import argparse import os from util import util import torch class BaseOptions(): def __init__(self): self.parser = argparse.ArgumentParser() self.initialized = False def initialize(self): # experiment specifics self.parser.add_argument('--name', type=str, default='label2city', help='name of the experiment. It decides where to store samples and models') 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('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here') self.parser.add_argument( '--model', type=str, default='pix2pixHD', help='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('--data_type', default=32, type=int, choices=[ 8, 16, 32], help="Supported data type i.e. 8, 16, 32 bit") self.parser.add_argument( '--verbose', action='store_true', default=False, help='toggles verbose') # input/output sizes self.parser.add_argument( '--batchSize', type=int, default=1, help='input batch size') self.parser.add_argument( '--loadSize', type=int, default=512, help='scale images to this size') self.parser.add_argument( '--fineSize', type=int, default=512, help='then crop to this size') self.parser.add_argument( '--label_nc', type=int, default=20, help='# of input label channels') 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', type=str, default='Data_preprocessing/') self.parser.add_argument('--datapairs', type=str, default='test_pairs.txt', help='train_pairs.txt/test_pairs.txt/test_pairs_same.txt etc.') self.parser.add_argument('--resize_or_crop', type=str, default='scale_width', help='scaling and cropping of images at load time [resize_and_crop|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 argumentation') self.parser.add_argument( '--nThreads', default=1, 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 displays self.parser.add_argument( '--display_winsize', type=int, default=512, help='display window size') self.parser.add_argument('--tf_log', action='store_true', help='if specified, use tensorboard logging. Requires tensorflow installed') # for generator self.parser.add_argument( '--netG', type=str, default='global', help='selects model to use for netG') 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=4, help='number of residual blocks in the global generator network') self.parser.add_argument('--n_blocks_local', type=int, default=3, help='number of residual blocks in the local enhancer network') self.parser.add_argument( '--n_local_enhancers', type=int, default=1, help='number of local enhancers to use') self.parser.add_argument('--niter_fix_global', type=int, default=0, help='number of epochs that we only train the outmost local enhancer') self.parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model') self.initialized = True def parse(self, save=True): 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) if save and not self.opt.continue_train: 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