The code is released for research purposes only and not for commercial purposes.
Before getting started, it's important to have a working environment with all dependencies satisfied. For this, we recommend using the Anaconda distribution of Python 3.5.
cd /tmp
curl -O https://repo.anaconda.com/archive/Anaconda3-2019.03-Linux-x86_64.sh
bash Anaconda3-2019.03-Linux-x86_64.sh
So PyTorch must be installed, please make sure that cuDNN is installed correctly (https://developer.nvidia.com/cudnn).
conda install pytorch torchvision cudatoolkit=9.2 -c pytorch
Then install the following libraries:
pip install torchvision
pip install opencv-python
pip install matplotlib
pip install tqdm
pip install textwrap3
pip install python-gflags
pip install text-to-image
Go to ./get_dataset folder then open the main file
Uncomment what you want to generate:
if __name__ == "__main__":
# Generation dataset
# getDataset()
# Generation HR1 images
# getHR1Domain_target()
# generation SATPdb peptides images
# getPep()
Then remember to change the paths within each function, or download the data (dataset-nConV-2019 and pepdata of SATPdb) from:
https://drive.google.com/open?id=1buUylzkMAM91Qs7z-ndvUKlvr4wbaiSq
After the unzip of data.zip you will find two folders:
i) ./data/Dataset-nConV-2019: the dataset that I used to train all the models ii) ./data/pepdata: The SATPdb peptide for the inference
Open the file train.py
Change the paths where you have located the dataset:
gflags.DEFINE_string ("train_path", "path-to-dataset/Dataset-nConV-2019/train", "training folder to be set")
gflags.DEFINE_string ("test_path", "path-to-dataset/Dataset-nConV-2019/test", "path of testing folder to be set")
gflags.DEFINE_string ("valid_path", "path-to-dataset/Dataset-nConV-2019/valid", "path of testing folder to be set")
gflags.DEFINE_string ("save_folder", "path-to-save-results", 'path of testing folder to be set!')
Single GPU:
gflags.DEFINE_string ("gpu_ids", "0", "gpu ids used to train")
Multi-GPU:
gflags.DEFINE_string ("gpu_ids", "0,1", "gpu ids used to train")
Number of CPU threads setting:
gflags.DEFINE_integer("workers", 8, "number of dataLoader workers")
For running the training:
sh run_train.sh
https://drive.google.com/open?id=18Zu05OagMmaHfQoRJ1_vgM0zwfWwM0Yy
Go to the folder ./inference and open the file inference.py and change the following paths:
model_path = "path-to-model/AlexNet_pretrain.pt"
train_path = "path-to-datset/train"
test_path = "path-to-datset/test"
valid_path = "path-to-datset/valid"
path_pep = "path-to-pep/pepdata"
path_hr1 = "./virus_genome/hr1"
then:
sh run_inference.sh
The pepdata folder is all 3027 SATPdb peptides (./data/pepdata)
- ** Nicolò Savioli, PhD **
Please if you find this code useful for all your research activities, cite it.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This license applies to all published OONI data.
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