Version 0.3.1

[0.3.1] - 2019-05-24

Added

• Added cyclic learning rate finder
• Added on_init callback hook to run at the end of trial init
• Added callbacks for weight initialisation in torchbearer.callbacks.init
• Added with_closure trial method that allows running of custom closures
• Added base_closure function to bases that allows creation of standard training loop closures
• Added ImagingCallback class for callbacks which produce images that can be sent to tensorboard, visdom or a file
• Added CachingImagingCallback and MakeGrid callback to make a grid of images
• Added the option to give the only_if callback decorator a function of self and state rather than just state
• Added Layer-sequential unit-variance (LSUV) initialization
• Added ClassAppearanceModel callback and example page for visualising CNNs
• Added on_checkpoint callback decorator
• Added support for PyTorch 1.1.0

Changed

• No_grad and enable_grad decorators are now also context managers

Deprecated

Removed

• Removed the fluent decorator, just use return self
• Removed install dependency on torchvision, still required for some functionality

Fixed

• Fixed bug where replay errored when train or val steps were None
• Fixed a bug where mock optimser wouldn't call it's closure
• Fixed a bug where the notebook check raised ModuleNotFoundError when IPython not installed
• Fixed a memory leak with metrics that causes issues with very long epochs
• Fixed a bug with the once and once_per_epoch decorators
• Fixed a bug where the test criterion wouldn't accept a function of state
• Fixed a bug where type inference would not work correctly when chaining Trial methods
• Fixed a bug where checkpointers would error when they couldn't find the old checkpoint to overwrite
• Fixed a bug where the 'test' label would sometimes not populate correctly in the default accuracy metric