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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.

[YY.MM.R] - YYYY-MM-DD

Added

  • catalyst-tune for Config API added #1411

Changed

Removed

Fixed

[22.02.1] - 2022-02-27

Added

  • catalyst-run for Config API support added #1406

Fixed

  • Logger API naming #1405

[22.02] - 2022-02-13

Tl;dr

Added

  • Additional tests for different hardware accelerators setups. Please check out the tests/pipelines folder for more information.
  • BackwardCallback and BackwardCallbackOrder as an abstraction on top of loss.backward. Now you could easily log model gradients or transform them before OptimizerCallback.
  • CheckpointCallbackOrder for ICheckpointCallback.

Changed

  • Minimal python version moved to 3.7, minimal pytorch version moved to 1.4.0.
  • Engines rewritten on top of Accelerate. First, we found these two abstractions very close to each other. Second, Accelerate provides additional user-friendly API and more stable API for "Nvidia APEX" and "Facebook Fairscale" - it does not support them.
  • SelfSupervisedRunner moved to the examples folder from the Catalyst API. The only Runners API, that will be supported in the future: IRunner, Runner, ISupervisedRunner, SupervisedRunner due to their consistency. If you are interested in any other Runner API - feel free to write your own CustomRunner and use SelfSupervisedRunner as an example.
  • Runner.{global/stage}_{batch/loader/epoch}_metrics renamed to Runner.{batch/loader/epoch}_metrics
  • CheckpointCallback rewritten from scratch.
  • Catalyst registry moved to full-imports-paths only.
  • Logger API changed to receive IRunner for all log_* methods.
  • Metric API: topk_args renamed to topk
  • Contrib API: init imports from catalyst.contrib - removed, use from catalyst.contrib.{smth} import {smth}. Could be change to full-imports-only in future versions for stability.
  • All quickstarts, minimal examples, notebooks and pipelines moved to new version.
  • Codestyle moved to 89 right margin. Honestly speaking, it's much easier to maintain Catalyst with 89 right margin on MBP'16.

Removed

  • ITrial removed.
  • Stages support removed. While we embrace stages in deep learning experiments, current hardware accelerators are not prepared well for such setups. Additionally, ~95% of dl pipelines are single-stage. Multi-stage runner support is under review. For multi-stage support, please define a CustomRunner with rewritten API.
  • Config/Hydra API support removed. Config API is under review. For now, you could write your own Config API with hydra-slayer if needed.
  • catalyst-dl scripts removed. Without Config API we don't need them anymore.
  • Nvidia Apex, Fairscale, Albumentations, Nifti, Hydra requiremets removed.
  • OnnxCallback, PruningCallback, QuantizationCallback, TracingCallback removed from callbacks API. Theese callbacks are under review now.

If you have any questions on the Catalyst 22 edition updates, please join Catalyst slack for discussion.

[21.12] - 2021-12-28

Added

  • MNIST dataset for SSL banchmark (#1368)
  • MoveiLens 20M dataset #1336
  • logger property for logging customization (#1372)
  • MacridVAE example (#1363)
  • SSL benchmark results (#1374)
  • Neptune example (#1377)
  • multi-node support for engines (#1364)

Changed

  • RL examples update to last version (#1370)
  • DDPLoaderWrapper updated to new version (#1385)
  • num_classes for classification metrics became optional (#1379)
  • colab ci/cd update to new verion

Removed

Fixed

  • requests requirements for catalyst[cv] added (#1371)
  • loader step counter (#1374)
  • detection example data preprocessing (#1369)
  • gradient clipping with fp16 runs (#1378)
  • config API fix for DDP runs (#1383)
  • checkpoint creation for fp16 engines (#1382)

[21.11] - 2021-11-30

Added

  • MultiVAE RecSys example (#1340)
  • Returned resume support - resolved #1193 (#1349)
  • Smoothing dice loss to contrib (#1344)
  • profile flag for runner.train (#1348)
  • MultiDAE RecSys example (#1356)
  • SETTINGS.log_batch_metrics, SETTINGS.log_epoch_metrics, SETTINGS.compute_per_class_metrics for framework-wise Metric & Logger APIs specification (#1357)
  • log_batch_metrics and log_epoch_metrics options for all available Loggers (#1357)
  • compute_per_class_metrics option for all available multiclass/label metrics (#1357)
  • pytorch benchmark script and simplified MNIST (#1360)

Changed

  • A few framework simplifications were made (#1346):
    • catalyst-contrib scripts reduced to collect-env and project-embeddings only
    • catalyst-dl scripts recuded to run and tune only
    • transforms. prefix deprecated for Catalyst-based transforms
    • catalyst.tools moved to catalyst.extras
    • task-dependent extensions from catalyst.data moved to catalyst.contrib.data
    • catalyst.data.transforms moved to catalyst.contrib.data.transforms
    • Normalize, ToTensor transforms renamed to NormalizeImage, ImageToTensor
    • metric learning extensions moved to catalyst.contrib.data
    • catalyst.contrib moved to code-as-a-documentation development
    • catalyst[cv] and catalyst[ml] extensions moved to flatten architecture design; examples: catalyst.contrib.data.dataset_cv, catalyst.contrib.data.dataset_ml
    • catalyst.contrib moved to flatten architecture design; exampels: catalyst.contrib.data, catalyst.contrib.datasets, catalyst.contrib.layers, catalyst.contrib.models, catalyst.contrib.optimizers, catalyst.contrib.schedulers
    • internal functionality moved to ***._misc modules
    • catalyst.utils.mixup moved to catalyst.utils.torch
    • catalyst.utils.numpy moved to catalyst.contrib.utils.numpy
  • default logging logic moved from "batch & epoch" to "epoch"-only to save computation time during logging; to respecify, please use:
    • SETTINGS.log_batch_metrics=True/False or os.environ["CATALYST_LOG_BATCH_METRICS"]
    • SETTINGS.log_epoch_metrics=True/False or os.environ["CATALYST_LOG_EPOCH_METRICS"]
  • default metrics computation moved from "per-class & aggregations" to "aggregations"-only to save computation time during logging; to respecify, please use:
    • SETTINGS.compute_per_class_metrics=True/False or os.environ["CATALYST_COMPUTE_PER_CLASS_METRICS"]
  • no transformations required for MNIST contrib dataset (#1360

Removed

  • A few framework simplifications were made (#1346):
    • catalyst.contrib.pandas
    • catalyst.contrib.parallel
    • catalyst.contrib.models.cv
    • a few catalyst.utils.misc functions
    • catalyst.extras removed from the public documentation

Fixed

  • documentation search error (21.10 only) (#1346)
  • docs examples (#1362)
  • Self-Supervised benchmark: (#1365), (#1361)

[21.10] - 2021-10-30

Added

  • RSquareLoss (#1313)
  • Self-Supervised example updates: (#1305), (#1322), (#1325), (#1335)
  • Albert training example (#1326)
  • YOLO-X (new) detection example and refactoring (#1324)
  • TopKMetric abstraction (#1330)

Changed

  • simlified readme (#1312)
  • improved DDP tutorial (#1327)
  • CMCMetric renamed from <prefix>cmc<suffix><k> to <prefix>cmc<k><suffix> (#1330)

Removed

Fixed

  • Zero seed error (#1329)
  • updated codestyle issues (#1331)
  • TopK metrics: (#1330), (#1334), (#1339)
  • --expdir param for catalyst-dl run (#1338)
  • ControlFlowCallback for distributed setup (#1341)

[21.09] - 2021-09-30

Added

  • CometLogger support (#1283)
  • CometLogger examples (#1287)
  • XLA docs (#1288)
  • Contarstive loss functions: NTXentLoss (#1278), SupervisedContrastiveLoss (#1293)
  • Self supervised learning: ISelfSupervisedRunner, SelfSupervisedConfigRunner, SelfSupervisedRunner, SelfSupervisedDatasetWrapper (#1278)
  • SimCLR example (#1278)
  • Superivised Contrastive example (#1293)
  • extra warnings for runner-callbacks interaction (#1295)
  • CategoricalRegressionLoss and QuantileRegressionLoss to the contrib (#1295)
  • R2 score metric (#1274)

Changed

  • Improved WandbLogger to support artifacts and fix logging steps (#1309)
  • full Runner cleanup, with callbacks and loaders destruction, moved to PipelineParallelFairScaleEngine only (#1295)
  • HuberLoss renamed to HuberLossV0 for the PyTorch compatibility (#1295)
  • codestyle update (#1298)
  • BalanceBatchSampler - deprecated (#1303)

Removed

Fixed

[21.08] - 2021-08-31

Added

  • RecSys loss functions: AdaptiveHingeLoss, BPRLoss, HingeLoss, LogisticLoss, RocStarLoss, WARPLoss (#1269, #1282)
  • object detection examples (#1271)
  • SklearnModelCallback (#1261)
  • Barlow Twins example (#1261)
  • TPU/XLA support (#1275)
  • native sync_bn support for all available engines (#1275)
    • Torch, AMP, Apex, FairScale

Changed

  • Registry moved to hydra-slayer (#1264))
  • (#1275)
    • batch metrics sync removed from ddp-runs to speedup training process
    • AccumulationMetric renamed to AccumulativeMetric
      • moved from catalyst.metrics._metric to catalyst.metrics._accumulative
      • accululative_fields renamed to keys

Removed

Fixed

  • PeriodicLoaderCallback docsting (#1279)
  • matplotlib issue (#1272)
  • sample counter for the loader (#1285)

[21.07] - 2021-07-29

Added

  • added pre-commit hook to run codestyle checker on commit (#1257)
  • on publish github action for docker and docs added (#1260)
  • MixupCallback and utils.mixup_batch (#1241)
  • Barlow twins loss (#1259)
  • BatchBalanceClassSampler (#1262)

Changed

Removed

Fixed

  • make expdir in catalyst-dl run optional (#1249)
  • Bump neptune-client from 0.9.5 to 0.9.8 in requirements-neptune.txt (#1251)
  • automatic merge for master (with Mergify) fixed (#1250)
  • Evaluate loader custom model bug was fixed (#1254)
  • BatchPrefetchLoaderWrapper issue with batch-based PyTorch samplers (#1262)
  • Adapted MlflowLogger for new config hierarchy (#1263)

[21.06] - 2021-06-29

Added

  • (#1230)
    • FairScale support
    • DeepSpeed support
    • utils.ddp_sync_run function for synchronous ddp run
    • CIFAR10 and CIFAR100 datasets from torchvision (no cv-based requirements)
    • Catalyst Engines demo
  • dataset_from_params support in config API (#1231)
  • transform from params support for config API added (#1236)
  • samplers from params support for config API added (#1240)
  • recursive registry.get_from_params added (#1241)
  • albumentations integration (#1238)
  • Profiler callback (#1226)

Changed

  • (#1230)
    • loaders creation now wrapper with utils.ddp_sync_run for utils.ddp_sync_run data preparation
    • runner support stage cleanup: loaders and callbacks will be deleted on the stage end
    • Apex-based engines now support both APEXEngine and ApexEngine registry names

Removed

Fixed

  • multiprocessing in minimal tests hotfix (#1232)
  • Tracing callback hotfix (#1234)
  • Engine hotfix for predict_loader (#1235)
  • (#1230)
    • Hydra hotfix due to 1.1.0 version changes
  • HuberLoss name conflict for pytorch 1.9 hotfix (#1239)

[21.05] - 2021-05-31

Added

  • Reinforcement learning tutorials (#1205)
  • customization demo (#1207)
  • FAQ docs: multiple input and output keys, engine tutorial (#1202)
  • minimal Config API example (#1215)
  • Distributed RL example (Catalyst.RL 2.0 concepts) (#1224)
  • SklearnCallback as integration of sklearn metrics (#1198)

Changed

  • tests moved to tests folder (#1208)
  • pipeline tests moved to tests/pipelines (#1215)
  • updated NeptuneLogger docstrings (#1223)

Removed

Fixed

  • customizing what happens in train() notebook (#1203)
  • transforms imports under catalyst.data (#1211)
  • change layerwise to layerwise_params (#1210)
  • add torch metrics support (#1195)
  • add Config API support for BatchTransformCallback (#1209)

[21.04.2] - 2021-04-30

Added

  • Weights and Biases Logger (WandbLogger) (#1176)
  • Neptune Logger (NeptuneLogger) (#1196)
  • log_artifact method for logging arbitrary files like audio, video, or model weights to ILogger and IRunner (#1196)

[21.04/21.04.1] - 2021-04-17

Added

  • Nifti Reader (NiftiReader) (#1151)
  • CMC score and callback for ReID task (ReidCMCMetric and ReidCMCScoreCallback) (#1170)
  • Market1501 metric learning datasets (Market1501MLDataset and Market1501QGDataset) (#1170)
  • extra kwargs support for Engines (#1156)
  • engines exception for unknown model type (#1174)
  • a few docs to the supported loggers (#1174)

Changed

  • TensorboardLogger switched from global_batch_step counter to global_sample_step one (#1174)
  • TensorboardLogger logs loader metric on_loader_end rather than on_epoch_end (#1174)
  • prefix renamed to metric_key for MetricAggregationCallback (#1174)
  • micro, macro and weighted aggregations renamed to _micro, _macro and _weighted (#1174)
  • BatchTransformCallback updated (#1153)

Removed

  • auto torch.sigmoid usage for metrics.AUCMetric and metrics.auc (#1174)

Fixed

  • hitrate calculation issue (#1155)
  • ILoader wrapper usage issue with Runner (#1174)
  • counters for ddp case (#1174)

[21.03.2] - 2021-03-29

Fixed

  • minimal requirements issue (#1147)
  • nested dicts in loaders_params/samplers_params overriding (#1150)

[21.03.1] - 2021-03-28

Added

  • Additive Margin SoftMax(AMSoftmax) (#1125)
  • Generalized Mean Pooling(GeM) (#1084)
  • Key-value support for CriterionCallback (#1130)
  • Engine configuration through cmd (#1134)
  • Extra utils for thresholds (#1134)
  • Added gradient clipping function to optimizer callback (1124)
  • FactorizedLinear to contrib (1142)
  • Extra init params for ConsoleLogger (1142)
  • Tracing, Quantization, Onnx, Pruninng Callbacks (1127)

Changed

  • CriterionCallback now inherits from BatchMetricCallback #1130)
    • united metrics computation logic

Removed

  • Config API deprecated parsings logic (1142) (1138)

Fixed

  • Data-Model device sync and Engine logic during runner.predict_loader (#1134)
  • BatchLimitLoaderWrapper logic for loaders with shuffle flag (#1136)
  • config description in the examples (1142)
  • Config API deprecated parsings logic (1142) (1138)
  • RecSys metrics Top_k calculations (#1140)
  • _key_value for schedulers in case of multiple optimizers (#1146)

[21.03] - 2021-03-13 (#1095)

Added

  • Engine abstraction to support various hardware backends and accelerators: CPU, GPU, multi GPU, distributed GPU, TPU, Apex, and AMP half-precision training.
  • Logger abstraction to support various monitoring tools: console, tensorboard, MLflow, etc.
  • Trial abstraction to support various hyperoptimization tools: Optuna, Ray, etc.
  • Metric abstraction to support various of machine learning metrics: classification, segmentation, RecSys and NLP.
  • Full support for Hydra API.
  • Full DDP support for Python API.
  • MLflow support for metrics logging.
  • United API for model post-processing: tracing, quantization, pruning, onnx-exporting.
  • United API for metrics: classification, segmentation, RecSys, and NLP with full DDP and micro/macro/weighted/etc aggregations support.

Changed

  • Experiment abstraction merged into Runner one.
  • Runner, SupervisedRunner, ConfigRunner, HydraRunner architectures and dependencies redesigned.
  • Internal settings and registry mechanisms refactored to be simpler, user-friendly and more extendable.
  • Bunch of Config API test removed with Python API and pytest.
  • Codestyle now supports up to 99 symbols per line :)
  • All callbacks/runners moved for contrib to the library core if was possible.
  • Runner abstraction simplified to store only current state of the experiment run: all validation logic was moved to the callbacks (by this way, you could easily select best model on various metrics simultaneously).
  • Runner.input and Runner.output merged into united Runner.batch storage for simplicity.
  • All metric moved from catalyst.utils.metrics to catalyst.metrics.
  • All metrics now works on scores/metric-defined-input rather that logits (!).
  • Logging logic moved from Callbacks to appropriate Loggers.
  • KorniaCallbacks refactored to BatchTransformCallback.

Removed

  • Lots of unnecessary contrib extensions.
  • Transforms configuration support through Config API (could be returned in next releases).
  • Integrated Python cmd command for model pruning, swa, etc (should be returned in next releases).
  • CallbackOrder.Validation and CallbackOrder.Logging
  • All 2020 year backward compatibility fixes and legacy support.

Fixed

  • Docs rendering simplified.
  • LrFinderCallback.

Release docs, Python API minimal examples, Config/Hydra API example.

[20.12.1] - XXXX-XX-XX

Added

  • Inference mode for face layers (#1045)

Fixed

  • Fix bug in OptimizerCallback when mixed-precision params set both: in callback arguments and in distributed_params (#1042)

[20.12] - 2020-12-20

Added

  • CVS Logger (#1005)
  • DrawMasksCallback (#999)
  • (#1002)
    • a few docs
  • (#998)
    • reciprocal_rank metric
    • unified recsys metrics preprocessing
  • (#1018)
    • readme examples for all supported metrics under catalyst.metrics
    • wrap_metric_fn_with_activation for model outputs wrapping with activation
    • extra tests for metrics
  • (#1039)
    • per_class=False option for metrics callbacks
    • PrecisionCallack, RecallCallack for multiclass problems
    • extra docs

Changed

  • docs update (#1000)
  • AMPOptimizerCallback and OptimizerCallback were merged (#1007)
  • (#1017)
    • fixed bug in SchedulerCallback
    • Log LRs and momentums for all param groups, not only for the first one
  • (#1002)
    • tensorboard, ipython, matplotlib, pandas, scikit-learn moved to optional requirements
    • PerplexityMetricCallback moved to catalyst.callbacks from catalyst.contrib.callbacks
    • PerplexityMetricCallback renamed to PerplexityCallback
    • catalyst.contrib.utils.confusion_matrix renamed to catalyst.contrib.utils.torch_extra
    • many parts of catalyst.data moved to catalyst.contrib.data
    • catalyst.data.scripts moved to catalyst.contrib.scripts
    • catalyst.utils, catalyst.data.utils and catalyst.contrib.utils restructured
    • ReaderSpec renamed to IReader
    • SupervisedExperiment renamed to AutoCallbackExperiment
  • gain functions renamed for dcg/ndcg metrics (#998)
  • (#1014)
    • requirements respecification: catalyst[cv], catalyst[dev], catalyst[log], catalyst[ml], catalyst[nlp],catalyst[tune]
    • settings respecification
    • extra tests for settings
    • contrib refactoring
  • iou and dice metrics moved to per-class computation (#1031)

Removed

  • (#1002)
    • KNNMetricCallback
    • sklearn mode for ConfusionMatrixLogger
    • catalyst.data.utils
    • unnecessary catalyst.tools.meters
    • todos for unnecessary docs
  • (#1014)
    • transformers-based contrib (too unstable)
  • (#1018)
    • ClasswiseIouCallback/ClasswiseJaccardCallback as deprecated on (should be refactored in future releases)

Fixed

  • prevented modifying config during the experiment and runner initialization (#1004)
  • a few test for RecSys MAP computation (#1018)
  • leave batch size the same for default distributed training (#1023)
  • (#1032)
    • Apex: now you can use apex for multiple models training
    • Apex: DataParallel is allowed for opt_level other than "O1"

[20.11] - 2020-11-12

Added

  • DCG, nDCG metrics (#881)
  • MAP calculations #968
  • hitrate calculations [#975] (catalyst-team#975)
  • extra functions for classification metrics (#966)
  • OneOf and OneOfV2 batch transforms (#951)
  • precision_recall_fbeta_support metric (#971)
  • Pruning tutorial (#987)
  • BatchPrefetchLoaderWrapper (#986)
  • DynamicBalanceClassSampler (#954)

Changed

  • update Catalyst version to 20.10.1 for tutorials (#967)
  • added link to dl-course (#967)
  • IRunner -> simplified IRunner (#984)
  • docs were restructured (#985)
  • set_global_seed moved from utils.seed to utils.misc (#986)

Removed

  • several deprecated tutorials (#967)
  • several deprecated func from utils.misc (#986)

Fixed

  • BatchTransformCallback - add nn.Module transforms support (#951)
  • moved to contiguous view for accuracy computation (#982)
  • fixed torch warning on optimizer.py:140 (#979)

[20.10.1] - 2020-10-15

Added

  • MRR metrics calculation (#886)
  • docs for MetricCallbacks (#947)
  • SoftMax, CosFace, ArcFace layers to contrib (#939)
  • ArcMargin layer to contrib (#957)
  • AdaCos to contrib (#958)
  • Manual SWA to utils (#945)

Changed

  • fixed path to CHANGELOG.md file and add information about unit test to PULL_REQUEST_TEMPLATE.md ([#955])(catalyst-team#955)
  • catalyst-dl tune config specification - now optuna params are grouped under study_params (#947)
  • IRunner._prepare_for_stage logic moved to IStageBasedRunner.prepare_for_stage (#947)
    • now we create components in the following order: datasets/loaders, model, criterion, optimizer, scheduler, callbacks
  • MnistMLDataset and MnistQGDataset data split logic - now targets of the datasets are disjoint (#949)
  • architecture redesign (#953)
    • experiments, runners, callbacks grouped by primitives under catalyst.experiments/catalyst.runners/catalyst.callbacks respectively
    • settings and typing moved from catalyst.tools.* to catalyst.*
    • utils moved from catalyst.*.utils to catalyst.utils
  • swa moved to catalyst.utils (#963)

Removed

Fixed

  • AMPOptimizerCallback - fix grad clip fn support (#948)
  • removed deprecated docs types (#947) (#952)
  • docs for a few files (#952)
  • extra backward compatibility fixes (#963)

[20.09.1] - 2020-09-25

Added

  • Runner registry support for Config API (#936)
  • catalyst-dl tune command - Optuna with Config API integration for AutoML hyperparameters optimization (#937)
  • OptunaPruningCallback alias for OptunaCallback (#937)
  • AdamP and SGDP to catalyst.contrib.losses (#942)

Changed

  • Config API components preparation logic moved to utils.prepare_config_api_components (#936)

Removed

Fixed

  • Logging double logging :) (#936)
  • CMCCallback (#941)

[20.09] - 2020-09-07

Added

  • MovieLens dataset loader (#903)
  • force and bert-level keywords to catalyst-data text2embedding (#917)
  • OptunaCallback to catalyst.contrib (#915)
  • DynamicQuantizationCallback and catalyst-dl quantize script for fast quantization of your model (#890)
  • Multi-scheduler support for multi-optimizer case (#923)
  • Native mixed-precision training support (#740)
  • OptiomizerCallback - flag use_fast_zero_grad for faster (and hacky) version of optimizer.zero_grad() (#927)
  • IOptiomizerCallback, ISchedulerCallback, ICheckpointCallback, ILoggerCallback as core abstractions for Callbacks (#933)
  • flag USE_AMP for PyTorch AMP usage (#933)

Changed

  • Pruning moved to catalyst.dl (#933)
  • default USE_APEX changed to 0 (#933)

Removed

Fixed

  • autoresume option for Config API (#907)
  • a few issues with TF projector (#917)
  • batch sampler speed issue (#921)
  • add apex key-value optimizer support (#924)
  • runtime warning for PyTorch 1.6 (920)
  • Apex synbn usage (920)
  • Catalyst dependency on system git (922)

[20.08] - 2020-08-09

Added

  • CMCScoreCallback (#880)
  • kornia augmentations BatchTransformCallback (#862)
  • average_precision and mean_average_precision metrics (#883)
  • MultiLabelAccuracyCallback, AveragePrecisionCallback and MeanAveragePrecisionCallback callbacks (#883)
  • minimal examples for multiclass and multilabel classification (#883)
  • experimental TPU support (#893)
  • add Imagenette, Imagewoof, and Imagewang datasets (#902)
  • IMetricCallback, IBatchMetricCallback, ILoaderMetricCallback, BatchMetricCallback, LoaderMetricCallback abstractions (#897)
  • HardClusterSampler inbatch sampler (#888)

Changed

  • all registries merged to one catalyst.registry (#883)
  • mean_average_precision logic merged with average_precision (#897)
  • all imports moved to absolute (#905)
  • catalyst.contrib.data merged to catalyst.data (#905)
  • {breaking} Catalyst transform ToTensor was renamed to ImageToTensor (#905)
  • TracerCallback moved to catalyst.dl (#905)
  • ControlFlowCallback, PeriodicLoaderCallback moved to catalyst.core (#905)

Removed

  • average_accuracy and mean_average_accuracy metrics (#883)
  • MultiMetricCallback abstraction (#897)

Fixed

  • utils.tokenize_text typo with punctuation (#880)
  • ControlFlowCallback logic (#892)
  • docs (#897)

[20.07] - 2020-07-06

Added

  • log parameter to WandbLogger (#836)
  • hparams experiment property (#839)
  • add docs build on push to master branch (#844)
  • WrapperCallback and ControlFlowCallback (#842)
  • BatchOverfitCallback (#869)
  • overfit flag for Config API (#869)
  • InBatchSamplers: AllTripletsSampler and HardTripletsSampler (#825)

Changed

  • Renaming (#837)
    • SqueezeAndExcitation -> cSE
    • ChannelSqueezeAndSpatialExcitation -> sSE
    • ConcurrentSpatialAndChannelSqueezeAndChannelExcitation -> scSE
    • _MetricCallback -> IMetricCallback
    • dl.Experiment.process_loaders -> dl.Experiment._get_loaders
  • LRUpdater become abstract class (#837)
  • calculate_confusion_matrix_from_arrays changed params order (#837)
  • dl.Runner.predict_loader uses _prepare_inner_state and cleans experiment (#863)
  • toml to the dependencies (#872)

Removed

  • crc32c dependency (#872)

Fixed

  • workflows/deploy_push.yml failed to push some refs (#864)
  • .dependabot/config.yml contained invalid details (#781)
  • LanguageModelingDataset (#841)
  • global_* counters in Runner (#858)
  • EarlyStoppingCallback considers first epoch as bad (#854)
  • annoying numpy warning (#860)
  • PeriodicLoaderCallback overwrites best state (#867)
  • OneCycleLRWithWarmup (#851)

[20.06] - 2020-06-04

Added

  • Mergify (#831)
  • PerplexityMetricCallback (#819)
  • PeriodicLoaderRunnerCallback (#818)

Changed

  • docs structure were updated during (#822)
  • utils.process_components moved from utils.distributed to utils.components (#822)
  • catalyst.core.state.State merged to catalyst.core.runner._Runner (#823) (backward compatibility included)
    • catalyst.core.callback.Callback now works directly with catalyst.core.runner._Runner
    • state_kwargs renamed to stage_kwargs

Removed

Fixed

  • added missed dashes in docker perfixes (#828)
  • handle empty loader in Runner (#873)

[20.05.1] - 2020-05-23

Added

  • Circle loss implementation (#802)
  • BatchBalanceSampler for metric learning and classification (#806)
  • CheckpointCallback: new argument load_on_stage_start which accepts str and Dict[str, str] (#797)
  • LanguageModelingDataset to catalyst[nlp] (#808)
  • Extra counters for batches, loaders and epochs (#809)
  • TracerCallback (#789)

Changed

  • CheckpointCallback: additional logic for argument load_on_stage_end - accepts str and Dict[str, str] (#797)
  • counters names for batches, loaders and epochs (#809)
  • utils.trace_model: changed logic - runner argument was changed to predict_fn (#789)
  • redesigned contrib.data and contrib.datasets (#820)
  • catalyst.utils.meters moved to catalyst.tools (#820)
  • catalyst.contrib.utils.tools.tensorboard moved to catalyst.contrib.tools (#820)

Removed

Fixed

[20.05] - 2020-05-07

Added

  • Added new docs and minimal examples (#747)
  • Added experiment to registry (#746)
  • Added examples with extra metrics (#750)
  • Added VAE example (#752)
  • Added gradient tracking (#679
  • Added dependabot (#771)
  • Added new test for Config API (#768)
  • Added Visdom logger (#769)
  • Added new github actions and templates (#777)
  • Added save_n_best=0 support for CheckpointCallback (#784)
  • Added new contrib modules for CV (#793)
  • Added new github actions CI (#791)

Changed

  • Changed Alchemy dependency (from alchemy-catalyst to alchemy) (#748)
  • Changed warnings logic (#719)
  • Github actions CI was updated (#754)
  • Changed default num_epochs to 1 for State (#756)
  • Changed state.batch_in/state.batch_out to state.input/state.output (#763)
  • Moved torchvision dependency from catalyst to catalyst[cv] (#738))

Removed

  • GanRunner removed to Catalyst.GAN (#760)
  • monitoring_params were removed (#760)

Fixed

  • Fixed docker dependencies ($753)
  • Fixed text2embeddding script (#722)
  • Fixed utils/sys exception (#762)
  • Returned detach method (#766)
  • Fixed timer division by zero (#749)
  • Fixed minimal torch version (#775)
  • Fixed segmentation tutorial (#778)
  • Fixed Dockerfile dependency (#780)

[20.04] - 2020-04-06

Added

Changed

Removed

Fixed

[YY.MM.R] - YYYY-MM-DD

Added

Changed

Removed

Fixed