def initialize(self): BaseOptions.initialize(self) self.is_Train = False self.parser.add_argument('--batchsize', type=int, default=3, help='input batch size')
def initialize(self): BaseOptions.initialize(self) self.parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.') self.parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc') #todo delete. self.parser.add_argument( '--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model' ) self.parser.add_argument('--num_aug', type=int, default=1, help='# of augmentation files') self.parser.add_argument('--dropout_p', type=float, default=0, help='dropout layer probability') self.is_train = False
def initialize(self): BaseOptions.initialize(self) self.is_Train = True self.parser.add_argument('--batchsize', type=int, default=64, help='input batch size') self.parser.add_argument('--lr_G', type=float, default=0.0002, help='initial learning rate of Generator') self.parser.add_argument('--lr_D', type=float, default=0.0002, help='initial learning rate of Discriminator') self.parser.add_argument('--count_epoch', type=int, default=0, help='the starting count epoch count') self.parser.add_argument('--epochs', type=int, default=10000, help='number of epochs for train') self.parser.add_argument('--beta1', type=float, default=0.5, help='adam optimizer parameter') self.parser.add_argument('--beta2', type=float, default=0.999, help='adam optimizer parameter') self.parser.add_argument('--save_epoch_freq', type=int, default=5, help='frequency of saving the latest results') self.parser.add_argument('--D_interval', type=int, default=20, help='the interval of each optimization of D') self.parser.add_argument('--w_L1', type=int, default=1, help='the weight of the L1 loss')
def initialize(self, parser): parser = BaseOptions.initialize(self, parser) parser.add_argument('--batch_size', type=int, default=4, help='input batch size') parser.add_argument('--mode', type=str, default='test') parser.add_argument('--model_load_path', type=str, default='checkpoints', help='dir for model saving') parser.add_argument('--save_path', type=str, default='', help='result saving path') return parser
def initialize(self, parser): parser = BaseOptions.initialize(self, parser) parser.add_argument('--batch_size', type=int, default=16, help='input batch size') parser.add_argument('--lr', type=float, default=0.0001, help='initial learning rate for Adam') parser.add_argument('--mode', type=str, default='train') parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float, metavar='W', help='weight decay (default: 1e-4) for Adam') parser.set_defaults(model_save_path='checkpoints') parser.set_defaults(epochs=21) parser.set_defaults(batch_size=8) return parser
def initialize(self): BaseOptions.initialize(self) self.parser.add_argument( '--display_freq', type=int, default=100, help='frequency of showing training results on screen') self.parser.add_argument( '--print_freq', type=int, default=100, help='frequency of showing training results on console') self.parser.add_argument('--save_latest_freq', type=int, default=1000, help='frequency of saving the latest results') self.parser.add_argument( '--save_epoch_freq', type=int, default=1, help='frequency of saving checkpoints at the end of epochs') self.parser.add_argument( '--continue_train', action='store_true', help='continue training: load the latest model') self.parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc') self.parser.add_argument( '--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model' ) self.parser.add_argument('--niter', type=int, default=10, help='# of iter at starting learning rate') self.parser.add_argument( '--niter_decay', type=int, default=10, help='# of iter to linearly decay learning rate to zero') self.parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam') self.parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam') self.parser.add_argument('--TTUR', action='store_true', help='Use TTUR training scheme') self.parser.add_argument('--gan_mode', type=str, default='ls', help='(ls|original|hinge)') self.parser.add_argument( '--pool_size', type=int, default=1, help= 'the size of image buffer that stores previously generated images') self.parser.add_argument( '--no_html', action='store_true', help= 'do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/' ) # for discriminators self.parser.add_argument( '--num_D', type=int, default=2, help='number of patch scales in each discriminator') self.parser.add_argument('--n_layers_D', type=int, default=3, help='number of layers in discriminator') self.parser.add_argument('--no_vgg', action='store_true', help='do not use VGG feature matching loss') self.parser.add_argument('--no_ganFeat', action='store_true', help='do not match discriminator features') self.parser.add_argument('--lambda_feat', type=float, default=10.0, help='weight for feature matching') self.parser.add_argument( '--sparse_D', action='store_true', help='use sparse temporal discriminators to save memory') # for temporal self.parser.add_argument('--lambda_T', type=float, default=10.0, help='weight for temporal loss') self.parser.add_argument('--lambda_F', type=float, default=10.0, help='weight for flow loss') self.parser.add_argument( '--n_frames_D', type=int, default=3, help='number of frames to feed into temporal discriminator') self.parser.add_argument( '--n_scales_temporal', type=int, default=2, help='number of temporal scales in the temporal discriminator') self.parser.add_argument( '--max_frames_per_gpu', type=int, default=1, help='max number of frames to load into one GPU at a time') self.parser.add_argument('--max_frames_backpropagate', type=int, default=1, help='max number of frames to backpropagate') self.parser.add_argument( '--max_t_step', type=int, default=1, help= 'max spacing between neighboring sampled frames. If greater than 1, the network may randomly skip frames during training.' ) self.parser.add_argument( '--n_frames_total', type=int, default=30, help='the overall number of frames in a sequence to train with') self.parser.add_argument( '--niter_step', type=int, default=5, help='how many epochs do we change training batch size again') self.parser.add_argument( '--niter_fix_global', type=int, default=0, help= 'if specified, only train the finest spatial layer for the given iterations' ) self.isTrain = True
def initialize(self): BaseOptions.initialize(self) self.parser.add_argument( '--print_freq', type=int, default=10, help='frequency of showing training results on console') self.parser.add_argument('--save_latest_freq', type=int, default=250, help='frequency of saving the latest results') self.parser.add_argument( '--save_epoch_freq', type=int, default=1, help='frequency of saving checkpoints at the end of epochs') self.parser.add_argument( '--run_test_freq', type=int, default=1, help='frequency of running test in training script') self.parser.add_argument( '--continue_train', action='store_true', help='continue training: load the latest model') self.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>, ...' ) self.parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc') self.parser.add_argument( '--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model' ) self.parser.add_argument('--niter', type=int, default=150, help='# of iter at starting learning rate') self.parser.add_argument( '--niter_decay', type=int, default=150, help='# of iter to linearly decay learning rate to zero') self.parser.add_argument('--beta1', type=float, default=0.9, help='momentum term of adam') self.parser.add_argument('--lr', type=float, default=0.0005, help='initial learning rate for adam') self.parser.add_argument( '--lr_policy', type=str, default='lambda', help='learning rate policy: lambda|step|plateau') self.parser.add_argument( '--lr_decay_iters', type=int, default=50, help='multiply by a gamma every lr_decay_iters iterations') self.parser.add_argument('--reg_weight', type=float, default=0, help='l1 regularization weight') # data augmentation stuff self.parser.add_argument('--num_aug', type=int, default=5, help='# of augmentation files') self.parser.add_argument( '--scale_verts', action='store_true', help='non-uniformly scale the mesh e.g., in x, y or z') self.parser.add_argument( '--slide_verts', type=float, default=0.1, help='percent vertices which will be shifted along the mesh surface' ) self.parser.add_argument('--flip_edges', type=float, default=0.1, help='percent of edges to randomly flip') # tensorboard visualization self.parser.add_argument('--no_vis', action='store_true', help='will not use tensorboard') self.parser.add_argument('--verbose_plot', action='store_true', help='plots network weights, etc.') self.parser.add_argument('--optim', type=str, default='Adam', help='plots network weights, etc.') self.parser.add_argument('--dropout_p', type=float, default=0, help='dropout layer probability') self.is_train = True
def initialize(self): BaseOptions.initialize(self) self.parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc') self.isTrain = False
def initialize(self): BaseOptions.initialize(self) # # visdom and HTML visualization parameters self.parser.add_argument( '--display_freq', type=int, default=400, help='frequency of showing training results on screen') # self.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.') # self.parser.add_argument('--display_id', type=int, default=1, help='window id of the web display') # self.parser.add_argument('--display_server', type=str, default="http://localhost", help='visdom server of the web display') # self.parser.add_argument('--display_env', type=str, default='main', help='visdom display environment name (default is "main")') # self.parser.add_argument('--display_port', type=int, default=8097, help='visdom port of the web display') self.parser.add_argument( '--update_html_freq', type=int, default=1000, help='frequency of saving training results to html') self.parser.add_argument( '--print_freq', type=int, default=100, help='frequency of showing training results on console') self.parser.add_argument('--eval_freq', type=int, default=100, help='frequency of evaluating results') # self.parser.add_argument('--no_html', action='store_true', help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/') # # network saving and loading parameters # self.parser.add_argument('--save_latest_freq', type=int, default=5000, help='frequency of saving the latest results') # self.parser.add_argument('--save_epoch_freq', type=int, default=5, help='frequency of saving checkpoints at the end of epochs') # self.parser.add_argument('--save_by_iter', action='store_true', help='whether saves model by iteration') self.parser.add_argument( '--continue_train', action='store_true', help='continue training: load the latest model') self.parser.add_argument( '--start_epoch', type=int, default=0, help= 'the starting epoch count, we save the model by <epoch_count>, <epoch_count>+<save_latest_freq>, ...' ) # self.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>, ...') # self.parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc') # # training parameters self.parser.add_argument( '--n_epochs', type=int, default=100, help='number of epochs with the initial learning rate') self.parser.add_argument( '--n_epochs_decay', type=int, default=100, help='number of epochs to linearly decay learning rate to zero') self.parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam') self.parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam') # self.parser.add_argument('--gan_mode', type=str, default='lsgan', help='the type of GAN objective. [vanilla| lsgan | wgangp]. vanilla GAN loss is the cross-entropy objective used in the original GAN paper.') # self.parser.add_argument('--pool_size', type=int, default=50, help='the size of image buffer that stores previously generated images') self.parser.add_argument( '--lr_policy', type=str, default='linear', help='learning rate policy. [linear | step | plateau | cosine]') self.parser.add_argument( '--lr_decay_iters', type=int, default=50, help='multiply by a gamma every lr_decay_iters iterations') self.parser.add_argument('--resume', type=bool, default=True, help='load pretrained model or not') self.initialized = True self.isTrain = True