robustness 1.2.1.post2

• Support for SqueezeNet architectures
• Fix incompatibility with PyTorch 1.7 (#83)
• Allow user to specify only some device ids for training through the dp_device_ids argument to train.train_model
• Update requirements.txt

robustness 1.2.1.post1

Small fixes in BREEDS dataset

robustness 1.2.1

Add BREEDS dataset, minor bug fixes

robustness 1.2-post1

• Restore ImageNetHierarchy class
• Improve type checking for dataset arguments

robustness v1.2

• 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

v1.1

Release stuff

v1.0-post1

Update README.rst