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.PADChest import PADChestBinaryTrainSplit
if __name__ == "__main__":
dataset = PADChestBinaryTrainSplit(root_path=os.path.join(args.root_path, "PADChest"), binary=True, expand_channels=False, downsample=64)
model = Generic_VAE(dims=(1, 64, 64), max_channels=512, depth=12, n_hidden=512)
#model = ALILikeAE(dims=(1, 64, 64))
AESetup.train_variational_autoencoder(args, model=model, dataset=dataset.get_D1_train(), BCE_Loss=False)
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.PADChest import PADChestBinaryTrainSplit
if __name__ == "__main__":
dataset = PADChestBinaryTrainSplit(root_path=os.path.join(
args.root_path, "PADChest"),
binary=True,
expand_channels=False,
downsample=64)
model = Generic_AE(dims=(1, 64, 64),
max_channels=512,
depth=12,
n_hidden=512)
#model = ALILikeAE(dims=(1, 64, 64))
AESetup.train_autoencoder(args,
model=model,
dataset=dataset.get_D1_train(),
BCE_Loss=True)
'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 = PADChestBinaryTrainSplit(root_path=os.path.join(
args.root_path, 'PADChest'),
binary=True)
D164 = PADChestBinaryTrainSplit(root_path=os.path.join(
args.root_path, "PADChest"),
binary=True,
downsample=64)
args.D1 = 'PADChest'
All_ODs = [
'UniformNoise',
'NormalNoise',
'MNIST',
'FashionMNIST',
'NotMNIST',
'CIFAR100',
'CIFAR10',