History

1.5.1 (2021-08-07)

• Added support for 2-dimensional inputs for AffineAdapterSigmoid
• Fixed CI issues

1.5.0 (2020-11-24)

• Added support for mixed-precision training using torch.cuda.amp (inputs fixed to float32 for now)
• Added support for PyTorch v1.7
• Dropped support for PyTorch < v1.0 and Python 2
• Removed the version limit for Pillow in the requirements

1.4.0 (2020-06-05)

• Added support for splitting on arbitrary dimensions to the Couplings. Big thanks to ClashLuke for the PR
• Added a preserve_rng_state option to the InvertibleModuleWrapper

1.3.2 (2020-03-05)

• Improved InvertibleModuleWrapper * Added support for multi input/output invertible operations! Big thanks to Christian Etmann for the PR
• Improved the is_invertible_module test * Added multi input/output checks * Fixed random seed per default * Additional warning checks have been added

1.3.1 (2020-03-02)

• HOTFIX InvertibleCheckpointFunction uses ref_count for inputs as well to avoid memory spikes

1.3.0 (2020-03-01)

• Updated underlying mechanics for the InvertibleModuleWrapper * Hooks have been replaced by a torch.autograd.Function called InvertibleCheckpointFunction * Identity functions are now supported
• Reported unstable memory behavior should be fixed now when using the InvertibleModuleWrapper!
• Minor changes to test suite

1.2.1 (2020-02-24)

• Added InvertibleModuleWrapper support to is_invertible_module test

1.2.0 (2020-01-19)

• Replaced TensorBoard logging with simple json file logging which removed the cumbersome TensorBoard and TensorFlow dependencies
• Updated the Dockerfile for Python37 and PyTorch 1.4.0
• Updated the CI tests Py36 versions to Py37, also added a new CI test for PyTorch 1.4.0

1.1.1 (2020-01-11)

• Fixed some versions in the requirements for TensorFlow and Pillow to avoid errors and segfaults
• The module auto documentation has been updated for the new API changes

1.1.0 (2019-12-15)

• A complete refactor of MemCNN with changes to the API
• Factored out the code responsible for the memory savings in a separate InvertibleModuleWrapper and reimplemented it using hooks
• The InvertibleModuleWrapper allows for arbitrary invertible functions now (not just the additive and affine couplings)
• The AdditiveBlock and AffineBlock have been refactored to AdditiveCoupling and AffineCoupling
• The ReveribleBlock is now deprecated
• The documentation and examples have been updated for the new API changes

1.0.1 (2019-12-08)

• Bug fixes related to SummaryIterator import in Tensorflow 2 (location of summary_iterator has changed in TensorFlow)
• Bug fixes related to NSamplesRandomSampler nsamples attribute (would crash if no-gpu and numpy.int were given)

1.0.0 (2019-07-28)

• Major release for completing the JOSS review:
• Anaconda cloud and codacy code quality CI
• Updated/improved documentation

0.3.5 (2019-07-28)

• Added CI for anaconda cloud
• Documented conda installation steps
• Minor test release for testing CI build

0.3.4 (2019-07-26)

• Performed changes recommended by JOSS reviewers:
• Added requirements.txt to manifest.in
• Added codacy code quality integration
• Improved documentation
• Setup proper github contribution templates

0.3.3 (2019-07-10)

• Added docker build triggers to CI
• Finalized JOSS paper.md

0.3.2 (2019-07-10)

• Added docker build shield
• Fixed a bug with device agnostic tensor generation for loss.py
• Code cleanup resnet.py
• Added examples to distribution with pytests
• Improved documentation

0.3.1 (2019-07-09)

• Added experiments.json and config.json.example data files to the distribution
• Fixed documentation issues with mock modules

0.3.0 (2019-07-09)

• Updated major bug in distribution setup.py
• Removed older releases due to bug
• Added the ReversibleBlock at the module level
• Splitted keep_input into keep_input and keep_input_inverse

0.2.1 (2019-06-06 - Removed)

• Patched the memory saving tests

0.2.0 (2019-05-28 - Removed)

• Minor update with better coverage and affine coupling support

0.1.0 (2019-05-24 - Removed)

• First release on PyPI