import argparse import os from util import util import torch class BaseOptions(): def __init__(self): self.parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) self.initialized = False def initialize(self): # experiment specifics self.parser.add_argument('--name', type=str, default='label-city', help='name of the experiment. It decides where to store samples and models') self.parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here') self.parser.add_argument('--model', type=str, default='CycleGAN', help='chooses which model to use.') self.parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization') self.parser.add_argument('--use_dropout', action='store_true', help='use dropout for the generator') self.parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU') self.parser.add_argument('--which_direction', type=str, default='AtoB', help='AtoB or BtoA') # input/output sizes self.parser.add_argument('--batchSize', type=int, default=1, help='input batch size') self.parser.add_argument('--loadSize', type=int, default=256, help='scale images to this size') self.parser.add_argument('--ratio', type=int, default=1, help='img width / height') self.parser.add_argument('--fineSize', type=int, default=256, help='then crop to this size') self.parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels') self.parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels') # for setting inputs self.parser.add_argument('--dataroot', required=True, help='path to images (should have subfolders trainA, trainB, testA, testB, etc)') self.parser.add_argument('--resize_or_crop', type=str, default='scale_width', help='scaling and cropping of images at load time [resize|crop|scale_width|scale_width_and_crop]') self.parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly') self.parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data augmentation') self.parser.add_argument('--nThreads', default=2, type=int, help='# threads for loading data') self.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.') # for display self.parser.add_argument('--display_winsize', type=int, default=256, help='display window size') self.parser.add_argument('--display_id', type=int, default=0, help='window id of the web display') self.parser.add_argument('--display_port', type=int, default=8097, help='visdom port of the web display') # fot generator self.parser.add_argument('--netG_A', type=str, default='global', help='selects model to use for netG_A') self.parser.add_argument('--netG_B', type=str, default='global', help='selects model to use for netG_B') self.parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in first conv layer') self.parser.add_argument('--n_downsample_global', type=int, default=4, help='number of downsampling layers in netG') self.parser.add_argument('--n_blocks_global', type=int, default=9, help='number of residual blocks in the global generator network') # for discriminator self.parser.add_argument('--netD', type=str, default='mult_sacle', help='selects model to use for netD') self.parser.add_argument('--num_D_A', type=int, default=1, help='number of discriminators to use') self.parser.add_argument('--num_D_B', type=int, default=1, help='number of discriminators to use') self.parser.add_argument('--n_layers_D', type=int, default=3, help='number of layers') self.parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in first conv layer') self.initialized = True def parse(self): if not self.initialized: self.initialize() self.opt = self.parser.parse_args() self.opt.isTrain = self.isTrain # train or test str_ids = self.opt.gpu_ids.split(',') self.opt.gpu_ids = [] for str_id in str_ids: id = int(str_id) if id >= 0: self.opt.gpu_ids.append(id) # set gpu ids if len(self.opt.gpu_ids) > 0: torch.cuda.set_device(self.opt.gpu_ids[0]) args = vars(self.opt) print('------------ Options -------------') for k, v in sorted(args.items()): print('%s: %s' % (str(k), str(v))) print('-------------- End ----------------') # save to the disk expr_dir = os.path.join(self.opt.checkpoints_dir, self.opt.name) util.mkdirs(expr_dir) file_name = os.path.join(expr_dir, 'opt.txt') with open(file_name, 'wt') as opt_file: opt_file.write('------------ Options -------------\n') for k, v in sorted(args.items()): opt_file.write('%s: %s\n' % (str(k), str(v))) opt_file.write('-------------- End ----------------\n') return self.opt