import argparse
import os
from util import util
import torch
class BaseOptions():
def __init__(self):
self.parser = argparse.ArgumentParser()
self.initialized = False
def initialize(self):
self.parser.add_argument('--name', required=True, type=str, help='name of the experiment. It decides where to store samples and models')
self.parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
self.parser.add_argument('--dataroot', required=True, type=str, help='path to images (should have subfolders trainA, trainB, valA, valB, etc)')
self.parser.add_argument('--n_domains', required=True, type=int, help='Number of domains to transfer among')
self.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.')
self.parser.add_argument('--resize_or_crop', type=str, default='resize_and_crop', help='scaling and cropping of images at load time [resize|resize_and_crop|crop]')
self.parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data augmentation')
self.parser.add_argument('--loadSize', type=int, default=286, help='scale images to this size')
self.parser.add_argument('--fineSize', type=int, default=256, help='then crop to this size')
self.parser.add_argument('--batchSize', type=int, default=1, help='input batch size')
self.parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels')
self.parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels')
self.parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in first conv layer')
self.parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in first conv layer')
self.parser.add_argument('--netG_n_blocks', type=int, default=9, help='number of residual blocks to use for netG')
self.parser.add_argument('--netG_n_shared', type=int, default=0, help='number of blocks to use for netG shared center module')
self.parser.add_argument('--netD_n_layers', type=int, default=4, help='number of layers to use for netD')
self.parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization')
self.parser.add_argument('--use_dropout', action='store_true', help='insert dropout for the generator')
self.parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU')
self.parser.add_argument('--nThreads', default=2, type=int, help='# threads for loading data')
self.parser.add_argument('--display_id', type=int, default=0, help='window id of the web display (set >1 to use visdom)')
self.parser.add_argument('--display_port', type=int, default=8097, help='visdom port of the web display')
self.parser.add_argument('--display_winsize', type=int, default=256, help='display window size')
self.parser.add_argument('--display_single_pane_ncols', type=int, default=0, help='if positive, display all images in a single visdom web panel with certain number of images per row.')
self.initialized = True
def parse(self):
if not self.initialized:
self.initialize()
self.opt = self.parser.parse_args()
self.opt.isTrain = self.isTrain # train or test
str_ids = self.opt.gpu_ids.split(',')
self.opt.gpu_ids = []
for str_id in str_ids:
id = int(str_id)
if id >= 0:
self.opt.gpu_ids.append(id)
# set gpu ids
if len(self.opt.gpu_ids) > 0:
torch.cuda.set_device(self.opt.gpu_ids[0])
args = vars(self.opt)
print('------------ Options -------------')
for k, v in sorted(args.items()):
print('%s: %s' % (str(k), str(v)))
print('-------------- End ----------------')
# save to the disk
expr_dir = os.path.join(self.opt.checkpoints_dir, self.opt.name)
util.mkdirs(expr_dir)
file_name = os.path.join(expr_dir, 'opt.txt')
with open(file_name, 'wt') as opt_file:
opt_file.write('------------ Options -------------\n')
for k, v in sorted(args.items()):
opt_file.write('%s: %s\n' % (str(k), str(v)))
opt_file.write('-------------- End ----------------\n')
return self.opt
