpdate (#288)
  • Moved pl_bolts.callbacks.self_supervised.SSLOnlineEvaluator to pl_bolts.callbacks.ssl_online.SSLOnlineEvaluator (#288)
  • Moved pl_bolts.datamodules.*_dataset to pl_bolts.datasets.*_dataset (#275)
  • Ensured sync across val/test step when using DDP (#371)
  • Refactored CLI arguments of models (#394)
  • Upgraded DQN to use .log (#404)
  • Decoupled DataModules from models - CPCV2 (#386)
  • Refactored datamodules/datasets (#338)
  • Refactored Vision DataModules (#400)
  • Refactored pl_bolts.callbacks (#477)
  • Refactored the rest of pl_bolts.models.self_supervised (#481, #479
  • Update [torchvision.utils.make_grid(https://pytorch.org/docs/stable/torchvision/utils.html#torchvision.utils.make_grid)] kwargs to TensorboardGenerativeModelImageSampler (#494)

Fixed

  • Fixed duplicate warnings when optional packages are unavailable (#341)
  • Fixed ModuleNotFoundError when importing datamoules (#303)
  • Fixed cyclic imports in pl_bolts.utils.self_suprvised (#350)
  • Fixed VAE loss to use KL term of ELBO (#330)
  • Fixed dataloders of MNISTDataModule to use self.batch_size (#331)
  • Fixed missing outputs in SSL hooks for PyTorch Lightning 1.0 (#277)
  • Fixed stl10 datamodule (#369)
  • Fixes SimCLR transforms (#329)
  • Fixed binary MNIST datamodule (#377)
  • Fixed the end of batch size mismatch (#389)
  • Fixed batch_size parameter for DataModules remaining (#344)
  • Fixed CIFAR num_samples (#432)
  • Fixed DQN run_n_episodes using the wrong environment variable (#525)

[0.2.5] - 2020-10-12

  • Enabled PyTorch Lightning 1.0 compatibility

[0.2.4] - 2020-10-12

  • Enabled manual returns (#267)

[0.2.3] - 2020-10-12

Added

  • Enabled PyTorch Lightning 0.10 compatibility (#264)
  • Added dummy datasets (#266)
  • Added KittiDataModule (#248)
  • Added UNet (#247)
  • Added reinforcement learning models, losses and datamodules (#257)

[0.2.2] - 2020-09-14

  • Fixed confused logit (#222)

[0.2.1] - 2020-09-13

Added

  • Added pretrained VAE with resnet encoders and decoders
  • Added pretrained AE with resnet encoders and decoders
  • Added CPC pretrained on CIFAR10 and STL10
  • Verified BYOL implementation

Changed

  • Dropped all dependencies except PyTorch Lightning and PyTorch
  • Decoupled datamodules from GAN (#206)
  • Modularize AE & VAE (#196)

Fixed

  • Fixed gym (#221)
  • Fix L1/L2 regularization (#216)
  • Fix max_depth recursion crash in AsynchronousLoader (#191)

[0.2.0] - 2020-09-10

Added

  • Enabled Apache License, Version 2.0

Changed

  • Moved unnecessary dependencies to __main__ section in BYOL (#176)

Fixed

  • Fixed CPC STL10 finetune (#173)

[0.1.1] - 2020-08-23

Added

  • Added Faster RCNN + Pscal VOC DataModule (#157)
  • Added a better lars scheduling LARSWrapper (#162)
  • Added CPC finetuner (#158)
  • Added BinaryMNISTDataModule (#153)
  • Added learning rate scheduler to BYOL (#148)
  • Added Cityscapes DataModule (#136)
  • Added learning rate scheduler LinearWarmupCosineAnnealingLR (#138)
  • Added BYOL (#144)
  • Added ConfusedLogitCallback (#118)
  • Added an asynchronous single GPU dataloader. (#1521)

Fixed

  • Fixed simclr finetuner (#165)
  • Fixed STL10 finetuner (#164)
  • Fixed Image GPT (#108)
  • Fixed unused MNIST transforms in tran/val/test (#109)

Changed

  • Enhanced train batch function (#107)

[0.1.0] - 2020-07-02

Added

  • Added setup and repo structure
  • Added requirements
  • Added docs
  • Added Manifest
  • Added coverage
  • Added MNIST template
  • Added VAE template
  • Added GAN + AE + MNIST
  • Added Linear Regression
  • Added Moco2g
  • Added simclr
  • Added RL module
  • Added Loggers
  • Added Transforms
  • Added Tiny Datasets
  • Added regularization to linear + logistic models
  • Added Linear and Logistic Regression tests
  • Added Image GPT
  • Added Recommenders module

Changed

  • Device is no longer set in the DQN model init
  • Moved RL loss function to the losses module
  • Moved rl.common.experience to datamodules
  • train_batch function to VPG model to generate batch of data at each step (POC)
  • Experience source no longer gets initialized with a device, instead the device is passed at each step()
  • Refactored ExperienceSource classes to be handle multiple environments.

Removed

  • Removed N-Step DQN as the latest version of the DQN supports N-Step by setting the n_step arg to n
  • Deprecated common.experience

Fixed

  • Documentation
  • Doct tests
  • CI pipeline
  • Imports and pkg
  • CPC fixes