Deep and machine learning for atomresolved data
Project description
AtomAI
What is AtomAI
AtomAI is a Pytorchbased package for deep/machine learning analysis of microscopy data, which doesn't require any advanced knowledge of Python or machine learning. The intended audience is domain scientists with basic knowledge of how to use NumPy and Matplotlib.
Why AtomAI
The purpose of the AtomAI is to provide an environment that bridges the instrument specific libraries and general physical analysis by enabling the seamless deployment of machine learning algorithms including deep convolutional neural networks, invariant variational autoencoders, and decomposition/unmixing techniques for image and hyperspectral data analysis. Ultimately, it aims to combine the power and flexibility of the PyTorch deep learning framework and simplicity and intuitive nature of packages such as scikitlearn, with a focus on scientific data.
Scientific papers that used AtomAI:

Exploring order parameters and dynamic processes in disordered systems via variational autoencoders. Science Advances 7, eabd5084 (2021). doi:10.1126/sciadv.abd5084

Disentangling Rotational Dynamics and Ordering Transitions in a System of SelfOrganizing Protein Nanorods via Rotationally Invariant Latent Representations. ACS Nano (2021). doi:10.1021/acsnano.0c08914

Tracking atomic structure evolution during directed electron beam induced Siatom motion in graphene via deep machine learning. Nanotechnology 32, 035703 (2020). doi:10.1088/13616528/abb8a6
Chat with us about DL/ML in Microscopy (beta)
Have some specific questions about applications of deep/machine learning (DL/ML) in microscopy? Feel free to ask them on Gitter!
How to use AtomAI
Quickstart: AtomAI in the Cloud
The easiest way to start using AtomAI is via Google Colab:

Train a Deep Fully Convolutional Neural Network for Atom Finding

Defect sniffer: Deep Learning and Graph Analysis to Locate Specific Types of Defects

Variational Autoencoders: Exploring Order Parameters in Disordered Systems from Atomic Movies
Semantic segmentation
If your goal is to train and/or apply deep learning models for semantic segmentation of your experimental images, it is recommended to start with atomai.models.Segmentor
, which provides an easy way to train neural networks (with just two lines of code) and to make a prediction with trained models (with just one line of code). Here is an example of how one can train a neural network for atom/particle/defect finding with essentially two lines of code:
import atomai as aoi # Initialize model model = aoi.models.Segmentor(nb_classes=3) # uses UNet by default # Train model.fit(images, labels, images_test, labels_test, # training data (numpy arrays) training_cycles=300, compute_accuracy=True, swa=True # training parameters )
Here swa
stands for stochastic weight averaging, which usually allows improving the model's accuracy and leads to better generalization. The trained model can be used to find atoms/particles/defects in new, previously unseen (by a model) data:
nn_output, coordinates = model.predict(expdata)
ImSpec models
AtomAI also provides models that can be used for predicting spectra from image data and vice versa. These models can be used for predicting property (functionality) from structure. An example can be predicting approximate scanning tulleling spectroscopy or electron energy loss spectroscopy spectra from structural images of local sample regions (the assumption is of course that there is only a small variability of spectral behaviour within each (sub)image). The training/prediction routines are very similar to those of the semantic segmentation model, with the main difference being that one has to specify the dimensions of input and output data:
in_dim = (16, 16) # Input dimensions (image height and width) out_dim = (64,) # Output dimensions (spectra length) # Initialize and train model model = aoi.models.ImSpec(in_dim, out_dim, latent_dim=10) model.fit(imgs_train, spectra_train, imgs_test, spectra_test, # training data (numpy arrays) full_epoch=True, training_cycles=120, swa=True # training parameters )
Make a prediction with the trained ImSpec model by running
prediction = model.predict(imgs_val, norm=False)
Deep ensembles
One can also use AtomAI to train an ensemble of models instead of just a single model. The average ensemble prediction is usually more accurate and reliable than that of the single model. In addition, we also get the information about the uncertainty in our prediction for each pixel/point.
# Ititialize and compile ensemble trainer etrainer = aoi.trainers.EnsembleTrainer("Unet", nb_classes=3) etrainer.compile_ensemble_trainer(training_cycles=500, compute_accuracy=True, swa=True) # Train ensemble of 10 models starting every time with new randomly initialized weights smodel, ensemble = etrainer.train_ensemble_from_scratch( images, labels, images_test, labels_test, n_models=10)
The ensemble of models can be then used to make a prediction with uncertainty estimates for each point (e.g. each pixel in the image):
predictor = aoi.predictors.EnsemblePredictor(smodel, ensemble, nb_classes=3) nn_out_mean, nn_out_var = predictor.predict(expdata)
Variational autoencoders (VAE)
AtomAI has builtin variational autoencoders (VAEs) for finding in the unsupervised fashion the most effective reduced representation of system's local descriptors. The available VAEs are regular VAE, rotationally and/or translationally invariant VAE (rVAE), classconditined VAE/rVAE, and joint VAE/rVAE. The VAEs can be applied to both raw data and NN output, but typically work better with the latter. Here's a simple example:
# Get a stack of subimages from experimental data (e.g. a semantically segmented atomic movie) imstack, com, frames = aoi.utils.extract_subimages(nn_output, coords, window_size=32) # Intitialize rVAE model input_dim = (32, 32) rvae = aoi.models.rVAE(input_dim, latent_dim=2) # Train rvae.fit( imstack_train, rotation_prior=np.pi/3, training_cycles=100, batch_size=100) # Visualize the learned manifold rvae.manifold2d();
One can also use the trained VAE to view the data distribution in the latent space. In this example the first 3 latent variables are associated with rotations and xytranslations (they are automatically added in rVAE to whatever number of latent dimensions is specified), whereas the last 2 latent variables are associated with images content.
encoded_mean, encoded_sd = rvae.encode(imstack) z1, z2, z3 = encoded_mean[:,0], encoded_mean[:, 1:3], encoded_mean[:, 3:]
Custom models
Finally, it is possible to use AtomAI trainers and predictors for easy work with custom PyTorch models. Suppose we define a custom denoising autoencoder in Pytorch as
import torch from atomai.nets import ConvBlock, UpsampleBlock torch_encoder = torch.nn.Sequential( ConvBlock(ndim=2, nb_layers=1, input_channels=1, output_channels=8, batch_norm=False), torch.nn.MaxPool2d(2, 2), ConvBlock(2, 2, 8, 16, batch_norm=False), torch.nn.MaxPool2d(2, 2), ConvBlock(2, 2, 16, 32, batch_norm=False), torch.nn.MaxPool2d(2, 2), ConvBlock(2, 2, 32, 64, batch_norm=False) ) torch_decoder = torch.nn.Sequential( UpsampleBlock(ndim=2, input_channels=64, output_channels=64, mode="nearest"), ConvBlock(2, 2, 64, 32, batch_norm=False), UpsampleBlock(2, 32, 32, mode="nearest"), ConvBlock(2, 2, 32, 16, batch_norm=False), UpsampleBlock(2, 16, 16, mode="nearest"), ConvBlock(2, 1, 16, 8, batch_norm=False), torch.nn.Conv2d(8, 1, 1) ) torch_DAE = torch.nn.Sequential(torch_encoder, torch_decoder)
We can easily train this model using AtomAI's trainers:
# Initialize trainer and pass our model to it trainer = aoi.trainers.BaseTrainer() trainer.set_model(torch_DAE) # Fix the initialization parameters (for reproducibility) aoi.utils.set_train_rng(1) trainer._reset_weights() trainer._reset_training_history() # Compile trainer trainer.compile_trainer( (imgdata_noisy, imgdata, imgdata_noisy_test, imgdata_test), # training data loss="mse", training_cycles=500, swa=True # training parameters ) # Train trained_model = trainer.run()
The trained model can be used to make predictions on new data using AtomAI's predictors:
p = aoi.predictors.BasePredictor(trained_model, use_gpu=True) prediction = p.predict(imgdata_noisy_test)
Not just deep learning
The information extracted by deep neural networks can be further used for statistical analysis of raw and "decoded" data. For example, for a single atomresolved image of ferroelectric material, one can identify domains with different ferroic distortions:
# Get local descriptors imstack = aoi.stat.imlocal(nn_output, coords, window_size=32, coord_class=1) # Compute distortion "eigenvectors" with associated loading maps and plot results: pca_results = imstack.imblock_pca(n_components=4, plot_results=True)
For movies, one can extract trajectories of individual defects and calculate the transition probabilities between different classes:
# Get local descriptors (such as subimages centered around impurities) imstack = aoi.stat.imlocal(nn_output, coordinates, window_size=32, coord_class=1) # Calculate Gaussian mixture model (GMM) components components, imgs, coords = imstack.gmm(n_components=10, plot_results=True) # Calculate GMM components and transition probabilities for different trajectories transitions_dict = imstack.transition_matrix(n_components=10, rmax=10) # and more
Installation
First, install PyTorch. Then, install AtomAI via
pip install atomai
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