def __init__(self, in_channels, act=F.elu, norm=None, opt=None):
super(InpaintingModel_GMCNN, self).__init__()
self.opt = opt
self.init(opt)
self.confidence_mask_layer = ConfidenceDrivenMaskLayer()
self.netGM = GMCNN(in_channels,
out_channels=3,
cnum=opt.g_cnum,
act=act,
norm=norm).cuda()
init_weights(self.netGM)
self.model_names = ['GM']
if self.opt.phase == 'test':
return
self.netD = None
self.optimizer_G = torch.optim.Adam(self.netGM.parameters(),
lr=opt.lr,
betas=(0.5, 0.9))
self.optimizer_D = None
self.wganloss = None
self.recloss = nn.L1Loss()
self.aeloss = nn.L1Loss()
self.mrfloss = None
self.lambda_adv = opt.lambda_adv
self.lambda_rec = opt.lambda_rec
self.lambda_ae = opt.lambda_ae
self.lambda_gp = opt.lambda_gp
self.lambda_mrf = opt.lambda_mrf
self.G_loss = None
self.G_loss_reconstruction = None
self.G_loss_mrf = None
self.G_loss_adv, self.G_loss_adv_local = None, None
self.G_loss_ae = None
self.D_loss, self.D_loss_local = None, None
self.GAN_loss = None
self.gt, self.gt_local = None, None
self.mask, self.mask_01 = None, None
self.rect = None
self.im_in, self.gin = None, None
self.completed, self.completed_local = None, None
self.completed_logit, self.completed_local_logit = None, None
self.gt_logit, self.gt_local_logit = None, None
self.pred = None
if self.opt.pretrain_network is False:
if self.opt.mask_type == 'rect':
self.netD = GlobalLocalDiscriminator(
3,
cnum=opt.d_cnum,
act=act,
g_fc_channels=opt.img_shapes[0] // 16 *
opt.img_shapes[1] // 16 * opt.d_cnum * 4,
l_fc_channels=opt.mask_shapes[0] // 16 *
opt.mask_shapes[1] // 16 * opt.d_cnum * 4,
spectral_norm=self.opt.spectral_norm).cuda()
else:
self.netD = GlobalLocalDiscriminator(
3,
cnum=opt.d_cnum,
act=act,
spectral_norm=self.opt.spectral_norm,
g_fc_channels=opt.img_shapes[0] // 16 *
opt.img_shapes[1] // 16 * opt.d_cnum * 4,
l_fc_channels=opt.img_shapes[0] // 16 *
opt.img_shapes[1] // 16 * opt.d_cnum * 4).cuda()
init_weights(self.netD)
self.optimizer_D = torch.optim.Adam(filter(
lambda x: x.requires_grad, self.netD.parameters()),
lr=opt.lr,
betas=(0.5, 0.9))
self.wganloss = WGANLoss()
self.mrfloss = IDMRFLoss()
def __init__(self, act=F.elu, opt=None):
super(InpaintingModel_DFBM, self).__init__()
self.opt = opt
self.init(opt)
self.confidence_mask_layer = ConfidenceDrivenMaskLayer()
self.netDFBN = DFBN().cuda()
init_weights(self.netDFBN)
self.model_names = ['DFBN']
if self.opt.phase == 'test':
return
self.netD = None
self.optimizers = []
self.optimizer_G = torch.optim.Adam(self.netDFBN.parameters(),
lr=opt.lr,
betas=(0.5, 0.9))
self.optimizers += [self.optimizer_G]
self.optimizer_D = None
self.zeros = torch.zeros((opt.batch_size, 1)).cuda()
self.ones = torch.ones((opt.batch_size, 1)).cuda()
self.aeloss = nn.L1Loss()
self.vggloss = None
self.G_loss = None
self.G_loss_mrf = None
self.G_loss_adv, self.G_loss_vgg, self.G_loss_fm_dis = None, None, None
self.G_loss_ae = None
self.loss_eta = 5
self.loss_mu = 0.03
self.loss_vgg = 1
self.BCEloss = nn.BCEWithLogitsLoss().cuda()
self.gt, self.gt_local = None, None
self.mask, self.mask_01 = None, None
self.rect = None
self.im_in, self.gin = None, None
self.completed, self.completed_local = None, None
self.completed_logit, self.gt_logit = None, None
def weight_fn(layer):
s = layer.shape
return 1e3 / (s[1] * s[1] * s[1] * s[2] * s[3])
self.weight_fn = weight_fn
self.pred = None
self.netD = GlobalLocalDiscriminator(3,
cnum=opt.d_cnum,
act=F.leaky_relu).cuda()
init_weights(self.netD)
self.optimizer_D = torch.optim.Adam(filter(lambda x: x.requires_grad,
self.netD.parameters()),
lr=opt.lr,
betas=(0.5, 0.9))
self.vggloss = VGGLoss()
self.optimizers += [self.optimizer_D]
self.schedulers = []
for optimizer in self.optimizers:
self.schedulers.append(
lr_scheduler.MultiStepLR(optimizer, [2000, 40000], 0.5))