README

About this Project

This repository contains the code implementation of the model described in the manuscript "Physics-guided deep learning strategy for 2D structure reconstruction from diffraction patterns".
Read Our Manuscript

Installation

To set up the environment for this project, you can use one of the following methods:

Using Conda:

conda env create -f environment_requests.yaml
conda activate dd2d

Using Pip:

pip install -r requirements.txt

Usage

Pre-trained Models

Pre-trained models are stored in the SavedModels folder as .pt files, which include the models showcased in the manuscript.

python pred.py

Example Usage

Detailed usage examples for the model can be found in the example folder, specifically in the ablation.ipynb notebook. This notebook demonstrates step-by-step how to use the model for reconstruction tasks and perform ablation studies.

# create the model
mconf = TransformerConfig(train_dataset.block_size,
                  n_layer=n_layer, n_head=n_head, n_embd=embeddingSize)
model = DD2D(mconf)
# initialize a trainer instance and kick off training
tconf = TrainerConfig(max_epochs=numEpochs, batch_size=batchSize, 
                      learning_rate=4e-4,
                      lr_decay=True, warmup_tokens=512*20, 
                      final_tokens=2*len(train_dataset)*blockSize,
                      num_workers=0, ckpt_path=ckptPath)
trainer = Trainer(model, train_dataset, val_dataset, tconf, bestLoss, device=device)
print('The following model {} has been loaded!'.format(ckptPath))

checkpoint = torch.load(ckptPath)
model.load_state_dict(checkpoint)
model = model.eval().to(trainer.device)
loader = torch.utils.data.DataLoader(
    test_dataset,
    shuffle=False,
    pin_memory=True,
    batch_size=len(test_dataset),
    num_workers=0)
for i, (x, y) in enumerate(loader):
    topN = 1
    acc = sample_from_model(model, y, x, topN=topN)
    print('Top {} accuracy: {}'.format(topN, acc))

Training a Custom Model

If you wish to train your own model from scratch, you can use the train.py script. The training process is configurable through the TrainerConfig, which includes the following parameters:

• max_epochs: Number of training epochs.
• batch_size: Batch size.
• learning_rate: Set to 4e-4 by default.
• lr_decay: Enable learning rate decay.
• warmup_tokens: Set to 512*20.
• final_tokens: Set to 2 * len(train_dataset) * blockSize.
• num_workers: Number of data loader workers (set to 0 by default).
• ckpt_path: Path to save the checkpoint.

For detailed experiments on the model structure and ablation studies, refer to the example folder's ablation.ipynb notebook.

Acknowledgement

If you utilize the data or code from this repository, please reference our paper "Reconstructing the 2D Material Structure from Diffraction Pattern with Physical-Sensitive Deep Learning".