in generation/options/base_options.py [0:0]
def initialize(self):
# experiment specifics
self.parser.add_argument('--name', type=str, default='label2city', help='name of the experiment. It decides where to store samples and models')
self.parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU')
self.parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
self.parser.add_argument('--model', type=str, default='pix2pixHD', help='which model to use: [pix2pixHD | cvae-pix2pixHD | cvae++pix2pixHD | bicycle-pix2pixHD]')
self.parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization for encoder and generator')
self.parser.add_argument('--d_norm', type=str, default='instance', help='[instance | batch | specatral] normalization for discriminator')
self.parser.add_argument('--use_dropout', action='store_true', help='use dropout for the generator')
self.parser.add_argument('--use_vae', action='store_true', help='use VAE as the encoder')
self.parser.add_argument('--faster', default=False, action='store_true', help='use faster forwarding for encoder')
self.parser.add_argument('--data_type', default=32, type=int, choices=[8, 16, 32], help="Supported data type i.e. 8, 16, 32 bit")
self.parser.add_argument('--verbose', action='store_true', default=False, help='toggles verbose')
# input/output sizes
self.parser.add_argument('--batchSize', type=int, default=1, help='input batch size')
self.parser.add_argument('--loadSize', type=int, default=1024, help='scale images to this size')
self.parser.add_argument('--fineSize', type=int, default=512, help='then crop to this size')
self.parser.add_argument('--label_nc', type=int, default=35, help='# of input label channels')
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')
# for setting inputs
self.parser.add_argument('--dataroot', type=str, default='./datasets/cityscapes/')
self.parser.add_argument('--label_dir', type=str, default='./datasets/cityscapes/')
self.parser.add_argument('--img_dir', type=str, default='./datasets/cityscapes/')
self.parser.add_argument('--color_mode', type=str, default='RGB', help='color mode of our color image [Lab|RGB]')
self.parser.add_argument('--resize_or_crop', type=str, default='scale_width', help='scaling and cropping of images at load time [resize_and_crop|crop|scale_width|scale_width_and_crop|pad_and_resize]')
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('--no_flip', action='store_true', help='if specified, do not flip the images for data argumentation')
self.parser.add_argument('--nThreads', default=2, type=int, help='# threads for loading data')
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.')
# for displays
self.parser.add_argument('--display_winsize', type=int, default=512, help='display window size')
self.parser.add_argument('--tf_log', action='store_true', help='if specified, use tensorboard logging. Requires tensorflow installed')
# for generator
self.parser.add_argument('--netG', type=str, default='global', help='selects model to use for netG')
self.parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in first conv layer')
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('--n_blocks_local', type=int, default=3, help='number of residual blocks in the local enhancer network')
self.parser.add_argument('--n_local_enhancers', type=int, default=1, help='number of local enhancers to use')
self.parser.add_argument('--niter_fix_global', type=int, default=0, help='number of epochs that we only train the outmost local enhancer')
# for instance-wise features
self.parser.add_argument('--no_instance', action='store_true', help='if specified, do *not* add instance map as input')
self.parser.add_argument('--instance_feat', action='store_true', help='if specified, add encoded instance features as input')
self.parser.add_argument('--label_feat', action='store_true', help='if specified, add encoded label features as input')
self.parser.add_argument('--feat_num', type=int, default=3, help='vector length for encoded features')
self.parser.add_argument('--load_features', action='store_true', help='if specified, load precomputed feature maps')
self.parser.add_argument('--n_downsample_E', type=int, default=4, help='# of downsampling layers in encoder')
self.parser.add_argument('--nef', type=int, default=16, help='# of encoder filters in the first conv layer')
self.parser.add_argument('--n_clusters', type=int, default=10, help='number of clusters for features')
# for (VA)encoder; DEPRECATED, would not be used in practical
self.parser.add_argument('--deterministic_vae', action='store_true', help='if specified, always same 1 as epsilon in VAE')
self.parser.add_argument('--pseudo_vae', action='store_true', help='if specified, , always same 0 as epsilon in VAE')
self.parser.add_argument('--sigma_ignored_vae', action='store_true', help='if specified, call another forward fucntion for VAE that ignores outputs of sigma')
self.parser.add_argument('--debug_cvae_ignore_loss', action='store_true', help='if specified, call another forward fucntion for VAE that ignores outputs of sigma')
self.parser.add_argument('--debug_cvae_rename_optimizer', action='store_true', help='if specified, call another forward fucntion for VAE that ignores outputs of sigma')
self.parser.add_argument('--debug_cvae_another_forward', action='store_true', help='if specified, call another forward fucntion for VAE that ignores outputs of sigma')
self.initialized = True