CHANGELOG.md

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] - 2021-MM-DD

Added

• Added YOLO model (#552)

• Added nn.Module support for FasterRCNN backbone (#661)

Changed

Deprecated

Removed

Fixed

[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