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| 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 | |