Class BaseOptions

options/base_options.py:9–164

This class defines options used during both training and test time. It also implements several helper functions such as parsing, printing, and saving the options. It also gathers additional options defined in functions in both dataset class and model class.

class BaseOptions():
    """This class defines options used during both training and test time.

    It also implements several helper functions such as parsing, printing, and saving the options.
    It also gathers additional options defined in <modify_commandline_options> functions in both dataset class and model class.
    """

    def __init__(self, cmd_line=None):
        """Reset the class; indicates the class hasn't been initailized"""
        self.initialized = False
        self.cmd_line = None
        if cmd_line is not None:
            self.cmd_line = cmd_line.split()

    def initialize(self, parser):
        """Define the common options that are used in both training and test."""
        # basic parameters
        parser.add_argument('--dataroot', default='placeholder', help='path to images (should have subfolders trainA, trainB, valA, valB, etc)')
        parser.add_argument('--name', type=str, default='experiment_name', help='name of the experiment. It decides where to store samples and models')
        parser.add_argument('--easy_label', type=str, default='experiment_name', help='Interpretable name')
        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')
        # model parameters
        parser.add_argument('--model', type=str, default='dcl', help='chooses which model to use.')
        parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels: 3 for RGB and 1 for grayscale')
        parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels: 3 for RGB and 1 for grayscale')
        parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in the last conv layer')
        parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in the first conv layer')
        parser.add_argument('--netD', type=str, default='basic', choices=['basic', 'n_layers', 'pixel', 'patch', 'tilestylegan2', 'stylegan2'], help='specify discriminator architecture. The basic model is a 70x70 PatchGAN. n_layers allows you to specify the layers in the discriminator')
        parser.add_argument('--netG', type=str, default='resnet_9blocks', choices=['resnet_9blocks', 'resnet_6blocks', 'unet_256', 'unet_128', 'stylegan2', 'smallstylegan2', 'resnet_cat'], help='specify generator architecture')
        parser.add_argument('--n_layers_D', type=int, default=3, help='only used if netD==n_layers')
        parser.add_argument('--normG', type=str, default='instance', choices=['instance', 'batch', 'none'], help='instance normalization or batch normalization for G')
        parser.add_argument('--normD', type=str, default='instance', choices=['instance', 'batch', 'none'], help='instance normalization or batch normalization for D')
        parser.add_argument('--init_type', type=str, default='xavier', choices=['normal', 'xavier', 'kaiming', 'orthogonal'], help='network initialization')
        parser.add_argument('--init_gain', type=float, default=0.02, help='scaling factor for normal, xavier and orthogonal.')
        parser.add_argument('--no_dropout', type=util.str2bool, nargs='?', const=True, default=True,
                            help='no dropout for the generator')
        parser.add_argument('--no_antialias', action='store_true', help='if specified, use stride=2 convs instead of antialiased-downsampling (sad)')
        parser.add_argument('--no_antialias_up', action='store_true', help='if specified, use [upconv(learned filter)] instead of [upconv(hard-coded [1,3,3,1] filter), conv]')
        # dataset parameters
        parser.add_argument('--dataset_mode', type=str, default='unaligned', help='chooses how datasets are loaded. [unaligned | aligned | single | colorization]')
        parser.add_argument('--direction', type=str, default='AtoB', help='AtoB or BtoA')
        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('--num_threads', default=4, type=int, help='# threads for loading data')
        parser.add_argument('--batch_size', type=int, default=1, help='input batch size')
        parser.add_argument('--load_size', type=int, default=286, help='scale images to this size')
        parser.add_argument('--crop_size', type=int, default=256, help='then crop to this size')
        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.')
        parser.add_argument('--preprocess', type=str, default='resize_and_crop', help='scaling and cropping of images at load time [resize_and_crop | crop | scale_width | scale_width_and_crop | none]')
        parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data augmentation')
        parser.add_argument('--display_winsize', type=int, default=256, help='display window size for both visdom and HTML')
        parser.add_argument('--random_scale_max', type=float, default=3.0,
                            help='(used for single image translation) Randomly scale the image by the specified factor as data augmentation.')
        # additional parameters
        parser.add_argument('--epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
        parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information')
        parser.add_argument('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{load_size}')