in separate_vae/options/base_options.py [0:0]
def initialize(self):
self.parser.add_argument('--dataroot', type=str, help='path to pickle file')
self.parser.add_argument('--label_dir', type=str, help='path to directory containing images (of segmentaiton labels)')
self.parser.add_argument('--label_txt_path', type=str, help='path to text file containing separation of labels')
self.parser.add_argument('--batchSize', type=int, default=2, help='input batch size')
self.parser.add_argument('--loadSize', type=int, default=286, help='scale images to this size')
self.parser.add_argument('--fineSize', type=int, default=256, help='then crop to this size')
self.parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels')
self.parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels')
self.parser.add_argument('--nz', type=int, default=8, help='#latent vector')
self.parser.add_argument('--nef', type=int, default=64, help='# of encoder filters in first conv layer')
self.parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in first conv layer')
self.parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in first conv layer')
self.parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2, -1 for CPU mode')
self.parser.add_argument('--name', type=str, default='', help='name of the experiment. It decides where to store samples and models')
self.parser.add_argument('--resize_or_crop', type=str, default='resize_and_crop', help='not implemented')
self.parser.add_argument('--dataset_mode', type=str, default='aligned', help='aligned,single')
self.parser.add_argument('--model', type=str, default='bicycle_gan', help='chooses which model to use. bicycle,, ...')
self.parser.add_argument('--which_direction', type=str, default='AtoB', help='AtoB or BtoA')
self.parser.add_argument('--nThreads', default=4, type=int, help='# sthreads for loading data')
self.parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
self.parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')
self.parser.add_argument('--display_winsize', type=int, default=256, help='display window size')
self.parser.add_argument('--display_id', type=int, default=1, help='window id of the web display')
self.parser.add_argument('--display_port', type=int, default=8097, help='visdom display port')
self.parser.add_argument('--display_server', type=str, default="http://localhost", help='visdom server of the web display')
self.parser.add_argument('--log_to_filename', type=str, default="/checkpoint/kimberlyhsiao/.visdom/", help='visdom log file location')
self.parser.add_argument('--use_dropout', action='store_true', help='use dropout for the generator')
self.parser.add_argument('--max_dataset_size', type=int, default=float("inf"),
help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.')
self.parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data argumentation')
# models
self.parser.add_argument('--share_decoder', action='store_true', help='if specified, combine the latent encoding to feed in to the shared decoder')
self.parser.add_argument('--share_encoder', action='store_true', help='if specified, different labels will share the same encoder weights')
self.parser.add_argument('--separate_clothing_unrelated', action='store_true', help='clothing related share an encoder while clothing unrelated share another')
self.parser.add_argument('--which_model_netG', type=str, default='unet_256', help='selects model to use for netG')
self.parser.add_argument('--which_model_netE', type=str, default='resnet_256', help='selects model to use for netE')
self.parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization')
self.parser.add_argument('--upsample', type=str, default='basic', help='basic | bilinear')
self.parser.add_argument('--nl', type=str, default='relu', help='non-linearity activation: relu | lrelu | elu')
self.parser.add_argument('--n_downsample_global', type=int, default=4, help='number of downsampling layers in netG')
self.parser.add_argument('--n_blocks_global', type=int, default=9, help='number of residual blocks in the global generator network')
self.parser.add_argument('--max_mult', type=int, default=16, help='the maximum times to multiply ngf')
self.parser.add_argument('--divide_by_K', type=int, default='16', help='dividing number of filters at each layer by K')
self.parser.add_argument('--bottleneck', type=str, default='2d', help='bottleneck dimension: 1d | 2d')
# extra parameters
self.parser.add_argument('--where_add', type=str, default='all', help='input|all|middle; where to add z in the network G')
self.parser.add_argument('--init_type', type=str, default='xavier', help='network initialization [normal|xavier|kaiming|orthogonal]')
self.parser.add_argument('--center_crop', action='store_true', help='if apply for center cropping for the test')
self.parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information')
self.parser.add_argument('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{which_model_netG}_size{loadSize}')
self.parser.add_argument('--tf_log', action='store_true', help='if specified, use tensorboard logging. Requires tensorflow installed')
# special tasks
self.initialized = True