CHANGELOG

robustness 1.2

• Biggest new features:

  • New ImageNet models
  • Mixed-precision training
  • OpenImages and Places365 datasets added

  • Ability to specify a custom accuracy function (custom loss functions

    were already supported, this is just for logging)

  • Improved resuming functionality

• Changes to CLI-based training:

  • --custom-lr-schedule replaced by --custom-lr-multiplier (same format)

  • --eps-fadein-epochs replaced by general --custom-eps-multiplier

    (now same format as custom-lr schedule)

  • --step-lr-gamma now available to change the size of learning rate

    drops (used to be fixed to 10x drops)

  • --lr-interpolation argument added (can choose between linear and step

    interpolation between learning rates in the schedule)

  • --weight_decay is now called --weight-decay, keeping with

    convention

  • --resume-optimizer is a 0/1 argument for whether to resume the

    optimizer and LR schedule, or just the model itself

  • --mixed-precision is a 0/1 argument for whether to use mixed-precision

    training or not (required PyTorch compiled with AMP support)

• Model and data loading:

  • DataParallel is now off by default when loading models, even when

    resume_path is specified (previously it was off for new models, and on for resumed models by default)

  • New add_custom_forward for make_and_restore_model (see docs for

    more details)

  • Can now pass a random seed for training data subsetting

• Training:

  • See new CLI features—most have training-as-a-library counterparts
  • Fixed a bug that did not resume the optimizer and schedule
  • Support for custom accuracy functions

  • Can now disable torch.nograd for test set eval (in case you have a

    custom accuracy function that needs gradients even on the val set)

• PGD:

  • Better random start for l2 attacks

  • Added a RandomStep attacker step (useful for large-noise training with

    varying noise over training)

  • Fixed bug in the with_image argument (minor)

• Model saving:

  • Accuracies are now saved in the checkpoint files themselves (instead of

    just in the log stores)

  • Removed redundant checkpoints table from the log store, as it is a

    duplicate of the latest checkpoint file and just wastes space

• Cleanup:

  • Remove redundant save_checkpoint function in helpers file
  • Code flow improvements

robustness 1.1.post2

• Critical fix in robustness.loaders.TransformedLoader(), allow for data shuffling

robustness 1.1

• Added ability to superclass ImageNet to make custom datasets (docs)
• Added shuffle_train and shuffle_test options to make_loaders()
• Added support for cyclic learning rate (--custom-schedule cyclic via command line or {"custom_schedule": "cyclic"} from Python
• Added support for transfer learning/partial parameter updates, robustness.train.train_model() now takes update_params argument, list of parameters to update
• Allow random_start (random start for adversarial attacks) to be set via command line
• Change defaults for ImageNet training (200 epochs instead of 350)
• Small fixes/refinements to robustness.tools.vis_tools module