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Latest version: v1.2.1.post1

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1.2.1.post1

Small fixes in BREEDS dataset

1.2.1

Add BREEDS dataset, minor bug fixes

1.2post1

- Restore ImageNetHierarchy class
- Improve type checking for dataset arguments

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

1.1

1.0post1

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