diff options
Diffstat (limited to 'options/base_options.py')
| -rwxr-xr-x | options/base_options.py | 95 |
1 files changed, 95 insertions, 0 deletions
diff --git a/options/base_options.py b/options/base_options.py new file mode 100755 index 0000000..863c061 --- /dev/null +++ b/options/base_options.py @@ -0,0 +1,95 @@ +### Copyright (C) 2017 NVIDIA Corporation. All rights reserved. +### Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode). +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('--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') + + # input/output sizes + self.parser.add_argument('--batchSize', type=int, default=1, help='input batch size') + self.parser.add_argument('--loadSize', type=int, default=1024, 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=35, 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='./datasets/cityscapes/') + 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=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 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=9, 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') + + # for instance-wise features + self.parser.add_argument('--no_instance', action='store_true', help='if specified, do *not* add instance map as input') + self.parser.add_argument('--instance_feat', action='store_true', help='if specified, add encoded instance features as input') + self.parser.add_argument('--label_feat', action='store_true', help='if specified, add encoded label features as input') + self.parser.add_argument('--feat_num', type=int, default=3, help='vector length for encoded features') + self.parser.add_argument('--load_features', action='store_true', help='if specified, load precomputed feature maps') + self.parser.add_argument('--n_downsample_E', type=int, default=4, help='# of downsampling layers in encoder') + self.parser.add_argument('--nef', type=int, default=16, help='# of encoder filters in the first conv layer') + self.parser.add_argument('--n_clusters', type=int, default=10, help='number of clusters for features') + + 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 |