in src/controlnet_aux/leres/pix2pix/options/base_options.py [0:0]
def initialize(self, parser):
"""Define the common options that are used in both training and test."""
# basic parameters
parser.add_argument('--dataroot', help='path to images (should have subfolders trainA, trainB, valA, valB, etc)')
parser.add_argument('--name', type=str, default='void', help='mahdi_unet_new, scaled_unet')
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='./pix2pix/checkpoints', help='models are saved here')
# model parameters
parser.add_argument('--model', type=str, default='cycle_gan', help='chooses which model to use. [cycle_gan | pix2pix | test | colorization]')
parser.add_argument('--input_nc', type=int, default=2, help='# of input image channels: 3 for RGB and 1 for grayscale')
parser.add_argument('--output_nc', type=int, default=1, 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', help='specify discriminator architecture [basic | n_layers | pixel]. 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', help='specify generator architecture [resnet_9blocks | resnet_6blocks | unet_256 | unet_128]')
parser.add_argument('--n_layers_D', type=int, default=3, help='only used if netD==n_layers')
parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization [instance | batch | none]')
parser.add_argument('--init_type', type=str, default='normal', help='network initialization [normal | xavier | kaiming | orthogonal]')
parser.add_argument('--init_gain', type=float, default=0.02, help='scaling factor for normal, xavier and orthogonal.')
parser.add_argument('--no_dropout', action='store_true', help='no dropout for the generator')
# 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=672, help='scale images to this size')
parser.add_argument('--crop_size', type=int, default=672, help='then crop to this size')
parser.add_argument('--max_dataset_size', type=int, default=10000, 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')
# 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('--load_iter', type=int, default='0', help='which iteration to load? if load_iter > 0, the code will load models by iter_[load_iter]; otherwise, the code will load models by [epoch]')
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}')
parser.add_argument('--data_dir', type=str, required=False,
help='input files directory images can be .png .jpg .tiff')
parser.add_argument('--output_dir', type=str, required=False,
help='result dir. result depth will be png. vides are JMPG as avi')
parser.add_argument('--savecrops', type=int, required=False)
parser.add_argument('--savewholeest', type=int, required=False)
parser.add_argument('--output_resolution', type=int, required=False,
help='0 for no restriction 1 for resize to input size')
parser.add_argument('--net_receptive_field_size', type=int, required=False)
parser.add_argument('--pix2pixsize', type=int, required=False)
parser.add_argument('--generatevideo', type=int, required=False)
parser.add_argument('--depthNet', type=int, required=False, help='0: midas 1:strurturedRL')
parser.add_argument('--R0', action='store_true')
parser.add_argument('--R20', action='store_true')
parser.add_argument('--Final', action='store_true')
parser.add_argument('--colorize_results', action='store_true')
parser.add_argument('--max_res', type=float, default=np.inf)
self.initialized = True
return parser