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import torch
from models.networks.base_network import BaseNetwork
from models.networks.loss import *
from models.networks.discriminator import *
from models.networks.generator import *
from models.networks.encoder import *
import util.util as util
def find_network_using_name(target_network_name, filename):
target_class_name = target_network_name + filename
module_name = 'models.networks.' + filename
network = util.find_class_in_module(target_class_name, module_name)
assert issubclass(network, BaseNetwork), \
"Class %s should be a subclass of BaseNetwork" % network
return network
def modify_commandline_options(parser, is_train):
opt, _ = parser.parse_known_args()
netG_cls = find_network_using_name(opt.netG, 'generator')
parser = netG_cls.modify_commandline_options(parser, is_train)
if is_train:
netD_cls = find_network_using_name(opt.netD, 'discriminator')
parser = netD_cls.modify_commandline_options(parser, is_train)
netE_cls = find_network_using_name('conv', 'encoder')
parser = netE_cls.modify_commandline_options(parser, is_train)
return parser
def create_network(cls, opt):
net = cls(opt)
net.print_network()
if len(opt.gpu_ids) > 0:
assert(torch.cuda.is_available())
net.cuda()
net.init_weights(opt.init_type, opt.init_variance)
return net
def define_G(opt):
netG_cls = find_network_using_name(opt.netG, 'generator')
return create_network(netG_cls, opt)
def define_D(opt):
netD_cls = find_network_using_name(opt.netD, 'discriminator')
return create_network(netD_cls, opt)
def define_E(opt):
# there exists only one encoder type
netE_cls = find_network_using_name('conv', 'encoder')
return create_network(netE_cls, opt)
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