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 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 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