David310/Detect_AI-generated_Image

1import argparse2import os3import util4import torch5 6 7class BaseOptions():8    def __init__(self):9        self.initialized = False10 11    def initialize(self, parser):12        parser.add_argument('--mode', default='binary')13        parser.add_argument('--arch', type=str, default='res50', help='see my_models/__init__.py')14        parser.add_argument('--fix_backbone', action='store_true')  15 16        # data augmentation17        parser.add_argument('--rz_interp', default='bilinear')18        parser.add_argument('--blur_prob', type=float, default=0.5)19        parser.add_argument('--blur_sig', default='0.0,3.0')20        parser.add_argument('--jpg_prob', type=float, default=0.5)21        parser.add_argument('--jpg_method', default='cv2,pil')22        parser.add_argument('--jpg_qual', default='30,100')23        24         25        parser.add_argument('--real_list_path', default=None, help='only used if data_mode==ours: path for the list of real images, which should contain train.pickle and val.pickle')26        parser.add_argument('--fake_list_path', default=None, help='only used if data_mode==ours: path for the list of fake images, which should contain train.pickle and val.pickle')27        parser.add_argument('--wang2020_data_path', default=None, help='only used if data_mode==wang2020 it should contain train and test folders')28        parser.add_argument('--data_mode',  default='ours', help='wang2020 or ours')29        parser.add_argument('--data_label', default='train', help='label to decide whether train or validation dataset')30        parser.add_argument('--weight_decay', type=float, default=0.0, help='loss weight for l2 reg')31        32        parser.add_argument('--class_bal', action='store_true') # what is this ?33        parser.add_argument('--batch_size', type=int, default=256, help='input batch size')34        parser.add_argument('--loadSize', type=int, default=256, help='scale images to this size')35        parser.add_argument('--cropSize', type=int, default=224, help='then crop to this size')36        parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0  0,1,2, 0,2. use -1 for CPU')37        parser.add_argument('--name', type=str, default='experiment_name', help='name of the experiment. It decides where to store samples and models')38        parser.add_argument('--num_threads', default=4, type=int, help='# threads for loading data')39        parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')40        parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')41        parser.add_argument('--resize_or_crop', type=str, default='scale_and_crop', help='scaling and cropping of images at load time [resize_and_crop|crop|scale_width|scale_width_and_crop|none]')42        parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data augmentation')43        parser.add_argument('--init_type', type=str, default='normal', help='network initialization [normal|xavier|kaiming|orthogonal]')44        parser.add_argument('--init_gain', type=float, default=0.02, help='scaling factor for normal, xavier and orthogonal.')45        parser.add_argument('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{loadSize}')46        self.initialized = True47        return parser48 49    def gather_options(self):50        # initialize parser with basic options51        if not self.initialized:52            parser = argparse.ArgumentParser(53                formatter_class=argparse.ArgumentDefaultsHelpFormatter)54            parser = self.initialize(parser)55 56        # get the basic options57        opt, _ = parser.parse_known_args()58        self.parser = parser59 60        return parser.parse_args()61 62    def print_options(self, opt):63        message = ''64        message += '----------------- Options ---------------\n'65        for k, v in sorted(vars(opt).items()):66            comment = ''67            default = self.parser.get_default(k)68            if v != default:69                comment = '\t[default: %s]' % str(default)70            message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment)71        message += '----------------- End -------------------'72        print(message)73 74        # save to the disk75        expr_dir = os.path.join(opt.checkpoints_dir, opt.name)76        util.mkdirs(expr_dir)77        file_name = os.path.join(expr_dir, 'opt.txt')78        with open(file_name, 'wt') as opt_file:79            opt_file.write(message)80            opt_file.write('\n')81 82    def parse(self, print_options=True):83 84        opt = self.gather_options()85        opt.isTrain = self.isTrain   # train or test86 87        # process opt.suffix88        if opt.suffix:89            suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else ''90            opt.name = opt.name + suffix91 92        if print_options:93            self.print_options(opt)94 95        # set gpu ids96        str_ids = opt.gpu_ids.split(',')97        opt.gpu_ids = []98        for str_id in str_ids:99            id = int(str_id)100            if id >= 0:101                opt.gpu_ids.append(id)102        if len(opt.gpu_ids) > 0:103            torch.cuda.set_device(opt.gpu_ids[0])104 105        # additional106        #opt.classes = opt.classes.split(',')107        opt.rz_interp = opt.rz_interp.split(',')108        opt.blur_sig = [float(s) for s in opt.blur_sig.split(',')]109        opt.jpg_method = opt.jpg_method.split(',')110        opt.jpg_qual = [int(s) for s in opt.jpg_qual.split(',')]111        if len(opt.jpg_qual) == 2:112            opt.jpg_qual = list(range(opt.jpg_qual[0], opt.jpg_qual[1] + 1))113        elif len(opt.jpg_qual) > 2:114            raise ValueError("Shouldn't have more than 2 values for --jpg_qual.")115 116        self.opt = opt117        return self.opt118