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| import argparse | |
| import os | |
| # import util | |
| import torch | |
| class BaseOptions(): | |
| def __init__(self): | |
| self.initialized = False | |
| def initialize(self, parser): | |
| parser.add_argument('--mode', default='binary') | |
| # data augmentation | |
| parser.add_argument('--rz_interp', default='bilinear') | |
| parser.add_argument('--blur_prob', type=float, default=0.5) | |
| parser.add_argument('--blur_sig', default='0.0,3.0') | |
| parser.add_argument('--jpg_prob', type=float, default=0.5) | |
| parser.add_argument('--jpg_method', default='cv2,pil') | |
| parser.add_argument('--jpg_qual', default='30,100') | |
| parser.add_argument('--data_label', default='train', help='label to decide whether train or validation dataset') | |
| parser.add_argument('--weight_decay', type=float, default=0.0, help='loss weight for l2 reg') | |
| parser.add_argument('--class_bal', action='store_true') # what is this ? | |
| parser.add_argument('--batch_size', type=int, default=16, help='input batch size') | |
| parser.add_argument('--loadSize', type=int, default=256, help='scale images to this size') | |
| parser.add_argument('--cropSize', type=int, default=224, help='then crop to this size') | |
| parser.add_argument('--gpu_ids', type=str, default='-1', 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('--name', type=str, default='experiment', help='name of the experiment. It decides where to store samples and models') | |
| 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('--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]') | |
| parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data augmentation') | |
| 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('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{loadSize}') | |
| self.initialized = True | |
| return parser | |
| def gather_options(self): | |
| # initialize parser with basic options | |
| if not self.initialized: | |
| parser = argparse.ArgumentParser( | |
| formatter_class=argparse.ArgumentDefaultsHelpFormatter) | |
| parser = self.initialize(parser) | |
| # get the basic options | |
| opt, _ = parser.parse_known_args() | |
| self.parser = parser | |
| return parser.parse_args() | |
| def print_options(self, opt): | |
| message = '' | |
| message += '----------------- Options ---------------\n' | |
| for k, v in sorted(vars(opt).items()): | |
| comment = '' | |
| default = self.parser.get_default(k) | |
| if v != default: | |
| comment = '\t[default: %s]' % str(default) | |
| message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment) | |
| message += '----------------- End -------------------' | |
| print(message) | |
| # save to the disk | |
| expr_dir = os.path.join(opt.checkpoints_dir, opt.name) | |
| # util.mkdirs(expr_dir) | |
| os.makedirs(expr_dir, exist_ok=True) | |
| file_name = os.path.join(expr_dir, 'opt.txt') | |
| with open(file_name, 'wt') as opt_file: | |
| opt_file.write(message) | |
| opt_file.write('\n') | |
| def parse(self, print_options=True): | |
| opt = self.gather_options() | |
| opt.isTrain = self.isTrain # train or test | |
| # process opt.suffix | |
| if opt.suffix: | |
| suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else '' | |
| opt.name = opt.name + suffix | |
| if print_options: | |
| self.print_options(opt) | |
| # set gpu ids | |
| str_ids = opt.gpu_ids.split(',') | |
| opt.gpu_ids = [] | |
| for str_id in str_ids: | |
| id = int(str_id) | |
| if id >= 0: | |
| opt.gpu_ids.append(id) | |
| if len(opt.gpu_ids) > 0: | |
| torch.cuda.set_device(opt.gpu_ids[0]) | |
| # additional | |
| #opt.classes = opt.classes.split(',') | |
| opt.rz_interp = opt.rz_interp.split(',') | |
| opt.blur_sig = [float(s) for s in opt.blur_sig.split(',')] | |
| opt.jpg_method = opt.jpg_method.split(',') | |
| opt.jpg_qual = [int(s) for s in opt.jpg_qual.split(',')] | |
| if len(opt.jpg_qual) == 2: | |
| opt.jpg_qual = list(range(opt.jpg_qual[0], opt.jpg_qual[1] + 1)) | |
| elif len(opt.jpg_qual) > 2: | |
| raise ValueError("Shouldn't have more than 2 values for --jpg_qual.") | |
| self.opt = opt | |
| return self.opt | |