lightning-bolts | PyTorch Ecosystem Directory

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lightning-bolts - Improved YOLO models Latest Release

Published by Borda over 1 year ago

[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)

New Contributors

@AdeelH made their first contribution in https://github.com/Lightning-Universe/lightning-bolts/pull/939
@AndresAlgaba made their first contribution in https://github.com/Lightning-Universe/lightning-bolts/pull/967
@Delaunay made their first contribution in https://github.com/Lightning-Universe/lightning-bolts/pull/982
@cdeepali made their first contribution in https://github.com/Lightning-Universe/lightning-bolts/pull/993
@NeoKish made their first contribution in https://github.com/Lightning-Universe/lightning-bolts/pull/944
@julien-blanchon made their first contribution in https://github.com/Lightning-Universe/lightning-bolts/pull/973
@qxcv made their first contribution in https://github.com/Lightning-Universe/lightning-bolts/pull/964
@vishnu-dev made their first contribution in https://github.com/Lightning-Universe/lightning-bolts/pull/965
@eggry made their first contribution in https://github.com/Lightning-Universe/lightning-bolts/pull/960
@heimish-kyma made their first contribution in https://github.com/Lightning-Universe/lightning-bolts/pull/851

Full Changelog: https://github.com/Lightning-Universe/lightning-bolts/compare/0.6.0...0.7.0

lightning-bolts - Minor patch release

Published by Borda almost 2 years ago

What's Changed

resolve require collisions by @Borda in https://github.com/Lightning-AI/lightning-bolts/pull/938
Metrics by @BaruchG in https://github.com/Lightning-AI/lightning-bolts/pull/892

Full Changelog: https://github.com/Lightning-AI/lightning-bolts/compare/0.6.0...0.6.0.post1

lightning-bolts - Revisions and compatibility

Published by rohitgr7 almost 2 years ago

[0.6.0] - 2022-11-03

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)

Contributors

@ArnolFokam, @Atharva-Phatak, @BaruchG, @Benjamin-Etheredge, @Borda, @Ce11an, @clementpoiret, @kfirgedal, @lijm1358, @matsumotosan, @nishantb06, @otaj, @rohitgr7, @shivammehta25, @TrellixVulnTeam

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lightning-bolts - More object detection models and backbones

Published by akihironitta almost 3 years ago

[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)

Contributors

@abhayraw1 @akihironitta @chris-clem @hoangtnm @nmichlo @oke-aditya @Programmer-RD-AI @senarvi

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lightning-bolts - More RL and callbacks

Published by Borda about 3 years ago

[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)

lightning-bolts - less softmax

Published by Borda over 3 years ago

[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)

lightning-bolts - updated LARS

Published by Borda over 3 years ago

[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)

lightning-bolts - typing friendly

Published by Borda over 3 years ago

[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)

lightning-bolts - compatibility PyTorch 1.8

Published by Borda over 3 years ago

[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)

Contributors

@akihironitta, @aniketmaurya, @BartekRoszak, @FlorianMF, @indigoviolet, @kaushikb11, @mxksowie, @wjn0

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lightning-bolts - major fixes & refactoring

Published by Borda almost 4 years ago

Detail chnages

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 option 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)

Contributors

@akihironitta, @ananyahjha93, @annikabrundyn, @awaelchli, @Borda, @briankosw, @chris-clem, @deng-cy, @hecoding, @miccio-dk, @oke-aditya, @SeanNaren, @sid-sundrani, @teddykoker, @zlapp

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lightning-bolts - Initial release

Published by Borda almost 4 years ago

[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.