A deep learning model for improving the image resolution of ultra-high-density arrays in DNBSEQ.
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Python 3.11.3
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Python venv
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Open a command window
#create
python -m venv DNBSRNenv
#activate
cd DNBSRNenv/Scripts && activate
#deactivate when not in use (optional)
deactivate
#Download DNBSRN from github
git clone https://github.com/BGIResearch/DNBSRN.git
#Go to the code folder
cd DNBSRN
pip install -r requirements.txt
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117
- Download training data from https://ftp.cngb.org/pub/CNSA/data2/CNP0005204/CNS0969113/train_image.zip, unzip and place them in 'DNBSRN/train_image'.
- Download test data from https://ftp.cngb.org/pub/CNSA/data2/CNP0005204/CNS0969113/test_image.zip, unzip and place them in 'DNBSRN/test_image'.
To train, test, and analyze the efficiency metrics of different networks, execute 'scripts/run.py'.
options:
- -B Execution of network training
- -C Execution of network testing
- -E Analyze the efficiency metrics
- -m Select which network to train, test, and analyze, choose from {DNBSRN, IMDN, RFDN, RLFN, EDSR, RDN, RCAN, DNBSRN_kernel_size_3, DNBSRN_kernel_size_5, DNBSRN_kernel_size_9, DNBSRN_delete_IIC, DNBSRN_delete_SRB}
- -t Select which dataset to test, choose from {dataset1, dataset2, dataset3, dataset4, dataset5, dataset6, dataset7, dataset8}
- -g Choose which gpu to use, default=0
- -p Whether to perform HM preprocessing when testing, choose from {true, false}, default=true
- -s Whether to save intermediate results of HM preprocessing, choose from {true, false}, default=false
More parameters can be modified in 'scripts/parameter.py'.
Specifically, to train, test, and analyze the efficiency metrics of DNBSRN
#train
python scripts/run.py -B -m DNBSRN
#test
python scripts/run.py -C -m DNBSRN -t dataset1 dataset2 dataset3 dataset4 dataset5 dataset6 dataset7 dataset8
#analyze the efficiency metrics
python scripts/run.py -E -m DNBSRN
The 'result' folder contains our trained models.