168 lines
9.7 KiB
Python
168 lines
9.7 KiB
Python
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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# basic parameters
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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('--use_idt', action='store_true', help='use_idt')
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parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
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# model parameters
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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_cond', choices=['basic_cond', '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_9blocks_mask', 'resnet_6blocks', 'unet_256', 'unet_128', 'stylegan2', 'smallstylegan2', 'resnet_cat', 'resnet_9blocks_cond'], 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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# dataset parameters
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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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# additional parameters
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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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# parameters related to StyleGAN2-based networks
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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: # check if it has been initialized
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parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser = self.initialize(parser)
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# get the basic options
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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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# modify model-related parser options
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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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print(parser)
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opt, _ = parser.parse_known_args() # parse again with new defaults
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else:
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opt, _ = parser.parse_known_args(self.cmd_line) # parse again with new defaults
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# modify dataset-related parser options
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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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# save and return the parser
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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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# save to the disk
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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 # train or test
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# process opt.suffix
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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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# set gpu ids
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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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