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- """
- Copyright (C) 2019 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 sys
- import argparse
- import os
- from util import util
- import models
- import data
- import pickle
-
-
- class BaseOptions():
- def __init__(self):
- self.initialized = False
-
- def initialize(self, parser):
- # experiment specifics
- parser.add_argument('--name', type=str, default='label2coco',
- help='name of the experiment. It decides where to store samples and models')
-
- parser.add_argument('--gpu_ids', type=str, default='0',
- 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('--model', type=str,
- default='pix2pix', help='which model to use')
- parser.add_argument('--norm_G', type=str, default='spectralinstance',
- help='instance normalization or batch normalization')
- parser.add_argument('--norm_D', type=str, default='spectralinstance',
- help='instance normalization or batch normalization')
- parser.add_argument('--norm_E', type=str, default='spectralinstance',
- help='instance normalization or batch normalization')
- parser.add_argument('--phase', type=str,
- default='train', help='train, val, test, etc')
-
- # input/output sizes
- parser.add_argument('--batchSize', type=int,
- default=1, help='input batch size')
- parser.add_argument('--preprocess_mode', type=str, default='fixed', help='scaling and cropping of images at load time.', choices=(
- "resize_and_crop", "crop", "scale_width", "scale_width_and_crop", "scale_shortside", "scale_shortside_and_crop", "fixed", "none"))
- parser.add_argument('--load_size', type=int, default=1024,
- help='Scale images to this size. The final image will be cropped to --crop_size.')
- parser.add_argument('--crop_size', type=int, default=512,
- help='Crop to the width of crop_size (after initially scaling the images to load_size.)')
- parser.add_argument('--aspect_ratio', type=float, default=1.3333333,
- help='The ratio width/height. The final height of the load image will be crop_size/aspect_ratio')
- parser.add_argument('--label_nc', type=int, default=29,
- help='# of input label classes without unknown class. If you have unknown class as class label, specify --contain_dopntcare_label.')
- parser.add_argument('--contain_dontcare_label', action='store_true',
- help='if the label map contains dontcare label (dontcare=255)')
- parser.add_argument('--output_nc', type=int, default=3,
- help='# of output image channels')
-
- # for setting inputs
- parser.add_argument('--dataroot', type=str,
- default='./datasets/cityscapes/')
- parser.add_argument('--dataset_mode', type=str, default='coco')
- 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('--no_flip', action='store_true',
- help='if specified, do not flip the images for data argumentation')
- parser.add_argument('--nThreads', default=0, type=int,
- help='# threads for loading data')
- parser.add_argument('--max_dataset_size', type=int, default=sys.maxsize,
- help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.')
- parser.add_argument('--load_from_opt_file', action='store_true',
- help='load the options from checkpoints and use that as default')
- parser.add_argument('--cache_filelist_write', action='store_true',
- help='saves the current filelist into a text file, so that it loads faster')
- parser.add_argument('--cache_filelist_read', action='store_true',
- help='reads from the file list cache')
-
- # for displays
- parser.add_argument('--display_winsize', type=int,
- default=400, help='display window size')
-
- # for generator
- parser.add_argument('--netG', type=str, default='spade',
- help='selects model to use for netG (pix2pixhd | spade)')
- parser.add_argument('--ngf', type=int, default=64,
- help='# of gen filters in first conv layer')
- parser.add_argument('--init_type', type=str, default='xavier',
- help='network initialization [normal|xavier|kaiming|orthogonal]')
- parser.add_argument('--init_variance', type=float, default=0.02,
- help='variance of the initialization distribution')
- parser.add_argument('--z_dim', type=int, default=256,
- help="dimension of the latent z vector")
-
- # for instance-wise features
- parser.add_argument('--no_instance', action='store_true',
- help='if specified, do *not* add instance map as input')
- parser.add_argument('--nef', type=int, default=16,
- help='# of encoder filters in the first conv layer')
- parser.add_argument('--use_vae', action='store_true',
- help='enable training with an image encoder.')
-
- 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, unknown = parser.parse_known_args()
-
- # modify model-related parser options
- model_name = opt.model
- model_option_setter = models.get_option_setter(model_name)
- parser = model_option_setter(parser, self.isTrain)
-
- # modify dataset-related parser options
- dataset_mode = opt.dataset_mode
- dataset_option_setter = data.get_option_setter(dataset_mode)
- parser = dataset_option_setter(parser, self.isTrain)
-
- opt, unknown = parser.parse_known_args()
-
- # if there is opt_file, load it.
- # The previous default options will be overwritten
- if opt.load_from_opt_file:
- parser = self.update_options_from_file(parser, opt)
-
- opt = parser.parse_args()
- self.parser = parser
- return opt
-
- 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)
-
- def option_file_path(self, opt, makedir=False):
- expr_dir = os.path.join(opt.checkpoints_dir, opt.name)
- if makedir:
- util.mkdirs(expr_dir)
- file_name = os.path.join(expr_dir, 'opt')
- return file_name
-
- def save_options(self, opt):
- file_name = self.option_file_path(opt, makedir=True)
- with open(file_name + '.txt', 'wt') as opt_file:
- for k, v in sorted(vars(opt).items()):
- comment = ''
- default = self.parser.get_default(k)
- if v != default:
- comment = '\t[default: %s]' % str(default)
- opt_file.write('{:>25}: {:<30}{}\n'.format(
- str(k), str(v), comment))
-
- with open(file_name + '.pkl', 'wb') as opt_file:
- pickle.dump(opt, opt_file)
-
- def update_options_from_file(self, parser, opt):
- new_opt = self.load_options(opt)
- for k, v in sorted(vars(opt).items()):
- if hasattr(new_opt, k) and v != getattr(new_opt, k):
- new_val = getattr(new_opt, k)
- parser.set_defaults(**{k: new_val})
- return parser
-
- def load_options(self, opt):
- file_name = self.option_file_path(opt, makedir=False)
- new_opt = pickle.load(open(file_name + '.pkl', 'rb'))
- return new_opt
-
- def parse(self, save=False):
-
- opt = self.gather_options()
- opt.isTrain = self.isTrain # train or test
-
- self.print_options(opt)
- if opt.isTrain:
- self.save_options(opt)
-
- # Set semantic_nc based on the option.
- # This will be convenient in many places
- opt.semantic_nc = opt.label_nc + \
- (1 if opt.contain_dontcare_label else 0) + \
- (0 if opt.no_instance else 1)
-
- # 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)
-
- # assert len(opt.gpu_ids) == 0 or opt.batchSize % len(opt.gpu_ids) == 0, \
- # "Batch size %d is wrong. It must be a multiple of # GPUs %d." \
- # % (opt.batchSize, len(opt.gpu_ids))
-
- self.opt = opt
- return self.opt
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