v3.2.4

Additions

• Added ConvDepth for depthwise convolution.
• Added Activation for activation functions.
• Added DropPath to drop samples.
• Added LayerNorm for layer normalisation.
• Added Scale for learnable scaling.
• Added SplineFlow for neural spline flow.
• Added optimiser and scheduler to BaseNetwork.
• Added module logger.
• Added ConvNeXt.
• Added kl_loss to Autoencoder.
• Added groups parameter to Conv layers.
• Added json net parameters check.
• Added names to sub-layers.
• Added invertible data transforms.
• Added support for epoch and loss schedulers.
• Added latent saving to Autoencoder predict.
• Added uncertainty propagation to transforms.
• Added in_transform attribute to BaseNetwork.
• Added progress to predict if verbose is full.
• Added input saving to Autoencoder predict.
• Added loss function attributes to Autoencoder and Decoder.
• Added samples transformation to NormFlow.
• Added transformation to max and meds in NormFlowEncoder.

Changes

• Changed BaseNetwork to accept any PyTorch Module.
• Changed NormFlowEncoder to require networks with SplineFlow as last layer rather than two networks.
• Changed Conv layers batch_norm to norm to support LayerNorm.
• Changed default dropout to 0.
• Changed Conv layers default padding to 0.
• Changed inceptionv4.json to use checkpoints.
• Changed setup.py to use version number from init.py.
• Changed BaseNetwork data transformation to accept transformation per output.
• Changed BaseNetwork header attribute to public.
• Changed get_device arguments to use 4 workers and persistent workers.

Removals

• Removed optimiser and scheduler from Network.
• Removed second optimiser and scheduler from NormFlowEncoder.
• Removed kl_loss_weight from Network.
• Removed kernel size check.
• Removed Pandas requirement.

Fixes

• Fixed Encoder batch_predict squeezing dimension.
• Fixed optimiser not changing device.
• Fixed Composite defaults overwriting network defaults rather than merging.