The following instructions are for UBUNTU-based systems. Start by creating a python environment and install the dependencies. Recomended is to use the following:
user@user:~/current_directory$ cd astrachallege/Code
user@user:~/astrachallnege/Code$ conda create -n env-name python=3.10
user@user:~/astrachallnege/Code$ conda activate env-name
user@user:~/astrachallnege/Code$ pip install -r requirements.txt --no-cache
WARNING! The environment for training and testing was created with CUDA 12.1 installed. If your systems uses different versions of CUDA, please update --extra-index-url in requirements.txt accordingly to your CUDA version. The lists of index-urls for Pytorch with given CUDA version can be found here: https://pytorch.org/get-started/previous-versions/ .
user@user:~/astrachallnege/Code$ python tumorigenesis.py --help
Automatic detection of tumorigenic ares
[-h] {compute,train} ...
positional arguments:
{compute,train}
compute Use this argument to segment tumorigenic areas with a trained AI model
train Use this argument to train an AI model to detect tumorigenic areas in MRI(s)
options:
-h, --help show this help message and exit
In a bash terminal, in the correct directory type the following to get information about the training mode:
user@user:~/astrachallnege/Code$ python tumorigenesis.py train --help
usage: Automatic detection of tumorigenic areas train [-h] [--configuration CONFIGURATION] [--mode MODE]
options:
-h, --help show this help message and exit
--configuration CONFIGURATION
Provide the path of the 'config.yaml' file with the training specifications
In the configuration file, you will find information about the parameters to train a new model from scratch. Have in mind that is better to have access to GPUs, otherwise the training will take significantly longer to converge. If the config.yaml file is located in the same directory as the rest of the code, to train a new model it is enough to type the following command in the terminal. Note that by doing this, you will use the same arguments as the submitted model.
user@user:~/astrachallnege/Code$ python tumorigenesis.py train
In a bash terminal, in the correct directory type the following to get information about the computing mode:
user@user:~/astrachallnege/Code$ python tumorigenesis.py compute --help
usage: Automatic detection of tumorigenic ares compute [-h] [--threshold THRESHOLD] [--device {cpu,cuda}] [--mode MODE] test_folder model_path model_name to_save
positional arguments:
test_folder Provide the directory storing the MRI(s)
model_path Provide the direcory storing the trained model
model_name Provide aproxy for the name of the model file
to_save Indicate the directory to save the predicted tumorigenic regions
options:
-h, --help show this help message and exit
--threshold THRESHOLD
Indicate the threshold to binarize the probability maps
--device {cpu,cuda} Run inference on CPU or GPU
python tumorigenesis.py compute testing-data-directory trained-model-location UNET results-directory