Installation

This section provides a tutorial on building a working environment for LibFewShot from scratch.

Get the LibFewShot library

Use the following command to get LibFewShot:

cd ~
git clone https://github.com/RL-VIG/LibFewShot.git

Configure the LibFewShot environment

The environment can be configured in any of the following ways:

  1. conda(recommend)

    cd <path-to-LibFewShot> # cd in `LibFewShot` directory
    conda env create -f requirements.yaml
    
  2. pip

    cd <path-to-LibFewShot> # cd in `LibFewShot` directory
    pip install -r requirements.txt
    
  3. or whatever works for you as long as the following package version conditions are meet:

    numpy >= 1.19.5
    pandas >= 1.1.5
    Pillow >= 8.1.2
    PyYAML >= 5.4.1
    scikit-learn >= 0.24.1
    scipy >= 1.5.4
    tensorboard >= 2.4.1
    torch >= 1.5.0
    torchvision >= 0.6.0
    python >= 3.6.0
    

Test the installation

  1. modify run_trainer.py:10

    # -*- coding: utf-8 -*-
    import sys
    
    sys.dont_write_bytecode = True
    
    from core.config import Config
    from core import Trainer
    
    if __name__ == "__main__":
        config = Config("./config/test_install.yaml").get_config_dict()
        trainer = Trainer(config)
        trainer.train_loop()
    
  2. modify data_root in config/headers/data.yaml to the path of the dataset to be used.

  3. run code

    python run_trainer.py
    
  4. If the first output is correct, it means that LibFewShot has been successfully installed.

Next

For model training and code modification, please see the train/test methods already integrated in LibFewShot and other sections of the tutorial.