vit4elm 1.0.1

Vision Transformers for Exotic Lattice Models

Applications of Vision Transformers for Exotic Lattice Models

About

Applications of Vision Transformers to Exotic Lattice Models. This repository contains all of the finetuned models as well as core scripts to reproduce results and experiment on new data. The experimentation suite that this repository builds on top of can be found in this website. The goal of this repository is to provide an end-to-end experimentation and evaulation of Vision Transfomers on these types of systems.

Raising issues are encouraged so we know what features to prioritize. We want to inevitably work towards predicting regions of interest using masked patch prediction, so this is our top most priority on our road map.

Documentation

Install Requirements

Nothing new here. Just make sure to create a new Python environment and run the following:

pip install -r requirements.txt

Format Data from JSON to User Friendly images

To process the data, ensure you have a data.json from statmechsims. This json will contain all of your simulation data. Say you have the following directory:

af_ising/
└── data.json

Running the below command will add to your main experiment directory af_ising with the following structure:

python3 data_handle.py --json_path="af_ising/data.json" --n_bins=4 --data_dir"af_ising" --stratified_shuffle=True --test_size=0.4

Binning is set by the temperature value, all of equal length between the minimum and maximum temperature.

NOTE: USE THE CLI IF YOU WANT TO USE EQUIDISTANT TEMPERATURE BOUNDARIES. ALSO, PLEASE MAKE SURE THE DIRECTORY (af_ising IN THIS EXAMPLE) DOES NOT EXIST. IF THE DIRECTORY EXISTS, IT WILL BE REMOVED AND REMADE

If you would like to add custom intervals, simply run the following in a Jupyter notebook or Python script:

# Import DataProcessor class
from data_handle import DataProcessor
import numpy as np

# Instantiate necessary parameters.
json_path = 'af_ising/data.json'
data_dir = 'af_ising'

# Please ensure that the shape of custom_intervals is (n_bins, 2)
custom_intervals = np.array([
    [0, 2],
    [2, 3.33],    # Notice the funky boundary lengths
    [3.33, 3.55], 
    [3.55, 4]
])

# Instantiate DataProcessor
processor = DataProcessor(json_path, data_dir, custom_intervals=custom_intervals)

# Let the processor process!
processor.process()

Should you decide to use custom intervals, please ensure the min/max temperatures (0 and 4, in this example) line up with your simulation experiments.

The result of either the following CLI call or running in a notebook/script will lead to a directory with the following structure.

af_ising/
├── csvs
│   ├── data.csv
│   ├── test.csv
│   ├── train.csv
│   └── validation.csv
├── data
│   ├── bin0
│   ├── bin1
│   ├── bin2
│   └── bin3
├── data.json
└── experiments.json

The only difference between these is the boundaries that determine which image goes into which bin based off of the temperature. Again, this is assuming you want to bin into 4 bins.

Training the Model

To train on the pretrained ViT Base 32 model with default configurations for feature extraction, run the following command.

python3 trainer.py --data_dir="af_ising" --num_runs=1 --batch_size=16 --lr=2e-4 --eval=True

The necessary parameters you must input is data_dir.

The last 4 parameters (num_runs, batch_size, lr, eval) are optional. The default values for those are 1, 16, 2e-4, and True respectively. Make sure that the data_dir parameter from both data_handle and trainer CLI calls are the same and there should be no issue. Furthermore, I highly recommend you increase the num_runs to at least 5 to construct solid evaluation. The instantiated ViT weights are randomized after all.

Evaluation will take place on a per run basis. There currently does not exist a seperate script that does evaluation independently, so keep eval to be True until it is allowed seperately. During evaluation, a confusion matrix will be saved as a numpy array. Furthermore, pyplot will display the confusion matrix. So, if you want to save the image, you can.

The results of the following run will be in your data_dir:

datasets/af_ising
├── csvs
│   ├── data.csv
│   ├── test.csv
│   ├── train.csv
│   └── validation.csv
├── data
│   ├── bin0
│   ├── bin1
│   ├── bin2
│   └── bin3
├── data.json
├── experiments.json
├── models
│   └── model_run_0
└── training_results
    └── training_results0

To Do

Smaller Stuff:

• Better model loading
• Add cover image for repo

Bigger Stuff:

• Head wise visualizations
• Add masker to images