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| import argparse | |
| import os | |
| from util import util | |
| import torch | |
| import models | |
| import data | |
| class BaseOptions: | |
| """This class defines options used during both training and test time. | |
| It also implements several helper functions such as parsing, printing, and saving the options. | |
| It also gathers additional options defined in <modify_commandline_options> functions | |
| in both dataset class and model class. | |
| """ | |
| def __init__(self): | |
| """Reset the class; indicates the class hasn't been initialized""" | |
| self.initialized = False | |
| def initialize(self, parser): | |
| """Define the common options that are used in both training and test.""" | |
| # basic parameters | |
| parser.add_argument('--dataroot', default='.', | |
| help='path to images (should have sub-folders trainA, trainB, valA, valB, etc)') | |
| parser.add_argument('--name', type=str, default='experiment_name', | |
| 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') | |
| # model parameters | |
| parser.add_argument('--model', type=str, default='painter', | |
| help='chooses which model to use.') | |
| parser.add_argument('--input_nc', type=int, default=3, | |
| help='# of input image channels: 3 for RGB and 1 for grayscale') | |
| parser.add_argument('--output_nc', type=int, default=3, | |
| help='# of output image channels: 3 for RGB and 1 for grayscale') | |
| parser.add_argument('--ngf', type=int, default=256, help='# of gen filters in the first conv layer') | |
| parser.add_argument('--layer_num', type=int, default=2, help='# of resnet block for generator') | |
| parser.add_argument('--init_type', type=str, default='normal', | |
| help='network initialization [normal | xavier | kaiming | orthogonal]') | |
| parser.add_argument('--init_gain', type=float, default=0.02, | |
| help='scaling factor for normal, xavier and orthogonal.') | |
| # dataset parameters | |
| parser.add_argument('--dataset_mode', type=str, default='single', | |
| help='chooses how datasets are loaded.') | |
| parser.add_argument('--direction', type=str, default='AtoB', help='AtoB or BtoA') | |
| 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('--num_threads', default=4, type=int, help='# threads for loading data') | |
| parser.add_argument('--batch_size', type=int, default=1, help='input batch size') | |
| parser.add_argument('--load_size', type=int, default=286, help='scale images to this size') | |
| parser.add_argument('--crop_size', type=int, default=256, help='then crop to this size') | |
| 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.') | |
| parser.add_argument('--preprocess', type=str, default='resize_and_crop', | |
| help='scaling and cropping of images at load time [resize_and_crop | crop | scale_width | ' | |
| 'scale_width_and_crop | none]') | |
| parser.add_argument('--no_flip', action='store_true', | |
| help='if specified, do not flip the images for data augmentation') | |
| parser.add_argument('--display_winsize', type=int, default=256, | |
| help='display window size for both visdom and HTML') | |
| # additional parameters | |
| parser.add_argument('--epoch', type=str, default='latest', | |
| help='which epoch to load? set to latest to use latest cached model') | |
| parser.add_argument('--load_iter', type=int, default='0', | |
| help='which iteration to load? if load_iter > 0, the code will load models by iter_[' | |
| 'load_iter]; otherwise, the code will load models by [epoch]') | |
| parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information') | |
| parser.add_argument('--suffix', default='', type=str, | |
| help='customized suffix: opt.name = opt.name + suffix') | |
| self.initialized = True | |
| return parser | |
| def gather_options(self): | |
| """Initialize our parser with basic options(only once). | |
| Add additional model-specific and dataset-specific options. | |
| These options are defined in the <modify_commandline_options> function | |
| in model and dataset classes. | |
| """ | |
| if not self.initialized: # check if it has been initialized | |
| parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) | |
| parser = self.initialize(parser) | |
| # get the basic options | |
| opt, _ = 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) | |
| opt, _ = parser.parse_known_args() # parse again with new defaults | |
| # modify dataset-related parser options | |
| dataset_name = opt.dataset_mode | |
| dataset_option_setter = data.get_option_setter(dataset_name) | |
| parser = dataset_option_setter(parser, self.isTrain) | |
| # save and return the parser | |
| self.parser = parser | |
| return parser.parse_args() | |
| def print_options(self, opt): | |
| """Print and save options | |
| It will print both current options and default values(if different). | |
| It will save options into a text file / [checkpoints_dir] / opt.txt | |
| """ | |
| 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) | |
| # save to the disk | |
| expr_dir = os.path.join(opt.checkpoints_dir, opt.name) | |
| util.mkdirs(expr_dir) | |
| file_name = os.path.join(expr_dir, '{}_opt.txt'.format(opt.phase)) | |
| with open(file_name, 'wt') as opt_file: | |
| opt_file.write(message) | |
| opt_file.write('\n') | |
| def parse(self): | |
| """Parse our options, create checkpoints directory suffix, and set up gpu device.""" | |
| opt = self.gather_options() | |
| opt.isTrain = self.isTrain # train or test | |
| # process opt.suffix | |
| if opt.suffix: | |
| suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else '' | |
| opt.name = opt.name + suffix | |
| self.print_options(opt) | |
| # 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) | |
| if len(opt.gpu_ids) > 0: | |
| torch.cuda.set_device(opt.gpu_ids[0]) | |
| self.opt = opt | |
| return self.opt | |