Class BaseOptions

options/base_options.py:16–178

class BaseOptions():
    def __init__(self):
        self.initialized = False

    def initialize(self, parser):
        # experiment specifics
        parser.add_argument('--name', type=str, default='label2coco', help='name of the experiment. It decides where to store samples and models')

        parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0  0,1,2, 0,2. use -1 for CPU')
        parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
        parser.add_argument('--model', type=str, default='pix2pix', help='which model to use')
        parser.add_argument('--norm_G', type=str, default='spectralinstance', help='instance normalization or batch normalization')
        parser.add_argument('--norm_D', type=str, default='spectralinstance', help='instance normalization or batch normalization')
        parser.add_argument('--norm_E', type=str, default='spectralinstance', help='instance normalization or batch normalization')
        parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc')

        # input/output sizes
        parser.add_argument('--batchSize', type=int, default=1, help='input batch size')
        parser.add_argument('--preprocess_mode', type=str, default='scale_width_and_crop', help='scaling and cropping of images at load time.', choices=("resize_and_crop", "crop", "scale_width", "scale_width_and_crop", "scale_shortside", "scale_shortside_and_crop", "fixed", "none"))
        parser.add_argument('--load_size', type=int, default=1024, help='Scale images to this size. The final image will be cropped to --crop_size.')
        parser.add_argument('--crop_size', type=int, default=512, help='Crop to the width of crop_size (after initially scaling the images to load_size.)')
        parser.add_argument('--aspect_ratio', type=float, default=1.0, help='The ratio width/height. The final height of the load image will be crop_size/aspect_ratio')
        parser.add_argument('--label_nc', type=int, default=182, help='# of input label classes without unknown class. If you have unknown class as class label, specify --contain_dopntcare_label.')
        parser.add_argument('--contain_dontcare_label', action='store_true', help='if the label map contains dontcare label (dontcare=255)')
        parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels')

        # for setting inputs
        parser.add_argument('--dataroot', type=str, default='./datasets/cityscapes/')
        parser.add_argument('--dataset_mode', type=str, default='coco')
        parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')
        parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data argumentation')
        parser.add_argument('--nThreads', default=0, type=int, help='# threads for loading data')
        parser.add_argument('--max_dataset_size', type=int, default=sys.maxsize, help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.')
        parser.add_argument('--load_from_opt_file', action='store_true', help='load the options from checkpoints and use that as default')
        parser.add_argument('--cache_filelist_write', action='store_true', help='saves the current filelist into a text file, so that it loads faster')
        parser.add_argument('--cache_filelist_read', action='store_true', help='reads from the file list cache')

        # for displays
        parser.add_argument('--display_winsize', type=int, default=400, help='display window size')

        # for generator
        parser.add_argument('--netG', type=str, default='spade', help='selects model to use for netG (pix2pixhd | spade)')
        parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in first conv layer')
        parser.add_argument('--init_type', type=str, default='xavier', help='network initialization [normal|xavier|kaiming|orthogonal]')
        parser.add_argument('--init_variance', type=float, default=0.02, help='variance of the initialization distribution')
        parser.add_argument('--z_dim', type=int, default=256,
                            help="dimension of the latent z vector")

        # for instance-wise features
        parser.add_argument('--no_instance', action='store_true', help='if specified, do *not* add instance map as input')
        parser.add_argument('--nef', type=int, default=16, help='# of encoder filters in the first conv layer')
        parser.add_argument('--use_vae', action='store_true', help='enable training with an image encoder.')

        self.initialized = True
        return parser

    def gather_options(self):
        # initialize parser with basic options