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import argparse |
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import os |
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from util import util |
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import torch |
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import models |
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import data |
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class BaseOptions(): |
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"""This class defines options used during both training and test time. |
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It also implements several helper functions such as parsing, printing, and saving the options. |
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It also gathers additional options defined in <modify_commandline_options> functions in both dataset class and model class. |
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""" |
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def __init__(self): |
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"""Reset the class; indicates the class hasn't been initailized""" |
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self.initialized = False |
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def initialize(self, parser): |
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"""Define the common options that are used in both training and test.""" |
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parser.add_argument('--dataroot', required=False, help='path to images (should have subfolders trainA, trainB, valA, valB, etc)') |
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parser.add_argument('--name', type=str, default='experiment_name', help='name of the experiment. It decides where to store samples and models') |
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parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU') |
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parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here') |
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parser.add_argument('--model', type=str, default='colorization', help='chooses which model to use. [cycle_gan | pix2pix | test | colorization]') |
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parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels: 3 for RGB and 1 for grayscale') |
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parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels: 3 for RGB and 1 for grayscale') |
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parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in the last conv layer') |
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parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in the first conv layer') |
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parser.add_argument('--netD', type=str, default='basic', help='specify discriminator architecture [basic | n_layers | pixel]. The basic model is a 70x70 PatchGAN. n_layers allows you to specify the layers in the discriminator') |
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parser.add_argument('--netG', type=str, default='resnet_9blocks', help='specify generator architecture [resnet_9blocks | resnet_6blocks | unet_256 | unet_128]') |
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parser.add_argument('--n_layers_D', type=int, default=3, help='only used if netD==n_layers') |
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parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization [instance | batch | none]') |
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parser.add_argument('--init_type', type=str, default='normal', help='network initialization [normal | xavier | kaiming | orthogonal]') |
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parser.add_argument('--init_gain', type=float, default=0.02, help='scaling factor for normal, xavier and orthogonal.') |
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parser.add_argument('--no_dropout', action='store_true', help='no dropout for the generator') |
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parser.add_argument('--dataset_mode', type=str, default='unaligned', help='chooses how datasets are loaded. [unaligned | aligned | single | colorization]') |
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parser.add_argument('--direction', type=str, default='AtoB', help='AtoB or BtoA') |
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parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly') |
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parser.add_argument('--num_threads', default=4, type=int, help='# threads for loading data') |
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parser.add_argument('--batch_size', type=int, default=1, help='input batch size') |
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parser.add_argument('--load_size', type=int, default=None, help='scale images to this size') |
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parser.add_argument('--crop_size', type=int, default=None, help='then crop to this size') |
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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.') |
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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]') |
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parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data augmentation') |
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parser.add_argument('--display_winsize', type=int, default=256, help='display window size for both visdom and HTML') |
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parser.add_argument('--epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model') |
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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]') |
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parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information') |
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parser.add_argument('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{load_size}') |
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parser.add_argument('--use_wandb', action='store_true', help='if specified, then init wandb logging') |
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parser.add_argument('--wandb_project_name', type=str, default='CycleGAN-and-pix2pix', help='specify wandb project name') |
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self.initialized = True |
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return parser |
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def parse(self): |
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if not self.initialized: |
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self.initialize() |
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self.opt = self.parser.parse_args() |
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self.opt.isTrain = self.isTrain |
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str_ids = self.opt.gpu_ids.split(',') |
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self.opt.gpu_ids = [] |
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for str_id in str_ids: |
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id = int(str_id) |
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if id >= 0: |
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self.opt.gpu_ids.append(id) |
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args = vars(self.opt) |
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print('------------ Options -------------') |
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for k, v in sorted(args.items()): |
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print('%s: %s' % (str(k), str(v))) |
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print('-------------- End ----------------') |
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expr_dir = os.path.join(self.opt.checkpoints_dir, self.opt.name) |
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util.mkdirs(expr_dir) |
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file_name = os.path.join(expr_dir, 'opt.txt') |
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with open(file_name, 'wt') as opt_file: |
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opt_file.write('------------ Options -------------\n') |
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for k, v in sorted(args.items()): |
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opt_file.write('%s: %s\n' % (str(k), str(v))) |
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opt_file.write('-------------- End ----------------\n') |
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return self.opt |
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