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from .base_options import BaseOptions |
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class TrainOptions(BaseOptions): |
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def initialize(self, parser): |
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parser = BaseOptions.initialize(self, parser) |
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parser.add_argument('--display_freq', type=int, default=400, help='frequency of showing training results on screen') |
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parser.add_argument('--display_ncols', type=int, default=4, help='if positive, display all images in a single visdom web panel with certain number of images per row.') |
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parser.add_argument('--update_html_freq', type=int, default=1000, help='frequency of saving training results to html') |
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parser.add_argument('--print_freq', type=int, default=100, help='frequency of showing training results on console') |
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parser.add_argument('--save_latest_freq', type=int, default=5000, help='frequency of saving the latest results') |
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parser.add_argument('--save_epoch_freq', type=int, default=5, help='frequency of saving checkpoints at the end of epochs') |
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parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model') |
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parser.add_argument('--epoch_count', type=int, default=1, help='the starting epoch count, we save the model by <epoch_count>, <epoch_count>+<save_latest_freq>, ...') |
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parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc') |
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parser.add_argument('--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model') |
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parser.add_argument('--niter', type=int, default=100, help='# of iter at starting learning rate') |
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parser.add_argument('--niter_decay', type=int, default=100, help='# of iter to linearly decay learning rate to zero') |
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parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam') |
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parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam') |
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parser.add_argument('--no_lsgan', action='store_true', help='do *not* use least square GAN, if false, use vanilla GAN') |
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parser.add_argument('--pool_size', type=int, default=50, help='the size of image buffer that stores previously generated images') |
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parser.add_argument('--no_html', action='store_true', help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/') |
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parser.add_argument('--lr_policy', type=str, default='lambda', help='learning rate policy: lambda|step|plateau|cosine') |
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parser.add_argument('--lr_decay_iters', type=int, default=50, help='multiply by a gamma every lr_decay_iters iterations') |
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parser.add_argument('--chamfer_loss', action='store_true', help='use chamfer loss') |
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parser.add_argument('--chamfer_2way', action='store_true', help='use chamfer loss 2 way') |
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parser.add_argument('--chamfer_only_line', action='store_true', help='use chamfer only on lines') |
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parser.add_argument('--lambda_chamfer', type=float, default=0.1, help='weight for chamfer loss') |
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parser.add_argument('--lambda_chamfer2', type=float, default=0.1, help='weight for chamfer loss2') |
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parser.add_argument('--dt_nonlinear', type=str, default='', help='nonlinear remap on dt [atan | sigmoid | tanh]') |
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parser.add_argument('--dt_xmax', type=float, default=10, help='first mutiply dt to range [0,xmax], then use atan/sigmoid/tanh etc, to have more nonlinearity (not much nonlinearity in range [0,1])') |
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parser.add_argument('--continuity_loss', action='store_true', help='use line continuity loss') |
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parser.add_argument('--lambda_continuity', type=float, default=10.0, help='weight for continuity loss') |
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parser.add_argument('--emphasis_conti_face', action='store_true', help='constrain conti loss to pixels in original lines (avoid apply to background etc)') |
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parser.add_argument('--facemask_dir', type=str, default='dataset/mask/face/', help='mask folder to constrain conti loss to pixels in original lines') |
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parser.add_argument('--netG_dt', type=str, default='unet_512', help='selects model to use for netG_dt, for chamfer loss') |
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parser.add_argument('--netG_line', type=str, default='unet_512', help='selects model to use for netG_line, for chamfer loss') |
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parser.add_argument('--discriminator_local', action='store_true', help='use six diffent local discriminator for 6 local regions') |
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parser.add_argument('--gan_loss_strategy', type=int, default=2, help='specify how to calculate gan loss for g, 1: average global and local discriminators; 2: not change global discriminator weight, 0.25 for local') |
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parser.add_argument('--addw_eye', type=float, default=1.0, help='additional weight for eye region') |
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parser.add_argument('--addw_nose', type=float, default=1.0, help='additional weight for nose region') |
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parser.add_argument('--addw_mouth', type=float, default=1.0, help='additional weight for mouth region') |
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parser.add_argument('--addw_hair', type=float, default=1.0, help='additional weight for hair region') |
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parser.add_argument('--addw_bg', type=float, default=1.0, help='additional weight for bg region') |
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parser.add_argument('--no_l1_loss', action='store_true', help='no l1 loss') |
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parser.add_argument('--no_G_local_loss', action='store_true', help='not using local transfer loss for local generator output') |
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parser.add_argument('--no_dtremap', action='store_true', help='no dt remap') |
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parser.add_argument('--no_dt', action='store_true', help='no dt') |
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parser.add_argument('--pretrain', action='store_true', help='pretrain stage, no dt loss, no ae') |
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self.isTrain = True |
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return parser |
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