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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, cmd_line=None): |
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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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self.cmd_line = None |
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if cmd_line is not None: |
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self.cmd_line = cmd_line.split() |
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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', default='placeholder', 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('--easy_label', type=str, default='experiment_name', help='Interpretable name') |
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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='cut', help='chooses which model to use.') |
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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', choices=['basic', 'n_layers', 'pixel', 'patch', 'tilestylegan2', 'stylegan2'], help='specify discriminator architecture. 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', choices=['resnet_9blocks', 'resnet_6blocks', 'unet_256', 'unet_128', 'stylegan2', 'smallstylegan2', 'resnet_cat'], help='specify generator architecture') |
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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('--normG', type=str, default='instance', choices=['instance', 'batch', 'none'], help='instance normalization or batch normalization for G') |
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parser.add_argument('--normD', type=str, default='instance', choices=['instance', 'batch', 'none'], help='instance normalization or batch normalization for D') |
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parser.add_argument('--init_type', type=str, default='xavier', choices=['normal', 'xavier', 'kaiming', 'orthogonal'], help='network initialization') |
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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', type=util.str2bool, nargs='?', const=True, default=True, |
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help='no dropout for the generator') |
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parser.add_argument('--no_antialias', action='store_true', help='if specified, use stride=2 convs instead of antialiased-downsampling (sad)') |
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parser.add_argument('--no_antialias_up', action='store_true', help='if specified, use [upconv(learned filter)] instead of [upconv(hard-coded [1,3,3,1] filter), conv]') |
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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=286, help='scale images to this size') |
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parser.add_argument('--crop_size', type=int, default=256, 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('--random_scale_max', type=float, default=3.0, |
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help='(used for single image translation) Randomly scale the image by the specified factor as data augmentation.') |
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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('--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('--stylegan2_G_num_downsampling', |
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default=1, type=int, |
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help='Number of downsampling layers used by StyleGAN2Generator') |
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self.initialized = True |
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return parser |
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def gather_options(self): |
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"""Initialize our parser with basic options(only once). |
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Add additional model-specific and dataset-specific options. |
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These options are defined in the <modify_commandline_options> function |
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in model and dataset classes. |
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""" |
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if not self.initialized: |
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parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) |
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parser = self.initialize(parser) |
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if self.cmd_line is None: |
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opt, _ = parser.parse_known_args() |
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else: |
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opt, _ = parser.parse_known_args(self.cmd_line) |
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model_name = opt.model |
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model_option_setter = models.get_option_setter(model_name) |
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parser = model_option_setter(parser, self.isTrain) |
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if self.cmd_line is None: |
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opt, _ = parser.parse_known_args() |
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else: |
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opt, _ = parser.parse_known_args(self.cmd_line) |
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dataset_name = opt.dataset_mode |
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dataset_option_setter = data.get_option_setter(dataset_name) |
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parser = dataset_option_setter(parser, self.isTrain) |
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self.parser = parser |
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if self.cmd_line is None: |
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return parser.parse_args() |
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else: |
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return parser.parse_args(self.cmd_line) |
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def print_options(self, opt): |
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"""Print and save options |
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It will print both current options and default values(if different). |
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It will save options into a text file / [checkpoints_dir] / opt.txt |
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""" |
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message = '' |
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message += '----------------- Options ---------------\n' |
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for k, v in sorted(vars(opt).items()): |
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comment = '' |
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default = self.parser.get_default(k) |
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if v != default: |
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comment = '\t[default: %s]' % str(default) |
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message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment) |
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message += '----------------- End -------------------' |
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print(message) |
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expr_dir = os.path.join(opt.checkpoints_dir, opt.name) |
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util.mkdirs(expr_dir) |
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file_name = os.path.join(expr_dir, '{}_opt.txt'.format(opt.phase)) |
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try: |
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with open(file_name, 'wt') as opt_file: |
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opt_file.write(message) |
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opt_file.write('\n') |
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except PermissionError as error: |
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print("permission error {}".format(error)) |
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pass |
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def parse(self): |
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"""Parse our options, create checkpoints directory suffix, and set up gpu device.""" |
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opt = self.gather_options() |
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opt.isTrain = self.isTrain |
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if opt.suffix: |
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suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else '' |
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opt.name = opt.name + suffix |
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self.print_options(opt) |
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str_ids = opt.gpu_ids.split(',') |
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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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opt.gpu_ids.append(id) |
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if len(opt.gpu_ids) > 0: |
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torch.cuda.set_device(opt.gpu_ids[0]) |
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self.opt = opt |
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return self.opt |
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