from __future__ import print_function
import os,sys,inspect
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(os.path.dirname(currentdir))
sys.path.insert(0,parentdir)
from utils.args import args
import setup.categories.ae_setup as AESetup
from models.autoencoders import *
from datasets.DRD import DRD
if __name__ == "__main__":
dataset = DRD(root_path=os.path.join(args.root_path, "diabetic-retinopathy-detection"), downsample=64)
model = ALILikeVAE(dims=(3, 64, 64))
AESetup.train_variational_autoencoder(args, model=model, dataset=dataset.get_D1_train(), BCE_Loss=True)
'knn/4',
'knn/8',
'vaemseaeknn/1',
'vaebceaeknn/1',
'mseaeknn/1',
'bceaeknn/1',
'vaemseaeknn/8',
'vaebceaeknn/8',
'mseaeknn/8',
'bceaeknn/8',
#'alivaemseaeknn/1', 'alivaebceaeknn/1', 'alimseaeknn/1', 'alibceaeknn/1',
#'alivaemseaeknn/8', 'alivaebceaeknn/8', 'alimseaeknn/8', 'alibceaeknn/8',
]
D1 = DRD(root_path=os.path.join(args.root_path,
'diabetic-retinopathy-detection'),
downsample=224)
D164 = DRD(root_path=os.path.join(args.root_path,
"diabetic-retinopathy-detection"),
downsample=64)
args.D1 = 'DRD'
All_ODs = [
'UniformNoise',
'NormalNoise',
'MNIST',
'FashionMNIST',
'NotMNIST',
'CIFAR100',
'CIFAR10',