def __init__(self, configer):
super(CycleGAN, self).__init__()
# load/define networks
# The naming conversion is different from those used in the paper
# Code (paper): G_A (G), G_B (F), D_A (D_Y), D_B (D_X)
self.configer = configer
self.netG_A = SubNetSelector.generator(
net_dict=self.configer.get('network', 'generatorA'),
use_dropout=self.configer.get('network', 'use_dropout'),
norm_type=self.configer.get('network', 'norm_type'))
self.netG_B = SubNetSelector.generator(
net_dict=self.configer.get('network', 'generatorB'),
use_dropout=self.configer.get('network', 'use_dropout'),
norm_type=self.configer.get('network', 'norm_type'))
self.netD_A = SubNetSelector.discriminator(
net_dict=self.configer.get('network', 'discriminatorA'),
norm_type=self.configer.get('network', 'norm_type'))
self.netD_B = SubNetSelector.discriminator(
net_dict=self.configer.get('network', 'discriminatorB'),
norm_type=self.configer.get('network', 'norm_type'))
self.fake_A_pool = ImagePool(
self.configer.get('network', 'imgpool_size'))
self.fake_B_pool = ImagePool(
self.configer.get('network', 'imgpool_size'))
# define loss functions
self.criterionGAN = GANLoss(
gan_mode=self.configer.get('loss', 'params')['gan_mode'])
self.criterionCycle = nn.L1Loss()
self.criterionIdt = nn.L1Loss()
def __init__(self, configer):
super(CycleGAN, self).__init__()
# load/define networks
# The naming conversion is different from those used in the paper
# Code (paper): G_A (G), G_B (F), D_A (D_Y), D_B (D_X)
self.configer = configer
self.netG_A = SubnetSelector.generator(
self.configer.get('network', 'generatorA'))
self.netG_B = SubnetSelector.generator(
self.configer.get('network', 'generatorB'))
self.netD_A = SubnetSelector.discriminator(
self.configer.get('network', 'discriminatorA'))
self.netD_B = SubnetSelector.discriminator(
self.configer.get('network', 'discriminatorB'))
self.fake_A_pool = ImagePool(
self.configer.get('network', 'discriminatorA')['pool_size'])
self.fake_B_pool = ImagePool(
self.configer.get('network', 'discriminatorB')['pool_size'])
# define loss functions
self.criterionGAN = GANLoss(
use_lsgan=self.configer.get('loss', 'use_lsgan'))
self.criterionCycle = nn.L1Loss()
self.criterionIdt = nn.L1Loss()
def __init__(self, configer):
super(Pix2Pix, self).__init__()
self.configer = configer
# load/define networks
self.netG = SubnetSelector.generator(net_dict=self.configer.get('network', 'generator'))
self.netD = SubnetSelector.discriminator(net_dict=self.configer.get('network', 'discriminator'))
self.fake_AB_pool = ImagePool(self.configer.get('network', 'imgpool_size'))
# define loss functions
self.criterionGAN = GANLoss(gan_mode=self.configer.get('loss', 'gan_mode'))
self.criterionL1 = nn.L1Loss()
def initialize(self, opt):
# load/define networks
self.netG = SubnetSelector.generator(
self.configer.get('network', 'generator'))
self.netD = SubnetSelector.discriminator(
self.configer.get('network', 'discriminator'))
self.fake_AB_pool = ImagePool(opt.pool_size)
# define loss functions
self.criterionGAN = GANLoss(
use_lsgan=self.configer.get('loss', 'use_lsgan'))
self.criterionL1 = nn.L1Loss()