Release Version 0.3.1 · pytorchbearer/torchbearer

[0.3.1] - 2019-05-24

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

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

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