Kaiqiang Wang and Edmund Y. Lam, "Harnessing Data and Physics for Deep Learning Phase Recovery", [Preprint].
- python == 3.8.18
- pytorch == 2.0.0
- matplotlib == 3.7.2
- numpy == 1.24.3
- opencv_python == 4.8.1.78
- scipy == 1.12.0
- scikit-image == 0.21.0
- tqdm == 4.65.0
The following software is recommended:
- CUDA == 11.7
- cuDNN == 8.5.0
DLPR
├── dataset_generation.py
├── train.py
├── one_infer.py
├── multiple_infer.py
├── functions
│ ├── data_read.py
│ ├── network.py
│ ├── propagation.py
│ └── train_func.py
├── datasets
│ ├── raw_images
│ │ ├── ...
│ ├── train_in
│ │ ├── ...
│ ├── train_gt
│ │ ├── ...
│ ├── test_in
│ │ ├── ...
│ └── test_gt
│ ├── ...
├── models_and_results
│ ├── model_weights
│ │ ├── ...
│ ├── results_DD_tPD_CD
│ │ ├── ...
│ └── results_uPD_tPDr
└── ├── ...
| Folders or files | Use |
|---|---|
dataset_generation.py |
generate paired hologram-phase datasets |
train.py |
train the neural network in the strategies of DD, tPD, and CD |
one_infer.py |
infer the neural network in the strategies of DD, tPD, or CD |
multiple_infer.py |
infer the neural network in the strategies of uPD or tPDr |
/functions/ |
contains functions to be called |
/datasets/raw_images/ |
contains raw images used for dataset generation |
/datasets/train_in/ |
contains the generated holograms for network training |
/datasets/train_gt/ |
contains the generated phases for network training |
/datasets/test_in/ |
contains the generated holograms for network inference |
/datasets/test_gt/ |
contains the generated phases for network inference |
/models_and_results/model_weights/ |
contains the trained network weights and training process |
/models_and_results/results_DD_tPD_CD/ |
contains the inference results of DD, tPD, and CD. |
/models_and_results/results_uPD_tPDr/ |
contains the inference results of uPD and tPDr |
- Download image datasets such as ImageNet, CIFAR, COCO, LFW, and MNIST. Alternatively, randimage can be used to generate random unnatural images.
- Put the downloaded or generated images in the folder
/datasets/raw_images/. - Run
dataset_generation.pyto generate paired hologram-phase datasets.
python dataset_generation.py- Then, hologram-phase datasets are generated in the folders
/datasets/train_in/,/datasets/train_gt/,/datasets/test_in/, and/datasets/test_gt/.
- Run
train.pyto train the neural network in the strategy of dataset-driven(DD), trained physics-driven (tPD), or co-driven (CD).
python train.py -s 'DD'python train.py -s 'tPD'python train.py -s 'CD'- Then,
.pth,.csv, and.pngare saved in the folder/models_and_results/model_weights/.
- Run
one_infer.py.
python one_infer.py -s 'DD'python one_infer.py -s 'tPD'python one_infer.py -s 'CD'- Then, inference results are saved in the folder
/models_and_results/results_DD_tPD_CD/.
Step 3.2: inference for the strategies of untrained physics-driven (uPD) and trained physics-driven with refinement (tPDr).
- Run
multiple_infer.py.
python multiple_infer.py -s 'uPD'Note that python multiple_infer.py -s 'uPD' can be run before Step 2.
python multiple_infer.py -s 'tPDr'- Then, inference results are saved in the folder
/models_and_results/results_uPD_tPDr/.
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