xiaojiuwo168 / enas-fernet

ENAS-FERNet

Code implementation of paper "Evolutionary neural architecture search for facial expression recognition " published on TETCI 2023.

Requirements

• Pytorch

Torch 1.1.0 or higher and torchvision 0.11.2 or higher are required.

(Example: torch == 1.10.1, torchvision == 0.11.2, numpy == 1.19.5)

How to run

Setp 1. Data Preparation

Download basic emotions dataset of RAF-DB, and make sure it have a structure like following:

- datasets/raf-basic/
         EmoLabel/
             list_patition_label.txt
	     new_10_noise.txt
	     new_20_noise.txt
         Image/aligned/
	     train_00001_aligned.jpg
             test_0001_aligned.jpg
             ...

Step 2. Then you need to change the path where the dataset is loaded to your dataset path. The changes can be found on lines 71 and 72 of the cifar10.py file in the template folder.

  trainloader = data_loader.get_train_loader('/home/dengshuchao/datasets/RafDb/raf-basic/',64,1,True,True)
  validloader = data_loader.get_valid_loader('/home/dengshuchao/datasets/RafDb/raf-basic/',64,1,False,True)

Step 3. Set hyperparameters in global.ini.

Ensure the following status before running：

[evolution_status]

is_running = 0

Step 4. Run python GA-FER-evolve.py or nohup python -u GA-FER-evolve.py > GA-FER-evolve.log 2>&1 &

If you have any questions, please feel free to raise "issues" for discussion.

Citing ENAS-FERNet

It would be greatly appreciated if the following paper can be cited when the code has helped your research.

@article{deng2023evolutionary,
  title={Evolutionary Neural Architecture Search for Facial Expression Recognition},
  author={Deng, Shuchao and Lv, Zeqiong and Galv{\'a}n, Edgar and Sun, Yanan},
  journal={IEEE Transactions on Emerging Topics in Computational Intelligence},
  year={2023},
  publisher={IEEE}
}