gedankennet
Codes and demo for the paper Self-supervised Learning of Hologram Reconstruction using Physics Consistency
Science Score: 36.0%
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○CITATION.cff file
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✓codemeta.json file
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✓.zenodo.json file
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○DOI references
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✓Academic publication links
Links to: nature.com -
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (9.4%) to scientific vocabulary
Repository
Codes and demo for the paper Self-supervised Learning of Hologram Reconstruction using Physics Consistency
Basic Info
- Host: GitHub
- Owner: PORPHURA
- Language: Python
- Default Branch: main
- Size: 20.8 MB
Statistics
- Stars: 45
- Watchers: 1
- Forks: 10
- Open Issues: 0
- Releases: 1
Metadata Files
README.md
Self-supervised learning of hologram reconstruction using physics consistency
Luzhe Huang, Hanlong Chen, Tairan Liu, Aydogan Ozcan, Self-supervised learning of hologram reconstruction using physics consistency. Nat Mach Intell 5, 895–907 (2023).
Environment requirements
The codes was tested on Windows 11 and Linux, with Python and PyTorch. Packages required to reproduce the results can be found in requirements.txt. The following software / hardware is tested and recommended:
- Python >= 3.9
- CUDA 11.2
- cuDNN >= 8.0
- Intel i9-12900F
- Nvidia RTX 3090
- RAM >= 64 GB
File structure
This repository contains codes for GedankenNet and GedankenNet-Phase, and demo models and data for each network.
GedankenNet
| README.md
| requirements.txt
|
|---GedankenNet
| | generate_random_image_parallel.py
| | train_Gedanken.py
| | test.py
| | ...
|
|---GedankenNet_Phase
| | train_GedankenP.py
| | testP.py
| | ...
|
|---Models
|
|---demo_data
| | stained_tissue
| | unstained_tissue
/GedankenNet and /GedankenNet_Phase contain the codes to train and test the two models.
/Models include two demo models for GedankenNet and GedankenNet-Phase respectively.
/demo_data/stained_tissue include two demo FOVs of lung and Pap smears (complex object), corresponding to the results of /GedankenNet.
/demo_data/unstained_tissue include two demo FOVs of kidney tissue (phase-only object), corresponding to the results of /GedankenNet_Phase.
Test
To test the two demo models (download via Google Drive) and reproduce some results shown in the paper, following these steps:
- Download checkpoints in /Models and mat files in /demo_data
- Change TEST_PATH in test.py or testP.py to the path of corresponding mat files
- Run test.py or testP.py. The outputs will be saved in outputs folders
Train
To train GedankenNet and GedankenNet-Phase models from scratch, follow these steps:
- Run /GedankenNet/generate_random_image_parallel.py to generate 100K artificial images, and then split these images into train, valid and test sets
- Change TRAIN_PATH and VALID_PATH to the locations of the generated artificial image datasets in train_Gedanken.py or train_GedankenP.py
- Run train_Gedanken.py or train_GedankenP.py, checkpoints will be saved in Models folders
Alternatively, we have uploaded one artificial image dataset with 99.9K training and 100 testing / validation images. Download from Google Drive.
Owner
- Name: Luzhe Huang
- Login: PORPHURA
- Kind: user
- Location: China
- Website: porphura.github.io
- Repositories: 1
- Profile: https://github.com/PORPHURA
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