options/base_options.py

import argparse
import os

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

    def initialize(self, parser):
        parser.add_argument('--dataroot', default='./dummy_path', 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('--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('--isTrain', action='store_true', help='if true, it denotes the training phase')
                # 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')
                # 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}')
        # wandb parameters
        parser.add_argument('--use_wandb', action='store_true', help='if specified, then init wandb logging')
        parser.add_argument('--wandb_project_name', type=str, default='CycleGAN-and-pix2pix', help='specify wandb project name')
                # 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=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', 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')

        self.initialized = True
        return parser

    def parse(self, use_cmd_line=True):
        if not self.initialized:
            parser = argparse.ArgumentParser(description="Base Options")
            parser = self.initialize(parser)
        if use_cmd_line:
            opt = parser.parse_args()
        else:
            opt = parser.parse_known_args()[0]
        self.opt = opt
        return self.opt