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',