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)
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)