Implementation of Auto Encoding Transformers for Art Style Recognition

python3 main.py --mode=0 -F=tmp_data/cifar --choose=0 --lr=0.002 --lr1=0.1 --batch_size=128 --num_workers=4 --type=0 --KL_Lambda=1.0 --lambda=10.0 --lambda1=7.5 --lambda2=5.0 --lambda3=2.0 --lambda4=0.5 --max_lambda=1 --max_lambda1=0.75 --max_lambda2=0.5 --max_lambda3=0.2 --max_lambda4=0.05 --portion=0.005 --beta=75 --mix_mode=1 --Mixmatch_warm=50 --dataset=cifar10

!cp "/content/drive/My Drive/Dataset/train.pickle" "train.pickle" !cp "/content/drive/My Drive/Dataset/test.pickle" "test.pickle"

python3 main.py -h --mode default:0, default mode to run -F training data path(Automatically download to this path) --choose use gpu id --lr default:0.002 learning rate for Adam optimizer for main backbone network --lr1 default:0.1 learning rate for SGD optimizer for AET regularization network --batch_size default:128 (Actually 256 is better, but one gpu can't support) --num_workers default:16 number of data loading workers for pytorch dataloader --type default:0 0:Wide ResNet-28-2, 1:Wide ResNet-28-2-Large --KL_Lambda default:1.0 hyper parameter for KL divergence to control consistency in the framework --lambda: warm factor for projective transformation AET regularization --lambda1: warm factor for affine transformation AET regularization --lambda2: warm factor for similarity transformation AET regularization --lambda3: warm factor for euclidean transformation AET regularization --lambda4: warm factor for CCBS transformation AET regularization --max_lambda: hyper-parameter for projective transformation in AET regularization. --max_lambda1: hyper-parameter for affine transformation in AET regularization. --max_lambda2: hyper-parameter for similarity transformation in AET regularization. --max_lambda3: hyper-parameter for eculidean transformation in AET regularization. --max_lambda4: hyper-parameter for CCBS transformation in AET regularization. --portion: specify the portion of data used as labeled data --beta: hyper parameter for the consistency loss in MixMatch part --mix_mode: default:1 specify to use Mosaic augmentation in MixMatch or not --Mixmatch_warm: warm factor for MixMatch beta hyper parameter --dataset: specify the dataset you will use for training