v0.0.1.dev13

Notable Changes:

• new Auto-Encoders:
  • Ae
  • TripletAe (Ae version of TripletVae)
  • AdaAe (Ae version of AdaVae)
  • AdaNegTripletAe (Ae version of AdaNegTripletVae)
• custom dataset MNIST example in the docs

Breaking Changes

• flattened disent.frameworks.vae and disent.frameworks.ae modules, unsupervised, weaklysupervised, and supervised submodules no longer exist.
• remove latent parameter classes from VAEs, VAEs now directly encode distributions with the encode_dists() function, this simplified a lot of other code.
• Datasets now only return 'x' in the observation dictionary if an augment is specified, ~5% performance boost
• some dependencies are optional, more work is still required to minimise dependencies
• Removed sample_random_traversal_factors, sample_random_cycle_factors from StateSpace and replaced with generic function sample_random_factor_traversal
• renamed all autoencoders AE to Ae

Other Changes:

• hdf5 dataset performance fix, now up to 5x faster when not loaded into memory
• all Auto-Encoders have new config options to disable the augment loss, recon loss, or detach the decoder so that no loss flows back through the encoder. VAEs can additionally have the regularisation loss disabled.
• new laplace latent distribution, can be specified in VAE configs.
• triplet loss helper functions
• flatness components metric helper functions for use elsewhere: compute_linear_score, compute_axis_score
• FftKernel augment module inheriting from torch.nn.Module, applies a channel-wise convolution to the input.
• to_standardised_tensor fix for non-PIL.Image.Image inputs
• more math helper functions:
  • torch_normalize normalise values along an axis between 0 and 1
  • torch_mean_generalized now supports the keepdim argument
• disent.visualise.visualise_module removed old redundant code adapted from disentanglement_lib
• disent.visualise.visualise_util additions
  • make_image_grid and make_animated_image_grid auto-detect border colour from input dtype
  • replaced cycle_factor with get_factor_traversal that accepts different modes: interval and cycle
• cleaned up experiments

++ many more additions and minor fixes ++