Python PADChestBinaryTrainSplit.get_D1_train Exemples

Exemple #1

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)

Exemple #2

Fichier : PADTrainVAEMSE.py Projet : caotians1/OD-test-master

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)