v0.2.1

Under the hood, quite a lot of code has been added or changed for this release, however the API remains very much the same.

Additions

• Wrapped datasets, instances of disent.dataset.wrapper.WrappedDataset are datasets that have some sort of mask applied to them that hides the true state space and resizes the dataset.
  • disent.dataset.wrapper.DitheredDataset applies an n-dimensional dithering operation to ground truth factors
  • disent.dataset.wrapper.MaskedDataset applies some provided boolean mask over the dataset
• disent.dataset.DisentDataset now supports wrapped datasets (instances of disent.dataset.wrapper.WrappedDataset). New methods and properties have been added to compliment this feature:
  • is_wrapped_data check if there is wrapped data
  • is_wrapped_gt_data check if there is wrapped data and the wrapped data is ground truth data
  • wrapped_data obtain the wrapped data, otherwise throw an error
  • wrapped_gt_data obtain the wrapped ground truth data, otherwise throw an error
  • unwrapped_disent_dataset creates a copy of the disent dataset with everything the same, except the data is unwrapped.
• disent.util.lightning.callbacks additions
  • Support for wrapped datasets. They automatically try to unwrap them to obtain the ground truth data which can be used to compute metrics and perform visualisations.
  • Support model output scaling to a certain range of values, fixing visualisations when using VaeLatentCycleLoggingCallback
• new utilities
  • disent.util.math.dither
  • disent.util.math.random
• Self contained HDF5 ground-truth datasets. These store all the information needed to construct the dataset and state space in one file, including the factor names.
  • Added disent.dataset.data.SelfContainedHdf5GroundTruthData to read these files
  • Added disent.dataset.util.H5Builder for creating these files. (API is not yet finalised)
• disent.dataset.util.StateSpace added helper function iter_traversal_indices
• disent.nn.transform added ToUint8Tensor which acts like ToStandardisedTensor, but instead of loading images as float32, it loads them as uint8. This is useful when you need to use datasets outside of a ML Model context, eg. performing analysis. This takes up less memory.
  • corresponding functional version exists to_uint_tensor complimenting to_standardised_tensor
• Begun work on a component & function registry, although do not use this as the API will change significantly.

API Breakages

• Under the hood, implementing wrapped data and DisentDataset copying requires the ability to copy samplers, so each sampler implementation should have the uninit_copy method implemented too.
• ArrayGroundTruthData is more strict about the observation_shape must be (H, W, C) or (C, H, W) depending on array_chn_is_last
• Removed reconstruction losses:
  • ReconLossHandlerMse4 aka. "mse4"
  • ReconLossHandlerMae2 aka. "mae2"
• Renamed disent.util.visualize.get_factor_traversal to get_idx_traversal

Deprecations

• GroundTruthData property aliases:
  • img_shape new property for the deprecated observation_shape
  • obs_shape new property for the deprecated x_shape
  • img_channels new property for the number of channels in the image

Fixes

• disent.util.inout.files.AtomicSaveFile minor fix to overwriting files
• disent.util.lightning.callbacks.LoggerProgressCallback fix to datatypes and potential crashes due to floats
• More stable experiment runs when performing sweeps. Better error handling, error messages and error catching.
• fixes to the various requirement*.txt files
• many other minor fixes