Atomai

Breaking changes
- out: *atomnet, atomstat*
- in: *models, trainers, predictors*

To install and use the old code run pip install git+https://github.com/ziatdinovmax/atomailegacy --upgrade

The new version provides an easy, Keras-like interface for training and applying models for semantic segmentation, image-to-spectrum conversion, as well as different forms of variational autoencoders. For example, to train a model for semantic segmentation of data, (for atom/defect finding) simply run:
python
model = Segmentor()
model.fit(X, y, X_test, y_test, training_cycles=300)

To make a prediction with a trained model, run:
python
output, coords = model.predict(expdata)

See the updated [documentation](https://atomai.readthedocs.io/en/latest/?badge=latest#) for more details.

New functionalities:
- ImSpec models for converting 2D images to 1D spectra and vice versa
- Graph analysis for identifying topologcial defects in the lattices
- Class-conditioned VAE and rVAE

Imrovements:
- AtomAI's trainers and predictors can now work with custom Pytorch models

New functionalities:
- Optional time-dependent weight perturbation w <- w + N(0, scale(t)) during NN model/ensemble training
- Vanilla Pytorch NN training loop (the customized on-the-fly data augmentation is not available in this mode)
- Basic utilities for working with graphs including:
- Construction of graph from NN output
- First-depth search for analyzing lattice topology
- Using connected/disconnected subgraphs to clean the NN predictions
- Plotting graphs

Bug fixes/improvements:
- fix bug that prevented NN training in a 'full_epoch' mode for multiclass case
- automatically load VAE's weights on cpu when cuda is not found
- return subimages together with VAE-encoded trajectories
- option to pass a custom latent variable range when plotting the VAE's manifold2d