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CHANGELOG.md

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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]

[0.7.3] - 2023-05-17

Fixed

  • Compatibility with newer PyTorch Benchmark version.

[0.7.2] - 2022-06-03

Added

  • Version for protobuf during build.
  • Conditional install of redis on win platforms

[0.7.1] - 2022-03-18

Fixed

  • Device transfer in benchmark.

[0.7.0] - 2022-03-18

Added

  • Defensive fallback for FLOPs measurement.
  • Add MultiStepLR optimizers.

Changed

  • Profiling to use pytorch_benchmark package.

Fixed

  • WandB logger log_dir extraction.

[0.6.1] - 2022-02-07

Changed

  • Profile only warms up on first inference.

[0.6.0] - 2022-01-27

Added

  • Memory profiling.

Fixed

  • Tune DeprecationWarning.

[0.5.1] - 2021-11-16

Added

  • Add pred and target dict support in Lifecycle.

Fixed

  • Avoid detaching loss in step.

[0.5.0] - 2021-11-12

Added

  • Add preprocess_batch method to Lifecycle.
  • Add option for string type in utils.name.
  • Add Metric Selector.

Fixed

  • Weight freezing during model loading.
  • Fix discriminative_lr param selection for NoneType parameters.
  • Fix wandb project naming during hparamsearch.
  • Optimizer Schedulers take accumulate_grad_batches into account.

Changed

  • Key debug statements while loading models to include both missing and unexpected keys.
  • Bumped PL to version 1.4. Holding back on 1.5 due to Tune integration issues.
  • Bumped Tune to version 1.8.

[0.4.6] - 2021-09-21

Fixed

  • Update profile to use model.call. This enable non-forward executions during profiling.
  • Add DefaultMethods Mixin with warm_up to make warm_up overloadable by Mixins.

[0.4.5] - 2021-09-08

Fixed

  • Fix warm_up function signature.
  • Requirement versions.

[0.4.4] - 2021-09-08

Added

  • warm_up function that is called prior to profil .

Fixed

  • Learning rate schedulers discounted steps.

[0.4.3] - 2021-06-03

Added

  • Logging of layers that are unfrozen.

Fixed

  • Cyclic learning rate schedulers now update on step.

[0.4.2] - 2021-06-02

Added

  • Added explicit logging of model profiling results.
  • Automatic assignment of hparams.num_gpus.

Fixed

  • Finetune weight loading checks.
  • Cyclic learning rate schedulers account for batch size.

[0.4.1] - 2021-05-27

Fixed

  • Feature extraction on GPU.

Added

  • Added explicit logging of hparams.

[0.4.0] - 2021-05-17

Fixed

  • Pass args correctly to trainer during testing.

Changed

  • CheckpointEveryNSteps now included in ModelCheckpoint c.f. pl==1.3.
  • Import from torchmetrics instead of pl.metrics .
  • Moved confusion matrix to RideClassificationDataset and updated plot.

Added

  • Feature extraction and visualisation.
  • Lifecycle and Finetuneable mixins always included via RideModule.
  • Support for pytorch-lightning==1.3.
  • Additional tests: Coverage is now at 92%.

Removed

  • Support for nested inheritance of RideModule.
  • Support for pytorch-lightning==1.2.

[0.3.2] - 2021-04-15

Fixed

  • Project dependencies: removed click and added psutil to requirements.
  • Logging: Save stdout and stderr to run.log.

Changed

  • Logged results names. Flattened folder structure and streamlines names.

Added

  • Docstrings to remaining core classes.
  • Tests that logged results exists.

[0.3.1] - 2021-03-24

Added

  • Add support for namedtuples in dataset input_shape and output_shape.
  • Add tests for test_enemble.
  • Expose more classes via from ride import XXX.
  • Fix import-error in hparamsearch.
  • Fix issues in metrics and add tests.
  • Remove unused cache module.

Change

  • Renamed Dataset to RideDataset.

[0.3.0] - 2021-03-24

Added

  • Documentation for getting started, the Ride API, and a general API reference.
  • Automatic import of SgdOptimizer.

Change

  • Renamed Dataset to RideDataset.

[0.2.0] - 2021-03-23

Added

  • Initial publicly available implementation of the library.